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Arterial stiffness and resting-state functional connectivity in older adults
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Arterial stiffness and resting-state functional connectivity in older adults
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i
ARTERIAL STIFFNESS AND RESTING-STATE FUNCTIONAL CONNECTIVITY IN
OLDER ADULTS
by
Anna E. Blanken
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
PSYCHOLOGY
August 2021
Copyright 2021 Anna E. Blanken
ii
Acknowledgments
There are more people who have contributed to my academic development then I can
name in these few pages of acknowledgments, but I would like to highlight some of them here. I
cannot thank my advisor, Dan Nation, enough for agreeing to meet with me after I cold emailed
him prior to applying to graduate school. There were whole stretches of time during this degree
when I realized that he believed in me more than I did in myself, and that was sometimes one of
the only motivators that kept me on track during some of the most personally and intellectually
challenging years of my life. I owe a tremendous amount of my academic, professional, and
personal development to him.
Within the USC community, I am grateful to my committee members who have inspired
and supported me for many years. I also consider Duke Han an honorary member of my
committee, because he sat with me for hours reviewing fMRI methods and results, and on more
than one occasion would say, ‘wait, I’m not on your committee?’ I have had so much support
from my labmates, Belinda Yew, Jean Ho, Shubir Dutt, Isabel Sible, Anisa Marshall, Aru
Kapoor, Aimee Gaubert, and Jung Jang. Jung never turns down a meeting with me and has sat
with me through multiple hours of tears and frustration this year. Jun Min has also provided me
with immense support in processing continuous blood pressure data, and discussing theory.
Finally, I extend thanks to my graduate cohort, Mona Khaled, Miriam Rubenson, Vanessa
Calderon, Elissa McIntosh, and Hannah Rasmussen. I hold your friendships dear to my heart.
Other USC friends who provided me immeasurable support in this process include, but are not
limited to, Narcis Marshall, Mariel Bello, Sarah Stoycos, and Sylvanna Vargas.
I found amazing mentors during my internship year at the San Francisco VA. Thank you
to my clinical supervisor, Holly Hamilton, and my research mentor, Carrie Gibson. They are
some of my greatest role models and their presence in my life has helped me overcome major
iii
obstacles and somehow still thrive. My SFVA intern cohort has also helped me survive this
pandemic year. In particular, thank you to Aly and Yashar for generously opening their home to
me during such an uncertain time and become some of my dearest friends.
Thank you to my parents, who have always encouraged my intellectual development
through reading, spending time outdoors, nerdy summer programs, travel, endless trips to the
science museum, zoo, and aquarium, and supporting my studies at one of the best colleges in this
country (that was more than 3000 miles from home).
I am grateful to USC, where my partner Casey’s path joined mine (although, we have
since learned that we had some near misses in our liberal arts college pursuits). Casey, forming
an unstoppable family team with you and Dr. Bobos, esteemed author of The BooBoo’s
Chronicles. We can and will support each other through all of the peaks and valleys of life. I am
so excited to earn a doctorate with you, and then make our family ‘official’ later this August.
Thank you for sticking with me and reminding me to be myself.
I am dedicating this dissertation to my four grandparents – Oma, Opa, Babcia, and Dzadziu.
Without their ambition and perseverance, I would never have made it here. Their risks and
sacrifices are beyond comprehension: surviving World War II, leaving friends and family to
move to a new country, or sending their only children across the world in hopes of providing a
better life while they stayed behind in hardship. I hope this achievement does their hard work
and undoubtedly painful life decisions some level of justice. In particular, to Opa, who inspired
in me a love of reading, learning, travel, and critical thinking. Watching you move through the
stages of dementia was unbearable. I hope that this work contributes to preventing that suffering
for other families across the world.
iv
TABLE OF CONTENTS
Acknowledgments ................................................................................................................. ii
List of Tables .............................................................................................................. ........... vi
List of Figures........................................................................................................................ vii
Abbreviations..........................................................................................................................viii
Abstract ................................................................................................................................. xi
General Introduction .............................................................................................................. 1
Specific Aims ……................................................................................................................ 20
Study 1: Relationship between blood pressure variability and static resting-state functional
connectivity in the default mode network.............................................................................. 21
Introduction ........................................................................................................................... 22
Methods ................................................................................................................................. 22
Results ................................................................................................................................... 26
Discussion .................................................................................................................. ........... 27
Tables .................................................................................................................................... 30
Figures ................................................................................................................................... 33
Study 2: Relationships between arterial stiffness and static resting-state functional connectivity
in the default mode network ................................................................................................. . 35
Introduction ........................................................................................................................... 36
Methods ................................................................................................................................. 36
Results ................................................................................................................................... 38
Discussion .................................................................................................................. ........... 39
v
Tables .................................................................................................................................... 46
Figures ………………………………………………………………………………....…... 47
Study 3: Relationship between blood pressure variability and amplitude of low frequency
fluctuations in resting-state functional MRI.......................................................................... 50
Introduction ........................................................................................................................... 51
Methods.................................................................................................................................. 52
Results ................................................................................................................................... 52
Discussion ............................................................................................................................. 53
Tables .................................................................................................................................... 55
Figures ................................................................................................................................... 56
Study 4: Short term variability in beat-to-beat blood pressure is related to dynamic
resting-state functional connectivity in the default mode network in non-demented
older adults ............................................................................................................................ 57
Introduction ........................................................................................................................... 58
Methods ................................................................................................................................. 61
Results ................................................................................................................................... 64
Discussion .............................................................................................................................. 65
Tables ..................................................................................................................................... 70
Figures ................................................................................................................................... 76
General Discussion ................................................................................................................ 80
References .............................................................................................................................. 87
Author Note ............................................................................................................................ 121
Supplementary Tables ............................................................................................................ 122
vi
List of Tables
1.1 VaSC MRI scan parameters ............................................................................................. 31
1.2 Demographic characteristics and mean values on hemodynamic measures in the VaSC study
sample .................................................................................................................................. 32
1.3 Summary of seed-to-voxel analyses: systolic blood pressure variability............................. 33
2.1 Summary of seed-to-voxel analyses: pulse wave velocity.................................................. 47
2.2 Summary of seed-to-voxel analyses: pulse pressure .......................................................... 48
3.1 Summary of ALFF analyses............................................................................................. 56
4.1 Resting-state functional connectivity analytical methods................................................... 71
4.2 LEMON rsfMRI scan parameters..................................................................................... 72
4.3 Demographic characteristics and mean (standard deviation) values on hemodynamic
measures in the LEMON study sample .................................................................................. 73
4.4 Association between systolic BPV and temporal variability in functional connectivity of
DMN hubs, controlled for age ........................................................................................ ……75
4.5 Association between systolic BPV and temporal variability in functional connectivity of the
hippocampus, controlled for age ............................................................................................ 76
vii
List of Figures
1. Graphical representation of the Windkessel effect..................................................................... 3
2. Visual representation of human vasculature.............................................................................. 5
3. Normal values for pulse wave velocity by age group................................................................ 7
4. Illustration of changes in waveform. ......................................................................................... 7
5. Rendering of the neurovascular unit........................................................................................ 11
6. Visual schematic of rsfMRI methods used in this dissertation ............................................... 14
7. Illustration of key regions in default mode network................................................................ 15
1.1. Higher systolic BPV was associated with lower connectivity between the ventral
DMN and anterior cingulate, bilateral supplementary motor areas, bilateral precentral gyri,
and left occipital regions ............................................................................................................. 35
1.2. Higher systolic BPV was associated with lower connectivity between the dorsal DMN
and left lateral frontal regions .................................................................................................... 36
2.1. Higher PWV was associated with lower functional connectivity between the ventral
DMN and the bilateral supramarginal gyri, the angular gyrus, the left parietal operculum
cortex, and the cingulate, the left planum temporale, left postcentral, and the left superior
temporal gyri ................................................................................................................................50
2.2. Higher PWV was associated with lower connectivity between the dorsal DMN and the
precuneus, the angular gyrus, the left occipital cortex, the left supramarginal gyrus, the
cingulate gyrus, the left superior temporal gyrus, and the left postcentral gyrus ........................ 51
3.1. Systolic BPV was associated with greater ALFF in regions overlapping with region
in the posterior DMN .................................................................................................................. 58
4.1 Higher systolic blood pressure variability was associated with higher temporal variability
viii
in the functional connectivity between the posterior cingulate cortex and areas in the
bilateral frontal lobes, right angular gyrus, and precuneus. Results were significant after
controlling for age and gender.................................................................................................... 78
4.2 Higher systolic blood pressure variability was associated with higher temporal variability
in the functional connectivity between the medial prefrontal cortex and areas in the bilateral
frontal lobe, bilateral supramarginal gyrus, left occipital lobe, and precuneus. Results were
significant after controlling for age and gender .......................................................................... 79
4.3 Higher systolic blood pressure variability was associated with higher temporal variability
in the functional connectivity between the lateral parietal regions of the DMN and areas in
the right supramarginal, left occipital cortex, right frontal pole, bilateral paracingulate, left
insula. Results were significant after controlling for age and gender.......................................... 80
4.4 Higher systolic blood pressure variability was associated with higher temporal variability
in the functional connectivity between the bilateral hippocampus and precuneus. Results
were significant after controlling for age and gender .................................................................. 81
ix
Abbreviations
AD Alzheimer’s disease
ALFF amplitude of low frequency fluctuations
ASL arterial spin labeling
BOLD blood-oxygenation-level-dependent
BP blood pressure
BPV blood pressure variability
CONN an open-source Matlab/SPM-based cross-platform
software for fMRI computation, display, and analysis
CBF cerebral blood flow
CompCor component-based noise-reduction method
CV coefficient of variation
DMN default mode network
FDR false discovery rate
fMRI functional MRI
FWHM full width at half maximum
GICA group-level ICA
GLM general linear model
ICA independent components analysis
L left
LEMON Leipzig Mind-Brain-Body Dataset
LP lateral parietal
MAP mean arterial pressure
MCI mild cognitive impairment
mmHg millimeters of mercury
MNI Montreal Neuroimaging Institute
x
MPFC medial prefrontal cortex
MPRAGE magnetization prepared rapid gradient echo
MRI magnetic resonance imaging
pcASL pseudo-continuous ASL
PCC posterior cingulate cortex
PP pulse pressure
R right
ROI region of interest
rsfMRI resting-state fMRI
RSN resting-state network
SD standard deviation
SPM statistical parametric mapping
TE echo time
TR repetition time
USC University of Southern California
VaSC Vascular Senescence & Cognition
xi
Abstract
The brain and heart communicate with each other in a dynamic and continuous concert of
nerve impulses, hormones, and pulse waves. The brain-heart connection is long-established, but
only recently has cerebrovascular function become a focus of brain aging and neurocognitive
disorder research. While studies indicate that vascular risk is associated with numerous
undesirable neurocognitive outcomes, the current literature focuses less on the interplay between
vascular and brain function. The overarching aim of this project is to examine links between
markers of age-related vascular dysfunction, arterial stiffness specifically, and brain function.
Studies 1 and 2 investigate the association between markers of arterial stiffness and static default
mode network (DMN) connectivity in non-demented community-living older adults. In Study 1,
we investigate the effect of long-term blood pressure variability (BPV) separately from other
more traditional indices of arterial stiffness used in Study 2 (e.g., pulse wave velocity and pulse
pressure) due to the unclear mechanistic pathways linking BPV and arterial stiffness. We extend
this investigation in Study 3 by examining the relationship between arterial stiffness and the
amplitude of low frequency fluctuations in rsfMRI signal, reflecting regional spontaneous
neuronal activity. Finally, Study 4 uses a sliding-window technique to investigate dynamic
relationships between simultaneously measured brachial artery blood pressure derived estimates
of arterial stiffness and temporal variability in DMN connectivity. This dissertation aims to
improve our understanding of how vascular factors affect brain health and function in later life.
Results from these studies may shed light on potential target mechanisms for interventions
aimed at maintaining optimal brain health and cognitive function in older adults.
1
1
GENERAL INTRODUCTION
‘[people] are as old as [their] arteries ...’
Thomas Sydenham (1624 – 1689)
2
Vascular dysfunction has long been thought to play a critical role in aging, the
development of cognitive impairment, and the risk of dementia (Breteler, 2000; Kivipelto et al.,
2001; Li et al., 2011; Snyder et al., 2015). However, the specifics of that role remain
incompletely understood. Though the brain represents about 2% of total human body mass, it
receives up to 20% of cardiac output and is responsible for about 20% of the body’s oxygen and
25% of its glucose consumption (Zlokovic, 2011). Comparatively, the amount of blood that
perfuses brain tissue is similar to the amount of blood the perfuses the skeletal muscles
throughout the whole body, which also receive about 20% of cardiac output while at rest (Xing
et al., 2017). The brain stores little energy, relying heavily on cerebral blood flow (CBF) for
immediate glucose supply (Gary F. Mitchell et al., 2011). To further underscore this, the brain
vasculature contains over 400 miles of vessels, with approximately a 1:1 ratio of capillary to
neuron (Zlokovic, 2005). Vascular aging brings about structural and functional changes within
the circulatory system that are linked to myriad structural and functional brain outcomes
affecting behavior, cognition, and related health risks (de la Torre, 2012; Gorelick et al., 2011;
Gary F. Mitchell et al., 2004; Sabayan et al., 2015). Stiffening of the large arteries is one
characteristic of cardiovascular aging that is linked to a higher risk of mortality, cardiovascular
disease, cerebrovascular events, and cognitive decline (Cooper & Mitchell, 2016; Mattace-Raso
et al., 2006; van Popele et al., 2001; van Sloten et al., 2015). However, it is unclear how arterial
stiffening relates to brain function, generally, or why it is associated with cognitive decline.
Understanding the role arterial stiffness plays in brain health, structural integrity, and function,
may allow for the development of targeted treatments that reduce the risk of associated
undesirable outcomes such as cerebrovascular injury, cognitive decline, and dementia.
3
Healthy Aortic and Large Artery Function.
With each contraction, the heart generates a pulse wave that directs blood flow through
the circulatory system. Compliance of the aorta and large blood vessels plays an integral role in
creating a steady and continuous peripheral blood flow (Gary F. Mitchell, 2008). By acting as an
elastic balloon that expands during the systole and contracts during the diastole, the aorta
dampens systolic arterial pressure and decreases the amplitude of the pulse wave as it travels
from larger arteries through to the microvasculature (Lyle & Raaz, 2017; G. F. Mitchell, 2011;
Gary F. Mitchell et al., 2004). This phenomenon is described as a Windkessel function (Figure
1) and is crucial for the health and function of the brain, a target-end organ that requires
continuous and steady blood perfusion (A. Benetos et al., 1997).
Figure 1. Graphical representation of the Windkessel effect. The aorta and large elastic arteries
expand during the systole to accommodate blood pumped from the heart, and contract to push
the blood forward during the diastole. The system works to produce continuous steady
perfusion throughout the circulatory system. Credit: Google image search.
