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The role of blood pressure variability in cognitive decline, cerebrovascular disease and Alzheimer’s disease

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The role of blood pressure variability in cognitive decline, cerebrovascular disease and Alzheimer’s disease

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Content The role of blood pressure variability in cognitive decline, cerebrovascular disease and
Alzheimer’s disease
by
Isabel J. Sible, M.A.
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PSYCHOLOGY)
May 2023
Copyright 2023 Isabel J. Sible
ii
Table of Contents
LIST OF TABLES ......................................................................................................................................... III
LIST OF FIGURES ....................................................................................................................................... IV
ABSTRACT....................................................................................................................................................V
INTRODUCTION .......................................................................................................................................... 1
DISCUSSION ................................................................................................................................................. 9
REFERENCES ............................................................................................................................................. 17
APPENDICES .............................................................................................................................................. 25
APPENDIX A – STUDY 1 ........................................................................................................................................... 25
APPENDIX B – STUDY 2............................................................................................................................................ 25
APPENDIX C – STUDY 3............................................................................................................................................ 25

iii
List of Tables
Table 1: Study 3 Table 1…………………………………………………………………………80
Table 2: Study 3 Table 2…………………………………………………………………………81
Table 3: Study 3 Table 3…………………………………………………………………………82
Table 4: Study 3 Supplementary Table 1………………………………………………………...84
Table 5: Study 3 Supplementary Table 2………………………………………………………...85

iv
List of Figures
Figure 1: Study 3 Figure 1……………………………………………………………………….78
Figure 2: Study 3 Figure 2……………………………………………………………………….79
Figure 3: Study 3 Graphical Abstract……………………………………………………………83
v
Abstract
To address these gaps in the aging literature, we aimed to investigate the hypothesis that elevated
BPV may convey susceptibility to dementia through links with cerebral microvascular
hypoperfusion (Study 1) and cerebrovascular dysfunction in response to stimuli (Study 2) and at
rest (Study 3) in brain regions vulnerable to aging and AD. All studies examined cross-sectional
relationships between continuous beat-to-beat BPV and concurrent functional neuroimaging
markers in community-dwelling older adults (aged 55-90 years) without history of dementia or
clinical stroke living in Los Angeles County and Orange County. Study 1 and Study 3 also
examined relationships in younger adult controls (aged 18-31 years) living in Los Angeles
County. Study 1 used pseudo-continuous arterial spin labelling (pCASL)-MRI to capture
regional cerebral blood flow during a 5-minute period of rest. Study 2 used pCASL-MRI during
5-minute visually guided hypercapnia and hypocapnia challenge to capture CVR response to
stimuli. Study 3 used resting state functional MRI (rsfMRI) to capture oscillations in regional
brain activity during a 7-minute period of rest. We studied functional activity in a priori selected
brain regions known to convey susceptibility to cerebrovascular insult and AD (Iadecola, 2004;
Vikner et al., 2021; Zlokovic, 2011), including the medial temporal lobe. We also examined
relationships between mean BP and functional neuroimaging markers to directly compare
potential effects with BPV.
1
Introduction
Blood pressure (BP) control is a promising therapeutic target for reducing dementia risk
(Barnes & Yaffe, 2011; Yaffe, 2019). Decades of work suggest that both high and low BP are
related to a myriad of poor brain health outcomes, including cognitive impairment and vascular
and neuronal brain pathology (Faraco & Iadecola, 2013; Iadecola et al., 2016; Qiu, Winblad, &
Fratiglioni, 2005). Additionally, there is strong evidence that mid-life BP, and in particular
hypertension, is associated with these outcomes and highlights the importance of early
intervention and management (Barnes & Yaffe, 2011; Lane et al., 2019; Muller et al., 2014; Qiu
et al., 2005). Importantly, the majority of cases with dementia have mixed vascular/Alzheimer’s
disease (AD) pathology (Gorelick et al., 2011; Schneider, Arvanitakis, Bang, & Bennett, 2007;
Toledo et al., 2013), suggesting shared underlying mechanisms – and potential shared therapies.
Unfortunately, clinical trials for AD have largely failed to show clinical/cognitive benefit,
despite some reducing hallmark AD pathophysiology amyloid-beta (Aß) (and to a lesser extent
tau) thought to drive cognitive impairment (Knopman, Jones, & Greicius, 2021). Additionally,
recent monoclonal antibody therapies targeting Aß, such as aducanumab (Aduhelm®) and
lecanemab (Leqembi®), have made headlines for adverse safety events, particularly those related
to cerebrovascular insult (i.e., amyloid related imaging abnormalities [ARIA]) (Knopman et al.,
2021; van Dyck et al., 2022; Woloshin & Kesselheim, 2022). Reported rates of ARIA have also
been higher in individuals with AD risk gene apolipoprotein (APOE) e4 (Sevigny et al., 2016;
van Dyck et al., 2022), who are at greatest risk for Aß accumulation and AD (Serrano-Pozo, Das,
& Hyman, 2021). These events have raised concern about lowering Aß burden (Woloshin &
Kesselheim, 2022) and have further implicated vascular mechanisms in neurodegenerative
processes (Iadecola, 2004; Zlokovic, 2011). In contrast, the recent Systolic Blood Pressure
Intervention Trial (SPRINT) Memory and Cognition in Decreased Hypertension (MIND) clinical
2
trial was able to safely lower BP levels in a matter of months in individuals with hypertension – a
major vascular risk factor for dementia - and showed cardiovascular, cerebrovascular, and
cognitive benefit (Nasrallah et al., 2019; Williamson et al., 2019; Wright et al., 2015).
Specifically, the results of the SPRINT MIND trial suggest that individuals who underwent
intensive BP lowering (target < 120 mmHg systolic BP), when compared to individuals who
underwent standard BP lowering (target < 140 mmHg systolic BP), had reduced risk of
cardiovascular events (e.g., stroke, myocardial infarction), reduced risk of mild cognitive
impairment (MCI) and a combined MCI/probable dementia outcome, and significantly slower
progression of white matter hyperintensities (Nasrallah et al., 2019; Williamson et al., 2019;
Wright et al., 2015), a hallmark feature of cerebrovascular disease. The trial did not prevent
dementia, but the data had been trending towards a benefit for dementia before being stopped
prematurely due to benefit finding for cardiovascular disease (Williamson et al., 2019). These
findings have fueled ongoing interest in understanding relationships between BP and brain health
and cemented BP as a promising strategy for lowering dementia risk.
Observational and interventional studies of BP have largely focused on capturing and
controlling mean BP levels (Wright et al., 2015). However, BP is naturally highly dynamic, and
fluctuations in levels reflect complex internal adjustments to ensure adequate pressure and flow
throughout the body (Parati, Ochoa, Lombardi, & Bilo, 2013). Until very recently, these
variations were often regarded as “noise” in BP management (Parati, Stergiou, Dolan, & Bilo,
2018), in much the same way that resting state brain networks were first considered something to
filter out when studying brain function (Greicius, Krasnow, Reiss, & Menon, 2003; Zuo et al.,
2010). However, just as heart rate variability is now considered to carry important information
about health beyond heart rate (Sturm, Brown, et al., 2018; Thayer, Åhs, Fredrikson, Sollers, &
3
Wager, 2012), there is a burgeoning interest to study the variability in BP independent of mean
levels. The focus on BP variability (BPV) as a distinct and important aspect of BP management
first emerged in cardiovascular studies suggesting elevated BPV is associated with arterial
remodeling and increased risk for stroke, renal damage, and chronic kidney disease (Bilo &
Parati, 2018; Parati et al., 2013; Rothwell, Howard, Dolan, O’Brien, et al., 2010). It was
hypothesized that excessive fluctuations in BP, regardless of mean BP, may introduce stress to
the cardiovascular system and promote degradation of cardiac structures and downstream renal
systems (Höcht, 2013; Kollias, Stergiou, Kyriakoulis, Bilo, & Parati, 2017; Parati et al., 2018).
Preclinical studies in rats and, later, clinical studies in humans, suggested a greater contribution
of BPV than mean BP in organ damage to the heart (e.g., left ventricular hypertrophy), blood
vessels (e.g., aortic hypertrophy, impaired arterial distensibility, intima-media thickness), and
kidneys (e.g., glomerular collapse) (Bilo & Parati, 2018; Höcht, 2013). As the heart-brain axis
has become increasingly appreciated in several scientific disciplines, BPV work has expanded to
the aging field to investigate relationships with target organ damage to the brain.
A growing number of studies indicate elevated BPV - whether determined over beat-to-
beat cycles, hours, days, months, or even years - is associated with numerous poor brain health
outcomes, such as cognitive impairment and decline and increased risk for and progression of
dementia, including AD (de Heus, Olde Rikkert, Tully, Lawlor, & Claassen, 2019; de Heus et
al., 2021; Lattanzi, Luzzi, Provinciali, & Silvestrini, 2015, 2014; Lattanzi, Viticchi, et al., 2014;
Rouch et al., 2020). Recent work suggests higher BPV is related to important longitudinal
markers of AD, such as medial temporal gray matter volume loss (Ma, Yilmaz, et al., 2020; Sible
& Nation, 2021), alterations in cerebrospinal fluid and plasma AD biomarkers Aß and
phosphorylated tau (Sible & Nation, 2022b; Sible, Yew, et al., 2022), tau accumulation in the
4
temporal lobes (Sible, Nation, et al., 2022), and neurofibrillary tangle pathology (Ma et al.,
2021). There is also a strong association between elevated BPV and severity and progression of
cerebrovascular disease on MRI (Brickman et al., 2010; Ma, Song, et al., 2020; Ma, Yilmaz, et
al., 2020; Tully et al., 2020) and postmortem evaluation (Ma et al., 2021; Sible, Bangen,
Blanken, Ho, & Nation, 2021). Importantly, many studies report BPV is more strongly related to
these outcomes when compared to effects with mean BP (de Heus et al., 2021; Nagai, Hoshide,
Dote, & Kario, 2015; Rouch et al., 2020), consistent with findings in cardiovascular research
(Höcht, 2013), and highlights the unique role BPV may play when considering antihypertensive
strategies for reducing dementia risk (Nagai & Kario, 2021; Nagai, Kato, & Dote, 2021).
