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Clinical neuroanatomy

Relevance of subcortical visual pathwaysdisruption to visual symptoms in dementia withLewy bodies

Stefano Delli Pizzi a,b,c, Valerio Maruotti b, John-Paul Taylor d,Raffaella Franciotti a,b,c, Massimo Caulo a,c, Armando Tartaro a,c,Astrid Thomas a,b, Marco Onofrj a,b and Laura Bonanni a,b,*

a Department of Neuroscience and Imaging, “G. d'Annunzio” University, Chieti, Italyb Aging Research Centre, Ce.S.I.,“G. d'Annunzio” University Foundation, Chieti, Italyc Institute for Advanced Biomedical Technologies (ITAB), “G. d'Annunzio” University Foundation, Chieti, Italyd Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, UK

a r t i c l e i n f o

Article history:

Received 28 February 2014

Reviewed 26 March 2014

Revised 14 April 2014

Accepted 10 July 2014

Action editor Marco Catani

Published online 22 July 2014

Keywords:

Dementia with Lewy bodies

Pulvinar

Thalamus

Visual hallucinations

Visual dysfunction

* Corresponding author. Department of NeuChieti, Italy.

E-mail address: l.bonanni@unich.it (L. Bohttp://dx.doi.org/10.1016/j.cortex.2014.07.0030010-9452/© 2014 Elsevier Ltd. All rights rese

a b s t r a c t

Visual hallucinations represent a core diagnostic criterion for dementia with Lewy bodies

(DLB).Wehypothesized that thalamic regions,whichare critically involved in themodulation

of visual transmission, may be differentially disrupted in DLB as compared to Alzheimer's

Disease (AD) and that these deficits could relate to visual dysfunction in DLB patients.

Magnetic Resonance and Diffusion Tensor Imaging (DTI) were performed with a 3 T

scanner on a sample population of 15 DLB patients, 15 AD patients and 13 healthy vol-

unteers. Regional thalamic micro-structural changes were assessed by parcelling the

thalamus based on its connectivity to cortex and to amygdala and by measuring the mean

diffusivity (MD) in each connectivity-defined sub-region.

Micro-structural grey matter damage associated to higher MD values was found bilat-

erally in DLB compared to controls in the sub-regions projecting from thalamus to pre-

frontal and parieto-occipital cortices. Right thalamic sub-region projecting to amygdala

and left thalamic sub-region projecting to motor cortex were also affected in DLB compared

to controls. Higher MD values were found bilaterally in AD compared to controls in the

thalamic sub-regions projecting to temporal cortex. Specific comparison between the two

forms of dementia found differences: the sub-regions which project from thalamus to

parieto-occipital cortex and to amygdala showed higher MD values in DLB compared to AD

patients. In DLB patients, correlation analysis showed a significant correlation between NPI

hallucinations item scores and MD values in the right thalamic sub-regions projecting to

parietal and occipital cortices.

The present study demonstrates how thalamic connectivity alterations between higher

and lower visual areas may be relevant in explaining visual hallucinations in DLB.

© 2014 Elsevier Ltd. All rights reserved.

roscience and Imaging, University G. d'Annunzio of Chieti-Pescara, Via dei Vestini, 66100

nanni).

rved.

Abbreviations

AD Alzheimer's Disease

BET Brain Extraction Tool

CAF Clinician Assessment of Fluctuations

df degree of freedom

DLB dementia with Lewy bodies

DRS-2 Dementia Rating Scale-2

DTI Diffusion Tensor Imaging

DWI-SE Diffusion Weighted Image Spin-Echo

EEG Electroencephalographic

FA Fractional Anisotropy

FAB Frontal Assessment Battery

FIRST FMRIB's Integrated Registration and

Segmentation Tool

FLAIR Fluid Attenuation Inversion Recovery sequence

FLIRT FMRIB's Linear Image Registration Tool

FSL Functional MRI of the Brain (FMRIB) Software

Library (FSL)

MD Mean Diffusivity

MMSE Mini Mental State Examination

MRI Magnetic Resonance Imaging

NPI Neuropsychiatric Inventory

PET Positron-Emission-Tomography

SPECT Single-Photon-Emission-Tomography

SUSAN Assimilating Nucleus Smallest Univalue

Segment

TE Echo Time

TFE Turbo Field-Echo

TR Repetition Time

c o r t e x 5 9 ( 2 0 1 4 ) 1 2e2 1 13

UPDRS Unified Parkinson's Disease Rating Scale

1. Introduction

Dementia with Lewy Bodies (DLB) is the second common

cause of dementia in the elderly after Alzheimer's Disease

(AD) (McKeith et al., 2005). However, despite the overlap of

clinical and pathological features between DLB and AD, the

former tends to be disproportionately affected by complex

recurrent visual hallucinations (Litvan et al., 2003). Indeed

visual hallucinations are a core symptom for the diagnosis of

DLB which, along with fluctuating cognition and parkin-

sonism, help to differentiate DLB from other forms of de-

mentia including AD. Often associated with visual

hallucinations in DLB, are significant visuo-perceptual and

visuo-spatial difficulties (Mosimann et al., 2004) and this

strongly suggests that pathophysiological alterations to the

cortical visual system occur in DLB.

