dissertations Miguel Ángel Muñoz-Ruiz | 235 | Miguel Ángel ... · MIGUEL ÁNGEL MUÑOZ-RUIZ...

Publications of the University of Eastern Finland

Dissertations in Health Sciences

isbn 978-952-61-1479-8

Publications of the University of Eastern FinlandDissertations in Health Sciencesd

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Miguel Ángel Muñoz-RuizDisease State Index

and Neuroimaging in Frontotemporal Dementia,

Alzheimer’s Disease and Mild Cognitive Impairment

Miguel Ángel Muñoz-Ruiz

Disease State Index and Neuroimaging in Frontotemporal Dementia, Alzheimer’s Disease and Mild Cognitive Impairment

Alzheimer’s disease (AD) is the most

prevalent disease of the dementia

diseases while frontotemporal dementia

(FTD) is relatively common in people

younger than 65 years of age. Early and

precise diagnosis of these two diseases

is a major challenge. There is a need to

identify new methods that could achieve

an earlier and more precise diagnosis, and

to integrate all these data originating from

multiple sources, in order to facilitate the

clinical diagnosis. This thesis introduces

the use of a new combination of different

methods in the differential diagnosis of

AD, mild cognitive impairment stages and

FTD, and a tool (Disease State Index and

Disease State Fingerprint) that collates

data from different sources to help

clinicians to profile a patient as having

either AD or FTD.

MIGUEL ÁNGEL MUÑOZ-RUIZ

Disease State Index and neuroimaging in frontotemporal dementia, Alzheimer’s disease and mild cognitive impairment

Neuroimaging and Disease State Index in dementia diseases

To be presented by permission of the Faculty of Health Sciences, University of Eastern Finland for

public examination in Canthia L3, Kuopio, on Wednesday, June 11th 2014, at 12 noon

Publications of the University of Eastern Finland Dissertations in Health Sciences

Number 235

Department of Neurology, Institute of Clinical Medicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland

Neurocenter / Neurology Kuopio University Hospital

Kuopio 2014

Kopijyvä Oy Kuopio, 2014

Series Editors:

Professor Veli-Matti Kosma, M.D., Ph.D. Institute of Clinical Medicine, Pathology

Faculty of Health Sciences

Professor Hannele Turunen, Ph.D. Department of Nursing Science


Professor Olli Gröhn, Ph.D. A.I. Virtanen Institute for Molecular Sciences


Professor Kai Kaarniranta, M.D., Ph.D. Institute of Clinical Medicine, Ophthalmology


Lecturer Veli-Pekka Ranta, Ph.D. (pharmacy) School of Pharmacy


Distributor: University of Eastern Finland

Kuopio Campus Library P.O.Box 1627

FI-70211 Kuopio, Finland http://www.uef.fi/kirjasto

ISBN (print): 978-952-61-1479-8 ISBN (pdf): 978-952-61-1480-4

ISSN (print): 1798-5706 ISSN (pdf): 1798-5714

ISSN-L: 1798-5706

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Author’s address: Department of Neurology, Institute of Clinical Medicine, School of Medicine University of Eastern Finland KUOPIO FINLAND

Supervisors: Professor Hilkka Soininen, M.D., Ph.D.

Department of Neurology, Institute of Clinical Medicine, School of Medicine University of Eastern Finland KUOPIO FINLAND Docent Päivi Hartikainen, M.D., Ph.D. Department of Neurology Kuopio University Hospital KUOPIO FINLAND Docent Yawu Liu, M.D., Ph.D. Department of Neurology, Institute of Clinical Medicine, School of Medicine University of Eastern Finland KUOPIO FINLAND

Reviewers: Professor Matti Viitanen, M.D., Ph.D. Department of Geriatrics University of Turku TURKU FINLAND

Associate Professor Vesa Kiviniemi, M.D., Ph.D. Department of Radiology University of Oulu OULU FINLAND

Opponent: Professor Alberto Lleó Bisa, M.D., Ph.D.

Hospital de la Santa Creu I Sant Pau BARCELONA SPAIN

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Muñoz-Ruiz, Miguel Ángel Disease State Index and neuroimaging in frontotemporal dementia, Alzheimer’s disease and mild cognitive impairment; Neuroimaging and Disease State Index in dementia diseases University of Eastern Finland, Faculty of Health Sciences Publications of the University of Eastern Finland. Dissertations in Health Sciences 235. 2014. 135 p. ISBN (print): 978-952-61-1479-8 ISBN (pdf): 978-952-61-1480-4 ISSN (print): 1798-5706 ISSN (pdf): 1798-5714 ISSN-L: 1798-5706 ABSTRACT: The differential diagnosis of dementia diseases represents a challenge particularly in early phases of the diseases. Many studies have focused on predictive factors for conversion from mild cognitive impairment (MCI) to dementia, most often to Alzheimer’s disease (AD). Several methods have been proposed for differentiating between AD and frontotemporal dementia (FTD), another relative common degenerative dementia. The differential diagnosis is not easy due to overlapping clinical and biomarker findings. This thesis introduces the use of a new combination of different methods in the differential diagnosis of AD, MCI and FTD, and describes a tool, Disease State Index (DSI) and its visual counterpart, Disease State Fingerprint which collates data from different modalities and facilitates clinicians to profile a patient as having either AD or FTD. The first publication compared the benefits of hippocampal volumetry (HV), tensor-based morphometry (TBM) and voxel-based morphometry (VBM), in order to identify the most accurate method for differentiating FTD from controls, AD, stable MCI and progressive MCI. Controls can clearly be differentiated from FTD by using HV (Accuracy=0.83), TBM (0.82) and VBM (0.83). VBM achieved the highest accuracy of the methods used in its ability to differentiate between FTD and AD (0.72). The second report described a comparison of FTD cases with AD, MCI and controls, including into the DSI in addition to the imaging methods assessed in study I, also values from CSF, APOE and MMSE. The highest accuracy was reached when comparing FTD with controls (0.84), followed by FTD compared with MCI (0.79) and AD (0.69). MRI is the most relevant feature in FTD in comparison to the situation for MCI and AD, however in the controls vs. FTD comparison, the most relevant feature was the MMSE. The third publication compared FTD cases with AD and controls, including in DSI data from clinical symptoms, Hachinski ischemic score, Webster total score, Hamilton depression scale, MMSE, and tests for assessing functions such as language, memory, visuo-construction and executive-function, MRI, SPECT, APOE genotype and CSF biomarker results. The highest accuracy was achieved in differentiating controls from patients with AD (0.99) and from FTD (0.97). In addition, AD could be differentiated from FTD with a high degree of accuracy (0.86). Clinical symptoms and neuropsychological tests were the most relevant categories in differentiating between AD and FTD. With respect to the imaging methods, MRI was particularly useful in differentiating a healthy state from AD, while SPECT was more relevant in separating FTD from controls and AD. The fourth publication investigated the generalizability of DSI in 875 MCI cases from four cohorts (ADNI, DESCRIPA, AddNeuroMed and Kuopio L-MCI). This report examined the

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accuracy to predict progression from MCI to AD and included MRI imaging analysis, HV, TBM, VBM and as well as CSF biomarkers, neuropsychological tests, MMSE and APOE. MRI features alone achieved good classification accuracies (0.67-0.81) in the four cohorts studied, which can be slightly improved by adding values from MMSE, APOE, CSF and neuropsychological test data. The results revealed that the prediction accuracy of the combined cohort (0.70) was close to the average of the individual cohort accuracies (0.68-0.82). It is feasible to use different cohorts as training sets for the DSI, as long as they are sufficiently similar. Results from this thesis point to the conclusion that HV, TBM and VBM provide accurate results when comparing the healthy state with disease and for predicting the conversion to AD and may also help in differentiating between AD and FTD. DSI incorporating data from several tests and biomarkers can be supportive in the differentiation of different patients group i.e. controls, MCI, AD, FTD.

National Library of Medicine Classification: WL 141.5, WL 358.5, WT 155, WN 185 Medical Subject Headings: Alzheimer Disease; Biological Markers; Diagnosis, Computer-Assisted; Dementia; Diagnosis, Differential; Frontotemporal Dementia; Hippocampus; Magnetic Resonance Imaging; Mild Cognitive Impairment; Neuroimaging; Neuropsychological Tests

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Muñoz-Ruiz, Miguel Ángel Disease State Index and neuroimaging in frontotemporal dementia, Alzheimer’s disease and mild cognitive impairment; Neuroimaging and Disease State Index in dementia diseases Itä-Suomen yliopisto, terveystieteiden tiedekunta Publications of the University of Eastern Finland. Dissertations in Health Sciences 235. 2014. 135 s. ISBN (print): 978-952-61-1479-8 ISBN (pdf): 978-952-61-1480-4 ISSN (print): 1798-5706 ISSN (pdf): 1798-5714 ISSN-L: 1798-5706 TIIVISTELMÄ:

Muistisairauden erotusdiagnoosi on haastavaa erityisesti sairauden alkuvaiheessa. Monet tutkimukset ovat keskittyneet tutkimaan niitä tekijöitä, jotka ennustavat lievän kognitiivisen heikentymisen (mild cognitive impairment, MCI)) etenemistä dementiaan, tavallisimmin Alzheimerin tautiin (AT). Useita menetelmiä on ehdottu erottelemaan AT ja otsalohko dementia (frontotemporaali dementia (FTD), joka on melko yleinen muistisairaus nuoremmissa ikäryhmissä. Erotusdiagnoosi ei ole helppoa, koska on kliinisissä oireissa ja biologisissa merkkiaineissa on osittain samankaltaisuutta näissä sairauksissa. Tässä väitöskirjassa tutkittiin uutta menetelmien yhdistelmää AT, MCI ja FTD välisessä erotusdiagnostiikassa. Työssä käytetään työkalua, Disease State Index (DSI, sairausindeksi) ja sen visuaalinen vastinetta, Disease State Fingerprint (taudin sormenjälki), mikä yhdistää tietoja ja helpottaa lääkäriä profiilimaan potilaan. Ensimmäisessä osatyössä vertailtiin hippokampuksen tilavuusmittauksen (volumetrian, HV), tensor-based morphometrian (TBM) ja voxel-based morphometrian (VBM) tarkkuutta erottaa FTD, kontrolleista sekä AT ja MCI potilaista. Kontrollit voitiin erottaa hyvin FTD potilaista käyttämällä HV (tarkkuus=0.83), TBM (0.82) ja VBM menetelmiä (0.83). VBM oli tarkin erottamaan FTD ja AT potilaat (0.72). Toisessa osatyössä verrattiin FTD, AT, MCI ja kontrolli ryhmiä siten, että DSI sisälsi MRI:n lisäksi myös likvorin (CSF) biologiset merkkiaineet, APOE ja MMSE testin tulokset. Paras tarkkuus saatiin FTD ja kontrolli ryhmien vertailussa (0.84), FTD MCI vertailussa (0.79) ja alhaisin FTD / AT vertailussa (0.69). MRI oli tärkein FTD /MCI ja FTD/AT erottelussa. Kolmannessa osatyössä FTD / AT / kontrollit vertailussa DSI sisälsi myös oireiden arviointiasteikkoja, laajempia neuropsykologisia testejä, MRI, SPECT, APOE ja CSF tuloksia. Paras tarkkuus saavutettiin kontrolli / AT (0.99) ja kontrolli / FTD (0.97) vertailuissa. Myös AT potilaat voitiin erottaa FTD potilaista (0.86). Kliiniset oireet ja neuropsykologiset testit olivat tärkeimmät AT ja FTD erottelussa. Kuvantamistutkimuksista MRI oli erityisen hyödyllinen erottamaan terveet AT potilaista, mutta SPECT oli merkityksellinen erottamaan FTD kontrolleista ja AT potilaista. Neljännessä osatyössä tutkittiin DSI:n yleistettävyyttä 875 MCI potilaalla neljässä kohortissa (ADNI, DESCRIPA, AddNeuroMed and Kuopio L-MCI). Tämä työ tutki tarkkuutta ennustaa MCI:n etenemistä dementiaan (AT). Analyysiin otettiin mukaan MRI (HV, TBM, VBM) sekä CSF tulokset, neuropsykologisia testejä, MMSE ja APOE. MRI yksin saavutti hyvän tarkkuuden (0.67-0.81) neljässä kohortissa. Tuloksen paranivat hieman lisäämällä MMSE:n arvot, APOE, CSF ja neuropsykologia testituloksia. Ennustearvon tarkkuus yhdistetyssä kohortissa (0.70) oli lähellä keskimääräisen yksittäisten kohorttien tarkkuutta (0.68-0.82). Tutkimus osoitti DSI:n yleistettävyyden myös eri kohortteja käytettäessä, jos kohortit ovat riittävän samanlaisia ja sisältävät samoja muuttujia.

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Tulokset osoittivat, että käytetyillä MRI menetelmillä päästään hyvään tarkkuuteen tutkittujen muistisairauksien erotusdiagnostiikassa. Testien, kuvantamisen ja biologisten merkkiaineiden tuloksia yhdistävä DSI voi tukea diagnostiikkaa muistisairauksissa. Luokitus: WL 141.5, WL 358.5, WT 155, WN 185 Yleinen Suomalainen asiasanasto: Alzheimerin tauti; merkkiaineet; Diagnoosi-tietokoneavusteisuus; Dementia; Erotusdiagnostiikka; Otsalohkodementia; Hippokampus; Magneettitutkimus; Neurologia-kuvantaminen; Neuropsykologia-testit

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To my family

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Acknowledgements

This work has been possible due to the multidisciplinary work of several partners, from a great variety of fields. I want to thank everyone who has helped, supported, shared moments with me, which are lots of people. Special thanks to: First of all, my greatest thanks to Professor Hilkka Soininen, main supervisor. Example of hard-working, leadership, always available although the lots of work she had, especially when she became dean of the Faculty of Health Sciences. For being an excellent supervisor and also an excellent person who has encouraged me to persuade in working hard for staying in Finland. Thanks to Päivi Hartikainen, co-supervisor. Still I remember when I was an exchange student at KYS and she gave me the chance to meet Hilkka and come back to Finland after my graduation as MD. A great supervisor and an excellent person, also with lots of work, who always has found time for attending me and supervising my research. Thanks to Yawu Liu, co-supervisor, for his great help in the daily work concerning my PhD articles and general research work. Thanks to Associate Professor Vesa Kiviniemi and Professor Matti Viitanen for reviewing this thesis and to Professor Alberto Lleó Bisa for being the opponent during the defense. Thanks to Anette Hall, for his enormous help and collaboration, always ready for answering every question kindly. Also for her help to write the methods part of this thesis and her work as co-author in the fourth article. Thanks to people working in PredictAD project, from which all the work has been developed. Particularly thanks to Jyrki Lötjönen, Juha Koikkalainen and Jussi Mattila from VTT in Tampere. Without their help, all these work could not have been done. It is always a pleasure visiting VTT and learning from them. Thanks to Ewen Macdonald, for making the language check of this thesis. Thanks to Professor Hannele Turunen for editing this thesis. Thanks to the other co-authors and collaborators, whose contribution has been essential for coming up with all the publications. Thanks to all the funding sources: Doctoral Programme in Molecular Medicine, European Union 7th Framework Program PredictAD, European Union 7th Framework Program VPH-DARE@IT (grant agreement No 601055), EVO grant from Kuopio University Hospital, Instrumentarium grant and the Faculty of Health Sciences. To Esa Koivisto, Tuija Parsons, Mari Tikkanen and Sari Palviainen from UEF and Tuula Toivanen from KYS, for their great help and assistance always when needed. To Merja Hallikainen, Tuomo Hänninen, Anne Koivisto, Professor Anne Remes and Professor Mikko Hiltunen for their comments when writing this thesis and/or preparing its’ defense. Thanks to people from VPH-DARE@IT in Sheffield, in particular to Professor Alejandro Frangi, Professor Annalena Venneri, Daerdre McGrath, Luigi Di Marco and Leandro Beltrachini. These few months in the UK have been a new and refreshing experience to me. To Álex for his tutoring and his kindness. To Annalena for her teaching about neuropsychological testing and her kindness during my time in Hallamshire Hospital. To

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Leandro, Daerdre and Luigi: working with them in CISTIB was an invaluable experience which I do recommend; I learned that engineers and medical people can do a great work together, no matter how distant our fields are. In particular thanks to Leo for his help writing the imaging section of the methods part and his time in Sheffield. We definitely have to go together to watch a Premier League match in Manchester! Thanks to Lauri Kivikoski for his collaboration within the PredictAD pilot project at KYS. To Sanna-Kaisa Herukka, Laura Kela, Maria Falkenberg, Ossi Nerg, Niina Happonen, Anna Railo and Terhi Laitinen for their support and help with Finnish language. Especially to Anna, who helped me during winter time in 2011. The first steps are always the hardest to take, and without her help it could not have been possible. Also to Ossi for being a friend who has made of me a new KalPa fan. To Taru Heikkinen for her friendship and the phrase of 2014: voe tokkiisa! Particularly thanks to Arja and Kari Savolainen, who have treated me like one of their children. I have learned Finnish with them, but because of them I have learned to understand and appreciate Finnish people like them, which is even more important. Most of the best moments I have lived in Finland have been with them. Nothing is more important than a family, and with them I have found a new one in Kuopio. Also special thanks to Alberto and Sharon Salgado, who have accompanied me during this time in Finland since the very first moment I met them in 2010. Thanks to Jussi Nokelainen and Valtteri Kokkonen. I met them in 2010 when they were my tutors, and then started an invaluable friendship that remains. Looking forward to watch the next Champions League match on TV with them! Thanks to Heli Nuutinen and her family for their support and friendship since 2010. Thanks to Harri Ruhanen for the Spanish conversations while having beers in Malja. To Carmen Plumed and Javier Ortega. With them I have felt like being in Spain again, despite the rainy and snowing days typical from the Finnish weather. To Bhima, Lili and Lan for their friendship and the funny moments lived; our coffee time at 16.00 is now a tradition that should continue. Especially to Bhima, for all the good moments lived since we met in the Winter School in 2012. Thanks to Anu Jormanainen, Vicent Tortosa, Alvaro Herrera and Pepe and his family, for the great moments we have shared either in Tampere, Helsinki, Jyväskylä or Joensuu. I remembered when I was with Alvaro in the EILC in Jyväskylä in 2010, when we wondered who would be coming back to Finland… and we both were here in Finland again during 2013. Life is always unpredictable! Thanks to Nacho Navero, best friend and permanent link from London, for the never-ending Facebook and Skype conversations, always there for cheering up my day. To Jordi Guinot for all the conversations about football, medicine and life in general. To Rogelio Monfort, Juan Hervás and Herminio Morillas for their friendship during these years. Thanks to Miguel Martínez for his support and guidance within the years. Last but not least, my biggest thanks go to my family: to my brothers Álex and Fran, and my parents Francisco and Trinidad. Always source of support and love, no matter how long is the distance. I would need all the pages in a book for thanking what they have done, and it never would be enough. They always supported me in the good and in the bad moments, encouraging me to study medicine at first and then doing a PhD in dementia and starting my medical specialty training in Finland. But above all, they encourage me to be a

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better person day after day and to be a good professional; this is working hard every day, with dignity and respect. It does not matter if it is big or small the goal you try to achieve, what matters is that you do your best for getting it. This thesis, or better saying, all the work done through these years, is entirely dedicated to my family. Finally, I would like to quote the words from Pascual Maragall, ex-former president of the Generalitat of Catalunya (Spain), currently suffering from Alzheimer’s disease, as our road route for studying dementia within the following years: “I want to help to defeat this disease; personally and collectively. Nowhere is it written that is invincible” Kuopio, May 2014 Miguel Ángel Muñoz-Ruiz

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List of the original publications

This thesis is based on the following original publications, referred by their Roman numbers in the text.

I. Muñoz-Ruiz MÁ, Hartikainen P, Koikkalainen J, Wolz R, Julkunen V, Niskanen E, Herukka S-K, Kivipelto M, Vanninen R, Rueckert D, Liu Y, Lötjönen J and Soininen H. Structural MRI in Frontotemporal Dementia: Comparisons between Hippocampal volumetry, Tensor-based morphometry and Voxel-based morphometry. PLoS ONE 7(12): e52531.

II. Muñoz-Ruiz MÁ, Hartikainen P, Hall A, Mattila J, Koikkalainen J, Herukka S-K,

Julkunen V, Vanninen R, Liu Y, Lötjönen J and Soininen H. Disease Fingerprint in frontotemporal degeneration with reference to Alzheimer’s disease and mild cognitive impairment. J Alzheimers Dis. 2013 Jan 1;35(4):727-39. doi: 10.3233/JAD-122260.

III. Muñoz-Ruiz MÁ, Hall A, Mattila J, Koikkalainen J, Herukka S-K, Husso M,

Hänninen T, Vanninen R, Liu Y, Hallikainen M, Lötjönen J, Soininen H and Hartikainen P. Validating the Disease State Fingerprint for diagnosing frontotemporal lobar degeneration. Submitted.

IV. Hall A*, Muñoz-Ruiz M*, Mattila J, Koikkalainen J, Tsolaki M, Mecocci P,

Kloszewska I, Vellas B, Lovestone S, Visser PJ, Lötjönen J, Soininen H and and collaborators from DESCRIPA, the Kuopio L-MCI study, AddNeuroMed consortium and Alzheimer Disease Neuroimaging Initiative. Generalizability of the Disease State Index in Predicting Mild Cognitive Impairment Progression to Alzheimer’s Disease in Four Different Cohorts. Submitted. * Authors have equal contribution to this study.

The publications were adapted with the permission of the copyright owners.

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Contents

1 INTRODUCTION ............................................................................... 1 2 REVIEW OF THE LITERATURE ..................................................... 3

2.1 Ageing and dementia .................................................................... 3 2.2 Mild cognitive impairment ........................................................... 5 2.3 Frontotemporal lobar degeneration............................................. 7

2.3.1 History and nosology ........................................................... 7 2.3.2 Epidemiology, classification and risk factors ................... 8 2.3.3 Neuropathology and pathophysiology ............................. 15

2.4 Alzheimer’s disease ....................................................................... 18 2.4.1 History and nosology ........................................................... 18 2.4.2 Epidemiology, classification and risk factors ................... 18 2.4.3 Neuropathology and pathophysiology ............................. 25

2.5 Imaging techniques ........................................................................ 28 2.5.1 Conventional MRI ................................................................ 28 2.5.2 Advanced MRI methods ....................................................... 31 2.5.3 SPECT ...................................................................................... 34 2.5.4 PET ........................................................................................... 35

2.6 Diagnostic methods and biomarkers ........................................... 38 2.6.1 Cerebrospinal fluid ............................................................... 39 2.6.2 Blood analysis ....................................................................... 40 2.6.3 Clinical and neuropsychological tests ............................... 41 2.6.4 Combination of biomarkers ................................................ 43

2.7 Predict AD ....................................................................................... 51 2.7.1 Automatic quantitative techniques .................................... 51 2.7.2 Disease State Index and Disease State Fingerprint ........... 51

3 AIMS OF THE STUDY ...................................................................... 53 4 SUBJECTS AND METHODS ........................................................... 55

4.1 Subjects ............................................................................................ 55 4.2 Acquisition ...................................................................................... 59

4.2.1 MRI ......................................................................................... 59 4.2.2 SPECT ..................................................................................... 59

4.3 Imaging and analysis methods ..................................................... 60 4.3.1 Volumetry .............................................................................. 60 4.3.2 Tensor-based morphometry ................................................ 62 4.3.3 Voxel-based morphometry .................................................. 62

4.4 Biomarkers....................................................................................... 63 4.4.1 CSF analysis ........................................................................... 63 4.4.2 APOE genotype ..................................................................... 64

4.5 Disease State Index and Disease State Fingerprint.................... 64 4.5.1 Disease State Index ............................................................... 64 4.5.2 Disease State Fingerprint ..................................................... 65 4.5.3 Evaluation .............................................................................. 67

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5 RESULTS .............................................................................................. 69 5.1 Study I .............................................................................................. 69 5.2 Study II ............................................................................................ 71 5.3 Study III ........................................................................................... 73 5.4 Study IV ........................................................................................... 77

6 DISCUSSION ...................................................................................... 81 6.1 Morphometric imaging methods (study I) ................................. 81 6.2 Comparison between diagnostic methods (studies II-IV)........ 83 6.3 General discussion (studies II-IV)................................................ 88 6.4 Disease State Index and Disease State Fingerprint (studies II-IV) 92 6.5 Future studies ................................................................................. 100

7 CONCLUSIONS ................................................................................. 101 8 REFERENCES ...................................................................................... 103 ORIGINAL PUBLICATIONS (I-IV)

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Abbreviations Aβ Amyloid beta

AD Alzheimer’s disease

ADL Activities of daily living

ALS Amyotrophic lateral sclerosis

APOE Apolipoprotein E

APP Amyloid precursor protein

ARWMC Age-related white matter

changes

ASL Arterial spin labelling

AV Automatic volumetry

BIBD Basophilic inclusion disease

BMI Body mass index

bvFTD Behavioral variant of FTD

BOLD Blood oxygen level

dependent

C Controls

C9ORF72 Chromosome 9 open reading frame 72

CBD Corticobasal degeneration

CBF Cerebral blood flow

CBS Corticobasal syndrome

CDR Clinical dementia rating

CHMP2B Charge multivesicular body

protein 2B

Cho Choline

COPD Chronic obstructive

pulmonary disease

Cr Creatine/phosphocreatine

CSF Cerebrospinal fluid

CTH Cortical thickness

DLB Dementia with Lewy bodies

DMN Default mode network

DSC Dynamic susceptibility

contrast

DSF Disease State Fingerprint

DSI Disease State Index

DTI Diffusion tensor-imaging

EOAD Early onset Alzheimer’s

disease

EOD Early onset dementia

FA Fractional anisotropy

FBI Frontal behavioral inventory

FLAIR Fluid-attenuated inversion

recovery

fMRI Functional MRI

FTD Frontotemporal dementia

FTLD Frontotemporal lobar

degeneration

FUS Fused in sarcoma

GDS Global deterioration scale

GM Grey matter

GRN Progranulin

GWAS Genome wide association

studies

HC Hippocampus

HIS Hachinski ischemic score

HV Hippocampal volumetry

LA Logopenic aphasia

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LOAD Late onset Alzheimer’s

disease

LOD Late onset dementia

MAPT Microtubule associated

protein tau

MB Microbleed

MBL Manifold-based learning

MCI Mild cognitive impairment

MD Mean diffusifity

MI Myoinositol

MMSE Mini-Mental State

Examination

MND Motoneuron disease

MRI Magnetic resonance imaging

MRS Magnetic resonance

spectroscopy

MTA Medial temporal lobe atrophy

MTL Medial temporal lobe

NAA N-acetyl aspartate

NFT Neurofibrillary tangles

NIFID Neuronal intermediate

filament inclusion disease

NPI Neuropsychiatric inventory

P-Tau Phosphorylated Tau

PET Positron emission

tomography

PIB Pittsburg Compound B

PMCI Progressive mild cognitive

impairment

PNFA Progressive non-fluent

aphasia

PPA Primary progressive aphasia

PS-1 Presenilin-1

PS-2 Presenilin-2

PSP Progressive supranuclear

palsy

PWI Perfusion weighted imaging

RF Risk factor

ROI Region of interest

RSN Resting state networks

SCI Subjective cognitive

impairment

SD Semantic dementia

SMCI Stable mild cognitive

impairment

SN Salience network

SPECT Single photon emission

computed tomography

TMT Trail making test

T-Tau Total Tau

TARDBP TAR DNA binding protein

TBM Tensor-based morphometry

UPS Unknown pathological

substrate

VaD Vascular dementia

VBM Voxel-based morphometry

VCP Valosin containing protein

WM White matter

WMS Weschler memory scale

1

1 Introduction

Among different memory patients there is always a real-life drama, with the patient as the main character and family and friends who have to live and help the patient - these are the secondary characters. The patient deserves a reasonable explanation and care for the memory or behavioral symptoms causing mental and physical disability. In 2008, the total cost of dementia in Europe was €177 billion, of which 56% were the costs attributable to informal care (Wimo et al. 2011). With respect to Finland, the latest report from 2004 estimated dementia to be the most expensive brain disorder, in particular due to the direct non-medical costs (Sillanpaa, Andlin-Sobocki & Lonnqvist 2008). It has been estimated than 35.6 million people were living with dementia worldwide in 2010 (Prince et al. 2013). Alzheimer’s disease (AD) is the most prevalent disease of the dementia diseases, followed by frontotemporal dementia (FTD) in people younger than 65 years of age (Ratnavalli et al. 2002). FTD is a neurodegenerative disease characterized by behavioural and/or language impairments (Neary et al. 1998). There is no cure, only symptomatic treatment and even its efficacy is not impressive. FTD has been reported to be under diagnosed in the elderly (Baborie et al. 2012, Rossor et al. 2010), being confused with psychiatric syndromes. AD is a neurodegenerative disease for which there is no cure. This disease starts with forgetfulness of minor things and then it spreads to affect several domains which hinder daily routines. There are some drugs available, however there is no current treatment which can delay or stop the onset of the disease. AD and other dementias are the 11th most important cause of disability-adjusted life years in western-Europe. The incidence of neurological disorders including dementia has increased from 1.9 to 3% over the two decades (Murray et al. 2012). Both early and precise diagnosis of these two dementia diseases is needed if one wishes to plan new drug trials. This is the only way to truly benefit these patients and it will require that these patients receive treatment as soon as possible. The prevention and control of risk factors (Qiu, Kivipelto & von Strauss 2009) have been stated as a door which can lead to a better understanding and hopefully to a decrease in the risk of developing AD. Several advances have been made in the recent years, especially in developing new diagnostic methods. New guidelines have been postulated for both FTD (Rascovsky et al. 2011) and AD (Dubois et al. 2010) have attempted to gather all of the possible findings that can lead to a certain diagnosis. There is still a need to identify new methods that could achieve an earlier and more precise diagnosis, and to integrate all these data originating from multiple sources, in order to facilitate the clinical diagnosis. This thesis introduces the use of a new combination of different methods in the differential diagnosis of AD, mild cognitive impairment stages and FTD, and a tool that collates data from different sources to help clinicians to profile a patient as having either AD or FTD.