4
In healthy younger adults, the peripheral arteries are smaller and stiffer than the
compliant aorta and larger arteries due to factors such as artery wall thickness and diameter
(Gary F. Mitchell et al., 2008). As a large artery approaches a target organ, the vessel branches
off into small and smaller vessels that ultimately form arterioles and, eventually, capillaries
(Figure 2, panel A). The structural differences between the large and small arteries create a
transition in impedance encountered by the pulsatile wave after heart contraction. The resistance
of the peripheral vasculature causes part of the pulse wave to reflect towards the heart in the late
systole, raising diastolic pressure that aids coronary perfusion (Nichols et al., 2011). The
reflected wave also reduces the amplitude of the next pulse wave it encounters (O’Rourke &
Hashimoto, 2007). The process of wave reflection works as a preservative function that buffers
against the pulsatile ejection of blood from the heart. Wave reflection prevents cerebrovascular
damage and dysfunction by precluding excessive pulsatile energy from entering the cerebral
microcirculation (Gary F. Mitchell et al., 2011).
5
Figure 2. Visual representation of human vasculature.
Panel A is a visual representation of the different vessels in the circulatory system. Panel
B demonstrates compositional differences in arteries, arterioles, and capillaries. Credit:
Publicly available image via google image search.
6
Aging and Artery Stiffening.
As the aorta and large arteries stiffen with increasing age, the vessels become less
compliant due to changes in composition. Large arteries are composed of several layers, each
with a unique function (Figure 2, panel B) (Kohn et al., 2015). Elastin fibers are a type of
connective tissue made of elastin protein that help to form the medial layer of a large artery
(Farand et al., 2007). These fibers are of great importance for the artery to stretch in response to
increased pressure (Samila & Carter, 1981). Notably, elastin has a slow turnover rate and is
estimated to last across an individual’s lifespan. The longevity of elastin allows for many years
of wear and an accumulation of changes, including fragmentation and calcification, that
contribute to the loss of elasticity (Schlatmann & Becker, 1977; Shapiro et al., 1991; Wagenseil
& Mecham, 2009). While elastin fibers decay, stiffer collagen fibers increase in concentration in
all layers of the arterial wall (Kohn et al., 2015; Schlatmann & Becker, 1977). The elastin-
tocollagen ratio shift first affects the elasticity of the aorta, followed by nearby large arteries, and
results in several hemodynamic changes.
For one, age-associated large artery stiffening results in reduced Windkessel effect and
increased pulse wave velocity (PWV), or rate of the forward moving pulse wave (Figure 3).
Subsequently, the reflected waves return to the heart earlier and have an additive, rather than
reductive, effect on the next pulse wave (Figure 4) (Gary F. Mitchell et al., 2004; O’Rourke &
Safar, 2005). The additive pulse wave effect gives rise to greater pulse pressure (PP), greater BP
variability (BPV), contributes to the pathogenesis of hypertension, and increases the risk of
major cardiovascular events (Gary F Mitchell, 2014). The brain relies on a high level of blood
flow which makes it particularly susceptible to damage and dysfunction, both of which are
associated with changes in the pulse wave. Damage and dysfunction are thought to occur whe n
7
cerebral microvascular impedance no longer matches the amount of wave reflection at the
junction between the aorta and peripheral arteries.
Figure 3. Normal values for pulse wave velocity by age group
(Reference Values for Arterial Stiffness’ Collaboration, 2010).
8
Figure 4. Illustration of changes in waveform.
Forward wave is denoted in blue, reflected wave in green. The red line is indicative of the
arterial pulse waveform. Credit: http://www.complior.com/info-center Arterial Stiffening
and Blood Pressure Variability.
BP fluctuates around an average value over long and short periods, representing
responses of cardiovascular mechanisms to both internal and external stimuli (Miyauchi et al.,
2019). Arterial stiffening and BPV demonstrate a synergistic relationship that is currently
without a well-characterized temporal sequence. Stiffening of the arterial tree leads to increased
BPV; however, data also suggests that increases in BPV trigger stiffening mechanisms, including
arterial modeling (Messerli et al., 2019). In a recent review of BPV and artery health, Miyauchi
et al. (2019) describe that higher long-term systolic BPV positively correlates with PWV and is
associated with less large vessel distensibility in cross-sectional and prospective studies. It is still
unclear whether BPV is a cause or marker of arterial stiffness.
Arterial Stiffening and Microvascular Dysfunction.
9
As large arteries stiffen, the abnormal physical forces due to increased blood flow
pulsatility trigger damage to the thinner, more fragile microvasculature (Gary F. Mitchell et al.,
2011). In response to increased pulsatile stress, the microvasculature undergoes structural
remodeling, including dilation and rearrangement in the distribution of wall thickness, in order to
constrict and increase vascular resistance to maintain optimal perfusion given increased
amplitude of arterial pulsation and changes in mean arterial pressure (MAP) (Jacobsen et al.,
2008; Gary F. Mitchell, 2008; Gary F. Mitchell et al., 2005, 2008; Safar et al., 2012). Higher
aortic stiffness and the resulting increase in pulsatility are also associated with blunted
microvascular reactivity, such that there is an impaired hyperemic response to ischemic stress
(Gary F. Mitchell et al., 2005). Impaired blood flow response has important implications for
brain function, which relies heavily on the ability of the vasculature to increase blood flow in
response to increased neuronal metabolic demand (Nippert et al., 2018). Furthermore, artery
remodeling and microvascular damage are critical parts of the proposed mechanisms by which
arterial stiffness affects cognition, as the brain is particularly vulnerable to the changes in
pressure and flow (Elias et al., 2009; Gary F. Mitchell, 2008; Gary F. Mitchell et al., 2011;
Singer et al., 2013; Zhong et al., 2014). There is evidence that cerebrovascular damage, as
measured by white matter lesion burden and cerebrovascular resistance, mediates the association
between arterial stiffening and cognition (Cooper et al., 2016; Cooper & Mitchell, 2016).
Arterial Stiffening and Neurovascular Dysfunction.
Arterial stiffening is associated with notable structural brain changes. Few studies have
looked at its relationship with brain function, despite good reason to believe there would be a
negative association. In response to excessive pulsatile energy, arteries and smaller arterioles
stiffen to restrict the level of pulsatile flow into capillary beds (Gary F. Mitchell, 2008).
Accordingly, pseudo-continuous arterial spin labeling (pcASL) studies have shown that arterial
10
stiffening is related to reduced whole-brain CBF (Jefferson et al., 2018). Additionally, there are
discernable reductions in regional CBF in areas of the brain that are vulnerable to both
microvascular damage and early-stage Alzheimer’s disease (AD) related pathophysiological
changes, such as the medial temporal lobes (Hughes et al., 2013; Jefferson et al., 2018; G. F.
Mitchell, 2011).The presence of vascular risk factors moderates the relationship between age and
CBF, such that those with greater vascular risk show reduction of CBF in frontal, parietal, and
medial temporal brain regions (Bangen et al., 2014). Individuals with elevated AD risk
demonstrate reduced and increased CBF, with greater CBF corresponding to superior cognitive
performance (Dai et al., 2009; Johnson et al., 2005; G. Xu et al., 2007).
The neurovascular unit (Figure 5) is responsible for maintaining functional hyperemia, or
the ability to increase blood flow in response to neural activation (Muoio et al., 2014; Zlokovic,
2011). The neurovascular unit consists of neurons, astrocytes, vascular smooth muscle cells
surrounding arteries and arterioles, pericytes enveloping capillaries, and vascular endothelial
cells. Increased pulsatility due to arterial stiffening may affect the neurovascular unit by
contributing to brain tissue damage in the form of white matter hyperintensities (Jolly et al.,
2013). Dysregulation of cerebrovascular autoregulation evidenced by changes in cerebral
perfusion may lead to loss of functional hyperemia (Iadecola, 2004). In other words, the
cerebrovascular system cannot adequately increase CBF to meet neuronal metabolic demands.
Patients with AD and cognitive impairment show decreased functional hyperemia (Nicolakakis
& Hamel, 2011). In a healthy brain, cerebral autoregulation maintains consistent CBF by
adjusting to fluctuations in MAP (Cerebral Autoregulation., 1990). Part of the autoregulation
mechanism is cerebrovascular resistance, equal to the ratio of cerebral perfusion pressure (MAP
– intracranial pressure (ICP)) to CBF. Because ICP is smaller than MAP and relatively constant
at rest, we can estimate a cerebrovascular resistance index with the ratio of MAP to CBF defines
11
(CVRi ≈ MAP/CBF). Under typical conditions, cerebrovascular resistance remains low because
the brain requires a high blood supply. Cerebrovascular resistance increases in aging and
neurodegenerative disease and associates with cognitive impairment and cognitive decline over
two years (Clark et al., 2015; Yew & Nation, 2017). Notably, MAP is also an important
determinant of arterial stiffness. As MAP increases, so does the recruitment of collagen fibers
and, accordingly, arteries grow stiffer (O’Rourke, 1976). Therefore, we need to consider MAP
when interpreting any relationship between arterial stiffness and neurovascular function.
Figure 5. Rendering of the neurovascular unit. The pial arteries give rise to the intracerebral
arteries which penetrate brain tissue. The intracerebral arteries branch into smaller arteries,
arterioles, and eventually capillaries. Credit: Zlokovic (2011).
Further investigation of the effects of arterial stiffness on brain neurovascular function is
needed. Brain hemodynamics are a vital contributor to blood-oxygenation-level-dependent
(BOLD) signal used in resting-state functional magnetic resonance imaging (rsfMRI) functional
12
connectivity analyses. BOLD is a blood-related signal that detects changes in blood flow,
volume, and oxygenation, delineating local neural activity. It is possible to evaluate brain activity
at rest or while the participant is engaged in a task. BOLD is a neurovascular signal that is not
independent of CBF. However, the overall understanding of how the level of CBF in regions
overlapping with a particular neural network might influence connectivity is poor.
To date, only one study (published during the current study) has examined the
relationship between age-related stiffening and rsfMRI functional connectivity in healthy
younger and middle-aged adults (Hussein, 2020). The primary result from this study was that
aortic PWV was negatively associated with resting-state functional connectivity, particularly in
the medial‐temporal, medial-frontal, and precuneus/posterior cingulate regions (Hussein et al.,
2020). There has also only been one task-based fMRI study in which healthy middle-aged adults
with stiffer arteries, measured outside of the scanner, showed decreased task-related brain
activation during a verbal working memory task (Jennings et al., 2017). More generally,
functional connectivity studies in those with higher vascular risk (e.g., hypertension, metabolic
syndrome, diabetes) are limited.
Default Mode Network.
The brain is constantly active, and even at rest, exhibits coherent fluctuations of activity
that organize into several resting-state networks (RSNs) (Damoiseaux et al., 2006).
Lowfrequency oscillations (<0.1 Hz) of the BOLD signal relay functional information about
brain networks (Biswal et al., 1995). The correlation of synchronized functional activation in
different brain regions, using pairwise Pearson’s correlation coefficients, is called functional
connectivity (Smitha et al., 2017). Research supports that anatomically disparate brain regions
that show a temporal correlation in BOLD signals are intrinsically functionally connected.
Therefore, most rsfMRI analysis techniques examine correlations between BOLD signal in
13
regions to assess functional connectivity. Figure 6 highlights the rsf MRI techniques used in this
dissertation project. One of the most common rsfMRI analysis techniques, seed-based
correlations (Figure 6a), examines the functional connectivity between a selected region (seed)
and every other voxel in the brain. Using a subtype of seed-based analysis, one can also look
specifically at the functional connectivity between two specific brain regions (known as ROI-to-
ROI functional connectivity). Functional connectivity provides information regarding
synchronization of activity in brain regions within a network, and therefore we can identify
network disruption. However, functional connectivity does not convey information about
regional changes in BOLD signal or pinpoint where abnormal spontaneous fluctuation in neural
activity is occurring in the brain. The amplitude of low-frequency fluctuations (ALFF) measures
BOLD signal magnitude on a voxel-by-voxel basis (Zou et al., 2008). ALFF is the root mean
square of the BOLD signal amplitude of every voxel in the brain that falls into the low-frequency
band (0.01-0.1Hz) ALFF
(Figure 6b) is thought to reflect properties of local spontaneous activity and offers
complementary information to functional connectivity analyses (Soares et al., 2016). The third
analysis we use in this dissertation, dynamic functional connectivity (Figure 6c), addresses
fluctuations in patterns of neural activity. In rsfMRI, most techniques assume that functional
connectivity remains constant throughout the scan time. In fact, there is a wealth of literature
suggesting that RSN functional connectivity metrics vary over time in response to both external
and internal stimuli. There are multiple approaches to quantifying these fluctuations, the most
common being sliding-window analysis (Allen et al., 2014; Hutchison et al., 2013). Dynamic
connectivity patterns represent a largely unexplored area of rsfMRI research. Although there are
significant challenges to choosing the best methodology and limitations in interpretation,
dynamic functional connectivity represents a new frontier in the study of brain connectivity as
14
we continue to advance the knowledge, the availability and development of tools, and the
available data in this area.
Figure 6. fMRI analysis methods used in this dissertation study. A) represents seed-
based correlations, B) represents amplitude of low frequency fluctuations (ALFF), and
C) represents dynamic functional connectivity.
Credit: This image was adapted from a figure published by Soares et al. (2016).
The default mode network (DMN) is one heavily studied RSN activated at rest and
attenuated during tasks. The DMN, which includes the posterior cingulate cortex (PCC), medial
prefrontal cortex (MPFC), inferior parietal, inferolateral temporal, anterior cingulate, precuneus,
and hippocampal brain regions (see Figure 7), is a network of focus in studies of brain
connectivity as these regions are more active during rest (Raichle, 2015). RSNs, particularly the
DMN, play an important role in maintaining brain function across the lifespan (Andrews-Hanna
et al., 2010). Whole-brain functional connectivity analysis in older adults has revealed reduced
connectivity within RSNs (Chan et al., 2014). More specifically, resting-state DMN connectivity
is inversely associated with age (Andrews-Hanna et al., 2007; Damoiseaux et al., 2008). Studies
report that in comparison to younger participants, older individuals show reduced task-related
15
deactivation of the DMN (Qin & Basak, 2020). This finding reflects a less efficient ability to
switch from “default mode” to activation of attentional networks required by the task and relates
to lower cognitive performance, a process known as neuromodulation (Kennedy et al., 2017; Qin
& Basak, 2020). Consistent with this, studies have shown that while aging is associated with
reduced within-network connectivity, older adults exhibit elevated connectivity between RSNs
(Chan et al., 2014). Of note, the PCC demonstrates robust connectivity with the hippocampus
(Maddock, 1999). Medial temporal lobe structures within the brain, including the hippocampus
and entorhinal cortex, are essential for memory function. These areas show functional and
volumetric abnormalities in those diagnosed with AD and mild cognitive impairment (MCI)
(Braak & Braak, 1991; Dickerson & Sperling, 2008; Squire & Zola-Morgan, 1991). The DMN
shows a negative association with cognitive engagement and increased activation during rest or
reflection, and the DMN plays a crucial role in episodic memory (Fox & Greicius, 2010;
Greicius, Krasnow, Reiss, & Menon, 2003; Raichle et al., 2001). DMN connectivity at rest also
predicts memory performance (Sala-Llonch et al., 2012; L. Wang et al., 2010). Wang et al.