Empirically, this concept is supported by a wide range of

studies in DLB patients applying multiple modalities including

positron-emission-tomography (PET) (Lim et al., 2009), perfu-

sion imaging using single-photon-emission-tomography

(SPECT) (Kemp, Hoffmann, Tossici-Bolt, Fleming, & Holmes,

2007), structural magnetic resonance imaging (MRI) (Delli Pizzi

et al., 2014), arterial-spin-labelling (Taylor et al., 2012) and

functionalMRI (Kenny, Blamire, Firbank,&O'Brien, 2012; Tayloret al., 2012). These studies together with neuropathological

(Harding, Broe, & Halliday, 2002; Mukaetova-Ladinska et al.,

2013; Yamamoto et al., 2006) and electrophysiological (Onofrj

et al., 2002; Taylor et al., 2011) studies have explored the

possible cortical areas involved in visual hallucinations pro-

duction, although, overall, the specific visual cortical loci (for

example, lower visual areas vs visual association areas

including occipito-parietal and occipito-temporal areas)

implicated in visuo-perceptive impairment and visual halluci-

nations occurrence appears to vary from study to study.

Connectivity between higher and lower visual areas may

also be relevant (Kenny, O'Brien, Firbank, & Blamire, 2013;

Watson et al., 2012): diffusion tensor imaging (DTI) studies in

DLB cohorts (Kiuchi et al., 2012; Lee et al., 2010) found specific

reductions in fractional anisotropy (FA) in the inferior longitu-

dinal fascicle and one study found an association between

disruption of this area and the occurrence of visual hallucina-

tions (Kantarci et al., 2010). Other studies have reported a

reduction of FA in parieto-occipital areas of DLB patients,

related to the occurrence of visual hallucinations and visuo-

perceptualdysfunction (Firbanketal., 2011;Watsonetal., 2012).

However a relativelyunexplored researcharea is the studyof

the role of subcortical structures in the aetiology of visual hal-

lucinations. The thalamus is critically involved in the modula-

tion of visual transmission to the cortex and between different

cortical areas. It modulates visual attention based on its wide-

spread connectivity with the visual cortex and the fronto-

parietal attention network (Saalmann & Kastner, 2011). Pa-

tients with pulvinar lesions have difficulties in coding spatial

information in the contralesional visual field (Ward & Arend,

2007) and in filtering distracting information (Snow, Allen,

Rafal, & Humphreys, 2009). The presence of these deficits is

classically associated with posterior parietal cortical lesions

(Friedman-Hill, Robertson, Desimone, & Ungerleider, 2003),

which underlines the functional relationship between the pul-

vinarandthosebrainareas thatsubservevisuo-spatial attention

(Arend, Rafal, & Ward, 2008; Snow et al., 2009). The thalamus

plays also a key role in visual perception and in relaying visual

information to amygdala (Day-Brown,Wei, Chomsung, Petry,&

Bickford, 2010; Lyon, Nassi, & Callaway, 2010). In this context,

recent theories (Shine,O'Callaghan,Halliday,& Lewis, 2014) and

DTI studies (Kantarci et al., 2010) have suggested the involve-

ment of amygdala in visual hallucinations of DLB.

We hypothesized that thalamic regions, which are criti-

cally involved in the modulation of visual transmission, may

be differentially disrupted in DLB as compared to AD and that

these deficits could be related to the occurrence of visual

hallucinations in DLB patients.

In the present study, we used combined MRI and DTI

techniques to clarify the role of the visual thalamus in DLB

patients contrasted against healthy controls.We also included

a comparison against an AD cohort to clarify that any patho-

logic changeswere specific to DLB and not arising as a result of

a dementia process per se.

2. Material and methods

2.1. Study sample

This study was approved by Local Institutional Ethics Com-

mittee and was carried out according to the Declaration of

c o r t e x 5 9 ( 2 0 1 4 ) 1 2e2 114

Helsinki and subsequent revisions (1997). All patients (or their

caregivers) and control subjects signed a written informed

consent. Seventeen AD and 16 DLB patients were recruited

fromour case cohorts of patientswho had been referred to our

Memory Clinic and Movement Disorder Clinic. Fifteen simi-

larly aged healthy volunteers who were matched for educa-

tional level with patients were recruited from our non-

demented case register cohorts. The diagnosis of probable

AD was based on National Institute of Neurological and

Communicative Diseases and Stroke/AD and Related Disor-

ders Association criteria (McKhann et al., 1984). The diagnosis

of probable DLB was based on consensus guidelines (McKeith

et al., 2005) with a specific restriction: we included only pa-

tients with the presence of complex, recurrent visual hallu-

cinations, plus at least one additional core feature (cognitive

fluctuations or parkinsonism) or one core and one suggestive

feature (McKeith et al., 2005). Each subject underwent

Computerized Tomography scan and/or MRI as part of their

clinical work up within six months before the inclusion in the

study. All of the DLB and AD patients were also scanned with

dopaminergic presynaptic ligand ioflupane SPECT (DAT scan).