2

3

2 Review of literature

2.1 AGEING AND DEMENTIA

Brain ageing can be classified as normal ageing and pathological aging (Barkhof et al. 2011). Within the normal group there are two apparent types i.e. successful aging and typical aging (Barkhof et al. 2011). With successful ageing, there is no any discernible deterioration i.e. minimal morphological and physiological loss relative to younger individuals. These are elderly people with borderline normal appearance of the brain when imaged. Some of these changes may appear gradually and do not represent disease. Typical (usual) ageing may encompass a variety of changes in the brain, not only overall shrinkage, but also distinct local alterations such as white matter changes or specific findings associated with vascular risk factors. There are some changes that can be identified in typical ageing and which are wrongly associated only with pathological ageing: brain volume loss, enlarged perivascular (Virchow-Robin) spaces, punctiform or minor white matter (WM) abnormalities associated with vascular risk factors and other cerebrovascular lesions also known as age-related white matter changes (ARWMC), iron accumulation in the basal ganglia, amyloid deposition, ventricular enlargement and cerebral microbleed (MB) (Barkhof et al. 2011). Finally, certain degenerative changes may make the individual susceptible to the appearance of certain age related diseases ultimately leading to ageing. There is a theory that aging starts when the brain, the last organ which concludes its development in early adolescence, starts to lose its volume steadily; this occurs through middle adulthood and sharply after 55 years of age (Courchesne et al. 2000). There is a more specific cut-off established usually at the age of 65 years of age, which basically matches the time when people can retire from work. Thus one can define early-onset dementia (EOD) as occurring below the age of 65 years, and late-onset dementia (LOD) after this age. According to WHO, dementia can be defined as a syndrome in which there is deterioration in memory, thinking, behavior and the ability to perform everyday activities. Dementia is a cause of disability and dependency among the elderly and it exerts a major physical, mental, social and economic impact on caregivers, families and society. There are two common assumptions made by many lay people: first, the tendency to consider dementia as a part of normal ageing, when it is not, although it is strongly associated with aging. In fact it is pathological aging. Second, dementia is a syndrome, it is not a disease. There are several diseases that can be considered to cause dementia. According to the widely accepted Diagnostic and Statistical Manual of Mental Disorders, 4th ed (DSM-IV) (American Psychiatric Association, 1994), the following criteria can be used for diagnosing dementia: memory deficits, one or more cognitive deficit as aphasia, apraxia, agnosia and executive dysfunction; the symptoms have to cause impairments in social and occupational functioning, registered by the activities of daily life questionnaire

4

(ADL); evidence for a systemic or brain disorder; and no clouding of consciousness, i.e. the deficits do not occur exclusively in the course of a period of delirium. Apraxia refers to difficulty in sequencing voluntary purposeful motor movements (such as dressing), agnosia is a difficulty in processing sensory input (i.e. in recognizing objects by sight), and disturbances in executive functioning to difficulty in planning and organizing activities (Kawas 2003). Aphasia refers to impairment in language function (i.e. difficulties in finding words, pronunciation or some other kind of speech problem). Another criteria classification widely accepted is that by the National Institute on Aging-Alzheimer’s Association (NIA-AA) workgroup or McKhann criteria (McKhann et al. 2011), in which have criteria for the threshold of dementia. It contains the requirements from DSM-IV, except that in NIA-AA criteria one does not necessarily need to identify a memory problem. Nonetheless the latest DSM-V includes a new modification: because of the stigmatization associated with the term dementia, it has been proposed that physicians should use the terms major and mild neurocognitive disorders instead of dementia. The International Classification of Diseases (ICD-10) used in clinical work also contains the term dementia, and different criteria for diagnosing various dementia diseases. One can classify dementia diseases into two groups: neurodegenerative or progressive and non-neurodegenerative or non-progressive dementia diseases. The neurodegenerative group contains the five most common dementia diseases: Alzheimer’s disease (AD), vascular dementia (VaD), frontotemporal dementia (FTD), dementia with Lewy-bodies (DLB) and Parkinson’s disease with dementia. The frequency of these main dementia diseases has been revised over the years. AD is by far the most common subtype (50-70%), followed by VaD (10-25%), DLB (15%) and FTD (5-10%) (Lobo et al. 2000, Fratiglioni et al. 2000, McKeith et al. 1996). Today, there have been proposals for a new distribution, and although AD still would be the most common subtype of dementia, FTD and DLB would have higher rates and a mixed-dementia due to the combination of AD and VaD would also display a higher rate. This could be a consequence of more precise and definitive diagnosis, and due to the fact that now there is considered that there are more dementias than simply those due to AD and it is accepted that mixed cases are more common than previously believed. Despite this new window for diagnosing each particular dementia disease and not simply considering all types of dementia as being AD, there is another problem; even in high income countries, only 20-50% of dementia cases are recognised and documented in primary care (alz.co.uk). The increase of age is the major risk factor for developing dementia nevertheless one should not forget that below 65 years of age there is the so-called pre-senile dementia or EOD, of which AD is the most common type followed by VaD and FTD (Vieira et al. 2013), and the oldest-old range (>85 years of age), where AD is the most common subtype, frequently associated with vascular cerebrovascular changes as demonstrated in some pathological studies (Corrada, Berlau & Kawas 2012). Within the non-neurodegenerative group, there are etiologies that one usually does not link with dementia, and these may be reversible at least to some extent, such as depression or hydrocephalus (Hejl, Hogh & Waldemar 2002).

5

2.2 MILD COGNITIVE IMPAIRMENT

The term mild cognitive impairment (MCI) generally describes the transitional zone between normal cognitive function and clinical AD (Winblad et al. 2004). If one considers that there is a continuum of brain ageing, then MCI would represent the intermediate stage on the route from age-associated memory impairment, the term used before MCI (Crook et al. 1986) , to AD (Small et al. 2008). According to NIA-AA, the main difference between MCI and dementia is based on whether there is interference in the ability to function at work or in usual daily activities (McKhann et al. 2011). The probability of developing dementia from MCI still is a question open to debate. One meta-analysis has suggested that less than half of the MCI cases actually progress to AD, with an annual rate of conversion of around 10%, and many cases do not convert even after 10 years of follow-up (Mitchell, Shiri-Feshki 2009). Nevertheless some studies have found a higher rate of progression, such as in the study from Bennet et al., with over 30% of MCI cases converting to AD on an average of 4.5 years’ follow-up (Bennett et al. 2002), and the study of Petersen et al., which described an up to 80% rate of conversion after 6 years (Petersen et al. 2001). MCI starts with a cognitive complaint: not normal for age, not for a demented subject, displaying a cognitive decline and maintaining normal functional activities. Once one concludes that these fulfill the criteria for MCI, we need to consider which cognitive domains are affected: if there is memory impairment, this is referred to as amnestic MCI or multi-domain amnestic MCI if the amnestic episode is accompanied by other features, while if there is no memory impairment, it may be necessary to distinguish between single non-memory MCI and multidomain non-amnestic MCI (Winblad et al. 2004). Most studies mainly focus on the amnestic and the multi-domain amnestic MCI subtypes, which are more likely to progress to AD (Petersen et al. 2001). However there is also a need to classify MCI according to the domains affected, and include all these subtypes into studies and trials, because most of the studies simply consider the amnestic subtype as MCI, and do not follow any standardized criteria (Christa Maree Stephan et al. 2013). In clinical practice it is usual to follow the criteria proposed by Petersen et al., (Petersen et al. 1997) (Table 1).

Table 1. Clinical diagnosis of Mild Cognitive Impairment devised by Petersen et al., (Petersen et al. 1997)

MCI

Diagnostic criteria A. Complaint of defective memory

B. Normal activities of daily living

C. Normal general cognitive function

D. Abnormal memory function for age

E. Absence of dementia

6

When MCI patients are followed over time, they may develop AD, some other type of dementia, some patients remain stable and a very few number of cases may even recover (Winblad et al. 2004). There are no large studies measuring the rate of progression of MCI to other types of dementia, only studies with small cohorts including MCI and neurodegenerative dementia subtypes (Galluzzi et al. 2013). The emergence of new biomarkers, and new research criteria for MCI and AD (Dubois et al. 2007, Sperling et al. 2011), could predict the appearance of dementia earlier than when symptoms arise. In order to clarify the use of the term MCI and AD, Dubois et al., (Dubois et al. 2010) have tried to collect the terms related to MCI in order that they can be more accurately defined. A summary with the terminology and the information required for each term can be found in Table 2.

Table 2. MCI lexicon. Modified from (Dubois et al. 2010)

MCI AD diagnosis Impairment in specific memory tests

Biomarkers in vivo

Additional requirements

MCI No Not required Not required Not required

Preclinical AD

- Asymptomatic at risk for AD

No Not present Required Absence of symptoms of AD

- Presymptomatic AD No Not present Not required

Prodromal AD Yes Required Required Absence of dementia

In addition, three terms should be considered: first, subjective cognitive impairment (SCI) as a phase prior to MCI where there are already differences in cognitive tests and hippocampal volumes between SCI subjects and age-matched non-SCI subjects (Reisberg et al. 2008); second, Albert et al., have categorized different MCI subgroups depending on the positivity of certain biomarkers which were already included in the Dubois criteria. According to this proposal, there is a high, an intermediate or no likelihood of having MCI due to AD (Albert et al. 2011). Third, vascular cognitive impairment and MCI are considered as different entities, and diseases and vascular risk factors are not usually considered in the criteria for MCI. It would be useful to study the association between MCI and these vascular risk factors, as half of the patients with vascular cognitive impairment do develop AD or mixed AD during follow-up (Stephan et al. 2009). Finally, there is an on-going effort to adapt the term MCI not only to general pre-dementia stage or pre-AD, but to other dementia diseases as proposed (Dubois, Albert 2004), such as VaD (Gorelick et al. 2011) or Parkinson’s disease (Goldman, Litvan 2011).

7

2.3 FRONTOTEMPORAL LOBAR DEGENERATION Frontotemporal lobar degeneration (FTLD) is an umbrella term that includes diseases which affect the frontal and temporal lobes in the brain, and cause behavioral and/or language impairment (Neary et al. 1998). FTLD is composed of a variety of different clinical and pathological syndromes caused by different histopathological atrophic changes in the anterior brain areas and related anatomical connections. Frontotemporal dementia (FTD) is the most common clinical subtype in FTLD. FTD consists of a variety of frontal type behavioral symptoms, referred to also as behavioural variant of FTD (bvFTD). FTD predominantly affects young patients, it has been underestimated as a cause of dementia in the elderly (Baborie et al. 2012), as it includes a symptomatic memory loss, resembling more AD than FTD (Baborie et al. 2012).

2.3.1 History and nosology Arnold Pick in 1892 described atrophy in the frontal and temporal lobes of patients who presented personality change and language impairment (Kertesz et al. 2005). Then Warrington (1975) and Mesulam (1982) described progressive language disorders in the Western literature. Warrington depicted a group of patients displaying a selective impairment of semantic memory (Warrington 1975). Mesulam depicted patients who were exhibiting progressive language problems that he called “primary progressive aphasia” (PPA), this included impairment in both production and comprehension (Mesulam 1982). In 1994 the Lund-Manchester criteria (The Lund and Manchester Groups, 1994) included in addition to the neuropathologic signs, clinical criteria for FTD. The FTD core diagnostic features consisted of a combination of behavioural disorders, affective symptoms, speech disorder, preservation of spatial orientation and praxis. Clinical motor neuron disease (MND) was noticed among supportive diagnostic features as well as the pathological changes of the MND type. The still current consensus clinical criteria for FTLD were described in 1998 by Neary and colleagues (Neary et al. 1998). The Neary criteria included clinical features for the bvFTD, progressive non-fluent aphasia (PNFA) and semantic dementia (SD). In 2001, McKhann and colleagues (McKhann et al. 2001) published a large detailed report about clinical and neuropathological correlations and specifications of terms. Furthermore some clarification of nosology was given after a consensus conference held in 2003 about Pick’s disease, originally regarded as a clinical disease, but subsequently the use of the term was narrowed to limited cases with specific Pick bodies as a microscopic finding in conjuction with macroscopic anterior brain atrophy. Pick type neuropathology was already described in the Lund & Manchester criteria. Pick’s disease manifests with frontotemporal dementia and typical Pick type neuropathological changes. Similarly, progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) were given more diverse meanings correlating with neuropathological changes in the spectrum of FTLD pathologies. In 2003, Kertesz described the so-called Pick complex or clinical Pick’s disease, which included the same entities as the McKhann criteria, adding the association of MND with FTD; the term Pick does not have to be associated with the appearance of Pick bodies, and this complex should be viewed as representing the commonalities between these diseases rather than the differences (Kertesz 2003).

8

2.3.2 Epidemiology, classification and risk factors Epidemiology: FTD is a frequent cause of dementia in people less than 65 years of age (Ratnavalli et al. 2002), consisting up to 27% of all cases (Vieira et al. 2013). Earlier, the diseases of the FTLD group were thought to represent only 5-10% of all dementia cases at different age groups (Lobo et al. 2000, Fratiglioni et al. 2000). FTLD syndromes commonly occurs when people are in their sixties (Hodges et al. 2004, Rosso et al. 2003a, Harvey, Skelton-Robinson & Rossor 2003) although it could start already in younger individuals in their thirties (Harvey, Skelton-Robinson & Rossor 2003) and also in the very old (Gislason et al. 2003, Pikkarainen, Hartikainen & Alafuzoff 2008). The mean of age at onset is lower in FTD compared to AD (Ratnavalli et al. 2002). Of all FTLD cases, only 20-25% occur in people over 65 years of age (Rosso et al. 2003a). In the elderly, FTLD syndromes are believed to be a different entity from early onset FTLD and it is considered to be underdiagnosed in the elderly (Baborie et al. 2012). Familial cases are more prevalent than sporadic cases, even in the elderly (Borroni et al. 2013). The average age of onset does not differ greatly between familial and sporadic cases (Piguet et al. 2004). Further, no male or female preponderance has been described (Rosso et al. 2003a, Borroni et al. 2011) albeit there are some studies which reported mild (Harvey, Skelton-Robinson & Rossor 2003) or even a high male predominance (Ratnavalli et al. 2002). In Japan, one study described the prevalence of FTLD as 12.7 % among all dementia cases (Ikeda, Ishikawa & Tanabe 2004); another study showed a frequency of 14.7% in early-onset dementia while in late-onset dementia FTLD accounted only for 1.6% of all cases (Yokota et al. 2005). The highest prevalence has been reported in two studies performed in the UK among patients between 45 and 65 years of age, with a prevalence rate between 81 and 98.1 per 100,000 inhabitants (Ratnavalli et al. 2002, Harvey, Skelton-Robinson & Rossor 2003). Harvey et al., stated that the prevalence increased with age. One northern Italian study found a FTLD prevalence of 22 per 100,000 inhabitants between 44 and 65 years of age, 78 per 100,000 inhabitants aged 66-74 and 54 per 100,000 inhabitants over 75 years of age (Borroni et al. 2010). Another study from the UK stated that FTLD accounted for 7.9% of all dementia cases among people over 65 years of age (Stevens et al. 2002). Very few studies have been conducted in the oldest-old however one can presume that there are FTLD cases, as was reported in one Swedish study which found a 3% of cases with the bvFTD in a population over 85 years of age (Gislason et al. 2003). The lowest rates were described in one study from the Netherlands where the prevalence of FTD was of 3.6 per 100,000 in patients aged 50-59, 9.4 per 100,000 at age 60-69 and 3.8 per 100.000 in patients between 70 and 79 years of age (Rosso et al. 2003a). This variability is among studies is due to the different settings and criteria established for selecting the patients. There is a strong association between age at onset of the diagnosis and length of survival. The mean survival ranged from 1.3 to 6.5 years depending on the study and whether only one disease was considered from the FTLD spectrum or a particular disease and the age of diagnosis. The lowest survival (1.3 years) was found when FTD was associated with MND (Brodaty, Seeher & Gibson 2012).

9

Classification: FTLD can be classified according to three patterns: clinical syndrome, neuropathological syndrome and molecular genetic syndrome (Barkhof et al. 2011, Sieben et al. 2012). These groups are divided as follows: Clinical syndromes: According to Neary and colleagues (Neary et al. 1998) (Table 3), FTLD includes 3 clinical variants which have brain atrophy or hypometabolism restricted to the frontal and mostly anterior regional brain areas, and display behavioural and/or language manifestation: bvFTD, and 2 language variants which are PNFA and SD. For PPA there are updated criteria in which PPA includes logopenic aphasia (LA) as well (Gorno-Tempini et al. 2011). Finally, there are other clinical presentations from the FTLD spectrum which present with a very low percentage of cases. The most common subtype of FTLD is bvFTD, accounting for between 30% to 50% of all cases depending on the study (Ikeda, Ishikawa & Tanabe 2004, Kertesz et al. 2007). BvFTD is characterized by changes in the personality and social conduct. Table 4 presents the recent criteria (Rascovsky et al. 2011), which have been proposed to replace FTD from the previous Neary criteria (Neary et al. 1998).

10

Tabl

e 3.

Dia

gnos

tic c

rite

ria

of F

TLD

acc

ordi

ng t

o N

eary

and

col

leag

ues

(Nea

ry e

t al

. 19

98)

FTD

P

NFA

S

D

Cor

e di

agno

stic

fea

ture

s (m

ust

be p

rese

nt for

mak

ing

a di

agno

sis)

:

A.

Insi

diou

s on

set

and

grad

ual p

rogr

essi

on

B.

Earl

y de

clin

e in

soc

ial i

nter

pers

onal

con

duct

C.

Early

impa

irm

ent

in r

egul

atio

n of

per

sona

l con

duct

D.

Earl

y em

otio

nal b

lunt

ing

E.

Early

loss

of in

sigh

t

Sup

port

ive

diag

nost

ic fea

ture

s:

A.

Beh

avio

ral d

isor

der

a.

Dec

line

in p

erso

nal h

ygie

ne a

nd g

room

ing

b.

Men

tal r

igid

ity a

nd in

flexi

bilit

y

c.

Dis

trac

tibili

ty a

nd im

pers

iste

nce

d.

Hyp

eror

ality

and

die

tary

cha

nges

e.

Pers

ever

ativ

e an

d st

ereo

type

d be

havi

our

f.

Util

izat

ion

beha

vior

B.

Spe

ech

and

lang

uage

a.

Alte

red

spee

ch o

utpu

t

b.

Ste

reot

ypy

of s

peec

h

c.

Echo

lalia

Cor

e di

agno

stic

fea

ture

s (m

ust

be p

rese

nt for

m

akin

g a

diag

nosi

s):

A.

Insi

diou

s on

set

and

grad

ual

prog

ress

ion

B.

Non

fluen

t sp

onta

neou

s sp

eech

Sup

port

ive

diag

nost

ic fea

ture

s:

A.

Spe

ech

and

lang

uage

a.

Stu

tter

ing

or o

ral a

prax

ia

b.

Impa

ired

rep

etiti

on

c.

Ale

xia,

agr

aphi

a

d.

Earl

y pr

eser

vatio

n of

wor

d m

eani

ng

e.

Late

mut

ism

B.

Beh

avio

r

a.

Earl

y pr

eser

vatio

n of

soc

ial

skill

s

b.

Late

beh

avio

ural

cha

nges

si

mila

r to

FTD

C.

Phys

ical

sig

ns:

late

con

tral

ater

al

prim

itive

ref

lexe

s, a

kine

sia,

rig

idity

, an

d tr

emor

D.

Inve

stig

atio

ns

Cor

e di

agno

stic

fea

ture

s (m

ust

be p

rese

nt for

m

akin

g a

diag

nosi

s):

A.

Insi

diou

s on

set

and

grad

ual

prog

ress

ion

B.

Lang

uage

dis

orde

r ch

arac

terize

d by

e.

Prog

ress

ive,

flu

ent,

em

pty

spon

tane

ous

spee

ch

f.

Loss

of w

ord

mea

ning

g.

Sem

antic

par

apha

sias

and

/or

perc

eptu

al d

isor

der

C.

Pres

erve

d pe

rcep

tual

mat

chin

g an

d dr

awin

g re

prod

uctio

n

D.

Pres

erve

d si

ngle

-wor

d re

prod

uctio

n

E.

Pres

erve

d ab

ility

to

read

alo

ud a

nd

wri

te t

o di

ctat

ion

orth

ogra

phic

ally

re

gula

r w

ords

Sup

port

ive

diag

nost

ic fea

ture

s:

A.

Spe

ech

and

lang

uage

: pr

ess

of

spee

ch,

idio

sync

ratic

wor

d us

age,

ab

senc

e of

pho

nem

ic p

arap

hasi

as,

surf

ace

dysl

exia

and

dys

grap

hia

B.

Beh

avio

r: lo

ss o

f sy

mpa

thy

and

empa

thy,

nar

row

ed p

reoc

cupa

tions

, pa

rsim

ony

10

11

d.

Pers

ever

atio

n

e.

Mut

ism

C.

Phys

ical

sig

ns

a.

Prim

itive

ref

lexe

s

b.

Inco

ntin

ence

c.

Aki

nesi

a, r

igid

ity a

nd t

rem

or

d.

Low

and

labi

le b

lood

pre

ssur

e

D.

Inve

stig

atio

ns

a.

Neu

rops

ycho

logy

: si

gnifi

cant

impa

irm

ent

of

fron

tal l

obe

test

s in

abs

ence

of se

vere

am

nesi

a,

apha

sia

or p

erce

ptuo

spat

ial d

isor

der

b.

EEG

: no

rmal

or

conv

entio

nal E

EG d

espi

te

clin

ical

ly e

vide

nt d

emen

tia

c.

Bra

in im

agin

g (s

truc

tura

l and

/or

func

tiona

l):

pred

omin

ant

fron

tal a

nd /

or a

nter

ior

tem

pora

l ab

norm

ality

a.

Neu

rops

ycho

logy

: no

nflu

ent

apha

sia

in t

he a

bsen

ce o

f se

vere

am

nesi

a or

pe

rcep

tuos

patia

l dis

orde

r

b.

EEG

: no

rmal

or

min

or

asym

met

ric

slow

ing

c.

Bra

in im

agin

g (s

truc

tura

l an

d/or

fun

ctio

nal)

: as

ymm

etri

c ab

norm

ality

pre

dom

inan

tly

affe

ctin

g do

min

ant

(usu

ally

le

ft)

hem

isph

ere

C.

Phys

ical

sig

ns:

abse

nt o

r la

te

prim

itive

ref

lexe

s, a

kine

sia,

rig

idity

, an

d tr

emor

D.

Inve

stig

atio

ns

a.

Neu

rops

ycho

logy

: pr

ofou

nd

sem

antic

loss

, co

mpr

ehen

sion

and

nam

ing

and/

or fac

e an

d ob

ject

re

cogn

ition

per

cept

ual

proc

essi

ng,

spat

ial s

kills

, an

d da

y-to

-day

mem

oriz

ing.

Pr

eser

ved

phon

olog

y an

d sy

ntax

, an

d el

emen

tary

b.

EEG

: no

rmal

c.

Bra

in im

agin

g (s

truc

tura

l an

d/or

fun

ctio

nal)

: pr

edom

inan

t an

teri

or

tem

pora

l abn

orm

ality

(s

ymm

etric

or a

sym

met

ric)

Tabl

e 3.

(co

ntin

ued)

Dia

gnos

tic c

rite

ria

of F

TLD

acc

ordi

ng t

o N

eary

and

col

leag

ues

(Nea

ry e

t al

. 19

98)

11

12

Table 4. Diagnostic criteria in the behavioral variant of frontotemporal dementia (Rascovsky et al. 2011) Criteria

Neurodegenerative disease Progressive deterioration of behavior and/or cognition evinced by observation or history

Possible bvFTD 3/6 of the following symptoms (persistent or recurrent symptoms):

A. Early behavioural disinhibition

B. Apathy or inertia

C. Early loss of sympathy or empathy

D. Early preservative, stereotyped or compulsive/ritualistic behavior

E. Hyperorality and dietary changes

F. Specific neuropsychological profile: executive/generation deficits with relative sparing of memory and visuospatial functions

Probable bvFTD Symptoms (A-C) + functional decline (caregiver report or CDR or Functional activities questionnaire) + imaging (frontal/temporal changes in MRI/CT or PET/SPECT)

Definite bvFTD Possible or probable criteria + Post-mortem pathology confirmation or genetic mutation

Exclusion criteria Pattern of non-neurodegenerative disorder + behavioral pattern due to psychiatric disorder + biomarker for AD (only in probable criteria)

The Neary criteria have been followed for determining a diagnosis of bvFTD. The clinical profile includes an altered behavioral and social conduct initially, with mild or no alteration of perception, spatial skills, praxis or memory. Mendez and colleagues reported a lack of sensitivity in the diagnosis of FTD if one used the Neary and Manchester-Lund criteria (Mendez et al. 2007, Mendez et al. 2008). This may due to the ambiguity of certain behavioral terms (such as “emotional blunting”), no levels for diagnosis certainty (no possible and probable categories), limited role of supportive features (such as imaging) and particularly the difficulty in diagnosing bvFTD in its earliest stages (Rascovsky et al. 2007). In order to solve these problems, Rascovsky and colleagues proposed new criteria in 2011. Clinical syndromes and features of FTD: Among FTD patients stereotypical movements are associated with compulsive-like behaviours (Mendez, Shapira & Miller 2005). Psychotic symptoms are rare (Mendez et al. 2008). Overeating and changing of dietary habits towards to a preference for sweet foods are typical of bvFTD but not for AD (Ikeda et al. 2002). The presence of amnesia should be considered also a possible and non-excluding feature of bvFTD (Graham et al. 2005). BvFTD and SD may have an earlier onset than PNFA (Johnson et al. 2005). The PPA syndrome belonging to FTLD caregory includes three language variants: PNFA, SD and logopenic (LA). Initially patients are diagnosed with progressive dysphatic language disorder suggestive to PPA, and then specific language findings refer to these

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three clinical variants (Gorno-Tempini et al. 2011). The inclusion and exclusion criteria for diagnosing PPA have been defined by Mesulam (Mesulam 2001). There are 3 inclusion criteria: the most prominent feature is difficulty with expressive language, followed by impaired daily living activities and finally aphasia is the most prominent deficit. The most prominent feature of PPA is difficulty with developing aphasia related to expressive language and the deficits are the principal cause of impaired daily living activities. The classification can be stated as “imaging-supported” if there is a specific and recognized pattern of atrophy, and “with definite pathology” if pathologic or genetic data confirm one of these variants (Gorno-Tempini et al. 2011). PNFA is a disorder where the patient experiences with difficulty in initiating speech, a slower rate of speech with phonological and grammatical mistakes and difficulties in reading and writing. There may be an impaired comprehension of syntactically complex sentences, however generally single-word comprehension and object knowledge are spared (Gorno-Tempini et al. 2011). As the diseases progresses, other symptoms can emerge such as behavioural changes which may resemble bvFTD (Knibb et al. 2006, Banks, Weintraub 2008). Nevertheless these behavioural abnormalities are less severe than in bvFTD or SD (Rosen et al. 2006). SD of FTLD category presents with impaired confrontation naming and single-word comprehension. There can be also impaired object knowledge and surface dyslexia or dysgraphia. However there are usually no alterations in repetition or speech production (Gorno-Tempini et al. 2011). SD is frequently associated with different behavioural changes (Rosen et al. 2006). SD patients show relative sparing of memory and spatial skills, and they show lack of empathy expressed as cold-hearted and self-centred behavior (Hodges, Patterson 2007, Irish, Hodges & Piguet 2014). LA represents a rare variant in the PPA group. Its core features are impaired single-word retrieval in spontaneous speech and naming and impaired repetition of sentences and phrases. Generally there are phonological errors in spontaneous speech and naming, no agrammatism and spared single-word comprehension and object knowledge and motor speech (Gorno-Tempini et al. 2011). Its major characteristic is the slow rate of speech accompanied by pauses (Chow, Alobaidy 2013). The peculiarity of LA is that is associated with a quite heterogeneous group of pathologies, especially AD pathology (Harris et al. 2013). However, it can be differentiated from AD based on several factors e.g. the onset or aggravation of anxiety disorders and a specific neuropsychological profile, including difficulties in mental calculation, preserved orientation in time and poor encoding performances in verbal memory tests (Magnin et al. 2013). In addition to these main clinical syndromes, there are other diseases which are included in the FTLD spectrum. These are PSP, CBD and MND (Kertesz 2003). PSP, corticobasal syndrome (CBS) and MND are considered parkinsonian phenotypes associated with FTLD (Espay, Litvan 2011). The association is defined with more emphasis placed on the pathological features than clinical presentations. The parkinsonian phenotypes display the main features of Parkinsonism (rigidity, postural tremor, postural instability and bradykinesia) may precede, coincide or follow the behavioural or language variant of FTD. PSP is characterized by postural instability with the patient often falling within the first year of the disease, slowing of vertical saccades and Parkinsonism minimally or unresponsive to levodopa. CBS is characterized by marked asymmetric Parkinsonism with

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ideomotor apraxia, myoclonus, dystonic posture, alien limb syndrome and sensory or visual neglect. About 35% of CBS patients exhibit CBD pathology (Espay, Litvan 2011). The major features of MND or amyotrophic lateral sclerosis (ALS) associated with FTD (FTD-MND) are a progressive frontal dementia, aphasia, Parkinsonism unresponsive to levodopa and weakness of limbs and orofacial muscles (Espay, Litvan 2011). Furthermore ALS is associated with cognitive impairment in more than 40% of the cases with no evidence of dementia, and 14% of ALS cases are associated with a dementia disease (Phukan et al. 2012). Delusions are particularly common in patients who develop FTD-MND (Lillo et al. 2010). Finally, there is a very rare disease, the inclusion body myopathy, Paget disease of bone and frontotemporal dementia (IBMPFD), caused by a mutation in valosin-containing protein (VCP) (Mackenzie et al. 2011). Histopathological syndromes: according to Mackenzie et al., (Mackenzie et al. 2010) there are four groups depending on the morphology and the immunological staining of the protein inclusions: tau (FTLD-Tau), TDP-43 (FTLD-TDP), ubiquitin proteasome system (UPS) (FTLD-UPS) and fused in sarcoma (FUS) (FTLD-FUS). Molecular genetic syndromes: Currently 7 genetic mutations have been described in FTLD: Chromosome 9 open reading frame 72 (C9ORF72), microtubule-associated protein tau (MAPT), progranulin (GRN), valosin containing protein (VCP), charged multivesicular body protein 2B (CHMP2B), TAR DNA binding protein (TARDBP) and fused in sarcoma (FUS) (Cruts, Theuns & Van Broeckhoven 2012). In addition, recent studies have associated a mutation in the SQSTM1 gene not only with Paget disease (Laurin et al. 2002) but also with FTD and ALS (Le Ber et al. 2013). Mutations C9ORF72, MAPT and GRN together explain over 80% of cases in FTLD with a strong autosomal dominant family history (Riedl et al. 2014). FTD can be classified into 3 categories according to its genetics: non-familial (sporadic), familial and autosomal dominant. The exact proportion of patients in each category has varied between studies, however most of the studies state that 40-50% of FTLD cases have a familial history (See et al. 2010). Between 10 and 40% of FTD patients have an autosomal dominant pattern of inheritance. This variability is probably due to geographical variations (See et al. 2010). MAPT and GRN mutations account for 1.9-8.9% and 4.8-10.7% respectively of all FTLD cases, the rate varying from one geographical area to another (Rohrer et al. 2009). MAPT and GRN account for a significant number of familial FTD cases, up to 43% and 20% respectively (See et al. 2010). Both MAPT and GRN genes are present in chromosome 17 (Arvanitakis 2010). VCP and CHMP2B account for less than 2% of all FTLD cases (Sieben et al. 2012). The hexanucleotide (GGGGCC) repeat expansion on chromosome 9 within the gene C9ORF72 has been associated with FTD-MND cases (Dejesus-Hernandez et al. 2012). It seems to be the most common genetic abnormality in familial FTD (25.1%) and familial ALS (37.6%) (Majounie et al. 2012). However, in the Finnish population, MAPT and GRN mutations are particularly rare (Kruger et al. 2009, Kaivorinne et al. 2008), instead the appearance of C9ORF72 repeat expansion is very common, explaining nearly 50% of familial FTD cases (Renton et al. 2011). Recently, the chromosome locus 9p21 in FTD-ALS was shown as a main cause of familial ALS in the Finnish population (Laaksovirta et al. 2010). Further investigation needs to be

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done in Finland to determine the distribution of patients with each mutation in the FTLD group. Nevertheless it is noteworthy that C9ORF72 may be present in patients who already have another gene mutation such as GRN or MAPT (van Blitterswijk et al. 2013), thus it is not possible to totally exclude the co-occurrence of two pathogenic mutations in the same patient. It is known that APOE, a gene usually linked to AD, can be present in FTD cases (Engelborghs et al. 2003). However there is some discrepancy about whether APOE ε4 and APOE ε2 can modify the risk of developing FTD (Bernardi et al. 2006, Lovati et al. 2010). Other genes which are usually associated with AD can also be present in FTD cases, such as PS1 and PS2 (Cruts, Theuns & Van Broeckhoven 2012) and the latest TREM2 (Borroni et al. 2013). Risk factors: There are very few studies concerning risk factors (RFs) for FTD. Genetic susceptibility: the major RF is the presence of a known gene mutation, already explained in the previous section. Vascular: the prevalence of heart disease was found to be lower in FTD as compared with other dementias (Kalkonde et al. 2012). Others: the prevalence of traumatic brain injury was significantly higher in FTD patients as compared with the other dementias (Kalkonde et al. 2012). Many patients with a neurodegenerative disease have received a prior psychiatric diagnosis, in particular patients with bvFTD, with depression being the most common diagnosis. Younger age, higher education and a family history of psychiatric illness increased the rate of prior psychiatric diagnosis in patients with bvFTD (Woolley et al. 2011). 2.3.3 Neuropathology and pathophysiology FTLD represents different findings in the pathology underlying same clinical diseases or syndromes (Mackenzie et al. 2010, Premi, Padovani & Borroni 2012).