(2010) reported that lower connectivity between the hippocampus and posterior DMN regions
(e.g., PCC and precuneus) was associated with worse episodic memory in healthy older adults.
16
Figure 7. Illustration of key regions in default mode network
Furthermore, patients with different neurological conditions exhibit additional
abnormalities in resting-state functional connectivity, including AD, Parkinson’s disease, and
frontotemporal dementia (Agosta et al., 2012; Filippi et al., 2013, 2017, 2019). Research has
reported lower functional connectivity of the DMN at rest amnestic MCI and across the AD
continuum (Cha et al., 2013; Eyler et al., 2019; Soman et al., 2020; Xue et al., 2019). Therefore,
abnormal activity of the DMN, and other RSNs, could be a noteworthy marker of cognitive
dysfunction in aging (Palop & Mucke, 2016). Other abnormalities include reduced activation,
compared to healthy older adults (joint height and extent thresholds of p < 0.05), of the posterior
cingulate, precuneus, and hippocampus. These are regions in which AD patients show
hypoperfusion, in other words, insufficient blood flow response to metabolic demand as detected
by arterial spin labeling (ASL) functional MRI (fMRI) studies (Alsop, Casement, de Bazelaire,
Fong, & Press, 2008; Dai et al., 2009; Greicius et al., 2004; Johnson et al., 2005). These regions
also overlap with areas showing lower CBF in individuals with high PWV (Jefferson et al.,
2018b; G. F. Mitchell, 2011). Thus, arterial stiffness may be one factor contributing to lower
brain connectivity.
Arterial Stiffening and Neurocognitive Function.
Altered cerebral hemodynamic response to neural activation may also be one way in
which aortic stiffening affects cognition. Cognitive function is an important determinant of
wellbeing and quality of life in older adults (Wilson et al., 2013). Memory complaints are
common in aging adults, yet they can arise from numerous sources, including neurodegeneration,
AD-related pathophysiology, cerebrovascular damage, drug and toxin exposure. There is
17
mounting evidence that arterial stiffening contributes to cognitive decline and risk of dementia
(Fujiwara et al.,
2005; Gary F. Mitchell et al., 2011; Poels et al., 2007; Waldstein et al., 2008). Individuals with
vascular dementia and AD exhibit higher PWV than non-impaired individuals (Hanon et al.,
2005). In contrast, others found no difference in PWV between vascular dementia, AD, or
cognitively normal participants (Dhoat et al., 2008). It is unclear whether arterial stiffening
directly affects cognition or indirectly via exacerbation of brain pathophysiological changes
leading to neurodegeneration. Many studies have linked PWV with cognitive function and
dementia, such that greater aortic stiffening is related to a higher risk of dementia, and with
lower performance on global cognition and domain-specific cognitive performance in memory,
executive function, and psychomotor speed (Cooper et al., 2016; Fukuhara et al., 2006; Gary F.
Mitchell et al., 2011; Nilsson et al., 2014; Poels et al., 2007; Scuteri et al., 2013; Tsao et al.,
2013; Waldstein et al., 2008; Watson et al., 2011; Zhong et al., 2014). However, there is some
evidence that PWV is most strongly associated with a decline in memory (Elias et al., 2009;
Gary F. Mitchell et al., 2011; Zhong et al., 2014). These results are present in patient
populations, including those with MCI and dementia, and in healthy older adults without
cognitive impairment (Fukuhara et al., 2006; Hanon et al., 2005; Watson et al., 2011; Zhong et
al., 2014). The relationship between stiffness and cognitive performance has been observed both
cross-sectionally (Fukuhara et al., 2006; Gary F. Mitchell et al., 2011; Matthew P Pase et al.,
2010; Poels et al., 2007; Scuteri et al., 2007) and longitudinally (Athanase Benetos et al., 2012;
Elias et al., 2009; M. P. Pase et al., 2012; Singer et al., 2013; Waldstein et al., 2008; Watson et
al., 2011). Individuals in the highest tertile of PWV exhibit a faster decline in global cognitive
function (Zeki Al Hazzouri et al., 2013). On the other hand, Poel et al. (2007) did not observe a
longitudinal relationship between PWV and cognitive performance, potentially due to their
18
young and healthy sample. Furthermore, the relationship between PWV and cognition
demonstrates independence from other cardiovascular factors, such as BP and heart rate (Hanon
et al., 2005; Kearney-Schwartz et al., 2009; Poels et al., 2007; Scuteri et al., 2007; Triantafyllidi
et al., 2009). Cognition is associated with other markers of arterial stiffness, including pulse
pressure and blood pressure variability (Lattanzi et al., 2014; Levin et al., 2020). While there is
accumulating support that arterial stiffness plays a role in cognition and that cerebrovascular
damage mediates this relationship, few have focused on how arterial stiffening affects the
functional contributions of arterial stiffness to modulating blood supply in response to brain
activation.
Clinical Significance.
Notably, existing evidence shows that BP-lowering treatment lowers PWV (Asmar, 2001;
Gary F. Mitchell et al., 2007). Subsequently, reduction of arterial stiffness also increases
baroreceptor sensitivity and reduces BPV (Chapleau, 2012). As hypertension is highly prevalent
and commonly treated in older adults, a better understanding of how arterial stiffening
contributes to cognitive decline and dementia, independent of sympathetic function and
autonomic control changes, may shed light on modifiable risk for aging individuals. This
knowledge is highly relevant for both clinical practitioners and public health outreach endeavors.
Hypertension is also of particular concern as the Center for Disease Control (CDC) reports that
only about half of hypertensive adults in the United States receive treatment, and about a third of
those with uncontrolled hypertension are not aware of their condition (Merai et al., 2016).
Hypertension control rates are even lower for adults older than 65, ranging from 33 to 46 percent
(Muntner et al., 2018). Poor control rates are of the utmost importance as lowering systolic BP
may significantly decrease the risk of MCI and dementia (Larstorp et al., 2012). In fact,
19
aggressive BP-lowering via pharmaceutical intervention is the only documented treatment that
has demonstrated a lower risk of MCI in a randomized control trial (SPRINT MIND
Investigators for the SPRINT Research Group et al., 2019).
20
SPECIFIC AIMS
In review, arteries grow stiffer during normal aging, largely due to loss of elasticity in the
vessel walls after a lifetime of mechanical stress. Stiffer arteries are associated with lower CBF,
suggesting that arterial stiffness may contribute to impaired regulation of blood supply to the
brain. Neural activity depends on vascular mechanisms that increase local CBF, enabling
adequate delivery of oxygen and nutrients to active neurons. The brain is always active, even at
rest, and activity organizes into functional RSNs characterized by synchrony of neuronal activity
between functionally linked brain regions. The DMN is one RSN that shows disruption in the
context of disordered aging, namely MCI and AD. While several studies have examined links
between arterial stiffness and structural brain biomarkers of MCI and AD (e.g., white matter
lesion burden, cortical atrophy, amyloid and tau burden), few have studied the role of arterial
stiffness in brain neurovascular function. Therefore, the present study sought to investigate
whether individuals with more arterial stiffness exhibit greater breakdown in resting-state DMN
connectivity.
This dissertation aims to provide new information about how arterial stiffness is related
to brain function. We measure arterial stiffness using PP, PWV, and BPV. We index brain
functional connectivity using two ROI-level analyses: 1) seed-based correlations between key
regions of interest (ROIs) in the DMN and all other voxels in the brain and 2) region-to-region
associations between key areas within the DMN. We build on functional connectivity analyses
by examining relationships between arterial stiffness and ALFF, a marker of spontaneous neural
activity. Finally, we characterize the relationship between synchronously measured BPV and
DMN functional connectivity variability. This research aims to fill gaps in our knowledge of the
vascular mechanisms underlying neurocognitive dysfunction in late life.
21
In Study 1 and 2, we first aim to characterize the relationship between markers of arterial
stiffness and static measures of resting-state functional connectivity of the DMN. In Study 3, we
next characterize the relationship between arterial stiffness and abnormal spontaneous neural
activity in the brain. Studies 1-3 aim to use a novel integration of neuroimaging (fMRI) and
physiological measures (BPV, PWV, PP) to evaluate the relationship between arterial
stiffness and rsfMRI metrics. The primary hypothesis tested in Studies 1 and 2 is that greater
arterial stiffness will be associated with greater DMN dysfunction, indexed by lower static
functional connectivity. Study 3 will build on functional connectivity studies by examining a
secondary exploratory hypothesis that greater arterial stiffness will be associated with greater
abnormalities in regional neural activity, as quantified by ALFF.
Study 4 aims to extend findings by investigating whether a dynamic relationship exists
between BP and brain function over a 15-minute-30-second MRI scan. This study capitalizes
on a unique dataset of simultaneously collected beat-to-beat BP and fMRI data to examine
how arterial stiffness (systolic BP & BPV) is related to temporal variability in resting-state
functional connectivity. Study 4 is the first to address synchrony between arterial stiffness
markers and brain functional connectivity in real-time. The primary hypothesis is that higher
systolic BP and BPV measured in the scanner will be associated with higher temporal variability
in resting-state DMN connectivity throughout the scan.
Together, these studies build on growing work investigating how cerebrovascular
dysfunction may be related to neurocognitive changes in older adults. The current project also
may provide necessary groundwork for future identification of novel treatment targets or
prevention strategies for optimal brain health throughout the lifespan.
22
Study 1: Relationship between blood pressure variability and static resting-state functional
connectivity in the default mode network
Anna E. Blanken, Jung Yun Jang, S. Duke Han, Isabel Sible, Belinda Yew, Yanrong Li, Daniel
A. Nation
23
INTRODUCTION
In brief review, researchers believe BPV reflects greater artery stiffening (Zhou et al.,
2018). Arterial stiffness and BPV may exhibit a synergistic relationship that negatively impacts
brain health and function (Messerli et al., 2019). The risks of higher BPV are just recently a
focus of neurocognitive research. For example, studies show that higher BPV associates with
poorer cognition. However, there is no clear mechanism for this relationship (Rouch et al.,
2020). It is possible that increased arterial stiffness, in combination with higher BP fluctuation,
causes damage to the white matter through cerebral hypoperfusion (Ma et al., 2020). Changes in
cerebral perfusion may also impact neural activity and functional connectivity, representing
another mechanistic pathway linking arterial stiffness to poorer cognition.
The current study examined the association between BPV and resting-state DMN
functional connectivity in older adults. The overall hypothesis was that greater BPV would be
associated with greater DMN disruption. We investigated both systolic and diastolic BPV as
regressors. Functional connectivity outcomes included seed-based correlations of ROIs within
the DMN to the whole brain and region-to-region correlations within the DMN.
METHODS
Participants.
Participants were drawn from the VaSC study, a longitudinal prospective study. The
VaSC study follows older adults ages 55+. We recruited the older adult participants in the VaSC
study from the Healthy Minds Volunteer Corp at USC. Healthy Minds is a pool of
communitydwelling older adults in the greater Los Angeles area. Subjects were excluded based
on known confounding factors in our vascular, brain, and cognitive function measures. Exclusion
criteria for all subjects were as follows: dementia diagnosis either by self-report or Mattis
Dementia
24
Rating Scale (DRS-2) score ≤ 126, history of stroke, lacunar infarct, head injury with loss of
consciousness greater than 15 minutes, substance abuse, drug use, any current neurological
disorder, weight > 270 pounds, history of multiple myocardial infarctions, any current organ
failure, any current psychiatric disorder, B12 deficiency, hypothyroidism, MRI contraindication,
use of medication likely to influence brain or vascular function, Type I diabetes, and English as a
second language.
Procedures.
We conducted eligibility screening over the phone. We invited eligible individuals to
participate in a two-day study visit conducted within two weeks. Study procedures included
physiological measurements (e.g., height, weight, BP, PWV), blood draws, questionnaires,
comprehensive neuropsychological evaluation, and an MRI scan. All participants completed
informed consent, and the USC Institutional Review Board approved the study protocol (#HS14-
00784). Participants also provided a study informant, who was interviewed over the phone and
completed several questionnaires regarding the participant’s cognitive and functional status.
Participants received monetary compensation for their participation after each study day.
Blood Pressure Variability Procedures and Calculation.
We collected four measurements of brachial artery BP. Two measurements occurred
before neuropsychological testing at visit one and two before MRI examination at visit two. An
examiner used either a calibrated mercury sphygmomanometer or Omron electric blood pressure
monitor to collect BP measurements while the participant was seated and resting. We calculated
average BP and BPV (SD, coefficient of variation [CV]) using the four BP measurements
collected. The current research defines CV as 100 × SD/mean.
MRI Data Acquisition.
25
Participants who passed eligibility and safety criteria for the MRI scan underwent a
standard protocol of structural and fMRI, using a 3T Siemens Prisma scanner with a 32-channel
head coil at USC. We asked participants to lie still with their eyes open. For structural data,
highresolution T1-weighted images are acquired using 3D magnetization prepared rapid gradient
echo (MPRAGE) sequences. Table 1.1 describes MRI sequence parameters.
rsfMRI Analyses.
All images were preprocessed and analyzed using CONN Toolbox Version 20b
(http://www.nitrc.org/projects/conn/) and SPM12 (http://www.fil.ion.ucl.ac.uk/spm).
rsfMRI Data Preprocessing.
We used a traditional preprocessing pipeline provided by CONN. Scan preprocessing
steps included realignment to the first scan, correction for slice-timing discrepancies, spatial
normalization to the Montreal Neuroimaging Institute (MNI) template in SPM12, and spatial
smoothing with an 8 mm full width at half maximum (FWHM) Gaussian kernel. Nuisance
signals were removed by applying temporal band-pass filtering (0.009 - 0.1 Hz) and a nuisance
regression approach to control nuisance variability and reduce the impact of physiological and
other non-neural artifacts on connectivity analyses. Nuisance variables included six motion
parameters and their first-order derivatives, five principal components each from white matter
and cerebrospinal fluid masks, a component-based noise correction strategy, CompCor (Behzadi
et al., 2007), and a linear detrending term (a total of 23 regressors). CONN also uses artifact
detection (ART) to scrub out data points contaminated by motion. We used a conservative
framewise displacement threshold of 0.55 millimeters (mm) and a global BOLD signal change
threshold of three standard deviations (SD) to determine outliers. We excluded participants who
had greater than 30% of their scan scrubbed. Participants who had head movement greater than
26
2.5 mm translation and 2.0° angular rotation in any axis during the scan were excluded from the
analysis (n=5) so that the final sample size was n=49.
