Measuring Cerebrovascular Pulsatility Using Cardiac Cycle
Fluctuations of fMRI BOLD Data
by
Athena Elizabeth Theyers
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Department of Medical Biophysics
University of Toronto
© Copyright by Athena Elizabeth Theyers, 2017
ii
Measuring Cerebrovascular Pulsatility Using Cardiac Cycle Fluctuations
of fMRI BOLD Data
Athena Elizabeth Theyers
Master of Science
Department of Medical Biophysics
University of Toronto
2017
Abstract
Arterial pulsatility is linked to cerebral small vessel damage and neurodegeneration, but
measuring cerebrovascular pulsatility in humans has largely been impeded by the skull. This
thesis describes a method that generates cerebrovascular pulsatility maps based on resorting
blood-oxygenation level dependent (BOLD) volumes according to their cardiac cycle position.
Sensitivity of this method was tested using 20 minutes of moderate-intensity exercise as an acute
physiological stressor in 45 healthy adolescents. Further examinations evaluated the influence of
repetition time (TR) and echo time (TE) via simulation and multi-echo data, respectively. There
were global, tissue-specific, and region-specific decreases in cerebrovascular pulsatility 20
minutes following exercise cessation. Cardiac-related pulsatility detection was comparable over
a range of TR and TE values, with highest detection during rapid TRs (≤300ms) or shorter TE
(~14ms). These results suggest that cardiac-related fMRI may represent a potent and easily
adoptable method of mapping cerebrovascular pulsatility influences with voxel-wise specificity.
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Acknowledgments
I would like to thank my supervisor, Dr. Bradley MacIntosh for his support and guidance
throughout this project. I have learned a great deal through all of the opportunities that you
provided me and I am grateful for the experience. I would also like to thank Drs. David Goertz
and Graham Wright for their advice as members of my supervisory committee.
The BOLD fMRI scans used in this thesis come from two larger studies. Members of the
MacIntosh and Goldstein labs performed the MRI data collection. There was no scientific
overlap between analyses described in this thesis and work set out by my colleagues. A special
thanks goes to Dr. Benjamin Goldstein for providing the adolescent fMRI scans and for his
extensive knowledge on bipolar disorder and to Dr. Andrew Robertson and Sarah Atwi for
collecting the multi-echo data and for all of their helpful suggestions throughout the past two
years. Thank you to Dr. Arron Metcalfe and Alvi Islam for their assistance with accessing
participant data for the exercise study and answering my various questions. Thank you also to
Zahra Shirzadi for her help and instruction with the ASL data.
And finally, I would like to thank my family and friends for their support and encouragement
during my Master’s degree. You helped me throughout everything and made sure I accomplished
all that I could. Thank you for everything.
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Table of Contents
Acknowledgments.......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Abbreviations and Symbols............................................................................................... vii
List of Tables ...................................................................................................................................x
List of Figures ................................................................................................................................ xi
Chapter 1 Introduction .....................................................................................................................1
1 Summary .....................................................................................................................................1
1.1 Cardiac Pulsatility and Vascular Aging ...............................................................................2
1.1.1 Vascular vs. Chronological Aging ...........................................................................2
1.1.2 Causes and Consequences........................................................................................4
1.2 Stroke ...................................................................................................................................5
1.3 Neurodegenerative Diseases ................................................................................................6
1.3.1 Cerebral Small Vessel Disease (SVD) and White Matter Hyperintensities
(WMHs) ...................................................................................................................6
1.3.2 Types and Prognosis of Dementia ...........................................................................7
1.3.3 Problems with Late Diagnosis .................................................................................8
1.4 Current Methods of Measuring Stiffness and Pulsatility .....................................................9
1.4.1 Pulse Wave Velocity (PWV) .................................................................................10
1.4.2 Transcranial Doppler (TCD) ..................................................................................10
1.4.3 Near Infrared Spectroscopy (NIRS) .......................................................................11
1.4.4 Pressure Sensors.....................................................................................................11
1.5 Magnetic Resonance Imaging (MRI) .................................................................................12
1.5.1 Magnetic Resonance Elastography (MRE) ............................................................13
1.5.2 Flow MRI ...............................................................................................................13
v
1.5.3 Current Missing Factors .........................................................................................15
1.6 Blood Oxygenation Level Dependent (BOLD) Functional MRI (fMRI) ..........................17
1.6.1 Composition of the BOLD Signal..........................................................................18
1.6.2 Previous Work with BOLD Pulsatility ..................................................................19
1.6.3 Using BOLD for Measuring Pulsatility .................................................................20
1.6.3.1 BOLD Scan Parameters ...........................................................................21
1.6.3.2 BOLD Signal Clean-up ...........................................................................23
1.7 Aerobic Exercise as a Physiological Stressor ....................................................................23
1.7.1 Acute Effects of Aerobic Exercise .........................................................................23
1.8 Aims and Hypothesis .........................................................................................................25
Chapter 2 Methods .........................................................................................................................27
2 Experiments and Participants ....................................................................................................27
2.1 Experiment 1 – Using Acute Exercise to Examine Intracranial BOLD Pulsatility
Session Effects ...................................................................................................................28
2.1.1 Aerobic Exercise Session .......................................................................................28
2.1.2 Data Acquisition ....................................................................................................28
2.1.3 T1 Segmentation ....................................................................................................29
2.1.4 BOLD Preprocessing .............................................................................................30
2.1.5 Method of Resorting Temporal Volumes Based on Cardiac Cycle Position.........30
2.2 Experiment 2 – Simulation to Evaluate the Influence of Repetition Time ........................34
2.3 Experiment 3 – Empirical Study to Evaluate the Influence of Echo Time Effects ...........36
2.4 Statistical Analysis .............................................................................................................37
Chapter 3 Results ...........................................................................................................................38
3 cr-fMRI Pulsatility Maps ..........................................................................................................38
3.1 Effect of Session ................................................................................................................39
3.2 Effect of Repetition Time ..................................................................................................42
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3.3 Effect of Echo Time ...........................................................................................................44
Chapter 4 Discussion and Conclusions ..........................................................................................45
4 Discussion .................................................................................................................................45
4.1 BOLD Intracranial Pulsatility Session Effect ....................................................................45
4.2 Effect of Repetition Time on BOLD Intracranial Pulsatility .............................................47
4.3 Effect of Echo Time on BOLD Intracranial Pulsatility .....................................................48
4.4 Limitations .........................................................................................................................48
4.5 Conclusions ........................................................................................................................49
Chapter 5 Future Directions ...........................................................................................................50
5 Possible Extensions ...................................................................................................................50
5.1 Using ASL Instead of BOLD .............................................................................................50
5.1.1 Pilot ASL-pulsatility mapping data from 2 participants ........................................52
5.1.2 Data Acquisition ....................................................................................................52
5.1.3 Preprocessing and Analysis ...................................................................................53
5.1.4 Results and Discussion ..........................................................................................54
5.1.5 Future Work ...........................................................................................................56
5.2 Investigating cr-fMRI among adolescents with bipolar disorder ......................................57
5.2.1 Bipolar Disorder.....................................................................................................57
5.2.2 Cardiovascular Risk and Increased WMH Lesion Burden ....................................58
5.2.3 Participants .............................................................................................................59
5.2.4 Study Description and Analysis .............................................................................61
5.2.5 Results and Discussion ..........................................................................................61
5.3 Thesis conclusion and expansion to Large Population cr-fMRI Studies ...........................63
References ......................................................................................................................................64
vii
List of Abbreviations and Symbols
∉ - Not an Element of
γ - Gyromagnetic Ratio
ℤ - Integer
AFNI - Analysis of Functional Neuro-Images
ANOVA - Analysis of Variance
ANTs - Advanced Normalization Tools
ASL - Arterial Spin Labeling
BD - Bipolar Disorder
BOLD - Blood Oxygenation Level Dependent
BMI - Body Mass Index
bpm - Beats Per Minute
CASL - Continuous ASL
CBF - Cerebral Blood Flow
CBV - Cerebral Blood Volume
CMRO2 - Cerebral Metabolic Rate of Oxygen
CO2 - Carbon Dioxide
cr-fMRI – Cardiac Related fMRI
CSF - Cerebral Spinal Fluid
CVD - Cardiovascular Disease
viii
EPI - Echo-Planar Imaging
FAST - FMRIB's Automated Segmentation Tool
FEAT - fMRI Expert Analysis Tool
fMRI - Functional Magnetic Resonance Imaging
FMRIB - Functional Magnetic Resonance Imaging of the Brain
FSL - FMRIB Software Library
FWHM - Full Width at Half Maximum
HC - Healthy Control
HR - Heart Rate
IQ - Intelligence Quotient
MATLAB - Matrix Laboratories
MRA - Magnetic Resonance Angiography
MRE - Magnetic Resonance Elastography
MRI - Magnetic Resonance Imaging
NIRS - Near Infrared Spectroscopy
NO - Nitric Oxide
ox-LDL – Oxidized Low-Density Lipoproteins
p* - p-value adjusted for multiple comparisons using 5000 permutations
PASL - Pulsed ASL
pCASL - Pseudo-Continuous ASL
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PP - Pulse Pressure
PWV - Pulse Wave Velocity
R2 - Coefficient of Determination
RETROICOR - Retrospective Image Correction
RF - Radiofrequency
RS - Resting State
SD - Standard Deviation
SLSD - Short-Label, Short-Delay
SNR - Signal to Noise Ratio
SVD - Small Vessel Disease
T1 - Longitudinal Relaxation Time
T2 - Transverse Relaxation Time
T2* - Effective Transverse Relaxation Time
TCD - Transcranial Doppler
TE - Echo Time
TIA - Transient Ischemic Attack
TR - Repetition Time
WMHs - White Matter Hyperintensities
x
List of Tables
Table 1.1: Summary of current potential methods for measuring cerebrovascular pulsatility or
stiffness, their typical applications and advantages / limitations strictly related to measuring
cerebrovascular pulsatility or stiffness. Last row contains features and desired elements for an
ideal method (Asmar et al., 1995; Ben-Shlomo et al., 2014; Ferrari et al., 2004; Kruse et al.,
2008; Medline Plus, 2015; Naqvi et al., 2013; Schneider et al., 2005; Stankovic et al., 2014) ... 15
Table 3.1: Participant Characteristics for Session Effect of Acute Exercise Study, BMI – Body
Mass Index, PP – Pulse Pressure, HR – Heart Rate, SD - Standard Deviation ............................ 39
Table 5.1: Advantages and disadvantages of using BOLD fMRI for measuring cerebrovascular
pulsatility (Borogovac and Asllani, 2012; Davis et al., 1998) ...................................................... 50
Table 5.2: Average percentage of pulsatile voxels in cr-fMRI ASL scans, separated by tissue
type, scan and method. Method 1: Subtracting, then cardiac-sorting of volumes. Method 2:
Cardiac-sorting followed by subtraction of volumes .................................................................... 56
Table 5.3: Participant Characteristics for Session Effect of Acute Exercise Study in Bipolar and
Healthy Adolescents, HC – Healthy Control, BD – Bipolar Disorder, BMI – Body Mass Index,
HR – Heart Rate, PP – Pulse Pressure, SD - Standard Deviation ................................................. 60
Table 5.4: Average percent tissue BOLD pulsatility in participants with bipolar disorder and
healthy controls. ............................................................................................................................ 62
xi
List of Figures
Figure 1.1: Changes to vascular wall structure and blood pressure profile of the vascular tree
with vascular aging. Note in particular, the increased systolic pressure in arteries and higher
pulsatility in microvasculature (top diagram), vascular wall thickening and disorganization,
lumen narrowing, and formation of cholesterol plaques (bottom image) with increasing vascular
age; ox-LDL – oxidized low-density lipoprotein (Nussenzweig et al., 2015; O’Rourke and
Hashimoto, 2007) ............................................................................................................................ 3
Figure 2.1: Schematic diagram of the resorting method with A) BOLD temporal volumes
matched to the pulse oximeter and sorted based on cardiac cycle position. B) Time series data
cardiac cycle resorted and fit with a 7-term Fourier series ........................................................... 31
Figure 2.2: A) Map of R2 values after voxel-wise fitting of a Fourier series model to cardiac-
sorted time series data B) A distribution of null fits generated using 45000 random permutations
of the BOLD time series data. R2 values greater than 5 standard deviations (green line) from the
null distribution mean (red line) are considered to be pulsatile. Subsequent black lines indicate
10, 20 and 30 standard deviations from the distribution mean, respectively. C) R2
values are
converted to deviations from null using the distribution in B). Correction for multiple
comparisons of the pulsatility maps was not conducted at this stage but performed for subsequent
group analyses ............................................................................................................................... 33
Figure 2.3: A) Example cardiac signal from a participant with an average heart rate of 80 bpm,
sampled at 1500ms (red), a multiple of the 750ms period for 80 bpm and 100ms (green), which
is faster than the Nyquist frequency of the cardiac signal (2.67 Hz or a period of 375ms). Sample
points resorted according to their position in the cardiac cycle are displayed below along with the
outline of a pulse from the original signal (blue trace) from the B) 1500ms and C) 100ms
sampling rates. Both collect samples from across the cardiac cycle, however, the faster sampling
rate allows for a more even distribution of points, potentially improving signal reconstruction
and cardiac pulsatility detection .................................................................................................... 35
Figure 3.1: A) Average cr-fMRI map of participant scans showing the twenty-eight consecutive
axial slices from inferior to superior. Scale indicates the number of deviations the R2 value for
xii
the pulsatility model is from the null fit distribution B) Example time-of-flight MRA image
depicting the cerebral arteries for visual comparison (Schuster, 2007) ........................................ 38
Figure 3.2: Average percentage of pulsatile voxels in each tissue category with standard error
bars and resampled p-values * p*<0.05, ** p*<0.01, RS - Resting State .................................... 40
Figure 3.3: A) Randomise results for voxel-wise analysis between baseline and post-exercise cr-
fMRI maps, showing a decrease in pulsatility after exercise, thresholded using a corrected p-
value < 0.05. Figure shows slices 4-21 for A) resting state and B) task scans. Note that the drop
in pulsatility after exercise is transient, with only a small segment of the insular middle cerebral
artery (arrow) showing a significant decrease after exercise cessation in the task scans taken
seven minutes after resting state ................................................................................................... 41
Figure 3.4: Calculated deviations from null fit for each simulated signal, plotted according to
average heart rate, TR and data cleaning method (uncorrected, high-pass filter and
RETROICOR). Larger markers indicate averages for each scenario ........................................... 43
Figure 3.5: Average percentage of pulsatile voxels in each of the tissue categories with standard
error bars and p-values adjusted for multiple comparisons using Holm’s method, **p<0.01 ..... 44
Figure 5.1: cr-fMRI maps overlaid with the CBF map from the participants' SLSD ASL scans for
select slices.................................................................................................................................... 55
Figure 5.2: Randomise results for voxel-wise analysis of post-exercise scans comparing cr-fMRI
maps between groups, thresholded using a corrected p-value < 0.05 and showing slices 3-19. All
significant differences were due to increased pulsatility in the bipolar adolescent group compared
to healthy controls ......................................................................................................................... 62
1
Chapter 1 Introduction
1 Summary
With the advent of new medicine and treatments, and better understanding of general health,
healthcare concerns have shifted towards aging gracefully. With the increase in life expectancy
over the last few decades, there has been a likewise rise in age-associated diseases, namely
dementia and stroke (Rocca et al., 2011). These chronic diseases currently have no cures, thus
they contribute to declining quality of life and increased burden on patients, their families and
friends, and the healthcare system. An estimated 564 000 people in Canada were living with
dementia in 2016, incurring an overall cost of $10.4 billion for that year alone. If current trends
hold, these numbers will increase to 937 000 patients with an associated cost of $16.6 billion by
2031 (Chambers et al., 2016). Similarly, stroke survivors living with debilitating side effects of
their stroke number around 426 000, costing roughly $3.6 billion/year in terms of necessary care
and lost productivity (Ontario Stroke Network, 2016). The toll from these two diseases is great;
both in terms of health and societal cost, emphasizing the need to gain a better understanding of
the diseases so as to develop better treatment and prevention methods.
Although both of these diseases directly affect the brain, research has shown a close connection
between cardiovascular health and cerebrovascular risk. While multiple studies have explored
these connections, (Grodstein, 2007; Kim et al., 2015; Mungas et al., 2001; Rocca et al., 2011;
Viswanathan et al., 2009) direct access to the brain’s blood vessels is limited due to the skull
which protects the brain but also impedes direct measures. As a result, methods that attempt to
empirically measure physiological or molecular processes typically rely on indirect or inferred
measurements. This thesis will focus on a potential solution for this existing gap, explore various
factors that could affect measurements and cover, in greater depth, the underlying motivation for
this work.
2
1.1 Cardiac Pulsatility and Vascular Aging
1.1.1 Vascular vs. Chronological Aging
Chronological age is often assumed when discussing the general topic of aging. One’s age
dictates if they can vote, if they are eligible to work, whether they qualify for certain health
examinations and treatment options. Our concept of aging, specifically vascular aging, is more
nuanced. For instance, two sixty-year-olds could have vastly different lifestyles and general
health; one could still be in peak condition, while the other’s health is rapidly declining. As such,
a distinction is frequently made between chronological and pathological aging (Jani and
Rajkumar, 2006).
In particular, the relative health of a person’s blood vessels and cardiovascular system in
comparison to the general population is one such important factor. The circulatory system acts as
one of the body’s major highways: carrying blood from the heart to distant organs and delivering
oxygen, nutrients and hormones down to the cellular level. At the same time it shuttles away
waste products, such as carbon dioxide that is expelled by the lungs. The circulation of blood
also serves to regulate body temperature, pH balance and cellular ion/water content, and contains
antibodies and white blood cells to protect the body from infection (Tortora and Derrickson,
2012). With so many broad and important roles, the health and function of the circulatory system
has a vast effect on overall health and can impact the health of other organs.
Arterial stiffening is one of the most salient signs of cardiovascular disease. Should one live long
enough, arterial stiffening is inevitable, but develops at different rates and to differing extents
across the population. This process is an example of vascular aging and is brought on by vascular
alterations that occur gradually over time. Specifically, when we are young and healthy, our
artery walls are comprised of neatly organized elastin and smooth muscle cells, allowing them to
stretch and flex in response to cyclic changes in blood pressure from the heart. The presence of
chronic, harmful factors – e.g. hypertension, inflammation and certain constituents of the blood –
can cause arterial stiffening; the result of deleterious vascular remodelling and the build-up of
cholesterol on vessel walls. With exposure to these processes, over months and years, the arterial
walls stiffen and pressure waves from the heart travel at faster velocities. Stiffening and related
damage to the arterial walls increase the risk of multiple diseases, (Ben-Shlomo et al., 2014; Jani
3
and Rajkumar, 2006; O’Rourke and Hashimoto, 2007; Ungvari et al., 2010) as will be described
in the following section.
Figure 1.1: Changes to vascular wall structure and blood pressure profile of the vascular
tree with vascular aging. Note in particular, the increased systolic pressure in arteries and
higher pulsatility in microvasculature (top diagram), vascular wall thickening and
disorganization, lumen narrowing, and formation of cholesterol plaques (bottom image)
with increasing vascular age; ox-LDL – oxidized low-density lipoprotein (Nussenzweig et
al., 2015; O’Rourke and Hashimoto, 2007)
4
1.1.2 Causes and Consequences
One of the drivers of increased arterial stiffness is cholesterol deposition within the artery walls.
Normally, cholesterol is found circulating in the blood stream, where it can occasionally
infiltrate the wall of endothelial cells and becomes trapped (Tortora and Derrickson, 2012). Over
time this creates plaques along the vessel walls, hardening them and narrowing the lumen. Added
to this, microscopic damage to the structure of the arterial walls forms through the everyday
stresses of pumping blood through the vascular system. This is the main underlying cause of
arterial stiffening and is not preventable, although certain risk factors can be avoided to slow this
progression (O’Rourke and Hashimoto, 2007).