2.2. Clinical and neuropsychological assessments

Global tests of cognition included Mini Mental State Exami-

nation (MMSE), Dementia Rating scale-2 (DRS-2) (Jurica,

Leitten, & Mattis, 2001) and the Frontal Assessment Battery

(FAB) (Dubois, Slachevsky, Litvan,& Pillon, 2000). Presence and

severity of extrapyramidal signs were rated using the motor

part of the Unified Parkinson's Disease Rating Scale (UPDRS)

(Fahn & Elton, 1987). Neuropsychiatric symptoms were

assessed by the Neuropsychiatric Inventory (NPI) (Cummings

et al., 1994). The NPI hallucinations item total scores

(frequency � severity) and hallucinations items subscores

(frequency and severity, separately) were used to determine

the association between severity and frequency of visual

hallucinations with imaging outomes. In detail, frequency

scores ranged from 1 (rarely) to 4 (very often), whereas

severity scores ranged from 1 (mild) to 3 (severe). Presence and

severity of cognitive fluctuations were evaluated with the

Clinician Assessment of Fluctuations (CAF) questionnaire

(Walker et al., 2000). REM sleep Behaviour Disorder (RBD) was

evaluated according tominimal International Classification of

Sleep Disorders criteria (World Health Organization, 1992).

Patients were also evaluated with electroencephalographic

(EEG) recordings as EEG abnormalities characterized by

parieto-occipital dominant frequency alterations have previ-

ously been demonstrated to reliably differentiate probable

DLB from AD patients (Bonanni et al., 2008).

The presence of vascular leukoencephalopathy (vascular

load) was assessed with a visual rating scale for white matter

hyperintensity (Rossini et al., 2004).

Subjects with uncontrolled hypertension, myocardial

ischaemia, peripheral vascular disease and chronic kidney

diseases were excluded from the study.

2.3. MR data acquisition

MRI and DTI were performed with a Philips Achieva 3 T

scanner (Philips Medical System, Best, the Netherlands)

equipped with 8-channel receiver coil. After scout and refer-

ence sequences, a 3-dimensional T1-Weighted Turbo Field-

Echo (3D T1-W TFE, TR/TE ¼ 11/5 msec, slice thickness of

.8 mm, FOV ¼ 256 � 192 � 170 mm) and Diffusion Weighted

Image Spin-Echo (DWI-SE; TR/TE ¼ 3691/67 msec, slice thick-

ness of 4 mm, FOV ¼ 230 � 230 � 139 mm, 15 diffusion-

sensitive gradient directions) sequences were performed. To

limit the risk of including participants with concomitant

vascular pathology or with white matter abnormalities

outside the normal range, a T2-weighted Fluid Attenuation

Inversion Recovery sequence (FLAIR, TR/TE¼ 11000/125msec,

slice thickness of 4 mm, FOV ¼ 240 � 129 � 222 mm) was also

performed. Participants with images characterized by motion

artifacts were excluded from the analysis.

2.4. MR data analysis

Structural MRI and DTI data analyses were performed by

using Functional MRI of the Brain (FMRIB) Software Library

(FSL) version 4.1 (http://www.fmrib.ox.ac.uk/fsl; Smith et al.,

2004).

2.4.1. Data preprocessingBefore data processing, noise reduction was carried out using

Smallest Univalue Segment Assimilating Nucleus (SUSAN)

algorithm on structural images and eddy-currents correction

on diffusion images. Brain Extraction Tool (BET) was used for

brain and skull extraction of the T1-W structural and DWI

images.

2.4.2. Segmentation of amigdalae and thalamiFor each subject, T1-W structural imageswere co-registered in

common space on the non-linear MNI152 template with

1 � 1 � 1 mm resolution, by means of affine transformations

based on 12� of freedom (three translations, three rotations,

three scalings and three skews) using FMRIB's Linear Image

Registration Tool (FLIRT). FMRIB's Integrated Registration and

Segmentation Tool (FIRST) were used to automatically

segment thalami and amygdalae (Patenaude, Smith, Kennedy,

& Jenkinson, 2011).

2.4.3. Mean diffusivity (MD) mapsFor each subject, MD maps were generated from a tensor-

model fit in FSL (FDT, FMRIB's Diffusion Toolbox).

2.4.4. Parcellation of thalamiThalami parcellation was performed according with methods

described by Behrens et al. (2003). Specifically, six cortical

masks including prefrontal, primary and secondary sensory,

pre- and primary motor, temporal, parietal and occipital re-

gions were defined using the Harvard Oxford cortical atlas

(Fig. 1A). Thalami and amygdala masks were obtained by

binarizing the FIRST outputs. All these masks were in MNI

space (1 � 1 � 1 mm).