Additionally, BPV does not correlate with other more traditionally studied BP measures (e.g.,
pulse pressure, mean arterial pressure, hypertension status), and may require distinct
interventions beyond standard BP control (Parati et al., 2013, 2018). Also, high BPV appears to
be more consistently related to poor outcomes than these traditional BP measures (Ma, Tully,
Hofman, & Tzourio, 2020), bolstering BPV as a new BP risk indicator strongly linked with brain
health and cognitive impairment.
Taken together, current BPV research suggests BPV may be related to both vascular and
neuronal vulnerabilities in aging. Although mechanisms linking BPV to increased dementia risk
remain understudied, it has been hypothesized that inflated BPV may alter processes both highly
dependent on BP and critical for cognition, such as cerebral perfusion (Lattanzi, Vernieri, &
Silvestrini, 2018; Ma, Tully, et al., 2020; Nagai et al., 2017; Yoo et al., 2020). Elevated BPV
may challenge cerebral autoregulation and risk cerebral hypoperfusion (Ma, Song, et al., 2020;
Nagai et al., 2017). Over time, chronic BP dipping may stress arterial walls and promote
microvascular damage, leading to blood-brain barrier breakdown, altered cerebral blood flow,
5
and subsequent neuronal damage (Lattanzi et al., 2018; Ma, Song, et al., 2020; Oishi et al.,
2017). Some have coined this process a “tsunami effect” (Saji, Toba, & Sakurai, 2016) to
emphasize the potential residual impact on cerebral arterial health. Consistent with this
hypothesis, recent studies reported that elevated BPV in older adults was associated with cerebral
perfusion decline in brain regions implicated in AD (Sible, Yew, et al., 2021) and greater
cerebrovascular disease lesion burden at postmortem evaluation (Ma et al., 2021; Sible, Bangen,
et al., 2021). Additional changes in cerebrovascular functioning that can occur as a result of
normal wear and tear over time, such as those related to arterial stiffening (Nichols, O’Rourke, &
Vlachopoulos, 2011; Zlokovic, 2011), may alter pulse wave dynamics and further exacerbate the
impact of BP fluctuations on brain health and cognition (Lattanzi et al., 2018; Ma, Tully, et al.,
2020; Nagai et al., 2017). Specifically, arterial stiffness may push the pressure wave deeper into
the brain’s vascular bed, the effects of which may be enhanced by greater fluctuation in the
pressure wave itself. These changes may convey particular risk to brain regions with both
vascular and neuronal vulnerability, such as the medial temporal lobes (Iadecola, 2004; Ma,
Song, et al., 2020; Vikner et al., 2021; Zlokovic, 2011), even in cognitively intact older adults.
Potentially synergetic properties between BPV, cerebral hypoperfusion, and age-related
cerebrovascular dysfunction prior to major neurocognitive change are not well understood and
may represent early pathways to cognitive impairment and dementia. Studying these
relationships in older adults with normal cognition or even mild cognitive deficits bolsters our
ability to identify assessment tools and therapies that can prevent dementia before symptoms
begin. Importantly, two recent studies reported that even in adults without dementia who had
strictly controlled mean BP, higher BPV was associated with cognitive decline and increased risk
for MCI and dementia (de Havenon et al., 2021; Sible & Nation, 2022a).
6
While dynamic autoregulatory functions governing the relationship between BP changes
and cerebral blood flow have been well studied using transcranial doppler (D’Andrea et al.,
2016), this literature is limited to studying blood flow in the larger intracranial arteries. Less is
known about the role of BPV in cerebral microvascular perfusion in specific brain regions
critical to cognitive function, and in smaller cerebrovascular compartments (e.g., arterioles,
capillaries) critical to blood-brain barrier integrity, nutrient influx, and waste clearance essential
for neuronal functioning (Kisler, Nelson, Montagne, & Zlokovic, 2017; Zlokovic, 2011).
Relatedly, evidence suggests a link between BPV and markers of cardiovascular dysfunction
(i.e., aortic stiffness, carotid artery remodeling) (Höcht, 2013; Lattanzi et al., 2018; Ma, Song, et
al., 2020), but fewer studies evaluate how BPV relates to markers of cerebrovascular dysfunction
that may predate frank markers of cerebrovascular disease (e.g., white matter hyperintensities on
MRI). Moreover, BPV is more strongly associated with stroke risk than with risk of heart disease
(Lattanzi, Słomka, & Divani, 2023), suggesting excessive BP fluctuation is especially important
for high-flow organs like the brain. Finally, a recent meta-analysis of BPV and cerebrovascular
disease showed greater effect sizes for shorter intervals of BPV (e.g., beat-to-beat, day-to-day)
when compared to longer intervals (e.g., month-to-month, year-to-year) (Tully et al., 2020),
underscoring the need for studies of concurrent brain and neurovascular functional changes
associated with acute BPV.
It is also unclear how aging may influence relationships between BPV, cerebral
perfusion, and cerebrovascular function. In contrast to the well-documented age-related decline
in heart rate variability due to diminished cardiovagal tone (Sturm, Brown, et al., 2018; Thayer et
al., 2012), BPV tends to increase with age, likely due to a combination of factors, including age-
related arterial stiffness and alterations in cardiovascular tone and autoregulatory mechanisms
7
(e.g., baroreflex function) (Höcht, 2013; Imai et al., 1997; Nagai et al., 2015; Parati et al., 2013).
Large fluctuations in BP are especially relevant to the brain and can risk frequent, but mild,
reductions in cerebral blood flow (i.e., oligemia) (Coats et al., 1991; Conway, Boon, Jones, &
Sleight, 1985; Imai et al., 1997). The brain’s microvasculature is particularly sensitive to
interrupted flow and studies on chronic oligemia report associations with severe cell disruption,
cognitive impairment, and increased presence of neurotoxic proteins that accumulate in the
brains of individuals diagnosed with AD (Iadecola, 2004; Zlokovic, 2011). Cerebrovascular
function, as can be indexed by cerebrovascular reactivity (CVR) – or the ability of the brain’s
blood vessels to dilate and constrict in response to stimuli – is also reduced with advancing age
(Sur et al., 2020). Some evidence indicates diminished CVR is associated with cognitive
impairment and may represent early cerebrovascular dysfunction relevant to cognitive function
(Markus & Cullinane, 2001; Reinhard et al., 2014; Sur et al., 2020). One recent study also
suggests that the CVR vasodilatory (i.e., hypercapnia) response (increases cerebral blood flow)
may be more affected by aging than the CVR vasoconstrictive (i.e., hypocapnia) response
(decreases cerebral blood flow) (Yew et al., 2022). This finding supports other work indicating
vasodilation relies heavily on the microvasculature (e.g., arterioles and capillaries), which is
particularly vulnerable in older adults (Zlokovic, 2011), whereas vasoconstriction is controlled
more by the larger cerebrovascular compartments (e.g., arteries) (Bagher & Segal, 2011;
Paniagua, Bryant, & Panza, 2001). Cerebrovascular function at rest may also be reduced in older
adults and reflect age-related declines in spontaneous brain activity possibly impacted by
cerebral arterial health (Hu, Chao, Zhang, Ide, & Li, 2014). One marker of basal cerebrovascular
function is amplitude of low frequency fluctuations (ALFF). ALFF is an index of oscillations in
the blood-oxygen level-dependent (BOLD) signal at rest and measures basal vascular and
8
neuronal brain activity (Biswal, Zerrin Yetkin, Haughton, & Hyde, 1995; Vigneau-Roy, Bernier,
Descoteaux, & Whittingstall, 2014). Some studies report reductions in ALFF, especially in
regions vulnerable to AD and microvascular insult, are related to cerebrovascular disease burden
and cognitive impairment (Ni et al., 2022; Wang et al., 2011; Zhang et al., 2021). ALFF may
also decline with age, representing less efficient neurovascular unit function at rest (Hu et al.,
2014). Together these studies suggest that changes in cerebrovascular functioning that occur
during the aging process may be related to cognitive decline through a cerebral hemodynamic
mechanism. It is possible that age-related cerebrovascular dysfunction – both at rest and in
response to stimuli – may underly the robust association between BPV, cerebrovascular disease,
and dementia risk.