Like all materials, both natural and synthetic, blood vessel walls will exhibit “wear and tear”
under constant application of force. Vascular remodelling of arteries is thus a response to the
continuous blood pressure oscillations. While each pulse wave is not enough to cause noticeable
damage, considering that an average heart rate of 70 beats per minute (bpm) would mean each
artery experiences approximately 3.7x107 pulses each year, the damage begins to add up. Under
this stress, the sheets of elastin become disorganized and begin to wear down. Since elastin is a
material that our bodies cannot readily replace, these gaps are typically filled by collagen, a
much stiffer material (O’Rourke and Hashimoto, 2007).
The human cardiovascular system is remarkably adept. Heart rate and blood pressure are the two
most obvious sources that contribute to arterial changes, but genetics, fitness level, diet, diabetes,
tobacco and alcohol are also examples of additional positive / negative sources. Remaining
active, consuming a healthy diet that avoids foods loaded with salt, cholesterol and sugars, along
with abstaining from smoking and heavy drinking, are all possible methods to lower
cardiovascular risk (O’Rourke and Hashimoto, 2007).
Consequences of stiff arteries go beyond commonly associated diseases, such as atherosclerosis,
heart attacks and strokes. Healthy, flexible arteries absorb pressure waves from the heart,
dispersing them so that they are at much lower pressure and near steady state by the time they
reach the microvasculature and tissue cells. With stiffening, arteries begin to lose this capability,
so that pulse wave oscillations travel faster and deeper into the vascular system, piling up and
adding constructively, causing hypertension. Over time, this causes damage to the
microvasculature and surrounding tissue, resulting in leaking vessels or cessation of blood flow,
5
which in turn results in tissue necrosis (O’Rourke and Hashimoto, 2007). Organs with high blood
flow rates and vascular density, such as the brain, heart and kidneys are especially vulnerable.
Damage to cerebrovasculature can lead to a number of increased risks for diseases and
deficiencies including cognitive decline, dementia and stroke (Gorelick et al., 2011; Makedonov
et al., 2013; Mungas et al., 2001; Pase et al., 2016; Webb et al., 2012).
1.2 Stroke
Stroke is a common, growing, debilitating disease with 50000 annual strokes in Canada,
resulting in a cost of $3.6 billion/year in patient care and reduced productivity (Ontario Stroke
Network, 2016). Strokes can be ischemic or hemorrhagic. Ischemic strokes are the most
common, accounting for 83-87% (American Heart Association, 2017; The New Jersey
Comprehensive Stroke Center, 2013) of all cases and are caused when a major blood vessel in
the brain is blocked. These are created by obstructions within the lumen, typically due to the
formation of a blood clot around a ruptured cholesterol plague or from one of these clots
breaking off and sealing the artery further downstream as the artery narrows. Sometimes these
blockages are broken down by the force of the blood, freeing up the pathway again before
serious damage is caused, which might cause transient ischemic attacks (TIAs). TIAs are, by
definition, brief periods of neurological symptoms, e.g. a few minutes with symptoms similar to
those in early stage strokes – dizziness, sudden muscle weakness, blurred vision, blindness in
one or both eyes, difficulty speaking or understanding others. People who have experienced
TIAs, have an increased the risk of stroke, with up to one third of those diagnosed with having a
TIA, experiencing a full stroke in less than a year (American Heart Association, 2017; The New
Jersey Comprehensive Stroke Center, 2013). Hypertension and high cholesterol levels, especially
in cases that result in atherosclerosis, heighten the risk of blood clots forming and breaking off.
More cholesterol is obviously connected to increased plaque formation, while hypertension
means greater force on the artery walls, increasing the likelihood of plaque rupture and blood
clot formation, as well as increasing the risk that those clots will break off (Texas Heart Institute,
2016). Hypertension is considered to be the primary cause in 35-50% of all strokes (Ontario
Stroke Network, 2016), while other risk factors associated with stroke risk are also tied to the
risk of having hypertension. For example, people with diabetes have a 2-3 fold increased risk of
having a stroke, while smokers have twice the risk as non-smokers. Stroke risk is also increased
by stress, drug abuse and obesity (Ontario Stroke Network, 2016).
6
Conversely, hemorrhagic strokes result when a blood vessel bursts, resulting in blood leaking
into the surrounding tissue, building up pressure and blocking the surrounding region from
receiving fresh blood. Ruptures typically occur in weakened or abnormally formed vessels but,
again, increased blood pressure and cerebrovascular pulsatility can increase the risk of this
occurring (American Heart Association, 2017; The New Jersey Comprehensive Stroke Center,
2013).
1.3 Neurodegenerative Diseases
Neurodegeneration is a blanket term that describes structural or functional changes in the brain
and spinal column. While there is a large assortment of disorders that fall under this category, the
most common and well known form is dementia (Gupta et al., 2015; National Institute of
Neurological Disorders and Stroke, 2017; UCSF Memory and Aging Centre, 2017). Dementia
refers to a broad category of symptoms that arise from the degeneration of signal pathways in the
brain and can be caused by a plethora of underlying pathological processes. In recent years,
vascular contributions are increasingly recognized for their deleterious role in progressing
neurodegeneration towards a dementia, for instance Alzheimer’s disease with cerebral small
vessel disease or vascular dementia (Breteler, 2000; Gorelick et al., 2011; Meyer et al., 2000;
Viswanathan et al., 2009).
1.3.1 Cerebral Small Vessel Disease (SVD) and White Matter Hyperintensities (WMHs)
When a large vessel is blocked or damaged within the brain, the effects tend to be obvious and/or
immediate. Whereas for cerebral small vessel disease (SVD) that impacts the brain’s smaller
vessels, the consequences are typically more subtle, including cognitive slowing, loss of motor
coordination, balance & gait instability, mood issues, urinary incontinence, and other symptoms
(Carmichael et al., 2012; de Groot et al., 2001; Gunning-Dixon and Raz, 2000; Mungas et al.,
2001; O’Sullivan et al., 2004; Pantoni, 2010; Sakakibara et al., 1999; van Norden et al., 2011;
Whitman et al., 2001).
Cerebral SVD refers to the damage or blockage of small blood vessels within the brain, including
arterioles, venules and capillaries. Such damage can be created by many different causes – e.g.
arteriolosclerosis, strokes or TIAs, inflammation, haemorrhaging, physical trauma – and can be
7
imaged as subsequent damage of tissue fed by these vessels. The cerebral SVD lesions may
appear as hyperintense lesions in white matter on computed topography and T2-weighted
magnetic resonance imaging (MRI) scans (Brickman et al., 2009; Marstrand et al., 2002;
O’Sullivan et al., 2004; Pantoni, 2010; Scuteri et al., 2007; Webb et al., 2012). The current
definition for these lesions is white matter hyperintensities (WMHs), which can range in size and
density from a single foci to multiple large hyperintense regions within the white matter of a scan
(Fazekas et al., 1987)
Increased WMH burden is linked to cognitive decline (Carmichael et al., 2012; de Groot et al.,
2001; Gunning-Dixon and Raz, 2000; Mungas et al., 2001), reduction of processing speed
(Carmichael et al., 2012; de Groot et al., 2001), mobility issues (Baloh et al., 1995; Whitman et
al., 2001), impairment to executive function (O’Sullivan et al., 2004), and an increased risk of
stroke (Debette and Markus, 2010) and the development of full-blown dementia (Debette and
Markus, 2010; Kim et al., 2015; Prins and Scheltens, 2015).
1.3.2 Types and Prognosis of Dementia
Dementia is the most common clinical expression of neurodegeneration, with upwards of 564
000 people currently living with the disease in Canada and more than 46.8 million people
worldwide. This results in $10.4 billion/year being spent on care for these patients in Canada and
$818 billion globally in 2015. These numbers include direct care within hospitals, residential
homes and community centres, as well as the indirect costs for family and friends (Chambers et
al., 2016). There is currently no cure for most forms of dementia, though early diagnosis and
intervention can alleviate or delay symptoms.
Dementia refers to a family of cognitive syndromes, the most common of which is Alzheimer’s
disease. Symptoms of Alzheimer’s are the product of characteristic plaques and tangles of
amyloid beta forming within the brain, which at excessive levels begin killing off surrounding
brain tissue. This disease is eventually fatal, with plaques impairing cognitive function, causing
confusion and memory loss. Making decisions and completing even simple tasks becomes
difficult, and behaviour is often impacted, so that the person becomes withdrawn or constantly
restless, acting out of character. Physical abilities can also be impacted, so that the person
afflicted experiences a reduction in mobility and coordination, as well as possible impairment of
speech. Alzheimer’s is rarely inherited, with less than 5% of cases considered to be familial, or
8
genetically caused. All other cases appear spontaneously, in connection with other risk factors
such as hypertension, presence of other neuronal diseases, smoking, age, etc. (Alzheimer Society
Canada, 2016)
The second most common form is vascular dementia where cell death due to prolonged oxygen
deprivation result in the onset of dementia-related symptoms (Alzheimer’s Society – United
against dementia, 2014; Alzheimer Society Canada, 2015a). This can occur due to silent or overt
strokes as well as other vascular diseases. A doctor may therefore choose to prescribe medicines
and therapies to target the responsible vascular disease in order to alleviate symptoms and slow
progression. In some cases, patients are diagnosed with a mixture of Alzheimer's and vascular
dementia, referred to as mixed dementia (Alzheimer’s Society – United against dementia, 2014;
Alzheimer Society Canada, 2015a).
As a side note, not all dementias are directly associated with cardiovascular risk factors. Lewy
body dementia comes from the formation of abnormal alpha-synuclein deposits, a protein that is
abundant in the brain and is thought to play a role in regulating neurotransmitters. These
formations interfere with neural signalling and cause the onset of dementia-type symptoms.
Other forms of dementia include frontotemporal dementia, which is limited to the frontal and
temporal lobes of the brain, and Parkinson’s disease dementia, which forms after the
development of Parkinson’s disease (Alzheimer Society Canada, 2015a). Dementia-like
symptoms can also be induced by other external factors like infection, certain medications, drug
abuse, brain tumours, nutritional or metabolic issues, depression, brain trauma or environmental
toxins. Unlike other forms of dementia, these symptoms can be reversed if found early enough,
once the underlying condition is removed or treated, but are rarer than their irreversible
counterparts (Gupta et al., 2015).
1.3.3 Problems with Late Diagnosis
Diagnosing a dementia is a challenge, while effective treatment options remain elusive. These
issues are reinforced by the broad set of underlying dementia pathologies. Thus a clinical work-
up tends to involve multiple diagnostic tests. Some dementias have markers that are not easily
identifiable; while others, like the plaques associated with Alzheimer’s disease, can only be
confirmed in an autopsy. In the absence of histological evidence from an autopsy, patients can, at
most, only be diagnosed with probable Alzheimer’s disease. This is based on a history of
9
worsening symptoms over a period of months or years that encompass a combination of
cognitive deficits that are severe enough to affect daily living, such as memory loss, impairment
to language or visuospatial processing, or executive dysfunction. This diagnosis is only made in
the absence of other unrelated causes that could produce similar symptoms, e.g. stroke, HIV
infection, Huntington’s disease, etc. (McKhann et al., 2011)
Currently, the various types of dementia are diagnosed through a mixture of cognitive and health
assessments, including looking at family history. Many of these tests are designed more to
eliminate the possibility of other diseases rather than to confirm the presence of dementia. Brain
imaging can play a critical clinical role, i.e. to locate brain regions that have unusual anatomy or
metabolism and thus can be used with other supportive measures, such as analysis of
cerebrospinal fluid, to assist in the diagnosis of suspected dementia cases (Alzheimer Society
Canada, 2015b).
Treatments can be prescribed to reduce or stabilize symptoms but there is no cure for
Alzheimer’s or many other forms of dementia. Currently, the best hope lies in prevention and
these efforts can be better guided by understanding the causes and sources of change that occur
within the brain prior to disease onset. Lifestyle interventions so far, seem to be the most
effective way of mitigating risk and must be made before the tell-tale symptoms are resolved, to
have their full effect (Alzheimer Society Canada, 2015c).
With growing evidence of a link between decline in vascular health and cognition, monitoring
cerebrovascular health would be a valuable tool in predicting the risk of developing Alzheimer’s
and vascular-related dementias and acting to prevent their occurrence. Low arterial stiffness and
cardiac pulsatility are indicators of healthy blood vessels and would be useful measures for
diagnosing cerebrovascular health.
1.4 Current Methods of Measuring Stiffness and Pulsatility
Increased arterial stiffness is generally first detected through increased systolic and/or diastolic
pressure; often measured using an arm cuff sphygmomanometer in a doctor’s office or clinic. In
many cases, clinical diagnosis will not proceed beyond this measurement; however, currently
there are several other methods to measure stiffness or vascular pulsatility, each with their
advantages and disadvantages, as well as primary applications where they excel. This section
10
will review briefly each of the common modalities, with particular consideration to how they
would pertain to cerebrovascular measurements.
1.4.1 Pulse Wave Velocity (PWV)
Pulse wave velocity (PWV) refers to the speed at which the arterial pulse wave travels
throughout the cardiovascular system. Applanation tonometry is the current gold standard PWV
method. It consists of using a pressure probe to measure pressure waves in two major arteries
sufficiently far away from another – usually the carotid and femoral arteries – to calculate the
difference in peak arrival time at each of the places, along with the physical distance between the
points. The ratio of these two measures gives the PWV, that can be used to represent arterial
stiffness and thus pulse wave characteristics (Asmar et al., 1995; Ben-Shlomo et al., 2014). This
method is cheap, fast, and simple to conduct, unfortunately, it only gives us a single value that
may or may not be related to values within the brain. Although correlations are likely between
PWV and cerebrovascular stiffness, it provides no specifics and results may vary between two
people with similar global vascular stiffness, but who have other factors that play a role in their
cerebrovascular health.
1.4.2 Transcranial Doppler (TCD)
Another modality that is commonly used for detecting pulse waves is Doppler ultrasound and in
the brain this is referred to as transcranial Doppler or TCD. The advantages of TCD are that the
equipment is low cost compared to many other modalities, continuous measurements are
possible and the device itself is portable. Additionally, while measurements are frequently
collected when the patient is lying or sitting down, TCD transducers can be attached to a
headband, which would allow for measurement during exercise or some other activity (Naqvi et
al., 2013).
TCD relies on the transmission and reception of ultrasound waves, which are heavily attenuated
by bone. Since our target is the brain, this presents a measurement challenge when accessing
signals from major brain arteries. To minimize attenuation, four ultrasound ‘windows’ are
typically used; these include through the eye (transorbital), through the temple (transtemporal),
from the base of the skull where it meets the spine (suboccipital) and beneath the jaw
(submandibular). This limits our ability to form an image with TCD. Additionally, an estimated
11
10-20% of the population lack the acoustic window for viable TCD. Even excluding this
demographic, TCD measurements provide us only with hemodynamic information from the
largest arteries and not the smaller vessels. Finally, due to the general requirement of manual
placement of the probe, combined with differences in skull geometry from person to person,
TCD measurements can vary greatly between each operator and session (Naqvi et al., 2013).
1.4.3 Near Infrared Spectroscopy (NIRS)
Near infrared spectroscopy (NIRS) uses the reflection and absorption of an infrared light to
determine the change in blood flow. While NIRS systems are low cost and portable like TCD,
imaging depth is limited by photon penetration through the skull and brain tissue, so that
practically, only a maximum depth of 1.5 cm from the scalp surface is accessible. This means
pulsatility for a large portion of the brain is undetectable and unlike TCD, this modality does not
even focus on what should be the largest sources of cardiac pulsatility within the brain.
Additionally, a combination of signal from blood vessels within the scalp and brain are generally
collected with NIRS, which could contaminate the desired cerebrovascular signal (Ferrari et al.,
2004).
1.4.4 Pressure Sensors
The most direct, yet impractical, measure of pulsatility is via physical sensors that detect
pressure changes on the surface of the brain. Getting such a sensor within the brain requires
drilling a hole in the skull, an invasive procedure that would introduce a collection of risks such
as potential brain damage, infection and issues with bleeding. Following this, placement and
removal of the sensor would introduce additional opportunities to cause damage or infection.
While intracranial sensors are used clinically, they are limited to situations such as monitoring
intracranial pressure in a patient with severe head trauma, to ensure that it does not build up to
the point of causing damage to the cerebral tissue. In those cases, the risk of drilling a hole and
placing the probe is less than the risk of not detecting increased pressure. Additionally, the
chance of a patient being inconvenienced by being hooked up to a machine to monitor their
intracranial pressure is unlikely as the patient would likely be bedridden in hospital and probably
unconscious at the time when this is required (Medline Plus, 2015).
12
1.5 Magnetic Resonance Imaging (MRI)
MRI is another modality that can be used to measure various hemodynamic parameters, which
includes arterial stiffness or and related blood flow parameters. Several methods have been
developed to accentuate different kinds of hemodynamic contrast that are described in this
section.
When a material is placed in a stable magnetic field, the average spin of the atoms within the
object tend to align along the direction of this main magnetic field. Atoms with an odd number of
neutrons and protons have a nonzero magnetic spin, thus are capable of a nuclear magnetic
signal. If a radio frequency (RF) pulse is applied perpendicular to the main field, the spins will
be excited to a higher energy state. After the RF pulse is turned off, the spins continue to precess,
or undergo gyroscopic motion around the direction of the main magnetic field, in a manner that
is detectable in the transverse plane. Over time, the spins lose the coherence of this signal, due to
macromolecular phenomenon and will experience recovery to their original orientation along the
main magnetic field. Phenomenologically, there are several ways to characterize the general
behaviour of these spins, forming the basis of MRI contrast. The first of these is the decay of the
signal in the transverse plane (T2 or T2* weighting), while the second is the recovery along the
longitudinal direction (T1 weighting). MRI scans are comprised of a combination of these
different contrasts, with their contributions dependent on the imaging parameters chosen. Tissues
with a longer T2 or T2*, i.e. slower decay rate, appear brighter in T2 or T2
* weighted scans, while
the reverse is true for ones with a longer T1, or recovery time, creating high contrast between
different types of soft tissue (Bernstein et al., 2004; Nishimura, 1996).
Spatial separation into volume units known as voxels is achieved through the application of a
gradient field to the imaging volume, which adds with the existing main field. The gradient field
varies in strength - most commonly in a linear fashion - along a chosen direction so that the
overall field strength is spatially dependent. Precession frequency of spins is related to the
gradient field strength through this equation: , where Δω is the change in angular
precession frequency, γ is the gyromagnetic ratio (2.67 x 108 rad s
-1 T
-1 for protons), Gz is the
gradient field strength in the z-direction and Δz is the slice thickness. Therefore, thin slices can
be isolated through the use of slice selective frequency pulses, i.e. a RF pulse tuned for the
13
appropriate Δω. A train of pulses can then be applied to collect multiple slices to form the entire
volume (Rosen and Wald, 2006).
MRI scans are commonly used to image soft tissue and can be collected as either structural (2-D
or 3-D) or functional (4-D) images, the latter of which can be designed to detect brain activity or
blood flow. Different contrasts and applications can be obtained by changing the sequence of
pulses applied, often by altering the repetition time (TR) and echo time (TE). TR refers to the
time between each series of pulses, while TE is used to describe the time between the initial
excitation and the beginning of signal collection. These two values are generally recorded in
milliseconds, with the combination of shorter TR and TE values lending to higher T1-weighted
signal and longer TR and TE values leading to higher T2-weighted signal (Nishimura, 1996).
1.5.1 Magnetic Resonance Elastography (MRE)
Elastography can be conducted using either ultrasound or MRI; however, as mentioned in the
previous section, ultrasound is heavily attenuated by the skull, leaving MRI as the only feasible
option to image the brain. Magnetic resonance elastography (MRE) measures tissue stiffness by
introducing small amplitude forces – often accomplished using a vibrating pad underneath the
head – to oscillate the tissue of interest while imaging, after which the measured displacement of
the tissue and the applied force are used to calculate stiffness, i.e.