After Bayesian Estimation of Diffusion Parameters ob-

tained using Sampling Techniques (BEDPOSTX), the DTI maps

were registered to MNI standard space using: 1. FLIRT to reg-

ister each subject's b0 image to its native structural image, and

2. FMRIB's Non-Linear Registration Tools (FNIRT) to register

the structural and diffusion images to MNI space

Fig. 1 e Thalamic structural connectivity. (A) Cortical rendering of target regions used for thalami parcellation (colours were

in agreement with thalamic connectivity). (B) Grand averages of connectivity-based subdivision of thalami are shown for

controls, dementia with Lewy bodies (DLB) and Alzheimer's Disease (AD). Within-group probabilistic tractography outputs

for each cortical target region were performed with “find the biggest” command line. Thalamic voxels are classified and

coloured according to the highest probability of connection to a specific cortical region. Blue ¼ connectivity-defined sub-

region (CDR) that projects from thalamus to prefrontal cortex; dark green¼ CDR that projects from thalamus tomotor cortex;

red ¼ CDR that projects from thalamus to primary and secondary somato-sensory cortex; yellow ¼ CDR that projects from

thalamus to parietal cortex; dark blue ¼ CDR that projects from thalamus to occipital cortex; green ¼ CDR that projects from

thalamus to amygdala; fuchsia¼ CDR that projects from thalamus to temporal cortex. (C) Thalami regions defined by Oxford

Thalamic Connectivity Atlas. (D) Within-group probabilistic tractography maps for each cortical target region. The

significant results are displayed by voxels rating from red to yellow (p < .05, FWE-corrected). No significant differences were

observed among groups.

c o r t e x 5 9 ( 2 0 1 4 ) 1 2e2 1 15

(1 � 1 � 1 mm). All masks were then propagated onto each

individual's DTI scalar maps using the inverse of the above

transformations. To exclude thalamic voxels that contained

cerebrospinal fluid (CSF), the b0 imageswere segmented using

FMRIB's Automated Segmentation Tool (FAST) and CSF

binarized to be used as exclusion mask. Additionally, further

manual editing was applied to exclude voxels out of the

thalamic range. Next, probabilistic tracking was performed by

PROBTRACKX tool. “Find the biggest” command line was used

to define the thalamic connectivity-defined sub-regions ac-

cording with their highest probability of connection (Fig. 1B).

Two experienced operators have visually inspected the “find

the biggest” outputs, verifying the anatomic correspondence

of each thalamic connectivity-defined sub-region among

subjects and their accordance with Oxford Thalamic Con-

nectivity Atlas (integrated in FSL, Johansen-Berg et al., 2005,

Fig. 1C).

Finally, MD values were calculated in each connectivity-

defined sub-region.

Given that some cortical regions in DLB and/or AD could be

characterized by alteration in regional cerebral blood flow

(Colloby et al., 2013) and the diffusion indices could be biased

by different location and volume of thalamic connectivity-

defined regions determined by the tracking algorithm, we

adopted three different strategies. Firstly, we verified whether

the spatial distribution and the connection probability within

each thalamic connectivity-defined region were altered by the

pathological underlying condition (Nair, Treiber, Shukla, Shih,

& Muller, 2013). In detail, fslroi command line was used to

align probtrackx outputs. This step is particularly important to

minimize themisalignment of thalami and of their probtrackx

output among subjects. Next, the probtrackx outputs were

overlapped on the thalami from a common MNI template. By

using “randomize” command line, we performed comparisons

within and between groups on the maps of connection prob-

ability generated by PROBTRACKX tool (Fig. 1D). Family-wise

error (FEW) correction was used to obtain the significant

voxels. Secondly, we verified whether the volumes of each

thalamic region defined by “find the biggest” were signifi-

cantly different among groups. To this aim, for each subject,

the volumes of thalamic regions were calculated by “fslstats”

command line and, next, they were normalizated for ipsilat-

eral thalamic volume. Third, we verified whether the MD

values in each thalamic region were related to differences

found by tracking probability. In detail, we defined the

thalamic regions from Oxford thalamic connectivity atlas and

Table 1 e Demographic and clinical features.

Characteristics Controls(n ¼ 13)

AD(n ¼ 15)

DLB(n ¼ 15)

Agea,b 76.0 ± 4.2 76.1 ± 4.9 76.3 ± 4.1

Male gender

(in percentage)c50% 47% 53%

c o r t e x 5 9 ( 2 0 1 4 ) 1 2e2 116

we extracted MD values from them. Because atlas did not

describe the thalamo-amygdala connectivity, this step was

performed only for the cortico-thalamic connectivity. Finally,

we performedmultivariate analysis of variance (MANOVA) on

MD values to test whether the two methods (probabilistic

parcellation and atlas) provided different results for thalamic

regions.

Disease duration

(years)de 3.1 ± .6 2.9 ± .7

Education level

(years)a,e7 ± 4 7 ± 4 7 ± 4

MMSEa,f 28.3 ± 1.5 16.7 ± 5.4 17.7 ± 5.2

DRSa,g 136.5 ± .7 91.0 ± 13.4 92.7 ± 4.0

FABa,h 17.8 ± .4 7.6 ± 3.9 7.2 ± 3.5

CAF .0 ± .0 .0 ± .0 5.6 ± 3.1

UPDRS III .0 ± .0 .0 ± .0 25.4 ± 10.4

NPI-item 2

hallucinations

.0 ± .0 .0 ± .0 5.8 ± 2.7

Values are expressed as mean ± standard deviation (SD).