FTLD with tau inclusions (FTLD-TAU): Tau is a microtubule-associated protein which is essential for microtubule stability and axonal transport. Thus it possesses a physiological and regulatory function in the healthy brain. However this protein can accumulate in different forms inside the brain and be present in many diseases complexes. There are several dementia and movement disorders which are characterized by neuropathological accumulations of abnormal filaments formed by tau. Collectively these are known as neurodegenerative tauopathies, and they include AD, FTD-ALS and PSP as well as some others (Lee, Goedert & Trojanowski 2001). FTLD-related tauopathies are classified based on the morphology and the biochemical composition of the tau inclusions. This is based on the relative proportion of three-repeat tau (3R) or four-repeat tau (4R). The classical Pick’s disease requires the presence of Pick’s bodies which are composed of ubiquitin and tau-positive inclusions. Pick bodies have predominantly 3R. With respect to the group with 4R, there are CBD and PSP presenting with parkinsonian features (Barkhof et al. 2011).

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There are less common diseases e.g. argyrophilic grain disease (4R), sporadic multisystem tauopathy with globular inclusions (4R) and diffuse neurofibrillary tangle dementia with calcifications (3R+4R) (Josephs et al. 2011).

FTLD with tau-negative and TDP-43-positive inclusions (FTLD-TDP): TDP-43 is a 414 aminoacid and the gene responsible for the production of transaction response element DNA-binding protein of 43 Kda. As described above TDP-43 pathology has ubiquitin-positive and tau-negative inclusions (FTLD-U). TDP-43 can be classified from A to D or 1 to 4 according to the characteristics of the inclusions, depending on the defining criteria (Mackenzie et al. 2011). Type A is characterized by numerous short dystrophic neurites and crescent-like or oval neuronal cytoplasmic inclusions, primarily in neocortical layer 2. Lentiform neuronal intranuclear inclusions may be present. It is noticed often with bvFTD and PNFA. Type B has a moderate numbers of neuronal cytoplasmic inclusions across all the cortical layers but very few dystrophic neurites. It is also a common type with bvFTD and MND-FTD. Type C has a predominance of elongated dystrophic neurites in upper cortical layers. It is associated with SD and bvFTD. Type D includes a large amount of short dystrophic neurites and frequent lentiform neuronal intranuclear inclusions. Type D is associated with familial IBMPFD.

FTLD with tau/TDP-43 negative and FUS-positive inclusions (FTLD-FUS): The group FTLD-U has proved to be linked mostly with TDP-43 pathology however a small amount of cases do not follow this pattern. Most of these cases had positive staining for FUS, and were classified as aFTLD-U (Mackenzie et al. 2008, Neumann et al. 2009). FUS-pathology can be present in 3 entities (Mackenzie et al. 2010): atypical FTLD with ubiquinated inclusions (aFTLD-U), cases with neuronal cytoplasmic inclusions of basophilic inclusion disease (BIBD) and cases with neuronal intermediate filament inclusion disease (NIFID). FUS-pathology is mainly linked with a frontal FTD phenotype, with behavioral changes and disinhibition among other manifestations (Mackenzie et al. 2008, Neumann et al. 2009, Hartikainen et al. 2012b). The association between FUS gene mutation and FUS pathology remains unclear (Snowden et al. 2011).

FTLD with positive immunochemistry against proteins of the ubiquitin proteasome system (FTLD-UPS): Only rarely, in FTLD-U pathology cases, the ubiquitinated protein is still unknown and these cases are classified as FTLD-UPS. So, many cases considered as being FTLD-UPS were discovered to display FUS-pathology (e.g. aFTLD-U) (Mackenzie et al. 2010).

Hypotheses: Unlike AD, in FTLD so far there is no clear hypothesis to explain the emergence and progression of each disease within the FTLD spectrum. Apart from the clearly key impact of the genetic inheritance, some roles have been proposed for certain pathological substrates, such as TDP-43 (Warraich et al. 2010). Although there can be cerebrovascular risk factors and lesions in the brain of FTLD cases, these are not different from controls, thus cerebrovascular pathology is not considered to enhance or modify the appearance or progression of FTLD (De Reuck et al. 2012b). However one imaging study indicated that WM damage should be considered as a part of the neurodegenerative process in FTLD (De Reuck et al. 2012a).

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Clinical, genetic and neuropathological correlations in FTLD: The question that remains after the description of the three classifications used in FTLD is: how are they related? Which genetic syndromes are present in which clinical syndromes? Which neuropathological finding correlates with each clinical entity and genetic mutation? Several consortiums and research groups have attempted to define the correlation between the clinical, genetical and neuropathological entities grouped in the FTLD category e.g. the diagram proposed by Sieben and colleagues (Sieben et al. 2012) (Figure 1). Other useful diagrams can be found elsewhere e.g. (Bhogal et al. 2013).

Figure 1. Diagram illustrating the clinical, genetic and neuropathological correlations in FTLD. The gray background in the genetics box represents the genetically unexplained fraction in FTLD cases overall. AGD = argyrophilic grain disease. From (Sieben et al. 2012). Reprinted from Acta Neuropathologica with permission from Springer.

It is the extensive overlapping between proteinopathies and clinical presentations, which makes the profiling of FTLD patients so difficult. A majority of FTLD-Tau cases present with bvFTD. C9ORF72 is likely to be associated with FTD-MND. PNFA and SD are associated with FTLD-TDP subtype A and C respectively. Nevertheless the associations depicted in the diagram do not always occur. There is one paper claiming that patients with FTD-MND displayed TDP-43 pathology but had no genetic mutations (Seelaar et al. 2007). Another classification proposed by Cairns et al., (Cairns et al. 2007) combined histopathological changes and genetic mutations in an attempt to classify the different diseases from the FTLD spectrum. It also compared the criteria themselves with the McKhann criteria for FTLD. Some of the clinical presentations may have AD pathology, in particular CBS and PNFA (Alladi et al. 2007).

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2.4 ALZHEIMER’S DISEASE

2.4.1 History and nosology On November 3, 1906, Alöis Alzheimer (1864-1915) during the 37th Meeting of South-West German Psychiatrists in Tübingen reported the follow-up case of Auguste Deter, a 50-year-old-woman he observed in Frankfurt, Germany, the first patient who suffered from what later would be called AD (Hippius, Neundorfer 2003). It was not until 1984 when the first clinical criteria were formulated for the diagnosis AD by McKhann and colleagues based on suggestions from the National Institute of Neurologic and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) (McKhann et al. 1984). The diagnosis of AD was based on progressive impairment of memory and other cognitive functions and the exclusion of other possible causes of dementia. Over the years, several consortia have defined new criteria for AD, including the incorporation of new diagnostic methods (Dubois et al. 2007) and determining different stages according to the findings (Albert et al. 2011). The latest proposals attempt to gather all the information coming from new diagnostic methods in order to diagnose AD at its earliest stages.

2.4.2 Epidemiology, classification and risk factors Epidemiology: AD is the most common dementia disease in every range of age (Alzheimer's Association 2012, Vieira et al. 2013). According to the Alzheimer’s Association’s report from 2012 (Alzheimer's Association 2012), one in eight people aged ≥65 years (13%) have AD. This increases with age such that about half of the people aged ≥85 years (45%) have AD. Within the AD patient group an estimated 4% are aged <65 years, 6% are 65 to 74 years, 44% are 75 to 84 years and 46% are ≥85. These are results from the USA; in different geographical areas, the proportion of patients with AD or another type of dementia may vary (Prince et al. 2013). The age of diagnosis is usually between 60 and 70 years of age, with a mean age of 65 although there can be cases in their early thirties as well as in their eighties. Dementia in general and AD in particular are classified according to the age of onset: below 65 years of age is named early-onset AD (EOAD) and over 65 years of age is referred to late-onset AD (LOAD) (Alzheimer's Association 2012). This is relevant because there may be different causes which lead to AD. In addition, the EOAD group may present at onset with a relative sparing of memory functions, while other domains are affected (Koedam et al. 2010, Smits et al. 2012), thus one needs to be aware of more signs than simply an isolated memory impairment. There are differences in gender, in the ratio of 2:1 for female-male (Alzheimer's Association 2012). The proportion of AD among females remains constant with age while it increases progressively in men (Sosa-Ortiz, Acosta-Castillo & Prince 2012). The mean survival ranges from 2.5 to 7.6 years depending on the age of diagnosis and certain clinical aspects (Brodaty, Seeher & Gibson 2012). Classification: The classical or typical AD is also known as the amnestic variant. It starts with an insidious cognitive decline characterized by a gradually worsening ability to remember new information and activities of daily living deteriorate. As the disease progresses, a wide

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variety of manifestations may occur e.g. impairment in planning or solving problems, confusion with time or place, problems with finding words in speaking or writing, poor insight and on the latest stages a decrease or poor judgment and changes in mood and personality (Alzheimer's Association 2012). In addition to the typical presentation of AD, there are atypical presentations such as the posterior cortical atrophy variant, in which the onset occurs with visual symptoms followed later by amnestic episodes. Furthermore, AD pathology can be present in patients with clinical presentations more typical of the FTLD spectrum, such as PNFA or CBS (Alladi et al. 2007). There are also cases with AD pathologies who mainly display behavioural changes, although it is usually is more common that behavioral symptoms appear in AD later once the memory impairment has become obvious (von Gunten et al. 2006). One study attempted to identify the presence of hallucinations, hypokinesia, paranoia, rigidity and tremors in AD population, since there are specific subgroups with these symptoms and determined the cerebrospinal fluid (CSF) profiles (Iqbal, Flory & Soininen 2013). In brief, when these atypical symptoms arise it is always imperative to make a differential diagnosis between AD and other possible causes (Kawas 2003). In clinical practice, the DSM-IV criteria for diagnosing dementia are used. The National Institute of Neurologic and Communicative Disorders and Stroke and Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria from 1984 (McKhann et al. 1984) (Table 5) are commonly used in making a diagnosis of AD. These criteria are based on the symptoms present in the patient, the speed and pattern of progression and the exclusion of other disorders that could act as confounders. A definite diagnosis only can be achieved by a neuropathological study.

Table 5. Clinical diagnosis of Alzheimer’s disease. Report of the NINCDS-ADRDA (McKhann criteria) 1984 (McKhann et al. 1984) Stage Clinical criteria

Probable AD Dementia established by clinical examination and documented by MMSE or similar cognitive scale, and confirmed by neuropsychological tests

Deficits in two or more areas of cognition

Progressive worsening of memory and other cognitive functions

No disturbance of consciousness

Onset between ages 40 and 90, most often after age 65

Absence of systemic disorders or brain diseases that in and of themselves could account for the progressive deficits in memory and cognition

Possible AD May be made on the basis of the dementia syndrome, in the absence of other neurologic, psychiatric, or systemic disorder sufficient to cause dementia, and in the presence of variations in the onset, in the presentation, or in the clinical course

May be made in the presence of a systemic or brain disorder sufficient to produce dementia, which is not considered to be the cause of dementia

Should be used in research studies when a single, gradually progressive severe cognitive deficit is identified in the absence of other identifiable cause

Definite AD Clinical criteria for probable AD + histopathological evidence

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An international working group published new criteria (Dubois et al. 2007) (Table 6).

Table 6. Dubois criteria. Modified from (Dubois et al. 2007) Stage

Probable AD

“A “ AND “B, C, D or E”

A. Presence of an early and significant episodic memory impairment

B. Presence of medical temporal lobe atrophy: volume of hippocampi, entorhinal cortex and amygdale evinced on MRI

C. Abnormal CSF biomarker: increased levels of T-tau or P-tau, decreased levels of Aβ or combination of the three of them

D. Specific pattern on PET: reduced glucose metabolism in bilateral temporal parietal regions OR other well validate ligands (PIB, FDDNP)

E. Proven AD autosomal dominant mutation within the immediate family

AD can be classified into “probable” AD and “definite” AD. For “probable” AD, the core feature is the presence of memory impairment supported by medial temporal lobe atrophy as detected with MRI, abnormal CSF biomarkers and certain changes displayed on positron emission tomography (PET). When the clinical criterion is accompanied by the presence of certain gene mutations (PS1, PS2 and APP) AD diagnosis is considered “definite” regardless of the neuropathological confirmation. In addition, a clinical criterion plus a neuropathological confirmation would be validated as “definite” AD. At the emergence of new biomarkers and the development in the field of dementia have incited the National Institute of Aging (NIA) to propose new criteria (McKhann et al. 2011) (Table 7). As well as the clinical diagnosis, it includes imaging markers such as structural MRI, FDG-PET and Aβ accumulation on PET imaging, and the measurement of Aβ and tau in the CSF.

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Table 7. Diagnosis of dementia due to Alzheimer’s disease. NIA clinical criteria for AD (McKhann criteria) 2011 (McKhann et al. 2011) Group Biomarker

probability of AD etiology

Aβ (PET or CSF) Neuronal injury (CSF tau, FDG-PET, structural MRI)

Dementia-unlikely due to AD

Lowest Negative Negative

Possible AD dementia (atypical clinical presentation)

- Based on clinical criteria

Uninformative Unavailable, conflicting, or indeterminate

Unavailable, conflicting, or indeterminate

- With evidence of AD pathophysiological process

High but does not rule out second etiology

Positive Positive

Probable AD dementia

- Based on clinical criteria

Uninformative Unavailable, conflicting, or indeterminate

Unavailable, conflicting, or indeterminate

- With three levels of evidence of AD pathophysiological process

Intermediate

Intermediate

High

Unavailable or indeterminate

Positive

Positive

Positive

Unavailable or indeterminate

Positive

These criteria take into consideration several traits that were not available or known in 1984. AD is now considered to be a neuropathological process which means that it is possible to draw a progression line from the diagnosis of AD to the post-mortem findings. Today there are several dementia diseases recognized, which were barely known two decades ago such as FTD or DLB. New biomarker e.g. imaging (MRI and PET) and CSF, already recommended in the Dubois criteria, and other possible presentations of AD are included and the diagnosis is not limited to the amnestic presentation. Genetic mutations are included as part of the criteria, and there is no cut-off between ages. Finally the NIA criteria include patients with possible or probable AD. Both McKhann and Dubois criteria are based on the assumption that AD dementia is mainly a clinical diagnosis. This means that although the presence of different biomarkers can increase the likelihood of AD, one cannot talk about AD before there has been symptom onset. Nevertheless, this assumption is not always defined for all criteria. The NIA also defined criteria for preclinical AD specifically for research purposes (Sperling et al. 2011) (Table 8). In this case, one could refer to a condition of preclinical AD and classify it into three stages where the symptoms are required only for the latest stage. This classification by Sperling et al. also includes functional MRI as a method for the diagnosis of AD and stages prior to AD. New biomarkers are expected to be included in the coming years, in order to define more accurately the changes in AD.

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Table 8. Preclinical stages of AD. Recommendations from the NIA (Sperling et al. 2011) Stage Description Aβ (PET or CSF) Neuronal injury

(CSF tau, FDG-PET, structural MRI)

Evidence of subtle cognitive change

Stage 1 Asymptomatic cerebral amyloidosis

Positive Negative Negative

Stage 2 1+neuronal injury Positive Positive Negative

Stage 3 1+2+subtle cognitive/behavioral decline

Positive Positive Positive

Finally, it is important to mention the classification criteria proposed by Vos and colleagues (Vos et al. 2013b) (Table 9), which includes another group in which there is no need to have amyloid for fulfilling the AD criteria. Amyloid and neuronal injury are defined by specific cut-offs in CSF levels of amyloid and tau. This classification claims that the absence of amyloid is not itself sufficient to prevent a patient being classified as having AD, i.e. there is one sub-entity of AD which presents only with neurodegeneration. Another study (Petersen et al. 2013) supports the existence of a non-AD pathophysiology group, although there has been criticism that the clinical dementia rating (CDR) is a staging and not a diagnostic instrument, and there is a lack of neuroimaging and other biomarkers in these criteria (Petersen 2013).

Table 9. Preclinical AD stages and symptomatic AD (Vos et al. 2013b) Group CDR

(0=no dementia)

Aβ (CSF) Neuronal injury (CSF)

Evidence of subtle cognitive decline

Normal group 0 Negative Negative Negative

Preclinical AD stage 1 0 Positive Negative Negative

Preclinical AD stage 2 0 Positive Positive Negative

Preclinical AD stage 3 0 Positive Positive Positive

Suspected non-AD pathophysiology group

0 Negative Positive Positive/negative

Unclassified group 0 Positive/negative Negative Positive

Symptomatic AD >0, memory and at least one other domain with >0.5, and probable AD according to NINCDS-ADRDA criteria

No No No

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Risk factors: Currently the field of RFs is one of the major challenges in AD. A summary with the main risk and protective factors for AD is displayed in Table 10. Most of these are cross-sectional studies and therefore they do not imply that there is a pathological or physiological correlation between the risk factor and dementia. AD is a multifactorial disease in which both genetic and lifestyle risk factors have been described. The level of epidemiologic evidence varies but it is strong for genetic factors, moderate for vascular and psychosocial factors, while nutrition and other factors still need to be studied further (Qiu, Kivipelto & von Strauss 2009). Several multidomain intervention trials have been performed which have attempted to assess how controlling the presence of risk factors can affect the progression of cognitive impairment. A comprehensive review can be found in (Imtiaz et al. 2014). Table 10. Risk and protective factors in AD. Modified from (Qiu, Kivipelto & von Strauss 2009, de la Torre 2004, Mangialasche et al. 2012)

Etiologic hypothesis

Risk Protective

Genetic susceptibility

� Aging � Positive familial history � APOE ε4 allele carriers � APP, PS1, PS2

� APOE ε2 allele carriers

Psychosocial � Lack of social network � Low education

� Socially active � High education � Mentally stimulation activities

Cardiovascular � Low physical activity � Saturated fatty acids � Smoking � High cholesterol level at midlife � High systolic blood-pressure at midlife

(hypertension) � Cerebrovascular disease (stroke,

arrhythmia, atherosclerosis, transient ischemic attack)

� COPD and asthma � Low diastolic blood pressure

(hypotension) � Diabetes, metabolic syndrome, high

BMI (obesity)

� Physical activity � Acetylsalicylic acid and non-

steroidal anti-inflammatory drugs

� Antihypertensive therapy

Other � Head injury � Depression � Overuse of alcohol � High homocysteine levels and

holotranscobalamin � Thyroid problems � Vitamin deficiencies: B12, folate,

antioxidants (vitamins A, E and C) � Hormone replacement therapy

� Light alcohol consumption � Nutritional antioxidants � Omega-3-fatty acids (fish),

antioxidants � Vegetable consumption � Coffee drinking � Mediterranean diet

Genetic susceptibility: the most important risk factor in AD is aging (Lobo et al. 2000, Plassman et al. 2007), as AD prevalence and incidence increase within the years. However having one or more first degree relatives with dementia also increases the risk of AD (van Duijn et al. 1991). The APOE ε4 allele is the strongest and best established risk gene for sporadic or LOAD. The carriers of the APOE ε4 allele are at a higher risk of developing AD, while APOE ε3/ε2 genotype and particularly the presence of the APOE ε2 are

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protective against the disease (Corder et al. 1994). In addition APOE ε4 is a genetic risk factor for dementia with stroke, including VaD and AD with cerebrovascular disease (Slooter et al. 1997). APP mutations were the first to be discovered (Goate et al. 1991), followed by the finding of PS1 and PS2 gene mutations as causes of EOAD (Sherrington et al. 1995, Levy-Lahad et al. 1995) (1995). Later, new risk genes for AD have been identified: PICALM (Harold et al. 2009), CLU (Harold et al. 2009) and CR1 (Lambert et al. 2009) in genome-wide association studies (GWAS) (2009), BIN1 (Seshadri et al. 2010) (2010), MS4A4/MS4A6E and CD2AP (Naj et al. 2011), SORL1 (Reitz et al. 2011) (2011) and the latest discovery of TREM2 (Guerreiro et al. 2013) (2012). In 2013, 11 new susceptibility loci for AD were identified in a meta-analysis including GWAS studies (Lambert et al. 2013).The data base to be found in alzgene.org summarizes the current gene findings. The most recently found risk genes have a much lower effect size than that of APOE. If one considers the above genes, then only APP, PS1 and PS2 have been associated with EOAD in family based studies. These three genes have a high impact, i.e. when these genes are mutated, there is a very high likelihood that a patient will develop AD. Nevertheless these genes are involved in only 2% of all AD cases (Paulson, Igo 2011). Most of these cases are due to PS1 and PS2. In PS1 mutation carriers, the disease starts on average 8.4 years earlier than in APP mutation carriers (average 51 years) and 14 years earlier than in PS2 mutation carriers (average 57 years) (Cruts, Theuns & Van Broeckhoven 2012). Furthermore there are a few AD cases which present gene mutations typically thought of as being from the FTLD spectrum, such as GRN (Cruts, Theuns & Van Broeckhoven 2012, Wojtas et al. 2012, Perry et al. 2013, Jin et al. 2012), MAPT (Cruts, Theuns & Van Broeckhoven 2012, Wojtas et al. 2012, Jin et al. 2012) or C9ORF72 (Wojtas et al. 2012, Harms et al. 2013). This suggests that when there is a suspicion of AD, especially when there is a strong family history, it is necessary to perform the genetic analyses not only for typical gene mutations for early onset AD (APP, PSEN-1 and PSEN-2) and APOE, but also for genes more typically associated with FTD. Psychosocial: according to the saying, “use it or lose it” the first risk factor for our brain is a lack of activity. Thus an individual who is socially active (Lipnicki et al. 2013) is likely to suffer less global cognitive decline. A high level of education is associated with a lower risk for AD (Anttila et al. 2002). Cardiovascular: a high systolic blood pressure and elevated serum cholesterol level at midlife is associated with a higher risk of AD (Kivipelto et al. 2001). Moreover a low diastolic blood pressure, the presence of atrial fibrillation and heart failure are also associated with a higher risk of developing AD (de la Torre 2012). A higher midlife body mass index (BMI) has been linked with dementia and AD (Tolppanen et al. 2014). Midlife smoking, chronic obstructive pulmonary disease (COPD) and asthma increase the risk of developing AD later in life (Rusanen et al. 2010, Rusanen et al. 2011, Rusanen et al. 2013). There is a report that limited alcohol intake in earlier adult life may be protective against incident dementia later (Peters et al. 2008). It is thought that physical activity may be protective against cognitive decline (Sofi et al. 2011), although one study found that more active men had higher rates of MCI or dementia (Lipnicki et al. 2013). Other factors: a healthy diet at midlife including coffee, fish, vegetables, monounsaturated acids and polyunsaturated fatty acids, is associated with a lower risk of AD (Eskelinen et al. 2011). Among coffee drinkers, the lowest risk was in those who drank between 3 and 5 cups

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per day (Eskelinen et al. 2009), but tea consumption did not change the risk of AD. There is a report that fat intake at midlife affects cognitive performance later in life (Eskelinen et al. 2008). The role of vitamin C, E, D, B6, B12 and folate is controversial (Coley et al. 2008). High homocysteine levels and holotranscobalamin have been linked with a poorer cognition performance in the elderly (Hooshmand et al. 2012) .

2.4.3 Neuropathology and pathophysiology AD is a disease characterized by the presence of extracellular plaques formed from amyloid beta (Aβ) and intracellular neurofibrillary tangles (NFT) including tau protein and hyperphosphorylated tau (P-Tau), which activate glial cells and evoke general inflammation which in turn causes neurotoxicity, and ultimately neuronal death. The NIA guidelines for the neuropathological assessment of AD (Hyman et al. 2012) include three different parameters in order to obtain an “ABC” score: Aβ plaque score from (Thal et al. 2013); NFT stage from (Braak, Braak 1991); and neuritic plaque score from (Mirra et al. 1991).

Amyloid pathology: Aβ is a protein cleaved from the amyloid precursor protein (APP). In the non-amyloidogenic pathway, α-secretase cleaves APP; the fragments of Aβ obtained are soluble and can be easily eliminated. The amyloidogenic pathway is attributable to hydrolysis by β-secretase and γ-secretase. γ-secretase is an intramembranous protease complex which includes 4 components: nicastrin, PEN-2 and APH-1 and presenilin, which is the active site. Most of β-secretase activity is due to the integral membrane aspartyl protease called β-site APP cleaving enzyme 1 (BACE1) (Blennow, de Leon & Zetterberg 2006). When β-secretase and γ-secretase sequentially break down APP, one obtains Aβ oligomers with longer chains of 40 (Aβ40) and 42 amino acids (Aβ42) respectively; because these are insoluble, they tend to aggregate into fibrils and in the end deposit into plaques. Plaques created of Aβ are named amyloid plaques or senile plaques. When amyloid plaques are surrounded by degenerating neurons filled with Tau protein, they are referred to as neuritic plaques (Barkhof et al. 2011, Hyman et al. 2012). Another classification can be made based on the composition of the plaques (Serrano-Pozo et al. 2011): amyloid plaques, dense-core plaques and diffuse plaques. When Aβ is deposited in the arterial walls and capillaries, it is called about cerebral amyloid angiopathy (CAA). Some degree of CAA is present in about 80% of AD cases. In CAA, primarily one can find Aβ40, while in dense-core plaques there is mainly Aβ42 in more insoluble amyloid fibrils. Aβ42 is more prevalent because of its higher rate of fibrillization and insolubility (Serrano-Pozo et al. 2011). Amyloid plaques accumulate mainly in the isocortex. Although the temporal pattern of progression is less predictable for amyloid deposition than for NFTs, two staging classifications have been proposed. Braak and Braak subdivided into 3 stages (Braak, Braak 1997): in stage A, amyloid patches are located in the basal portions of the neocortex, particularly in the temporal lobe; in stage B, the amyloid deposits have spread to the adjacent neocortical areas and the hippocampus; in stage C, amyloid has propagated to primary isocortical areas and it can be present throughout all of the cortex, including densely myelinated areas of the neocortex.

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Thal et al., identified 5 stages (Serrano-Pozo et al. 2011, Thal et al. 2002): in stage 1, Aβ is located in the neocortex; in stage 2, it spreads to allocortical brain regions (entorhinal cortex, hippocampus, amygdale, insula, cingulated cortices); in stage 3, amyloid accumulation involves the striatum, diencephalic nuclei and the cholinergic nuclei of the basal forebrain; in stage 4, brainstem structures are affected, including the red nuclei, substantia nigra, reticular formation of the medulla oblongata and superior and inferior colliculi; and in stage 5, the cerebellum and the pons (reticular formation, raphe nuclei and locus coeruleus) have amyloid enclosed.

Tau pathology: Tau is a protein which stabilizes the cytoskeleton of neurons made by tubulin. When tau is hyperphosphorylated, the microtubules become destabilized and form tangles which disrupt the microtubule normal functioning, accumulation into NFT. These NFT become extraneuronal (“ghost” tangles) when tangle-bearing neurons die. These NFT can be accompanied by neuropil threads, which are axonal and dendritic segments containing aggregated and hyperphosphorylated tau (Serrano-Pozo et al. 2011). Tau phosphorylation is regulated by the balance between multiple kinases and phosphates. Since tau becomes hyperphosphorylated, tau and other microtubule-associated proteins are sequestered causing microtubule disruptions and thus impaired axonal transport that compromises neuronal and synaptic function. Tau is more prone to aggregation and clustering into NFT, compromising neuronal functioning (Blennow, de Leon & Zetterberg 2006). Tau accumulation in the brain usually follows a specific dissemination pattern, as ordered in the stages identified by Braak and Braak in 1997 (Braak, Braak 1997). There are 6 stages which tend to depict the extent, location and sequence of progression tau within the regions in the brain: I-II stages in transentorhinal area, with no symptoms; III-IV stages in the limbic area, when clinical symptoms start to appear; and V-VI stages in the neocortical areas, when AD has already developed. In stage I, NFTs appear in the transentorhinal (perirhinal) region, spreading to the whole entorhinal cortex. In stage II, NFTs reach the CA1 region of the hippocampus. In stages III and IV, NFTs accumulate in the limbic structures such as the subiculum of the hippocampal formation (stage III) and the amygdala, thalamus and claustrum (stage IV). In stage V, the isocortical areas are affected, finally reaching the primary motor, sensory and visual areas in stage VI, although in relative terms there is some sparing of these areas (Braak, Braak 1991, Serrano-Pozo et al. 2011). These stages appear in AD patients with very few individual variations. Tau is a key factor for diagnosis and prognosis, because the first symptoms appear to be dependent on the location and amount of tau, and second for a post-mortem diagnosis one requires that there is the presence of neurofibrillary changes (Hyman et al. 2012). Nevertheless tau is not specific for AD because it can be present in many other tauopathies (Avila 2000).

Other pathologies: Although amyloid and tau are the main key players in AD pathology, usually these are not the only findings. AD generally occurs in mixed-forms, where there can be neocortical Lewy bodies and microvascular infarcts (Schneider et al. 2009). Reactive astrogliosis is also an important process in AD. These cells surround the amyloid plaques and diffuse deposits of amyloid. It is believed that this increasing astrogliosis

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occurs as a response of the amyloid accumulation, trying to encapsulate it as a neuroprotective barrier. The intensity of reactive astrogliosis increases in parallel with Braak stages (Sofroniew, Vinters 2010).

Hypotheses: Amyloid hypothesis (Hardy, Selkoe 2002): at present the most widely accepted theory about the origin of AD is the amyloid hypothesis elaborated by Hardy and Selkoe (Hardy, Selkoe 2002). The amyloid cascade hypothesis states that the entire process starts with the abnormal cleavage of APP, leading to an imbalance in the production and clearance of Aβ. The Aβ forms tend to aggregate from Aβ oligomers into Aβ fibrils, and these deposit into the plaques, known as senile plaques. This finally causes neuronal and synaptic loss. Despite its level of acceptance, it has not yet provided all of the answers. First, the extent of amyloid deposition does not correlate with the symptom severity or progression nor with the level of neurodegeneration extension, whereas tau does correlate with the extent of neurodegeneration. Secondly, the role of tau is not explained in this hypothesis. Therefore even though Aβ may be one of the triggering events in AD, the field is open to find other initiators of AD pathology, especially if one could generate a more general hypothesis. Vascular hypothesis (de la Torre 2000): during the last few years, this has been proposed as an alternative to the amyloid hypothesis (de la Torre 2004). Different manifestations of cerebrovascular disease such as MBs (Benedictus et al. 2013) and white matter hyperintensities (Debette, Markus 2010), are more prominent in the AD population than in controls, and thus these vascular changes have been considered as a possible explanation for the cascade of events that leads to AD. Due to the theory that amyloid is a key player in AD, and the amyloid hypothesis does not explain fully LOAD, a theory is emerging that amyloid and vascular processes are part of a common spectrum causing AD and vascular dementia and not two totally different entities. MBs may be particularly important linking the amyloid and the vascular hypotheses, as proposed in one review paper (Cordonnier, van der Flier 2011). Tau hypothesis: even though most of the studies claim that the Aβ misbalance precedes tau pathology and neurodegeneration (Zetterberg, Blennow 2012) there are some studies stating that tau pathology precedes amyloid, since it is even possible to find evidence of tau deposition in absence of Aβ in some children and young adults (Braak et al. 2011). In this theory, the process does not start in the transentorhinal area, but in the lower brainstem. Therefore tau and Aβ would follow two different pathways which are also separated in time: first tau pathology appears, probably in the childhood and later in years Aβ would emerge. Capillary dysfunction hypothesis: one study group has proposed a model which places the hypoperfusion, capillary changes and capillary flow patterns as being antecedent to AD (Ostergaard et al. 2013). Others: there are several hypotheses to account for many of the molecular and pathological aspects of AD. Many of these hypotheses have been rejected due to failures in clinical trials with specific drugs. The cholinergic hypothesis did lead to the development of the current symptomatic treatment of AD (Davies 1999). Much work still needs to be done in order to determine the key initial factors which trigger the cascade that leads to the manifestations typical of AD. However the most plausible hypothesis so far seems to involve the integration of the amyloid and vascular

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hypotheses, as being combined and not exclusive initiators of AD processes, with the additional influence of environmental and genetic risk factors which may modify the processing of tau, Aβ or enhance the associated inflammatory response. Today, there are many studies elucidating this possibility (Hunter, Arendt & Brayne 2013).

2.5 IMAGING TECHNIQUES

The imaging methods to be described in this section are summarized in Table 11.

Table 11. Imaging techniques and the biological process they measure. Modified from (Small et al. 2008).