While this is a good method for imaging diseases such as normal pressure hydrocephalus, where
large scale changes in tissue properties can be expected (Fattahi et al., 2016), MRE is a measure
of tissue stiffness, not arterial stiffness. Additionally, these two factors cannot be simply
substituted for each other. Tissue stiffness has been shown not to vary with age (Kruse et al.,
2008), as would be expected to occur with vascular stiffness and has actually been seen to
decrease in cases of frontotemporal dementia, possibly due to the presence of plaques which can
lower the stiffness of the grey and white matter (Huston et al., 2016). As such, it is impractical to
try to track cerebrovascular stiffness using this method.
1.5.2 Flow MRI
MRI is ideally suited to measure fluid and blood flow in the body. As such MRI flow techniques
like time-of-flight and phase contrast angiography can also be used to try to track hemodynamic /
14
pulsatility features of blood vessels. Both time-of-flight and phase contrast are limited to
hemodynamic measures within larger vessels, much like TCD; however they form a complete
image with little dependence on the user.
Time-of-flight MRI applies a series of RF pulses to an imaging plane, thereby reducing signal
from static tissue (an approach known as signal saturation). While static MRI signals are low,
spins from upstream in the blood have not been saturated so that when they flow into the
imaging plane, it contributes to a signal intensity that is comparably higher. Thus the image
delineates vessels hence the term MR angiography (MRA). MRA scans can either be done as 2-
D or 3-D acquisitions with 3-D generally used for arterial flow imaging, while 2-D is more
appropriate for venous flow, due to its greater sensitivity for slower flows. While time-of-flight
is great for blood vessel visualization, actual quantification is hampered by several factors.
Typical time-of-flight MRA provides a snapshot of blood flow, allowing a measure of the
average velocity through the vessels but not a quantitative measure of flow. Even if normal
protocols are adapted to acquire multiple time points, image quality depends on width and
tortuosity of the blood vessels, so that narrower, more tortuous vessels - which are common with
aging and in certain diseases - may produce poor estimates (Han, 2012; Schneider et al., 2005).
In phase contrast MRA spins are exposed to a bipolar gradient that varies spatially along a
chosen direction causing these spins to develop a phase shift which depends on their location
within the scan. This bipolar gradient is comprised of two consecutive gradient lobes of equal
magnitude and length, but in opposite directions. Since these are applied consecutively, the net
change in phase for stationary spins is zero but spins moving along the direction of the bipolar
pulse will experience two different gradient strengths during the two lobes, resulting in a net
phase shift that is proportional to the velocity of the particle. Careful selection of the amplitude
and duration of the applied bipolar gradient must be made so that velocity resolution is small
enough to differentiate various speeds and large enough to ensure that higher velocities are not
aliased. Phase contrast can be done as 1-D, 2-D, 3-D or 4-D images, with 4-D scans required to
track changes over time for imaging pulsatility (Stankovic et al., 2014; Wåhlin et al., 2013). One
of the downsides of using this method is that the accuracy of flow measurement of flow could be
affected by motion artifacts, phase errors and insufficient velocity resolution due to bipolar
gradient design, an important consideration when trying to measure pulsatility through changes
15
in flow over time, instead of the average flow rate (Schneider et al., 2005; Stankovic et al.,
2014).
1.5.3 Current Missing Factors
All of the previously mentioned methods have their strengths and limitations (Table 1.1), but
there is one important factor that has not been addressed. With the exception of MRE, none of
these methods provide a convenient means of imaging pulsatility across the whole brain. Even
time-of-flight and phase contrast MRI, are limited to the major blood vessels.
Table 1.1: Summary of current potential methods for measuring cerebrovascular
pulsatility or stiffness, their typical applications and advantages / limitations strictly related
to measuring cerebrovascular pulsatility or stiffness. Last row contains features and
desired elements for an ideal method (Asmar et al., 1995; Ben-Shlomo et al., 2014; Ferrari et
al., 2004; Kruse et al., 2008; Medline Plus, 2015; Naqvi et al., 2013; Schneider et al., 2005;
Stankovic et al., 2014)
Modality
Value
Measured
/Output
Common
Application(s) Advantages
Disadvantages/
Limitations
Applanation
Tonometry
Pulse wave
velocity
Measure of
arterial stiffness
-Rapid collection
- Inexpensive
- Simple to
measure/perform
- Non-invasive
- Portable
- Single, global
measure
- Not specific to
the brain
TCD Pulsatility Index
Detecting
vasospasm in
sickle cell
anemia,
intracranial
bleeding, acute
ischemic stroke,
intra- and
extracranial
- Inexpensive
- Provides
continuous
measurements
- Can be conducted
during physical
activity
- Non-invasive
- Portable
- Operator
dependent
- Limited to major
vessels
- Ultrasound
attenuation limits
imaging to specific
windows which are
unavailable in 10-
16
stenosis,
intraoperative
monitoring,
brain stem
death
20% of the
population
NIRS
Tissue O2
saturation,
changes in
deoxy- and
oxyhaemoglobin
concentration
Functional
brain mapping,
detecting
intracranial
bleeding,
measuring
muscular blood
flow and
volume
- Inexpensive
- Provides
continuous
measurements
- Can be conducted
during physical
activity
- Non-invasive
- Portable
- Limited depth:
1.5 cm from scalp
- Signal
confounded by
scalp blood vessels
Pressure
Sensors
Intracranial
Pressure
Intracranial
pressure
monitoring after
traumatic injury
- Direct
measurement of
pressure
- Invasive
- Global brain
measure
MRE Tissue Stiffness
Investigation
and detection of
abnormal and
pathological
tissue –
fibrosis,
tumours, etc.
- Whole brain
coverage
- Non-invasive
- Resolution too
low to measuring
vascular stiffness
- Does not display
stiffness changes
with aging
PC MRA Blood Flow
Angiography,
scout image for
vascular
procedures,
blood flow and
velocity
quantification
and pattern
evaluation
- Non-invasive
- Whole brain
coverage
- High spatial
resolution
- Quantitative
- Limited to major
vessels
- Susceptible to
phase artifacts
17
TOF MRA Blood velocity Angiography
- Non-invasive
- Whole brain
coverage
- High spatial
resolution
- Limited to major
vessels
-Difficult to
measure slow flows
- Quality of images
depends on blood
vessel width,
thickness and
tortuosity
Ideal
Method
Image-based
pulsatility scale
across whole
brain
Cerebrovascular
pulsatility
- Sensitive to
changes in
cerebrovascular
pulsatility
- Non-invasive
- Whole brain
coverage
- Rapid
collection/full
temporal coverage
- Inexpensive
- Simple to
measure/perform
- High spatial
resolution
What is required to fill this gap is a method that can image the whole brain and index pulsatile
features throughout. This means it will need to not only provide signal collection across the
entire brain, but also be able to perform collection repeatedly and rapidly through time, to
capture changes present due to pulsatility.
1.6 Blood Oxygenation Level Dependent (BOLD) Functional MRI (fMRI)
One technique that has yet to be introduced is blood oxygenation level dependent (BOLD)
functional MRI (fMRI). BOLD contrast arises from T2* decay (Bernstein et al., 2004) and is
18
commonly used to measure neuronally mediated changes in blood oxygenation. Deoxy- and
oxyhaemoglobin are molecules in red blood cells; they influence the magnetic field differently
since the binding of oxygen to the heme complex that completes the conformational change of
deoxyhaemoglobin into oxyhaemoglobin, also changes the magnetism of the molecules from
paramagnetic to diamagnetic. As a paramagnetic molecule, deoxyhaemoglobin has a stronger
local effect on the magnetic field so that the measured spins decay faster, decreasing the signal in
the transverse plane in comparison to oxyhaemoglobin. Unlike many other cells, neurons do not
have a back-up of glucose and oxygen, requiring a near immediate response from the
cardiovascular system to maintain bioenergetic conditions. This way, when a neuronal
population has higher firing rates and related metabolic activity, oxygenated blood requirement
increases; thus neurovascular signalling leads to local vasodilation. When neurons are less
activate, oxygenation levels drop back down to normal levels through vasoconstriction and
decreased oxygen consumption. BOLD fMRI works by taking a rapid series of images while the
participant rests or conducts a predetermined task, followed by correlating measured increases in
blood oxygenation within a spatial unit with increases in brain activity (Brain Research Imaging
Centre Edinburgh, 2017).
1.6.1 Composition of the BOLD Signal
BOLD fMRI is an indirect measure of neuronal activation. The BOLD signal is affected by
multiple factors including changes in cerebral blood flow (CBF), cerebral blood volume (CBV)
and cerebral metabolic rate of oxygen (CMRO2). Change in the BOLD signal can be described in
terms of these factors using the equation:
, where
M is the proportionality constant reflecting the regional maximum potential BOLD response, and
β (1.5) is the proportionality constant between deoxyhaemoglobin and the BOLD signal (Davis
et al., 1998). CMRO2 and CBF can be estimated through several different imaging modalities
such as NIRS, calibrated fMRI and arterial spin labelling (ASL) fMRI, while CBF and CBV are
often assumed to be related through this ratio
, where α = 2.63 (Wu et al.,
2002). All of these factors are affected by the level of neuronal activity but also depend strongly
on other factors such as their location within the cardiac cycle. Non-neuronal sources of BOLD
contrast are well documented and will be described in further detail. Respiratory and cardiac
19
cycles are two such non-neuronal sources; they each contribute to BOLD signal variance and can
be used to generate additional information on brain physiology as demonstrated by the
pioneering work by Dagli and colleagues (Dagli et al., 1999).
The concentration of oxy- and deoxyhaemoglobin in a voxel fluctuates based on numerous
physiological processes, which may or may not depend on the underlying neuronal activity. Over
one heartbeat, for instance, the volume of blood in a voxel might vary. Peak blood volume would
correspond to the arrival of the induced wave from systole and the minimum, likewise, with
diastole. In 1999, Dagli and colleagues showed that when temporal volumes collected as part of
a BOLD scan were reordered based on when in the cardiac cycle an image had been collected, a
clear and periodic trend in the signal could be seen. Not only that, but this effect was also shown
to be spatially variant, with the largest signal variation occurring in voxels where we would
expect to see a large cardiac effect, such as around the major cerebral arteries (Dagli et al.,
1999).
1.6.2 Previous Work with BOLD Pulsatility
Even before Dagli’s work, there have been several suggestions to improve the detection of the
BOLD signal by addressing confounding physiological sources. The simplest of these methods
was the use of a digital filter, to remove frequencies within the cardiac and respiratory range,
prior to data analysis (Biswal et al., 1996). One year after Dagli's paper, Glover and colleagues
developed a new method called retrospective image correction (RETROICOR), an approach that
converts physiological traces collected during an fMRI scan into a Fourier series that is used to
regress out their influences on the fMRI time series. After this has been completed, analysis of
the BOLD signal can then be conducted on the corrected images (Glover et al., 2000). This
method has gained popularity because it improves the accuracy of detecting activation signals by
removing unwanted physiological noise, in a manner that is more precise than other techniques,
like bandpass filtering. On the other hand, the use of a filter has the advantage of being simple
since it does not rely on collecting physiological traces. A filter can also remove other nuisance
sources that are not accounted for in RETROICOR, such as fluctuations in arterial CO2
concentration, which in turn affects deoxyhaemoglobin concentrations. Where RETROICOR
wins out is in its ability to correctly handle aliased frequencies, which digital filters do not
address. Since most fMRI scans are collected at sampling rates that are slower than human heart
20
and respiratory rates, higher frequency signals, like that of cardiac and respiratory, are aliased
and appear as lower frequencies, which are then not removed. Conversely, while an ideal filter
would remove all of the frequencies outside the specified values and keeps all of the frequencies
within the desired range, in practice this is not the case. As such, there is typically a slight
decrease in the targeted frequencies, especially if those happen to be near the edge of the
bandpass range, potentially reducing the desired neuronal signal (Murphy et al., 2013).
Research on RETROICOR has been extended by several laboratories, either looking to remove
other noise frequencies such as low frequency oscillations in the data (Shmueli et al., 2007; Tong
et al., 2016), or to adjust the method so that the accuracy of removing the cardiac and respiratory
influences is improved (Beall and Lowe, 2007; Chang et al., 2009; Deckers et al., 2006;
Desjardins et al., 2001). And yet, all of these groups still treat the cardiac and respiratory signals
as signals of non-interest. A few exceptions exist including work done by Makedonov et al.
(2013) and Tong et al. (2014). Makedonov and colleagues sought to correlate WMH severity
with the percent contribution of cardiac frequencies to the BOLD signal (Makedonov et al.,
2013), while Tong et al. adapted the Dagli method to produce an angiography image from BOLD
data (Tong et al., 2014; Tong and Frederick, 2014). Both recognized that the cardiac effect is not
just a nuisance variable but could have important information about the patient's
cerebrovasculature, while introducing the possibility of using this information for more specific
analyses.
1.6.3 Using BOLD for Measuring Pulsatility
In many ways BOLD fMRI is an ideal modality for detecting cerebrovascular pulsatility. It is a
non-invasive method and by adopting a method similar to Dagli’s, voxel-wise assessment of
pulsatility can be obtained, providing a full, 3-D map within the brain. Another advantage is that,
in most cases, this information can be ascertained and evaluated, retrospectively. FMRI is a
mature research field, thus there are thousands of fMRI scans from human participants from the
past few decades and as long as cardiac pulse traces were collected during the scan, pulsatility
can be measured. Currently, several large databases exist of fMRI BOLD scans, such as the UK
Biobank and the Human Connectome Project, which together number in the thousands of
participants, with those numbers still growing (UK biobank, 2016; Van Essen et al., 2013).
Unlike other methods, which generally require a special acquisition to measure pulsatility, using
21
BOLD means this data can be extracted from scans that were initially collected for other
purposes. The use of pulse oximeters and respiratory bellows for collecting physiological signals
is already commonly undertaken during BOLD scans so those signals can be removed to
improve the quality of the neuronal activation signal. With this in mind, no extra work would
have to be done to make such scans available for pulsatility analysis using this method.
Before this can be applied across the board, however, there are several factors that should be
taken into consideration corresponding to experimental design.
1.6.3.1 BOLD Scan Parameters
As previously stated in section 1.5, MRI contrast and feature detection depends heavily on the
scan parameters, TR and TE. TR in fMRI refers to the time between the collection of each
subsequent temporal volume or, in other words, the sampling rate of the data. Standard TRs for
BOLD scans are within the range of 1-3 seconds (0.33-1 Hz), fast enough to correctly sample a
standard BOLD signal response to a stimuli, a task, or spontaneous resting state neuronal
fluctuations; the range of these neurovascular signals is approximately 0.01-0.1 Hz (Murphy et
al., 2013). On the other hand, this TR is too slow to critically sample certain physiological
cycles, like the cardiac, which ranges between 0.83-1.67 Hz in normal, healthy adults (Schriger,
2012). Nyquist’s theorem states that to accurately collect all of a signal’s features, a sampling
rate that is at least twice as fast as the highest frequency within the signal, should be used
(Wescott, 2016). Assuming a cardiac frequency upper limit of 1.67 Hz for adult humans (100
bpm) (Schriger, 2012), to satisfy Nyquist’s rule, sampling should then occur at, at least 3.33 Hz
or once every 0.3 s. Sampling at this speed across the whole brain requires the use of specialized
sequences, like multiband echo planar imaging such that multiple slices are collected
simultaneously, which is often not practical for multiple reasons. Multiband sequences are
currently not standard for most clinical scanners and would have to be implemented specially,
eliminating the previously stated benefit of being able to utilize this method in combination with
other BOLD analysis. Multiband data also suffers from higher image distortion, requiring extra
care during preprocessing and increased potential for inaccurate measurements of pulsatility
(Scheel et al., 2014; Van Essen et al., 2013).
Fortunately, there is an exception to the Nyquist rule, which works in our favour when dealing
with cardiac-related pulsatility. If a signal is periodic and the sampling rate is chosen such that
22
the same point(s) are not measured in every cycle (i.e.
∉ ℤ, where Tsample is the period of
the sampling rate, Tsignal is the period of the signal and ℤ is the integer set), all of the signal’s
features can be reconstructed, even with a sampling rate that is slower than the signal’s frequency
(Wescott, 2016). Sampling the cardiac cycle satisfies both criteria; changes in blood flow follow
a periodic cycle but the exact length of each cycle varies slightly for each heartbeat. This ensures
that if enough volumes are collected, sampling will be staggered throughout the cardiac cycle, no
matter the participant’s average heart rate or the TR that is used for the scanning protocol.
Another possible way of influencing cardiac pulsatility in the BOLD signal is by changing the
TE. Most single echo BOLD scans will have a TE of around 30ms at 3T, chosen to maximize the
T2* contrast of deoxy- and oxyhaemoglobin
(Bernstein et al., 2004). However, this is not the only
option; a growing number of studies use multi-echo scans, collecting each image volume at
successive TEs. This strategy of collecting more data per volume provides the opportunity for
‘data cleaning’, such as correcting for artifacts like patient motion and inhomogeneities in the
static magnetic field. With the additional TEs – usually no more than four – it is possible to use
the extra collected images to differentiate background noise from the neuronal signal. While the
BOLD signal is dependent on the TE of acquisition, most other components such as field
inhomogeneities are not, so that they would be consistent across the multiple echo times (Liu et
al., 2006; National Institute of Mental Health, 2017; Poser and Norris, 2009). The BOLD signal
can then be separated from these factors by modelling changes in signal intensity and their
dependence on the acquired TEs. But unlike non-physiological noise, physiological sources of
signal change, like the cardiac cycle, also depend on the TE of acquisition (Gorno-Tempini et al.,
2002; Krüger and Glover, 2001; Liu et al., 2006; National Institute of Mental Health, 2017;
Poser and Norris, 2009). As such, the ability to detect cardiac pulsatility using BOLD data would
probably vary with the chosen TE, either for a multi-echo protocol or a single-echo scan with a
TE other than 30 ms.
Varying either of these parameters could affect pulsatility detection results, an important
consideration for any future analysis that may wish to compare scans from different studies.
Additionally, as more studies are completed and the field of fMRI expands, there are a higher
number of scans collected with 'non-conventional' scan parameters, increasing the need to
explore possible variations in results that could be caused from these choices.
23
1.6.3.2 BOLD Signal Clean-up
As already discussed, the BOLD signal is comprised of multiple sources that depend not only on
the neuronal activation response but on cardiac and respiratory cycle position, as well as low
frequency oscillations from sources such as arterial CO2 concentration and changes in cardiac
and respiratory rates. As mentioned previously, there are two main ways of removing
physiological noise, retrospectively - through the use of a bandpass filter or via the use of
physiological trace derived regressors, the most common of which is RETROICOR (Murphy et
al., 2013). Since the target here is the cardiac signal, to clean the collected data, a slight deviation
on standard image processing practice can be made by switching to a high pass filter with a cut-
off slightly above the upper range of the measured respiratory signal, and by setting the
RETROICOR procedure to only remove the respiratory signal.
1.7 Aerobic Exercise as a Physiological Stressor
Whenever a new method or technique is developed or discovered, the standard practice is to
compare it to the current gold standard or to expected textbook results. Since neither of these
possibilities is readily available for whole brain cardiac pulsatility, another direction must be
taken to evaluate experimental viability. One possible choice is to induce a physiological change
that would influence pulsatility. This could be achieved by drugs, such as vasodilator or
vasoconstriction agents. Another sensible choice is aerobic exercise because of the profound
effects it has on the cardiovascular system. Aerobic exercise during physical activities tends to
stress the body: muscles require more energy and oxygen, while this requirement in turn
increases the demand for oxygen from the lungs, ejection fraction of the heart increases, and so
on. This added stress induces multiple reactions from the body to cope with the increased
demand, as outlined below.