To address these gaps in the aging literature, we aimed to investigate the hypothesis that
elevated BPV may convey susceptibility to dementia through links with cerebral microvascular
hypoperfusion (Study 1) and cerebrovascular dysfunction in response to stimuli (Study 2) and at
rest (Study 3) in brain regions vulnerable to aging and AD. All studies examined cross-sectional
relationships between continuous beat-to-beat BPV and concurrent functional neuroimaging
markers in community-dwelling older adults (aged 55-90 years) without history of dementia or
clinical stroke living in Los Angeles County and Orange County. Study 1 and Study 3 also
examined relationships in younger adult controls (aged 18-31 years) living in Los Angeles
County. Study 1 used pseudo-continuous arterial spin labelling (pCASL)-MRI to capture
regional cerebral blood flow during a 5-minute period of rest. Study 2 used pCASL-MRI during
5-minute visually guided hypercapnia and hypocapnia challenge to capture CVR response to
stimuli. Study 3 used resting state functional MRI (rsfMRI) to capture oscillations in regional
brain activity during a 7-minute period of rest. We studied functional activity in a priori selected
9
brain regions known to convey susceptibility to cerebrovascular insult and AD (Iadecola, 2004;
Vikner et al., 2021; Zlokovic, 2011), including the medial temporal lobe. We also examined
relationships between mean BP and functional neuroimaging markers to directly compare
potential effects with BPV.
Findings from these studies will help elucidate possible mechanisms linking BPV to
dementia and improve the use of BP as a risk indicator. These findings may have therapeutic
implications and immediate impact on dementia prevention strategies since some existing
antihypertensive medications have specific effects on BPV, independent of traditionally targeted
mean BP levels (Rothwell, Howard, Dolan, Brien, et al., 2010; Webb, Fischer, Mehta, &
Rothwell, 2010). While recent clinical trials and studies of BP lowering have changed guidelines
on BP control (Whelton et al., 2018; Williamson et al., 2019; Wright et al., 2015), these
guidelines do not currently consider BPV. Additionally, BP is both highly modifiable and readily
accessible in most clinical care settings (Kollias et al., 2017; Parati et al., 2018), suggesting that
findings with BPV could have a far clinical reach. Importantly, at least 60% of adults will go on
to take medications to control BP (CDC, 2021), and even slight improvements in BP
management – perhaps by incorporating BPV into monitoring and treatment plans – have the
potential to reduce dementia risk worldwide (Barnes & Yaffe, 2011; Yaffe, 2019).
Discussion
Findings suggest elevated short-term BPV, independent of mean BP, in older adults
without dementia is associated with concurrent cerebral microvascular hypoperfusion (Study 1)
and cerebrovascular dysfunction both in response to stimuli (Study 2) and at rest (Study 3).
These relationships were robustly observed in medial temporal regions, especially in older
adults. In contrast, mean BP was not significantly related to these measures - in any brain region
- in older or younger adults, underscoring the unique role BPV may play in functional
10
cerebrovascular health. Findings were largely consistent across studies using three different
neuroimaging modalities to study cerebrovascular function, highlighting the potential vascular
and neuronal selective vulnerability of the medial temporal lobe to large fluctuations in BP.
Results from Study 1 are consistent with a recent study showing higher visit-to-visit BPV
over one year was associated with cerebral perfusion decline over the same one-year period in
AD vulnerable brain regions (Sible, Yew, et al., 2021), including the medial temporal lobe.
Study 1 findings add to this work by utilizing a novel approach of monitoring BP during
assessment of regional microvascular perfusion and was thus able to appreciate relationships
between short-term BPV and concurrent regional cerebral blood flow in the brain’s smallest
vascular compartments. Additionally, no significant relationships were observed between BPV
and cerebral perfusion in younger adults or in regions beyond the medial temporal lobe. These
findings suggest a possible age-dependent relationship between short-term BPV and cerebral
blood flow, specifically within brain regions highly sensitive to hypoxic-ischemic injury
(Iadecola, 2004; Zlokovic, 2011).
There is a strong link between elevated BPV and increased risk for stroke (Kollias et al.,
2017; Parati et al., 2018) and frank markers of cerebrovascular disease observable on structural
MRI (Ma, Song, et al., 2020; Tully et al., 2020) that impact cognitive functioning. Findings from
Study 2 and Study 3 provide novel evidence of a relationship between higher BPV and early
markers of cerebrovascular dysfunction that may presage vascular brain injury relevant to
dementia risk. Specifically, Study 2 found that elevated BPV was associated with diminished
CVR in response to both hypercapnia and hypocapnia challenge. These findings suggest that
BPV may be related to the ability of the brain’s blood vessels to dilate and constrict in response
to salient stimuli and have an impact on functional hyperemia. Interestingly, elevated BPV may
11
increase risk for both cerebral small vessel disease (e.g., arteriolosclerosis) and large vessel
disease (e.g., atherosclerosis in the Circle of Willis) (Ma et al., 2021; Sible, Bangen, et al., 2021).
Findings from Study 2 support this hypothesis, since hypercapnia may rely heavily on the
microvasculature (i.e., arterioles, capillaries) and hypocapnia on larger cerebral compartments
(i.e., arteries) (Bagher & Segal, 2011; Paniagua et al., 2001; Zlokovic, 2011). In addition to
cerebrovascular dysfunction following an environmental probe (e.g., hypercapnia/hypocapnia
challenge), results from Study 3 raise the possibility that BPV may also be related to
cerebrovascular dysfunction at rest. Higher BPV was associated with lower ALFF, which reflects
oscillations in vascular and neuronal basal brain activity (Biswal et al., 1995; Vigneau-Roy et al.,
2014). Importantly, relationships were observed in medial temporal regions in older adults and
younger adults, but findings in younger adults were less robust and limited to fewer subregions.
It could be that increasingly variable BP is associated with age-related declines in neurovascular
unit functioning efficiency even at baseline. Study 3 was observational and cross-sectional but
one recent longitudinal interventional study suggests that ALFF can be increased through
biofeedback training of heart rate variability, at least in healthy younger adults (Nashiro et al.,
2021). Specifically, participants were randomized to an active 5-week intervention group of daily
biofeedback training aimed at increasing heart rate variability or a control group. The
intervention group not only increased their resting heart rate variability, an important index of
health (Sturm, Sible, et al., 2018; Thayer et al., 2012), but also their ALFF. In contrast, these
effects were not observed in the control group. These results are important for the interpretation
of Study 3 findings, since heart rate variability and BPV are inversely correlated (Parati et al.,
2013) and suggest that behavioral interventions targeting heart rate variability may also have a
positive impact on BPV and brain health. Although the intervention was conducted with healthy
12
younger adults, effects could be even stronger in older adults given that findings with BPV in
Study 3 were more robust and widespread in older vs younger adults. Additionally, many of the
younger adult participants in Study 3 are the same as those in the intervention study, and future
work examining these possibilities could have implications for behavioral interventions for
reducing BPV.
BPV appeared to be particularly associated with function in medial temporal regions that
are critical for cognition and sensitive to hypoxic-ischemic injury and AD neurodegenerative
disease process (Iadecola, 2004; Zlokovic, 2011). Several new studies also link higher BPV with
medial temporal vulnerability, including gray matter volume loss (Sible & Nation, 2021) and tau
accumulation (Sible, Nation, et al., 2022). Interestingly, BPV was robustly associated with these
important markers of AD in APOE e4 carriers. Additional lines of evidence suggest other
vascular contributions of APOE e4 to cognitive dysfunction in AD, such as blood-brain barrier
breakdown in the medial temporal lobe (Montagne et al., 2020; Nation et al., 2019). Although
the smaller sample sizes in the present studies limited our ability to examine associations with
APOE e4, it is possible that the robust relationships observed between BPV and medial temporal
cerebrovascular dysfunction are driven by APOE e4 carriers. Future studies with larger samples
will be able to investigate this possibility and add to ongoing work detailing relationships
between BPV, cerebrovascular disease, and AD risk.
It is very interesting that mean BP was not significantly associated with outcomes in
these studies. Additionally, we calculated intraindividual BPV as variability independent of
mean, an increasingly used index of BPV uncorrelated with mean levels (Rothwell, Howard,
Dolan, O’Brien, et al., 2010) (we also used the more common standard deviation and coefficient
of variation). Most observational and interventional BP work has focused on mean levels
13
(Nasrallah et al., 2021; Williamson et al., 2019; Wright et al., 2015) and the present findings
indicate that BPV may be an understudied aspect of BP control relevant to disease prevention. It
may be that, beyond overall pressure, there is something unique about variability in BP levels
that poses a risk to cerebrovascular health. Findings from the three studies raise the possibility
that large fluctuations in BP could cause mechanical injury to the brain. For example, chronic BP
surging could distend the arterial walls beyond repair, which in turn could promote
microvascular insult and disrupt cerebrovascular function (Ma, Song, et al., 2020; Nagai et al.,
2017). Additionally, changes in sheer stress associated with higher BPV likely trigger
transcriptomic changes in endothelial cells through mechanoreceptors on their surface (Höcht,
2013). These changes could powerfully modulate endothelial – and therefore ultimately
neurovascular – unit functioning (Höcht, 2013) and neural milieu in terms of inflammation,
oxidative stress, hypertrophy, and blood-brain barrier breakdown, ultimately contributing to
neurodegeneration. Importantly, the brain’s endothelium is highly sensitive to circulatory
changes, including changes in hemodynamics and BP (Chen et al., 2020). Additionally, a salt-
rich diet in mice leads to cognitive impairment associated with a nitric oxide (potent vasodilator)
deficit in endothelial cells and hypoperfusion (Faraco et al., 2018). One recent study in mice also
found that a high salt diet induced tau hypophosphorylation and subsequent cognitive
dysfunction, but that these effects were prevented by restoring endothelial nitric oxide
production (Faraco et al., 2019). These findings are important when considering recently
reported links between higher BPV and tau (Ma et al., 2021; Sible & Nation, 2022b; Sible,
Nation, et al., 2022), given that nitric oxide released from the endothelium causes beat-to-beat
fluctuations in BP (Höcht, 2013). Age-related arterial stiffening may exacerbate these effects
(Imai et al., 1997). Alternatively, BPV could be an index, driver, or consequence of arterial
14
stiffening (Lattanzi et al., 2018). Future studies that can directly address the role of arterial
stiffening and other potential mechanisms are warranted. It is also possible that
neurodegenerative effects on autonomic control centers could result in diminished BP control
(and increase BPV) and cerebrovascular function (Kitamura et al., 2020; Nagai et al., 2017). The
present studies were all cross-sectional and longitudinal designs able to disentangle the temporal
order of relationships will help elucidate the strong link between BPV and dementia risk.