ROI Definitions.
CONN provides a list of ROIs used for network analysis. Regions that represent the
DMN include the posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), left lateral
parietal (L LP), and right lateral parietal (R LP) regions. Mean activity in the ROIs was
computed by extracting and averaging BOLD time series from all voxels within each ROI and
used as the reference (i.e., seed).
Parcellating the DMN into subnetworks allows for the investigation of functionally
distinct contributions to cognitive function (J. E. Chen et al., 2017; Shirer et al., 2012). The
ventral DMN, which includes the medial and posterior temporal regions of the DMN, is thought
to play a role in decision making, semantic, and episodic memory. The ventral DMN largely
overlaps with areas in the medial temporal lobe, including the hippocampal, parahippocampal,
retrosplenial, ventromedial prefrontal, and posterior parietal cortex. The dorsal DMN subregion,
which includes anterior and dorsal DMN regions, is involved in more complex cognitive tasks
related to affect and self-reflection (Andrews-Hanna et al., 2010; Doucet et al., 2011). The dorsal
DMN contains the medial prefrontal cortex and posterior cingulate cortex. Publicly available
functional ROI masks for the ventral and dorsal DMN subnetworks were downloaded from the
Functional Imaging in Neuropsychiatric Disorders (FIND) lab at Stanford University. We used
these functional ROI masks that averaged across the regions of each subnetwork, ventral and
dorsal DMN, as seeds for the final analyses unless otherwise noted
(https://findlab.stanford.edu/functional_ROIs.html). Mean activity in the ROIs was computed by
extracting and averaging BOLD time series from all voxels within each ROI and used as the
reference (i.e., seed).
27
Seed-to-Voxel Connectivity Analyses.
CONN created functional connectivity maps for each participant based on Fisher’s r-to-z
transformed correlations between the mean time series in each ROI seed and the time series of
every other voxel in the whole brain. The association between BPV and ROI functional
connectivity was then estimated by including systolic BPV and diastolic BPV as regressors in
general linear models (GLM), adjusting for age, gender, MAP, and systolic BP. Analyses were
one-tailed because we expected an inverse association between arterial stiffness and functional
connectivity. We determined statistical significance by voxel-threshold of p < 0.001 and a false
discovery rate (FDR) cluster threshold of p < 0.05.
ROI-to-ROI Connectivity Analyses.
Functional connectivity within each large-scale network was estimated through bivariate
Fisher’s r-to-z correlations, representing the level of functional connectivity, between each pair
of ROIs consisting of the network (again, CONN computes the mean activity in the ROIs by
extracting and averaging BOLD time series from all voxels within each ROI). CONN generated
correlation matrices for each participant. We used GLMs to test associations between BPV and
connectivity between pairs of ROIs within the DMN, adjusting for age and gender. Analyses
were one-tailed because we expected an inverse association between arterial stiffness and
functional connectivity. We determined statistical significance by voxel-threshold of p < 0.001
and an FDR cluster threshold of p < 0.05.
RESULTS
VaSC participants included 49 older adults (mean age = 71.8 years, mean education level
= 15.8 years, 65.3% men). Table 1.2 displays demographic characteristics and mean values on
hemodynamic measures for the VaSC study sample.
Seed-to-Voxel Analyses.
28
Blood Pressure Variability
Higher systolic BPV (CV) was significantly associated with lower functional
connectivity between the ventral DMN and the following ROIs: left occipital cortex, bilateral
SMA, anterior cingulate, bilateral precentral gyrus (Figure 1.1) (p
FDR
=0.026). Table 1.3 includes
their anatomical labels and MNI coordinates.
We observed a trend-level relationship such that higher systolic BPV (CV) was
associated with lower functional connectivity between regions of the dorsal DMN and left frontal
brain regions (Figure 1.2) (voxel-threshold set at p<0.008; p
FDR
=0.014). Table 1.3 includes their
anatomical labels and MNI coordinates.
ROI-to-ROI Analyses.
We did not observe a significant relationship between BPV and functional connectivity
between the two major ROIs of the DMN (e.g., PCC and MPFC).
DISCUSSION
We investigated the relationship between BPV and DMN connectivity. Individuals with
higher systolic BPV exhibited lower functional connectivity between both subregions of the
DMN and the left frontoparietal regions (e.g., left frontal pole, precentral gyrus, supplementary
motor cortex). Higher BPV was also associated with reduced connectivity between the ventral
DMN and the anterior cingulate and the left occipital lobe. Interestingly, studies have linked
higher BPV to power processing speed, attention, and executive functions which are attributed to
frontoparietal regions. The mechanisms underlying these associations remain unclear.
Highly variable pressure is counteracted by homeostatic mechanisms, including
baroreflex function and cerebral autoregulation, to ensure continuous and stable brain perfusion
(Aaslid et al., 1989; Schillaci et al., 2012). BPV may signal blood flow instability, leading to
damage to the cerebrovasculature and surrounding tissue, leading to changes in brain structure
29
and function (Schillaci et al., 2012). BPV may also stress the vascular wall, leading to
microvascular damage and arterial remodeling (Zhou et al., 2018). Damaged cerebral
microvessels could impair blood-brain-barrier function and increase vascular permeability,
leaving the brain vulnerable to toxic byproducts (Sweeney et al., 2018). Researchers report a
stronger association between systolic BPV and dementia risk in the presence of impaired
baroreflex function and higher arterial stiffness (Rouch et al., 2020). As there is no clear causal
explanation for the link between BPV and dementia, it is also plausible that BPV could represent
preclinical neurodegenerative processes in areas implicated in BP control (Nagai et al., 2010).
Most BP-lowering medications focus on lowering average BP, but the potential role of BPV in
refined diagnosis, treatment, and prevention of neuropathological processes warrants further
investigation.
Strengths
To our knowledge, Study 1 is the first to use rsfMRI to examine the effects of BPV on
neural connectivity in older adults. Study 1 also represents only the second known examination
of the relationships between arterial stiffness and rsfMRI metrics in humans. The results from
Study 1 point to potential neurovascular mechanisms that may underlie the relationship between
BPV and neurocognitive dysfunction or dementia risk in older adults.
Limitations
We were unable to thoroughly assess the potential effects of antihypertensive treatment
on our results due to sample size. Previous literature recommends using >3 BP measurements to
estimate BPV (Lim et al., 2019). Although we included 4 BP measurements in our estimate of
BPV, measurements occurred over only two visits. On average, visits occurred within one month
of each other. There is currently no gold standard for BPV measurement, and it is unknown to
30
what extent measurement method, number of visits, number of measurements, or amount of time
in-between visits may influence our and other BPV findings.
Clinical Significance
Understanding how BPV affects brain function may allow for improved intervention
targets for neurogenerative diseases. For example, lowering average BP is one of the only
interventions that has ever shown evidence of preventing MCI in a clinical trial, and effects of
BP-lowering treatment on neurocognitive outcomes continues to be actively investigated (Ho et
al., 2017; SPRINT MIND Investigators for the SPRINT Research Group et al., 2019; Yaffe,
2019). However, BPV also highlights autonomic function and baroreceptor sensitivity as a
potentially modifiable treatment target for AD or other disorders of aging. Research observe
reduced baroreflex in AD and MCI; however, the literature is not consistent concerning this
finding. (De Heus et al., 2018; Meel-van den Abeelen et al., 2013)
CONCLUSION
Study 1 findings are consistent with the hypothesis that age-related artery stiffening plays
a significant role in the altered structure and function of the aging brain. Higher BPV may
explain observed links between cardiovascular risk and neurocognitive dysfunction. BPV is a
potentially modifiable target for intervention and prevention strategies in disorders of aging.
However, whether BPV causes or simply reflects increased arterial stiffness is unclear.
Therefore, we build on Study 1 with Study 2 by examining relationships between arterial
stiffening and brain functional connectivity using more direct measures of large artery stiffness,
including the gold-standard metric: PWV.
31
TABLES
Table 1.1 VaSC MRI scan parameters.
Parameter T1 rsfMRI
Volumes, n 176 140
TR, ms 2300 3000
TE, ms 2.98 30
Flip angle, ° 9 80
Matrix, mm 256 × 256 × 176 64x64
Voxel size, mm 1x1x1.2 3.3
Duration, min 9 min 7 min 11 sec
Note. Scan protocol of whole-brain near-isotropic 3-dimensional T1-weighted (3DT1w) and resting-state
functional T2*-weighted MRI (rs-fMRI) on a Siemens Prisma 3T scanner with a 32-channel head coil at
the University of Southern California.
TR, repetition time; TE, echo time.
32
Table 1.2 Demographic characteristics and mean values on hemodynamic measures in the VaSC
study sample.
Variable n Mean (SD)
Age (years) 49 71.8 (6.8)
Gender (M:F) Frequency (%) 49 35%:65%
Education (years) 49 15.8 (2.8)
Pulse Wave Velocity (m/s) 24 7.8 (1.8)
Systolic BPV (CV) 49 0.06 (0.03)
Diastolic BPV (CV) 49 0.08 (0.07)
PP (mmHg) 49 54.0 (9.1)
Systolic BP (mmHg) 49 132.1 (4.4)
Diastolic BP (mmHg) 49 78.0 (9.3)
MAP (mmHg) 48 96.0 (7.7)
34
Table 1.3 Summary of seed-to-voxel analyses: systolic blood pressure variability.
p-FDR Voxels t p-uncorr
Systolic
BPV
Ventral DMN
Occipital Pole Left (194)
Cingulate Gyrus, anterior division (135)
Juxtapositional Lobule Cortex (125)
Lateral Occipital Cortex, inferior division Left (78)
Juxtapositional Lobule Cortex (54)
Precentral Gyrus Right (36)
Precentral Gyrus Left (25)
0.026
439
2.69 0.000 -02 -02 40
Dorsal DMN
Frontal Pole Left (305)
Middle Frontal Gyrus Left (239)
Superior Frontal Gyrus Left (44)
0.014 675 2.50 0.000 -14 60 12
Note. We identified anatomical regions within clusters with AAL atlas through CONN toolbox. Numbers in parentheses are the number of voxels that belonged
to the labeled region. Voxel-level results were thresholded at p<.005 for the ventral DMN and p<.008 (trend) for the dorsal DMN analyses. Cluster-level results
were thresholded at p < .05, using false discovery rate (FDR) methods to account for multiple comparisons. x, y, z are coordinates of peak locations in the
Montreal Neuroimaging Institute (MNI) space. Covariates entered in the GLM include age, gender, mean systolic BP, and mean arterial pressure (MAP).
Regressor
Seed ROI
Cluster Labels (# voxels)
Cluster - Level
Voxel - Level
x
y
z
32
34
35
Figure 1.1. Higher systolic BPV was associated with lower connectivity between the ventral
DMN and anterior cingulate, bilateral supplementary motor areas, bilateral precentral gyri, and
left occipital regions. Color bar represents t-value.
Figure 1.2. Higher systolic BPV was associated with lower connectivity between the dorsal
DMN and left lateral frontal regions. Color bar represents t-value.
35
37
36
Study 2: Relationships between markers of arterial stiffness and static resting-state
functional connectivity in the default mode network
Anna E. Blanken, Jung Yun Jang, S. Duke Han, Isabel Sible, Belinda Yew, Yanrong Li, Daniel
A. Nation
37
INTRODUCTION
In review, arterial stiffness associates with worse executive function and memory. Both
executive functioning and memory relate to RSN integrity, particularly of the DMN. The
mechanisms driving these links, for example how arterial stiffness impacts brain function, are
unestablished. Few studies have used functional neuroimaging techniques to explore associations
between any type of cardiovascular risk and brain function. To date, no study has examined the
relationships between measures of arterial stiffness and resting-state functional connectivity
metrics in older adults. We address this gap by investigating whether arterial stiffening is
associated with functional connectivity of the DMN in community-dwelling older adults.
The overall hypothesis was that more evidence of arterial stiffness would be associated
with greater DMN disruption. We investigated the relationship between arterial stiffness and
DMN connectivity using data from the VaSC study. We examined two arterial stiffness metrics:
PWV and PP. Functional connectivity metrics included seed-based correlations of ROIs within
the DMN to the whole brain and region-to-region correlations within the DMN.
METHODS
See Study 1 for a description of the VaSC participants, overall study procedures, fMRI
preprocessing, and ROI definitions.
Participant Subset
There were 24 individuals who underwent PWV data collection (mean age = 73.8 years,
9 males, mean education = 16.3 years).
PWV Procedures and Measurement
38
Participants rested in a supine position for 10 minutes at room temperature in a room with
dimmed lighting. We asked the participants to refrain from caffeine or vigorous exercise
beginning the day before their visit. A trained operator (AEB) collected data with the
SphygmoCor device. The SphygmoCor uses applanation tonometry to detect the pulse waveform
at the carotid and femoral arteries. SphygmoCor calculates PWV by dividing the distance
between each pulse recording site by the transit time between the carotid and femoral pulse.
PWV is expressed in meters per second.
Blood Pressure Measures
Seated brachial artery systolic and diastolic blood pressures were obtained and PP was
calculated as systolic pressure minus diastolic pressure. MAP was calculated as diastolic
pressure plus one-third the pulse pressure
Seed-to-Voxel Connectivity Analyses.
CONN created functional connectivity maps for each participant based on Fisher’s r-to-z
transformed correlations between the mean time series in each ROI seed and the time series of
every other voxel in the whole brain. The association between arterial stiffness (PWV and PP)
and ROI functional connectivity was then estimated by including arterial stiffness metrics (PWV
and PP) as regressors in general linear models (GLM), adjusting for age, gender, MAP, and
systolic BP separately. Analyses were one-tailed because we expected an inverse association
between arterial stiffness and functional connectivity. We determined statistical significance by
voxel-threshold of p < 0.001 and a false discovery rate (FDR) cluster threshold of p < 0.05.
ROI-to-ROI Connectivity Analyses.
Functional connectivity within each large-scale network was estimated through bivariate
Fisher’s r-to-z correlations, representing the level of functional connectivity, between each pair
39
of ROIs consisting of the network (again, CONN computed the mean activity in the ROIs by
extracting and averaging BOLD time series from all voxels within each ROI). CONN generated
correlation matrices for each participant. We used GLMs to test associations between arterial
stiffness metrics (PWV and PP) and connectivity between pairs of ROIs within the DMN,
adjusting for age. Analyses were one-tailed because we expected an inverse association between
arterial stiffness and functional connectivity. We determined statistical significance by
voxelthreshold of p < 0.001 and an FDR cluster threshold of p < 0.05.
RESULTS
VaSC participants included 24 older adults (mean age = 71.8 years, mean education level
= 15.8 years, 65.3% men). Table 1.2 displays demographic characteristics and mean values on all
hemodynamic measures for the VaSC study sample.