1.7.1 Acute Effects of Aerobic Exercise
Heart rate and blood pressure increase during exercise or physical activity to satisfy increased
blood flow demands from the worked muscles. On average, moderate intensity exercise will
raise a person's heart rate to 50-70% of their maximum
and 70-90% during intense exercise (American Heart Association, 2015; Londeree and
Moeschberger, 1982) while systolic blood pressure can normally reach up to 180 mm Hg during
24
aerobic exercise (Plowman and Smith, 2008). After ceasing exercise, most people will not
experience an immediate return to resting blood pressure but instead experience a temporary
‘overshoot’ period of hypotension, before the resting blood pressure level is recovered (Boone et
al., 1992; Chen and Bonham, 2010; Hagberg et al., 1987; Kaufman et al., 1987; Kenney and
Seals, 1993). The degree and duration of post-exercise hypotension does not appear to depend on
intensity or duration of the exercise (MacDonald et al., 2000) but instead appears to depend on
resting blood pressure and arterial stiffness, with more prominent and long lasting drops seen in
hypertensive individuals (Chen and Bonham, 2010; Kenney and Seals, 1993). In particular, it is
the systolic pressure that is most affected by this phenomenon, with an average drop of 18-20
mm Hg in hypertensives and 8-10 mm Hg in normotensive individuals. On the other hand,
diastolic pressure typically only displays drops of 7-9 and 3-5 mm Hg in hypertensives and
normotensives, respectively, (Kenney and Seals, 1993) and has been noted not to vary from
resting values in several studies (Floras et al., 1989; MacDonald et al., 1999). This behaviour
reduces the difference in pressure between systole and diastole, or the pulse pressure, and is a
sign of lowered arterial stiffness. Reduction in systemic arterial stiffness has also been confirmed
separately during exercise recovery (Seo et al., 2013), with a greater reduction observed in the
arteries of the muscles that have been exercised (Sugawara et al., 2003).
Reducing blood pressure can translate to associated reduced arterial stiffness through systematic
vasodilation, resulting primarily from an increased production of nitric oxide (NO). NO is a
potent vasodilator and inhibitor of vasoconstriction, and is upregulated when the vascular
endothelium experiences increasing shear and oxidative stress. Both conditions occur with
exercise-induced increases in blood flow, leading to higher concentrations of NO in the blood for
even a short period after cessation, with reduced stiffness observed for 30 minutes after exercise
cessation in healthy adults (Kojda and Hambrecht, 2005; Sugawara et al., 2003). Reduced NO
production, due to sedentary lifestyles or declining health, increases risk of hypertension and
promotes an elevated response to exercise compared to active persons (Kojda and Hambrecht,
2005). This coincides with observations of more dramatic and prolonged presence of post-
exercise hypotension in hypertensive individuals versus normotensives (Chen and Bonham,
2010; Cléroux et al., 1992; Kenney and Seals, 1993; Pescatello et al., 1991).
While vascular endothelial NO upregulation is a potential mechanism for post-exercise
hypotension and observed decrease in arterial stiffness, this is not the only possible cause. Boone
25
et al. (1992) found that an injection of naloxone, a drug that blocks opioid receptors and is
typically used to treat opioid overdoses, seven minutes into the resting period reversed the effects
of post-exercise hypotension for a period of 15-27 minutes, while the same injection had no
effect on heart rate or pre-exercise resting blood pressure. The exact reason for this behaviour
was unknown though it was suggested that naloxone could disrupt the function of natural
opioids, which help regulate vascular tone following stress and inhibit the release of
norepinephrine, a stress hormone known to raise blood pressure. Whatever the reason, this effect
seems to be transient, as participants began to experience a second dip in systolic blood pressure
roughly 15 minutes after naloxone injection (Boone et al., 1992).
Other studies have shown that exercise training in patients with chronic heart failure increases
the abundance of messenger ribonucleic acids that encode for two antioxidative enzymes,
superoxide dismutase and glutathione peroxide. These enzymes convert oxidative molecules,
such as superoxide, into less harmful species and increase the effectiveness of NO, indirectly
(Ennezat et al., 2001). In animal models, superoxide dismutase activity was also found to
increase with physical activity, accompanied by a decrease in nicotinamide adenine dinucleotide
oxidase, the primary source of vascular superoxide (Fukai et al., 2000; Higashi and Yoshizumi,
2004; Rush et al., 2003). Increased oxidative stress is linked to increased arterial stiffness,
indicating another potential source of exercise-induced changes (Patel et al., 2011).
1.8 Aims and Hypothesis
In summary, the goal of this thesis is to establish a non-invasive technique of measuring cardiac
pulsatility throughout the whole brain through the following specific aims:
1) Modify the temporal resorting method of fMRI BOLD data first described by Dagli et al.
(1999), so that it can be used to estimate cerebrovascular pulsatility based on the degree to which
signal intensity changes are related to position in the cardiac cycle.
2) Test the capability of this method to detect cerebrovascular pulsatility changes in response to a
stressor, such as an acute aerobic exercise session in healthy adolescents.
26
3) Evaluate the generalizability of this technique, by assessing the effect of varying common
fMRI scan parameters, namely TR and TE, using computer simulations and pilot multi-echo data
in young healthy adults, respectively.
In relationship to these aims, the main hypotheses of this thesis are:
1) Cardiac cycle resorting of BOLD temporal volumes will be amenable to detecting changes in
cerebrovascular pulsatility corresponding to the expected reduction of arterial stiffness and
vascular tension acutely after aerobic exercise.
2) Simulation and multi-echo data can inform us of this method’s detectability of pulsation,
based upon variation of TR and TE.
27
Chapter 2 Methods
The main focus of this thesis was to develop a method of detecting cerebrovascular pulsatility
using BOLD fMRI. To test method sensitivity, this technique was used for four different sets of
BOLD scans – two collected at baseline and two acutely following a single aerobic exercise
session from healthy adolescent volunteers. Analysis was then conducted on the resulting
pulsatility maps to see if a session difference in pulsatility could be detected due to acute aerobic
exercise as a physiological stressor. Adolescents were chosen here to reduce potential confounds
to the data due to the effects of vascular aging, for this proof of concept experiment.
Two supporting analyses were conducted to complement the main objective. The first simulated
the influence of TR and retrospective data cleaning methods, on the ability to detect a cardiac
pulse trace in BOLD data. The second empirically investigated the influence of TE on the ability
to detect a cardiac pulse trace in BOLD data, using multi-echo scans.
2 Experiments and Participants
English-speaking participants between 13-20 years of age were recruited for the main study via
advertisements in the community. Although this research may be directed to a clinical
population, as in Chapter 5, Chapters 2-4 focus on healthy adolescents. Therefore, participants
were excluded if they had been previously diagnosed with or prescribed medication for a
cardiovascular, metabolic, auto-immune or inflammatory disease; had an anxiety disorder, or
alcohol or drug dependence within the past 3 months; had been diagnosed with or had a family
history of neurological impairment, mood disorders, psychotic disorders, or autism; had an IQ <
80; or had a contraindication to exercise (cardiovascular disease, motor impairment, bone or joint
problems etc.) or MRI testing (claustrophobia, metal implants, etc.). IQ was tested using the
Wechsler Abbreviated Scale of Intelligence (Wechsler, 1999). Seven participants with
incomplete scans, or inadequate cardiac or respiratory traces, were removed prior to final
analysis, leaving forty-five participants (24 female, age: 16 ± 2 years) for analysis. Participants
and their parents / guardians provided written informed consent. Adolescents received $110 in
compensation for their participation, while parents were given $50 to cover travel expenses.
28
Ten English-speaking participants without any major illnesses, psychiatric disorders, or
contraindications to either exercise or MRI testing were recruited (3 female, age: 25 ± 3 years)
and provided written informed consent for the experiment concerning influence of TE on
pulsatility detection. All studies were approved by the Sunnybrook Research Ethics Board.
2.1 Experiment 1 – Using Acute Exercise to Examine Intracranial BOLD Pulsatility Session Effects
2.1.1 Aerobic Exercise Session
After the baseline MRI, participants were taken to the Movement and Exercise lab on M6 at
Sunnybrook where they performed aerobic exercise on a semi-recumbent cycle ergometer (ISO
1000R, SciFit, Tulsa USA). Heart rate was recorded every minute. The exercise session
consisted of a 5-minute low intensity warm-up, a 20-minute steady state exertion at a target heart
rate of 70% age-predicted maximum (i.e. 220 bpm – age in years) (Londeree and Moeschberger,
1982), and a 2-minute cool down. Participants whose heart rates deviated by more than 5 bpm
from their target during the 20 minutes of exercise, were instructed to adjust their intensity to
stay within a consistent heart rate zone.
2.1.2 Data Acquisition
Four fMRI BOLD scans were acquired for each participant across two sessions: one resting state
and one task-based scan (described below) at baseline, which were then repeated 20 minutes
following cessation of exercise. Neuroimaging was performed using a 3 T system (Achieva,
Philips Healthcare, Best NL) with a body coil transmitter and an 8-channel head coil receiver.
Single-echo BOLD echo-planar image (EPI) acquisition parameters were similar for all scans
(TR = 1500 ms, TE = 30 ms, 28 slices, 4 mm thickness, 80 x 80 matrix, 3 x 3 mm resolution, flip
angle 70°) with the exception that 230 volumes were acquired for resting state and 240 volumes
were acquired for the task-based scans. The resting state scans were acquired before the task, by
design, in both the baseline and post-exercise sessions. T1-weighted images were collected post-
exercise using fast-field echo 3-D imaging (TR = 9.5 ms, TE = 2.3 ms, TI = 1400 ms, 256 x 164
x 140 matrix, 0.94 x 1.17 x 1.2 mm resolution). Cardiac and respiratory traces were digitized at
500 Hz during all scans using a pulse oximeter and respiratory bellows, respectively.
29
BOLD fMRI scans can be done either under resting state or under a task condition, where resting
state is meant to characterize the brain’s inherent functional connectivity in the absence of
external stimuli or performing tasks during the BOLD scan. Whereas the task BOLD fMRI in
this study consisted of repeated visual stimuli to guide motor responses experiment in blocks and
separated by rest periods. Both the resting state and task BOLD data are expected to produce
neurovascular activation patterns; however, the hypothesis is that the underlying pulsatility is
relatively independent of the BOLD activation, so measurement of this value should not be
affected by the chosen imaging paradigm. Since both scan paradigms are commonly utilized by
current fMRI studies, both resting state and task BOLD were explored in this thesis.
The task for this experiment was designed to be a low level of difficulty to allow for a series of
successive trials without mental fatigue. During both task BOLD scans (i.e. one before and one
after exercise), participants viewed a screen using a mirror inside the head coil. They were
instructed to pay attention to the visual cues on the screen and press a response button using their
index finger on the right hand whenever a number between 1 and 9 appeared on the screen,
unless it was the number 3, in which case they were instructed to refrain from acting. Numbers
were displayed for 250 ms, followed by 900 ms of fixation, in a pseudorandom order, with the
number 3 appearing in 15% of all trials. This task was delivered in six 34.5s duration blocks of
30 trials, with a 19.5 s rest period on either side of each block, for a total of seven rest periods
(E-prime v.1.2.1.94, Psychology Software Tools, Pittsburgh, PA, USA). The task fMRI data
were analyzed by a postdoctoral fellow in the lab as part of a separate brain activation study
(Metcalfe et al., 2016).
2.1.3 T1 Segmentation
T1-weighted images were segmented into three tissue classes: cerebral spinal fluid (CSF), grey
matter and white matter tissue types using the Oxford Centre for Function MRI of the Brain
(FMRIB)'s Automated Segmentation Tool (FAST) from the FMRIB Software Library (FSL;
version 4.1; URL: https://fsl.fmrib.ox.ac.uk/fsl) (Jenkinson et al., 2012). Since cardiac-related
pulsatility within the CSF was observed to be virtually non-existent outside of the ventricles,
CSF was further classified as ventricular CSF of interest or sulcal CSF of non-interest, using an
in-house MATLAB script. The segmented masks were then transformed to align to the BOLD
data for subsequent analysis by tissue-type.
30
2.1.4 BOLD Preprocessing
Analysis of Functional Neuro-Images (AFNI)’s RETROICOR freeware (URL:
https://afni.nimh.nih.gov) was used to remove respiratory sources in the BOLD data using a
program called 3dretroicor (Cox, 1996). Next, brain from head extraction was performed to
focus the analysis on brain voxels. Other established fMRI image processing steps included: slice
timing correction, spatial smoothing of 5 mm full width at half maximum (FWHM), and motion
correction using FSL fMRI Expert Analysis Tool (FEAT). BOLD images were registered to a
T1-weighted template image that was created from all participants using the Advanced
Normalization Tools (ANTs) software package (Avants et al., 2011). The choice of this hybrid
software was based on the MacIntosh lab’s image processing practises, given the recognized
strength of each library / tool.
The digitized cardiac trace was synchronized to the onset of BOLD and analyzed offline. The
first step involved identifying and removing BOLD volumes that occurred during a spurious
cardiac pulse interval recording. Spurious cardiac traces were defined as being greater than 0.3
seconds above/below the mean heart rate, which were viewed as measurement errors and non-
physiological pulse traces; these spurious cardiac traces occurred during a minority of BOLD
volumes. The cut-off for minimum number of usable volumes was to be set to 138, i.e. 60% of
the total volumes per scan, chosen to maximize the number of volumes and the number of
participants that could be used for analysis. Participants were excluded from analysis if any
individual scan had an insufficient number of viable volumes. To ensure equality in the number
of data points that were analyzed for each participant, 139 volumes (lowest number of usable
volumes in any of the scans after exclusions) were randomly sampled to perform the pulsatility
model fitting.
2.1.5 Method of Resorting Temporal Volumes Based on Cardiac Cycle Position
BOLD volumes were sorted according to their position in the cardiac cycle (Figure 2.1A).
Subsequent image processing analysis was performed on a voxel-wise basis. To establish
evidence of a cardiac pulse trace in a voxel, a 7-term Fourier series was fit to the cardiac-sorted
BOLD time series (Figure 2.1B). This model was chosen, as a Fourier series would correctly
emulate the cyclic nature of the cardiovascular signal response, while seven terms provided the
31
model with the best separator between noise and true cardiac cycle influence. Fewer than this
and the model would not be sufficient to correctly fit slightly more complicated relations (e.g.
multiple minor peaks along with the main pulse, sharper increases/decreases in signal intensity),
while more terms increased the risk of overfitting the data and including fluctuations that were
unrelated to cardiac cycle, into the model.
Goodness of fit was defined by the coefficient of determination (R2), a statistical metric that
reflects the proportion of a signal’s variation that can be predicted using an independent variable,
in other words, a measure of the accuracy of the model used compared to the available empirical
data. R2 values range from 0 (model does not predict any of the data variance) to 1 (model
predicts data with perfect accuracy).
Figure 2.1: Schematic diagram of the resorting method with A) BOLD temporal volumes
matched to the pulse oximeter and sorted based on cardiac cycle position. B) Time series
data cardiac cycle resorted and fit with a 7-term Fourier series
32
Given that the Fourier fitting of the pulse trace was performed on a voxel-wise basis and the
measured R2 values within the scans followed a right-skewed distribution, the choice was made
to evaluate this procedure (map) using non-parametric statistics. Specifically, in addition to
fitting the cardiac sorted BOLD time series, the goodness of fit was evaluated from a null
distribution, derived from 45000 random permutations of the sorted data based on the
hypothesis-testing method described by Lim and colleagues (Lim et al., 2014). The number of
null conditions was chosen to roughly match the number of voxels with signal in a single
volume. Unlike when working with exact values like amplitudes, the goodness of fit of a low
order Fourier series model using more than 100 data points is not dependent on the absolute
signal intensities of any of the data points, but rather on the strength of the underlying cardiac
cycle trend in the data. Therefore, when generating a permutation-based distribution of null fits,
data from a single distribution calculated from one voxel can be used for the whole brain to
improve computational efficiency.
The number of standard deviations between the actual R2 (Figure 2.2A) and the mean null R
2
(Figure 2.2B) was then calculated to determine the degree of influence from the cardiac pulse
wave, i.e. cardiac related fMRI (cr-fMRI) (Figure 2.2C). The percentage of significant or
pulsatile voxels was calculated as the percentage of the total voxels with a R2
value greater than 5
deviations from null (non-parametric p < 0.001).
33
Figure 2.2: A) Map of R2 values after voxel-wise fitting of a Fourier series model to
cardiac-sorted time series data B) A distribution of null fits generated using 45000 random
permutations of the BOLD time series data. R2 values greater than 5 standard deviations
(green line) from the null distribution mean (red line) are considered to be pulsatile.
Subsequent black lines indicate 10, 20 and 30 standard deviations from the distribution
mean, respectively. C) R2
values are converted to deviations from null using the
distribution in B). Correction for multiple comparisons of the pulsatility maps was not
conducted at this stage but performed for subsequent group analyses
34
2.2 Experiment 2 – Simulation to Evaluate the Influence of Repetition Time
As mentioned in section 1.6.3.1, reconstruction of a signal using a lower frequency sampling rate
is usually prohibited due to Nyquist’s Rule (i.e. sampling rate must be greater than twice the
signal’s highest frequency for accurate reconstruction), with an exception for periodic signals
where the sampling rate has been chosen so as to collect multiple data points at different
positions in the cycle. In this case, this occurs regardless of the chosen TR and the participant’s
average heart rate due to the variability in pulse length for each heartbeat (e.g. a participant with
an average heart rate of 80 bpm, could easily experience a range of pulse interval lengths
between 0.67 and 0.85s in length, instead of just the expected 0.75s). With a long enough
collection time, this allows for a dense spread of sample points through the cardiac cycle, making
proper reconstruction of the signal possible, even if the average cardiac frequency is an integer
multiple of the sampling rate (Figure 2.3A-B).
While this means that reconstruction across the cardiac signal is possible, in general, the use of
higher sampling rates allows for a more evenly spread distribution of points collected from
across the signal’s period (Figure 2.3C) and increases the accuracy of a signal’s reconstruction.
This raises the question of whether or not the choice of TR would affect the detection of
cerebrovascular pulsatility using this method.
Another question is whether detection could also be affected by which data cleaning method was
used to remove unwanted influences from the signal prior to analysis. Previous activation studies
have commonly used either digital filters or a regression model based on collected physiological
traces, with advantages and disadvantages to both methods in terms of their ability to reduce
unwanted physiological sources and preserve the desired signal (Murphy et al., 2013); there is no
reason to believe that this might not similarly hold true with pulsatility detection.
35
Figure 2.3: A) Example cardiac signal from a participant with an average heart rate of 80
bpm, sampled at 1500ms (red), a multiple of the 750ms period for 80 bpm and 100ms
(green), which is faster than the Nyquist frequency of the cardiac signal (2.67 Hz or a
period of 375ms). Sample points resorted according to their position in the cardiac cycle
are displayed below along with the outline of a pulse from the original signal (blue trace)
from the B) 1500ms and C) 100ms sampling rates. Both collect samples from across the
cardiac cycle, however, the faster sampling rate allows for a more even distribution of
points, potentially improving signal reconstruction and cardiac pulsatility detection
36
To answer these questions, simulated BOLD time series data was generated using R (version
3.2.0), composed primarily of cardiac (target physiological signal) and respiratory (non-target
physiological noise) sources. The cardiac signal was modelled after a normal physiological trace
collected using the pulse oximeter, by the function
, where f is the
frequency of the heart rate in Hz and t is time in seconds. Cardiac frequencies of 50, 60, 70, 80,
90 and 100 bpm were tested, with a ± 5 bpm variance for each. The respiratory signal was
represented by a series of repeated normal distributions, to simulate respiratory bellow
recordings at a frequency of 16 ± 5 breaths per minute (Schriger, 2012). Periodic trends,
representing fluctuations in the cardiac and respiratory cycles, were modelled as low frequency
sine waves (range: 0.01-0.1 Hz). Non-periodic white noise, representing patient motion and
thermal noise, was also added (amplitude: ~10% of signal). One hundred randomly-generated
data sets were created for each cardiac frequency.
Nine TR values were selected from between 33 ms (Bianciardi et al., 2016) and 3000 ms (Dagli
et al., 1999). Each value was chosen from previous fMRI experiments (Bianciardi et al., 2016;
Dagli et al., 1999; Metcalfe et al., 2016; Rajna et al., 2015; Van Essen et al., 2013). One hundred
thirty-nine points were then collected from the generated signals with spacing equal to that of
each of the chosen TRs, equivalent to the number of volumes in Experiment #1.