Imaging technique Biological process

Conventional MRI Atrophy, space-occupying lesions, focal cerebrovascular lesions

Susceptibility weighted imaging Microbleeds

ASL Perfusion

Functional MRI Blood flow, functional connectivity

DTI Neural connectivity, WM integrity

1H-MRS Metabolite concentrations

99mcTc-HMPAO SPECT Blood flow

99mTc-ECD SPECT Blood flow

18F-FDG PET Glucose metabolism (functional activity)

11C-PIB PET Amyloid plaques

18F-FDDNP PET Amyloid plaques and tangles

18F-BAY94-9172-PET Amyloid plaques

18F-MPPF-PET Hippocampal neuronal integrity

ASL: Arterial Spin Labelling; DTI: Diffusion Tensor Imaging; MRS: Magnetic Resonance Spectroscopy; SPECT: Single Photon Emission Computed Tomography; PET: Positron Emission Tomography 2.5.1 Conventional MRI It is widely accepted that there is an association between the neurofibrillary pathology and the atrophy displayed in structural MRI (Whitwell et al. 2008), which is the most common marker used for diagnosing and monitoring the progression of AD, while there is no association between the beta-amyloid burden and the extent of brain atrophy (Josephs et al. 2008). In a standard conventional MRI protocol 3 types of sequences are usually applied: coronal 3D T1-weighted, transverse T2-weighted and transverse fluid-attenuated inversion recovery (FLAIR). Transverse T2* imaging, such as susceptibility weighted imaging, sometimes is included as a way to detect MBs. These sequences provide a minimum set of information to answer two questions: the extent and pattern of brain atrophy and the

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degree of vascular damage (Barkhof et al. 2011). T1 describes structural changes, T2 and FLAIR reflects pathological changes and T2* is sensitive to haemorrhages. Many factors should be considered in evaluating an imaging protocol for investigating dementia (Barkhof et al. 2011, Bhogal et al. 2013): (1) are any reversible causes of dementia syndrome present (e.g. normal pressure hydrocephalus and subdural haematoma); (2) the presence, extent and location of small cerebrovascular diseases; (3) the presence and distribution of MBs; (4) degree and pattern of general cortical atrophy; (5) focal atrophy, especially in the hippocampus, medial temporal lobe, frontal lobes, posterior cingulate, precuneus, cerebellum and pons. Visual ratings provide a qualitative study of the images; they are fast with some scales which are recognized for being specific in evaluating the changes in dementia patients. There are several scales available: global cortical atrophy, medial temporal lobe (MTL) atrophy using the Scheltens scale (Scheltens et al. 1995) and ARWMC using the Fazekas scale (Fazekas et al. 1987). The assessment of the MTL atrophy in coronal T1 weighted MRI is considered to be the gold standard for the diagnosis of AD, as the hippocampal atrophy rate varies as MCI progresses to AD (Jack et al. 2008). The problem with these ratings is the subjective component inherent in the final decision; in addition, experience is needed in using these scales. Furthermore it is not easy to monitor the progression with respect to time. In AD, there is usually a marked atrophy of the hippocampus (Scheltens 2009) (Figure 2). It has been reported that there is an annual atrophy rate of around 1.55% in controls and 4% in AD patients (Jack et al. 1998). The amygdala also undergoes atrophy in AD (Cavedo et al. 2011). Structural MRI assessments of the entorhinal cortex and the hippocampus, particular the CA1 region, are associated with a higher risk of developing AD (Stoub et al. 2005, Apostolova et al. 2006). The atrophy starts in the entorhinal cortex, progresses to the hippocampus and medial temporal lobe and finally to the parietal cortex (Jack 2012). BvFTD can usually detect a marked atrophy in the frontal lobes and anterior temporal lobes, and this can be evaluated visually (Bhogal et al. 2013). Hippocampal atrophy may be detected also in bvFTD cases (Figure 2), thus it is not a phenomenon restricted to AD (van de Pol et al. 2006). FTD patients have a higher rate of whole-brain atrophy in comparison with AD cases (Chan et al. 2001).

Figure 2. From left to right, coronal T1-weighted MRI scans of: control case (Scheltens score = 0), AD case (Scheltens score=4) and bvFTD case (Scheltens score = 2; prominent frontal atrophy). Courtesy of Dr. Yawu Liu, Kuopio University Hospital.

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A visual assessment can also identify other changes such as lacunes, MBs or the enlargement of the ventricles, all of which can be altered in the presence of normal ageing and dementia diseases. Manual volumetry (outlining) (Figure 3) is useful, but it is time consuming. Furthermore there is no standard technique for delineating the hippocampus in the MRI images.

Figure 3. Visual rating system for assessing hippocampal atrophy: on the left, the hippocampus outlined in red shows mild atrophy (score=2). Courtesy of Dr. Yawu Liu, Kuopio University Hospital.

The use of automatic and semi-automatic techniques is intended to exclude the subjective component that biases the decision of individual clinicians. Moreover in automatic techniques, one can select specific regions-of-interest (ROIs) and quantify their volumes in order to monitor the rate of atrophy with respect to time, thus it is possible to study more regions of the brain. A large number of different methods exist for quantifying atrophy, as reviewed (Soininen et al. 2012a). Five of these automatic methods are as follows: automatic hippocampal volumetry (HV), tensor-based morphometry (TBM), voxel-based morphometry (VBM), manifold-based learning (MBL) and cortical thickness (CTH). HV displays the total volume of the left, right and whole hippocampus (Wolz et al. 2011). Hippocampal atrophy is not a phenomenon restricted to AD, although the rate, amount and pattern atrophy can differentiate AD from FTD (van de Pol et al. 2006, Frisoni et al. 1999). Furthermore HV can evaluate specific regions in the hippocampus, such as conducted in one study comparing the accuracy between global hippocampal atrophy and selected atrophy in CA1, which found maximum atrophy in the CA1 in amnestic MCI and AD patients (La Joie et al. 2013). TBM calculates the average Jacobian of atrophic voxels within a ROI, weighted based on voxel-wise p-values (Wolz et al. 2011). In other words, the voxel-wise proportion of variance in volumes is computed for specific ROIs for one disease versus another disease (Brun et al. 2009). One study using support vector machines and linear discriminant analysis in ADNI data was able to differentiate healthy controls from AD patients with an accuracy of 87%, and stable-MCI (SMCI) from progressive-MCI (PMCI) with an accuracy of 64% (Wolz et al. 2011). Brambatti et al., compared FTD cases and controls after 1 year follow-up, and in the whole brain analysis, a significant atrophy change was detected in the anterior cingulated and paracingulate gyri (Brambati et al. 2007). Another study observed different atrophy patterns SD and PNFA (Lu et al. 2013). VBM involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects (Ashburner, Friston 2000). It has been widely used in the field of dementia. One study indicated that in contrast to controls, MCI cases displayed significant

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unilateral atrophy in the medial temporal lobe (Pennanen et al. 2005). In addition, one study surveyed the conversion from amnestic MCI to AD after an 18 month follow-up; it found a significant GM loss in converters relative to non-converters in the hippocampal area, temporal gyrus, posterior cingulated and precuneus (Chetelat et al. 2005). A meta-analysis revealed GM changes in the frontal-striatal-limbic brain areas in patients with bvFTD compared to controls (Pan et al. 2012). One study using statistical parametric mapping detected decreased GM volume in the frontal and anterior temporal lobes in a FTD group while in AD there were decreased volumes bilaterally in the posterior cingulated gyri and parietal lobules (Kanda et al. 2008). MBL estimates the coordinates of a subject in a low-dimensional manifold space learned from pairwise image similarities (Wolz et al. 2011). It has been shown to differentiate controls and AD cases with a high degree of sensitivity (90%) (Wolz et al. 2011). CTH computes the average thickness within a ROI defined based on a group-level statistical analysis (Wolz et al. 2011). One ADNI study revealed that normalized thickness could distinguish AD patients from controls with an accuracy of 85% and predicted conversion from MCI to AD in 76% of the subjects (Querbes et al. 2009). When applied to compare FTD with MCI and controls, if one determined cortical thinning in the frontal and temporal poles, then no statistical differences were found in the AD vs. FTD group comparison (Hartikainen et al. 2012a).

2.5.2 Advanced MRI methods There are some other MRI techniques which are not widely used but they perhaps could contribute new information for the understanding of dementia diseases. These techniques are based on perfusion, diffusion and spectroscopy. In order to determine the cerebral perfusion by using MRI, one can utilize two perfusion based techniques: MR perfusion weighted imaging (PWI) and functional MRI (fMRI). PWI can be conducted invasively by injecting a paramagnetic contrast bolus, or non-invasively by applying a magnetic tag. When applying the bolus-tracking paramagnetic contrast, also named dynamic susceptibility contrast (DSC), one measures the signal changes during the bolus passage through the brain, which appear as a decrease in the signal intensity of T2* images (Calamante 2010). DSC illustrates regional alterations in cerebral blood flow (CBF) when comparing AD with MCI and controls (Hauser et al. 2013). The non-invasive technique is the so-called arterial spin labeling (ASL). ASL produces a flow-sensitized image or “labelled” image and a “control” image in which the static tissue signals are identical, but where there are differences in the extent of magnetization of the inflowing blood (Petersen et al. 2006). One study showed differences in regional CBF in AD patients as compared to controls using ASL with a 3 Tesla scanner (Yoshiura et al. 2009). Another study reported that frontal and parietal perfusion in ASL could differentiate AD and FTD with an accuracy of 87% (Du et al. 2006). Recently there has been an interest to introduce ASL into clinical practice, particularly in the field of dementia (Golay, Guenther 2012). Even though there are many techniques which describe the dynamic patterns of function or activity taking place in the brain, when one uses the term fMRI, this is usually referring to blood oxygen-level dependent (BOLD) and resting-state networks (RSN).

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In BOLD fMRI the difference on magnetic susceptibility is used as an intrinsic contrast to compare conditions (e.g. a particular response against a stimulus). The technique is based on the differences in the paramagnetic properties between oxygenated hemoglobin and deoxygenated hemoglobin (Buxton 2013). Deoxygenated hemoglobin is paramagnetic whereas oxygenated hemoglobin is not, which causes local dephasing of protons and consequently reduces the signal being emitted from those tissues. CBF increases more than oxygen metabolism when local neural activity increases; oxygenated hemoglobin is introduced into the tissue and deoxygenated hemoglobin is removed, thus tissue experiencing a change in oxygenation is detected. Several studies have been performed in which AD and FTD patients have been asked to perform a specific task, e.g. performace in tasks targeting social cognitive deficits in bvFTD patients correlate with functional abnormalities in frontal and limbic regions in fMRI, which reflect emotional abnormalities (Virani et al. 2013); memory recall (encoding task) identifies changes in the limbic brain (Filippini et al. 2009). RSN have gained popularity during recent years. It is based on the fact that the brain never rests completely, and thus it is possible to measure the differences on brain activation from an active state to a state of relative inactivity i.e. the subject is not doing any particular task. In other words one conducts a BOLD fMRI without asking the patient to perform a specific task, simply to be at rest. Resting state fMRI (RSfMRI) also allows the investigation of multiple regions at once, at the same time making it possible to map specific circuits. In AD, RSN detected a reduced activation in the so-called default-mode network (DMN) (Greicius et al. 2004). DMN is the analogue of consciousness and becomes less active during the performance of a task. DMN involves the posterior cingulated cortex, the hippocampus, the inferior parietal lobule and the prefrontal cortex (Buckner, Andrews-Hanna & Schacter 2008). The DMN becomes atrophied in AD while in bvFTD it is the salience network (SN) which undergoes atrophy. One study (Zhou et al. 2010) conducted with free-task fMRI by using independent component analysis, and found that in AD the DMN was attenuated, mostly in posterior hippocampus, medial cingulo-parieto-occipital regions and the dorsal raphe nucleus; however the SN connectivity was intensified. In bvFTD, there was reduced SN connectivity, mostly in the frontoinsular, cingulated, striatal, thalamic and brainstem nodes, while DMN connectivity was enhanced. However, it has also been reported that apart from the SN, the DMN and the fronto-parietal network can be affected in bvFTD (Filippi et al. 2013). AD patients display a lower functional connectivity within the DMN as compared with controls (Binnewijzend et al. 2012, Wang et al. 2007), while MCI functional connectivity values lie between AD and controls (Binnewijzend et al. 2012, Zhou et al. 2008). Interestingly, in the posterior DMN there is decreased connectivity while in the anterior DMN there is increased activity in AD versus controls. At follow-up, all functional activity decreased within all default mode networks (Damoiseaux et al. 2012). The initial higher connectivity in the frontal regions may be a compensatory mechanism attempting to overcome the functional loss in posterior regions (Damoiseaux 2012). Some studies also have found more decreased functional connectivity in the superior parietal lobules and inferior frontal gyrus in MCI as compared to controls (Sorg et al. 2007) and in the connections between the temporal lobe and thalamus and corpus striatum in AD (Supekar et al. 2008).

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Diffusion weighted MRI (DWI) is a non-invasive method which allows the mapping of the diffusion of free water molecules in in-vivo biological tissues, working as a contrast. The diffusion throughout tissues is not random since it is delimited by all the barriers that could be present. The water motion can be described in statistical terms by a displacement distribution, which represents the proportion of molecules that go into a specific direction and till a specific distance (Hagmann et al. 2006). These diffusion patterns may reveal details about the microstructure. In the central nervous system, the water molecules are subjected to less hindrance when parallel to the white matter tracts than when they are perpendicular to these tracts, and this difference can be used to evaluate the integrity of white matter tracts in vivo. There are different diffusion based techniques; diffusion weighted-imaging, diffusion tensor imaging (DTI) and diffusion spectrum imaging (Hagmann et al. 2006). DTI is the diffusion method most commonly used in the study of dementia diseases. It measures microstructural changes in WM. These are usually measured by the fractional anisotropy (FA) and the mean diffusivity (MD). A meta-analysis including studies which had compared controls with AD and/or MCI revealed that FA was decreased in AD in all regions with the exception of the parietal WM and internal capsule, while MCI patients had lower FA values in all regions except the parietal and occipital areas. MD was increased in AD in all regions, while in MCI MD was increased in all areas other than the occipital and frontal regions (Sexton et al. 2011). Another study showed that AD patients had lower FA than controls at baseline and 3 months later in the fornix and the anterior portion of the cingulum bundle, and lower FA in these regions and the splenium at baseline and after 3 months compared to MCI (Mielke et al. 2009). If one uses Tract-Based Spatial Statistics, then it is possible to identify a so-called FA-skeleton; this is the major white matter structures, improving the sensitivity, objectivity and interpretability of DTI analysis. This type of technique has revealed a significant FA decrease in the parahippocampal white matter, cingulum, uncinate fasciculus, inferior and superior longitudinal fasciculus, corpus callosum and cerebellar tracts in AD compared to controls, with the MCI values being between the AD and control patients (Liu et al. 2011). DTI has also been used for evaluating FTD. One study including controls, AD and FTD cases, showed that FTD patients had reductions in FA in frontal and temporal regions including the anterior corpus callosum, bilateral anterior and descending cingulum tracts, and uncinate tracts, compared to controls. AD patients exhibited reductions in FA in the parietal, temporal and frontal regions, including the left anterior and posterior cingulum tracts, bilateral descending cingulum tracts and left uncinate tracts, as compared to controls. In the FTD vs. AD comparison, FTD was associated with greater reductions of FA in frontal brain regions, while in AD no region showed a greater reduction of FA as compared to FTD (Zhang et al. 2009). Another approach is to measure the concentration of particular metabolites in the brain. This can be done using Proton Magnetic resonance spectroscopy (1H-MRS); this is a non-invasive method for characterizing the cellular biochemistry within the brain. Specific metabolites are associated with specific locations and functions (e.g. N-acetylaspartate (NAA) is an axonal marker localized in the central and peripheral nervous systems), and their levels may correlate with specific pathology and in this way help to identify a specific disease pattern. There are four metabolites which can be quantified using 1H-MRS: NAA, a marker of healthy neuronal density; myoinositol (MI), reflecting glial cell proliferation;

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creatine/phosphocreatine (Cr) and choline-containing compounds (Cho), which reflect the products of membrane phosphotidylcholine (Kantarci 2007). Neurodegenerative dementia is characterized by elevated myoinositol and decreased NAA levels (Kantarci 2013). The increase in MI seems to precede the decline in the NAA levels in AD. The NAA/MI ratio in the posterior cingulated gyri decreases with an increasing burden of AD pathology (Kantarci 2013). Glutamate plus glutamine levels are also decreased in AD (Antuono et al. 2001). These changes on metabolites concentrations in AD seem to be widespread (Tedeschi et al. 1996), involving the parietal and frontal lobes (Schuff et al. 1998) and the hippocampus (Schuff et al. 1997). The levels of NAA correlate with dementia severity (Kwo-On-Yuen et al. 1994) and psychotic symptoms (Sweet et al. 2002), suggesting that NAA could represent a marker for AD severity in certain clinical features. In MCI and pre-symptomatic AD 1H-MRS shows elevation of MI/Cr ratio and reduction in NAA/Cr ratio (Kantarci 2007). In the pathologic progression of AD, the first difference to appear is the increase in MI/Cr ratio, and later in the disease course there is a reduction in the in NAA/Cr ratio and an increase in the Cho/Cr ratio (Kantarci et al. 2000). In FTD, 1H-MRS displays the same pattern as in AD i.e. a decrease in the NAA/Cr ratio and an increase in the MI/Cr ratio (Kantarci 2007). Both diseases display decreased NAA levels in the posterior cingulate cortex however this is lower in the frontal region in the FTD group than in the AD group. In addition, there is a report that the MI/Cr ratio was higher in the frontal region of the FTD group in comparison with the AD group (Mihara et al. 2006).

2.5.3 SPECT Functional tomographic technology or molecular imaging using a high resolution compound is a technique which has been widely applied in dementia diseases over the last decades. These techniques include positron emission tomography (PET) and photon emission computed tomography (SPECT) (Jagust 2004). Both methodologies rely on the detection of radioactive signals from a labelled compound (tracer) that selectively binds somewhere in the brain. The tracers used in SPECT are photon-emitting isotopes. The emitted gamma-rays are detected by a rotating camera which converts the radioactive rays into an electrical signal. In AD it has been common to evaluate cerebral perfusion or CBF with 99mcTc-HMPAO (hexamethylpropylene amine oxime) as the tracer (Herholz 2011). There is bilateral hypoperfusion in the temporoparietal-occipital regions in AD, while in FTD one typically detects hypoperfusion in the frontal and anterior temporal region (Neary et al. 1998, Rollin-Sillaire et al. 2012) (Figure 4 and 5). It has been reported than SPECT can differentiate accurately AD from FTD (McNeill et al. 2007). It can also help to reveal imaging and clinical correlations among the entities within FTLD (Borroni et al. 2007).

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Figure 4: Figure 5:

Figure 4: Typical SPECT of AD: bilateral temporoparieto-occipital hypoperfusion. Figure 5: Typical SPECT of bvFTD: anterior (internal frontal and dorsal frontolateral) hypoperfusion. Reprinted from (Rollin-Sillaire et al. 2012), Journal of Alzheimer’s disease, with permission from IOS Press.

One study showed that visual ratings of SPECT cannot help to predict conversion from MCI to AD, however quantified blood flow in parietal and temporal regions predicted conversion from MCI to AD. The same study claimed that the predictive value of SPECT declined in the presence of other stronger predictors such as verbal memory or hippocampal volume (Devanand et al. 2010). Another study showed a rate of progression around 10% from MCI to dementia, mostly to AD (Nobili et al. 2009). Another tracer which is available is 99mTc-ECD. This can differentiate an AD-like pattern in FTD (hypoperfusion in precuneus, temporal and parietal areas) from a non AD-like pattern (Padovani et al. 2013). In addition, it indicates that frontal, prefrontal and left parietal areas were sensitive markers of the progression from MCI to AD (Encinas et al. 2003). 99mcTc-HMPAO and 99mTc-ECD may perform differently in particular brain areas in AD, with different rates of hypoperfusion. 99mcTc-HMPAO demonstrated significantly higher uptake values in the hippocampus (Koulibaly et al. 2003). Besides SPECT technique has been of major importance in the diagnosis of DLB, using dopamine transporter tracers (Donnemiller et al. 1997).

2.5.4 PET PET is another technique which uses tracers. There are tracers which reflect the brain metabolism (18F-FDG), amyloid deposition (11C-PIB) and recently there has been an attempt made to map the tau deposition in the brain with several on-going studies using different tracers (Chien et al. 2014, Chien et al. 2013). FDG-PET can reveal decreased glucose metabolism in the temporal and parietal regions in AD. FDG-PET in AD found evidence of hypometabolism in the posterior regions, particularly in the posterior cingulated cortex (Minoshima et al. 1997). FTD displays a typical pattern of frontal hypometabolism, accompanied by hypometabolism in the anterior temporal cortex and the anterior cingulated cortex (Ishii et al. 1998). One study pointed out the need for not discounting the suspicion of FTLD as soon

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as there was evidence found of temporoparietal metabolism if other areas were affected (Womack et al. 2011). Another study recommended the use of FDG-PET when there would be doubt about making a diagnosis of FTD or AD based on clinical findings (Foster et al. 2007). It has been proposed that FDG-PET can differentiate MCI from controls, and predict conversion from MCI to AD (Rinne, Nagren 2010, Mosconi et al. 2007). A study in the ADNI cohort indicated that FDG-PET could predict changes in cognition and functional abilities in MCI and AD (Landau et al. 2011). The typical pattern encountered in AD includes decreased uptake in the lateral temporoparietal cortex and posterior cingulated precuneus, with a similar pattern but less evident in MCI (Jack 2012) (Figure 6).

Figure 6. Typical FDG PET uptake pattern in AD. Decreased FDG uptake in lateral temporoparietal cortex (arrows) and posterior cingulate–precuneus area is seen in the AD subject and less clearly in the MCI subject. Reprinted from (Jack 2012), Radiology, with permission from the Radiological Society of North America.

Even though reduced glucose metabolism could be considered as being non-specific because it is associated with many neurodegenerative diseases, the pattern of abnormalities could be helpful in elucidating which disease it is in its early stages, because hypometabolism precedes tissue loss that can be visualized with structural MRI. There are no perfect similarities between the areas affected in MRI and FDG-PET. Nevertheless one study has attempted to examine the relationship between the hippocampal atrophy, the WM integrity and the GM metabolism using a voxel-based approach; by linking the findings in MRI (hippocampal atrophy) and FDG-PET (posterior cingulate cortex hypometabolism) (Villain et al. 2008). More studies will be needed in order to study the association between the structural changes and the metabolic alterations in the brain. The neurotransmitter concentrations such as acetylcholine, serotonin or dopamine can also be assessed indirectly in the brains of dementia patients (Kadir, Nordberg 2010). 18F-MPPF-PET selectively binds to serotonin 5HT receptors, particularly those present in the hippocampus, thus the signal of the binding is reduced in MCI and AD as compared to controls (Small et al. 2008). The last decade has seen the widespread emergence of tracers which bind to amyloid. According to the amyloid hypothesis, the increase in the level of amyloid in some specific brain region would happen at the earliest stages of the disease, and therefore if one could recognize this pattern change, it could be possible to tackle the disease in a phase when the

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brain has still not been structurally damaged. The tracer most widely used is the 11C-Pittsburg Compound B (PIB) (11C-PIB).

Figure 7. 11C-PIB standardized uptake images show a higher PiB retention in a 79-year-old patient with AD than in a 67-year-old healthy control. Reprinted from (Small et al. 2008, Klunk et al. 2004), with permission from Elsevier.

AD cases display more amyloid retention in more cortical areas than controls (Figure 8) (Klunk et al. 2004). One study from the AIBL cohort found that PIB PET results were closely related to postmortem data, suggesting that amyloid deposition had preceded the diagnosis of AD by 15 years (Rowe et al. 2010). The same study revealed that 33% of the healthy controls were positive for this biomarker, although this result may be skewed due to the fact that there was inclusion of a majority of healthy controls with APOE ε4. Nonetheless the amyloid burden seemed to remain at the same levels in AD cases at all ages (Rowe et al. 2010). One review stated that between 10 and 30% of older healthy controls have PiB positive scans (Quigley, Colloby & O'Brien 2011). Every second MCI patient exhibits an AD-pattern of amyloid deposition within the brain (Kemppainen et al. 2007, Rowe et al. 2007). Many studies report contradictory results with respect to the association between the amyloid burden in the brain and the development or worsening of cognitive impairment (Mormino et al. 2009, Aizenstein et al. 2008). However, an association between amyloid and connectivity disruption (Sperling et al. 2009) and brain volume (Dickerson et al. 2009) was reported in asymptomatic subjects, suggesting that amyloid imaging could be used for predicting the progression to AD even though no symptoms were being manifested. Amyloid deposition seems to take place prior to any cognitive decline and the presence of amyloid alone is not sufficient to cause a cognitive decline; the neurodegenerative changes are the substrate to permit a cognitive decline. Amyloid changes occur early and neurodegeneration occurs later in life, supporting the combination of MRI and PIB studies rather than one single method alone (Jack et al. 2009). There is a report that PIB-PET can help to differentiate AD from FTLD (Rabinovici et al. 2007) and another report that amyloid deposition is higher in AD as compared to controls and FTD patients (Quigley, Colloby & O'Brien 2011). FDG-PET seems to be a slightly more accurate and sensitive method than SPECT for the diagnosis of AD and other neurodegenerative dementias (Davison, O'Brien 2013). One study comparing SPECT and FDG-PET reported that while in the temporo-parietal regions

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there can be significant differences between probable AD and controls, only PET could detect differences in the frontal area (Messa et al. 1994). However, more studies will be needed to clarify in detail the advantages and disadvantages of both methods. One study reported that FDG-PET and PIB-PET had similar accuracies differentiating AD from FTLD (Rabinovici et al. 2011). The problem with 11C-PIB is the cost and the short half-life of the tracer (20 minutes) which complicates its transport over long distances. This may explain why it is still unavailable in many clinical centres (Villemagne, Rowe 2013). In attempts to overcome the problem of the short half-life, several tracers with fluorine-18 (half life 110 minutes) have been developed. Several studies have replicated the results obtained with PiB by using 18F-flutemetamol, 18F-florbetaben, 18F-florbetapir and 18F-AZD4694 (Villemagne, Rowe 2013). 18F-BAY94-9172-PET has been claimed to be able to differentiate AD from FTD and healthy controls (Rowe et al. 2008). Apart from amyloid, there are other pathologic markers which can be measured in the brains of dementia patients: tau protein, microglia and astrocytes (Kadir, Nordberg 2010). 18FDDNP binds to both amyloid plaques and tau NFT and differentiates AD from MCI and controls (Small et al. 2006). There are also new MRI techniques which are starting to replace PET and SPECT, such as fMRI using BOLD contrast which can identify those areas that are activated under specific task or deprivation conditions. However this technique may prove to be unspecific because the BOLD signal and task assigned are subject to large interindividual variability which although this can be handled in a statistical manner in the comparison between study groups, it might not be appropriate for an individual diagnosis (Herholz 2011). Other techniques which have been suggested include ASL or RS fMRI (Herholz 2011).

2.6 DIAGNOSTIC METHODS AND BIOMARKERS

The Biomarkers Definitions Working Group of the National Institutes of Health defined a biomarker as ‘‘a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention’’ (Hampel et al. 2011, Biomarkers Definitions Working Group. 2001). According to this definition, one can state that several types of biomarker are now available: imaging methods, CSF proteins, blood analysis and genetic profiling. All of these could be classified as biomarkers, but neuropsychological and clinical testing should be considered as separate entities. However, when the term “biomarker” is used, and no clear distinction is made between each diagnostic category, then it can be interpretated as any diagnostic method that can be used for the diagnosis of a disease. More than two decades ago, the future Molecular and Biochemical Markers of Alzheimer’s Disease working group defined what would represent an ideal biomarker for AD (The Ronald and Nancy Reagan Research Institute of the Alzheimer's Association and the National Institute on Aging Working Group, 1998): “the ideal biomarker for AD should detect a fundamental feature of neuropathology and be validated in neuropathologically-confirmed cases; it should have a sensitivity > 80% for detecting AD and a specificity of > 80% for distinguishing other dementias; it should be reliable, reproducible, non-invasive, simple to perform and inexpensive”. Still today, this goal is far from being achieved – there

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is no ideal biomarker. According to the guidelines (Dubois et al. 2007) several types of biomarker are available for AD: hippocampal atrophy and PIB PET for amyloid deposition as imaging methods, protein markers in CSF and blood and mutations in APP, PS1 and PS2.Since the imaging methods were described in the previous section, this section will focus on other diagnostic methods that could be used for diagnosing dementia: molecular analysis, i.e. CSF and blood analysis including genetic profiling, and possible clinical and neuropsychological scales.

2.6.1 Cerebrospinal fluid During the last decades, protein markers significant for AD and other dementia diseases in cerebrospinal fluid (CSF) have been investigated. Unlike blood, CSF molecules exist in a constant exchange process with their counterparts in the brain and do not have to pass through the blood-brain-barrier. Thus CSF depicts more directly the situation in the brain. CSF bathes the ventricles close to the brain and passes down the spinal cord. It is taken by lumbar puncture between L4 and L5 vertebrae, a cheap and relatively simple procedure although it is not free of some risks and secondary effects such as headache, discomfort, rarely haematoma. Variations in CSF flow, pressure, and its contents of various proteins can help to define the neurological status of a patient and to diagnose several neurological processes (Wright, Lai & Sinclair 2012). In the clinical protocols for dementia diseases, three proteins are measured: Aβ42, total tau (T-tau) and phospho tau (P-tau). The Aβ42 levels are reduced in the CSF of AD patients whereas T-tau and P-tau are elevated, in FTD one encounters a decrease in the levels of Aβ42 and an increase in T-tau compared but to a lesser extent than in AD, with P-tau being invariable in FTD (Schoonenboom et al. 2012). However there is some discrepancy about the level of tau in FTD (Grossman et al. 2005). The presence of intracerebral tau pathology does not necessarily result in higher tau levels in CSF, as evidenced in FTLD patients with tau mutations (Rosso et al. 2003b). In AD, the levels of T-tau (Samgard et al. 2010) and P-tau (Tapiola et al. 2009) seem to correlate with a reduction in the cognitive decline and the presence of neurofibrillary pathology respectively. It has been reported that the numbers of amyloid plaques and the levels of P-tau from brain biopsies correlate with concentrations of Aβ42, T-tau and P-tau in CSF (Seppala et al. 2012) and that the combination of low levels of Aβ42 and high tau predict the presence of AD pathologic features in post-mortem brain with high accuracy (Tapiola et al. 2009). Aβ42, T-tau and P-tau levels can help to differentiate AD from FTLD and other dementia diseases (Schoonenboom et al. 2012). Kapaki et al., claimed that the T-tau/Aβ42 ratio was the most sensitive (90.3%) while P-tau was the most specific (85.7%) biomarker for differentiating AD from FTLD (Kapaki et al. 2008). The importance of P-Tau for differentiating FTD from AD has also been highlighted in other studies (Blennow 2004, Bibl et al. 2011). The tau/Aβ42 ratio is lower in FTLD as compared to AD cases (Bian et al. 2008, de Souza et al. 2011). One study comparing AD patients and patients with the frontal variant of FTD showed significantly different levels of P-tau, Aβ and P-tau/Aβ (de Rino et al. 2011). An assessment of CSF can also help to differentiate between the frontal and temporal variants of FTLD, with higher levels of tau in the temporal variants (Pijnenburg et al. 2006).

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Many studies have confirmed that CSF biomarkers can predict conversion from MCI to AD (Hansson et al. 2007, Hansson et al. 2006, Zetterberg, Wahlund & Blennow 2003). When MCI patients are classified into amnestic and non-amnestic subtypes, the amnestic MCI cases progressed to AD at a higher rate (Vos et al. 2013a). Up to 90% of MCI cases may develop AD after 9-10 years if they display Aβ42 changes in the CSF (Buchhave et al. 2012). The CSF biomarkers have been recommended as being for diagnostic tools even in older age groups (Mattsson et al. 2012). However, multicenter studies have found lower accuracies of CSF in diagnosing incipient AD when compared to the accuracy reported in single-center studies (Mattsson et al. 2009). The lack of standardization may be a feasible explanation for the variability of results between centres, thus by applying a quality control program (Mattsson et al. 2011) and harmonization of sample collection and handling it would be possible to facilitate the comparison among different centres (Mattsson, Zetterberg & Blennow 2010). There are two main techniques which can be used for extracting and processing the CSF samples: ELISA and xMAP Luminex technology. No differences have been found between both techniques when comparing different clinical groups, however the results from either assay types could not be recalculated and compared with the type of assay (Reijn et al. 2007); it seems that the concentrations of CSF protein levels vary depending on which assay is being used. Recently a model for converting between xMAP and ELISA levels of Aβ42, T-tau and P-tau (Wang et al. 2012) was proposed, and this would make possible a relevant comparison of results from different centres. Aβ42 is the amyloid marker which has been most frequently studied in CSF, however it is not the only parameter. For example, levels of other amyloid peptides have also been investigated in both in AD and FTLD (Bibl et al. 2011, Pijnenburg et al. 2007, Bibl et al. 2012). Thus in AD, elevated levels of BACE-1 in AD patients have been described (Zetterberg et al. 2008).