Seed-to-Voxel Analyses.
The Seed-to-Voxel results we reported in Study 2 using PWV as a regressor controlled
for the effects of age. We included the results from the GLMs using PWV as a regressor and
control for all other covariates (gender, MAP, and systolic BP) in the Supplementary Data
section. The GLMs using PP as a regressor include all the covariates listed in the methods
section.
Pulse Wave Velocity
Higher PWV was significantly associated with lower functional connectivity between the
ventral DMN and the following ROIs: left parietal operculum cortex, bilateral supramarginal
gyrus, and right angular gyrus (p
FDR
<0.001). Figure 2.1 displays the significant clusters of ROIs,
and Table 2.1 includes their anatomical labels and MNI coordinates.
40
Higher PWV was significantly associated with lower functional connectivity between the
dorsal DMN and the following ROIs: precuneus, right lateral occipital cortex (p
FDR
=0.001).
Figure 2.2 displays the significant clusters of ROIs, and Table 2.1 includes their anatomical labels
and MNI coordinates. Reported associations were consistent across models controlling for age,
gender, MAP, and average systolic BP separately.
Pulse Pressure
Higher PP was significantly associated with greater functional connectivity between the
ventral DMN and regions in the right occipital cortex (p
FDR
=0.017). Table 2.2 includes their
anatomical labels and MNI coordinates. Higher PP was not associated with functional
connectivity of regions in the dorsal DMN.
ROI-to-ROI Analyses.
We did not observe a significant relationship between arterial stiffness and functional
connectivity between the two major ROIs of the DMN (e.g., PCC and MPFC).
DISCUSSION
To our knowledge, this is the first study to extensively investigate relationships between
markers of arterial stiffness and resting-state BOLD fMRI metrics in healthy older adults. This
study is the first to examine relationships between PP and resting-state BOLD fMRI metrics in
human subjects. PP is related to arterial stiffening but also provides unique information about
aspects of vascular function. The overall findings we present here suggest that there are
important age-related vascular effects on DMN functional connectivity, which may play a role in
neurocognitive health and interpretation of fMRI BOLD findings in older adults.
ROI-Level Findings.
41
Higher PWV was associated with lower connectivity between the ventral DMN and
areas in the superior temporal and inferior parietal lobule (e.g., supramarginal gyri, superior
temporal gyrus, angular gyrus, etc.). Coactivation between ventral regions of the DMN,
including the PCC and the temporoparietal junction, is thought to support memory and social
cognitive functions (S. Wang et al., 2020). Decreases in DMN connectivity, particularly between
the PCC, precuneus, and parietal cortex, are noted across the spectrum of healthy aging, MCI,
and AD (Carbonell et al., 2020; Hafkemeijer et al., 2012; Huang et al., 2015; D. Tomasi &
Volkow, 2012). Regions of the DMN show reduced CBF in aging and susceptibility to AD
pathology (Bero et al., 2011). PWV was also specifically related to reduced connectivity with the
right angular gyrus, which shows both metabolic abnormalities and reduced CBF in AD, which
although not studied here, have been shown to correlate with declines in ideational praxis and
memory (Yoshii et al., 2018). The angular gyrus is conceptualized as a “connector hub” with
involvement in several functional networks, including the DMN, the cingulo-opercular,
frontoparietal, and ventral attention networks (X. Xu et al., 2016). In review, the angular gyrus is
thought to be responsible for combining and integrating multisensory information to give
meaning to events, for mental manipulation, problem-solving, episodic memory, and redirecting
attention to salient information (Seghier, 2013; X. Xu et al., 2016). The disrupted function of the
posterior regions of the DMN has multiple cognitive repercussions, including impaired memory
and executive functions, that map onto cognitive domains in which individuals with greater
arterial stiffness show impairment (Alvarez-Bueno et al., 2020).
Brain regions with high metabolic demand may be the most vulnerable to the effect of
pulse-wave-related damage and (Lamballais et al., 2018). There is some evidence that arterial
stiffness affects memory and cognition via cerebrovascular disease damage (e.g., white matter
42
lesion burden) (Cooper et al., 2016). However, others have shown that disruption in vascular
function (e.g., hypertension) in pediatric populations is also correlated with poorer cognition,
suggesting that an alternative mechanism may also be present (Lande et al., 2017). Lamballais et
al. (2018) suggest cerebrovascular reactivity, or the ability of cerebral vessels to dilate and
constrict to modulate blood flow, is one likely mechanism underlying the relationship between
stiffness and cognition. Across the lifespan, studies have shown associations between
hypertension and reduced cerebrovascular reactivity, particularly in regions overlapping with the
DMN (Haight et al., 2015). Others have shown that higher PWV is linked to reduced CBF in
healthy older adults even in the context of preserved cerebrovascular reactivity, but that those
associations disappear in patients with hypertension and MCI (Jefferson et al., 2018). Evidence
from mouse studies, whose brains are highly vascularized, points to multiple deleterious effects of
large artery stiffening on brain function. These include altered regulation of CBF, decreased
integrity of the cerebral vasculature, and higher blood-brain-barrier permeability, all of which are
associated with cognitive impairment (Muhire et al., 2019). Taken together, these results and ours
suggest that arterial stiffness has multifold effects on cerebrovascular function, including a
reduction in CBF, which compromises delivery of oxygen and nutrients necessary to support
neuronal function that may lead to neuronal death and impaired cognition.
It is difficult to interpret the findings showing that higher PP was associated with higher
connectivity between the ventral DMN and right occipital regions as there is no basis for this
relationship in the literature. In their study of young-to-middle-aged adults, Hussein et al. (2020)
also did not observe an association among BP, PP, and rsfMRI metrics. In our study, and theirs,
although we expected to find an effect of systolic BP, which is a well‐established covariate of
PWV, we did not observe any effect of mean BP on functional connectivity or any confounding
43
effect in the association between PWV and functional connectivity (Gesche et al., 2012;
Waldstein et al., 2008). Notably, PP is a suboptimal estimate of arterial stiffness compared to
other measures (e.g., PWV). In fact, PP and PWV are often conflicting in individuals, and there
is some evidence that in the context of high PWV, both low and high PP associate with worse
cardiovascular outcomes (Niiranen et al., 2016). Accordingly, previous research suggests an
‘inverted-U’ relationship between PP and cognition, such that both low and high PP are
detrimental in some contexts (Qiu et al., 2003). Healthy individuals in our study with higher PP
and lower PWV may benefit from intact or compensatory CBF mechanisms that continue to
support neurovascular function (Novak & Hajjar, 2010).
The findings in Studies 1 and 2 showed altered connectivity between these two DMN
hubs and other brain regions in individuals with higher BPV and PWV. However, neither study
revealed any evidence of associations between arterial stiffness and within-network disruptions
in connectivity, specifically when examining region-to-region correlations between the PCC and
MPFC in the DMN. Our study may be underpowered in this analysis, as a prior post-doc test
indicated a minimum sample size of 50 older adults would be necessary to detect an effect.
Very few studies have examined the relationship between vascular factors and rsfMRI.
However, one study in healthy adults reported an association between PWV and rsfMRI metrics
(Hussein et al., 2020). In this study of healthy young to middle‐aged adults, there was a
significant negative association between PWV and rsfMRI low‐frequency amplitude that was
spatially distinct from the effect of age on resting‐BOLD signal amplitude. Consistent with our
findings, PWV was negatively associated with resting‐state functional connectivity in regions
overlapping with the DMN (Hussein et al., 2020). Our results extend these findings in multiple
areas. For one, we examine PWV and brain function in a sample of older adults who are at-risk
44
for developing MCI or dementia rather than younger or middle-aged adults. Our study also uses
more conventional and more highly sampled rsfMRI BOLD scan acquisition parameters and
hypothesis-driven seed-based correlations. We leverage the gold standard non-invasive measure
of arterial stiffness (carotid-femoral PWV). We also use multiple indicators of artery stiffening
and hemodynamic function (e.g., PP and BPV) and examine the effect of PWV on DMN
subnetwork connectivity, which adds more within-network specificity to previously observed
findings.
Strengths
To our knowledge, our study is the first to use rsfMRI to examine the effects of arterial
stiffness on neural connectivity in older adults, and only the second to examine relationships
between arterial stiffness and rsfMRI metrics in humans. Overall, there are very few studies that
investigate relationships between cardiovascular dysfunction and functional brain activation.
This study addresses a critical gap in knowledge of the potential mechanisms underlying the
relationship between cardiovascular dysfunction and neurocognitive function and provides the
groundwork for future investigation. Future studies using imaging techniques that better
distinguish neuronal activity from vascular, both reflected in the BOLD signal, will help further
our understanding of the links between arterial stiffening and neural function. As stiffening does
not occur uniformly throughout the arterial tree, future studies that directly assess changes in
stiffness of the cerebral arteries may also help understand how vascular stiffness affects the
supply of blood to the neurons and potentially affects neural function.
Limitations
The small sample size of the PWV study calls into question the statistical power and may
be related to null findings. We were also unable to thoroughly assess the potential effects of
45
antihypertensive treatment on our results. Although small sample sizes decrease the study power
and make it more challenging to interpret the observed effects, our sample size for PWV
analyses is consistent with two similar studies of arterial stiffness and both resting and task-
based fMRI outcomes in healthy middle-aged adults (Hussein, 2020: n=25; Steward, 2014:
n=28) (Hussein et al., 2020; Midlife Arterial Stiffness and Brain Activation During Working
Memory Task, 2014).
Clinical Significance
Cardiovascular disease is one area of modifiable risk for cognitive decline and dementia.
BP-lowering medication has also been shown to decrease PWV and seems to convey cognitive
benefits in some cases (Ho & Nation, 2017; Nation et al., 2016; SPRINT MIND Investigators for
the SPRINT Research Group et al., 2019). One recent study observed that in mid-to-late life,
faster rates of arterial stiffening related to greater pathological differences in white matter
structure and cerebral perfusion observed in the following years (Suri et al., 2021). It is possible
that targeting artery stiffening in mid- or even late-life could help prevent or reduce brain
structure and function changes associated with poorer cognitive outcomes. Future studies
exploring potential moderating effects of sex/gender, race/ethnicity, genetic risk, and other
important demographic factors are necessary for characterizing this relationship. Continued
studies of vascular risk, including arterial stiffness, in larger sample sizes, may allow for the
development of fMRI biomarkers to facilitate and refine diagnosis, develop targeted
interventions, and possibly prevent cognitive decline and neurodegenerative disease.
CONCLUSION
Current findings are consistent with the hypothesis that age-related artery stiffening plays
a significant role in the altered structure and function of the aging brain. Higher arterial stiffness
46
may represent a causal link between cardiovascular risk and neurocognitive dysfunction, making
it a worthwhile target for developing therapeutic interventions and prevention strategies.
Functional connectivity calculation depends on local slow and rhythmic spontaneous oscillations
of cerebral and peripheral blood flow. In Study 3, we aim to build on the functional connectivity
analyses performed in Studies 1 and 2 by focusing on the relationship between arterial stiffness
and spontaneous neuronal activity, indexed by ALFF. ALFF may be linked to functional
connectivity measures via neurovascular coupling and offers further information about potential
abnormalities in resting-state activity across the whole brain.
Table 2.1 Summary of seed-to-voxel analyses: pulse wave velocity.
p-FDR Voxels t p-uncorr
PWV Ventral
DMN
Parietal Operculum Cortex Left (211)
Supramarginal Gyrus, anterior division Left (128)
Supramarginal Gyrus, anterior division Right (124)
Supramarginal Gyrus, posterior division Right (115)
Angular Gyrus Right (96)
Supramarginal Gyrus, posterior division Left (83)
Cingulate Gyrus, posterior division (77)
Planum Temporale Left (37)
Postcentral Gyrus Left (30)
Central Opercular Cortex Left (20)
Cingulate Gyrus, anterior division (18)
Superior Temporal Gyrus, posterior division Left (15)
0.000 371 -3.53 0.000 -54 -28 20
Dorsal
DMN
Precuneus (188)
Lateral Occipital Cortex, superior division Right (107)
Occipital Pole Right (59)
0.001 551 -3.06 0.000 20 -66 36
Note. We identified anatomical regions within clusters with AAL atlas through CONN toolbox. Numbers in parentheses are the number of voxels th at belonged to
the labeled region. Voxel-level results were thresholded at p<.001 and uncorrected for multiple comparisons (p < .001). Cluster-level results were thresholded at p
< .05, using false discovery rate (FDR) methods to account for multiple comparisons. x, y, z are coordinates of peak locations in the Montreal Neuroimaging
Institute (MNI) space. Age was included as a covariate for these analyses.
Regressor
Seed ROI
Cluster Labels (# voxels)
Cluster - Level
Voxel - Level
x
y
z
46
Table 2.2 Summary of seed-to-voxel analyses: pulse pressure.
Regressor Seed ROI Cluster Labels Cluster-Level Voxel-Level x y z
(# voxels)
p-FDR Voxels t p-uncorr
PP Ventral
DMN
Occipital Pole Right (103)
Lateral Occipital Cortex, inferior division Right (54)
0.017 175 -3.28 0.002 32 -92 -04
Note. We identified anatomical regions within clusters with AAL atlas through CONN toolbox. Numbers in parentheses are the number o f voxels that belonged to
the labeled region. Voxel-level results were thresholded at p < .001. Cluster-level results were thresholded at p < .05, using false discovery rate (FDR) methods to
account for multiple comparisons. x, y, z are coordinates of peak locations in the Montreal Neuroimaging Institute (MNI) space. Covariates entered in the GLM
include age, gender, mean systolic BP, and mean arterial pressure (MAP).
47
49
50
FIGURES
Figure 2.1. Higher PWV was associated with lower functional connectivity between the ventral
DMN and the bilateral supramarginal gyri, the angular gyrus, the left parietal operculum cortex,
and the cingulate, the left planum temporale, left postcentral, and the left superior temporal gyri.
Color bar represents t-value.
50
Figure 2.2. Higher PWV was associated with lower connectivity between the dorsal DMN and
the precuneus, the angular gyrus, the left occipital cortex, the left supramarginal gyrus, the
cingulate gyrus, the left superior temporal gyrus, and the left postcentral gyrus. Color bar
represents t-value.
51
Study 3: Relationship between blood pressure variability and amplitude of low frequency
fluctuations in resting-state functional MRI
Anna E. Blanken, Jung Yun Jang, S. Duke Han, Isabel Sible, Belinda Yew, Yanrong Li, Daniel
A. Nation
52
INTRODUCTION
rsfMRI is a continually growing field of research that is not yet completely understood.
In rsfMRI studies, there are several ways to gather information about neuronal processes at the
voxel-level. Traditional rsfMRI studies examine synchrony in BOLD-signal oscillations between
two functionally related brain regions. On the other hand, ALFF quantifies the amplitude of
BOLD signal fluctuations of each voxel’s signal in the low frequency range (Zang, 2007).