Taken together, findings from all three studies suggest that elevated BPV, independent of
mean BP, is associated with cerebral hypoperfusion and cerebrovascular dysfunction, and the
impact of excessive BP fluctuations on the brain’s structural and functional integrity may
increase with advancing age. BPV elevation was recently shown to predate major neurocognitive
disorder (Sible & Nation, 2020) and these findings highlight the possibility that BPV may also be
related to early markers of cerebrovascular dysfunction that precede frank cerebrovascular insult
impacting cognitive function. Therefore, it is possible that BPV is an early vascular risk factor
for dementia, with potential therapeutic implications.
The three studies provide new information on the role of BPV in cerebrovascular
functioning that may predate cerebrovascular disease. Relationships were examined in both older
adults and younger adults and improves our understanding of age-related differences.
Additionally, BPV and functional neuroimaging markers were collected concurrently and add to
prior work largely utilizing BPV captured over months to years (Ma, Song, et al., 2020; Tully et
al., 2020). We used multiple neuroimaging techniques to capture cerebrovascular function in the
brain, even in the smallest vascular compartments vulnerable to cerebrovascular disease and AD.
We also assessed cerebrovascular function at rest and in response to salient stimuli to appreciate
basal vs functional changes. There are several important limitations. First, the sample sizes of
15
each of the studies are relatively small and validation with larger samples is needed. Due to
limited sample sizes, we were not able to test for effects with antihypertensive medications.
Some evidence suggests certain classes of antihypertensive agents may have differential effects
on BPV independent of mean BP (Höcht, 2013; Rothwell, Howard, Dolan, Brien, et al., 2010;
Webb et al., 2010). Future work that is able to adequately examine these potential effects – both
for monotherapy and combination therapy – is essential for clinical translation of work on BPV.
Relatedly, many older adults take multiple medications, sometimes several BP agents (CDC,
2021), and finding existing antihypertensives that can address mean BP and BPV have the
potential to reduce the effects of polypharmacy and improve precision medicine approaches to
dementia care. We assessed BPV on a beat-to-beat scale using a device that has been validated
with intra-arterial BP monitoring (Biopac, 2019; Gratz et al., 2017; Kwon et al., 2022). While
continuous BP monitoring is not standard practice in most clinical settings, new advances in
wearable technology will be able to test the validity and reliability of our findings. Relatedly, the
BPV field is emerging and gold standards in methodology are not yet fully established (Parati et
al., 2013, 2018). However, several lines of evidence converge to suggest that higher BPV,
whether measured over seconds, hours, days, months, or years, is robustly associated with poor
brain health outcomes (de Heus et al., 2021; Ma, Song, et al., 2020; Tully et al., 2020). Our
samples included community-dwelling older adults without major neurocognitive disorder and
with limited vascular risk (e.g., ~65-75% with Fazekas score (Fazekas, Chawluk, & Alavi, 1987)
≤ 1). Investigating relationships with cerebrovascular function in samples with greater cognitive
impairment and vascular burden is needed. Additionally, including AD biomarkers will aid in
our understanding of the nuances in the relationship between BPV and cerebrovascular function
across the AD biomarker spectrum. While our study samples were comprised of individuals
16
reflecting the racial and ethnic diversity of Los Angeles and Orange County regions, we were not
able to test for potential differences by race and ethnicity due to limited sample sizes. One study
that was adequately powered for this analysis found no racial/ethnic differences (white vs Black
vs Hispanic vs other) in the relationship between BPV and markers of cerebrovascular disease on
MRI (Brickman et al., 2010). On the other hand, a recent meta-analysis provided some evidence
that the risk for cerebral small vessel disease attributed to BPV was higher for Asians than non-
Asians (Tully et al., 2020). Recent work suggests a disproportionate burden of dementia and
MCI among older Black and Hispanic adults (Manly et al., 2022). Similarly, rates of
hypertension are higher among non-Hispanic Black adults (56%) than non-Hispanic white adults
(48%), non-Hispanic Asian adults (46%), or Hispanic adults (39%) (CDC, 2021). Additionally,
among those recommended to take antihypertensive medication, BP control is lowest among
non-Hispanic Asian adults (19%) compared to non-Hispanic white adults (32%), non-Hispanic
Black adults (25%), and Hispanic adults (25%) (CDC, 2021). Therefore, examining BPV in
samples that are racially and ethnically diverse is essential for our larger understanding of health
disparities in vascular risk relevant to dementia. Finally, the cross-sectional nature of all three
studies limits interpretation of findings. Longitudinal, and hopefully later, interventional designs
have the possibility of informing therapeutic targets for dementia prevention.
17
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25
Appendices
Appendix A – Study 1
Study 1 - Sible, I.J., Yew, B., Dutt, S., Li, Y., Blanken, A.E., Jang, J., Ho, J.K., Marshall, A.,
Kapoor, A., Gaubert, A., Bangen, K.J., Sturm, V.E., Shao, Z., Wang, D.J.J., & Nation, D.A.
(2022). Selective vulnerability of medial temporal regions to short-term blood pressure
variability and cerebral hypoperfusion in older adults. NeuroImage:Reports, 2(1), 100080.
Appendix B – Study 2
Study 2 - Sible, I.J., Jang, J., Dutt, S., Yew, B., Alitin, J.P.M., Li, Y., Blanken, A.E., Ho, J.K.,
Marshall, A., Kapoor, A., Shenasa, F., Gaubert, A., Nguyen, A., Rodgers, K.E., Sturm,
V.E., Shao, X., Wang, D.J., & Nation, D.A. (2022). Older adults with higher blood
pressure variability exhibit cerebrovascular reactivity deficits. American Journal of
Hypertension, hpac108.
Appendix C – Study 3
Study 3 - Sible, I.J., Yoo, H.J., Min, J., Nashiro, K., Change, C., Nation, D.A, & Mather, M.
(2023). Short-term blood pressure variability is inversely related to regional amplitude of
low frequency fluctuations in older and younger adults [Manuscript under review].
Department of Psychology, University of Southern California.

Selective vulnerability of medial temporal regions to short-term
blood pressure variability and cerebral hypoperfusion in older
adults
Isabel J. Sible, MA
a
, Belinda Yew, PhD
a
, Shubir Dutt, MA
a,b
, Yanrong Li, BA
c
, Anna E.
Blanken, PhD
a
, Jung Yun Jang, PhD
c
, Jean K. Ho, PhD
c
, Anisa J. Marshall, MS
a
, Arunima
Kapoor, MSc
d
, Aimée Gaubert, BA
c
, Katherine J. Bangen, PhD
e,f
, Virginia E. Sturm, PhD
g,h,i
,
Xingfeng Shao, PhD
j
, Danny J. Wang, PhD
j
, Daniel A. Nation, PhD
c,d,*
a
Department of Psychology, University of Southern California, Los Angeles, CA 90089, USA
b
Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
c
Institute for Memory Impairments and Neurological Disorders, University of California, Irvine,
Irvine, CA 92697, USA
d
Department of Psychological Science, University of California Irvine, Irvine, CA 92697, USA
e
Research Service, Veteran Affairs San Diego Healthcare System, San Diego, CA 92161, USA
f
Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA
g
Department of Neurology, University of California, San Francisco, San Francisco, CA, 94158,
USA
h
Department of Psychiatry, University of California, San Francisco, San Francisco, CA, 94158,
USA
i
Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, 94158,
USA
j
Laboratory of Functional MRI Technology, Mark and Mary Stevens Neuroimaging and Informatics
Institute, University of Southern California, Los Angeles, CA, 90033, USA
Abstract
Blood pressure variability is an emerging risk factor for stroke, cognitive impairment, and
dementia, possibly through links with cerebral hypoperfusion. Recent evidence suggests visit-to-
visit (e.g., over months, years) blood pressure variability is related to cerebral perfusion decline
1
Abbreviations: BP = blood pressure; BPV = blood pressure variability; AD = Alzheimer’s disease; CBF = cerebral blood flow;
pCASL-MRI = pseudo-continuous arterial spin-labelling MRI; ROI = regions-of-interest; mOFC = medial orbitofrontal cortex; IPC =
inferior parietal cortex; ITC = inferior temporal cortex; rMFG = rostral middle frontal gyrus; VIM = variation independent of mean;
CV = coefficient of variation; BMI = body mass index; DRS-2 = Mattis Dementia Rating Scale – 2; FDR = false discovery rate; FWE
= family-wise error; APOE ϵ4 = apolipoprotein ϵ4
*
Corresponding Author: Daniel A. Nation, Ph.D., Associate Professor, University of California Irvine, Department of Psychological
Science, 4201 Social and Behavioral Sciences Gateway, Irvine, CA 92697-7085, Phone: (949) 824-9339, dnation@uci.edu.
Conflict of interest/disclosure statement
The authors report no disclosures.
HHS Public Access
Author manuscript
Neuroimage Rep. Author manuscript; available in PMC 2022 July 01.