Two data cleaning methods were evaluated for their ability to remove the respiratory signal: 1)
RETROICOR in AFNI (Cox, 1996; Glover et al., 2000) and 2) a first-order Butterworth high-
pass filter using a frequency cut-off of 0.27 Hz, implemented in R (version 3.2.0). Cr-fMRI
pulsatility was calculated for each cardiac frequency, TR and data cleaning method using the
resorting method described in Experiment #1.
2.3 Experiment 3 – Empirical Study to Evaluate the Influence of Echo Time Effects
The effect that echo time has on intracranial BOLD pulsatility was also considered. For this
experiment multi-echo fMRI BOLD volumes (TR = 2300 ms, TE1 = 13.8 ms, TE2 = 35.4 ms and
TE3 = 57.0 ms, 28 slices, 4 mm thickness, 80 x 80 matrix, 3 x 3 mm resolution, flip angle 70°,
195 volumes) and T1-weighted images (TR = 9.5 ms, TE = 2.3 ms, TI = 1400 ms, 256 x 164 x
140 matrix, 0.94 x 1.17 x 1.2 mm) were acquired using the same 3 T MRI system as Experiment
37
#1. Cardiac and respiratory traces were digitized at 500 Hz during all scans using a pulse
oximeter and respiratory bellows, respectively.
All preprocessing and cr-fMRI calculation steps were the same as described for Experiment #1,
save that the threshold for acceptable data was reduced to 129 useable volumes, instead of 139,
to reflect the fewer acquired volumes.
2.4 Statistical Analysis
Non-parametric statistical tests or multiple comparison corrections of p-values were used, as
appropriate. The choice of non-parametric statistics was based on a desire to make the fewest
assumptions about the BOLD data. The choice to correct for multiple comparisons is based on
one of the main challenges of voxel-wise analyses, which is to avoid false positive findings.
In Experiment #1, mean percentage of pulsatile voxels was compared across session (i.e.
baseline vs post-exercise) and fMRI condition (i.e. resting state vs task BOLD) using a two-way
repeated-measures ANOVA, with sex, age and heart rate as covariates. Separate comparisons
were performed for each tissue type. Significant session by scan condition interactions were
followed up with paired t-tests, with p-values adjusted for multiple comparisons using
resampling of 5000 permutations, which shall henceforth be specified as p*. In Experiment #3,
mean percentage of pulsatile voxels were compared between the 3 TEs using the Friedman test,
separately for each tissue type. Following a significant Friedman test, Wilcox signed rank test
was performed between each pair of echoes and adjusted for multiple comparisons using Holm's
method (Holm, 1979).
Voxel-wise analysis was conducted using FSL Randomise to compare differences in cr-fMRI
maps between baseline and post-exercise sessions (Experiment #1), including sex and age as
covariates and correcting for family-wise error with 5000 permutations. Cr-fMRI maps were
spatially blurred by 5 mm FWHM to account for interparticipant differences in feature location
prior to voxel-wise analysis.
38
Chapter 3
Results
3 cr-fMRI Pulsatility Maps
On average, cr-fMRI maps identified the major cerebral arteries, ventricles and the superior
sagittal sinus as the highest sources of cardiac pulsatility. Visually, intersubject spatial agreement
of the pulsatility maps was high; all major features were in close alignment, with small variations
likely due to normative interparticipant variability in exact cerebrovasculature location (Figure
3.1A). From visual inspection, the major sources of cerebrovascular pulsatility also seem to
agree with the vascular anatomy (Figure 3.1B), with major landmarks such as the Circle of
Willis, middle and anterior cerebral artery visible.
Figure 3.1: A) Average cr-fMRI map of participant scans showing the twenty-eight
consecutive axial slices from inferior to superior. Scale indicates the number of deviations
the R2 value for the pulsatility model is from the null fit distribution B) Example time-of-
flight MRA image depicting the cerebral arteries for visual comparison (Schuster, 2007)
39
3.1 Effect of Session
Table 3.1: Participant Characteristics for Session Effect of Acute Exercise Study, BMI –
Body Mass Index, PP – Pulse Pressure, HR – Heart Rate, SD - Standard Deviation
Characteristic Mean ± SD
N 45
Sex (Female/Male) 24/21
Age (Years) 16.3 ± 1.8
Adjusted BMI (kg/m2) 20.3 ± 2.6
Resting PP (mm Hg)* 39 ± 9
Exercise PP (mm Hg)* 50 ± 15
HR – pre-exercise scans (bpm) † 70 ± 9
HR – post-exercise scans (bpm) † 72 ± 8
HR – during exercise (bpm) † 143 ± 10
Work Rate (W) 65 ± 23
Time between exercise cessation and
resting state scan (min) 19.8 ± 1.1
Time between resting state and task scans
(min) 6.8 ± 0.9
Final perceived exertion ‡ 12.4 ± 2.0
* Blood pressure calculated using 39 participants, data missing/incorrectly recorded for 6 participants
† Heart rate calculated using only 44 participants, data missing for 1 participant
‡ Self-reported using Borg scale: 6 (No Exertion) – 20 (Maximal Exertion)
Results for the two-way repeated-measures ANOVA comparing percentage of pulsatile voxels
showed a significant session effect (ventricular CSF: F(1,25) =9.53, p=0.0049; grey matter: F(1,25)
=14.0, p=0.00096; white matter: F(1,25) =8.92, p=0.0062) and session by scan condition
interaction (ventricular CSF: F(1,25) = 9.44, p = 0.0051; grey matter: F(1,25) = 5.65, p = 0.025;
white matter: F(1,25) = 5.58, p = 0.026). Sex, age and heart rate did not have a significant effect on
the percentage of pulsatile voxels in ventricular CSF (F(1,25)<0.77, p>0.39), grey (F(1,25)<1.13,
p>0.30) or white matter (F(1,25)<3.75, p>0.064). There was no difference found between the two
baseline scans (ventricular CSF: p* = 0.10; grey matter: p* = 0.97; white matter: p* = 0.67);
however, after exercise the resting state scans had a lower percentage of pulsatile voxels than the
task scans in grey (p* = 0.027) and white matter (p* = 0.014) but not in CSF (p* = 0.17), after
40
adjustment. This was a consequence of a decrease in the percentage of pulsatile voxels for all
tissue types in the resting state scans compared to baseline (p* < 0.0025), with no significant
change in the task scans (p* > 0.12) (Figure 3.2).
Figure 3.2: Average percentage of pulsatile voxels in each tissue category with standard
error bars and resampled p-values * p*<0.05, ** p*<0.01, RS - Resting State
Voxel-wise session comparison of the resting state data revealed a significant decrease in
pulsatility, in terms of deviations from null, in the major blood vessels and ventricles after
exercise (Figure 3.3A). Whereas a significant post-exercise decrease in pulsatility was only
observed in the left insula, in proximity to the middle cerebral artery territory, for the task BOLD
datasets (Figure 3.3B).
41
Figure 3.3: A) Randomise results for voxel-wise analysis between baseline and post-exercise
cr-fMRI maps, showing a decrease in pulsatility after exercise, thresholded using a
corrected p-value < 0.05. Figure shows slices 4-21 for A) resting state and B) task scans.
Note that the drop in pulsatility after exercise is transient, with only a small segment of the
insular middle cerebral artery (arrow) showing a significant decrease after exercise
cessation in the task scans taken seven minutes after resting state
42
3.2 Effect of Repetition Time
Simulations showed that the ability to detect cr-fMRI pulsatility decreased with increasing TR,
for sampling rates higher than the Nyquist frequency of the cardiac signal (i.e. TR < 300–600
ms) (Figure 3.4). For TRs > 500 ms, the goodness of fit no longer depends on the sampling rate,
averaging around 9 deviations from null fitting for the uncorrected raw signal and 13 deviations
from null fitting when RETROICOR is used. The high pass filter performed the best for TRs <
500 ms, with the average pulsatility measure reaching around 30 deviations from null at 33 and
100 ms, 36% higher than the RETROICOR results at the same TRs. Unfortunately this method
became problematic at slower sampling rates, due to large, inconsistent effects at certain TRs,
depending on the average heart rate (i.e. ℤ
where the affected TR is in seconds, HR is
the average heart rate in Hz and ℤ is an integer).
43
Figure 3.4: Calculated deviations from null fit for each simulated signal, plotted according to average heart rate, TR and data
cleaning method (uncorrected, high-pass filter and RETROICOR). Larger markers indicate averages for each scenario
44
3.3 Effect of Echo Time
Friedman tests found a significant echo time dependence on the percentage of pulsatile voxels in
grey (χ2 = 16.8, p < 0.001) and white matter (χ2
= 15.2, p < 0.001) but not in ventricular CSF (χ2
= 4.1, p = 0.13). Post hoc analysis with the Wilcox signed-rank test found that the percentage of
pulsatile voxels was significantly higher in the shortest echo time compared to the two longer
echo times for both grey (p = 0.0059) and white matter (p = 0.0059) (Figure 3.5).
Figure 3.5: Average percentage of pulsatile voxels in each of the tissue categories with
standard error bars and p-values adjusted for multiple comparisons using Holm’s method,
**p<0.01
45
Chapter 4
Discussion and Conclusions
4 Discussion
Results presented in Chapter 3 suggest that cardiac pulsatility is detectable in BOLD data using
cardiac cycle resorting of volumes and non-parametric model evaluation. Highest likelihood of
cardiac-driven pulsatility occurred near major cerebral arteries, lateral ventricles and the superior
sagittal sinus. The amplitude of this effect was reduced acutely after aerobic exercise. The
current findings help to establish that cardiac-related pulsatility in fMRI reflects physiologically-
relevant haemodynamic alterations within the cerebral vasculature. To improve characterization
of these cr-fMRI pulsatility effects, pulsatility detection was found to be most achievable with
shorter TR and TE parameters, from simulated and empirical data, respectively.
4.1 BOLD Intracranial Pulsatility Session Effect
In support of the primary hypothesis for this experiment, cr-fMRI pulsatility effects decreased
after exercise, as observed in both tissue-based and voxel-wise analyses. While the percentage of
pulsatile voxels was lower in both of the post-exercise scans compared to baseline, the difference
was only significant for the first post-exercise scan, which occurred roughly 20 minutes after
cessation of aerobic exercise. No difference in pulsatility was detected between these two scans
at baseline, both in terms of average percentage of pulsatile voxels for each tissue type and in the
subsequent voxel-wise analysis. Conversely, the post-exercise task scan showed markedly higher
pulsatility in grey and white matter than the corresponding resting state scan, with 16% and 20%
more pulsatile voxels, respectively. These data suggests a temporal evolution and recovery of
intracranial pulsatility in grey and white matter tissues, which was not significant for ventricular
CSF.
Some context is provided in interpreting these findings. First, the baseline comparison showed
no difference in pulsatility between successive resting state and task BOLD scans, which were 7
minutes apart. Whereas after exercise cessation, the same 7-minute time period may have
facilitated a more pronounced recovery from exercise, resulting in a significant change in
pulsatility results from the first to the second post-exercise scan. Blood pressure and flow within
the body, is regulated through the use of vasodilators and vasoconstrictors to control vascular
46
tone. Increased pulsatile pressure from the heart and demand for higher blood flow during
exercise results in the upregulation of these vasodilators in response (Hagberg et al., 1987;
Higashi and Yoshizumi, 2004; Kenney and Seals, 1993; Kojda and Hambrecht, 2005; Sugawara
et al., 2003), temporarily reducing the stiffness of the arteries (Seo et al., 2013; Sugawara et al.,
2003). In hypertensive individuals, this effect is particularly dramatic since the baseline
production of vasodilators like NO has been reduced due to disease, aging, sedentary lifestyle,
substance abuse or a combination of those factors (Kojda and Hambrecht, 2005). Acute effects
like post-exercise hypotension and reduced arterial stiffness are increasingly noticeable in these
individuals and can last for hours after exercise cessation (Hagberg et al., 1987; Hara and Floras,
1995; Pescatello et al., 1991; Wilcox et al., 1982), while in normotensive individuals post-
exercise hypotension is typically less pronounced (Chen and Bonham, 2010; Kenney and Seals,
1993) and begins to normalize after as little as 30 minutes (Cléroux et al., 1992; Pescatello et al.,
1991), with the largest decrease occurring between 15 and 30 minutes post-exercise (MacDonald
et al., 2000, 1999; Wilcox et al., 1982). The notable increase from the first to the second post-
exercise scans appears to be in keeping with the previously observed rapid recovery in
normotensives.
The largest cr-fMRI pulsatility effect occurred in proximity to arteries and ventricles at baseline,
which is in keeping with the literature (Dagli et al., 1999; Tong et al., 2014; Tong and Frederick,
2014). The voxel-wise session-analysis showed decreased cr-fMRI pulsatility at the level of
Circle of Willis, the lateral ventricles, the sagittal sinus and notably, a portion of the superior
parietal white matter, along a watershed territory between the anterior and posterior
cerebrovascular circulation (Howard and Singh, 2017). The discovery of this marked difference
in white matter was surprising as white matter pulsatility in this region was often sub-threshold,
as defined by a conservative cut-off of 5 deviations from null for pulsatile voxels. This
demonstrates that even though the effect of the cardiac cycle was low or modest in these areas to
be directly detected at baseline, changes in pulsatility can still be detected following a
physiological stressor.
While there was no significant reduction in the percentage of voxels that met the cr-fMRI
detection threshold from baseline to post-exercise task scans, voxel-wise analysis through
Randomise showed that exact cr-fMRI values were still significantly reduced from baseline
within a section of the left insula, a region known to affect the parasympathetic and sympathetic
47
systems that regulate heart rate and blood pressure (Nagai et al., 2010; Oppenheimer et al.,
1992). Although there was no a priori hypothesis concerning this region, it is interesting to see
that the one area of the brain where cardiac pulsatility remains significantly decreased in the
second scan after exercise is one that would influence the underlying cerebrovascular stiffness
following the introduction or removal of a temporary stressor.
4.2 Effect of Repetition Time on BOLD Intracranial Pulsatility
Simulation has shown that the cardiac effect can still be reliably detected with longer TRs,
despite failing to satisfy the Nyquist sampling criteria, as the signal was periodic and sampled
data was sufficiently staggered throughout the cardiac cycle in all cases, to allow for signal
reconstruction in all cases. Additionally, increasing TR does not seem to decrease the ability to
detect this effect, once the sampling rate becomes slower than the Nyquist frequency for the
cardiac signal, when using either the RETROICOR-corrected or raw signal. This suggests that
there would be no inherent difference, due to sampling rate, between scans conducted within the
normal range of TRs used in clinical BOLD scans.
At very short TRs, high-pass filtering produced much better results for removing unwanted
signal influences and detecting cardiac pulsatility than either RETROICOR or the uncorrected
signal, but this advantage disappeared as soon as the TR was longer than the Nyquist frequency
of the cardiac signal. The cardiac signal was almost entirely lost after the filter was applied,
whenever the TR was an integer multiple of the average period between simulated heartbeats,
making it an unreliable method to use for any BOLD scans collected at conventional TRs. As an
example, the choice of a TR of 1500ms for the exercise study would have resulted in very low
pulsatility detection in participants with an average heart rate around 80 bpm, if the data had
been cleaned using a high-pass filter, instead of RETROICOR, as was done.
This follows from sampling theory where a signal can only be properly sampled at a lower
frequency if the frequency of interest is not an integer multiples of the sampling frequency
(Wescott, 2016). In this case, it would appear that while the variability between each pulse
interval staggers the sampling points enough to reconstruct the cardiac signal, this is not enough
to allow for the application of a filter before analysis.
48
4.3 Effect of Echo Time on BOLD Intracranial Pulsatility
For grey and white matter, the proportion of pulsatile voxels in the BOLD scans was highest for
the shortest TE, while the difference between TE1 and TE2 was larger than the difference
between TE2 and TE3. In ventricular CSF, however, the proportion of pulsatile voxels does not
vary significantly with TE, and actually increases slightly for the longest TE. This trend may be
related to T2* decay, or a drop in signal intensity with longer TEs. In tissues with shorter T2
*
values (i.e. faster decay rates), like grey and white matter, the signal intensity would be
significantly reduced at the later echo times chosen for this experiment, but would be less
affected in CSF, whose signal decays much more slowly (Haacke et al., 2014; Macintosh and
Graham, 2013).
As to the unexpected slight increase in the percentage of pulsatile voxels within CSF, this could
be due to the fact that the ventricles account for a far smaller volume of the brain than either grey
or white matter and typically have a higher percentage of detected pulsatile volume; a small
variance in pulsatile voxel detection due to random influences can therefore result in either a
several percent point increase or decrease, potentially causing this apparent disparity.
4.4 Limitations
One of the main limitations of this study is the lack of a direct gold-standard measure of
cerebrovascular pulsatility for comparison. Although the present data shows the expected
decrease in pulsatility associated with the post-exercise phenomenon, the validity of this
technique cannot currently be verified through direct comparison.
Additionally, angiography scans were not collected for any of the participants, so it is not
possible to confirm that the detected pulsatile features coincide directly with the major
cerebrovasculature, although previous literature would suggest good agreement (Tong et al.,
2014). A third limitation is that pulsatility cannot be compared to pulse pressure readings within
the MRI scanner as blood pressure was not measured throughout any of the scans, due to
practicality and equipment restrictions. Continuous monitoring would require an automated,
MRI-compatible blood pressure monitor; however, such a device was not available for use at the
time of these experiments.
49
4.5 Conclusions
Resorting BOLD fMRI data according to their position in the cardiac cycle provides a reliable
method to visualize and quantify the brain’s inherent cardiac pulsatility. Cardiac pulsatility in
BOLD data was markedly reduced in grey and white matter, and CSF compartments after
recumbent cycling aerobic exercise among healthy adolescents. The implicated exercise-based
physiological alterations likely include changes in cardiac output, vascular tone, arterial stiffness,
and blood pressure. Additionally, there was a persisting regional pulsatility decrease in the left
insula, a brain region known to regulate autonomic signals. Finally, while the use of very short
TRs and TEs improves pulsatility detection, detection is still possible with conventional scan
parameters, making the proposed cr-fMRI approach amenable to retrospective BOLD fMRI, so
long as cardiac and respiratory traces have been collected during the scan. Therefore, while this
study helps to establish the feasibility of mapping BOLD cardiac pulsatility changes in response
to aerobic exercise, this work is readily amenable to broader application in research on
hypertension or vascular aging, where vascular remodelling and arterial stiffening are well
documented.
50
Chapter 5 Future Directions
5 Possible Extensions
This thesis focuses on designing, implementing and testing the feasibility of a BOLD-based
pulsatility technique in healthy young people. This chapter extends this work along two
directions. The first is to consider a non-BOLD MRI technique to assess brain pulsatility, namely
through arterial spin labeling (ASL). The second future direction in this chapter is to look at
expanding this method to clinical groups, both within adolescents as previously studied in this
thesis and within larger population cohorts.
5.1 Using ASL Instead of BOLD
The thesis has thus far investigated resorting BOLD fMRI to evaluate the cardiac pulse trace in
the brain. There are advantages and disadvantages of this approach that are listed in Table 5.1.
One of the major limitations of BOLD is the composite nature of its signal. BOLD signal is
affected by changes in CBF, CBV and CMRO2, creating potential confounds to the pulsatility
measurement (Davis et al., 1998). To address these concerns, an alternative approach can be
undertaken by substituting BOLD with a similar fMRI technique, known as ASL.
Table 5.1: Advantages and disadvantages of using BOLD fMRI for measuring
cerebrovascular pulsatility (Borogovac and Asllani, 2012; Davis et al., 1998)
Advantages Disadvantages
Non-invasive
Inexpensive (If coupled with
other BOLD analyses)
Whole brain coverage
Sufficient spatial and
temporal resolution for voxel-
wise analysis
Scanner available at many
sites
Non-quantitative
Composite nature of signal
introduces potential
confounds to measurements
Sensitive to motion
Requires good quality
recording of physiological
traces
51
There are many reasons why ASL is an attractive pulse sequence for measuring brain pulsatility.