Researchers believe ALFF reflects spontaneous neuronal activity, constituting up to 95% of the
brain’s metabolism (Fox & Raichle, 2007). ALFF was first used to quantitatively measure
regional BOLD signal variation in attention deficit hyperactivity disorder (Zang et al., 2007) and
has since been used to characterize the abnormal spontaneous neuronal activity of the brain,
namely lower ALFF, in MCI and AD (Cha et al., 2015; Z. Wang et al., 2011).
ALFF is influenced mainly by both neuronal activity and cerebrovascular hemodynamic
factors. Therefore, ALFF also reflects regional changes in cerebral hemodynamics (e.g., CBF,
cerebral blood volume, blood oxygenation, pulsatile BP). CBF fluctuations intrinsically link to
neuronal activities via neurovascular coupling (Vanzetta & Grinvald, 2008). However, there is
also clear evidence that BOLD signal fluctuations reflect neuronal activity, as evidenced by
associations of ALFF and local functional connectivity density with brain glucose metabolism, a
marker of neuronal activity (Dardo Tomasi & Volkow, 2019). It remains unclear to what extent
vascular vs. neuronal activity contributes to BOLD signal changes, but many indications suggest
that studying ALFF, in addition to functional connectivity, can provide important information
about abnormalities in brain function.
The present study sought to examine associations between arterial stiffness, measured
outside the scanner from the peripheral arteries, and rsfMRI ALFF. We hypothesize that greater
53
arterial stiffness will be associated with greater abnormalities in regional neural activity, as
quantified by ALFF.
METHODS
See Study 1 for a description of participants, procedures, BPV, and fMRI preprocessing
methods. We describe the additional methodology for this study below.
Voxel-Level Resting-State Analyses.
The CONN toolbox creates ALFF maps by calculating the root mean square of BOLD
signal at each voxel after band-pass filtering (Yang et al., 2007). We used GLMs to test
associations between arterial stiffness metrics (BPV, PWV, and PP) and ALFF. Analyses were
two-tailed. We determined statistical significance by voxel-threshold of p < 0.001 and an FDR
cluster threshold of p < 0.05.
RESULTS
VaSC participants included 49 older adults (mean age = 71.8 years, mean education level
= 15.8 years, 65.3% men). Table 1.2 displays demographic characteristics and mean values on
hemodynamic measures for the VaSC study sample.
Voxel-Level Analyses.
There was a significant positive relationship between systolic BPV (both CV and SD)
with ALFF in the following ROIS: left occipital, precuneus, bilateral postcentral gyrus, PCC,
angular gyrus, occipital pole, left planum temporale, right precentral gyrus, lef t temporal gyrus
(superior, supramarginal, and middle) (p
FDR
<0.001) (Figure 3.1). Table 3.1 includes their
anatomical labels and MNI coordinates.
We did not observe any significant relationship between diastolic BPV, PWV PP, and
ALFF.
54
DISCUSSION
We found that higher systolic BPV was also associated with higher ALFF in the
precuneus, bilateral postcentral gyrus, left occipital cortex, left angular gyrus, cingulate gyrus,
lower parietal lobule, and superior temporal lobe. In terms of the current literature, others have
shown decreased ALFF in the precuneus in patients with greater aggregate vascular risk, and
some studies have shown hypometabolism of the precuneus in MCI and AD (Zhuang et al.,
2020). Patients with subcortical ischemic vascular dementia also exhibit lower ALFF in the
precuneus (Liu et al., 2014). However, in one study of age on ALFF within a predefined
functional language network, older age was related to greater ALFF but decreased connectivity
within the network (Zhang et al., 2021). Others have shown that age is associated with higher
ALFF in the thalamus (Mather & Nga, 2013). Because ALFF measures the BOLD signal
fluctuations, we might see increases or decreases in ALFF reflecting abnormality of the BOLD
signal.
In an early work studying cerebral hemodynamics in animal models, Roy and
Sherrington note that “the brain expands with each rise of the blood pressure and contracts with
each successive fall” during the frequently detected spontaneous Mayer blood-pressure waves
(Roy & Sherrington, 1890). While others have interpreted pulsation as a nuisance noise signal in
the study of functional connectivity using the BOLD signal, physiological pulsations are an
emerging signal of interest in studying disease states (Raitamaa et al., 2021). For example, there
is a significant alteration in the variance of cardiovascular pulsation in the brain of AD patients.
ALFF may represent a new technique of characterizing changes in brain hemodynamics, such as
arterial stiffness. In fact, studies have shown that BOLD signal oscillations in the low-frequency
band exhibit similar spectral power distribution to CBF velocity in the middle cerebral artery
55
(Zhu et al., 2015).
Furthermore, recent work has demonstrated that respiratory and cardiovascular
pulsations contribute substantially to ALFF and suggests that ALFF measurements may measure
cerebral pulse wave amplitude. This measure may be comparable to pulse data measured
outside of the scanner via finger photoplethysmogram (Raitamaa et al., 2021). Interestingly, this
study also found that in areas of the DMN, neuronally-related very-low-frequency power
contributed to BOLD signal over respiratory or cardiovascular fluctuations. Future work focused
on continuing to parse out the neural vs. vascular elements of the BOLD signal may improve
functional connectivity results and clarify whether ALFF could serve as a surrogate of changes
in cerebral pulse waves, and subsequently, a marker of stiffness of the cerebral arteries.
CONCLUSION
BPV was correlated with higher ALFF in brain regions highly overlapping with the
DMN. Abnormal BOLD signal can also stem from changes in CBF and glucose metabolism.
Patients with higher BPV may exhibit greater arterial stiffness, greater PWV, and greater CBF,
possibly explaining why we observed a positive relationship with ALFF in DMN areas.
TABLES
Table 3.1 Summary of ALFF analyses.
p-FDR voxels t p-uncorr
Systolic
BPV
Lateral Occipital Cortex, left superior division (616)
Precuneous (440)
Postcentral Gyrus Right (290)
Postcentral Gyrus Left (182)
Cingulate Gyrus, posterior division (166)
Angular Gyrus Left (162)
Occipital Pole Left (136)
Planum Temporale Left (73)
Precentral Gyrus Right (72)
Supramarginal Gyrus, posterior division Left (72) Superior
Temporal Gyrus, posterior division Left (67)
Middle Temporal Gyrus, temporooccipital Left (39)
Supramarginal Gyrus, anterior division Right (28)
Central Opercular Cortex Left (12)
Supramarginal Gyrus, anterior division Left (11)
0.000 2178 .27 0.000 -50 -60 44
Note. We identified anatomical regions within clusters with AAL atla s through CONN toolbox. Numbers in parentheses are the number of voxels that belonged
to the labeled region. Voxel-level results were uncorrected for multiple comparisons (p < .001). Cluster-level results were thresholded at p < .05, using and false
discovery rate (FDR) methods to account for multiple comparisons. x, y, z are coordinates of peak locations in the Montreal Neuroimaging Institute (MNI) space.
Covariates entered in the GLM include age, gender, mean systolic BP, and mean arterial pressure (MAP).
Regressor
Cluster Labels
Cluster - Level
Voxel - Level
x
y
z
55
57
FIGURES
Figure 3.1. Systolic BPV was associated with greater ALFF in regions overlapping with regions
in the posterior DMN. Color bar represents t-value.
58
Study 4: Synchronous evaluation of blood pressure variability and variability in
restingstate functional connectivity of the default mode network in older adults
Anna E. Blanken, Jungwon Min, Jung Yun Jang, S. Duke Han, Isabel Sible, Belinda Yew,
Yanrong Li, Daniel A. Nation
59
INTRODUCTION
Studies utilizing rsfMRI typically conceptualize functional connectivity between brain
regions as a static value across the scan duration. There are new methodological approaches that
facilitate the study of short-term fluctuations in connectivity, called dynamic functional
connectivity (Hutchison et al., 2013). Table 4.1 describes the differences and advantages
between static and dynamic functional connectivity methods. One dynamic connectivity rsfMRI
analysis uses a designated window of predetermined length that moves incrementally across the
BOLD signal to produce a continuous series of functional connectivity measurements. We can
then analyze this series to characterize changes in networks over time (Leonardi & Van De Ville,
2015). Some have used this technique, termed sliding window analysis, to investigate dynamic
connectivity in healthy aging, neurodegenerative disorders, and multiple other neurological
conditions (Y. Chen et al., 2017; Fiorenzato et al., 2019; Kim et al., 2017; Park et al., 2017).
Understanding the dynamic characteristics of functional connectivity may provide a novel
perspective on the temporal aspects of network function and disruption complementary to
traditional static functional connectivity analyses (Schumacher et al., 2019).
Higher temporal variability in resting-state functional connectivity is notable in multiple
neuropsychiatric populations, including attention-deficit hyperactive disorder, major depressive
disorder, and schizophrenia (Demirtaş et al., 2016; Rolls et al., 2021). Very few studies have
investigated variability in functional connectivity as it relates to healthy and pathological aging.
However, several studies suggest that examining variability in functional connectivity may help
characterize changes in neural activity related to cognitive function. In one study of healthy
adults, higher whole-brain variability in resting-state functional connectivity associates with
worse attention (Fong et al., 2019). Others have reported that higher resting-state temporal
60
variability within the DMN was associated with worse Stroop test performance in healthy adults,
whereas higher task-state variability was associated with improved performance (Douw et al.,
2016). Higher variability within DMN dynamic functional connectivity is notable in older
compared to younger adults (Madhyastha & Grabowski, 2014). Greater age-related changes in
dynamic functional connectivity may help to characterize cognitive decline.
Dynamic connectivity in highly efficient networks, including the DMN, may reflect brief,
systematic, and reoccurring patterns of increased CBF that the brain relies on to create a balance
between efficient information processing and energy expenditure (Zalesky et al., 2014). There is
an increasing focus on age-related changes in CBF in late-life brain health studies. In
nonhypertensive older adults, higher BP has been linked with decreased CBF globally and in the
hippocampus (Glodzik et al., 2019). This same study showed that hypertensive patients exhibited
a quadratic relationship between BP and CBF, such that there was a middle range of BP values
that appeared to optimally facilitate brain perfusion (Glodzik et al., 2019). While the relationship
between BP and CBF is still under investigation, a growing number of studies have linked high
BP with impaired neurocognitive function later in life (Forte et al., 2019; Iadecola & Gottesman,
2019). High BP is also an important risk factor for vascular dementia and AD in older adults
(Sweeney et al., 2019). Many studies have investigated links between BP and both aging and
disease-related structural brain changes, but few have investigated the effects of vascular risk
factors on brain function, especially using fMRI techniques. Several studies report that
hypertensive individuals with and without AD exhibit abnormalities in resting-state functional
connectivity (Carnevale et al., 2020; Son et al., 2015). Understanding how components of
vascular aging, including changes in BP, play a role in brain connectivity in normal aging may
61
facilitate our understanding of the mechanisms driving the relationships among high BP,
cognitive impairment, and neurodegenerative disease risk.
BP also fluctuates over short (e.g., minute-by-minute, 24-hour) and long (days, weeks,
months, years) periods (Mancia, 2012). A dynamic interplay between the brain and heart leads to
fluctuations in BP that are responses to changes in the external environment, individual
behavioral factors, and internal cardiovascular regulatory mechanisms (Parati et al., 2013). A
growing literature suggests that BPV increases during aging and conveys risk for cardiovascular
disease, stroke, cognitive impairment, and dementia in older adults, even in the context of
wellcontrolled average BP (Nagai et al., 2015; Oishi et al., 2017; Rouch et al., 2020; Yoo et al.,
2020). Beat-to-beat BP monitoring more accurately reflects the dynamic characteristics of BP,
provides information about short-term BPV, and has scarcely been utilized in clinical aging
research. Beat-to-beat BPV has been associated with a higher risk of dementia after controlling
for age, sex, genetic risk, and mean BP (Ma et al., 2021). Higher BPV may reflect disruptions in
vascular function that could have a meaningful impact on brain function and integrity (Yano et
al., 2017). To our knowledge, no study has investigated the relationship between BPV and
dynamic functional connectivity using rsfMRI data.
During aging, there may be disruption of the synchrony between important brain regions
and the heart. This study investigated the potential relationships between beat-to-beat BP
recordings and rsfMRI BOLD signal used to assess regional neural activity in 17 healthy older
adults in the Leipzig Mind-Brain-Body Dataset (LEMON) (Babayan et al., 2019). To our
knowledge, this is the first study to examine temporal dynamics of BP and functional
connectivity. Hypotheses were as follows:
62
1) We expected that higher BP would correlate with more variable functional connectivity in
the DMN.
2) Greater BPV would be associated with more variable functional connectivity in the
DMN.
METHODS
We included participants from the publicly available LEMON dataset. The data and
Public Domain Dedication and License (PDDL) are available at
http://fcon_1000.projects.nitrc.org/indi/retro/MPI_LEMON.html. The whole dataset included
228 healthy participants comprising a young (N=154, 25.1±3.1 years, range 20–35 years, 45
female) and an elderly group (N=74, 67.6±4.7 years, range 59–77 years, 37 female) acquired
cross-sectionally in Leipzig, Germany, between 2013 and 2015 to study mind-body-emotion
interactions. Participants completed MRI at 3 Tesla (rsfMRI, MPRAGE) during rest. During
task-free rsfMRI, researchers continuously acquired cardiovascular measures (BP, heart rate,
pulse, respiration). Further detailed information on study design and methodology is available in
the published dataset descriptor paper (Babayan et al., 2019). Complete rsfMRI and BP data
were available for 17 older adults.
Physiological Recordings: Non-Invasive Beat-to-Beat Blood Pressure
Continuous recordings of brachial BP were obtained during the rsfMRI using Biopac
MP150 bioamplifiers and a computer equipped with data acquisition software. The study obtained
beat-to-beat BP at the brachial artery of the left arm with an air-filled pressure-sensitive sensor.
Pulse Decomposition Analysis transformed the pressure signal into systolic and diastolic
BP (Baruch et al., 2011). A custom pipeline scripted in MATLAB preprocessed raw data
63
(Version R2019b). Briefly, the script marked the signature components of each waveform. We
determined outliers in the raw data as signals +/− 3 SD from the mean level during the data
collection period. The Matlab script interpolated these periods if their duration was three seconds
or less and deleted them if their duration was longer than three seconds. We excluded subjects
who had greater than 30% interpolated data. Finally, we visually examined interpolated data for
gross abnormalities and outliers. Data quality checks resulted in the exclusion of 27 older adults
due to inadequate data quality. We averaged BP for the entire scan and each separate 60-second
sliding window. We calculated BPV as the coefficient of variation (CV = σ/μ) for the overall scan
and in each 60-second sliding window.
MRI Analyses.