Abbreviations: AD ¼ Alzheimer's Disease; CAF ¼ Clinician Assess-

ment of Fluctuations; DLB ¼ dementia with Lewy bodies;

DRS ¼ Dementia Rating Scale; FAB ¼ Frontal Assessment Battery;

MMSE ¼ Mini Mental State Examination; NPI ¼ Neuropsychiatric

Inventory; UPDRS ¼ Unified Parkinson's Disease Rating Scale.a the p-values were calculated using the one-way ANOVA; Tukey'sHSD post-hoc test was also performed when F-test was significant.b main interaction among groups: F ¼ .023, df ¼ (2,42), p ¼ .977.c the p-values were calculated using chi-squared test: c2 ¼ .023,

df ¼ 1, p ¼ .879.d

2.5. Statistical analysis

SPSS version 14.0 was used for statistical analysis.

ANOVA among groups and Tukey's HSD post-hoc test were

performed on demographic and clinical data. Chi-squared test

was carried out for gender.

We performed MANCOVA to exclude the possibile effect of

cognitive impairment on different imaging results obtained

from demented patients and controls. Next, MANOVA was

carried out to test the differences among groups (AD, DLB and

controls). Tukey's HSD post-hoc test was performed for

assessing pair-wise differences between groups.

Relatively to DLB patients, linear regressionwas performed

to assess the relationship between imaging outcomes (MD

values) and NPI hallucinations item scores. MMSE, CAF and

UPDRS scores were included as nuisance factors to exclude

the effect of cognitive impairment, cognitive fluctuation and

extrapyramidal signs, respectively.

the p-values were calculated using the independent-samples t-

test: t ¼ �.561, df ¼ 28, p ¼ .579.e main interaction among groups: F ¼ .048, df ¼ (2,42), p ¼ .953.f main interaction among groups: F ¼ 24.904, df ¼ (2,42), p < .001;

post-hoc: controls vs AD, p < .001; controls vs DLB, p < .001 and AD

vs DLB, p ¼ .815.g main interaction among groups: F ¼ 53.701, df ¼ (2,42), p < .001;

post-hoc: controls vs AD, p < .001; controls vs DLB, p < .001 and AD

vs DLB, p ¼ .934.

3. Results

One DLB, 2 AD and 2 control subjects were excluded from the

study due to the presence ofmotion artifacts onMRI, leaving a

total study cohort of 15 DLB, 15 AD and 13 controls. subjects.

h main interaction among groups: F ¼ 219.263, df ¼ (2,42), p < .001;

post-hoc: controls vs AD, p < .001; controls vs DLB, p < .001 and AD

vs DLB, p ¼ .721.

3.1. Demographic and clinical features

The three groups were similar in terms of age, gender and

educational level (Table 1). Neuropsychological test scores are

presented in Table 1. AD and DLB patients exhibited no dif-

ferences on global test of cognition (DRS-2, MMSE). The

severity of frontal dysfunction, as assessed by FABwas similar

in DLB and AD patients. All DLB patients had recurrent VH,

whereas none of the AD patients had VH. Parkinsonian signs

were only present in DLB. Cognitive fluctuations and RBDwere

present only in DLB patients. 13 DLB patients had RBD. All DLB

patients showed abnormal EEG pattern profile consistent with

the diagnosis of DLB while none of the AD patients or controls

had EEG abnormalities.

The degree of vascular load and white matter alterations

were comparable in all the participants and no focal lacunar

infarcts at thalamic level were found.

At SPECT-DAT scan all DLB patients showed dopamine-

transporter hypocaptation in the caudate nuclei, bilaterally

in 11 patients. None of the AD patients or control subjects

showed SPECT-DAT scan abnormalities.

The patients were on a range of medications including L-

Dopa (all DLB patients), rivastigmine or donepezil (all AD and

DLB patients with no differences in daily dosages between the

two groups of patients), quetiapine (8 DLB and 6 AD), clozapine

(4 DLB), and risperidone (4 AD) and clonazepam (the 13 DLB

patients with RBD).

3.2. Thalamo-cortical structural connectivity

The thalamic connectivity-defined sub-regions projecting to:

� prefrontal cortex included the ventro-anterior and dorso-

medial parts of the thalamus;

� supplementary motor and motor cortices included the

superior region of ventro-lateral parts of the thalamus;

� sensory cortex included the inferior region of ventro-

lateral parts of the thalamus;

� parietal cortex included the ventro-posterior part of the

thalamus;

� occipital cortex included the superior portion of

pulvinar;

� amygdala included the medial portion of pulvinar;

� temporal cortex included the dorso-anterior part of the

thalamus and the inferior portion of pulvinar.

c o r t e x 5 9 ( 2 0 1 4 ) 1 2e2 1 17

Fig. 1D shows the maps of connection probability of each

thalamic sub-region to specific cortical regions and amygdala,

for each group. When comparisons among groups were per-

formed, no significant differences were observed on tracking

probability. As assessed by visual inspection of “find the

biggest” outputs, the location of each connectivity-defined

thalamic region showed good correspondence among groups

and it was in agreement with Oxford Thalamic Connectivity

Atlas (Fig. 1C). Furthermore, the volumes of each thalamic

region defined by “find the biggest” were not different among

groups (Suppl. Table 1). Therefore, the anatomy for each

thalamic connectivity-defined region did not significantly

change in pathological conditions respect to controls.