First and foremost, ASL is a quantitative CBF technique; it achieves perfusion-weighted contrast
through pairs of image called control and tagged images. The tag is created endogenously by
applying an inversion RF pulse(s) at the level of main supplying brain arteries, i.e. upstream of
the volume of interest. The three main labelling approaches are continuous (CASL), pseudo-
continuous (pCASL) or pulsed (PASL) labelling methods. Both CASL and pCASL rely on a
long labelling period to a single plane, using a single long pulse in the case of CASL or a rapid
series of pulses for pCASL. PASL, on the other hand, uses a much shorter labelling pulse over a
much thicker volume slab to create the tagged blood. Each approach has advantages: PASL
provides better labelling efficiency, while pCASL and CASL have better signal-to-noise ratio
(SNR). PCASL is the preferred ASL technique because of the higher SNR and unlike CASL,
there is no need for a specialized head coil (Alsop et al., 2015).
Regardless of the labelling method, tagged images are collected after a predefined delay,
allowing the labelled blood to travel into the brain and diffuse into the tissue. ASL acquisition
involves repeating the tag and control collection numerous times to build up CBF image quality
through averaging of the difference images. It is common to apply 30 averages in a 2D EPI ASL
acquisition. Since this imaging technique measures blood flow directly, it is a sole contrast
functional technique, unlike BOLD where signal intensity changes are the combined influences
from CBF, CBV and CMRO2 (Borogovac and Asllani, 2012). It is important to note that ASL
should in theory have no influence from CSF pulsatility. This added potential specificity to
arterial pulsatility makes ASL a compelling choice.
One disadvantage of conventional ASL is the length required for each scan. Since a minimum
time is needed to both label the blood and wait for this tagged blood to travel through the brain's
vascular system, there are limited options available for speeding up the scan. A TR of 4 seconds
is fairly common in ASL (Ferré et al., 2013), which amounts to roughly five minutes to acquire
30 control-tag pairs. On the other hand, to collect 230 pairs, similar to the BOLD study, the
resulting ASL scan would be 30 minutes. This is a long time for a participant to remain still.
Such a long scan duration also increases the likelihood of drift in the main magnetic field, loss of
the cardiac trace from the pulse oximeter if it shifts on the participant's finger, etc. While a
BOLD scan of around 200 volumes or more is common for other fMRI studies, allowing this
measurement to be done without additional scans and cost, typical ASL scans are much shorter.
52
These scans would have to be done separately or protocols adjusted if an ASL scan was already
planned for the study. In comparison, even if a separate BOLD scan must be made for this
measurement, collecting around 200 volumes would only take roughly 6 minutes compared to
the 30+ minutes necessary for an equivalent ASL scan.
Certain adjustments can be made to address an ASL-pulsatility scan. At the cost of some of the
temporal quality for reconstructing the pulsatility trace, the number of collected pairs can be
reduced to a minimum of 90, which would shorten the scan time from 30 to 13 minutes. This
would lower the sensitivity of this method to detect pulsatility and prohibit rejection of any
poorly collected cardiac traces, but this is off-set against ASL’s improved specificity to CBF
changes. If possible, securing the pulse oximeter with tape to the participant’s finger during the
scan could reduce the risk of movement and the odds of poor collection. It is also advisable to
use electrocardiography leads to digitize exact location in the cardiac cycle.
Another way to reduce the scan time would be to use a short-label, short-delay (SLSD,
parameters provided below) ASL scan, instead of the conventional label and delay periods.
Normally, long label and delay periods are required to allow for tagged blood to travel to the
smaller arterioles and perfuse into the tissue; with a shorter delay, most of the tagged blood will
still be in the large vessels. But since we are not interested in a perfusion map, and - as shown
with previous BOLD experiments - the majority of the cardiac-related signal comes from the
large vessels, this is probably not a concern. After making both of these adjustments, the required
scan time drops to a little over four and a half minutes; similar to that of more typical ASL
experiments.
5.1.1 Pilot ASL-pulsatility mapping data from 2 participants
To test the feasibility of using ASL for this measurement, two participants (both female, 27 ± 3
years) were scanned as part of a pilot study. One received both conventional and SLSD scans
while the other received only the SLSD scan.
5.1.2 Data Acquisition
ASL scans (18 slices, 5 mm thickness, 64 x 64 matrix, 3 x 3 x 5 mm resolution, flip angle 90°)
with two different sampling rates were collected on the same 3 T scanner used for the BOLD
scans: conventional (labelling duration = 1650 ms, post label delay = 1600 ms, TR = 4000 ms),
53
and SLSD (labelling duration = 500 ms, post label delay = 400 ms, TR = 1300 ms). Pseudo-
continuous labelling was used in both cases, with the labelling plane set above the bifurcation of
the carotid artery. Ninety control-tag pairs were collected to allow for proper analysis using the
same method as for the BOLD. Respiratory and cardiac waveforms were digitized at a rate of
500 Hz during the scan using a respiratory belt and pulse oximeter, as was done with the BOLD
scans. T1-weighted images were collected for both participants using fast-field echo imaging
(TR = 9.5 ms, TE = 2.3 ms, TI = 1400 ms, 256 x 164 x 140 matrix, 0.94 x 1.17 x 1.2 mm
resolution).
5.1.3 Preprocessing and Analysis
Similar preprocessing steps to those used for the BOLD scans were conducted, with brain
segmentation, motion and slice time correction, and spatial smoothing of 5mm FWHM
completed using FSL FEAT and T1 segmentation conducted with FSL FAST (Jenkinson et al.,
2012). Tag volumes were then subtracted from their corresponding control volumes. These
difference volumes were aligned with the corresponding cardiac cycle positions during the
collection of each pair’s control volume. Signal intensities within each voxel were sorted based
upon their position in the cardiac cycle, fitted with a 7-term Fourier series and the goodness of fit
was compared against a distribution of 45000 null values, as before. Poor cardiac collection was
noted but volumes were not discarded, due to the limited number of pairs available. Since this
exercise was solely to judge the feasibility of the method and not to provide an exact,
quantitative measurement or comparison, variation in inter-scan cardiac trace quality is not a
concern.
To test whether the relative distance between control and tag image collection, in terms of
cardiac cycle position, would affect the ability to detect cerebrovascular pulsatility, analysis was
repeated by first resorting image volumes, followed by creating the difference images. In this
case, control/tag pairs were formed based on where they fell in the cardiac cycle, minimizing the
difference in position. As before, difference images were aligned with the corresponding cardiac
cycle position of the control image. The resultant maps were then compared to the ones obtained
from the previous method.
54
5.1.4 Results and Discussion
Pulsatility was successfully detected in all three ASL scans. Exact cr-fMRI values were lower
than in the BOLD scans, likely due to the fewer number of volumes, in addition to the inability
to discard volumes with incorrectly collected cardiac pulse traces. Potential variation in labelling
efficiency across the tagged volumes is another possible source of noise that would not be
present in the BOLD scans, which could also reduce pulsatility detection.
Signal was limited to the arteries, with the superior sagittal sinus showing up very faintly in both
participants and below the 5 deviations from null threshold, while the ventricles are completely
invisible for participant 1. The second participant showed a much stronger pulsatile signal, with
more of the vessels showing up clearly. This also resulted in pulsatility detection within the
ventricles as well, despite the limited signal in that region due to CBF providing the primary
source of contrast in ASL.
55
Figure 5.1: cr-fMRI maps overlaid with the CBF map from the participants' SLSD ASL
scans for select slices
Both label and delay durations had their advantages and limitations. For the participant with both
scans, detected pulsatility was higher in the ASL scan with conventional label and delay
parameters, perhaps due to the longer tagging and delay periods providing more tagged blood
56
and higher signal, as well as providing additional time for the signal to become more spatially
distributed. Unfortunately, the longer scan duration seems to have increased the risk of a
corrupted cardiac trace, with the conventional ASL scan having 15 of the 90 control images
corresponding to poorly collected pulse intervals, while the SLSD scans only had 7 and 5
incorrectly aligned images for the same and second participant, respectively. However, given the
limited number of scans in this pilot, conclusions stating that the use of a shorter label and delay
period lowers pulsatility detection or improves cardiac pulse interval collection cannot be made
with any certainty.
Resorting, then differencing the control/tag images reduced the sensitivity for detecting
pulsatility within all scans; blood vessels were still partially visible but only if the threshold was
set lower than 5 deviations from null, which would also increase the amount of background noise
included within the analysis. In typical ASL studies, difference images are calculated from
consecutively collected control and tagged images to reduce artifacts due to motion and scanner
drift; in this case it seems that any benefit that may have been gained from reducing the cardiac
positioning differences between pairs was far outweighed by increased noise from other sources.
Table 5.2: Average percentage of pulsatile voxels in cr-fMRI ASL scans, separated by
tissue type, scan and method. Method 1: Subtracting, then cardiac-sorting of volumes.
Method 2: Cardiac-sorting followed by subtraction of volumes
Participant 1, Conv. Participant 1, SLSD Participant 2, SLSD
Tissue Type Method 1 Method 2 Method 1 Method 2 Method 1 Method 2
CSF 3.01% 0.00% 4.86% 3.24% 19.97% 4.60%
Grey Matter 3.86% 0.12% 1.83% 0.51% 9.16% 3.39%
White Matter 2.18% 0.08% 1.06% 0.47% 5.42% 1.98%
Overall 2.88% 0.10% 1.44% 0.52% 7.41% 2.64%
5.1.5 Future Work
Although current ASL-pulsatility work is pilot data from two participants, there are some
conclusions that can be drawn. First, the ASL-pulsatility maps share a close resemblance to those
provided in section 3. Second, the percentage of pulsatile voxels is comparable to the BOLD
57
approach. As a future direction for the ASL-pulsatility work, it would be advisable to increase
the number of collected control-tag pairs to at least 100+ pairs so that volumes with poorly
collected cardiac could be excluded from analysis. This would require slightly longer scan times
(~5 min and ~14 min with SLSD and conventional parameters, respectively) but would allow for
better comparison of the two scans.
5.2 Investigating cr-fMRI among adolescents with bipolar disorder
This thesis focuses on young, healthy people, but there is potential application of this work in
studying clinical groups. Measuring brain pulsatility may help predict disease risk or further our
understanding of how cerebrovascular diseases progress with time. Thus a future direction for
this thesis is to compare the current cohort with a second cohort of participants with bipolar
disorder because of the stress that this mood disorder can exert on blood vessels, including those
in the brain. Indeed, this thesis is the culmination of collaboration with Sunnybrook’s Centre for
Youth Bipolar Disorder.
In essence, this future direction seeks to answer two questions: first, if it is possible to detect a
difference in cerebrovascular pulsatility between healthy adolescents and those diagnosed with
bipolar disorder using the cr-fMRI method, and second, if this difference is dampened or
exacerbated after exposure to a stressor, like exercise.
5.2.1 Bipolar Disorder
Bipolar disorder is a mood disorder that comprises of episodes of mania/hypomania and
depression. People that suffer from bipolar disorder will experience extended periods of time
with manic/hypomanic or depressive symptoms. There are two major types of bipolar disorder:
type I where the person suffers from at least one manic episode which is frequently accompanied
by depressive episodes as well, while people with type II experience episodes of depression and
hypomania. Type II can be mistaken for major depressive disorder, or unipolar depression, if
hypomanic episodes are subtle enough or not reported. A third classification – not otherwise
specified – is used when patients do not display one of the standard trends in their episodes, such
as rapid swapping between manic and depressive symptoms or only having hypomanic episodes
(Depression and Bipolar Support Alliance, 2016).
58
Mania and hypomania are mood states characterized as elated and/or irritable moods and are
expressed by a combination of the following symptoms: inflated self-esteem, being easily
distracted, impulsive tendencies towards high-risk behaviour, talkativeness, racing thoughts,
psychomotor agitation, requiring less sleep and/or increased goal-driven activity. Depression can
be expressed through persistent sadness or irritability; lack of interest in normal activities; severe
loss or gain of appetite; persistent fatigue; diminished capability to think, concentrate or make
decisions; recurrent thoughts of suicide or actual suicide attempts. What separates these episodes
from similar behaviour in people without mood disorders is that they are not brought about by
external causes such as drugs, illness or traumatic/joyous events that could normally induce
strong feelings of depression or elation. Such events in individuals with bipolar disorder are also
persistent for a long period of time (>4 days for hypomania, >1 week for mania and >2 weeks for
depression) (Goldstein et al., 2015).
Bipolar disorder is present in about 2.6% of the population older than 18 years of age, with age
of onset occurring at a median of 25 years and ranging from early childhood to the late 50s. Risk
of bipolar disorder is increased dramatically when one (15-30%) or both (50-75%) parents have
been diagnosed with the mood disorder. Those diagnosed with bipolar disorder are expected to
have an average reduction of 9.2 years to their life span, with one in five committing suicide
(Depression and Bipolar Support Alliance, 2016).
5.2.2 Cardiovascular Risk and Increased WMH Lesion Burden
In addition to increased risk of suicide among individuals with bipolar disorder, there is also an
increased risk of cardiovascular disease (CVD) (Goldstein et al., 2015). Even after controlling
for body mass index, substance abuse, alcohol consumption and sedentary lifestyle, which are
typically higher in psychiatric populations, those with either bipolar or major depressive disorder
have an adjusted hazard ratio of 3.7 for ischemic heart disease mortality. This ratio increased to
7.12 within the subset of those who had previously attempted suicide (Shah et al., 2011).
This increased CVD risk is visible in the brain as well, sometimes in the form of microvessel
damage manifesting as an increased prevalence of white matter lesions or hyperintensities
(WMHs). A meta-analysis conducted in 2009 concluded that persons with bipolar disorder had
an odds ratio of 2.5 for WMH prevalence in comparison to those without diagnosis of a
psychiatric disorder (Beyer et al., 2009). Specifically in children and adolescents, those who
59
suffered from major depression were significantly more likely to have WMHs than their non-
depressed peers, while those diagnosed with bipolar disorder had severer WMH burden than
even those with unipolar depression (Lyoo et al., 2002). Conversely, increased volume of white
matter lesions was found to be linked with late onset of bipolar disorder, i.e. those who
experienced their first manic episode in middle age (de Asis et al., 2006; Dupont et al., 1995a,
1995b; Fujikawa et al., 1995; Hickie et al., 1995) and that individuals with a high number of
WMHs also had a higher prevalence of psychiatric disorders among their family members
(Dupont et al., 1995b).
Severity of the disorder also appears to play a role in the level of WMH burden. Those classified
as having a good outcome (full recovery from each manic/depressive episode with at least 8
weeks between each episode) showed no higher prevalence of lesions than healthy controls. In
contrast, those with poor outcome (less than 8 weeks between manic/depressive episodes with
lingering symptoms and a poor response to lithium treatment) had significantly higher
prevalence of lesions in deep white matter and higher average grade of periventricular lesions
than healthy controls and those within the good outcome group (Moore, 2001). Whether
increased WMH burden leads to worse outcome for the mood disorder, worse symptoms lead to
increased risk of developing WMHs, or this effect is completely bidirectional, there is a clear
connection between severity of bipolar disorder and white matter lesions of presumed vascular
origin. As white matter lesions are both an indicator of poor cerebrovascular health and are
thought to be linked to various forms of impaired cognitive function, detecting a heightened
BOLD pulsatility response in a bipolar disorder cohort seems both plausible and a potentially
useful tool in assessment of cerebrovascular health and mood disorder severity.
5.2.3 Participants
This study was conducted in conjunction with the exercise session experiment within healthy
adolescents, with bipolar adolescents recruited from the Centre for Youth Bipolar Disorder at
Sunnybrook Health Sciences Centre. Similar exclusion criteria were used as with the healthy
controls, with the exception of diagnosis and family history of mood disorders. As before,
participants were excluded from analysis if they had missing scans, or poorly collected
physiological traces. This resulted in 27 bipolar adolescents (11 female, age: 17.3 ± 1.4 years)
who were compared with 27 age-matched healthy controls (11 female, age: 17.2 ± 1.5 years)
60
taken from the main experiment (Table 5.3). Only resting state scans were considered for the
current analysis.
Table 5.3: Participant Characteristics for Session Effect of Acute Exercise Study in Bipolar
and Healthy Adolescents, HC – Healthy Control, BD – Bipolar Disorder, BMI – Body Mass
Index, HR – Heart Rate, PP – Pulse Pressure, SD - Standard Deviation
Characteristic HC (Mean ±
SD)
BD (Mean ±
SD) Statistic p-value
N 27 27 - -
Sex (Female/Male) 11/16 11/16 - -
Age (Years) 17.2 ± 1.5 17.3 ± 1.4 t = 0.30 0.76
Adjusted BMI (kg/m2) 21.1 ± 2.7 23.6 ± 3.4 t = 2.92 0.0052
Resting PP (mm Hg)* 40 ± 10 47 ± 11 t = 2.43 0.019
Exercise PP (mm Hg)* 50 ± 14 60 ± 21 t = 2.09 0.042
HR – pre-exercise scans (bpm) † 66 ± 8 73 ± 12 t = 2.56 0.014
HR – post-exercise scans (bpm)
† 69 ± 7 77 ± 11 t = 2.91 0.0055
HR – during exercise (bpm) † 143 ± 9 142 ± 6 t = -0.56 0.58
Work Rate (W) 68 ± 26 70 ± 25 t = 0.28 0.78
Time between exercise cessation
& scan (min) 19.7 ± 0.9 19.9 ± 2.4 t = 0.32 0.75
Final perceived exertion ‡ 12.2 ± 1.8 11.5 ± 1.7 t = -1.48 0.15
* Blood pressure calculated using 24 participants in HCs and 26 participants in BDs, data missing/ incorrectly
recorded for 3 and 1 participants, respectively
† Heart rate calculated using only 26 participants in both cohorts, data missing for 1 participant
‡ Self-reported using Borg scale: 6 (No Exertion) – 20 (Maximal Exertion)
As mentioned in the previous section, individuals with bipolar disorder have a higher propensity
towards increased weight and poorer cardiovascular health than their age-matched, non-
depressed counterparts. This was visible in the present cohort with the bipolar participants
having significantly higher average BMI, pulse pressure, and resting and recovery heart rates
than the healthy adolescents (Table 5.3).
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5.2.4 Study Description and Analysis
Bipolar disorder participants underwent the same scanning and exercise paradigm as their
healthy counterparts, as described in Chapter 2.1. Similar statistical analysis was conducted for
between session effects in the bipolar group. Additionally, average percentage of pulsatile voxels
was compared between the two groups using t-tests for both scans and all tissue types. Between-
group voxel-wise analysis of the BOLD pulsatility maps was conducted using FSL Randomise.
The p-values from all statistical tests were adjusted for multiple comparison using 5000
permutations.
5.2.5 Results and Discussion
Table 5.4 summarizes the average percentage of pulsatile voxels for each tissue type, scan, and
participant group. Even within the baseline scans, bipolar adolescents exhibited a higher average
percentage of pulsatile voxels than healthy controls in all tissue types. This is consistent with
what would be expected due to the generally poorer cardiovascular heath in this demographic
and higher pulse pressure measured in this cohort, though the increase in pulsatile voxels was
only significant at baseline in white matter (p* = 0.047) after correction. However, with the
introduction of the exercise stressor, the gap between the average percent pulsatility in the two
cohorts increases. While both cohorts experience a decrease in pulsatility after exercise
cessation, this drop is only significant in healthy controls (CSF: p* = 0.0024; grey matter: p* =
0.0048; white matter: p* = 0.0024). Due to this discrepancy in behaviour 20 minutes after
exercise cessation, the bipolar adolescents’ post-exercise scans show a markedly higher
percentage of pulsatile voxels in all three tissue types with the difference in grey and white
matter pulsatility remaining significant after adjustment for multiple comparisons (p* = 0.0096
and p* = 0.0060, respectively) and nearly significant in ventricular CSF (p* = 0.058).
62
Table 5.4: Average percent tissue BOLD pulsatility in participants with bipolar disorder
and healthy controls.