All images were preprocessed and analyzed using CONN Toolbox Version 20b
(http://www.nitrc.org/projects/conn/) and SPM12 (http://www.fil.ion.ucl.ac.uk/spm). Table 4.1
summarizes the methodological approaches. Table 4.2 summarizes LEMON MRI acquisition
parameters.
MRI Data Preprocessing.
Scan preprocessing steps included correction for slice-timing discrepancies, realignment
to the first scan, spatial normalization to the MNI template in SPM12, and spatial smoothing
with an 8-mm FWHM Gaussian kernel. Nuisance signals were removed by applying temporal
band-pass filtering (0.009 - 0.1 Hz) and a nuisance regression approach to control nuisance
variability and reduce the impact of physiological and other non-neural artifacts on connectivity
analyses. Nuisance variables included six motion parameters and their first derivatives, five
principal components each from white matter and cerebrospinal fluid masks, f ollowing a
component-based noise correction strategy, CompCor (Behzadi et al., 2007), and a linear
64
detrending term (a total of 23 regressors). CONN also uses artifact detection (ART) to scrub out
data points contaminated by motion. We used a conservative f ramewise displacement threshold
of 0.55 millimeters (mm) and a global BOLD signal change threshold of three standard
deviations (SD) to determine outliers. We determined to exclude participants who had greater
than 30% of their scan scrubbed. We also determined to exclude participants who had head
movement greater than 2.5 mm translation and 2.0° angular rotation in any axis during the scan
from the analysis. We did not have to remove any subjects based on these criteria.
ROI Definitions.
CONN provides a list of ROIs used for network analysis. Regions that represent the
DMN include the PCC, MPFC, L LP, and R LP regions. Mean activity in the ROIs was
computed by extracting and averaging BOLD time series from all voxels within each ROI and
used as the reference (i.e., seed).
Seed-to-Voxel Analyses.
CONN created functional connectivity maps for each participant based on Fisher’s r-to-z
transformed correlations between the mean time series in each ROI seed and the time series of
every other voxel in the whole brain. We estimated the association between BPV and ROI
functional connectivity for selected seeds within the DMN using GLM, adjusting for age and
gender.
ROI-to-ROI Analyses.
CONN estimated functional connectivity within each large-scale network through
bivariate Fisher’s r-to-z correlations, representing the level of functional connectivity, between
each pair of ROIs consisting of the network (again, CONN computed the mean activity in the
ROIs by extracting and averaging BOLD time series from all voxels within each ROI). CONN
65
generated correlation matrices for each participant. We performed GLM analyses to test
associations between BPV and connectivity between pairs of ROIs within the DMN, adjusting for
age and gender.
Dynamic Functional Connectivity Analyses.
We established a 60-second window length to “slide” through the entire rsfMRI data in
steps of 30 seconds for dynamic connectivity analyses. Accordingly, we estimated each subject’s
rsfMRI data into 30 sliding windows 60 seconds in length and overlapping by 30 seconds. We
determined window length and step length using published guidelines (Leonardi & Van De
Ville, 2015; Zalesky & Breakspear, 2015). CONN derived Fisher’s Z transformed seed-based
correlation maps for each window. CONN calculated beta values for correlations between
regions of the DMN and extracted them from each sliding window. We conducted group-level
dynamic analyses by performing group-level statistics on the SD in beta values at each voxel.
We used GLMs to test associations between each temporal variability of the four
CONNprovided DMN regions as dependent variables, systolic and diastolic BP and BPV as
regressors, and age and gender as covariates. Researchers have described a similar dynamic
analytic approach in clinical populations (Yue et al., 2018).
RESULTS
Demographics.
Table 2. summarizes the demographic data for 20 older adults (6 men and 14 women) in
the current study. Out of the 20 total subjects, systolic BP data was available for 17 participants,
and diastolic BP data was available for 17 participants.
Dynamic Functional Connectivity.
66
Tables 4.4 and 4.5 summarize results from dynamic seed-based correlations that were
significantly related to BPV. Higher systolic BPV was significantly associated with higher
dynamic connectivity between the PCC and regions predominately located in the bilateral frontal
gyri (middle and superior), the precuneus, and the right angular gyrus (p
FDR
<0.001) (Figure 4.1).
Higher systolic BPV was significantly associated with higher dynamic connectivity between the
MPFC and regions, including the bilateral frontal poles, the left occipital cortex, bilateral
supramarginal gyri, precuneus, and bilateral middle frontal gyri (p
FDR
<0.001) (Figure 4.2).
Higher systolic BPV was significantly associated with higher dynamic connectivity between the
right and left lateral parietal areas of the DMN and regions predominately located in the bilateral
supramarginal gyrus, bilateral occipital cortex, bilateral paracingulate gyri, left insular cortex,
and anterior cingulate gyrus (p
FDR
<0.001) (Figure 4.3).
Table 4.5 summarizes results from dynamic seed-based correlations between the bilateral
hippocampi and every other voxel in the brain significantly related to BPV. Higher BPV was
significantly associated with higher dynamic connectivity between the bilateral hippocampi and
the precuneus (p
FDR
<0.001) (Figure 4.4). The reported results include age as a covariate, and
results did not change after controlling for gender.
Diastolic BPV was not associated with temporal variability in functional connectivity
between regions of the DMN and all other voxels in the brain. Mean BP was not associated with
temporal variability in functional connectivity between regions of the DMN.
We did not observe a significant relationship between mean BP or BPV and temporal
variability of functional connectivity between the two major ROIs of the DMN (e.g., PCC and
MPFC).
DISCUSSION
67
We used rsfMRI to explore relationships between DMN dynamic functional connectivity
and BP and BPV in a cohort of healthy older adults at risk of dementia. Greater BPV was
significantly associated with higher temporal variability in functional connectivity in brain areas
within the DMN, whereas average BP was not significantly associated.
To our knowledge, this is the only study investigating the relationship between BP, BPV,
and dynamic functional connectivity. In healthy adults, the DMN has shown low-to-moderate
variability in functional connectivity. Therefore, an increase in variability of DMN connectivity
in older adults may signify a breakdown of temporal dynamics of the oscillatory network
patterns that support brain function (Mueller et al., 2013; Zalesky et al., 2014). Notably, the
results presented here are in line with those of brain dynamics in MCI and AD. Studies have
shown that patients with amnestic MCI and AD exhibit higher variability in functional
connectivity of RSNs, including the DMN and frontal-parietal networks (Niu et al., 2019). Both
DMN and frontal-parietal networks contain regions susceptible to AD pathology early in disease
progression (Palmqvist et al., 2017).
Furthermore, increases in resting-state variability in functional connectivity of the DMN
are associated with worse scores on tests of global cognition, cognitive flexibility, and recognition
memory (Mini-Mental Status Exam-MMSE; Stroop Color-Word test; Auditory Verbal Learning
Test, Recognition Trial; AVLT) in these patient groups (Douw et al., 2016; Niu et al., 2019). This
study suggests that altered hemodynamic function, as captured by BPV, may play an important
role in disrupted dynamic brain connectivity. These alterations may underlie declines in cognitive
function.
Although the mechanism driving these changes is unclear, an increase in BPV points to
several possible pathways. The DMN may be specifically vulnerable to neurovascular
68
compromise. Several studies have demonstrated reduced cerebrovascular function in the brain
regions overlapping with the DMN (Claus et al., 1998; Dai et al., 2009). Higher BPV places more
mechanical stress on arterial walls, leading to large artery remodeling and stiffening (Zhou et al.,
2018). Higher stiffening is associated with higher flow pulsatility, damaging the fragile cerebral
microcirculation (Gary F. Mitchell et al., 2011). Proper function of the cerebral microcirculation
supports functional hyperemia, which is necessary for neuronal metabolic function (Nippert et al.,
2018).
An alternative mechanistic perspective considers links between BPV and sympathetic
nervous system function. The arterial baroreceptor reflex comprises a sympathetic negative
feedback system that regulates BP (Julien, 2008). Arterial baroreceptors monitor BP changes and
signal the brain, triggering shifts in sympathetic and parasympathetic nerve activity to modulate
multiple cardiovascular processes and stabilize BP (Armstrong & Moore, 2019). Increases in
BPV may signify a breakdown of baroreceptor activity, leading to shifts in the timing of
communication of cardiovascular changes to the brain and impaired ability to adapt in response to
changes in external and internal stimuli. Support for the effects of reduced baroreceptor function
on cognitive functions is a mixed but growing area of research. Baroreflex function attenuates in
aging, and more so in MCI and AD than age-matched healthy controls (Meel-van den Abeelen et
al., 2013; Ogoh & Tarumi, 2019). The directionality of baroreflex function and
AD remains to be elucidated and remains an important area of investigation.
Strengths and Limitations.
Cross-sectional design limits the interpretation of our results, as this design does not
allow us to examine how within-person age-related change in vascular function over several
years is tied to change in brain function. Small sample size limits the power of our study. Small
69
sample size partially reflects the novelty of these measures, and validation in larger study
samples will be necessary.
Non-neuronal factors, including breathing and spontaneous head motion, also contribute
to variability in dynamic functional connectivity (Nikolaou et al., 2016). Although these factors
can complicate the interpretation of findings, there is still much scientific value in using
restingstate BOLD fMRI for studies of time-varying neural interactions. Although we applied a
wellvalidated pre-processing pipeline, we cannot entirely remove non-neuronal effects.
Confounding factors are an important consideration in aging research as older adults are more
likely to move in the scanner than younger adults. The potential confounding effects of non -
neuronal factors highlight the need for validation studies and extension of findings through
multiple neuroimaging modalities.
Our study also exhibits strengths. The LEMON study utilized a 15minute and 30 second
long multiband accelerated echo-planar imaging with a high sampling rate. Faster sampling rates
and longer scan times empower higher sensitivity in detecting resting-state fluctuations,
improved ability to control for physiological confounds, and greater degrees of freedom (Smith
et al., 2013). Even longer scan times will allow for several repetitions of oscillatory network
patterns and may improve estimates of variability and characterization of dynamic brain
behavior. We also were able to characterize short-term BPV using novel noninvasive beat-tobeat
BP monitoring data. Dynamic functional connectivity analysis has helped examine changes in
cognitive states and disease progression; however, few have applied dynamic analytical methods
to rsfMRI studies of healthy aging or MCI and AD. Our study contributes unique information
regarding relationships between brain and cardiovascular dynamics in healthy older adults.
70
CONCLUSION
Study 4 shows that higher variability in continuous beat-to-beat systolic BP is associated
with higher temporal variability in DMN functional connectivity, specifically between the
bilateral hippocampi and the precuneus. Changes in cerebrovascular function may underlie
disruption of DMN connectivity and changes in cognition associated with aging and
neurodegenerative disease. Future investigations are needed to expand our understanding of the
effect of BPV on neurovascular function.
TABLES
Table 4.1 Resting-state functional connectivity analytical methods.
Functional
Connectivity
Relationships between spatially separated
brain regions inferred from statistically
correlated brain activity. Standard functional
connectivity measures include Pearson
correlation coefficient, coherence, phase lag
index, and phase‐locking value.
Assessment of functional connectivity changes
over seconds or minutes.
Allows noninvasive in-vivo measurement of the brain state
at a macroscopic level. The measured state results from
combined excitatory and inhibitory activity at any given time
window and, in general, is not capable of differentiating
excitatory from inhibitory activity. Calculating functional
connectivity is computationally straightforward and may
have greater test‐retest reliability but does not provide
information on directionality.
Describes the temporal evolution of brain networks. We
cannot measure functional connectivity instantaneously, and
so we use time-series data. Therefore, decisions about time
window length, size, and computational methods may affect
results.
Dynamic
Functional
Connectivity
70
Table 4.2 LEMON rsfMRI scan parameters.
Note. TR, repetition time; TE, echo time.
Parameter
Volumes, n 657
TR, ms 1400
TE, ms 30
Flip angle, ° 69
Matrix, mm 88x88
Voxel size, mm 2.3
Duration, min 15 min 30 sec
71
Table 4.3 Demographic characteristics and mean (standard deviation) values on hemodynamic measures in the LEMON study sample.
Variable Mean (SD)
n 20
Age (years) 67 (4.7)
Gender (M:F) Frequency (%) 30:70
Education (Gymnaisum:Realschule) Frequency (%) 55:45
Systolic BPV (CV) 0.03 (0.02)
Diastolic BPV (CV) 0.08 (0.1)
Systolic BP (mmHg) 147.0 (24.0)
Diastolic BP (mmHg) 64.7 (11.7)
MAP (mmHg) 93.2 (7.2)
Gymnasium = 13 years of formal education in Germany; Realschule = 10 years of formal education in Germany; BPV = blood
pressure variability; CV=coefficient of variation; PP = pulse pressure; MAP = mean arterial pressure; mmHg = millimeters of
mercury.
72
Table 4.4 Association between systolic BPV and temporal variability in functional connectivity of DMN hubs, controlled for age.
Seed Region Cluster Labels p-FDR voxels t p-uncorr
x y z
Posterior
cingulate
cortex
Middle Frontal Gyrus Right (491)
Middle Frontal Gyrus Left (299)
Precuneous Cortex (233)
Frontal Pole Right (190)
Angular Gyrus Right (156)
Lateral Occipital Cortex, superior Right (87)
Superior Frontal Gyrus Right (78)
Superior Frontal Gyrus Left (66)
Frontal Pole Left (59)
Inferior Frontal Gyrus, R pars opercularis (45)
Supramarginal Gyrus, anterior division Right (44)
Vermis 7 (40)
Lateral Occipital Cortex, superior Left (37)
Cerebellum 8 Left (31)
Precentral Gyrus Right (21)
Precentral Gyrus Left (18)
Paracingulate Gyrus Right (17)
Angular Gyrus Left (12)
Paracingulate Gyrus Left (8)
0.000 617 4.14 0.000 46 26 42
73
Medial
Prefrontal
Cortex
Frontal Pole Left (186)
Frontal Pole Right (62)
Lateral Occipital Cortex, superior division Left (46)
Middle Frontal Gyrus Left (42)
Supramarginal Gyrus, posterior division Left (38)
Precuneous Cortex (33)
Supramarginal Gyrus, anterior division Right (32)
Superior Frontal Gyrus Right (29)
Middle Frontal Gyrus Right (23)
Middle Temporal Gyrus, temporooccipital part Right (18)
Superior Parietal Lobule Left (17)
Angular Gyrus Left (12)
0.000 220 4.14 0.000 -32 40 14
Right and Left
Lateral
Parietal
Supramarginal Gyrus, anterior division Right (187)
Occipital Pole Left (133)
Lateral Occipital Cortex, superior division Right (128)
Frontal Pole Right (102)
Paracingulate Gyrus Left (66)
Paracingulate Gyrus Right (63)
Insular Cortex Left (59)
Supramarginal Gyrus, posterior division Right (58)
Lateral Occipital Cortex, superior division Left (57)
Middle Frontal Gyrus Left (54)
Superior Parietal Lobule Right (49)
Cingulate Gyrus, anterior division (46)
Superior Frontal Gyrus Right (40)
Frontal Pole Left (36)
Frontal Operculum Cortex Left (22)
Postcentral Gyrus Right (11)
Parietal Operculum Cortex Right (10)
Angular Gyrus Right (7)
Temporal Pole Left (6)
0.000
294
8.93
0.000
64
-
24
32
* Note: Results were similar when controlling for Gender
74
Table 4.5 Association between systolic BPV and temporal variability in functional connectivity of the hippocampus, controlled for
age.