Published in final edited form as:
Neuroimage Rep. 2022 March ; 2(1): . doi:10.1016/j.ynirp.2022.100080.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
in brain regions vulnerable to Alzheimer’s disease. However, less is known about relationships
between short-term (e.g., < 24 hours) blood pressure variability and regional cerebral perfusion,
and whether these relationships may differ by age. We investigated short-term blood pressure
variability and concurrent regional cerebral microvascular perfusion in a sample of community-
dwelling older adults without history of dementia or stroke and healthy younger adults. Blood
pressure was collected continuously during perfusion MRI. Cerebral blood flow was determined
for several brain regions implicated in cerebrovascular dysfunction in Alzheimer’s disease.
Elevated systolic blood pressure variability was related to lower levels of concurrent cerebral
perfusion in medial temporal regions: hippocampus ( β = −.60 [95% CI −.90, −.30]; p < .001),
parahippocampal gyrus ( β = −.57 [95% CI −.89, −.25]; p = .001), entorhinal cortex (β = −.42
[95% CI −.73, −.12]; p = .009), and perirhinal cortex (β = −.37 [95% CI −.72, −.03]; p = .04),
and not in other regions, and in older adults only. Findings suggest a possible age-related selective
vulnerability of the medial temporal lobes to hypoperfusion in the context of short-term blood
pressure fluctuations, independent of average blood pressure, white matter hyperintensities, and
gray matter volume, which may underpin the increased risk for dementia associated with elevated
BPV .
1
Keywords
blood pressure variability; cerebral hypoperfusion; aging; medial temporal lobes
1. INTRODUCTION
Blood pressure (BP) and cerebral perfusion are related to dementia risk (Lane et al., 2019;
Mattsson et al., 2014; Wolters et al., 2017; Yew and Nation, 2017; Zlokovic, 2011). Growing
evidence suggests that increased BP variability (BPV) over the long-term (e.g., months,
years, also known as “visit-to-visit BPV”) and short-term (e.g., < 24 hours) are also
associated with increased risk for, and predictive of, cognitive impairment and dementia,
including Alzheimer’s disease (AD) and vascular dementia, regardless of average BP levels
(De Heus et al., 2021; Lattanzi et al., 2018, 2014; Ma et al., 2020b; Nagai et al., 2017;
Oishi et al., 2017; Qin et al., 2016; Tully et al., 2020). A recent study found that BPV
elevation may occur before the onset of major neurocognitive dysfunction and in the context
of ongoing AD pathophysiology, suggesting a potentially useful early marker of disease
progression that is both readily accessible and easily modifiable (Sible et al., 2020). It is
widely appreciated that vascular factors contribute to 30–75% of cases of AD (Schneider
et al., 2007) and a growing number of studies now focus on how BPV may be one such
vascular factor linked to increased dementia risk.
Although possible mechanisms linking BPV to increased dementia risk are not well known,
it has been proposed that elevated BPV may jeopardize cerebral perfusion and disrupt
neuronal functioning (Lattanzi et al., 2018; Ma et al., 2020b). Chronic large fluctuations
in BP may stress arterial walls beyond repair and promote arterial remodeling and
microvascular damage (Ma et al., 2020c, 2020a). Indeed, elevated BPV has been linked
with stroke (Kato et al., 2010; Parati et al., 2018), arterial stiffness (Schillaci et al., 2012;
Tatasciore et al., 2020; Xia et al., 2017), and cerebrovascular disease burden on MRI (e.g.,
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white matter hyperintensities, cortical infarcts, cerebral microbleeds) (Ma et al., 2020a) and
at postmortem evaluation (e.g., atherosclerosis in the Circle of Willis, arteriolosclerosis)
(Sible et al., 2021a), independent of average BP. These microvascular changes may alter
blood-brain barrier integrity and risk hypoperfusion injury (Lattanzi et al., 2018). Consistent
with this hypothesis, a recent study found that elevated BPV was related to cerebral
perfusion decline in brain regions associated with cerebrovascular dysfunction in AD (Sible
et al., 2021b). Importantly, this repeated “tsunamic effect” in the cerebral parenchyma may
be particularly relevant in aging, due to the possibility that age-related arterial stiffening may
further amplify BPV (Ma et al., 2020b; Nagai et al., 2017; Schillaci et al., 2012; Tatasciore
et al., 2020; Xia et al., 2017; Yoo et al., 2020), and in brain areas both highly sensitive to
BP-related hypoxic-ischemic injury and vulnerable to early blood-brain barrier breakdown
and AD, such as the medial temporal lobes (Iadecola, 2004; Ma et al., 2020b; Nation et al.,
2019; Schmidt-Kastner and Freund, 1991; Vikner et al., 2021).
While the dynamic relationship governing BP changes and cerebral blood flow (CBF)
have been well-studied using transcranial doppler (Aaslid et al., 1989), the majority
of this literature is limited to studying blood flow velocity in the larger intracranial
arteries (D’Andrea et al., 2016). Less is known about the role of BPV in regional
cerebral microvascular perfusion, where smaller vascular compartments are critical to blood-
brain barrier nutrient transfer, nutrient influx, and waste clearance essential for neuronal
functioning (Kisler et al., 2017; Zlokovic, 2011). It is also unclear whether relationships
may differ by age. To investigate these possibilities, we studied short-term BPV during
concurrent assessment of regional cerebral microvascular perfusion in a sample of older and
younger adults.
2. MATERIAL AND METHODS
2.1 Participants
Study participants were drawn from the Vascular Senescence and Cognition Lab at the
University of Southern California (USC), an ongoing research study aimed at detailing
various vascular mechanisms of cognitive decline and dementia. Participants were recruited
from the community via flyers and related research list-serves at USC. Inclusion criteria
included aged 55–90 and living independently in the greater Los Angeles area. Participants
were excluded for history of dementia, stroke, traumatic brain injury, learning disability,
or other systemic or neurological disorder known to affect the central nervous system.
Additionally, all research participants underwent neuropsychological testing that included
the Mattis Dementia Rating Scale – 2 (DRS-2) (Griffiths et al., 2011), a widely used
measure of global cognition. Remaining eligible participants were further excluded based on
a DRS-2 total score ≤ 126, an established cutoff to rule out major neurocognitive impairment
(Griffiths et al., 2011). A sample of healthy younger adults were also recruited from the
USC campus. The study was approved by the Institutional Review Board at USC and all
participants provided their written informed consent.
The present study included 33 older adult participants (ages 55–87) and 26 younger adult
participants (ages 18–28) who underwent resting pseudo-continuous arterial spin-labelling
MRI (pCASL-MRI) with simultaneous, continuous BP monitoring.
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2.2 Measures
2.2.1 Cerebral perfusion assessment—CBF was determined from a 5-minute
resting pCASL-MRI on a single 3 Tesla Siemens® MAGNETOM Prisma MRI using a
pCASL method with background suppressed gradient and spin echo (GRASE) readout.
The following parameters were used: TR = 5000ms; TE = 36.3ms; FOV = 240mm;
resolution = 2.5×2.5×3.4mm
3
; slice thickness = 3.42mm; number of slices = 24; number
of measurements = 1 M0 image + 1 dummy image + 15 pairs of tag-control images; total
scan time = 5:25. pCASL scans were co-registered to structural T1 MRI scans collected
during the same session. CBF was determined for several a priori regions-of-interest (ROI)
linked to hypoperfusion-related dementia risk (Mattsson et al., 2014; Yew and Nation,
2017): hippocampus, entorhinal cortex, posterior cingulate, precuneus, medial orbitofrontal
cortex (mOFC), inferior parietal cortex (IPC), inferior temporal cortex (ITC), and rostral
middle frontal gyrus (rMFG). We also included ROIs for other medial temporal regions
(e.g., parahippocampal gyrus and perirhinal cortex), given the known associations with both
BP-related hypoxic-ischemic injury vulnerability and AD pathology in this region (Iadecola,
2004; Schmidt-Kastner and Freund, 1991; Vikner et al., 2021; Zlokovic, 2011). ROIs were
derived from the AAL3 atlas (Rolls et al., 2020) and warped into MNI standard space. CBF
values were normalized to precentral gyrus CBF during the MRI, due to the relative sparing
of this region in AD, and consistent with other studies of perfusion MRI in older adults,
as described elsewhere (Mattsson et al., 2014; Sible et al., 2021b; Yew and Nation, 2017).
Finally, values were averaged across hemispheres and used in all analyses. Whole brain CBF
was also determined.
2.2.2 BP assessment—BP was collected continuously using a Biopac® MRI-
compatible BP monitoring device during the 5-minute resting pCASL-MRI scan. BP data
were then processed offline using a custom pipeline scripted in AcqKnowledge®, as
described elsewhere (Sturm et al., 2018b). Briefly, BP signals were first visually inspected
for mechanical errors and noise (e.g., signal dropout due to sensor interference) and then
algorithms detected and removed any outliers defined as +/− 3 standard deviations (SD)
from the average over the entire resting 5-minute scan. Intraindividual BPV was calculated
as variation independent of mean (VIM), a commonly used measure of BPV uncorrelated
with mean BP levels (de Heus et al., 2019; Peter M. Rothwell et al., 2010; Rouch et
al., 2020; Sible et al., 2021b, 2020; Xia et al., 2017). VIM was calculated as: VIM =
SD/mean
x
, where the power x was derived from non-linear curve fitting of BP SD against
average BP using the nls package in R Project, as previously described (Peter M. Rothwell
et al., 2010; Sible et al., 2021b, 2021a, 2020). To use measures of BPV that may be
more readily computed in clinical settings, intraindividual BPV was also calculated as the
SD and coefficient of variation (CV [100 × SD/mean]) (see Supplementary Materials).