Healthy Bipolar
Baseline Post-Exercise Baseline Post-Exercise
Ventricular CSF 44.62 ± 14.4% 34.90 ± 16.5% 46.57 ± 19.2% 46.55 ± 16.7%
Grey Matter 10.64 ± 3.7% 8.29 ± 3.8% 12.98 ± 5.7% 12.34 ± 5.8%
White Matter 8.29 ± 3.2% 6.64 ± 3.3% 11.06 ± 5.0% 10.78 ± 5.6%
The difference in BOLD pulsatility between the two cohorts appears to be greatest in white
matter after exercise cessation, both in terms of average percentage pulsatility in the tissue and in
the Randomise comparison of BOLD pulsatility maps. While analysis of baseline maps did not
find any voxel-based regions of significantly higher pulsatility in bipolar participants, post-
exercise comparison of bipolar participants with healthy controls demonstrated higher pulsatility
primarily within white matter near the ventricles and subcortical grey matter, forming a distinct
line in the watershed region in the upper slices of the brain (Figure 5.2).
Figure 5.2: Randomise results for voxel-wise analysis of post-exercise scans comparing cr-
fMRI maps between groups, thresholded using a corrected p-value < 0.05 and showing
slices 3-19. All significant differences were due to increased pulsatility in the bipolar
adolescent group compared to healthy controls
63
In summary, it would appear based on this data that BOLD pulsatility differences in grey and
white matter are visible at baseline between bipolar and healthy adolescents but are even further
accentuated under exposure to a stressor like exercise. This suggests that changes in
cerebrovascular pulsatility in an at-risk cohort can be detected after the introduction of a stressor
like exercise, and possibly at baseline as well.
5.3 Thesis conclusion and expansion to Large Population cr-fMRI Studies
This thesis demonstrates that it is possible to sort individual volumes in BOLD and ASL fMRI
datasets to evaluate evidence of a pulse trace in each voxel. These brain pulsatility maps were
consistent across the present cohorts of young people. Significant changes in brain pulsatility
patterns were also observed in response to acute aerobic exercise as a physiological stressor.
Data in this thesis consisted of N = 45 for the main objectives, N = 10 for the multi-echo data, N
= 2 for the pilot ASL analysis and an additional N = 27 adolescents with bipolar disorder for the
preliminary clinical work. These sample sizes are modest and illustrate modest to high effect
sizes associated with brain pulsatility differences. Relative to the many fMRI readouts that are
available, e.g. default mode network functional connectivity, BOLD pulsatility thus may yield
fruitful discovery in large cohort studies. Therefore future directions for BOLD pulsatility would
be to access publicly available fMRI databases, like the Human Connectome project, which has
two resting state and seven task based BOLD scans for more than 900 participants (Van Essen et
al., 2013), or the UK Biobank with brain imaging data available from 5000 participants to date,
and aims to recruit 100,000 participants (UK biobank, 2016). For pathology specific studies,
collaborations could be formed between other groups, especially those conducting longitudinal
studies, to see if this cerebrovascular pulsatility measure could indeed predict the risk of
developing a disease such as Alzheimer’s disease.
64
References
Alsop, D.C., Detre, J.A., Golay, X., Günther, M., Hendrikse, J., Hernandez-Garcia, L., Lu, H.,
MacIntosh, B.J., Parkes, L.M., Smits, M., van Osch, M.J.P., Wang, D.J.J., Wong, E.C.,
Zaharchuk, G., 2015. Recommended implementation of arterial spin-labeled perfusion MRI
for clinical applications: A consensus of the ISMRM perfusion study group and the
European consortium for ASL in dementia. Magn. Reson. Med. 73, 102–116.
doi:10.1002/mrm.25197
Alzheimer’s Society – United against dementia, 2014. Factsheet 402 - What is vascular
dementia?
Alzheimer Society Canada, 2016. Alzheimer’s disease | Alzheimer Society of Canada [WWW
Document]. URL http://www.alzheimer.ca/en/About-dementia/Alzheimer-s-disease
Alzheimer Society Canada, 2015a. Other dementias | Alzheimer Society of Canada [WWW
Document]. URL http://www.alzheimer.ca/en/About-dementia/Dementias
Alzheimer Society Canada, 2015b. Diagnosis | Alzheimer Society of Canada [WWW
Document]. URL http://www.alzheimer.ca/en/About-dementia/Diagnosis
Alzheimer Society Canada, 2015c. Treatment options | Alzheimer Society of Canada [WWW
Document]. URL http://www.alzheimer.ca/en/About-dementia/Treatment-options
American Heart Association, 2017. Types of Stroke [WWW Document]. URL
http://www.strokeassociation.org/STROKEORG/AboutStroke/TypesofStroke/Types-of-
Stroke_UCM_308531_SubHomePage.jsp
American Heart Association, 2015. Target Heart Rates [WWW Document]. URL
http://www.heart.org/HEARTORG/HealthyLiving/PhysicalActivity/FitnessBasics/Target-
Heart-Rates_UCM_434341_Article.jsp#.WTgcddy1uUk
Asmar, R., Benetos, A., Topouchian, J., Laurent, P., Pannier, B., Brisac, A.-M., Target, R., Levy,
B.I., 1995. Assessment of Arterial Distensibility by Automatic Pulse Wave Velocity
Measurement : Validation and Clinical Application Studies. Hypertension 26, 485–490.
65
doi:10.1161/01.HYP.26.3.485
Avants, B.B., Yushkevich, P., Pluta, J., Minkoff, D., Korczykowski, M., Gee, J.C., 2011. The
Optimal Template Effect in Hippocampus Studies of Diseased Populations. Neuroimage 49,
2457. doi:10.1016/j.neuroimage.2009.09.062.
Baloh, R.W., Yue, Q., Socotch, T.M., Jacobson, K.M., 1995. White matter lesions and
disequilibrium in older people. I. Case-control comparison. Arch. Neurol. 52, 970–974.
Beall, E.B., Lowe, M.J., 2007. Isolating physiologic noise sources with independently
determined spatial measures. Neuroimage 37, 1286–1300.
doi:10.1016/j.neuroimage.2007.07.004
Ben-Shlomo, Y., Spears, M., Boustred, C., May, M., Anderson, S.G., Benjamin, E.J.,
Boutouyrie, P., Cameron, J., Chen, C.-H., Cruickshank, J.K., Hwang, S.-J., Lakatta, E.G.,
Laurent, S., Maldonado, J., Mitchell, G.F., Najjar, S.S., Newman, A.B., Ohishi, M., Pannier,
B., Pereira, T., Vasan, R.S., Shokawa, T., Sutton-Tyrell, K., Verbeke, F., Wang, K.-L.,
Webb, D.J., Willum Hansen, T., Zoungas, S., McEniery, C.M., Cockcroft, J.R., Wilkinson,
I.B., 2014. Aortic Pulse Wave Velocity Improves Cardiovascular Event Prediction. J. Am.
Coll. Cardiol. 63, 636–646. doi:10.1016/j.jacc.2013.09.063
Bernstein, M.A., King, K.F., Zhou, X.J., 2004. Handbook of MRI Pulse Sequences. Elsevier
Inc., Oxford, UK.
Beyer, J.L., Young, R., Kuchibhatla, M., Krishnan, K.R.R., 2009. Hyperintense MRI lesions in
bipolar disorder: A meta-analysis and review. Int. Rev. Psychiatry 21, 394–409.
doi:10.1080/09540260902962198
Bianciardi, M., Toschi, N., Polimeni, J.R., Evans, K.C., Bhat, H., Keil, B., Rosen, B.R., Boas,
D.A., Wald, L.L., 2016. The pulsatility volume index: an indicator of cerebrovascular
compliance based on fast magnetic resonance imaging of cardiac and respiratory pulsatility.
Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374, 20150184. doi:10.1098/rsta.2015.0184
Biswal, B., DeYoe, E.A., Hyde, J.S., 1996. Reduction of physiological fluctuations in fMRI
using digital filters. Magn. Reson. Med. 35, 107–113. doi:10.1002/mrm.1910350114
66
Boone, J.B., Levine, M., Flynn, M.G., Pizza, F.X., Kubitz, E.R., Andres, F.F., 1992. Opioid
receptor modulation of postexercise hypotension. Med. Sci. Sport. Exerc. 24, 1108–1113.
doi:10.1249/00005768-199210000-00007
Borogovac, A., Asllani, I., 2012. Arterial Spin Labeling (ASL) fMRI: Advantages, Theoretical
Constrains and Experimental Challenges in Neurosciences. Int. J. Biomed. Imaging 2012,
1–13. doi:10.1155/2012/818456
Brain Research Imaging Centre Edinburgh, 2017. Functional MR Imaging (fMRI) [WWW
Document]. URL http://www.sbirc.ed.ac.uk/research/techniques/functional.html
Breteler, M.M.., 2000. Vascular risk factors for Alzheimer’s disease: An epidemiologic
perspective. Neurobiol. Aging 21, 153–160. doi:10.1016/S0197-4580(99)00110-4
Brickman, A.M., Zahra, A., Muraskin, J., Steffener, J., Holland, C.M., Habeck, C., Borogovac,
A., Ramos, M.A., Brown, T.R., Asllani, I., Stern, Y., 2009. Reduction in cerebral blood
flow in areas appearing as white matter hyperintensities on magnetic resonance imaging.
Psychiatry Res. Neuroimaging 172, 117–120. doi:10.1016/j.pscychresns.2008.11.006
Carmichael, O., Mungas, D., Beckett, L., Harvey, D., Tomaszewski Farias, S., Reed, B.,
Olichney, J., Miller, J., DeCarli, C., 2012. MRI predictors of cognitive change in a diverse
and carefully characterized elderly population. Neurobiol. Aging 33, 83–95.
doi:10.1016/j.neurobiolaging.2010.01.021
Chambers, L.W., Bancej, C., McDowell, I., 2016. Report summary – Prevalence and monetary
costs of dementia in Canada (2016): a report by the Alzheimer Society of Canada. Heal.
Promot. Chronic Dis. Prev. Canada 36, 231–232. doi:10.24095/hpcdp.36.10.04
Chang, C., Cunningham, J.P., Glover, G.H., 2009. Influence of heart rate on the BOLD signal:
The cardiac response function. Neuroimage 44, 857–869.
doi:10.1016/j.neuroimage.2008.09.029
Chen, C.-Y., Bonham, A.C., 2010. Postexercise Hypotension. Exerc. Sport Sci. Rev. 38, 122–
127. doi:10.1097/JES.0b013e3181e372b5
Cléroux, J., Kouamé, N., Nadeau, A., Coulombe, D., Lacourcière, Y., 1992. Aftereffects of
67
exercise on regional and systemic hemodynamics in hypertension. Hypertens. (Dallas, Tex.
1979) 19, 183–191.
Cox, R.W., 1996. AFNI: Software for Analysis and Visualization of Functional Magnetic
Resonance Neuroimages. Comput. Biomed. Res. 29, 162–173. doi:10.1006/cbmr.1996.0014
Dagli, M.S., Ingeholm, J.E., Haxby, J. V., 1999. Localization of Cardiac-Induced Signal Change
in fMRI. Neuroimage 9, 407–415. doi:10.1006/nimg.1998.0424
Davis, T.L., Kwong, K.K., Weisskoff, R.M., Rosen, B.R., 1998. Calibrated functional MRI:
mapping the dynamics of oxidative metabolism. Proc. Natl. Acad. Sci. U. S. A. 95, 1834–
1839. doi:10.1073/pnas.95.4.1834
de Asis, J.M., Greenwald, B.S., Alexopoulos, G.S., Kiosses, D.N., Ashtari, M., Heo, M., Young,
R.C., 2006. Frontal Signal Hyperintensities in Mania in Old Age. Am. J. Geriatr. Psychiatry
14, 598–604. doi:10.1097/01.JGP.0000200603.70504.d5
de Groot, J.C., de Leeuw, F.-E., Oudkerk, M., Hofman, A., Jolles, J., Breteler, M.M.B., 2001.
Cerebral white matter lesions and subjective cognitive dysfunction: The Rotterdam Scan
Study. Neurology 56, 1539–1545. doi:10.1212/WNL.56.11.1539
Debette, S., Markus, H.S., 2010. The clinical importance of white matter hyperintensities on
brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 341, c3666.
doi:10.1136/bmj.c3666
Deckers, R.H.R., van Gelderen, P., Ries, M., Barret, O., Duyn, J.H., Ikonomidou, V.N.,
Fukunaga, M., Glover, G.H., de Zwart, J.A., 2006. An adaptive filter for suppression of
cardiac and respiratory noise in MRI time series data. Neuroimage 33, 1072–1081.
doi:10.1016/j.neuroimage.2006.08.006
Depression and Bipolar Support Alliance, 2016. Bipolar Disorder Statistics - Depression and
Bipolar Support Alliance [WWW Document]. URL
http://www.dbsalliance.org/site/PageServer?pagename=education_statistics_bipolar_disord
er
Desjardins, A.E., Kiehl, K.A., Liddle, P.F., 2001. Removal of Confounding Effects of Global
68
Signal in Functional MRI Analyses. Neuroimage 13, 751–758. doi:10.1006/nimg.2000.0719
Dupont, R.M., Butters, N., Schafer, K., Wilson, T., Hesselink, J., Gillin, J.C., 1995a. Diagnostic
specificity of focal white matter abnormalities in bipolar and unipolar mood disorder. Biol.
Psychiatry 38, 482–486. doi:10.1016/0006-3223(95)00100-U
Dupont, R.M., Jernigan, T.L., Heindel, W., Butters, N., Shafer, K., Wilson, T., Hesselink, J.,
Gillin, J.C., 1995b. Magnetic Resonance Imaging and Mood Disorders. Arch. Gen.
Psychiatry 52, 747–755. doi:10.1001/archpsyc.1995.03950210041009
Ennezat, P. V, Malendowicz, S.L., Testa, M., Colombo, P.C., Cohen-Solal, A., Evans, T.,
LeJemtel, T.H., 2001. Physical training in patients with chronic heart failure enhances the
expression of genes encoding antioxidative enzymes. J. Am. Coll. Cardiol. 38, 194–198.
doi:10.1016/S0735-1097(01)01321-3
Fattahi, N., Arani, A., Perry, A., Meyer, F., Manduca, A., Glaser, K., Senjem, M.L., Ehman,
R.L., Huston, J., 2016. MR Elastography Demonstrates Increased Brain Stiffness in Normal
Pressure Hydrocephalus. Am. J. Neuroradiol. 37, 462–467. doi:10.3174/ajnr.A4560
Fazekas, F., Chawluk, J., Alavi, A., Hurtig, H., Zimmerman, R., 1987. MR signal abnormalities
at 1.5 T in Alzheimer’s dementia and normal aging. Am. J. Roentgenol. 149, 351–356.
doi:10.2214/ajr.149.2.351
Ferrari, M., Mottola, L., Quaresima, V., 2004. Principles, Techniques, and Limitations of Near
Infrared Spectroscopy. Can. J. Appl. Physiol. 29, 463–487. doi:10.1139/h04-031
Ferré, J.-C., Bannier, E., Raoult, H., Mineur, G., Carsin-Nicol, B., Gauvrit, J.-Y., 2013. Arterial
spin labeling (ASL) perfusion: Techniques and clinical use. Diagn. Interv. Imaging 94,
1211–1223. doi:10.1016/j.diii.2013.06.010
Floras, J.S., Sinkey, C.A., Aylward, P.E., Seals, D.R., Thoren, P.N., Mark, A.L., 1989.
Postexercise hypotension and sympathoinhibition in borderline hypertensive men.
Hypertension 14, 28–35. doi:10.1161/01.HYP.14.1.28
Fujikawa, T., Yamawaki, S., Touhouda, Y., 1995. Silent Cerebral Infarctions in Patients With
Late-Onset Mania. Stroke 26, 946–949. doi:10.1161/01.STR.26.6.946
69
Fukai, T., Siegfried, M.R., Ushio-Fukai, M., Cheng, Y., Kojda, G., Harrison, D.G., 2000.
Regulation of the vascular extracellular superoxide dismutase by nitric oxide and exercise
training. J. Clin. Invest. 105, 1631–1639. doi:10.1172/JCI9551
Glover, G.H., Li, T., Ress, D., 2000. Image‐ based method for retrospective correction of
physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162–167.
doi:10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.3.CO;2-5
Goldstein, B.I., Carnethon, M.R., Matthews, K.A., McIntyre, R.S., Miller, G.E., Raghuveer, G.,
Stoney, C.M., Wasiak, H., McCrindle, B.W., 2015. Major Depressive Disorder and Bipolar
Disorder Predispose Youth to Accelerated Atherosclerosis and Early Cardiovascular
Disease. Circulation 132, 965–986. doi:10.1161/CIR.0000000000000229
Gorelick, P.B., Scuteri, A., Black, S.E., Decarli, C., Greenberg, S.M., Iadecola, C., Launer, L.J.,
Laurent, S., Lopez, O.L., Nyenhuis, D., Petersen, R.C., Schneider, J.A., Tzourio, C., Arnett,
D.K., Bennett, D.A., Chui, H.C., Higashida, R.T., Lindquist, R., Nilsson, P.M., Roman,
G.C., Sellke, F.W., Seshadri, S., 2011. Vascular Contributions to Cognitive Impairment and
Dementia A Statement for Healthcare Professionals From the American Heart Association /
American Stroke Association. Stroke 42, 2672–2713. doi:10.1161/STR.0b013e3182299496
Gorno-Tempini, M.L., Hutton, C., Josephs, O., Deichmann, R., Price, C., Turner, R., 2002. Echo
Time Dependence of BOLD Contrast and Susceptibility Artifacts. Neuroimage 15, 136–
142. doi:10.1006/nimg.2001.0967
Grodstein, F., 2007. Cardiovascular risk factors and cognitive function. Alzheimer’s Dement. 3,
S16–S22. doi:10.1016/j.jalz.2007.01.001
Gunning-Dixon, F.M., Raz, N., 2000. The cognitive correlates of white matter abnormalities in
normal aging: A quantitative review. Neuropsychology 14, 224–232. doi:10.1037//0894-
4105.14.2.224
Gupta, R., Chari, D., Ali, R., 2015. Reversible dementia in elderly: Really uncommon? J.
Geriatr. Ment. Heal. 2, 30–37. doi:10.4103/2348-9995.161378
Haacke, E., Brown, R., Thompson, M., Venkatesan, R., 2014. Magnetic resonance imaging, 2nd
70
ed. John Wiley & Sons, Inc., Hoboken, NJ.
Hagberg, J.M., Montain, S.J., Martin, W.H., 1987. Blood pressure and hemodynamic responses
after exercise in older hypertensives. J. Appl. Physiol. 63, 270–276.
Han, H.C., 2012. Twisted blood vessels: Symptoms, etiology and biomechanical mechanisms. J.
Vasc. Res. 49, 185–197. doi:10.1159/000335123
Hara, K., Floras, J.S., 1995. Influence of naloxone on muscle sympathetic nerve activity,
systemic and calf haemodynamics and ambulatory blood pressure after exercise in mild
essential hypertension. J. Hypertens. 13, 447–461.
Hickie, I., Scott, E., Mitchell, P., Wilhelm, K., Austin, M.-P., Bennett, B., 1995. Subcortical
hyperintensities on magnetic resonance imaging: Clinical correlates and prognostic
significance in patients with severe depression. Biol. Psychiatry 37, 151–160.
doi:10.1016/0006-3223(94)00174-2
Higashi, Y., Yoshizumi, M., 2004. Exercise and endothelial function: Role of endothelium-
derived nitric oxide and oxidative stress in healthy subjects and hypertensive patients.
Pharmacol. Ther. 102, 87–96. doi:10.1016/j.pharmthera.2004.02.003
Holm, S., 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70.
Howard, J., Singh, A., 2017. Neurology image-based clinical review. Springer, New York, NY.
Huston, J., Murphy, M.C., Boeve, B.F., Fattahi, N., Arani, A., Glaser, K.J., Manduca, A., Jones,
D.T., Ehman, R.L., 2016. Magnetic resonance elastography of frontotemporal dementia. J.
Magn. Reson. Imaging 43, 474–478. doi:10.1002/jmri.24977
Jani, B., Rajkumar, C., 2006. Ageing and vascular ageing. Postgrad. Med. J. 82, 357–362.
doi:10.1136/pgmj.2005.036053
Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M., 2012. FSL.