2.6.2 Blood analysis Recently, some interest has been focused on searching for possible biomarkers in blood. A lumbar puncture needed for CSF sample is invasive and needs expertise and accordingly its use in diagnostic procedures varies from country to country. Therefore a blood biomarker would be preferable, the sample would be convenient to collect and be accepted by patients. Thus it has long been believed that blood would be the preferred source to seek diagnostic markers. Many markers have been identified in blood, however none of them has demonstrated reliability, sensitivity and specificity for the diagnosis of AD. For this reason, the levels of Aβ42, T-tau and P-tau measured in CSF are still considered as more reliable markers (Humpel, Hochstrasser 2011). Some investigators, such as the work of Hu et al., have reported that specific metabolites were associated with the diagnosis of very mild dementia (Hu et al. 2012), but these will have to be validated in the future. Metabololomic analysis of blood has also shown promising results in AD and in the prediction of progression to AD among MCI subjects (Oresic et al. 2011). Finally blood analysis can be viewed as a source for DNA which makes it possible to detect specific mutations related to AD and FTLD. Genetic profiling has also been investigated but it has not gained an established position in clinical use as yet.

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2.6.3 Clinical and neuropsychological tests The field of cognitive tests, neuropsychological scales and clinical tests that could be performed is massive and many classifications have been described in the literature. Here only a brief mention will be made of some of these scales and tests based on a three-group classification: assessment of cognition by global cognitive function and, assessment of specific cognitive domains, clinical and behavioral scales, as well as performance scales.

Assessment of global cognitive functions: The Mini-Mental State Examination (MMSE) is a 30-item cognitive screening instrument (Folstein, Folstein & McHugh 1975) used in most clinical sites as the first line in order to identify a cognitive impairment. It has been proved its utility in predicting conversion from MCI to AD, although it is recommended to be used in combination with episodic memory tests (Pozueta et al. 2011). The MMSE score does not discriminate between AD and FTD. However, another study indicated that patterns of longitudinal decline in the MMSE score could differ in bvFTD and AD groups (Tan et al. 2013). The Alzheimer’s Disease Assessment Scale (ADAS-cog) is a commonly used cognitive test in clinical trials (Doraiswamy et al. 2001). The Consortium to Establish a Registry for Alzheimer’s disease Neuropsychological Battery (CERAD-NB) includes the latest version of MMSE and 4 other tests: Verbal Fluency, modified Boston naming test, Word List Memory and Constructional Praxis (Paajanen et al. 2010). It has been proved to be sensitive in differentiating controls from MCI and for determining AD status (Barth et al. 2005). One study from the AddNeuroMed cohort (including 6 European centres from different countries) showed that although the overall level of the CERAD score varied between the different countries, it remained accurate in differentiating controls from MCI subjects in each individual country (Paajanen et al. 2010). Furthermore, this study claimed that the CERAD total score was superior to the MMSE or any individual CERAD subtest when comparing MCI and controls. Memory functions: in the early stages of the disease, FTD patients tend to have better performance on word-list learning and the delayed verbal recall and a well-preserved recognition memory in comparison to AD patients who may be more likely to have impaired access to semantic representations (Harciarek, Jodzio 2005). AD patients perform poorly in memory tests such as the Rey Complex Figure Test, the Logical memory subtest from the Wechsler Memory Scale (WMS) and the Auditory Verbal Learning Test (AVLT), while FTD patients perform poorly in verbal ability tests such as Graded Naming Test or Word-Picture Matching (Hutchinson, Mathias 2007). FTD patients perform significantly better than AD patients on word-list learning and in delayed verbal recall tests (Diehl, Kurz 2002). Executive (attention) function: changes in attention are a common feature in both AD and FTD, however there are some particular differences which make possible a differentiation between AD and FTD in the earliest stages of the disease. FTD patients are relatively more impaired in their executive functions tasks which require planning. BvFTD patients suffer very serious difficulties in tests examining attention and executive functions, especially when they are compared with other cognitive tests scores, and this can help in differentiating bvFTD from the other FTD subtypes (Harciarek, Jodzio 2005).

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One study showed that the combination of trail making test (TMT) B and word list delayed recall reached the highest area under the curve (AUC) value in differentiating presymptomatic AD and non-demented cases (Chen et al. 2000). There is a report that word list delayed recall, delayed recall of Rey Complex Figure and TMT-B could predict the conversion from MCI to AD (Gainotti et al. 2013). The Wisconsin card sorting test is thought to be specific for frontal lobe damage. However some studies have claimed that this test may not differentiate AD from FTD, as in AD executive-functions may also be impaired in the earliest stages (Siri et al. 2001). Language: language problems are frequently observed in AD and in FTD. Both types of patients can exhibit aphasia with preserved repetition abilities. FTD patients may experience word-finding problems, although they more commonly present with impoverished spontaneous speech. The AD patients display more severe comprehension difficulties and their discourse is usually disturbed (Harciarek, Jodzio 2005). One study could detect no differences in naming (Boston naming test) and verbal fluency tasks between AD and FTD patients (Diehl, Kurz 2002). Visuo-construction (visuo-spatial): FTD patients usually perform better than AD patients in visuo-spatial measures tests, although this could be also due to the poor planning and failure to apply logical strategy in FTD patients. Copying tasks do not clearly differentiate AD from bvFTD. The clock-drawing test and Rey Complex figure show a significant impairment in AD patients (Harciarek, Jodzio 2005). Although usually the sequence of drawing and colour use while doing the test usually denote more of a FTD than a AD pattern, one study could detect no differences in Rey Complex figure scores in AD and FTD patients nor did execution-strategies differ between these two patient groups (Gasparini et al. 2008). FTD and AD patients both achieve lower overall scored in the clock-drawing test as compared with controls, but FTD patients scored significantly higher than their AD counterparts (Blair et al. 2006).

Clinical and behavioral scales: Clinical scales: Hachinski ischemic score (HIS) (Hachinski et al. 1975) is still the most common questionnaire used for diagnosing VaD (Hachinski et al. 2012). It has been widely used and it differentiates VaD and AD with a sensitivity of 89% and a specificity of 89% (Knopman et al. 2001), although it cannot differentiate clearly pure VaD and the mixed dementias (Moroney et al. 1997), i.e. patients with the combination of AD and VaD. HIS identifies the vascular component of cognitive disorders and these risk factors which modulate its severity and no item in HIS per se is oriented toward a diagnosis of dementia (Hachinski et al. 2012). Therefore its function is not to diagnose dementia as such, but rather to identify vascular factors which could be treatable and in this way to possibly improve the overall status of the dementia patient. The Webster scale (Webster 1968) recognizes extrapyramidal signs and in that way it may help to identify specific diseases from the FTLD spectrum such as PSP, PDD or MND associated with FTD. Its utility in AD and other dementias is limited due to fact that these patients fail to show any extrapyramidal signs. Behavioral scales: for evaluation of depressive symptoms, one can use Hamilton Depression Scale and the Geriatric Depression Scale. Both scales show discrepant results between each other (Lichtenberg et al. 1992). Present and past depression are associated

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with dementia at old age (Olazaran, Trincado & Bermejo-Pareja 2013), thus this is a major factor that needs to be assessed in all dementia patients. The frontal behavioural inventory (FBI) is a 24-item caregiver-based behavioural questionnaire designed for the diagnosis of FTD symptoms (Kertesz et al. 2003). FBI could classify correctly 92.7% of FTD cases as compared with non-FTD cases. Perseveration, indifference, inattention, inappropriateness and loss of insight were rated highest in FTD (Kertesz et al. 2000). The neuropsychiatric inventory (NPI) (Cummings et al. 1994) can also differentiate bvFTD from other dementias (Hirono et al. 1999). The NPI questionnaire is a brief validated caregiver questionnaire, which utilizes specific questions taken from the wider NPI; the results of assessments made by the NPI questionnaire differed by less than 5% from conclusions from the NPI (Kaufer et al. 2000).

Performance scales: Alzheimer’s Disease Co-operative Study – Activities of Daily Living Inventory (ADCS-ADL) is a test that can be used when one wishes to discern between MCI and AD (Winblad et al. 2004). Clinical Dementia Rating (CDR) (Morris 1993) score and the Global Deterioration Scale (GDS) (Reisberg et al. 1982) are useful tests if one wishes to track the progression through aging and it may help in predicting conversion from MCI to AD. It does assume the patients will receive a score from each test and it needs to be interpreted cautiously and it does not necessarily imply that a patient clearly has either MCI or AD (Petersen 2004).

2.6.4 Combination of biomarkers Many guidelines have been published in an attempt to identify the hallmarks of dementia diseases at an early stage. In addition, several studies have attempted to find the best combination of biomarkers in order to come to an earlier diagnosis of Alzheimer’s disease. Several models have been proposed which try to stratify the progression from a healthy status to the indisputable appearance of dementia.

Figure 8. Hypothetical model of dynamic biomarkers of the AD expanded to explicate the preclinical phase. Reprinted from (Sperling et al. 2011), Alzheimer’s & Dementia, with permission from Elsevier

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Based on the model first proposed by Jack (Jack et al. 2013), Aisen (Aisen et al. 2011) and then extended by Sperling (Sperling et al. 2011) (Figure 8), it has been proposed that there is the following AD time progression: PIB-PET / CSF Aβ (Aβ) > FDG-PET (metabolism) / fMRI (function, activity) > DTI (diffusion) > structural MRI > neuropsychological tests > activities of daily life > CDR. When one refers to inherited AD or EOAD, Aβ can be found already in a time scale from 15 to 25 years before the onset or symptoms arise in the disease, and the rest of the methods can become significant gradually in the years before the patient receives a symptomatic diagnosis. However, when one refers to sporadic AD or LOAD, all the methods tend to find significant changes at the stage very close to the symptomatic phase of AD. The same concept can be applied to FTD: functional changes > WM damage > vulnerable network atrophy > whole brain atrophy (Filippi 2013, Agosta 2013). Many studies have attempted to find the best combination of biomarkers for diagnosing MCI, AD or FTD. Some of these studies have been summarized in Table 12. The Area Under the Receiver Operator Characteristic Curve (AUC), correct classification accuracy (CCA), sensitivity (SS) and specificity (SP) are presented when available. Most of these studies have sought to elucidate the most accurate or useful biomarker from a specific pool, but do not attempt to find the best combination or to examine whether the combination of all the biomarkers would be more powerful than the use of one individual marker on its own. One exception is the study of Kantarci et al., where DLB and AD were differentiated first by each single biomarker, then by different combinations, and finally by all the biomarkers together (Kantarci 2013). This summary does not include those studies using one single biomarker (e.g. only FDG-PET) comparing MCI, AD or FTD, as these have been reviewed in the previous sections. The majority of the studies have tried to predict the conversion from MCI to AD. Six of the studies utilized data coming from ADNI, three studies have included patients from the VU medical center (VUmc) and DESCRIPA, and two studies evaluated patients only from VUmc. The rest of the cohorts have been used once. In addition, one meta-analysis is included. In the comparison of FTD with AD, the studies have found different results concerning the value of FDG-PET and MRI. Mendez et al., (Mendez et al. 2007) stated that SPECT/PET was the most sensitive method for diagnosing FTD. Neuropsychological tests initially did not differentiate FTD from other dementias, although after a 2-year follow-up, the pattern of progression (worse naming and pourer executive-functions but preserved constructional ability) did help in recognizing FTD. Kipps et al., (Kipps et al. 2009) showed that only FTD patients with abnormal MRI displayed differences in FDG-PET hypometabolism as compared to controls. Dukart et al., (Dukart et al. 2011)stated that although FDG-PET was more accurate than structural MRI, the combination of MRI and FDG-PET could differentiate more accurately FTLD from AD than the use of only MRI or FDG-PET. Most of the studies have found the combination of different biomarkers to be more accurate or sensitive in predicting conversion from MCI to AD than the use of one single biomarker e.g. MRI+MRS (Westman et al. 2010), MMSE+CSF+clock drawing test (Palmqvist et al. 2012). The meta-analysis (Bloudek et al. 2011) highlighted the utility of FDG-PET in comparing AD and non-demented controls, demented controls and other dementias. It was stated that the addition of CSF P-Tau and SPECT helped to differentiate AD from non-AD dementias.

45

A report from the AddNeuroMed cohort compared different classifiers; here the best techniques reached a sensitivity of 83% and a specificity of 87% in the ability to distinguish controls from AD patients (Aguilar et al. 2013). With respect to the different MRI markers, Clerx et al., reported that both automatic and manual HV were better predictors of progression to AD from MCI than the MTA score and lateral ventricle value. Both HV methods correlated with tau levels in CSF, while MTA score and lateral ventricle correlated with amyloid levels in CSF (Clerx et al. 2013). Wolz et al., combined different automatic MRI techniques (HV, CTH, MBL, TBM) by using linear discriminant analysis: they found that the combination of all techniques was more accurate, sensitive and specific than any individual method in its ability to differentiate controls from AD, and SMCI from PMCI (Wolz et al. 2011). One study supported the use of DTI for the diagnosis of AD among 1H-MRS and PWI (Zimny et al. 2011), by examining the correlation between the three methods and neuropsychological tests. One interesting group classification was proposed by Prestia et al., classifying MCI patients into 5 groups according to several possible combinations including Aβ42 levels in CSF, FDG-PET and HV (Prestia et al. 2013). The first 4 groups represented the typical progression to AD (Aβ42>metabolic changes>hippocampal atrophy), while the fifth group included every other type of combination; 22% of the patients were included in the last group, suggesting that not all the MCI cases follow the typical progression depicted in the model proposed by Jack and colleagues (Jack et al. 2010). Nevertheless the incidence of dementia did increase from group 1 to 4, in the same order as indicated with the different stages in the progression model. A similar group classification was used by Galluzi et al., adding also a group with no biomarker positivity. This is the only study to have investigated the percentage of conversion from MCI to other dementias after 36 months (Galluzzi et al. 2013) One study surveyed the progression from clinically ambiguous dementia to dementia diseases (Boutoleau-Bretonniere et al. 2012). It was found that the CSF biomarkers predicted the evolution towards AD, while imaging helped to differentiate AD from other dementias. CSF biomarkers showed the highest sensitivity in the diagnosis of AD (SS=100%), also helping to the diagnosis of FTD and VaD. The combination of MRI and SPECT increased the specificity for the AD diagnosis (SS=93%, SP=88%), although they decreased the sensitivity when compared to the single use of MRI (SS=100%, SP=71%). MRI and SPECT could contribute to the diagnosis of FTD (SS=73%, SP=78%). Independent of the clinical diagnosis, MTL atrophy and T-Tau were best correlated with the cognitive decline at 2 years. Last, there is one prospective study currently being carried out in Amsterdam (Krudop et al. 2013), which involves bvFTD patients aged 45-75 years who have undergone a neurological and psychiatric evaluation and been subjected to MRI, CSF and PET examinations. These patients will be observed over a period of two years in order to assess the diagnostic value of these methods.

46

Tabl

e 12

. Stu

dies

usi

ng d

iffer

ent

indi

vidu

al b

iom

arke

rs a

nd/o

r co

mbi

natio

ns o

f bi

omar

kers

incl

udin

g co

ntro

ls,

MCI,

AD

and

FTD

gro

ups

Stu

dy

Yea

r C

ohor

t G

rou

ps

and

sam

ple

Fo

llow

-up

M

eth

ods

Dia

gn

osi

s A

UC

/C

CA

/S

S/

SP

Agu

ilar

et a

l.

(Agu

ilar

et a

l. 20

13)

2013

Add

Neu

roM

ed

116

AD

, 11

9 M

CI,

100

C

1 ye

ar

MRI+

educ

atio

n (O

PLS)

AD

vs.

C

-/85

.8/8

0.2/

91.8

Blo

udek

et

al.

(Blo

udek

et

al.

2011

)

2011

M

eta-

anal

ysis

11

9 st

udie

s -

FDG

-PET

CSF

P-Ta

u

SPE

CT

AD

vs.

non

-de

men

ted

C

AD

vs.

de

men

ted

C

/MCI

AD

vs.

non

-AD

de

men

tias

96/-

/90/

89

91/ -

/92/

78

86

86

Bou

tole

au-B

reto

nnie

re e

t al

.

(Bou

tole

au-B

reto

nnie

re e

t al

. 20

12)

2012

N

ante

s 18

AD

, 11

FTD

, 8

VaD

,

7 Ps

y 24

mon

ths

CSF

Aβ

CSF

T-Ta

u

CSF

P-Ta

u

CSF

Am

yloi

d Ta

u In

dex

≥2

CSF

biom

.

MRI

Sch

elte

ns s

core

SPE

CT

AD

typ

e

AD

vs.

non

-AD

72

/-/7

8/64

93/ -

/100

/74

84/-

/100

/64

90/-

/94/

71

-/-/

100/

71

68/-

/56/

86

-/-/

43/7

4

Cle

rx e

t al

.

(Cle

rx e

t al

. 20

13)

2013

D

ESCR

IPA

and

Vum

c 32

8 M

CI

2 ye

ars

Aut

omat

ic H

V

Man

ual H

V

MTL

atr

ophy

sco

re

Late

ral v

entr

icle

AD

0.

71

0.71

0.65

0.60

46

47

Tabl

e 12

. (c

ontin

ued)

Stu

dies

usi

ng d

iffer

ent

indi

vidu

al b

iom

arke

rs a

nd/o

r co

mbi

natio

ns o

f bi

omar

kers

incl

udin

g co

ntro

ls,

MCI,

AD

and

FTD

gro

ups

Stu

dy

Yea

r C

ohor

t G

rou

ps

and

sam

ple

Fo

llow

-up

M

eth

ods

Dia

gn

osi

s A

UC

/C

CA

/S

S/

SP

Dav

atzi

kos

et a

l.

(Dav

atzi

kos

et a

l. 20

11)

2011

AD

NI

239

MCI

12±

6 m

onth

s SPA

RE-

AD

and

tau

SPA

RE-

AD

and

Aβ4

2

SPA

RE-

AD

and

tau

and

Aβ4

2

- -

Duc

kart

et

al.

(Duk

art

et a

l. 20

11)

2011

Le

ipzi

g 21

AD

, 14

FTL

D,

13 C

-

FDG

-PET

and

MR

I AD

vs.

FTL

D

AD

vs.

C

FTLD

vs.

C

-/94

/95/

92

-/10

0/10

0/10

0

-/93

/86/

100

Gal

luzz

i et

al.

(Gal

luzz

i et

al.

2013

)

2013

Bre

scia

58

MCI

36 m

onth

s -

Con

vers

ion

to

prob

able

AD

, po

ssib

le A

D,

DLB

or

FTD

-

Kan

tarc

i et

al.

(Kan

tarc

i et

al.

2012

)

2012

M

ayo

Clin

ic A

DRC

21

AD

, 21

DLB

, 42

C

- PI

B+

FDG

PIB+

MR

I

FDG

+M

RI

All

AD

vs.

DLB

94

96

96

98

Kip

ps e

t al

.

(Kip

ps e

t al

. 20

09)

2009

Cam

brid

ge

24 b

vFTD

, 12

C

- FD

G-P

ET

MRI

-

Kru

dop

et a

l.

(Kru

dop

et a

l. 20

13)

2013

VU

mc

BvF

TD 4

5-75

yea

rs

2 ye

ars

Neu

rops

ycho

logi

cal t

ests

, M

RI,

FD

G-P

ET,

CSF

- Pr

ospe

ctiv

e st

udy

47

48

Tabl

e 12

. (c

ontin

ued)

Stu

dies

usi

ng d

iffer

ent

indi

vidu

al b

iom

arke

rs a

nd/o

r co

mbi

natio

ns o

f bi

omar

kers

incl

udin

g co

ntro

ls,

MCI,

AD

and

FTD

gro

ups

Stu

dy

Yea

r C

ohor

t G

rou

ps

and

sam

ple

Fo

llow

-up

M

eth

ods

Dia

gn

osi

s A

UC

/C

CA

/S

S/

SP

Land

au e

t al

.

(Lan

dau

et a

l. 20

10)

2010

AD

NI

193

AD

, 22

9 C,

28 P

MCI,

57

SM

CI

2-3

year

s FD

G-P

ET

HV

CSF

Aβ4

2

CSF

T-Ta

u

CSF

P-Ta

u

AVLT

All

88/7

6/82

/70

89/8

1/79

/82

81/7

6/82

/70

80/7

5/80

/70

80/7

4/71

/77

95/9

0/93

/88

Men

dez

et a

l.

(Men

dez

et a

l. 20

07)

2007

U

nive

rsity

of

Cal

iforn

ia,

Los

Ang

eles

63 F

TD,

71 n

on-F

TD

2 ye

ars

Con

sens

us c

rite

ria

MRI

SPE

CT/

PET

FTD

dia

gnos

is

-/-/

36.5

/100

-/-/

63.5

/70.

4

-/-/

90.5

/74.

6

Palm

qvis

t et

al.

(Pal

mqv

ist

et a

l. 20

12)

2012

M

alm

ö SM

CI

and

PMC

I (A

D,

PSP,

VaD

, D

LB,

tum

or)

6 ye

ars

MM

SE,

clo

ck d

raw

ing

test

and

CSF

CSF

Cog

nitiv

e te

sts

MCI-

AD

vs.

M

CI-

othe

r de

men

tias

-/93

/-/-

-/89

/-/-

-/85

/-/-

Pres

tia e

t al

.

(Pre

stia

et

al.

2013

)

2013

Bre

scia

, VU

mc,

Kar

olin

ska

73 M

CI

- CSF

Aβ4

2, F

DG

-PET

HV

- -

Roe

et

al.

(Roe

et

al.

2013

)

2013

Kni

ght

AD

RC

N

orm

al a

dults

age

d 45

to

88

Up

to 7

.5

year

s Am

yloi

d im

agin

g, C

SF

- -

48

49

Tabl

e 12

. (c

ontin

ued)

Stu

dies

usi

ng d

iffer

ent

indi

vidu

al b

iom

arke

rs a

nd/o

r co

mbi

natio

ns o

f bi

omar

kers

incl

udin

g co

ntro

ls,

MCI,

AD

and

FTD

gro

ups

Stu

dy

Yea

r C

ohor

t G

rou

ps

and

sam

ple

Fo

llow

-up

M

eth

ods

Dia

gn

osi

s A

UC

/C

CA

/S

S/

SP

Sch

uff et

al.

(Sch

uff et

al.

2009

)

2009

AD

NI

111

cont

rols

, 22

6 M

CI,

96

AD

6,

12

mon

ths

MRI,

CSF,

APO

E -

-

Tosu

n et

al.

(Tos

un e

t al

. 20

12)

2012

M

emor

y an

d Agi

ng

cent

er o

f th

e U

nive

rsity

of

Cal

iforn

ia

12 b

vFTD

, 12

C

- M

RI,

ASL

- -

Van

Ros

sum

et

al.

(van

Ros

sum

et

al.

2012

)

2012

VU

mc

91 M

CI-

AD

,

112

MCI -

othe

r -

CSF,

MTA

, APO

E, M

MSE

- -

Vem

uri e

t al

., 2

009

(Vem

uri e

t al

. 20

09)

2009

AD

NI

109

C,

192

aMCI,

98

AD

-

STA

ND

sco

re (

MR

I),

T-ta

u,

Aβ4

2, P

-tau

CSF

and

MR

I

- -

Vos

et

al.

(Vos

et

al.

2012

)

2012

D

ESCR

IPA

and

VU

mc

153

MCI

2 ye

ars

CSF

and

HV

- -

Vos

et

al.

(Vos

et

al.

2013

a)

2013

D

ESCR

IPA

and

VU

mc

226

naM

CI,

399

aM

CI,

19

4 aM

CI

sing

le-

dom

ain,

225

aM

CI

mul

ti-d

omai

n

Up

to 5

ye

ars

CSF,

HV,

APO

E -

-

Wal

hovd

et

al.

(Wal

hovd

et

al.

2010

)

2010

AD

NI

42 C

, 73

MC

I, 3

8 AD

(a

fter

fol

low

-up:

C,

51 M

CI,

25

AD

)

2 ye

ars

MRI,

FD

G-P

ET,

CSF

- -

49

50

Tabl

e 12

. (c

ontin

ued)

Stu

dies

usi

ng d

iffer

ent

indi

vidu

al b

iom

arke

rs a

nd/o

r co

mbi

natio

ns o

f bi

omar

kers

incl

udin

g co

ntro

ls,

MCI,

AD

and

FTD

gro

ups

Stu

dy

Yea

r C

ohor

t G

rou

ps

and

sam

ple

Fo

llow

-up

M

eth

ods

Dia

gn

osi

s A

UC

/C

CA

/S

S/

SP

Wes

tman

et

al.

(Wes

tman

et

al.

2010

)

2010

ART

30 A

D,

36 C

-

MRI

MRS

MRI

and

MR

S

- -/

-/93

/86

-/-/

76/8

3

-/-/

97/9

4

Wol

z et

al.

(Wol

z et

al.

2011

)

2011

AD

NI

231

C,

238

SM

CI,

167

PMCI,

198

AD

Abo

ut 1

8 m

onth

s H

V,

MBL,

CTH

, TB

M

C v

s. A

D

SM

CI

vs.

PMCI

C v

s. P

MCI

-

Zim

ny e

t al

.

(Zim

ny e

t al

. 20

11)

2011

W

rocl

aw

30 A

D,

23 a

MCI,

15

C

- 1 H

-MRS

PWI

DTI

AD

aMCI

AD

aMCI

AD

aMCI

82/-

/53/

100

47/-

/09/

100

87/-

/87/

80

67/-

/61/

73

95/-

/83/

100

79/-

/87/

67

50

51

2.7 PREDICT AD

Despite all the advances that have been made, still there is no definitive biomarker or test that could be said to be specific of AD or FTD and could clearly differentiate one disease from another especially in atypical presentations. The Predict AD project (www.predictad.eu, 6/2008-11/2011) has attempted to identify new biomarkers and to combine these biomarkers in order to reach a more precise diagnosis of AD or FTD. This work has been particularly focused on the development of automatic quantitative techniques for MRI imaging analysis and a tool that could compose the various biomarkers that could be available, this is the combination of Disease State Index (DSI) and Disease State Fingerprint (DSF).

2.7.1 Automatic quantitative techniques Automatic MRI methods are becoming more important in the field of research in order to mitigate two common problems in the daily clinic work: the unavoidable subjective component when a radiologist interprets the images and the lack of quantifiable data. Still the gold standard for imaging in AD is to visually interpret the shape and size of the hippocampus in a coronal T-1 weighted MRI, i.e. assessing the MTL atrophy using a qualitative visual scale. In addition, it does not require much time to conduct a visual scoring. Manual volumetry is the other gold standard technique, although it is time consuming. However, recent research criteria have provided the opportunity to start to utilize quantitative techniques. This quotation recommends their use: “Volume loss of hippocampi, entorhinal cortex, amygdala evidenced on MRI with qualitative ratings using visual scoring (referenced to well characterized population with age norms) or quantitative volumetry of regions of interest (referenced to well characterized population with age norms)” (Dubois et al. 2007). Nonetheless automatic quantitative techniques are not still in widespread use. This may be due to the lack of standardization among methods and centers, which does not allow a direct comparison between results from different sites. Therefore these quantitative techniques are restricted to research centres.

2.7.2 Disease State Index and Disease State Fingerprint Together denominated as a PredictAD tool, it is a so-called decision support system for profiling patients (Mattila et al. 2011, Simonsen et al. 2012). It aims to help to integrate all the heterogeneous data originating from multiple sources e.g. CSF, imaging, genetics or neuropsychological tests, providing a holistic view, illustrating each individual biomarker and their combination at a glance, helping the clinician in the assessment of all these data before making a diagnosis. It provides objective information about the status of a patient as compared with data from a large number of previous cases. It computes an index between 0 and 1 which describes the progress level of the disease and represents the information also graphically.

52

53

3 Aims of the study

This project is intended to evaluate the combination of multiple different methods for profiling patients used in the diagnosis between FTD and AD compared with MCI stages. We use automatic imaging methods HV, TBM and VBM, and functional imaging 99mcTc-HMPAO SPECT, CSF biomarker candidates and neuropsychological tests integrated in the DSI and DSF tools. A case profile of DSF for diagnostic purpose was developed in studies II and III. The aims were to study:

1. The classification accuracy of HV, TBM and VBM in differentiating the healthy state from dementia and FTD from AD (Study I)

2. DSI performance as a classifier in differentiating study groups and DSF in profiling a patient as being healthy, having AD or FTD (Studies II-IV)

3. The usefulness of imaging methods from Study I, MMSE, CSF and APOE in differentiating AD from FTD (study II)

4. The importance of SPECT, manual hippocampal volumetry, MMSE and a battery of neuropsychological tests, CSF and APOE in differentiating AD from FTD (Study III)

5. The usefulness of SPECT over manual volumetry and a battery of neuropsychological tests over MMSE (study III)

6. The usefulness of imaging methods from study I, MMSE, CSF, APOE and a different batteries of neuropsychological tests in predicting conversion from MCI to AD (Study IV)

7. The generalizability of DSI in a study including multiple cohorts in predicting the conversion from MCI to AD by studying the performance of a combined cohort compared with each individual cohort performance and by using different cohorts as training sets compared with intra-cohort cross-validation (Study IV)

54

55

4 Subjects and methods

4.1 SUBJECTS

The following cohorts were investigated in the thesis: three different patient groups recruited from Kuopio University Hospital (KUH) (studies I, II and III), Kuopio L-MCI, ADNI, AddNeuroMed and DESCRIPA (study IV). In studies I, II, III and in Kuopio L-MCI cohort from study IV, informed written consent was obtained from all subjects according to the Declaration of Helsinki and the study was approved by the ethics committee of The North-Savo Hospital district. In study IV, informed consents and approvals by ethic committees had been obtained for all the cohorts included. A summary of the patients and methods included in each study can be found in Table 13.