3.3. MD changes within thalamic regions

Table 2 shows grand mean MD values for each connectivity-

defined sub-region obtained from tractography-based subdi-

vision of thalami. Effects among groups from MANCOVA and

MANOVA analyses were provided in Suppl. Table 2. Suppl.

Table 3 shows grand mean MD values for each connectivity-

defined sub-region obtained from Oxford thalamic connec-

tivity atlas. No differences were found between MD values

obtained with probabilistic parcellation and MD values ob-

tained with Oxford thalamic connectivity atlas (Suppl. Table

4).

DLB patients showed bilateral increase of MD values in the

thalamic connectivity-defined sub-regions projecting to pre-

frontal and parieto-occiptal cortices compared to controls.

Right thalamic sub-region projecting to amygdala and left

thalamic sub-region projecting tomotor cortex showed higher

MD values in DLB respect to controls. AD patients showed

bilateral increase of MD values in the thalamic connectivity-

defined sub-regions projecting to the temporal cortex

Table 2 e Mean diffusivity (MD) values for each “connectivity-deparcellation of thalami.

CDR Controls AD DLB

PFC (R) 760 ± 22 795 ± 75 818 ± 41

PFC (L) 752 ± 18 795 ± 77 813 ± 45

MOT (R) 731 ± 22 747 ± 79 783 ± 52

MOT (L) 725 ± 21 743 ± 69 779 ± 51

SEN (R) 738 ± 18 740 ± 79 773 ± 48

SEN (L) 733 ± 24 752 ± 67 777 ± 42

AMG (R) 786 ± 26 790 ± 29 816 ± 29

AMG (L) 763 ± 25 794 ± 47 799 ± 50

PAR (R) 761 ± 17 771 ± 75 821 ± 51

PAR (L) 760 ± 28 790 ± 70 815 ± 47

OCC (R) 749 ± 46 780 ± 79 858 ± 93

OCC (L) 785 ± 54 788 ± 97 891 ± 97

TEM (R) 812 ± 47 868 ± 64 833 ± 45

TEM (L) 789 ± 32 851 ± 70 826 ± 41

MD values (�10�4mm2/sec) are expressed asmean± standard deviation (S

Significant mean differences were found among groups by MANOVA (F ¼Abbreviations: R ¼ right; L ¼ left; AD ¼ Alzheimer's Disease; DLB ¼ dement

from thalamus to: PFC ¼ prefrontal cortex, MOT ¼ pre- and primary mo

cortex, OCC ¼ occipital cortex, TEM ¼ temporal cortex.a p values in the table were referred to Tukey's HSD post-hoc.

compared to controls. The thalamic connectivity-defined sub-

region which projects to parietal (right) and occipital (bilater-

ally) cortices and to the right amygdala showed higher MD

values in DLB than in AD.

Fig. 2 and Table 3 show the close relationship between NPI

hallucinations item scores and MD values in the connectivity

sub-regions projecting from right thalamus to parietal and

occipital cortices. As reported in Table 4, we observed that the

MD values in thalamic region projecting to parietal cortex

were associated to both frequency and severity of visual hal-

lucinations whereas, MD values in thalamic region projecting

to occipital cortex were linked to severity of visual

hallucinations.

4. Discussion

The present study demonstrates how posterior thalamic al-

terations may be relevant in explaining visual dysfunctions in

DLB patients.

The thalamus contains primary relay nuclei which

transmit information to specific cortical areas via topo-

graphically organized cortical projections. Particularly, the

pulvinar is a well-differentiated thalamic nucleus which

shows reciprocal connections with visual cortical areas

(Saalmann & Kastner, 2012). Several studies supported the

idea that posterior thalamic lesion might be linked to visual

hallucinations (Catafau, Rubio, & Serra, 1992; de Morsier,

1969a, 1969b; Noda, Mizoguchi, & Yamamoto, 1993). Particu-

larly, more recent connectivity models argued for a specific

role of pulvino-cortical circuits in visual hallucinations

(ffytche, 2008).

Few DTI studies have investigated the thalami involve-

ment in DLB, providing controversial results. Specifically,

Firbank et al. (2007) by using a manually ROI-based approach

fined region” (CDR) obtained from tractography-based

Statistical comparison

AD vs Controlsa DLB vs Controlsa AD vs DLBa

p ¼ .234 p ¼ .015 p ¼ .392

p ¼ .100 p ¼ .011 p ¼ .596

p ¼ .761 p ¼ .053 p ¼ .194

p ¼ .609 p ¼ .024 p ¼ .166

p ¼ .996 p ¼ .236 p ¼ .248

p ¼ .566 p ¼ .053 p ¼ .331

p ¼ .931 p ¼ .020 p ¼ .039

p ¼ .150 p ¼ .078 p ¼ .939

p ¼ .877 p ¼ .017 p ¼ .044

p ¼ .292 p ¼ .022 p ¼ .399

p ¼ .534 p ¼ .001 p ¼ .020

p ¼ .997 p ¼ .007 p ¼ .006

p ¼ .036 p ¼ .601 p ¼ .228

p ¼ .008 p ¼ .150 p ¼ .406

D); bold characters indicate significant results after post-hoc analysis.