Neuroimage 62, 782–790. doi:10.1016/j.neuroimage.2011.09.015
Kaufman, F.L., Hughson, R.L., Schaman, J.P., 1987. Effect of exercise on recovery blood
71
pressure in normotensive and hypertensive subjects. Med. Sci. Sport. Exerc. 19, 17–20.
doi:10.1249/00005768-198702000-00004
Kenney, M.J., Seals, D.R., 1993. Postexercise hypotension. Key features, mechanisms, and
clinical significance. Hypertension 22, 653–664. doi:10.1161/01.HYP.22.5.653
Kim, S., Choi, S.H., Lee, Y.M., Kim, M.J., Kim, Y.D., Kim, J.Y., Park, J.H., Myung, W., Na,
H.R., Han, H.J., Shim, Y.S., Kim, J.H., Yoon, S.J., Kim, S.Y., Kim, D.K., 2015.
Periventricular white matter hyperintensities and the risk of dementia: a CREDOS study.
Int. Psychogeriatrics 27, 2069–2077. doi:10.1017/S1041610215001076
Kojda, G., Hambrecht, R., 2005. Molecular mechanisms of vascular adaptations to exercise.
Physical activity as an effective antioxidant therapy? Cardiovasc. Res. 67, 187–197.
doi:10.1016/j.cardiores.2005.04.032
Krüger, G., Glover, G.H., 2001. Physiological noise in oxygenation‐sensitive magnetic
resonance imaging. Magn. Reson. Med. 46, 631–637. doi:10.1002/mrm.1240.abs
Kruse, S.A., Rose, G.H., Glaser, K.J., Manduca, A., Felmlee, J.P., Jack, C.R., Ehman, R.L.,
2008. Magnetic resonance elastography of the brain. Neuroimage 39, 231–237.
doi:10.1016/j.neuroimage.2007.08.030
Lim, A.S.P., Srivastava, G.P., Yu, L., Chibnik, L.B., Xu, J., Buchman, A.S., Schneider, J.A.,
Myers, A.J., Bennett, D.A., De Jager, P.L., 2014. 24-Hour Rhythms of DNA Methylation
and Their Relation with Rhythms of RNA Expression in the Human Dorsolateral Prefrontal
Cortex. PLoS Genet. 10, e1004792. doi:10.1371/journal.pgen.1004792
Liu, C.-S.J., Miki, A., Hulvershorn, J., Bloy, L., Gualtieri, E.E., Liu, G.T., Leigh, J.S.,
Haselgrove, J.C., Elliott, M.A., 2006. Spatial and Temporal Characteristics of Physiological
Noise in fMRI at 3T. Acad. Radiol. 13, 313–323. doi:10.1016/j.acra.2005.10.018
Londeree, B.R., Moeschberger, M.L., 1982. Effect of Age and Other Factors on Maximal Heart
Rate. Res. Q. Exerc. Sport 53, 297–304. doi:10.1080/02701367.1982.10605252
Lyoo, I.K., Lee, H.K., Jung, J.H., Noam, G.G., Renshaw, P.F., 2002. White matter
hyperintensities on magnetic resonance imaging of the brain in children with psychiatric
72
disorders. Compr. Psychiatry 43, 361–368. doi:10.1053/comp.2002.34636
MacDonald, J.R., MacDougall, J.D., Hogben, C.D., 2000. The effects of exercise duration on
post-exercise hypotension. J. Hum. Hypertens. 14, 125–129. doi:10.1038/sj.jhh.1000953
MacDonald, J.R., MacDougall, J.D., Interisano, S.A., Smith, K.M., McCartney, N., Moroz, J.S.,
Younglai, E. V., Tarnopolsky, M.A., 1999. Hypotension following mild bouts of resistance
exercise and submaximal dynamic exercise. Eur. J. Appl. Physiol. 79, 148–154.
doi:10.1007/s004210050488
Macintosh, B.J., Graham, S.J., 2013. Magnetic resonance imaging to visualize stroke and
characterize stroke recovery: a review. Front. Neurol. 4, 60. doi:10.3389/fneur.2013.00060
Makedonov, I., Black, S.E., MacIntosh, B.J., 2013. BOLD fMRI in the White Matter as a Marker
of Aging and Small Vessel Disease. PLoS One 8, e67652.
doi:10.1371/journal.pone.0067652
Marstrand, J.R., Garde, E., Rostrup, E., Ring, P., Rosenbaum, S., Mortensen, E.L., Larsson,
H.B.W., 2002. Cerebral Perfusion and Cerebrovascular Reactivity Are Reduced in White
Matter Hyperintensities. Stroke 33, 972–976. doi:10.1161/01.STR.0000012808.81667.4B
McKhann, G., Knopman, D.S., Chertkow, H., Hymann, B., Jack, C.R., Kawas, C., Klunk, W.,
Koroshetz, W., Manly, J., Mayeux, R., Mohs, R., Morris, J., Rossor, M., Scheltens, P.,
Carrillo, M., Weintrub, S., Phelphs, C., 2011. The diagnosis of dementia due to Alzheimer’s
disease: Recommendations from the National Institute on Aging- Alzheimer’s Association
workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 263–
269. doi:10.1016/j.jalz.2011.03.005.The
Medline Plus, 2015. Intracranial pressure monitoring [WWW Document]. URL
https://medlineplus.gov/ency/article/003411.htm
Metcalfe, A.W.S., MacIntosh, B.J., Scavone, A., Ou, X., Korczak, D., Goldstein, B.I., 2016.
Effects of acute aerobic exercise on neural correlates of attention and inhibition in
adolescents with bipolar disorder. Transl. Psychiatry 6, e814. doi:10.1038/tp.2016.85
Meyer, J.S., Rauch, G.M., Rauch, R.A., Haque, A., Crawford, K., 2000. Cardiovascular and
73
Other Risk Factors for Alzheimer’s Disease and Vascular Dementia. Ann. N. Y. Acad. Sci.
903, 411–423. doi:10.1111/j.1749-6632.2000.tb06393.x
Moore, P.B., 2001. Cerebral white matter lesions in bipolar affective disorder: relationship to
outcome. Br. J. Psychiatry 178, 172–176. doi:10.1192/bjp.178.2.172
Mungas, D., Jagust, W.J., Reed, B.R., Kramer, J.H., Weiner, M.W., Schuff, N., Norman, D.,
Mack, W.J., Willis, L., Chui, H.C., 2001. MRI predictors of cognition in subcortical
ischemic vascular disease and Alzheimer’s disease. Neurology 57, 2229–2235.
doi:10.1212/WNL.57.12.2229
Murphy, K., Birn, R.M., Bandettini, P.A., 2013. Resting-state fMRI confounds and cleanup.
Neuroimage 80, 349–359. doi:10.1016/j.neuroimage.2013.04.001
Nagai, M., Hoshide, S., Kario, K., 2010. The insular cortex and cardiovascular system: a new
insight into the brain-heart axis. J. Am. Soc. Hypertens. 4, 174–182.
doi:10.1016/j.jash.2010.05.001
Naqvi, J., Yap, K.H., Ahmad, G., Ghosh, J., 2013. Transcranial Doppler Ultrasound: A Review
of the Physical Principles and Major Applications in Critical Care. Int. J. Vasc. Med. 2013,
1–13. doi:10.1155/2013/629378
National Institute of Mental Health, 2017. Multi-Echo fMRI | Section on Functional Imaging
Methods [WWW Document]. URL https://fim.nimh.nih.gov/projects/multi-echo-fmri
National Institute of Neurological Disorders and Stroke, 2017. All Disorders | National Institute
of Neurological Disorders and Stroke [WWW Document]. URL
https://www.ninds.nih.gov/Disorders/all-disorders
Nishimura, D.G., 1996. Overview, in: Principles of Magnetic Resonance Imaging. pp. 33–53.
Nussenzweig, S.C., Verma, S., Finkel, T., 2015. The role of autophagy in vascular biology. Circ.
Res. 116, 480–488. doi:10.1161/CIRCRESAHA.116.303805
O’Rourke, M.F., Hashimoto, J., 2007. Mechanical Factors in Arterial Aging. J. Am. Coll.
Cardiol. 50, 1–13. doi:10.1016/j.jacc.2006.12.050
74
O’Sullivan, M., Morris, R.G., Huckstep, B., Jones, D.K., Williams, S.C., Markus, H.S., 2004.
Diffusion tensor MRI correlates with executive dysfunction in patients with ischaemic
leukoaraiosis. J. Neurol. Neurosurg. Psychiatry 75, 441–447. doi:10.1136/jnnp.2003.014910
Ontario Stroke Network, 2016. Stroke Stats and Facts | Ontario Stroke Network [WWW
Document]. URL http://ontariostrokenetwork.ca/information-about-stroke/stroke-stats-and-
facts/
Oppenheimer, S.M., Gelb, A., Girvin, J.P., Hachinski, V.C., 1992. Cardiovascular effects of
human insular cortex stimulation. Neurology 42, 1727–1727. doi:10.1212/WNL.42.9.1727
Pantoni, L., 2010. Cerebral small vessel disease: from pathogenesis and clinical characteristics to
therapeutic challenges. Lancet Neurol. 9, 689–701. doi:10.1016/S1474-4422(10)70104-6
Pase, M.P., Beiser, A., Himali, J.J., Tsao, C., Satizabal, C.L., Vasan, R.S., Seshadri, S., Mitchell,
G.F., 2016. Aortic Stiffness and the Risk of Incident Mild Cognitive Impairment and
Dementia. Stroke 47, 2256–2261. doi:10.1161/STROKEAHA.116.013508
Patel, R.S., Al Mheid, I., Morris, A.A., Ahmed, Y., Kavtaradze, N., Ali, S., Dabhadkar, K.,
Brigham, K., Hooper, W.C., Alexander, R.W., Jones, D.P., Quyyumi, A.A., 2011.
Oxidative stress is associated with impaired arterial elasticity. Atherosclerosis 218, 90–95.
doi:10.1016/j.atherosclerosis.2011.04.033
Pescatello, L.S., Fargo, A.E., Leach, C.N., Scherzer, H.H., 1991. Short-term effect of dynamic
exercise on arterial blood pressure. Circulation 83, 1557–1561.
Plowman, S.A., Smith, D.L., 2008. Cardiovascular Responses to Exercise, in: Exercise
Physiology for Health Fitness and Performance. Lippencott Williams & Wilkins,
Philadelphia, PA, pp. 351–382.
Poser, B.A., Norris, D.G., 2009. Investigating the benefits of multi-echo EPI for fMRI at 7 T.
Neuroimage 45, 1162–1172. doi:10.1016/j.neuroimage.2009.01.007
Prins, N.D., Scheltens, P., 2015. White matter hyperintensities, cognitive impairment and
dementia: an update. Nat. Rev. Neurol. 11, 157–165. doi:10.1038/nrneurol.2015.10
75
Rajna, Z., Kananen, J., Keskinarkaus, A., Seppänen, T., Kiviniemi, V., 2015. Detection of short-
term activity avalanches in human brain default mode network with ultrafast MR
encephalography. Front. Hum. Neurosci. 9, 448. doi:10.3389/fnhum.2015.00448
Rocca, W.A., Petersen, R.C., Knopman, D.S., Hebert, L.E., Evans, D.A., Hall, K.S., Gao, S.,
Unverzagt, F.W., Langa, K.M., Larson, E.B., White, L.R., 2011. Trends in the incidence
and prevalence of Alzheimer’s disease, dementia, and cognitive impairment in the United
States. Alzheimer’s Dement. 7, 80–93. doi:10.1016/j.jalz.2010.11.002
Rosen, B., Wald, L., 2006. MR Image Encoding, in: HST.584J: Magnetic Resonance Analytic,
Biochemical, and Imaging Techniques. Cambridge, MA: Harvard-MIT Division of Health
Sciences and Technology.
Rush, J.W.E., Turk, J.R., Laughlin, M.H., 2003. Exercise training regulates SOD-1 and oxidative
stress in porcine aortic endothelium. Am. J. Physiol. - Hear. Circ. Physiol. 284, H1378–
H1387. doi:10.1152/ajpheart.00190.2002
Sakakibara, R., Hattori, T., Uchiyama, T., Yamanishi, T., 1999. Urinary function in elderly
people with and without leukoaraiosis: relation to cognitive and gait function. J. Neurol.
Neurosurg. Psychiatry 67, 658–660. doi:10.1136/jnnp.67.5.658
Scheel, N., Chang, C., Mamlouk, A.M., 2014. The Importance of Physiological Noise
Regression in High Temporal Resolution fMRI, in: Wermter, S., Weber, C., Duch, W.,
Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., Villa, A.E.P. (Eds.), Artificial
Neural Networks and Machine Learning -- ICANN 2014: 24th International Conference on
Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings.
Springer International Publishing, Cham, pp. 829–836. doi:10.1007/978-3-319-11179-
7_104
Schneider, G., Prince, M.R., Meaney, J.F.M., Ho, V.B. (Eds.), 2005. Magnetic Resonance
Angiography. Springer-Verlag, Milan. doi:10.1007/b138651
Schriger, D., 2012. Approach to the Patient with Abnormal Vital Signs, in: Goldman, L.,
Schafer, A. (Eds.), Goldman’s Cecil Medicine. Elsevier/Saunders, Philadelphia, p. 27.
76
Schuster, S., 2007. Radiology, News, Education, Service [WWW Document]. URL
http://www.auntminnie.com/index.aspx?sec=ser&sub=def&pag=dis&ItemID=76901
(accessed 8.25.17).
Scuteri, A., Tesauro, M., Appolloni, S., Preziosi, F., Brancati, A.M., Volpe, M., 2007. Arterial
stiffness as an independent predictor of longitudinal changes in cognitive function in the
older individual. J. Hypertens. 25, 1035–1040. doi:10.1097/HJH.0b013e3280895b55
Seo, J.-B., Chung, W.-Y., Kim, S.-H., Kim, M.-A., Zo, J.-H., 2013. Immediate impact of
exercise on arterial stiffness in humans. World J. Cardiovasc. Dis. 3, 40–45.
doi:10.4236/wjcd.2013.31009
Shah, A.J., Veledar, E., Hong, Y., Bremner, J.D., Vaccarino, V., 2011. Depression and History
of Attempted Suicide as Risk Factors for Heart Disease Mortality in Young Individuals.
Arch. Gen. Psychiatry 68, 1135. doi:10.1001/archgenpsychiatry.2011.125
Shmueli, K., van Gelderen, P., de Zwart, J.A., Horovitz, S.G., Fukunaga, M., Jansma, J.M.,
Duyn, J.H., 2007. Low-frequency fluctuations in the cardiac rate as a source of variance in
the resting-state fMRI BOLD signal. Neuroimage 38, 306–320.
doi:10.1016/j.neuroimage.2007.07.037
Stankovic, Z., Allen, B.D., Garcia, J., Jarvis, K.B., Markl, M., 2014. 4D flow imaging with MRI.
Cardiovasc. Diagnosis Ther. 4, 173–192. doi:10.3978/j.issn.2223-3652.2014.01.02
Sugawara, J., Otsuki, T., Tanabe, T., Maeda, S., Kuno, S., Ajisaka, R., Matsuda, M., 2003. The
Effects of Low-Intensity Single-Leg Exercise on Regional Arterial Stiffness. Jpn. J. Physiol.
53, 239–241. doi:10.2170/jjphysiol.53.239
Texas Heart Institute, 2016. Vulnerable Plaque [WWW Document]. URL
http://www.texasheart.org/HIC/Topics/Cond/vulplaq.cfm
The New Jersey Comprehensive Stroke Center, 2013. UH Stroke Center | University Hospital,
Newark, NJ [WWW Document]. URL http://www.uhnj.org/stroke/types.htm
Tong, Y., Frederick, B. deB., 2014. Tracking cerebral blood flow in BOLD fMRI using
recursively generated regressors. Hum. Brain Mapp. 35, 5471–5485.
77
doi:10.1002/hbm.22564
Tong, Y., Hocke, L.M., De Frederick, B.B., 2014. Short repetition time multiband echo-planar
imaging with simultaneous pulse recording allows dynamic imaging of the cardiac pulsation
signal. Magn. Reson. Med. 72, 1268–1276. doi:10.1002/mrm.25041
Tong, Y., Hocke, L.M., Lindsey, K.P., Erdoğan, S.B., Vitaliano, G., Caine, C.E., Frederick, B.
deB., 2016. Systemic Low-Frequency Oscillations in BOLD Signal Vary with Tissue Type.
Front. Neurosci. 10, 313. doi:10.3389/fnins.2016.00313
Tortora, G.J., Derrickson, B., 2012. Principles of Anatomy and Physiology 13th ed, Willey 2012.
doi:10.1007/s13398-014-0173-7.2
UCSF Memory and Aging Centre, 2017. Disease Fact Sheets | UCSF Memory and Aging Center
[WWW Document]. URL http://memory.ucsf.edu/education/diseases
UK biobank, 2016. Brain imaging results from 5,000 subjects | UK Biobank [WWW Document].
URL https://www.ukbiobank.ac.uk/2016/09/brain-imaging-results-from-5000-subjects/
Ungvari, Z., Kaley, G., de Cabo, R., Sonntag, W.E., Csiszar, A., 2010. Mechanisms of Vascular
Aging: New Perspectives. Journals Gerontol. Ser. A Biol. Sci. Med. Sci. 65A, 1028–1041.
doi:10.1093/gerona/glq113
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E.J., Yacoub, E., Ugurbil, K., 2013. The
WU-Minn Human Connectome Project: An overview. Neuroimage 80, 62–79.
doi:10.1016/j.neuroimage.2013.05.041
van Norden, A.G., de Laat, K.F., Gons, R.A., van Uden, I.W., van Dijk, E.J., van Oudheusden,
L.J., Esselink, R.A., Bloem, B.R., van Engelen, B.G., Zwarts, M.J., Tendolkar, I., Olde-
Rikkert, M.G., van der Vlugt, M.J., Zwiers, M.P., Norris, D.G., de Leeuw, F.-E., 2011.
Causes and consequences of cerebral small vessel disease. The RUN DMC study: a
prospective cohort study. Study rationale and protocol. BMC Neurol. 11, 29.
doi:10.1186/1471-2377-11-29
Viswanathan, A., Rocca, W.A., Tzourio, C., 2009. Vascular risk factors and dementia: How to
move forward? Neurology 72, 368–374. doi:10.1212/01.wnl.0000341271.90478.8e
78
Wåhlin, A., Ambarki, K., Birgander, R., Wieben, O., Johnson, K.M., Malm, J., Eklund, A., 2013.
Measuring Pulsatile Flow in Cerebral Arteries Using 4D Phase-Contrast MR Imaging. Am.
J. Neuroradiol. 34, 1740–1745. doi:10.3174/ajnr.A3442
Webb, A.J.S., Simoni, M., Mazzucco, S., Kuker, W., Schulz, U., Rothwell, P.M., 2012.
Increased Cerebral Arterial Pulsatility in Patients With Leukoaraiosis: Arterial Stiffness
Enhances Transmission of Aortic Pulsatility. Stroke 43, 2631–2636.
doi:10.1161/STROKEAHA.112.655837
Wechsler, D., 1999. Wechsler Abbreviated Scale of Intelligence.
Wescott, T., 2016. Sampling: What Nyquist Didn’t Say, and What to Do About It. Wescott
Semin. 1–26.
Whitman, G.T., Tang, Y., Lin, A., Baloh, R.W., Tang, T., 2001. A prospective study of cerebral
white matter abnormalities in older people with gait dysfunction. Neurology 57, 990–994.
Wilcox, R.G., Bennett, T., Brown, A.M., Macdonald, I.A., 1982. Is exercise good for high blood
pressure? Br. Med. J. (Clin. Res. Ed). 285, 767–769.
Wu, G., Luo, F., Li, Z., Zhao, X., Li, S.J., 2002. Transient relationships among BOLD, CBV,
and CBF changes in rat brain as detected by functional MRI. Magn. Reson. Med. 48, 987–
993. doi:10.1002/mrm.10317