Evidence suggests all three indices capture relationships between BPV and cardiovascular
and cognitive outcomes (de Heus et al., 2019; Parati et al., 2018, 2013; Peter M. Rothwell
et al., 2010; Yano, 2017), but unlike SD, CV and VIM offer information uncorrelated with
mean levels. Furthermore, VIM was recently shown to better predict all-cause mortality in
the SPRINT dataset when compared to other indices of BPV (Cheng et al., 2021).
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2.2.3 Brain gray matter volume assessment—Participants underwent structural
brain MRI to obtain T1-weighted magnetization prepared rapid gradient-echo (MP-RAGE)
sequence for high resolution anatomical images using the following scan parameters: TR =
2300 ms; TE = 2.98 ms; TI = 900 ms; slice thickness = 1.20 mm; flip angle = 9°; field
of view = 256 mm. Structural T1 scans were processed using Freesurfer (Dale et al., 1999;
Ségonne et al., 2004) and gray matter volumes were extracted for the same ROIs in CBF
analyses described above.
2.2.4 White matter hyperintensity assessment—A subset of older adult
participants (n = 32 out of 33) also underwent T2-fluid attenuated inversion recovery
(FLAIR) MRI sequence for the evaluation of white matter lesions. The following parameters
were used: TR = 10000 ms; TE = 91 ms; TI = 2500 ms; slice thickness = 5.0 mm; flip angle
= 150°; field of view = 220 mm. Severity of white matter hyperintensities was determined
by visual grading with a recognized scale (Fazekas et al., 1987): 0 = “absent”; 1 = “caps
or pencil-thin lining around ventricles or discrete diffuse lesions”; 2 = “smooth halo around
ventricles or beginning of confluence of foci”; 3 = “irregular PVH extending into the DWM
or large confluent areas”.
2.2.5 Other measurements—Demographic, clinical, and cognitive information was
determined from study screening. Height and weight were collected to determine body mass
index (BMI) (weight (kg) / height (meters) squared). Medication use was also assessed,
and participants were categorized as those taking antihypertensive medication (all classes)
versus those not taking antihypertensive medication. Older adult participants also underwent
neuropsychological testing that included the DRS-2 (Griffiths et al., 2011), a widely used
measure of global cognition. Total raw scores (max score = 144) were then converted to
age-corrected scaled scores (Lucas et al., 1998).
2.3 Statistical Analysis
BPV values were log-transformed to approach a normal distribution. ANOV A and chi-
square tests compared older and younger adults on clinical and demographic variables as
well as CBF levels. Multiple linear regression was first used to investigate the relationship
between BPV and regional CBF in older and younger adults separately. To limit the risk
of type 1 error and leverage knowledge gained from prior ASL-MRI studies in older adults
(Mattsson et al., 2014; Yew and Nation, 2017), voxel-wise multivariable regressions were
then performed with explicit masks of ROIs found to be significant in initial analyses.
All models controlled for age, sex, and antihypertensive medication use. Supplementary
analyses explored associations using the SD and CV indices of BPV , as well as relationships
between 1) BPV and whole brain CBF, and 2) average BP and regional CBF. Elevated BPV
is thought to be influenced by sympathetic nervous system overactivity (Imai et al., 1997;
Mancia et al., 1997; Parati et al., 2018, 2013), which has been shown to be lateralized
to the brain’s right hemisphere in studies of older adults (Guo et al., 2016; Sturm et al.,
2018a). Therefore, exploratory analyses examined relationships between BPV and CBF by
hemisphere in ROIs found to be significant in initial analyses. Potential lateralization effects
were formally tested by adding an interaction term of hemisphere to regression models.
Based on a power analysis for detecting moderate-to-large effect sizes using G*Power (Faul
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et al., 2009), multiple linear regression (α = .05, 3 covariates) with a sample size of 28 older
adults will yield 85% power. As such, the current study is adequately powered to detect
moderate-to-large effect sizes in analyses of older adults, while analyses of younger adults
should be regarded as exploratory. Sensitivity analyses tested the robustness of primary
findings by substituting antihypertensive use, to preserve statistical power, for 1) severity
of white matter hyperintensities (e.g., Fazekas score), 2) brain gray matter volume in the
corresponding ROI, 3) BMI, 4) years of education, and 5) global cognition (e.g., DRS-2 total
scaled score). All analyses were 2-sided with significance set at p < .05 for ROI regression
and family-wise error (FWE) corrected p < .05 for voxel-wise regression. False Discovery
Rate (FDR) (Benjamini and Hochberg, 1995) was set at p < .05 for ROI analyses. All
analyses were carried out in R Project (R Core Team, 2018).
3. RESULTS
As shown in Table 1, older adults were significantly older, had greater BMI, and had greater
systolic BP when compared to younger adults. Additionally, CBF was significantly lower
in older adults when compared to younger adults in the following ROIs: hippocampus,
posterior cingulate, precuneus, mOFC, IPC, ITC, and rMFG.
3.1 ROI analyses
ROI analyses revealed elevated systolic BPV in older adults was related to lower CBF in
medial temporal lobe regions: hippocampus ( β = −.60 [95% CI −.90, −.30]; p < .001; R
2
=
.43), parahippocampal gyrus (β = −.57 [95% CI −.89, −.25]; p = .001; R
2
= .35), entorhinal
cortex ( β = −.42 [95% CI −.73, −.12]; p = .009; R
2
= .42), and perirhinal cortex (β = −.37
[95% CI −.72, −.03]; p = .04; R
2
= .25), but findings did not reach statistical significance
in other regions (p’s = .08 to .99) (Figure 1). Diastolic BPV in older adults was not related
to CBF in any region (p’s = .16 to .99) (Data not shown). BPV in younger adults was not
related to regional CBF (systolic: p’s = .18 to .95; diastolic: p’s = .12 to .90) (Data not
shown).
After FDR correction, systolic BPV findings in older adults remained significant for all
regions except for perirhinal cortex (q-value = .10).
3.2 Voxel-wise analyses
V oxel-wise analyses restricted to medial temporal lobe showed elevated systolic BPV in
older adults was related to lower cerebral perfusion in the bilateral medial temporal lobe
with the right-sided clusters significant after FWE correction (20, −32, −12; peak T = 3.93)
(Figure 2).
3.3 Supplementary analyses
Supplementary analyses using the SD and CV indices of BPV revealed similar relationships
with regional CBF in older adults (see Supplementary Results).
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No significant relationships were observed between BPV and whole brain CBF (p’s = .48 to
.70), or between average BP and CBF in any ROI in either older adults or younger adults
(p’s = .11 to .97 ) (see Supplementary Results).
3.4 Exploratory analyses
Primary results did not differ significantly by hemisphere (p’s = .63 to .91) (Data not
shown).
3.5 Sensitivity analyses
Primary findings were essentially unchanged when controlling for severity of white matter
hyperintensities, brain gray matter volume in the corresponding ROI, BMI, years of
education, and global cognition (see Supplementary Results).
4. DISCUSSION
Findings indicate that elevated short-term systolic BPV was related to lower concurrent
CBF in medial temporal regions known to convey hypoperfusion-related dementia risk in
older adults, independent of average BP levels. In contrast, no relationships between BPV
and CBF were observed in younger adults or in regions beyond the medial temporal lobe,
suggesting an age-dependent relationship between short-term BPV and CBF specifically
within medial temporal regions.
The present study used a novel approach of monitoring BP during assessment of regional
microvascular perfusion and was thus able to appreciate any relationships between
short-term BPV and concurrent regional CBF. While a recent study found that visit-to-
visit BPV is linked with cerebral perfusion decline in several brain regions associated
with cerebrovascular dysfunction in AD, including medial temporal lobes (Sible et
al., 2021b), the present investigation suggests the medial temporal lobes may also be
particularly susceptible to short-term BP fluctuations. Prior studies suggest selective regional
vulnerability of medial temporal structures to hypoperfusion during hypotensive crisis
(Schmidt-Kastner and Freund, 1991). Animal models have also shown that CA1 neurons
and capillaries in the hippocampus are sensitive to impaired microcirculation (De Jong et
al., 1999). Additionally, a recent study found that hippocampal vascularization patterns were
related to memory performance and hippocampal volume, especially in individuals with
cerebrovascular disease (Perosa et al., 2020). Furthermore, hypoperfusion in the medial
temporal lobes has been linked with cognitive impairment, including worse performance
on tests of memory, and increased risk for AD (Bangen et al., 2021; Wierenga et al.,
2014). Although causality cannot be determined based on cross-sectional observations, the
present study findings could suggest a selective vulnerability of medial temporal structures
to hypoperfusion in the context of short-term BP fluctuation. It is, therefore, possible that
the increased risk for dementia associated with elevated short-term BPV may be related to
hypoperfusion of medial temporal regions critical for memory function.