2.413, df ¼ (28,54), p ¼ .003).

ia with Lewy bodies; CDR ¼ connectivity-defined region that projects

tor cortices, SEN ¼ sensory cortex, AMG ¼ amygdala, PAR ¼ parietal

Fig. 2 e Linear regression analysis of NPI hallucinations item scores (frequency£ severity) with MD values. The scatter plots

describe the correlation between MD values (£10¡4 mm2/sec) in the right thalamic connectivity-defined sub-regions (CDRs)

that project to parietal (panel A) and occipital (panel B) cortices and NPI hallucinations item scores.

c o r t e x 5 9 ( 2 0 1 4 ) 1 2e2 118

did not observe differences in the thalami of DLB as compared

with AD and controls. More recently, Watson et al. (2012), by

using a voxel-based approach, have observed a micro-

structural damage of thalami in DLB as compared to AD and

controls. In the current study, we combined structural MRI

and DTI to parcellate thalami according to its connectivity

with specific cortical areas and amygdala. MD has been spe-

cifically assessed because its values are related to a decrease

in membrane density and cell loss of both neurons and glia.

Therefore, MD is an index for both grey and white matters

damage (Canu et al., 2010). Strengths of our methods are: 1.

the automatic segmentation of thalami that resolves the

partial volume contamination due to manually ROI definition;

2. the direct and quantitative measurement of MD in

connectivity-defined sub-regions.

We found micro-structural alteration in the connectivity-

defined sub-regions projecting from thalamus to the amyg-

dala in DLB patients as compared to controls and AD.

The amygdala is involved earliest and most severely in the

LB pathology (Braak et al., 2003). Interesting, it was demon-

strated that DLB patients with visual hallucinations were

characterized by high density of LB in the amygdala (Harding

et al., 2002). Furthermore, the more recent model on visual

hallucinations, underlines the role of the amygdala to coor-

dinate the dorsal and ventral visuo-attentional networks

(Shine et al., 2014). Because the pulvinar relays information

from the superficial layers of the superior colliculus to

amygdala (Day-Brown et al., 2010), we hypothesized that

alteration of thalamo-amygdala structural connectivity may

Table 3 e Linear regression of NPI hallucination item scores wit

CDR (right) NPI hallucinations

MM

b t p R b

Parietal .77 4.18 .002 .80 .02 .

Occipital .65 2.38 .039 .60 �.15 �.

Amygdala .32 1.08 .308 .43 .43 1.

Bold characters highlight statistically significant results.

Abbreviations: CDR ¼ connectivity-defined region (projecting from right th

be associated to visuo-perceptual dysfunction in DLB patients.

We did not observe significant correlation between MD values

in the thalamic regions projecting to amygdala and NPI hal-

lucinations item scores, however further investigation should

be considered to verify whether these changes could be

related to the distress/expression of emotion related to visual

dysfunction.

We found micro-structural alteration in the connectivity-

defined sub-regions projecting from pulvinar to the parietal

and occipital cortices in DLB patients as compared to controls

and AD.

Pulvinar plays key roles in visual cortical function

(Sherman & Guillery, 2011) and visual attention (Saalmann &

Kastner, 2011). Similar to patients with lesions in the poste-

rior parietal cortex (Friedman-Hill et al., 2003) and in the

extrastriate cortex (Gallant, Shoup, & Mazer, 2000), patients

with pulvinar damage also showdeficits in filtering distracting

information (Arend et al., 2008; Ward & Arend, 2007). In this

context, we would argue that the micro-structural pulvinar

changes evidenced in the present study in DLB patients may

be an important contributor to the marked visual and atten-

tion deficits that occur in this condition.

Furthermore, we observed a close relationship between

NPI hallucinations item scores andMD values within thalamic

regions projecting to parietal (both frequency and severity)

and occipital (severity) cortices. The same association was not

found for MMSE, CAF and UPDRS, supporting the hypothesis

that pulvinar changes were specific to visual hallucinations

and not to DLB symptoms in general.

h imaging outcomes.

Nuisance factors

SE CAF UPDRS

t p b t p b t p

15 .88 .14 .78 .46 �.18 �1.17 .27

69 .50 .06 .22 .83 �.17 �.75 .47

86 .09 .32 1.08 .31 .17 .72 .49

alamus to parietal and occipital cortices and amygdala).

Table 4 e Linear regression of visual hallucinations frequency and severity with imaging outcomes.