Table 13. Main characteristics of the study groups

Study Subjects (n) Age mean± SD

Methods Project Follow-up (years)

I AD (46)

FTD (37)

MCI:

P-MCI (16)

S-MCI (48)

Controls (26)

74±6

66±9

73±5

72±6

74±4

� HV, TBM, VBM � HARDEM, KUH � Kuopio L-MCI

3

II AD (35)

FTD (37)

MCI (64)

Controls (26)

74±6

66±9

73±5

74±4

� HV, TBM, VBM � MMSE � CSF � APOE

� HARDEM, KUH � Kuopio L-MCI

3

III AD (57)

FTD (38)

Controls (22)

70±8

65±9

71±4

� MRI manual outlining

� SPECT � Neuropsychological

tests � MMSE � CSF � APOE

� KUH

IV MCI: (875)

ADNI (370)

AddNeuroMed (123)

DESCRIPA (237)

Kuopio L-MCI (145)

75±7

74±6

70±8

72±5

� HV, TBM, VBM � Neuropsychological

tests � MMSE � CSF � APOE

� ADNI � AddNeuroMed � DESCRIPA � Kuopio L-MCI

2.9

1

2.2

2.6

56

In studies I and II, subjects derived from the same database obtained in KUH, although study I also included 11 AD cases more than in study II and MCI was divided into SMCI and PMCI in study I. There were 37 patients who fulfilled the diagnostic criteria for FTD (Neary et al. 1998) and for whom there were technically satisfactory brain images according to the quality control criteria set for the images. All FTD patients were examined in the Kuopio University Hospital by an experienced neurologist (Päivi Hartikainen). From these subjects, MMSE, APOE genotype and CSF values were included as parameters in study II. Twenty seven of these FTD patients were originally recruited to the rare dementias project, and ten cases were included in the Demspect project. The clinical diagnosis of all FTD patients was ascertained after three years follow-up that consisted of a thorough clinical history, physical examination, neuropsychological testing, blood tests, MRI and SPECT. The majority of these cases displayed the behavioral variant of FTD, however not all these cases followed this clinical pattern. Four cases displayed the combination of the behavioral variant of FTD with signs of MND. Three cases had the C9ORF72 gene mutation. Nine cases underwent autopsy and exhibited TDP (TAR DNA-binding protein of 43 kDa) positive brain immunochemistry. MRI images and neuropsychological tests were carried out soon after the initial visit. The following scales and neuropsychological tests were used: MMSE, CDR (Hughes et al. 1982), Boston naming test, Vocabulary subtest of the Wechsler Adult Intelligence scale using every second item, Word list learning test (Helkala et al. 1988), Heaton Visual Reproduction test of geometric figures immediately (WMS figures) and after 30 minutes, Story recall, Story recall after 30 minutes, Trail making A test in which a maximum time of 150 s was used, and Verbal fluency PAS test. All controls (n = 26) were selected from a large population study related to an MCI population-based project. Those cases were healthy people without any neuropsychological or psychiatric history (Kivipelto et al. 2001, Pennanen et al. 2005, Julkunen et al. 2010, Hanninen et al. 2002). MCI subjects were selected from the Kuopio MCI, a population-based study database gathered at University of Kuopio (Julkunen et al. 2010, Julkunen et al. 2009). MCI was diagnosed using the criteria originally proposed by the Mayo Clinic Alzheimer’s Disease Research Center (Petersen et al. 1997, Petersen et al. 1995): (1) memory complaint by patient, family, or physician; (2) normal activities of daily living; (3) normal global cognitive function; (4) objective impairment in memory or in one other area of cognitive function as evident by scores > 1.5 standard deviation below the age-appropriate mean; (5) clinical dementia rating score of 0.5; and (6) absence of dementia. All the MCI subjects were considered as displaying the amnestic subtype of the syndrome. In study I, those MCI subjects who developed AD during the course of the follow-up were considered as PMCI subjects (n = 16) and those whose status remained stable were considered as having SMCI (n = 48). In study I, 46 patients fulfilled the diagnostic criteria of AD according to the DSM-IV-TR (American Psychiatric Association, 1994). Four AD patients had a pathologically confirmed post-mortem AD diagnosis. In study II, 35 AD patients were included following the same criteria as in study I. In study III, a total of 117 subjects were included. All subjects were examined in the KUH. Forty cases fulfilled the clinical diagnostic criteria for FTD (Neary et al. 1998) and for whom there were technically valid brain images after image quality control. All FTD patients were clinically examined in the KUH by an experienced neurologist (Päivi Hartikainen). Nine

57

cases had autopsy confirmation. Fifty-six patients fulfilled the diagnostic criteria of AD according to the DSM-IV-TR (American Psychiatric Association, 1994). Twelve AD cases had autopsy confirmation. Twenty-one healthy controls were included. Several parameters were grouped into 6 main categories: clinical, neuropsychological, genetic, MRI, SPECT and CSF. The following clinical features were included symptoms record (amnesia, dysphasia, confusion, psychosis, paranoia, depression, apathy, sleeping disorder, frontal symptom, tremor, myoclonus, and disinhibition) and scales for assessing particular changes, such as HIS for assessing vascular changes, Webster total score for extrapyramidal signs or Hamilton depression scale. The following neuropsychological tests were used: MMSE, language set (Boston naming test, vocabulary, verbal fluency PAS, verbal fluency animals), memory set (word list learning, WMS figures, story immediate recall, word list recognition, word list recognition deletion, word list recognition false positive, WMS figures recall, story recall), visuo-construction set (block design, WMS figures copying, cubic copying, drawing clock), executive function (trail making A, trail making A mistakes, trail making A deletions, trail making B, trail making B mistakes, trail making B deletions, Wisconsin card sorting categories, Wisconsin card sorting mistakes, Wisconsin card sorting perseveration, Wisconsin card sorting right, praxias). Two genetic factors were recorded: the number of APOE-4 alleles and the presence of dementia in the family as genetic factors. In addition, manual volumetric MRI and SPECT were included as imaging methods, and CSF biomarkers (Aβ42, T-Tau and P-Tau). Study IV included MCI patients from 4 different cohorts: DESCRIPA (Visser et al. 2008), ADNI (Mueller et al. 2005, Weiner et al. 2013), AddNeuroMed (Lovestone et al. 2009) and the Kuopio longitudinal-MCI cohort (Kivipelto et al. 2001, Hanninen et al. 2002, Julkunen et al. 2009, Julkunen et al. 2010). A total of 875 subjects were involved. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of AD (Weiner et al. 2013). It involves 50 medical center and university sites across the United States and Canada. ADNI is supported by the NIH, private pharmaceutical companies, and nonprofit organizations, and has the primary goal of evaluating MRI, PET, CSF, and clinical measures acquired serially over 2–3 years (Landau et al. 2010). The ADNI dataset was downloaded from the ADNI database (www.loni.ucla.edu/ADNI). ADNI subjects aged 55 to 90 from over 50 sites across the US and Canada have participated in the project. More detailed information is available at www.adni-info.org. Inclusion criteria for MCI patients were MMSE score between 24 and 30, memory problems with objective memory loss, CDR score of 0.5, not impaired significantly in other cognitive domains, preservation of activities of daily living and absence of dementia. The ADAS-cog was selected as the neurological test set to be included in DSI for ADNI. In addition CSF and APOE genotype data were determined. This study from this cohort included 370 subjects, 36% female, with an average follow-up time 2.9 years. The ‘Development of Screening Guidelines and Clinical Criteria for Predementia AD’ (DESCRIPA) study is an European multicentre study which intends to investigate which combination of variables can best identify subjects with predementia AD (Visser et al. 2008). Memory clinics across Europe recruited patients aged 55 years and older with cognitive complaints, but without a diagnosis of dementia or a somatic, psychiatric or neurological disorder that

58

could have caused the cognitive impairment. Study IV included neuropsychological tests, CSF values and APOE genotype information. The neuropsychological tests were divided into domains of learning, memory, language, executive function, and visuoconstruction. As the subjects were collected from several different countries, the exact type of cognitive tests administered varied from center to center. This database included 237 subjects, 57% female. The average follow-up time was 2.2 years. The study subjects displayed had an observable cognitive impairment, defined as a z-scored result of -1.5 or lower in neuropsychological tests. The diagnosis of AD was based on the NINCDS-ADRDA criteria. AddNeuroMed is a multi-center European study aimed at validating and identifying plasma-based and neuroimaging biomarkers for AD (Lovestone et al. 2009). The data was collected from six centers and contained control, MCI and AD patients; from this database 123 MCI patients (50% female) with MRI results were included in study IV. In addition to the MRI, the MMSE, CERAD battery and APOE genotype were assessed. The follow-up time for MCI to AD conversion was one year and the AD diagnosis was made according to the NINCDS-ADRDA criteria for probable AD. At baseline, all MCI subjects fulfilled the following components of the diagnostic criteria for amnestic MCI (Petersen et al. 1999, Petersen et al. 2001), i.e., (1) memory complaint by patient, family, or physician; (2) normal activities of daily living; (3) MMSE score (Folstein, Folstein & McHugh 1975) range between 24 and 30; (4) Geriatric Depression Scale score less than or equal to 5; (5) subject aged 65 years or above; (6) CDR memory score (Hughes et al. 1982) of 0.5 or 1; and (7) absence of dementia according to the NINCDS-ADRDA criteria (McKhann et al. 1984). Patients with subjective memory complaints, but with a CDR memory and total score of 0 were not included in the MCI group. A total of 145 subjects diagnosed with mild cognitive impairment were included in the Kuopio L-MCI study. These patients were pooled from two populations based studies gathered in the University of Eastern Finland (Kivipelto et al. 2001, Pennanen et al. 2005, Hanninen et al. 2002, Julkunen et al. 2009, Julkunen et al. 2010). One of the two studies used the MMSE for screening, with those patients scoring under 24 or less being invited to participate in the clinical phase (Kivipelto et al. 2001). MCI was diagnosed using the following criteria originally proposed by the Mayo Clinic Alzheimer’s Disease Research Center: 1) memory complaint by patient, family, or physician; 2) normal activities of daily living; 3) normal global cognitive function; 4) objective impairment in memory or in one other area of cognitive function as evident by scores > 1.5 S.D. below the age appropriate mean; 5) CDR score of 0.5; and 6) absence of dementia. All the MCI subjects were considered as having the amnestic subtype of the syndrome at the time of recruitment. The diagnosis of AD was based on the NINCDS-ADRDA criteria. The neuropsychological data after feature selection by relevance for Kuopio MCI included the Logical Memory Test from the Wechsler Memory Scale–Revised, Block Design and Vocabulary from the Wechsler Adults Intelligence Scale, Buschke Selective Reminding Test, Copy a Cube-test and Constructional Praxis for CERAD. Details on the battery of neuropsychological tests can be found elsewhere (Kivipelto et al. 2001) and (Hanninen et al. 2002). In addition APOE genotype and CSF values were included as parameters in study IV.

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4.2 ACQUISITION 4.2.1 MRI In studies I, II, III and in Kuopio L-MCI cohort from study IV, all MRI images were acquired with one of three different 1.5 T scanners (two Siemens Magnetom Visions, one Siemens Magnetom Avanto, all Siemens Medical Systems, Erlangen, Germany) in the Department of Clinical Radiology, Kuopio University Hospital. In all cases, MRI images were obtained using a T1-weighted 3D MPRAGE sequence. Detailed information of the imaging sequences can be found within Study I. In AddNeuroMed, the MRI data was acquired with 6 different 1.5T MR systems (Simmons et al. 2011). The data acquisition was designed to be compatible with ADNI (Jack et al. 2008). The imaging protocol in ADNI and AddNeuroMed included a high-resolution sagittal 3D T1 –weighted MPRAGE volume and axial proton density/T2-weighted fast spin echo images. In DESCRIPA, the MRI scans were conducted in 9 different centers, each with their own scanners and protocols. The scanning was performed at either 1.0 or 1.5 Tesla and included a 3D T1 weighted gradient echo and a fast fluid attenuated inversion recovery (FLAIR) sequence (van de Pol et al. 2009).

4.2.2 SPECT In study III, the regional cerebral blood flow ratios referred to the cerebellum were examined by 99m Tc-HMPAO. Detailed information has been described previously (Lehtovirta et al. 1998, Lehtovirta et al. 1996). The regions of interest were frontal cortex, temporal region, parietal cortex, occipital, basal ganglia and amygdala-hippocampus.

60

4.3 IMAGING AND ANALYSIS METHODS

Studies I, II and IV involved automatic hippocampal volumetry (HV), tensor-based morphometry (TBM) and voxel-based morphometry (VBM). The following seven regions-of-interest (ROIs) were included: hippocampus/amygdala, posterior temporal lobe, gyri hippocampalis et ambiens, lateral ventricle (frontal horn, central part and occipital horn), lateral ventricle (temporal horn), anterior cingulate gyrus and superior frontal gyrus. These regions were selected out of a template of 83 ROIs, taken from the Hammer’s atlas. The ROIs displaying the highest accuracy in the comparisons of the study groups were selected resulting in 7 ROIs being chosen from that pool. Skull stripping was applied, i.e., the separation of the brain from other areas of the head, was applied as a pre-processing step before TBM and VBM. In study III, the analysis of regional MRI volumes was performed using manual outlining of the ROIs as described previously (Laakso et al. 2000). The ROIs were frontal lobe, temporal lobe and hippocampus.

4.3.1 Volumetry We computed automatically the volume of the hippocampi using a modified multi-atlas segmentation framework. It has been used previously (Lotjonen et al. 2011, Lotjonen et al. 2010) detecting no differences as compared with semiautomatic segmentation, while the automatic approach was much less time consuming, requiring a mere 2 minutes. Studies I and II involved an analysis for normalized HV and unnormalized HV. Normalization was conducted for age, gender and intracranial volume. No clear differences were found between both approaches therefore the normalized hippocampal volumes were presented. The technique of automatic volumetry (AV) was conducted in a step-by-step basis as follows (figure 9):

1. Register using affine and smooth non-rigid transformations all atlases to a template image, which is an image selected from the atlas library (this is an offline step that does not need to be performed for each patient separately).

2. Register the patient image using affine and smooth non-rigid transformations to the template.

3. Register non-rigidly all selected atlases to the patient image. 4. Propagate all class labels (WM, GM, CSF, hippocampus, background) of the atlases

to the co-ordinate system of the patient image and construct a probabilistic atlas for each class.

5. Perform a tissue classification using the expectation maximization algorithm.

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62

4.3.2 Tensor-based morphometry Morphometric methods are nowadays widely used in the field of neuroscience. Three main techniques were initially described (Ashburner, Friston 2000): deformation-based morphometry, tensor-based morphometry (TBM) and voxel-based morphometry (VBM). In deformation and tensor-based morphometry, a template image i.e. a randomly selected patient image or a mean image computed from multiple subjects, is deformed non-rigidly to all images of the training set. In other words, a vector is defined from each voxel of the template to the anatomically corresponding location in the target image. It was intended to show that the deformation vectors may be different for healthy controls, MCI, AD and FTD. For example, the ventricles usually are enlarged in AD as compared to the normal population, thus the mean image template is deformed outwards in AD cases (large ventricles) and inwards in normal cases (small ventricles). TBM localize regions of shape differences among groups of brains based on deformation fields. This is conducted using the determinant of the Jabobian matrix (so called the Jacobian) which measures the local volume changes. Tensor-based morphometry step-by-step (figure 9):

1. Compute a mean anatomical template of the 30 ADNI images, here called template image.

2. Skull-strip the patient image (extract brain tissue from the study image). 3. Register (non-rigidly) the template image with the study image. 4. Compute the Jacobian (i.e., the determinant of the Jacobian matrix) for each voxel. 5. Compute the ROI-wise mean values of the log-Jacobian

The local volume change is computed for each voxel of the template relative to the template image. In study I, II and IV, a multi-template TBM was used, where these steps were repeated independently for the 30 templates, and then in step 4 the Jacobians of all the templates were averaged for each voxel (Brun et al. 2009, Koikkalainen et al. 2011).

4.3.3 Voxel-based morphometry VBM examines the differences in GM local concentration between two groups of patients (Ashburner, Friston 2000). Voxel-based morphometry is described on a step-by-step basis (figure 9):

1. Compute a mean anatomical template of the 30 ADNI images, here called template image.

2. Skull-strip the study image (extract brain tissue from the study image). 3. Make the tissue segmentation of the study image using the expectation

maximization algorithm. 4. Use a smooth non-rigid transformation to register the template image with the study

image. 5. Propagate the tissue segmentation (GM segmentation) to the template space. 6. Extract the GM segmentation and smooth it with a Gaussian filter. 7. Compute the ROI-wise mean values for the GM concentration.

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Application of TBM and VBM TBM and VBM produce voxel-wise results. In order to quantify these results for classification, the ROI-wise average was computed. All the classification results are from the ROI analysis and no voxel-wise classifications were performed. Since the features are calculated by summing the voxel-wise values for all the voxels inside a ROI, if a ROI includes both atrophic and expanding regions in TBM, these opposite effects may cancel out each other. In other words, the total volume change may be zero, even if there are significant atrophic and expanding sub-regions within the ROI. The equation

��

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0)(0)(

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)()()()(

xTxT

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xJxwxJxwF is used to take this into account: the regions that are

known to be atrophic and expanding are computed separately, thus it was decided to search for the AD-like pattern in the Jacobian values. This applies to the VBM for regions with increased and decreased GM concentration in VBM within a ROI.

Classification methods: The Hammers’ atlas of 83 structures was used to define the ROIs in studies I, II and IV. A very basic linear classifier was used. It was decided to predict the class from the features using a regression model: class = a1*f1 + a2*f2 + … + an*fn + constant, where ax are the regression weights computed from the training set, and fx are the feature values. In studies I, II and IV, the imaging results were corrected for age and gender as described elsewhere (Koikkalainen et al. 2012). The correction was performed using linear regression models determined between each MRI feature and age and gender using the control subjects of the ADNI study. By using the control subjects, only the variations related to normal ageing and gender differences could be removed but all the disease specific variations were preserved.

4.4 BIOMARKERS

4.4.1 CSF analysis In studies I, II, III and Kuopio L-MCI from study IV, the CSF levels of Aβ42, T-Tau, and P-Tau were measured by commercial ELISA kits Innotest beta-amyloid1-42, InnotestTau-Ag, InnotestPhosphotau (181P), (Innogenetics, Ghent, Belgium) according to the manufacturer’s protocol. All samples were analyzed in duplicate and blinded to the clinical diagnosis. Study II also included the so-called AD-profile, expressed as: (Hulstaert et al. 1999, Visser et al. 2009).

In ADNI, levels of CSF concentrations of Aβ42, t-tau, and p-tau were measured by flow cytometry using monoclonal antibodies provided in the INNOBIA Alz Bio3 immunoassay kit (Innogenetics, Ghent, Belgium) with xMAP technology (Luminex, Austin, TX) (Weiner et al. 2013). In DESCRIPA all CSF samples were analyzed at Sahlgrenska University Hospital, Mölndal in Sweden. The concentrations of Aβ42, T-tau and P-tau were measured with single parameter ELISA (Innotest β-amyloid 1-42; Innotest hTAU-Ag; Innogenetics, Ghent, Belgium) (Visser et al. 2009).

64

4.4.2 APOE genotype In studies I, II, III and Kuopio L-MCI cohort from study IV, the APOE genotype was determined from blood leukocytes. DNA was extracted by a standard phenol-chloroform extraction, and APOE genotypes were analyzed by polymerase chain reaction and HhaI digestion as described previously (Tsukamoto et al. 1993). In ADNI, the APOE genotyping was performed at the time of participant enrolment and it was included in the ADNI database using DNA extracted by Cogenics from a 3 mL aliquot of EDTA blood, as described earlier in the text. Polymerase chain reaction amplification was followed by HhaI restriction enzyme digestion, resolution on 4% Metaphor Gel, and visualization by ethidium bromide staining (Saykin et al. 2010). In AddNeuroMed, the APOE genotype was determined from blood leukocytes (Hixson, Vernier 1990). In DESCRIPA, the APOE genotype was determined on genomic DNA extracted from EDTA blood with the polymerase chain reaction (Norberg et al. 2011).

4.5 DISEASE STATE INDEX AND DISEASE STATE FINGERPRINT

DSI and its counterpart for visualization the DSF are methods that help the clinician in coming to a diagnosis and assist in the follow-up of dementia patients. The tool resulting from the combination of DSI and DSF methods is known as the PredictAD tool (Simonsen et al. 2012). It integrates heterogeneous data from a patient and compares these variables to data from previous subjects. It is a support tool for profiling patients. It has been developed and patented by VTT Technical Research Centre in Tampere (patents: A METHOD FOR INFERRING THE STATE OF A SYSTEM. U.S. Patent No. 7,840,510. STATE INFERENCE IN A HETEROGENEOUS SYSTEM PCT/FI2010/050545, Inferring a state of a system over time FI20125177).

4.5.1 Disease State Index Disease State Index (DSI) is a measure of the state of a disease and/or its progression in a scale from 0 to 1. DSI estimates quantitatively the likelihood of a particular patient to exist in a state corresponding to the controls or the positive groups from the training set. It attempts to solve two main questions: 1. Is the biomarker indicating towards healthy or disease? DSI evaluates the similarity or fitness of the patient’s data compared to previously diagnosed cases having the disease (positive group) in contrast to cases not having the disease (control group). In other words, the fitness defines the location of a particular test result among the training test cases. This training test is composed of previously diagnosed cases, including a control and a positive group.

2. Is the biomarker good in separating health and disease cases? Compare the distributions of the biomarker for healthy and disease cases.

65

Define the distance or relevance between the distributions by value 0-1 and by box size. The relevance is independent of patient measurements, only considers the two selected populations’ databases. For each measurement type, or feature, a relevance value is calculated. The relevance value ranges from 0 to 1 and indicates the feature’s ability to separate between stable and progressive populations. The relevance is calculated from the sensitivity and specificity of the feature:

.1�� yspecificitysensitivitrelevance

The relevance of zero denotes a feature completely unable to separate between the two populations, and a value of one is a feature for which there is absolutely no overlap between the two groups. Patient measurements are combined into a composite DSI value using a weighted average, where the fitness values are weighted according to their relevance:

.�

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relevancefitnessrelevance

=DSITotal

This process of evaluating DSI and relevance and combining features by weighted average can then be repeated recursively, until an overall DSI is obtained for the subject. Different population sizes exert no effect since the DSI operates in the context of the distributions resulting from the raw population data. False positives and false negatives can of course occur, as always when making a classification. The DSI is nothing more than simply another supervised classification method. Relevance is the weight of each parameter included in the profile. All the parameters do not have the same weight. The composite index e.g. created from 4 parameters is the weighted average of relevance and fitness computed for those parameters. If the relevance is low, even large fitness values do not have a high impact on decision making. Since low relevance => low weighting factor; then this means that other more relevant features influence results more when calculating the weighted average. Detailed information about the Matlab Fingerprint toolbox can be found elsewhere (Mattila et al. 2011, Cluitmans et al. 2013).

4.5.2 Disease State Fingerprint Disease State Fingerprint (DSF) is a graphical visualization representing the status of a patient using colored boxes (Figure 10). The size of the box indicates the relevance/importance of the measure in separating the two groups being compared. The location of the patient relative to healthy and diseased cases is represented by the disease state index 0-1, shown numerically in parenthesis, and by different color intensity. Lower values (disease index value between 0 – 0.5) and shades of blue indicate the patient’s similarity to the first group while higher values (disease index value between 0.5 – 1) and shades of red designate a patient’s similarity to the second group in the training data. A white colour (near 0.5) does not point to either group. The value in parenthesis is the DSI value, reflected also by the colour as explained above. The DSF shows the index value for all data, i.e. individual features, the composite features, and the total DSI value for all data

66

available. The relevance is indicated by the box size; the larger the box, the more relevant is that feature (this applies to original data and composite features as well). All the measurements are ordered in a hierarchical tree-like presentation according to the relevance values starting with the highest on the top, from total index value to group features and finally to single measurements.

Figure 10: DSF hierarchical tree of one single case in an AD vs. FTD comparison

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Figure 11: DSI distribution curves in AD vs. FTD. The single case is represented by the vertical black line. The more separate the two distributions are the better differentiation that can be obtained between the groups with DSI values. The vertical axis represents the probability density and the horizontal axis is a DSI scale from 0.0 to 1.0. A DSI value closer to zero denotes data similarity to the first state in the comparison, whereas a DSI value closer to one indicates data similarity to the second state in the comparison

We can also see DSI distributions in a separate view (Figure 11). The probability density functions are computed from the training data. The area of both probability density functions is one. This means that the visualization of the distributions do not consider any possible prevalence differences between the control and the positive groups in the training set. The probability density is the probability that a certain case is located in a particular range of the disease state index distribution. The performance of DSI depends on the training set, i.e. on the population. The size of the samples (1) is an important factor. The smaller the number of the database samples, the less reliable will be the estimates of the probability density functions and the disease state index computed from these functions. The parameters included in (2) establish the results that need to be obtained, determining the relevance of a feature. For example, if the relevance of CSF is low between the AD and the FTD groups, then although in a particular case there could be striking results in CSF pointing to AD, the feature will have a low relevance value, which means that the information is considered as being less important than other values. There are two completely independent implementations of the DSI and DSF. One is for Matlab (mainly for research purposes) and the other for Windows applications (mainly for clinical application purposes). We will refer to the latest version in the dicussion section.

4.5.3 Evaluation DSI classification results were calculated with 10-fold cross-validation in studies II and IV and with leave-one-out cross-validation in study III. In addition, in study IV each cohort was also tested by using the three other cohorts as the training group. From the DSI values, the AUC, CCA, SS and SP were calculated.

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If these statistical measures are computed directly from the training set data without cross validation, the results may overestimate the true performance. Thus it is essential to apply cross-validation in the studies. Cross-validation is used when there is a single dataset, which needs to be analyzed instead of having separate training and test sets. Ten-fold cross-validation takes the following steps: 1) the training set is randomly divided into 10 parts, 2) nine parts are used to train the classifier (the disease state index used as a classifier) and the tenth part is used to compute the statistical measures, 3) Step 2 is repeated by selecting each part separately for computing the statistical measures (Step 2 is performed 10 times), and 4) the mean and the standard deviation of the statistical measures are computed. The accuracy-value obtained is computed for the whole training set, containing all the cases. However, it is also possible to calculate the accuracy for a particular DSI range, then only reporting the classification accuracies reported for subjects having that particular DSI value i.e. DSI<X (most probable control cases) and DSI>(1-X) (most probable positive cases). The SS, SP and CCA are calculated from the true positives, false postives, false positives and false negatives. SS = TP / (TP+FN). The relevance is calculated from the SP and the SS as mentioned previously. Apart from the DSI values for AUC, CCA, SS and SP, we can obtain the fingerprint of a particular case for its individual study, as conducted in studies II and III. DSI has been used previously in AD and MCI patients (Mattila et al. 2011, Simonsen et al. 2012, Mattila et al. 2012, Soininen et al. 2012b), while other studies are currently being done. Some of these studies will be referred to in the discussion section.

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5 Results

5.1 STUDY I

Table 14 illustrates the demographic and clinical data results. The groups did not differ in terms of gender or years of education. With respect to age there were significant differences between the groups, where the FTD group differed from all other groups. The FTD patients were significantly younger than subjects in the other groups. The MMSE scores differed significantly across the groups, being lower for FTD and AD groups compared to controls, but no significant difference was found between FTD and AD. Significant results were obtained for normalized hippocampal volumes.

Table 14. Demographic and clinical data of study groups

C FTD AD SMCI PMCI

Number of subjects 26 37 46 48 16

Gender male/female 12/14 20/17 14/32 18/30 7/9

Age in years* 74±4

(66-81)

66±9

(41-80)

74±6

(58-88)

73±5

(63-82)

72±6

(55-78)

Education in years 7±2

(4-12)

8±4

(4-20)

8±3

(4-20)

NA NA

MMSE* 27±2

(24-30)

20±7

(6-29)

20±4

(9-24)

26±2

(23-30)

25±3

(20-30)

Total Hippocampal Volumes (ml)*

4346±499

(3535-5865)

3788±620

(2588-5712)

3440±741

(2028-5662)

4191±527

(3288-5262)

3741±437

(2976-4717)

Normalized Hippocampal Volumes (zero mean, unit standard deviation)*

0.69±0.65

(-0.50-2.40)

−0.48±0.98

(−2.85-1.76)

−0.52±1.11

(−2.71-2.23)

0.56±0.59

(-0.73-1.69)

−0.21±0.61

(−1.20-0.95)

Results are expressed as mean ± standard deviation, range in parenthesis. *p<0.05 Figure 12 illustrates the accuracy results for each comparison performed with HV, TBM and VBM. A high accuracy was obtained with all the methods when comparing FTD and controls. In the comparison between AD and FTD, VBM reached the highest accuracy, with a poor accuracy obtained by using HV or TBM. There were no differences among the three methods in the AD vs. controls comparison. In FTD vs. PMCI, the highest accuracy was reached by HV. When comparing controls and PMCI, VBM achieved the highest accuracy.

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Figure 12. Accuracies (%) obtained using HV, TBM, and VBM for comparisons C vs. FTD, AD vs. FTD, SMCI vs. FTD, C vs. AD, C vs. PMCI

For each method, a total of 7 ROIs was studied as described in the methods section. The results of volumetry were not impressive, thus it was decided to perform the volumetry analysis only in the hippocampus, as has been widely recommended in the literature because it is considered a hallmark lesion in AD. For TBM and VBM, the analysis was conducted in the 7 ROIs. The most accurate results for each method and the possible group comparisons were as follows: In HV, the highest accuracy was achieved in differentiating FTD from controls and SMCI. In TBM, the highest accuracy was reached when comparing FTD with controls and SMCI, and controls with PMCI. In VBM, the highest accuracy was reached when comparing FTD with controls and SMCI, and controls with PMCI.

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5.2 STUDY II

The demographics and clinical data for the study groups are shown in Table 15. The groups did not differ in terms of gender or years of education. There were age-related differences between FTD and the other groups, i.e. the FTD cases were significantly younger. The other groups did not differ in terms of age. The MMSE scores differed between the groups, being lower for FTD and AD as compared to controls. However, there were no differences between AD and FTD. There were differences among the groups with respect to hippocampal volumes. Both FTD and AD had smaller hippocampal volumes than controls and MCI. In subjects there were significant differences in APOE between the groups. Two APOE ε4 alleles were detected only in the AD and MCI groups. In AD 60% were carriers at least of one APOE ε4 allele, whereas in FTD the percentage of cases was around 20%.

Table 15. Demographic and clinical data of the study groups

C MCI AD FTD

Number of subjects 26 64 35 37

Gender (Male/Female) 12/14 25/39 10/25 20/17

Age in years* 74±4

(66-81)

73±5

(55-82)

74±6

(58-88)

66±9

(41-80)

Education in years 7±2

(4-12)

NA 7±3

(4-20)

8±4

(4-20)

MMSE* 27±2

(24-30)

26±3

(20-30)

20±4

(9-24)

20±7

(6-29)

Hippocampal volumes* 2108±287

(1714-2902)

1978±303

(1307-2790)

1714±354

(1095-2704)

1792±282

(1276-2599)

APOE ε4 (0/1/2)*

APOE ε4 carrier % (non-

carrier/carrier)

13/12/0

52/48

33/15/5

62/38

9/12/8

31/69

16/4/0

80/20

Results are expressed as mean ± standard deviation, range in parenthesis. * P-value<0.05

Table 16 shows the classification results using DSI in the group comparisons. The highest accuracy was reached when comparing controls with FTD (0.84). The accuracy for AD compared with FTD was low (0.69).

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Table 16. The classification results for the Disease state index (DSI) classification.

C vs. FTD MCI vs. FTD AD vs. FTD

AUC 0.91±0.13 0.90±0.10 0.78±0.21

Accuracy 0.84±0.13 0.79±0.12 0.69±0.18

Sensitivity 0.84±0.18 0.78±0.22 0.70±0.27

Specificity 0.83±0.27 0.80±0.17 0.71±0.27

DSI calculated with 10-fold cross validation. Includes: APOE (number of APOE ε4 alleles), CSF (Aβ42, P-Tau, T-Tau), MRI (HV, TBM, VBM), clinical and cognitive test (MMSE).

Results are expressed as mean±standard deviation.

The relevance for each parameter can be found in Table 17. MMSE and MRI were the most useful parameters for differentiating controls from FTD. MMSE was also relevant in distinguishing MCI from FTD, however it was not useful in distinguishing between AD and FTD. MRI was the most useful parameter differentiating FTD from MCI and AD, being also useful APOE and CSF in AD vs. FTD. CSF also helped to differentiate MCI fron FTD.

Table 17. The relevance values for each measure and their combinations

Relevance C vs FTD MCI vs FTD AD vs FTD

Total Index 0.75±0.04 0.65±0.03 0.52±0.07

MMSE 0.69±0.08 0.51±0.03 0.00C

MRI 0.65±0.04 0.63±0.03 0.56±0.05

- HV 0.64±0.03 0.48±0.03 0.12±0.04

- TBM 0.56±0.05 0.28±0.04 0.18±0.05

- VBM 0.67±0.04 0.54±0.03 0.40±0.06

APOE 0.28±0.05 0.18±0.03 0.49±0.04

CSF − 0.31±0.06 0.50±0.07

- CSF value A − 0.35±0.06 0.41±0.06

- CSF profile B − 0.09±0.04 0.30±0.07

ACSF value: CSF includes P-Tau, T-Tau and Aβ42 BCSF profile: AD profile. CRelevance below 0.1 Results are expressed as mean ± standard deviation. High relevance indicates that the feature is good at differentiating between both states.