Seed Region Cluster Labels (# voxels) p-FDR voxels t p-
unorr
Left and Right Hippocampus Precuneus (183) 0.000 186 8.93 0.000 -12 -54 46
*Results were similar when controlling for Gender
Cluster-Level Voxel-Level x y z
75
77
FIGURES
Figure 4.1 Higher systolic blood pressure variability was associated with higher temporal
variability in the functional connectivity between the posterior cingulate cortex and areas in the
bilateral frontal lobes, right angular gyrus, and precuneus. Results were significant after
controlling for age and gender.
78
Figure 4.2 Higher systolic blood pressure variability was associated with higher temporal
variability in the functional connectivity between the medial prefrontal cortex and areas in the
bilateral frontal lobe, bilateral supramarginal gyrus, left occipital lobe, and precuneus. Results
were significant after controlling for age and gender.
79
Figure 4.3 Higher systolic blood pressure variability was associated with higher temporal
variability in the functional connectivity between the lateral parietal regions of the DMN and
areas in the right supramarginal, left occipital cortex, right frontal pole, bilateral paracingulate,
left insula. Results were significant after controlling for age and gender.
80
Figure 4.4 Higher systolic blood pressure variability was associated with higher temporal
variability in the functional connectivity between the bilateral hippocampus and precuneus.
Results were significant after controlling for age and gender.
81
GENERAL DISCUSSION
82
This dissertation represents the first attempt to test the relationship between age-related
artery stiffening and functional brain connectivity in older adults. In Study 3, we establish that
greater arterial stiffness (BPV) is associated with greater ALFF in the DMN, reflecting
abnormalities in neuronal activity and possibly greater amplitude of cerebral pulse wave. In
Studies 1 and 2, we identified an association between arterial stiffness markers and functional
connectivity of the DMN. We report on BPV separately in Study 1 due to the unclear
mechanistic pathways and crosstalk between BPV and arterial stiffness. Consistent with our
hypotheses, higher arterial stiffness was associated with reduced functional connectivity between
DMN subnetworks and important brain regions. However, ROI-to-ROI analyses did not reveal
an association between higher stiffness and lower connectivity between the two major hubs of
the DMN, possibly due to lack of power. ROI-to-ROI and seed-based correlation analyses
demonstrate different sensitivities which may explain a discrepancy in results. Finally, Study 4
suggests that greater BPV, and not average BP, was associated with higher temporal variability
of DMN functional connectivity. This study is among the first to examine the dynamic nature of
the interactive relationship between vascular and brain function. There are many areas of future
investigation that may provide more insight into these dynamics. Furthermore, future
investigation can elucidate whether the breakdown in synchrony between neural and vascular
activation underly age-related and pathological changes in cognitive function.
These studies provide complementary information about the relationships between
markers of vascular dysfunction and RSN function. Both offer novel perspectives as few studies
have investigated links between cardiovascular risk and brain function, as measured by fMRI.
Furthermore, there are scarcely any studies in aging populations that approach functional
connectivity from a dynamic framework. Dynamic models are thought to represent brain activity
better but are computationally complicated.
83
Strengths
This dissertation draws on two well-designed datasets focused on brain health and aging
(VaSC and LEMON) and contributes novel vascular and neural data in older adults. This set of
studies is the first to investigate relationships between age-related artery stiffening and brain
function in older adults. Studies 1 and 2 are the first to examine how BPV is related to rsfMRI
metrics in any population. BP measurements are much less expensive and much more accessible
across settings and populations. In terms of clinical utility, BP and BP-derived measures of
vascular function, such as BPV, are therefore worthy of investigation.
Study 4 is one of few studies that simultaneously examines collected BP and rsfMRI data
in humans. The LEMON dataset also utilizes highly sampled fMRI data, allowing for better
identification and control of cardiac and respiratory confounds.
Limitations
Small sample sizes limit several models described within this set of studies (24 older
adults with PWV; 20 older adults with beat-to-beat BP) and lack of power to detect the effect of
interest. We were delayed in the VaSC study data collection for this dissertation in 2019 due to
administrative changes, and then data collection was rendered impossible by the COVID-19
pandemic in 2020. I, therefore, sought out other publicly available data sources for which I did
not have input in study design or data collection. Furthermore, the main variables in both studies,
including PWV, beat-to-beat BP, and rsfMRI, are susceptible to noise from various confounds,
require a great deal of data cleaning, and are particularly challenging to assess in an elderly
population accurately. Subsequently, we discarded five rsfMRI scans, approximately 25% of
PWV data, and over 50% of beat-to-beat BP data did not meet quality control standards.
However, the sample size and results for the PWV analyses align with a similar study published
by Hussein et al. (2020) investigating the relationship of MRI-derived aortic stiffness with
84
pCASL derived BOLD data in younger and middle-aged adults (25 middle-aged parents and 24
adult children). Moreover, in-scanner physiological data monitoring, especially BP, is scarce in
neuroimaging studies despite its known utility. Lack of in-scanner physiological data is likely
due to the specialty equipment necessary to measure BP in the MRI machine and myriad
difficulties with data quality control as described. Our study is one of very few to publish on
synchronous BP and fMRI data. Nevertheless, it would benefit the field to replicate findings in a
larger sample.
Several other noted limitations were beyond the scope of this work. The cross-sectional
study design does not allow us to make any strong inferences about the direction of the observed
relationships. Data on non-Caucasian individuals was limited, preventing meaningful analysis of
racial or ethnic differences. Vascular disease is disproportionately present in Black Americans
(Graham, 2015). Black populations exhibit greater arterial stiffness and greater cardiovascular
risk that may reflect effects of psychosocial stressors disproportionately experienced by these
populations, such as discrimination (Buie et al., 2019). Additional research conducted within
more diverse populations could considerably progress our understanding of the factors
contributing to risk and the potential for interventions that specifically target those at the highest
risk.
It is important to note that the rsfMRI BOLD signal is neurovascular and captures both
neural and non-neural effects. From this data, we cannot assess the degree to which motion
arising from arterial pulsation, respiration, or head movements contribute to the observed
associations with functional connectivity. However, the CONN toolbox utilizes a wellestablished
denoising procedure to remove potential confounding components related to motion,
cerebrospinal fluid, and white matter, from the BOLD signal (Behzadi et al., 2007). Furthermore,
we applied band-pass filtering to include only low-frequency fluctuations, which minimizes the
85
influence of physiological, head-motion, and other noise sources. Finally, after denoising, we
used visual inspection and quality control metrics provided by CONN to further evaluate the
quality of data for each subject and removed any subject with notable motion artifacts after
preprocessing steps were complete. These steps, and the consistency between our study and other
investigations of arterial stiffness with measures of brain function (CBF), support that the
observed associations are not solely attributable to the vascular components of the BOLD signal.
Future Directions
The driving question behind this study is: How is age-related vascular dysfunction,
namely arterial stiffness, and BPV, tied to brain dysfunction later in life? Future studies are
needed to test if lower CBF, or impaired neural function, represent mechanistic pathways linking
higher arterial stiffness to poorer cognition. One unexplored hypothesis is that increased pulse
wave velocity slowly decouples vascular and neuronal oscillations. In other words, changes in the
timing of the pulse wave, or other changes in pulse wave characteristics, may negatively impact
the resting organization and correlations of spontaneous neuronal activity that typically form
networks that support cognition. The emerging literature increasingly recognizes the existence
and importance of synchrony between physiological pulsations (e.g., the cardiac, respiratory, and
neuronal systems). Future work focused on the desynchronization of these vital bodily systems
has great potential in informing our understanding of brain disorders. Ruptures in the brain-heart
connection have a great capacity to cause injury and dysfunction.
A better understanding of how age-related breakdown of the cardiovascular system,
including changes in artery structure and function, influences brain health and function may
guide the development of interventions that effectively promote optimal neurocognitive health in
late life. For example, can improvement in BP control reduce BPV and, in turn, improve
maintenance of functional connectivity later in life? Different classes of anti-hypertensive
86
medications have shown varying effectiveness in reducing BPV. Similarly, classes of
antihypertensive drugs have differential effects on cognition in older adults. Understanding these
differences may lead to improved personalized medicine and better maintenance of brain health
and function throughout the lifespan.
Clinical Implications
Understanding the role of BPV in aging and neurodegenerative disorders has valuable
clinical significance as it reflects new and emerging perspectives on vascular contributions to
brain dysfunction, may have utility for early diagnosis, and point to new therapeutic targets.
Patients routinely measure BP at home and healthcare appointments. Tracking visit-to-visit
variability, rather than just single measurements, could be leveraged to improve older adults'
health monitoring. Furthermore, as reviewed in the introduction, there is evidence that several BP
stabilizing treatments reduce BPV and PWV, and improve baroreceptor function, all of which
could significantly benefit brain tissue health, neurovascular function, and cognition (Nardin et
al., 2019; Safar, 2017; Tohyama et al., 2020). As BP control is the only treatment that has ever
prevented MCI in a clinical trial, age-and-neurodegenerative disorder-related intervention science
must continue to explore BP and the arterial stiffness targets investigated here as targets.
CONCLUSION
Altogether, this group of studies contributes to knowledge concerning potential mechanisms
that may link processes of vascular aging to brain health and aging and potentially contribute to
pathological aging. The findings are consistent with the hypothesis that age-related artery
stiffening impairs CBF, negatively impacting RSN function. Impaired neuronal function may
represent one pathway linking arterial stiffness to cognition.
87
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Author Note
The National Institute of Health Grants R01 AG064228 and R01 AG060049 supported
data in Studies 1-3. The National Institute of Health Grants R01 AG064228 and R01 AG060049
supported data used in Study 4.
Supplementary Tables
Supplemental Table 1. Summary of seed-to-voxel analyses: pulse wave velocity. Covariates: gender, systolic BP, MAP.
PWV Gender Ventral DMN
Dorsal DMN
Systolic BP Ventral DMN
Dorsal DMN
MAP Ventral DMN Parietal
Operculum Cortex Left (197)
Supramarginal Gyrus, anterior division Right (149)
Supramarginal Gyrus, posterior division Right (123)
Supramarginal Gyrus, anterior division Left (114)
Angular Gyrus Right (85)
Cingulate Gyrus, posterior division (52)
Supramarginal Gyrus, posterior division Left (49)
Postcentral Gyrus Left (37)
Central Opercular Cortex Left (21)
Cingulate Gyrus, anterior division (17)
Planum Temporale Left (11)
Postcentral Gyrus Right (6)
Precuneus (166)
Lateral Occipital Cortex, superior division Right (94)
Occipital Pole Right (45)
Supramarginal Gyrus, posterior division Right (252)
Supramarginal Gyrus, anterior division Right (208)
Postcentral Gyrus Right (136)
Angular Gyrus Right (132)
Parietal Operculum Cortex Right (21)
Precentral Gyrus Right (12)
Central Opercular Cortex Left (6)
Occipital Pole Right (99)
Lateral Occipital Cortex, superior division Right (86)
Precuneous Cortex (61)
Cuneal Cortex Right (8)
Supramarginal Gyrus, posterior division Right (302)
Angular Gyrus Right (215)
Supramarginal Gyrus, anterior division Right (201)
Parietal Operculum Cortex Right (14)
124
0.000 507 -3.53 0.000 -54 -28 20
0.016 364 -3.06 0.000 18 -66 38
0.021 246 -2.10 0.000 -54 -28 20
0.026 435 -2.83 0.000 20 -64 38
Regressor
Covariate
Seed ROI
Cluster Labels (# voxels)
Cluster - Level
Voxel - Level
x
y
z
p - FDR
Voxels
t
p - uncorr
0.047 646 -2.21 0.000 64 -42 32
ARTERY STIFFNESS AND BRAIN FUNCTIONAL CONNECTIVITY
Dorsal DMN Precentral Gyrus Left (236) 0.003 603 -2.41 0.000 -32 -16 60
Middle Frontal Gyrus Left (176)
Superior Parietal Lobule Right (159)
Postcentral Gyrus Right (123)
Postcentral Gyrus Left (77)
Frontal Operculum Cortex Left (65)
Precentral Gyrus Right (40)
Frontal Pole Left (32)
Superior Frontal Gyrus Left (30)
Inferior Frontal Gyrus, pars triangularis Left (30) Inferior
Frontal Gyrus, pars opercularis Left (30)
Note. We identified anatomical regions within clusters with AAL atlas through CONN toolbox. Numbers in parentheses are the number of voxels that belonged to the labeled
region. Voxel-level results were thresholded at p<.001 and uncorrected for multiple comparisons (p < .001). Cluster-level results were thresholded at p < .05, using false discovery
rate (FDR) methods to account for multiple comparisons. x, y, z are coordinates of peak locations in the Montreal Neuroim aging Institute (MNI) space. Covariates in the GLMs
reported in this table include gender, mean systolic blood pressure (BP), and mean arterial pressure (MAP).
Abstract (if available)
Abstract
The brain and heart communicate with each other in a dynamic and continuous concert of nerve impulses, hormones, and pulse waves. The brain-heart connection is long-established, but only recently has cerebrovascular function become a focus of brain aging and neurocognitive disorder research. While studies indicate that vascular risk is associated with numerous undesirable neurocognitive outcomes, the current literature focuses less on the interplay between vascular and brain function. The overarching aim of this project is to examine links between markers of age-related vascular dysfunction, arterial stiffness specifically, and brain function. Studies 1 and 2 investigate the association between markers of arterial stiffness and static default mode network (DMN) connectivity in non-demented community-living older adults. In Study 1, we investigate the effect of long-term blood pressure variability (BPV) separately from other more traditional indices of arterial stiffness used in Study 2 (e.g., pulse wave velocity and pulse pressure) due to the unclear mechanistic pathways linking BPV and arterial stiffness. We extend this investigation in Study 3 by examining the relationship between arterial stiffness and the amplitude of low frequency fluctuations in rsfMRI signal, reflecting regional spontaneous neuronal activity. Finally, Study 4 uses a sliding-window technique to investigate dynamic relationships between simultaneously measured brachial artery blood pressure derived estimates of arterial stiffness and temporal variability in DMN connectivity. This dissertation aims to improve our understanding of how vascular factors affect brain health and function in later life. Results from these studies may shed light on potential target mechanisms for interventions aimed at maintaining optimal brain health and cognitive function in older adults.
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Arterial stiffness and resting-state functional connectivity in older adults
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