CDR (right) NPI hallucinations (frequency) NPI hallucinations (severity) Nuisance factors

MMSE CAF UPDRS

b t p R b t p R p p p

Parietal .46 3.05 .014 .62 .59 4.85 .001 .80 .66a .37b .10c

Occipital .11 .61 .555 .17 .78 5.58 <.001 .84 .48d .43e .08f

Amygdala .53 1.95 .083 .48 �.20 �.89 .397 .24 .09g .39h .30i

Bold characters highlight statistically significant results.

Abbreviations: CDR ¼ connectivity-defined region (projecting from right thalamus to parietal and occipital cortices and amygdala).a b ¼ .052, t ¼ .452.b b ¼ .147, t ¼ .944.c b ¼ �.220, t ¼ �1.810.d b ¼ �.096, t ¼ �.732.e b ¼ .148, t ¼ .831.f b ¼ �.276, t ¼ �1.987.g b ¼ .403, t ¼ 1.932.h b ¼ .253, t ¼ .901.i b ¼ .244, t ¼ 1.110.

c o r t e x 5 9 ( 2 0 1 4 ) 1 2e2 1 19

In agreement with these findings, interactive models of

visual hallucinations (Collerton Perry, & McKeith, 2005;

Diederich, Goetz, & Stebbins, 2005; Shine Halliday, Naismith,

& Lewis, 2011; Shine et al., 2014) have proposed that distrib-

uted dysfunction in both visuo-perceptive and attentional

systems is necessary for visual hallucinations to occur and

given the centrality of the pulvinar in modulating the trans-

mission of visual information between cortical areas, consis-

tently, and relevantly, depending upon attention demands

(Saalmann & Kastner, 2011), we suggest that this structure

may have a putative role in the manifestation of visual

hallucinations.

In the present study, we observed that themicro-structural

damage in DLB patients was predominantly in the right

hemisphere. This finding is in accordance with previous re-

ports fromour group showing reduced functional connectivity

between prefrontal and parietal areas (Franciotti et al., 2006,

2013) and parieto-occipital thinning (Delli Pizzi et al., 2014) in

the right hemisphere. Therefore, differential dysfunction of

this hemisphere may have an intrinsic role in the manifes-

tation of DLB visuo-spatial attentional dysfunction and this is

consistent with the established and dominant role of right

hemisphere in visuo-spatial attention (Thiebaut de Schotten

et al., 2011). Further studies, however, across both structural

and functional neuroimaging modalities, are required to test

whether right hemispheric dysfunction is a specific feature of

DLB.

Strengths to our study include clinically well defined and

worked-up patient groups, a disease comparator group (AD)

and the use of detailed parcellation of subcortical structures

which as far as we are aware have not been reported in the

study of DLB.

Despite the core of tracking connectivity for each thalamic

region was clearly defined, within-group probabilistic trac-

tography maps showed a minimal overlap, limited to border

voxels, between thalamic regions. This issue is commonly

reported in literature (Behrens et al., 2003; Johansen-Berg

et al., 2005; Nair et al., 2013). Johansen-Berg et al. (2005)

argued that methodological factors, such as the limited

spatial resolution andmotion artefacts due to pulsation of the

CSF in the third ventricle, could contribute to the fuzziness of

borders in thalamus parcellation. Additionally, it was also

hypothesized that the lack of sharp borders could also reflect

inter-individual variability in thalamic anatomy (Johansen-

Berg et al., 2005). Particularly, the inter-individual variation

in connectivity-defined regions could reflect difficulties to

precisely match variations in brain and thalamic sizes and

shapes when within-group images were registered. We have

minimized possible registration bias by using “fslroi” com-

mand line and we have resolved any competition of voxels for

multiple cortical areas by using “find the biggest” command

line. In this way, thalamic regions, which were used for MD

values measurements, were unambiguously defined on the

basis of the highest connection probability of each voxel to

specific cortical region.

5. Conclusions

The present study demonstrates how thalamic alterationmay

be relevant in explaining visual dysfunction in DLB. We

identified important neural correlates of DLB visual pathology,

highlighting, firstly, the association between the disruption of

thalamic regions projecting to parieto-occipital cortex and the

presence and occurrence of visual hallucinations and, sec-

ondly, the damage of thalamic region which is anatomically

connected to amygdala.

Disclosure statement for authors

M. Onofrj has served as a consultant for UCB, Novartis,

Lundbeck Medtronic Newron Boheringher Ingelheim; serves

on speakers' bureaus for the Movement Disorders Society,

World Parkinson Association, and on the editorial board of

European Neurological Journal.

Dr. John-Paul Taylor is supported by a Wellcome Trust In-

termediate Clinical Fellowship and by the National Institute

for Health Research (NIHR) Biomedical Research Unit at

Newcastle Hospitals NHS Foundation Trust and Newcastle

c o r t e x 5 9 ( 2 0 1 4 ) 1 2e2 120

University. He has, in the past, acted as a consultant for

Novartis. All authors declare no conflicts of interest.

Acknowledgements

This study was supported by the Italian National Institute of

Health (Grant Young Researcher 2007).

Supplementary data

Supplementary data related to this article can be found at

http://dx.doi.org/10.1016/j.cortex.2014.07.003.

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