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5.3 STUDY III

Table 18 shows the demographic and clinical data of the study groups. The groups did not differ in terms of gender. Instead the groups differed with respect to age, with FTD differing from controls and AD i.e. the FTD patients were the youngest. There were also significant differences concerning the years of education between the groups. The groups also differed in terms of APOE ε4; 74% of AD patients were carriers of one or two e4 alleles while only 16% of FTD patients were APOE ε4 carriers. With respect to the CSF values, AD patients had higher levels for T-Tau and P-Tau and lower Aβ42 levels as compared to FTD patients


C AD FTD

Number of subjects 22 57 38

Gender (Female/Male) 14/8 31/26 20/18

Age in years* 71±4

(65-79)

70±8

(50-83)

65±9

(45-80)

Education in years* 10±4

(4-16)

7±3

(2-22)

8±4

(4-20)

APOE (2/3, 2/4, 3/3, 3/4, 4/4)*

APOE ε4 carrier % (non-carrier/carrier)

2/0/15/3/1

82/18

2/0/12/27/15

26/74

3/0/23/5/0

84/16

CSF T-Tau pg/ml* NA 555±342

(138-1542)

321±203

(90-835)

CSF P-Tau pg/ml* NA 77±35

(29-168)

50±30

(14-147)

CSF Aβ42 pg/ml* NA 526±176

(125-955)

677±230

(246-1101)

Right hippocampus* 2154±262 1557±398 1862±350

Left hippocampus* 2147±243 1555±404 1786±395

Results are expressed as mean ± standard deviation, range in parenthesis. NA = Non Available. P-value<0.05.

Table 19 shows the classification results for the DSI classification. The highest accuracy was reached when comparing controls with AD (0.99), with a similar accuracy for controls vs. FTD (0.97). The comparison between AD and FTD groups also achieved a high accuracy value (0.86).

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Table 19. The classification results with the Disease state index (DSI)

C vs. FTD

C vs. AD

AD vs. FTD

AUC 0.99 1.00 0.90

Accuracy 0.97 0.99 0.86

Sensitivity 0.95 0.98 0.84

Specificity 1.00 1.00 0.88

DSI classification results calculated with leave-one-out crossvalidation

The relevance for each parameter can be found in Table 20. The clinical symptoms record and the neuropsychological tests were the most relevant categories for distinguishing AD from FTD.

Table 20. The relevance values for each measure and their combinations.

Relevance C vs FTD C vs AD AD vs FTD

Total index 0.99±0.03 0.98 0.70±0.05

Clinical 0.95±0.16 0.98 0.60±0.08

� Symptom 0.95±0.16 0.98 0.59±0.09

- Amnesia 0.15±0.01 0.82±0.01 0.67±0.01

- Dysphasia 0.39±0.01 NR 0.32±0.01

- Confusion 0.12±0.01 NR NR

- Psychosis 0.15±0.01 NR NR

- Paranoia NR 0.11 NR

- Depression 0.42±0.01 0.40±0.01 NR

- Apathy 0.64±0.01 0.65±0.01 NR

- Sleeping disorder 0.12±0.01 0.13±0.01 NR

- Frontal symptoms 0.59±0.01 0.18±0.01 0.41±0.01

- Tremor 0.28±0.01 0.24±0.01 NR

- Myoclonus NR NR NR

- Disinhibition 0.43±0.01 0.13±0.02 0.31±0.02

� Hachinski ischemic score NA NA NR

� Webster total score NA NA 0.29±0.01

� Hamilton depression scale

NA NA 0.12±0.01

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Neuropsychological 0.79±0.07 0.87±0.08 0.52±0.06

� MMSE 0.62±0.01 0.67±0.01 NR

� Language 0.58±0.04 0.92±0.09 0.19±0.02

- Boston naming test 0.56±0.02 0.56±0.02 NR

- Vocabulary 0.61±0.01 0.94 NR

- Verbal fluency PAS NA NA 0.20±0.02

- Verbal fluency animals NA NA 0.15±0.01

� Memory 0.84±0.03 0.98 0.40±0.03

- Word list learning 0.73±0.09 0.94 0.12±0.01

- WMS figures 0.73±0.04 0.81±0.01 NR

- Story immediate recall NA NA NR

- Word list recognition 0.86±0.01 0.78±0.01 0.19±0.01

- Word list recognition deletion NA NA 0.30±0.01

- Word list recognition false positive NA NA 0.37±0.01

- WMS figures recall 0.83±0.01 0.96 0.25±0.01

- Story recall NA NA 0.21±0.01

� Visuo-construction 0.81±0.04 0.79±0.03 0.19±0.01

- Block design 0.79±0.02 0.76±0.02 0.17±0.01

- WMS figures copying 0.86±0.02 0.59±0.02 0.20±0.01

- Cubic copying 0.62±0.03 0.56±0.02 NR

- Drawing clock 0.70±0.04 0.57±0.03 NR

� Executive-function 0.78±0.05 0.81±0.02 0.33±0.04

- Trail making A 0.76±0.02 0.76±0.02 NR

- Trail making A mistakes 0.36±0.01 0.26±0.01 0.10±0.01

- Trail making A deletions 0.36±0.01 0.40±0.01 NR

- Trail making B 0.80±0.02 0.80±0.02 0.13±0.01

- Trail making B mistakes 0.66±0.02 0.57±0.02 NR

- Trail making B deletions 0.74±0.01 0.78±0.01 NR

- Wisconsin card sorting categories 0.63±0.02 0.69±0.02 0.17±0.01

- Wisconsin card sorting mistakes 0.35±0.9 0.48±0.04 NR

- Wisconsin card sorting perseveration 0.49±0.05 0.49±0.03 NR

- Wisconsin card sorting right 0.61±0.03 0.64±0.05 0.16±0.03

- Praxias

NA NA 0.37±0.01

Table 20. (continued) The relevance values for each measure and their combinations.

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Genetic NR 0.56±0.01 0.47±0.03

� APOE 4 alleles NR 0.56±0.01 0.59±0.01

� Dementia in family NA NA 0.34±0.01

� APOE genotype NR 0.56±0.01 0.59±0.01

MRI 0.43±0.02 0.44±0.07 0.32±0.09

� Frontal lobe 0.47±0.03 0.28±0.03 0.17±0.02

- Frontal lobe right 0.40±0.01 0.31±0.01 0.14±0.01

- Frontal lobe left 0.37±0.01 0.24±0.03 0.15±0.02

� Temporal lobe 0.16±0.02 0.24±0.02 0.14±0.01

- Temporal lobe right NR 0.17±0.02 0.14±0.01

- Temporal lobe left 0.16±0.02 0.16±0.02 NR

� Hippocampus 0.44±0.03 0.62±0.02 0.29±0.01

- Right hippocampus 0.31±0.03 0.62 0.33±0.02

- Left hippocampus 0.49±0.03 0.62±0.01 0.24±0.03

SPECT 0.65±0.05 0.22±0.04 0.46±0.02

� Frontal cortex 0.17±0.04 0.21±0.04 NR

- Frontal cortex right 0.20±0.05 0.21±0.04 NR

- Frontal cortex left 0.18±0.02 NR NR

� Temporal region NR 0.23±0.05 0.37±0.02

- Temporal region right NR 0.23±0.04 0.33±0.01

- Temporal region left NR NR 0.31±0.01

� Parietal cortex 0.39±0.03 0.27±0.04 0.49±0.02

- Parietal cortex right 0.39±0.02 0.29±0.05 0.54±0.01

- Parietal cortex left 0.21±0.03 0.22±0.03 0.39±0.02

� Occipital 0.26±0.04 0.26±0.03 NR

- Occipital right 0.16±0.03 0.26±0.02 NR

- Occipital left 0.30±0.02 0.38±0.03 NR

� Basal ganglia 0.42±0.03 0.24±0.03 0.18±0.02

- Basal ganglia right 0.31±0.06 0.20±0.04 0.25±0.01

- Basal ganglia left 0.49±0.03 0.39±0.02 0.11±0.01

� Amygdala-hippocampus NR 0.21±0.04 0.38±0.09

- Amygdala-hippocampus right NR 0.21±0.04 0.15±0.01

- Amygdala-hippocampus left

NR NR 0.47±0.01


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CSF NA NA 0.49±0.02

� Amyloid β42 NA NA 0.14±0.01

� Tau NA NA 0.38±0.02

- Total Tau NA NA 0.36±0.01

- Phospho Tau NA NA 0.37±0.01

Features with relevance < 0.1 were omitted from the final analysis Results are expressed as mean ± standard deviation. NR= Not relevant, relevance is less than 0.1 and feature is omitted from the final analysis NA=Not available, data is not available for controls Relevance values: 0 = no separation between both states, 1 = perfect separation, no overlap

5.4 STUDY IV

Table 21 shows the demographic and clinical data for the study groups. There were significant differences between the cohorts in terms of age, gender and years of education. The average age of the ADNI, which was the oldest cohort, was about 5 years higher than that for DESCRIPA, the youngest one. In the ADNI cohort, 36% were women, while 67% were women in the Kuopio MCI cohort. The ADNI cohort also differed from the others significantly because of its high average in years of education, 15.6 while the Kuopio MCI cohort had lowest level of education with an average of only 7.0 years. The AddNeuroMed study had a fixed follow-up time of 1 year, while the other three cohorts had average follow-up times of over 2 years. In this respect, the AddNeuroMed study also had the lowest overall conversion percentage of 19%, while ADNI had the highest i.e. 44%. The average MMSE scores for ADNI, AddNeuroMed and DESCRIPA were equivalent, while the average score for the Kuopio MCI cohort displayed significantly lower scores and also lower educational level that may explain this difference. This was also reflected in the one-way ANOVA and post-hoc analyses. Hippocampal volumes differed significantly between the groups, although in the individual group comparisons, only significant differences were detected between the ADNI and DESCRIPA cohorts. Pearson Chi-Square test revealed significant differences between the groups for the APOE ε4 allele. The highest percentage was present in ADNI where 55% were APOE ε4 carriers of 1 or 2 alleles, and the lowest frequency was present in AddNeuroMed, with 36% of carriers.


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ADNI AddNeuroMed Descripa Kuopio MCI

MCI patients 370 123 237 145

Age (years)* 75.3 (7.3) 73.8 (5.7) 70.2 (7.9) 71.5 (5.0)

Gender (female)* 134 (36%) 61 (50%) 136 (57%) 97 (67%)

Education (years)* 15.6 (3.1) 8.9 (4.3) 9.3 (4.0) 7.0 (2.5)

Follow-up time (years) 2.9 (0.6) 1.0 (0.0) 2.2 (1.1) 2.6 (1.8)

Converted to AD 163 (44%) 23 (19%) 57 (24%) 54 (37%)

MMSE scoreA*

27.0 (1.8)

(N=370) 27.1 (1.7)

27.1 (2.3)

(N=236) 23.1 (3.6)

CSF Aβ-42 (pg/ml) 161 (52) (N=187) - 529 (272) (N=87) 572 (220) (N=40)

CSF T-Tau (pg/ml) 104 (61) - 507 (375) 456 (232)

CSF P-Tau (pg/ml) - - 77 (51) 77 (25)

Hippocampal volume* 3753 (612) 3912 (624) 3891 (652) 3840 (567)

APOE ε4 0/1/2* 167/158/45

(45%/42%/12%) 70/35/5

(64%/32%/5%) 111/75/19(N=205)

(54%/37%/9%) 76/51/16(N=143) (53%/36%/11%)

Values are mean (standard deviation) or number (percentage) P-value<0.05. MRI features alone achieved good accuracies (0.67-0.81) in predicting the progression from MCI to AD for the four cohorts studied. The accuracy was slightly improved by the addition of MMSE, APOE, CSF and neuropsychological tests (Table 22).

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Table 22. The classification results with the Disease state index (DSI)

AUC ACC SEN SPEC

MRI with intra-cohort cross-validation ADNI 0.72 (0.07)

[0.70 - 0.73] 0.67 (0.06)

[0.66 - 0.69] 0.69 (0.11)

[0.66 - 0.71] 0.66 (0.10)

[0.64 - 0.68] AddNeuroMed 0.79 (0.22)

[0.75 - 0.84] 0.81 (0.11)

[0.78 - 0.83] 0.74 (0.34)

[0.67 - 0.80] 0.82 (0.12)

[0.80 - 0.84] DESCRIPA 0.77 (0.12)

[0.74 - 0.79] 0.72 (0.08)

[0.70 - 0.74] 0.62 (0.22)

[0.57 - 0.66] 0.75 (0.10)

[0.73 - 0.77] Kuopio MCI 0.73 (0.14)

[0.71 - 0.76] 0.67 (0.12)

[0.65 - 0.70] 0.69 (0.20)

[0.65 - 0.72] 0.67 (0.15)

[0.64 - 0.70] Combined cohort 0.74 (0.06)

[0.73 - 0.75] 0.69 (0.05)

[0.68 - 0.70] 0.69 (0.08)

[0.67 - 0.70] 0.69 (0.06)

[0.68 - 0.70] MRI, APOE and MMSE with intra-cohort cross-validation

ADNI 0.74 (0.07) [0.73 - 0.76]

0.69 (0.07) [0.67 - 0.70]

0.71 (0.11) [0.69 - 0.73]

0.67 (0.11) [0.65 - 0.69]

AddNeuroMed 0.82 (0.20) [0.78 - 0.86]

0.82 (0.11) [0.80 - 0.84]

0.80 (0.28) [0.75 - 0.86]

0.83 (0.11) [0.81 - 0.85]

DESCRIPA 0.78 (0.13) [0.75 - 0.80]

0.75 (0.09) [0.74 - 0.77]

0.68 (0.22) [0.63 - 0.72]

0.78 (0.10) [0.76 - 0.80]

Kuopio MCI 0.74 (0.11) [0.72 - 0.76]

0.68 (0.10) [0.66 - 0.70]

0.70 (0.18) [0.66 - 0.74]

0.67 (0.16) [0.64 - 0.70]

Combined cohort 0.76 (0.06) [0.75 - 0.78]

0.70 (0.05) [0.69 - 0.71]

0.70 (0.08) [0.69 - 0.72]

0.70 (0.05) [0.69 - 0.71]

MRI, APOE, MMSE, CSF and Neuropsychology with intra-cohort cross-validation ADNI 0.76 (0.08)

[0.75 - 0.78] 0.70 (0.07)

[0.68 - 0.71] 0.74 (0.10)

[0.72 - 0.76] 0.66 (0.10)

[0.64 - 0.68] AddNeuroMed 0.83 (0.20)

[0.79 - 0.86] 0.82 (0.12)

[0.79 - 0.84] 0.78 (0.30)

[0.72 - 0.83] 0.83 (0.12)

[0.80 - 0.85] DESCRIPA 0.81 (0.10)

[0.79 - 0.83] 0.75 (0.08)

[0.74 - 0.77] 0.66 (0.20)

[0.62 - 0.70] 0.78 (0.10)

[0.76 - 0.80] Kuopio MCI 0.76 (0.13)

[0.73 - 0.78] 0.70 (0.11)

[0.68 - 0.72] 0.71 (0.20)

[0.67 - 0.75] 0.70 (0.15)

[0.67 - 0.73] MRI using other cohorts as a training group

ADNI 0.71 0.65 0.74 0.57 AddNeuroMed 0.75 0.74 0.70 0.75 DESCRIPA 0.72 0.67 0.60 0.69 Kuopio MCI 0.74 0.68 0.54 0.77

MRI, APOE and MMSE using other cohorts as a training group ADNI 0.73 0.66 0.75 0.58 AddNeuroMed 0.78 0.78 0.74 0.79 DESCRIPA 0.75 0.73 0.67 0.76 Kuopio MCI 0.76 0.69 0.61 0.74

Results from cross-validated cohorts are in the form of mean (standard deviation) [95% confidence interval].

Based on the mean AUCs, AddNeuroMed provided the highest classification results of the cohorts, while ADNI and Kuopio MCI displayed the lowest values. The AUCs obtained for the DSI analyzed including only MRI data were 0.72 for ADNI, 0.73 for Kuopio MCI, 0.77 for DESCRIPA, 0.79 for AddNeuroMed and 0.74 for the combined cohort. With the addition of the other biomarkers into the analysis, the AUC for the ADNI cohort increased to 0.74 with APOE and MMSE and to 0.76 also with CSF and neuropsychology. For DESCRIPA, there was also an increase with the addition of all biomarker data. There was

80

no discernible change for Kuopio MCI or AddNeuroMed outside of the 95% confidence intervals. A comparison of these inter-cohort results with those obtained through intra-cohort analysis revealed that the only notable decrease in AUC was found for the DESCRIPA MRI data decreasing from 0.77 to 0.72. The AUC for each parameter can be found in Table 23. Concerning imaging methods, TBM was the best classifier of all cohorts except for AddNeuroMed, where the VBM performance was better. CSF was one of the best classifiers in both the Kuopio MCI and DESCRIPA cohorts, although in ADNI it was strikingly less relevant.

Table 23. Classification AUC of the feature groups using intra-cohort cross-validation

AUC ADNI AddNeuroMed DESCRIPA Kuopio MCI Combined

CSF 0.65 (0.13) NA 0.79 (0.21) 0.78 (0.28) NA Neuropsychological 0.71 (0.08) 0.62 (0.24) 0.67 (0.13) 0.70 (0.21) NA

MMSE 0.59 (0.08) NR 0.65 (0.15) NR 0.60 (0.06) Genetic (APOE ε4) 0.63 (0.08) 0.69 (0.19) 0.62 (0.13) 0.59 (0.15) 0.64 (0.06)

VBM 0.66 (0.09) 0.82 (0.17) 0.68 (0.13) 0.69 (0.15) 0.67 (0.06) mTBM 0.71 (0.08) 0.77 (0.25) 0.74 (0.10) 0.70 (0.14) 0.74 (0.06)

Hippocampus volume 0.66 (0.08) 0.67 (0.24) 0.69 (0.14) 0.66 (0.16) 0.68 (0.07)

Results are in the form of the mean (standard deviation). NA = Not Available, NR = Not Relevant

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6 Discussion

6.1 MORPHOMETRY IMAGING METHODS (study I)

Our results show that controls can clearly be differentiated from FTD by using HV (hippocampus left side), TBM (hippocampus and amygdala) and VBM (all the regions). The main interest of this study was to promote the use of automatic methods, and to see whether they could differentiate AD from FTD. Automatic methods have been previously used to differentiate controls from MCI and AD, for example in the report by Wolz et al., (Wolz et al. 2011) which included HV and TBM. However, the differential diagnosis of AD and from other dementia diseases is also challenging, thus it was decided to focus on FTD, a dementia disease of high importance and research of interest. The comparison between AD and controls has been included to highlight the usefulness of these methods, as it makes it possible to compare these results with previous studies, for example with those of Wolz et al., (Wolz et al. 2011) which already included HV and TBM in the analysis of the ADNI data. This thesis does not intend to be an exhaustive and comprehensive description of these three methodologies, but to highlight the main differences and to determine the accuracy that can be obtained in differentiating between the different disease categories. At the date of writing the thesis, this was apparently the first study which aimed to compare volumetry, TBM and VBM in differentiation between AD and FTD. Therefore there are no previous results which could be used as reference data. TBM evaluates the changes in volume, whereas VBM measures the concentration on GM. TBM and HV are performed using a multi-template approach. The highest accuracy in comparisons between AD vs. FTD was reached by using VBM, where the three methods achieved similar levels of accuracy in differentiating controls from FTD and AD. The three methods are more accurate when comparing controls with FTD than controls with AD. By using multiple-ROIs, VBM was the most accurate method in the AD vs. FTD comparison and also in controls vs. PMCI. HV was the most accurate method for differentiating PMCI from FTD. HV in MCI: with HV, the left hippocampus was more accurate than the right hippocampus in all the comparisons, except in differentiating between controls and AD. In Wolz et al., (Wolz et al. 2011) TBM and HV reached better accuracies than achieved here in differentiating controls from AD and PMCI. Clerx et al., (Clerx et al. 2013) included 4 different MTL measurements studies in MCI patients at baseline and after 2 years. After 2 years, the AUC value was higher in the automated atlas-based hippocampal measurement and manual hippocampus measurement and lower in the MTL atrophy score and lateral ventricle. Chincarini et al., (Chincarini et al. 2013) reported that the AUC value achieved with automatic temporal lobe atrophy was higher for subjective memory complaint vs. converters than for converters vs. non-converters.

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HV in FTD: hippocampal volumetry can differentiate FTD from controls with 83% of accuracy, while in the FTD vs. AD comparison the accuracy reached was much lower (55%). Van de pol et al., stated that it was possible to differentiate FTD from controls and from AD by using hippocampal volumetry and MTA ratings scale (van de Pol et al. 2006). Thus HV is demonstrated to be an accurate method for diagnosing AD and FTD, but not for differentiating between these diseases, i.e. as the hippocampus is a region that is affected in both diseases TBM and VBM: by using single-ROI analysis, in TBM the most accurate region for differentiating FTD from AD was the superior frontal gyrus, while when comparing between FTD and controls, the most accurate regions were the hippocampus-amygdala, the superior frontal gyrus and the lateral ventricle (frontal horn). Brambati et al., (Brambati et al. 2007) used a specific ROIs analysis when comparing controls with bvFTD patients and reported that the ROIs in the superior frontal gyrus, amygadala and hippocampus displayed significant atrophy changes, results in line with the present study and evidence supporting the use of TBM for tracking longitudinal changes in FTD. In VBM all the regions achieved a high accuracy in differentiating controls from FTD, although the most accurate region was the lateral ventricle (frontal horn, central part, occipital horn). The highest accuracy was achieved for differentiating AD from FTD in the lateral ventricle (frontal part, central part and occipital horn) (CCA=0.73). The reason why VBM reach overall higher accuracy results than TBM and volumetry may be that VBM measures more directly the tissue loss than TBM or HV since it estimates the local amount or the density of GM. VBM quantifies the GM concentration. There are volume changes in both diseases, atrophy in lobules and expansion in the ventricles, but probably the study of the GM is more precise. On the other hand, TBM is based on defining one-to-one mappings between images through registrations, which are run typically for gray-scale images. This may lead to inaccuracies in characterizing the narrow cortical GM. The strength of TBM is that it can assess the local size and shape differences (morphometric changes) of sub-cortical structures for which one-to-one mappings are more clearly defined. TBM may reveal changes in GM, WM and CSF regions, as all the brain voxels were included in the analysis. The accuracy of some regions in VBM results was somewhat surprising e.g. in the lateral ventricle (frontal horn, central part, occipital horn) which does not contain GM. There are two interpretations for this phenomenon: neighboring GM structures (e.g. thalamus, precuneus) are included in this ROI due to a smoothing and segmentation error and appear as the amount of GM decreases; and the ratio of WM/CSF causes a partial volume effect on the GM of the ROI. For either of these reasons, VBM detects a GM loss that is actually a ventricular expansion. The real effect of GM density on the regions close to the lateral ventricle should be studied in detail in future studies. In TBM only the volume was included as a feature. However in future studies, other features such as rotation could be included as these may be better able to define the changes in a particular ROI. VBM measures the concentration of GM but in addition can measure the concentration of WM and CSF and quantify it. ROIs: the entire analysis was performed for the 83 ROIs described in the Hammer atlas, and then 7 regions were selected which achieved the highest accuracy for controls, AD and FTD. These ROIs were selected based on the classification accuracy. According to the literature, some specific ROIs which may be more relevant, such as the posterior cingulate gyrus in AD, were not included in the pool of 7 ROIs. All the analyses were conducted for

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right and left sides, in order to find asymmetry, which may appear in AD and particularly in bvFTD, with a predominance of atrophy in the right frontal and right temporal lobes. However it was not possible to detect any significant findings, thus the left-right results were considered only in HV. Initially, the volumetric changes of the selected ROIs were studied, but the overall results were not impressive, therefore it was decided to focus only on the volumetric study of the hippocampus, as it is actually the gold standard study recommended by research criteria guidelines (Dubois et al. 2007). Future studies should strive to determine the differences between the anterior and posterior regions of the hippocampus, as these may help in differentiating accurately AD from bvFTD i.e. in AD there is a marked atrophy in the posterior hippocampus but in bvFTD it occurs in the anterior hippocampus. As far as is known, no previous study has used the same ROIs as proposed here. Some of the regions resemble the same areas for AD and FTD (hippocampus, posterior temporal lobe, superior frontal gyrus) and others have not been extensively assessed in this respect (lateral ventricle divided into 2 regions, gyri hippocampalis et ambiens), which complicates their comparison with the previous literature. Limitations and future directions: This study has some limitations. First, autopsy proven finding were availale for only a minority of FTD and AD cases. Nine cases had a pathological confirmation of FTLD with TDP positive brain immunochemistry and four AD cases had post-mortem confirmation. Three FTD cases had C9ORF72 but knowledge about APOE was not included in these groups. It would be interesting to divide FTD, MCI and AD cases according to the genetic background that could be present, as it is known that certain genetic features such as APOE ɛ4 allele, GRN, MAPT or C9ORF72 are associated with a specific pattern of brain atrophy. It has been reported that healthy controls who are APOE ɛ4 allele carriers have a reduced volume in the hippocampus bilaterally, particularly in the right hippocampus, in comparison to non-carriers (O'Dwyer et al. 2012). Conclusions: it is concluded that HV and morphometric methods (TBM and VBM) should not be exclusively conducted, as they may produce different results in certain brain regions.

6.2 COMPARISON BETWEEN DIAGNOSTIC METHODS (studies II-IV)

Single cohort:

Study II: This is the first study to apply the PredictAD tool (DSI and DSF) in FTD. There was almost the same population as in Study I, with the main differences of a few cases added to the AD group and the combination of SMCI and PMCI into one single MCI group. The highest accuracy was reached when comparing FTD with controls (0.84), followed by FTD compared with MCI (0.79) and AD (0.69). The lower accuracy in FTD vs. AD may be explained by the shared findings in some of the parameters included in DSI. When one focusses on each individual parameter, it was apparent that MRI was the most relevant feature when FTD were compared to MCI and AD, however in controls vs. FTD, the most relevant feature was the MMSE.

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MMSE: In the controls vs. FTD comparison the most relevant feature was the MMSE. Instead MMSE was not useful differentiating AD from FTD. This may be due to two reasons: the non-specificity of MMSE for helping to make a specific dementia diagnosis (Kertesz et al. 2003), and the recruitment of FTD patients already in a more advanced stage of the disease, where there may be an overlap between symptoms (memory impairment, behavioural changes, disinhibition) between bvFTD and AD, while other symptoms that could be differentiating such as apathy (Chow et al. 2009), are not assessed in MMSE. The MMSE score includes questions concerning attention, orientation, language and memory. At least the two first parameters are usually impaired in bvFTD (these patients may have language difficulties and amnestic episodes, but these usually appear later as the disease progresses and they are not always present), and this can help in differentiating between healthy controls and bvFTD. MCI patients could be differentiated from bvFTD with using MMSE, probably because of the typical amnestic-presentation of the MCI group. DSI reflects the profile of cognition The scores for each individual item in MMSE, could provide support in differentiating AD from FTD e.g. a low score in recall questions leans more to AD while a low score in attention questions is more indicative of FTD. Nevertheless, one should remember that MMSE is a screening tool. In the follow-up procedure, MMSE could help to differentiate AD from bvFTD, as both diseases exhibit different rates of decline (Tan et al. 2013). Imaging methods: VBM was the most relevant technique in all the comparisons, although HV is highly relevant if one wishes to differentiate FTD from controls and MCI. This highlights the fact that the hippocampus is a region also affected in FTD (van de Pol et al. 2006), thus when comparing FTD with MCI and in particular AD one needs to study more regions than the hippocampus alone. CSF and APOE: CSF and APOE were valuable when comparing FTD with MCI and AD. CSF samples or APOE data were not available for controls. Interestingly, only P-Tau achieved statistically significant differences. This supports the belief that P-Tau can differentiate AD from other dementia diseases (Schoonenboom et al. 2012), although Aβ42 and T-Tau could help to make a MCI or AD diagnosis at an earlier stage. More studies with more incipient MCI cases and FTD and AD cases recruited at an earlier stage are needed. Furthermore, there is no specific CSF profile for FTD (Chow, Alobaidy 2013). In addition to the combination of P-Tau, T-Tau and Aβ42, the so-called CSF profile or AD profile, defined as was also included. This profile was less relevant than the CSF values in AD vs. FTD, and had markedly lower relevance in the MCI vs. FTD comparison. Thus it is recommended to use the normal CSF values and to combine them with values from DSI; it is not recommended to use the so-called CSF profile. The APOE genotype was particularly relevant in AD vs. FTD. In the AD group, 69% were carriers of one or two APOE ε4 alleles, whereas in FTD there were only 20% patients carrying one APOE ε4 allele and there were no ε4/ ε4 carriers. The proportion of AD ε4 carriers is in line with the published literature, and the ε4 frequency in the FTD group was comparable with the previous literature (Engelborghs et al. 2003, Lovati et al. 2010). The APOE genotype is not usually investigated in clinical practice, as the DSM-IV criteria for AD do not include this parameter. However, it could be advisable to include it since it is a major risk factor for AD and this has been recommended in recent guidelines (Dubois et al. 2007). However, it should be stressed that the presence of APOE ε4 is not exclusively a

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property of AD, this allele may be present in FTD and other diseases (Engelborghs et al. 2003, Lovati et al. 2010). Limitations and future directions: The main limitation of this study is the single inclusion of MMSE as a neuropsychological test, which was non-specific for AD or FTD. Study III tried to tackle this issue.

Study III: This study was planned in order to amend the limitations of study II, in particular with respect to the presence of MMSE as the only neuropsychological test (which is a screening test and thus does not differentiate AD from FTD) (Kertesz et al. 2003), and to compare the importance of the manual volumetric analysis and SPECT, which is used in clinical practice mostly in uncertain cases because it has proven utility in differentiating FTD from AD (McNeill et al. 2007). The highest accuracy was reached in differentiating controls from AD (0.99) and FTD (0.97). In addition, AD could be differentiated from FTD also with a high accuracy (0.86). When one focusses into each individual category, then it seemed that clinical symptoms record and neuropsychological tests were the most relevant groups in differentiating AD from FTD. Symptoms: among each category, the clinical category, in particular the symptoms record, was the most relevant group. This is as expected, as in the majority of AD and FTD cases the symptom profile differs, i.e. AD is characterized by amnesia and FTD by frontal symptoms (e.g. behavioural changes, alterations in conduct). However the simplicity of the recording of these two symptoms may mask differences in the pattern or profile of memory (Bertoux et al. 2013) and behavioral conduct (Bathgate et al. 2001); these are important in differentiating AD from FTD, and there is always a need to perform specific neuropsychological tests. Although apathy and disinhibition are usually more frequent in FTD than in AD (Bathgate et al. 2001, Leger, Banks 2013), only the presence of disinhibition was found to be relevant in this study in helping to differentiate both diseases. In our initial population there were also patients diagnosed as PNFA, SD or PSP, but these were excluded from the final study due to the low number of cases. Dysphasia, confusion and psychosis were relevant only for differentiating FTD from controls, while paranoia only differentiated controls from AD. Depression, apathy, sleeping disorders and tremor helped to differentiate controls from FTD and AD, however they were not relevant when comparing both dementia diseases. Previous studies have shown that depression is a common finding in AD and FTD (Leger, Banks 2013). Clinical scales: HIS was not relevant in AD vs. FTD. As stated in the review of literature, HIS only identifies the vascular component; therefore its utility may be reduced in these cases where AD displays a clear manifestation of a vascular disorder, or for differentiating AD from VaD (Hachinski et al. 2012, Knopman et al. 2001). Webster total score differentiated AD from FTD, as extrapyramidal and other parkinsonian signs may be associated with FTD (Espay, Litvan 2011) and are less frequent in AD (Duker et al. 2012). The Hamilton depression scale is relevant to a minor extent in differentiating AD from FTD. The reason why this scale is relevant, whereas the isolated symptom of depression is not helpful, may be that Hamilton depression scale includes some questions concerning loss of insight, agitation and compulsive behavior, which point more to FTD than to AD.