Arterial stiffening may influence the relationship between BPV and CBF. Specifically,
arterial stiffening may alter pulse wave dynamics and amplify BPV , further exacerbating the
impact of BP changes on brain health and cognition (Lattanzi et al., 2018; Ma et al., 2020b;
Sible et al. Page 7
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Nagai et al., 2017; Tatasciore et al., 2020; Yoo et al., 2020; Zhou et al., 2018). Older adults
may be particularly vulnerable to these vascular changes, given the significant role age is
known to play in the development of arterial stiffening (Zhou et al., 2018). Consistent with
this hypothesis, several studies report links between elevated BPV in older adults, arterial
stiffness, and cognitive impairment (Schillaci et al., 2012; Winder et al., 2021; Xia et al.,
2017). Additionally, arterial stiffening may contribute to the growing evidence linking BPV
to cerebrovascular disease by disrupting tissue perfusion to the brain’s microvasculature
(Ma et al., 2020a). Arterial stiffness is strongly associated with atherosclerosis (Van Popele
et al., 2001), a well-studied risk factor for AD, possibly through links with altered CBF
and subsequent microinfarction (Arvanitakis et al., 2016; Hofman et al., 1997; Toledo
et al., 2013; White et al., 2002). Recent evidence suggests elevated BPV is related to
increased severity of atherosclerosis in the Circle of Willis and arteriolosclerosis in the
brain’s smaller arteries relevant to microvascular perfusion (Ma et al., 2021; Sible et al.,
2021a). Importantly, the Circle of Willis includes the posterior cerebral artery and anterior
choroidal artery, which both perfuse the highly vulnerable hippocampi (Perosa et al., 2020).
While the current study did not directly investigate links with arterial stiffness, more studies
are needed to determine whether this may be related to the increased risk for AD associated
with elevated BPV .
Other age-related vascular changes, such as diminished baroreflex sensitivity, may also be
responsible for the observed relationship between short-term BPV and medial temporal
hypoperfusion in older adults only (Lattanzi et al., 2018; Nagai et al., 2017). Several
autoregulatory mechanisms, including baroreflex sensitivity, work to steady naturally
dynamic BP levels in order to maintain steady perfusion levels needed to meet the brain’s
high metabolic demand (Parati et al., 2013; Zlokovic, 2011). However, age-related declines
in baroreflex sensitivity may leave BP less regulated and risk frequent, but mild reductions
in CBF (Coats et al., 1991; Conway et al., 1985; Imai et al., 1997). These reductions, known
as oligemia, have been associated with severe cell disruption and cognitive impairment
(Zlokovic, 2011). Future work will examine links with baroreflex sensitivity.
BPV is linked with antemortem and postmortem markers of cerebrovascular disease (Ma
et al., 2020a; Sible et al., 2021a). Study findings suggest short-term BPV is related to
lower levels of medial temporal lobe CBF, even in a sample of older adults with limited
cerebrovascular disease. Results remained even after sensitivity analyses controlled for
severity of white matter hyperintensities; however, the sample notably had minimal white
matter hyperintensity burden (e.g., 72% had Fazekas scores ≤ 1). More studies should
investigate relationships in samples with varying levels of cerebrovascular disease burden.
Neurodegeneration effects on cortical control of the autonomic nervous system could
alternatively help explain the study findings. Specifically, atrophy of these regions may
disrupt autonomic function and leave both BP and CBF less regulated (Kitamura et al.,
2020). However, consistent with a recent study of visit-to-visit BPV and cerebral perfusion
decline (Sible et al., 2021b), findings were independent of gray matter volume, lending
further support to the hypothesis that BPV may be related to CBF through a vascular
mechanism.
Sible et al. Page 8
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AD risk gene apolipoprotein ϵ4 (APOE ϵ4) has been associated with neuronal and
neurovascular dysfunction in the medial temporal lobes (Burggren et al., 2008; Palop and
Mucke, 2011). It was recently reported that APOE ϵ4 carriers display early blood-brain
barrier breakdown in the medial temporal lobes (Montagne et al., 2020), and that this was
predictive of cognitive impairment (Nation et al., 2019), independent of AD pathology.
Larger studies adequately powered to examine relationships between BPV and CBF based
on APOE ϵ4 carrier status will help elucidate vascular contributions of APOE ϵ4 to AD risk.
A study strength is the continuous collection of BP during perfusion MRI, which allowed
exploration of concurrent relationships between short-term BPV and CBF. Additionally,
the study used pCASL-MRI to quantify microvascular perfusion, which captures flow in
the smallest vascular compartments critical to nutrient transfer and blood-brain barrier
integrity, in brain areas with known importance for cognition and AD. The study adds
to our understanding of potential age-related relationships between short-term BP changes
and cerebral perfusion by utilizing a sample of both older and younger adults. Findings
were present in a sample of community-dwelling older adults without history of dementia
or stroke, even after controlling for global cognition, bolstering prior evidence suggesting
BPV elevation precedes major neurocognitive dysfunction (Sible et al., 2020), with potential
therapeutic implications. Study findings add to the growing clinical relevance of BPV as
a risk factor for and predictor of cognitive decline (De Heus et al., 2021; Lattanzi et al.,
2014) and cerebrovascular disease severity (Tully et al., 2020), above and beyond average
BP levels.
The current study is limited by the small sample size. Relatedly, while findings controlled
for antihypertensive medication use, the study was not adequately powered to detect
potential class effects of antihypertensive treatment on BPV and CBF. Some evidence
suggests differential class effects on BPV in risk for stroke, independent of average BP
levels (Peter M Rothwell et al., 2010; Webb et al., 2010). Future studies with large sample
sizes that investigate possible class effects of monotherapy and combination therapy may
have the potential to inform antihypertensive treatment decisions. Other indices of short-
term BPV , such as 24-hour monitoring, provide information on BP changes relevant to
the circadian rhythm, such as nighttime “dipping” (Parati et al., 2013). Relatedly, scans
were collected at variable times throughout the day (e.g., between the hours of 8 AM
and 5 PM), which may obscure the influence of typical daytime/nighttime BP changes
(Parati et al., 2013). While the methods used in the current investigation do not capture this
phenomenon, beat-to-beat BPV offers assessment of cardiovascular modulation that 24-hour
BPV does not. Nevertheless, both indices of BPV have been linked with cardiovascular and
cognitive outcomes (Cho et al., 2018; Oishi et al., 2017; Parati et al., 2013; Schillaci et
al., 2012; Tatasciore et al., 2020; Xia et al., 2017; Zhou et al., 2018) and the reliability
of these different time windows remains unclear (Parati et al., 2013). Additionally, the
cross-sectional design limits interpretation of findings. Another study limitation is the lack
of characterization of AD biomarkers amyloid-beta and phosphorylated tau. While findings
were largely unchanged when controlling for one marker of neurodegeneration (e.g., gray
matter volume), including AD biomarkers will help elucidate potential relationships in the
context of ongoing AD biomarker abnormality.
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5. CONCLUSIONS
Elevated short-term BPV is related to lower, concurrent levels of CBF specifically in the
medial temporal lobes, independent of average BP, in older adults only. Medial temporal
lobes may have an age-related selective vulnerability to hypoperfusion in the context of
short-term BP fluctuation, which may underpin the increased risk for dementia associated
with elevated BPV .
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
ACKNOWLEDGEMENTS
Study funding
The study data and analysis were supported by National Institute of Health/National Institute on Aging
grants (R01AG064228, R01AG060049, P50AG016573, and P01 AG052350), National Science Foundation grant
DGE1418060, and Alzheimer’s Association grant AARG-17-532905.
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Sible et al. Page 14
Neuroimage Rep. Author manuscript; available in PMC 2022 July 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Figure 1. Elevated short-term systolic BPV is related to lower concurrent medial temporal
cerebral perfusion in older adults
Scatterplots display the relationship between short-term systolic BPV and corrected
microvascular perfusion in medial temporal regions in older adults. CBF values were
adjusted for age, sex, and antihypertensive medication use.
Abbreviations: BPV = blood pressure variability
Sible et al. Page 15
Neuroimage Rep. Author manuscript; available in PMC 2022 July 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Figure 2. Voxel-wise associations between short-term BPV and medial temporal perfusion in
older adults
Results of voxel-wise analyses in the medial temporal lobes in older adults.
Sible et al. Page 16
Neuroimage Rep. Author manuscript; available in PMC 2022 July 01.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Sible et al. Page 17
Table 1.
Demographic and clinical information for older and younger adults.
Older Adult (n=33) Younger Adult (n=26) F or χ
2
p-value
Age (years) 68.7 (8.5) 22.0 (2.7) 725.3 <.001
Sex (male/female) 14/19 6/20 1.64 .20
Education (years) 16.3 (2.8) -- -- --
APOE-ϵ4 carriers (n, %) 12 (36.4%) -- -- --
DRS-2 total (scaled score) 11.6 (2.5) -- -- --
Body mass index (kg/m
2
) 26.5 (5.5) 23.0 (3.5) 7.90 .007
Fazekas score (n, %)
0 4 (12.5%) -- -- --
1 19 (59.4%) -- -- --
2 7 (21.9%) -- -- --
3 2 (6.3%) -- -- --
Antihypertensive use (n, %) 13 (39.4%) -- -- --
Systolic BP (mmHg) 132.5 (22.0) 110.0 (21.8) 15.26 <.001
Diastolic BP (mmHg) 79.2 (20.4) 78.7 (25.7) .01 .94
Systolic BPV (mmHg) 10.1 (2.0) 9.4 (1.8) 2.00 .16
Diastolic BPV (mmHg) 5.7 (1.0) 6.3 (1.1) 3.96 .05
CBF (ml/100g/min)
Hippocampus 39.2 (6.5) 44.3 (5.9) 4.99 .03
Parahippocampal gyrus 44.5 (6.4) 44.5 (6.0) 2.59 .11
Entorhinal cortex 50.4 (7.5) 48.9 (5.2) .78 .38
Perirhinal cortex 43.0 (6.5) 45.6 (6.7) .002 .97
Posterior cingulate 51.8 (10.7) 65.4 (12.6) 20.14 <.001
Precuneus 46.8 (7.5) 57.1 (9.7) 21.03 <.001
mOFC 45.7 (7.7) 57.8 (7.2) 38.14 <.001
IPC 41.9 (20.9) 54.6 (11.0) 7.82 .007
ITC 43.8 (7.6) 50.1 (7.9) 5.10 .03
rMFG 52.6 (7.6) 61.9 (8.1) 20.52 <.001
Means and SDs shown unless otherwise indicated.