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Neuropsychological tests: with respect to the neuropsychological tests, memory tests were clearly the most relevant group when comparing FTD with controls and AD, and separating controls from AD. The next most useful tests were those assessing executive-functions. The overall inclusion of all the neuropsychological tests was more relevant than the single use of MMSE when differentiating controls from patients with AD and patients with FTD, and in distinguishing AD from FTD. Genetics: two genetic factors were included: APOE genotype (APOE ε4 alleles) and if there were other family members with dementia. APOE genotype was relevant in differentiating AD from controls and FTD, as the APOE ε4 allele is clearly more prevalent in the AD population (74%) than in controls (18%) or FTD (16%). In FTD, the proportion of family members with the disease has been described as being as high as 50% (See et al. 2010), while in AD it is significantly lower, although having a family member with AD is also a risk factor for developing AD (van Duijn et al. 1991). This explains why this is a relevant parameter in the FTD vs. AD comparison. Imaging methods: MRI is particularly useful in differentiating the healthy state from both AD and FTD, while SPECT is more relevant in comparing between AD and FTD. With respect to the ROIs to be incorporated into MRI, the hippocampus was the most relevant area in AD in the comparison with controls and FTD. In the FTD vs. controls comparison, both the hippocampus and the frontal lobe were particularly relevant. This implies that atrophy is present in both frontal lobes and hippocampus in FTD, but the frontal atrophy differences between AD and FTD are less marked than those in the hippocampus. This is not in line with the previous literature; there is one study which compared AD and FTLD proven autopsy cases, indicating that the MTL was a common area of GM reduction in both diseases and thus it did not help in discriminating between them, whereas atrophy in frontal cortices did differentiate AD from FTLD (Rabinovici et al. 2007). With respect to asymmetries, one can observe that there were striking differences present in the temporal lobe, where only the left temporal lobe was relevant in the controls vs. FTD and the right temporal lobe in AD vs. FTD. One study reported that the left temporal lobe was smaller in AD than it was in FTD, while the right temporal lobe was smaller in FTD (Fukui, Kertesz 2000). This present study revealed that the left temporal lobe and the right frontal lobe were the regions that best discriminated AD from FTD (Fukui, Kertesz 2000). Here the right hippocampus and the hippocampus were more relevant when comparing FTD with both AD and controls. This is in line with the results using HV in study I. However asymmetries in the hippocampus for AD and bvFTD are not clearly defined in the literature. One meta-analysis found only minor differences among left and right hippocampal atrophy, although a left-less-than-right atrophy has been described (Shi et al. 2009). With respect to ROIs in SPECT, the frontal cortex displayed some relevance in the left side when comparing between controls and FTD, otherwise it was not relevant. The parietal cortex and the left amygdala-hippocampus were the most relevant areas for differentiating AD from FTD. Previous studies also revealed that the parietal lobe could discriminate AD from FTD (McNeill et al. 2007). CSF: Aβ42 is relevant in distinguishing AD from FTD, although to a much lower extent than Tau. This is in line with previous studies and also found in study II. The reason for this may be the age and stage of the disease of the patients recruited. The Aβ42 level is

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considered to be the first marker which detects subjects with MCI and AD (Jack et al. 2010), and this occurs when there are few neurodegenerative changes causing the atrophy in the brain and as a result there is no marked elevation of Tau levels in CSF. AD patients, with a mean age of 70, may have already higher levels of Tau and P-Tau which can help to differentiate them from FTD. Studies on FTD cases have described discrepant profiles for Tau in CSF (Schoonenboom et al. 2012, Grossman et al. 2005). Limitations and future directions: this study evaluated manual MRI volumetry and SPECT. Although volumetric MRI with manual outlining of the hippocampus has been used for the diagnosis of AD, one can predict that it will be replaced by automatic methods that allow the quantification of the atrophy in several regions more accurately and more quickly. The MRI methods from study I were not included, instead the data evaluated included a manual outlining of the ROIs (frontal lobe, temporal lobe and hippocampus) and SPECT (frontal region, temporal region, parietal cortex, occipital area, basal ganglia and amygdala-hippocampus). SPECT has been more widely used during the last decades because it is cheaper and more widely available than FDG-PET or PIB-PET. However nowadays FDG-PET is starting to substitute SPECT in many centres. In future studies it would be interesting to examine the importance of more biomarkers and tests (e.g. FDG-PET, FBI) and their combinations in bvFTD not only at baseline but in the follow-up, and to attempt to determine whether their predictive value varies over time, as has been proposed (Krudop et al. 2013). Conclusions: study III showed that a wider battery of neuropsychological tests and a detailed symptoms record was more useful than using MMSE alone, and SPECT could be useful in the differentiation of AD from FTD. Volumetric MRI is useful when comparing the healthy state with both AD and FTD. There were no automatic analyses performed (e.g. HV, TBM and VBM) because this package of data was already closed. It would be interesting to compare the manual volumetric study of the main ROIs (e.g. frontal lobe, temporal lobe, parietal lobe) with their performance when they are determined by automatic methods.

Multiple-cohorts:

Study IV: This study aimed to investigate the value of DSI at predicting the conversion from MCI to AD in four different cohorts. In the prediction of how MCI subjects progressed to AD MRI features alone gave good accuracies (0.67-0.81) for the four cohorts studied. The accuracy was slightly improved if one incorporated MMSE, APOE, CSF and neuropsychological tests. MRI: TBM was the most accurate method in all the cohorts except in AddNeuroMed, where VBM displayed the highest accuracy. Wolz et al., used two classifiers including several imaging methods from ADNI cohort, where TBM and HV accuracies were somewhat similar in differentiating SMCI from PMCI, and TBM was more accurate than HV in the controls vs. AD comparison (Wolz et al. 2011). In both methods, the accuracies in differentiating SMCI from AD were similar to the accuracies in all the cohorts included in this study (Wolz et al. 2011), except for the situation with AddNeuroMed, where both TBM and VBM achieved higher accuracies. The amygdala was the most accurate ROI again with

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the exception of the ADNI cohort, where the most useful ROI was the right lateral ventricle (temporal horn). Neuropsychological tests: In ADNI, the neuropsychological tests were the most accurate category, while in the other cohorts they were less accurate. This may be explained by the number of sets available in each cohort. From the ADNI the ADAS-cog test was included, where word recall, orientation and delayed word recall were the most relevant features, and only word recognition test was less accurate, while in the other three cohorts, other tests were included whose accuracies were very low and therefore they reduced the overall accuracy of the neuropsychological test category. Word list recall, Buschke test and word list recognition were the most accurate subtests in DESCRIPA, Kuopio-MCI and AddNeuroMed respectively. MMSE: In Kuopio-MCI and AddNeuroMed, MMSE was not sufficiently relevant to be useful. The highest accuracy was reached in ADNI, although it was still not very high. These accuracies compared to the neuropsychological tests accuracies, confirm that MMSE is a screening test for detecting a cognitive impairment but it is not specific for predicting conversion to AD. CSF: In the DESCRIPA and Kuopio-MCI cohorts, CSF was the most accurate biomarker while in ADNI it was less accurate. This may be explained by the different analytical procedure followed, as in Kuopio-MCI and DESCRIPA ELISA was used while ADNI utilized xMAP technology. APOE: With respect to the genetic biomarkers, only APOE genotyping was available. Its accuracy was higher in AddNeuroMed as compared with the other cohorts. Recently a study was conducted using ADNI data (Munoz-Ruiz et al. 2013), in an attempt to predict the conversion from MCI to AD. The combination of neuropsychological tests, CSF, APOE, MRI and PET achieved the highest accuracy, although MRI and neuropsychological tests together were almost as accurate. Of all the parameters, neuropsychological tests and semi-automated HV achieved the highest accuracy. These results are partly in line with the results in the present study, where neuropsychological tests were the most accurate feature, although HV was not particularly accurate, and TBM was the most accurate imaging method. Limitations and future directions: MCI is recognized as a heterogeneous entity. Data derived from four large cohorts, each one of which had its own criteria for selecting MCI patients, slightly different methods and follow-up times. It would be preferable to standardize the selection criteria in order to obtain more homogenized cohorts and to harmonize the methods. Conclusions: DSI can compare and combine data coming from different populations. The prediction accuracy resulting from the combined cohort is close to each individual cohort accuracy. This is feasible if the different datasets are relatively similar.

6.3 GENERAL DISCUSSION (studies II-IV)

This section attempts to clarify why the data in these four studies were included, and then strives to determine which could be the most useful biomarkers according to the present results and the literature available at the time of writing.

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Data in studies I-IV: studies I and II involved patients from the Kuopio L-MCI cohort (Julkunen et al. 2010) and KUH database. For this cohort, only MMSE, CSF and T1 images were available; from T1 images HV, TBM and VBM values could be obtained. Study I only compared three imaging methods and had no intention of combining different biomarkers. The results were adjusted for age, gender and intracranial volumes. Study III focused on the importance of the symptoms record, neuropsychological batteries and tests and on importance of the difference concerning SPECT and MRI. HV, TBM and VBM were applied identically in studies I, II and IV. Study IV combined different cohorts, thus it was necessary to select data that was similar in these cohorts. The four studies included biomarkers which are accepted criteria for MCI (Petersen et al. 1997), FTD (Neary et al. 1998) and AD (McKhann et al. 1984). It would be advisable to use new criteria in future studies, for example the novel criteria proposed by Rascovsky et al., for bvFTD (Rascovsky et al. 2011). In future studies, it would be interesting to include not only FDG-PET, but also the novel imaging techniques such as DTI and RSfMRI in attempts to differentiate AD from bvFTD in their early stages. MRI can also evaluate the number of MBs and white matter hyperintensitites which are associated more with AD or VaD rather than with any disease from the FTLD spectrum. We had autopsy prove for only a minority of cases in studies I, II and III. It could be advisable in the future to perform studies with cases including autopsy confirmation and try to see the correlation between the neuropathology findings and each specific biomarker. Biomarkers: demonstrated in Table 12 in the review of literature, there are many studies which have attempted to predict conversion from MCI to AD with a variety of biomarkers, however very few have specifically compared bvFTD and AD. The differential diagnosis between AD and bvFTD starts with a comparison between the profiles of the symptoms. The CSF biomarkers may help in distinguishing between both diseases, although the biomarker most commonly used is FDG-PET, since it reveals different patterns of hypometabolism in AD and FTD (Berti, Pupi & Mosconi 2011). PIB-PET exhibits no or lower accumulation of amyloid in FTD cases as compared to AD (Engler et al. 2008), but this technology is available in only a few centres. Furthermore it does not provide a conclusive diagnosis, since about 10% of AD cases are PIB negative and 15% of FTD cases are PIB positive (Rabinovici et al. 2011). Novel techniques are emerging such as DTI or RSfMRI (Frisoni et al. 2010). However, structural MRI is the only method which correlated with the extent of neurodegeneration and for that reason it is recognized as is the gold-standard for categorizing the state of the disease. Some studies have detected a correlation between the amyloid burden and inactivation of default-mode circuits (Sperling et al. 2010). Thus RSfMRI as PIB-PET could be used when a patient is classified as MCI or in the early stages of AD or bvFTD, and in the follow-up structural MRI should be used as has been already proposed (Jack et al. 2009). AD and FTD are characterized by the default-mode network and salience network respectively (Zhou et al. 2010), however more studies will be needed to assess the predictive value of RSfMRI in the conversion from MCI to AD or FTD. Structural MRI: Which methods should be used? As reported in the literature and also in this thesis, automatic techniques can achieve accurate results. In study II, MRI was found to be more relevant than the manual outlining MRI conducted in study III when one is

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comparing FTD patients with controls and AD. However, the manual outlining of the hippocampus was clearly more relevant in the AD vs. FTD comparison conducted in study III than HV was in study II. Nonetheless, it is recommended that HV, TBM and VBM should be conducted as automatic techniques for four reasons: they can be easily obtained from a single 3DT1 image, they are not long time-consuming, they are good biomarkers at least to some extent, and they can be easily added to the other biomarkers (tests, genetic profile, CSF) within the PredictAD tool. It is true that PET amyloid imaging is more specific for obtaining a differential diagnosis between AD and FTD. However its cost and non-availability in many centres hinders its widespread use, while automatic MRI methods are readily obtained from one T1-volumetric MRI scan. TBM and particularly VBM give accurate results to help in the diagnosis of AD and bvFTD. Not only the hippocampus, but all the MTL (entorhinal cortex, parahippocampus, hippocampus, amygdala) should be studied, as the entorhinal cortex and CA1 of the hippocampus are severely affected. With respect to CSF, recent guidelines state that MCI and AD can be predicted by quantifying the levels of Aβ, P-Tau and T-Tau, and CDR testing (Vos et al. 2013b). The present studies strongly support the use of CSF in differentiating AD from FTD. However, discrepancies were found between the individual importance of Aβ, P-Tau and T-Tau in studies II and III. With respect to the ability to predict conversion from MCI to AD, study IV reveals that CSF was the most accurate biomarker in the Kuopio MCI and DESCRIPA cohorts, although it was less accurate in ADNI. This may be due to the different procedures used in CSF in the different centres; i.e. either ELISA or xMAP Luminex. Finally, in all of these studies, it was possible to detect that differences in APOE genotype clearly help to differentiate AD from FTD. Currently, genetic studies in FTLD should be considered if there is a first-degree relative with the diseases and if there is an early age of onset in the actual patient, because it is rare that a patient with FTLD has any mutation without a positive familial history (Chow, Alobaidy 2013). However, there may be cases with a family history and no positive results in the genetic testing. Although the proportion FTLD having a known genetic mutation (MAPT, GRN, C9ORF72) is much greater than the corresponding situation for AD cases, genetic testing should not be recommended for differentiating AD from FTLD, as so few FTLD cases carry any recognized mutation (Chow, Alobaidy 2013). Genetic studies are usually performed in AD only if the onset is below the age of 65 years. However although in the clinical practice there is no strong impulse to do genetic profiling in dementia patients, it could well be recommended to do so in research since this may help to clarify the real percentage of some genetic mutations (e.g. C9ORF72) and especially in LOAD, to determine whether certain combinations of genes mutations are significant for the development of the disease, contributing more than a single-gene mutation. In these studies only the APOE genotype was included in DSI. We identified some FTD cases as displaying the C9ORF72 mutation, although this was considered as demographic information. In future studies involving Finnish cohorts, it would be advisable to check whether or nor these patients exhibit the C9ORF72 repeat expansion, due to its presumably high frequency (Renton et al. 2011).

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A family history for dementia should be always recorded, as it is a major risk factor for developing both AD and FTLD. With regard to cognitive symptoms, AD patients suffer early memory and orientation impairment while FTD subjects display deficits in executive functions and behavior, nonetheless when both diseases progress, all of these functions can become impaired, e.g. FTD patients exhibiting memory disturbances and AD cases displaying behavioral changes. However in daily practice, these limits are not that clearly established and there is a broader range of impaired functions at onset, such as impairment in executive functions in early AD stages, and conversely patients with FTD can suffer memory problems in the first stages of the disease. There may also be language problems in both AD and bvFTD, and these could well confuse the diagnosis by pointing towards PNFA or SD, or even logopenic aphasia. Furthermore it would be advisable to try to correlate the different symptoms and performance in specific tests and the brain areas affected in MCI, AD and FTLD patients, as conducted before with VBM (Mahoney et al. 2011, Venneri et al. 2011, Bruen et al. 2008). Only MMSE was utilized for the assessment of global cognitive functions. In future studies it could be advisable to use CERAD (which includes MMSE) for diagnosing and assessing the progression of MCI (Paajanen et al. 2014), and NPI (Hirono et al. 1999) and/or FBI capturing neuropsychiatric symptoms (Kertesz et al. 2003) for FTD, as well as executive-function tests that are not covered in CERAD. It would be interesting to combine different behavioral rating scales (e.g. FBI, NPI) with neuropsychological scales (e.g. word list recognition, TMT B, clock drawing test). In summary, the recommendation is that after an initial interview assessing the symptomatic situation, if there is a suspicion of dementia (MCI, AD or FTD) the clinical work-up should include : neuropsychological (MMSE, depression scales, language, memory, visuo-construction and executive-function batteries) and structural MRI (manual volumetry or automatic methods), supported by APOE genotype and CSF for the study of MCI, AD and bvFTD, and supplementation with FDG-PET/SPECT when it is not possible to discriminate between AD and bvFTD. It is recommended that SPECT should be used based on the results of study III, and FDG-PET based on the previous literature. APOE genotyping could be included on a routine basis, but the study of other genes is recommended only if there is an early age of onset and/or there is previous familial history of AD or FTD. The stage and severity of the disease are important when considering the importance of biomarker combinations for diagnosis and monitoring progression. In AD, the amyloid accumulation is present at the earliest stages, i.e. in MCI, but the amyloid reaches its maximum level relatively quickly. Therefore in order to monitor the disease progression one could use structural MRI for assessing atrophy changes which are related to the elevated tau levels and not to amyloid accumulation. Perhaps metabolic and functional imaging could be more linear in the progression of the disease and also could help in the diagnosis in the earliest stages of the disease and also be useful in monitoring disease progression. Secondly standardization for collecting data from each biomarker is needed, as well of standardization of patient classifications. There are several studies which have incorporated a great number of biomarkers, but because of the different methodologies and the different criteria for classifying the patients, it has not proved to be feasible to compare

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different studies. Finally more work needs to be done in the evaluation of different factors e.g. lifestyle and genetics RFs and cognitive reserve (Jack et al. 2013).

6.4 DISEASE STATE INDEX AND DISEASE STATE FINGERPRINT (studies II-IV)

The combination of DSI and DSF has created a tool that integrates data coming from multiple sources, displaying it in an easy and understandable manner that could help the clinician to come to a confident diagnosis. This thesis obtained results gathered by using DSI. The DSI integrates the data and helps the clinician to recognize the importance of each biomarker, by comparing the marker originating from a particular patient to healthy and diseased populations which have been already registered. The goal of DSI is to try to resolve the question of how one can best combine the plethora of data in a logical manner to obtain a precise diagnosis of AD. Particularly, it helps to assess the results, since it takes into account which method or test would be more relevant for achieving a certain diagnosis.

What to include in DSI Another major issue is which parameter should be included in DSI. DSI is a statistical tool for collecting, analyzing data and finally displaying it in a comprehensible graphical manner via the DSF. This means it could include almost any parameter available in an appropiate format. For example it could be used for many other diseases. However, it can be predicted that clinicians would prefer to use a tool that would help them in their daily work. The DSI and DSF are designed for this task since they provide an unbiased perspective of the patient. It is important to consider the variation due to gender, age and education (Koikkalainen et al. 2012). These studies did not include age, gender or education as parameters in the DSI although their importance is noted in the literature. Most of the AD cases are women (Alzheimer's Association 2012) and while there are no differences in terms of age in FTD (Rosso et al. 2003a); most of FTD cases are younger than AD cases (Ratnavalli et al. 2002). It is known that a lower level of education is associated with a higher risk of developing AD (Anttila et al. 2002). The problem is that not all the cases follow the same pattern concerning age and gender, giving those parameters weightings on the DSI would probably bias the results, and could well blur the overall outcome. If a parameter can be considered by the clinician without using the tool and that parameter does not necessarily follow a pattern (e.g. AD cases are usually women, but that condition is not helpful in making a diagnosis, it is simply a risk factor; the same is the case for age or the level of education), then it is better not to include it in DSI. On the other hand, the volume of the hippocampus or the amount of amyloid present in the CSF are biomarkers, not risk factors, and in these cases DSI can help to determine their importance for coming to a diagnosis. Another question which could be asked is whether it is advisable to include medications, other risk factors and symptoms on DSI.

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Study III had access to a larger battery of data, which included knowledge about the medications used by AD and FTD cases, and RFs present in the same group of patients. What are the reasons for not incorporating medications or RFs into the tool? Medications do represent an enormous list of variables, and knowing that a patient is taking some particular medication could help us to deduce whether the symptoms are iatrogenic and thus the reason why they are present in that patient, but again this could blur other results. Thus it may well be better to consider this as additional information and interpret it on an individual basis (e.g. a patient taking NMDA inhibitor and not displaying any clear benefit, more likely could be suffering from FTD than AD). However, in terms of lifestyle and vascular RFs, it could be really advisable to include these parameters into DSI when predicting AD. During the last decade some studies have demonstrated the benefits of controlling both vascular and other risk factors (lack of exercise, no diet), and therefore it would be very advantageous if it could be possible to integrate these kinds of factors postulated to influence the development of AD. A different question would be how should this kind of risk factor data be collected, and not simply incorporated as a binary (yes/no) scale. Instead, it would be preferable to register actual values of midlife of heart rate, sleeping patterns, gait, combined with more precise questionnaires on diet and exercise. In selecting the ROIs used in study I and II and IV the Hammer atlas was utilized (Heckemann et al. 2006). This has been used previously in other studies; here it was decided to gather data from a total of 83 regions from this atlas, and then the ROIs with the highest accuracy were selected. This thesis has compared bvFTD with AD, MCI and controls. However, bvFTD only represents about half of the cases within the FTLD spectrum. BvFTD was chosen because it may be difficult to differentiate this particular syndrome from AD e.g. a patient over 65 years of age presenting with amnestic memory, an AD pattern on imaging or even APOE ε4 allele. In future studies, features from other dementia diseases such as other forms of the FTLD spectrum and VaD should be investigated. It is important not only to make a differential diagnosis between the major dementia diseases, but also to identify MCI cases and to be able to predict their probable conversion to dementia. It is also important to make a differential diagnosis in the mixed cases which tend to be the rule in very old patients. It is concluded that conceivably DSI can be a supportive tool for profiling patients and it can help the clinician to make as accurate a diagnosis as possible. The combination of several biomarkers has been recently conducted by applying many different procedures. There are many classifiers such as support vector machines or linear discriminant analysis (Wolz et al. 2011, Mattila et al. 2011). However within the dementia literature, only one multivariate data analysis has been done that attempted to combine different features and display the findings in a graphical manner in a similar way to the DSF: the orthogonal projections to latent structures (OPLS) (Westman et al. 2011). The use of MRI data predicted conversion from MCI to AD in the AddNeuroMed cohort over a 1 year period with an accuracy of 86% (Aguilar et al. 2013). In that study, the combination of CSF and MRI parameters at baseline, the accuracy for discriminating between AD and controls, MCI and controls and for predicting future conversion from MCI to AD, was higher than the accuracy obtained by using either MRI or CSF separately (Westman, Muehlboeck & Simmons 2012).

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Diagnosis using PredictAD tool by the clinician: There are two clinical studies describing the use of DSI. Both studies utilized data from ADNI, and in both cases it helped the clinician in the task of classifying subjects with MCI into six possible stages, ranging from non-clear AD to clear AD (non-clear AD, probable non-AD, subtle non-AD, subtle AD, probable AD and clear AD). Simonsen et al., (Simonsen et al. 2012) reported that the classification accuracy with the tool (70%) was higher than without its use (62.6%). Liu et al., (Liu et al. 2013) the accuracy reached by the PredictAD itself and the clinician using the tool was very similar (72% vs. 71%).

DSI in clinical practice: Finally, one question to remains to be discussed: when should DSI best be used in clinical practice? It has recognized utility comparing population datasets for research purposes, and it is also known how it performs in the examination of a single case in determining whether he/she is more likely to be AD or FTD. Nevertheless the studies in this thesis do not investigate when this tool could become part of the daily clinical routine. Since this issue will be relevant in the near future, its possibilities will be discussed in this final section. It can be a long road from the time when a patient presents in the physician’s office with a memory complaint or behavioural problem (or other cognitive function) until there is an initial diagnosis; the tests or methods which can be applied to ascertain that diagnosis and then to monitor the patient in follow-up, are depicted in Figure 16. This is a general overview, not all of the steps are taken nor are all the tests or methods applied in every single patient. One very useful stepwise approach provided for the diagnosis and assesses of AD in the primary care was depicted in the guideline by Galvin and Sadowsky (Galvin, Sadowsky & NINCDS-ADRDA 2012). The figure illustrates one possible line to follow, as is done in Kuopio region, Finland. With respect to the screening test, one could recommend the well-known MMSE test, or CERAD such as is the case in Finland; CERAD includes MMSE as well as some other tests. CERAD contains sections more focused on the study of memory and also behavioural components, therefore it can initially point towards to a more amnestic or behavioural presentation. Additionally the performance of the patient while undertaking daily life activities is conducted with ACDS-ADL. These tests along with a detailed interview of the patient and the caregiver, lead to the initial evaluation, i.e. deciding if the patient needs to be refered for further examinations. A detailed interview provides the highest amount of information, in particular when the first symptoms appeared and the age of the patient, which usually orientates more to either AD or FTD, although one should remember that both diseases can be present in every range of age, and both diseases can start with amnesia or behavioural symptomatology. Once the patient is referred to the hospital, a new evaluation is done and it is then decided if the diagnosis would benefit from the input of some other tests and/or imaging. If the tests have been conducted 3 months earlier than the present time, they will be required to be done again in the hospital because it is essential to have access to up-to-date data. This initial evaluation is followed by a first visit to the memory clinic and subsequent visits if needed. AD patients usually are directed to the memory clinic, while patients with

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a behavioral profile may initially be referred to a psychiatrist. If there is a suspicion of FTD, the FBI battery is performed. Finally, one has gathered a detailed clinical story of the patient, along with several neuropsychological tests, MRI (normal protocol; or memory protocol, focusing on temporal lobe atrophy in the hippocampus with Scheltens scale, general and focal atrophy, sulcus widening, ventricular enlargement, white matter hyperintensities in Fazekas scale, MBs. Visual analysis is done although quantitative techniques are starting to become available), and possibly CSF biomarkers and SPECT or FDG-PET. Although there have been used different versions of the PredictAD tool, Figure 13 shows a screenshot of the latest version of PredictAD (Windows application). The MRI images go through a pipeline that estimates the results for HV, TBM and VBM (Figures 14 and 15).

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Figure 14. TBM analysis results visualised for one AD case (left) and for one healthy control (right). Shades of red and blue indicate areas which expand and shrink, respectively, in a way typical to AD. The AD case has clear expansion in the ventricles and shrinking in the MTL, not visible in the healthy control. Courtesy of Dr. Jyrki Lötjönen, VTT.

Figure 15. VBM analysis results visualised for one AD case (left) and for one healthy control (right). Shades of red and blue indicate areas which have higher or less GM concentration, respectively, in a way typical to AD. The AD case has clear less GM concentration in the MTL, not visible in the healthy control. Courtesy of Dr. Jyrki Lötjönen, VTT.

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These three automatic methods are applied from a basic T1 image, and since it uses a sophisticated and elaborated protocol, this is not a time consuming procedure to conduct these studies, and could provide additional information for the clinician. Data has originated from multiple sources, which different levels of importance, thus combining the information using the PredictAD tool could be useful. The question then is should it become part of the routine in the first visit and follow-up? Or should it be reserved for the situation when there is a plethora of biomarkers and tests? Perhaps it should be only utilized just in complicated and difficult cases? To start, there is a basic problem: the availability of data. Not simply if these tests are done or not, or if the procedures for collecting data and samples are homogeneus, but the possibility of collating all of them together in comparable software format in a reasonable time. Even today most of the information is collected on paper, in some cases that is the only way it can be done (e.g. drawing tests). ACDS-ADL, GDS, CDR and FBI tests are not added to any software, sometimes the score is mentioned in the clinical anamnesis but often is not. With imaging, there is a different problem: images are in different formats, and one would need a direct transfer of these images to PredictAD, in order that they can be utilized. Finally, other tests are rarely conducted, such as taking a CSF sample in order to analyse biomarkers or alternatively conducting genetic profiling. Nevertheless now ideally one has access to all the appropriate data and next one must decide whether or not to use PredictAD. There are two occasions when the PredictAD tool could be wisely used: the first time the patient comes to the memory clinic, if the clinician is unexperienced or not familiar with dementia diseases, this tool could be supportive or help to point to a certain diagnosis. However, in pure dementia cases, the clinician might not need any supportive tool, nonetheless in rare forms of dementia, where the different biomarkers do not clarify the state of the patient, the PredictAD tool might be helpful to the clinician. An initial diagnosis could be done, to allow initiation of therapy and counselling. The second occasion is that in the follow-up, one could attempt to use PredictAD. In the future, more tests or biomarkers will be added with the intention of confirming the initial diagnosis. Furthermore, sometimes diagnoses need to be updated or modified. It is important that with ageing there tends to be an overlap of comorbidities, which may complicate the diagnosis e.g. the appearance of depression, extrapyramidal signs, cardiovascular diseases. PredictAD may help the clinician to elucidate this overlap and derive a diagnosis from it. Finally, it is worth mentioning that while it is recommended to utilize the PredictAD for research purposes, it can only be viewed as a supportive tool in the clinic. A positive biomarker identified in the PredictAD tool just as any other individual biomarker (e.g. medial temporal lobe atrophy) is not in itself sufficient to diagnose a disease, it is simply a risk for having a disease. In other words, the tool provides information which always has to be interpreted.

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6.5 FUTURE STUDIES

Future projects will no doubt attempt to find new biomarkers and to combine novel biomarkers in order to come to an accurate diagnosis as early as possible in AD and FTD. In fact the work done in this thesis will be developed further in three projects: PredictAD pilot project, PredictND and VPH-DARE@IT. Nowadays this PredictAD tool is being used in a pilot clinical project, the PredictAD pilot project, in two centers in Finland: Kuopio University Hospital and Turku University Hospital, to determine whether it helps clinicians to make a differential diagnosis of patients with dementia. The VPH-DARE@IT will validate previously known methods or develop new biomarkers, attempting to combine the mechanistic and phenomenological models of the ageing brain and how these are influenced by environmental factors. This is a consortium involving 21 European centres and it started in 2013. There are new methods being assessed e.g. magnetic resonance elastography, RSfMRI, ASL, a model for integrating risk factors and DTI. All these biomarkers presumably could be integrated into the PredictAD tool. PredictND which started in 2014 is intended to use biomarkers and tests from daily clinical use and develop PredictAD tool further also for other neurodegenerative diseases. Finally, DSI could be used not only for diagnosing a patient at a specific moment from certain data, but to monitor the progression of the disease and determine whether the patient status has changed with time, in a longitudinal study (Runtti et al. 2014).

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7 Conclusions

1. HV, TBM and VBM provide accurate results in these research studies when comparing the healthy state with disease and for predicting the conversion to AD. They may also help in differentiating between AD and FTD (study I).

2. DSI collates data from several tests and biomarkers, and can be supportive in the profiling of a patient with a certain dementia disease, i.e. whether it is MCI, FTD or AD. DSF can help to profile a particular patient by displaying the findings in an easy-to-interpret picture format (studies II-IV).

3. Imaging is the most relevant feature in differentiating FTD from MCI and AD in comparison with MMSE, CSF and APOE, while MMSE is the most useful test distinguishing a healthy state from FTD (study II).

4. Clinical symptoms and neuropsychological tests are the most important studies in differentiating a healthy state from dementia and in distinguishing AD from FTD. MRI and particularly SPECT, APOE genotyping and CSF can be useful in distinguishing patients with AD from those with FTD (study III).

5. SPECT differentiates FTD from both controls and AD while manual hippocampal volumetry may be particularly helpful in the differential diagnosis between a healthy state and AD. It is recommended that a broad battery of neuropsychological tests is conducted rather than the single use of MMSE in the differentiation of a healthy state from dementia and AD from FTD (study III)

6. MRI features alone achieve good accuracies in predicting the progression from MCI to AD, this is only slightly improved by the addition of MMSE, APOE, CSF and neuropsychological tests (study IV)

7. The combination of data coming from multiple-center studies and their comparison is feasible using DSI. The accuracy of the combined cohort is close to the average of the individual cohorts. It is feasible to use different cohorts as training sets for the DSI, as long as they are sufficiently similar (study IV)

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Publications of the University of Eastern Finland

Dissertations in Health Sciences

isbn 978-952-61-1479-8

Publications of the University of Eastern FinlandDissertations in Health Sciences

dissertatio

ns | 235 | M

igu

el Án

gel M

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oz-R

uiz | D

isease State In

dex and N

euroimagin

g in Frontotem

poral D

ementia, A

lzheim

er’s...

Miguel Ángel Muñoz-RuizDisease State Index

and Neuroimaging in Frontotemporal Dementia,

Alzheimer’s Disease and Mild Cognitive Impairment

Miguel Ángel Muñoz-Ruiz

Disease State Index and Neuroimaging in Frontotemporal Dementia, Alzheimer’s Disease and Mild Cognitive Impairment

Alzheimer’s disease (AD) is the most

prevalent disease of the dementia

diseases while frontotemporal dementia

(FTD) is relatively common in people

younger than 65 years of age. Early and

precise diagnosis of these two diseases

is a major challenge. There is a need to

identify new methods that could achieve

an earlier and more precise diagnosis, and

to integrate all these data originating from

multiple sources, in order to facilitate the

clinical diagnosis. This thesis introduces

the use of a new combination of different

methods in the differential diagnosis of

AD, mild cognitive impairment stages and

FTD, and a tool (Disease State Index and

Disease State Fingerprint) that collates

data from different sources to help

clinicians to profile a patient as having

either AD or FTD.

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