Abbreviations: DRS-2 = Dementia Rating Scale – second edition; BPV = blood pressure variability; mOFC = medial orbital frontal cortex; IPC =
inferior parietal cortex; ITC = inferior temporal cortex; rMFG = rostral medial frontal gyrus; CBF = cerebral blood flow
Neuroimage Rep. Author manuscript; available in PMC 2022 July 01.
American Journal of Hypertension 36(1) January 2023 63
ORIGINAL ARTICLE
1
Department of Psychology, University of Southern California, Los Angeles,
CA 90089, USA;
2
Institute for Memory Impairments and Neurological
Disorders, University of California, Irvine, Irvine, CA 92697, USA;
3
Davis
School of Gerontology, University of Southern California, Los Angeles,
CA 90089, USA;
4
Department of Rehabilitation and Human Performance,
Icahn School of Medicine at Mount Sinai, New Y ork, NY , 10029, USA;
5
San
Francisco Veterans Affairs Health Care System, San Francisco, CA, 94121,
USA;
6
Department of Psychiatry and Behavioral Sciences, University of
California, San Francisco, San Francisco, CA, 94158, USA;
7
Department of
Psychological Science, University of California Irvine, Irvine, CA 92697,
USA;
8
Department of Neurology, University of California, San Francisco,
San Francisco, CA, 94158, USA;
9
Global Brain Health Institute, University
of California, San Francisco, San Francisco, CA, 94158, USA;
10
Center for
Innovation in Brain Science, Department of Pharmacology, The University
of Arizona, Tucson, AZ, 85721, USA;
11
Laboratory of Functional MRI
Technology, Mark and Mary Stevens Neuroimaging and Informatics
Institute, University of Southern California, Los Angeles, CA, 90033, USA.
© The Author(s) 2022. Published by Oxford University Press on
behalf of American Journal of Hypertension, Ltd. All rights reserved.
For permissions, please e-mail: journals.permissions@oup.com
Correspondence: Daniel A. Nation (dnation@uci.edu).
Initially submitted August 10, 2022; date of fi rst revision September 14,
2022; accepted for publication September 21, 2022; online publication
September 26, 2022.
Older Adults With Higher Blood Pressure Variability Exhibit
Cerebrovascular Reactivity Defi cits
Isabel J. Sible,
1,
* Jung Yun Jang,
2,
* Shubir Dutt,
1,3
Belinda Yew,
1,4
John Paul M. Alitin,
2
Yanrong Li,
2

Anna E. Blanken,
5,6
Jean K. Ho,
2
Anisa J. Marshall,
1
Arunima Kapoor, MSc
7
Fatemah Shenasa,
7

Aimée Gaubert,
2
Amy Nguyen,
2
Virginia E. Sturm,
6,8,9
Mara Mather,
3
Kathleen E. Rodgers,
10

Xingfeng Shao,
11
Danny J. Wang,
11
and Daniel A. Nation
2,7,
BACKGROUND
Elevated blood pressure (BP) variability is predictive of increased risk
for stroke, cerebrovascular disease, and other vascular brain injuries,
independent of traditionally studied average BP levels. However, no
studies to date have evaluated whether BP variability is related to
diminished cerebrovascular reactivity, which may represent an early
marker of cerebrovascular dysfunction presaging vascular brain
injury.
METHODS
The present study investigated BP variability and cerebrovascular
reactivity in a sample of 41 community-dwelling older adults (mean
age 69.6 [SD 8.7] years) without a history of dementia or stroke.
Short-term BP variability was determined from BP measurements
collected continuously during a 5-minute resting period followed by
cerebrovascular reactivity during 5-minute hypocapnia and hyper-
capnia challenge induced by visually guided breathing conditions.
Cerebrovascular reactivity was quantifi ed as percent change in cere-
bral perfusion by pseudo-continuous arterial spin labeling (pCASL)-
MRI per unit change in end-tidal CO
2
.
RESULTS
Elevated systolic BP variability was related to lower whole brain ce-
rebrovascular reactivity during hypocapnia (ß = −0.43 [95% CI −0.73,
−0.12]; P = 0.008; adjusted R
2
=.11) and hypercapnia (ß = −0.42 [95% CI
−0.77, −0.06]; P = 0.02; adjusted R
2
= 0.19).
CONCLUSIONS
Findings add to prior work linking BP variability and cerebrovascular
disease burden and suggest BP variability may also be related to prod-
romal markers of cerebrovascular dysfunction and disease, with po-
tential therapeutic implications.
GRAPHICAL ABSTRACT
Keywords: aging; blood pressure; blood pressure variability; cerebro-
vascular reactivity; hypertension.
https://doi.org/10.1093/ajh/hpac108
*Authors contributed equally.
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64 American Journal of Hypertension 36(1) January 2023
Sible et al.
Hypertension is strongly linked with increased risk for
stroke, cerebrovascular disease, and dementia.
1,2
Th e es-
tablished link was further supported by fi ndings from the
SPRINT trial suggesting intensive blood pressure (BP)
lowering was associated with fewer cardiovascular event
outcomes (e.g., stroke),
3
slower progression of white matter
hyperintensities,
4
and decreased risk for incident mild
cognitive impairment.
5
Th ese results have fueled interest
in other aspects of BP control that could potentially fur-
ther reduce cardiovascular, cerebrovascular, and dementia
risk.
6
BP variability (BPV), or the change in BP over a pe-
riod of seconds to minutes (known as “short-term” BPV) or
months to years (known as “long-term” BPV or “visit-to-
visit” BPV), is now an emerging risk factor for stroke, cere-
brovascular disease, and dementia, independent of average
BP levels.
7–9
Specifi cally, a growing number of studies have
found that elevated BPV is related to cognitive decline,
8

progressions of dementia,
10,11
and greater cerebrovascular le-
sion burden on MRI (e.g., white matter hyperintensities, ce-
rebral infarcts, and cerebral microbleeds)
9
and postmortem
evaluation (e.g., atherosclerosis in the Circle of Willis and
arteriolosclerosis).
12,13
However, less is known about the
relationships between BPV and putative markers of cere-
brovascular dysfunction or prodromal disease that may be
important for cognitive functioning. One such marker is ce-
rebrovascular reactivity (CVR), which represents the ability
of the brain’s vessels to dilate and constrict in response to vas-
oactive stimuli.
14
Diminished CVR is predictive of stroke and
transient ischemic attack,
15,16
and lower CVR is associated
with cognitive impairment in older adults.
17
Additionally,
CVR and cognitive functioning were improved in patients
with carotid artery atherosclerosis aft er carotid endarterec-
tomy.
18
Furthermore, 1 recent study found that, compared to
healthy younger adults, cognitively unimpaired older adults
had attenuated CVR in response to both hypocapnia and hy-
percapnia breathing conditions.
19
Together these fi ndings
support the hypothesis that CVR may be an early marker of
vascular dysfunction that predates vascular brain injury rel-
evant to dementia risk.
Hypertension can diminish CVR, possibly through
increased tortuosity, arterial remodeling, or shift s in the
cerebral autoregulatory curve.
20,21
However, less is known
about how other aspects of BP , such as BPV , may be re-
lated to CVR. Th e present study investigated the relation-
ship between BPV collected continuously over a 5-minute
resting period and CVR during hypocapnia and hypercapnia
breathing conditions during pseudo-continuous arterial spin
labeling (pCASL)-MRI in a sample of community-dwelling
older adults.
METHODS
Participants
Study participants were recruited from ongoing studies of
aging at the University of California Irvine (UCI) and the
University of Southern California (USC), and from the local
Orange County and Los Angeles communities via fl yers,
word-of-mouth, and community outreach events. Inclusion
criteria required participants to be aged 55–90 years and
living independently in the community. Exclusionary
criteria included: History of dementia, stroke, traumatic
brain injury, learning disability, or other major systemic,
psychiatric, or neurological disorder known to aff ect the
central nervous system. All research participants underwent
neuropsychological testing and obtained a Mattis Dementia
Rating Scale- 2 (DRS-2)
22
total score > 126, an established
cutoff to rule out major neurocognitive impairment.
22
Th e
study was approved by the Institutional Review Boards at
UCI and USC and all participants provided their written in-
formed consent.
BPV data was not collected on all participants enrolled
in the combined ongoing studies at USC and UCI (n = 126)
and some participant data were not included due to proce-
dural errors or noise. Th erefore, the present study included
41 older adult participants (aged 55–88 years) who under-
went continuous BP monitoring over a 5-minute resting pe-
riod and breath control tasks during pCASL-MRI to induce
hypocapnia and hypercapnia and determine CVR.
Measures
MRI protocols. Participants underwent brain MRI on
the same make and model device (3T Siemens MAGNETOM
Prisma) at either UCI (n = 23) or USC (n = 18). Th ree types
of scans were collected from all participants: (i) structural
MRI; (ii) cerebral perfusion pCASL-MRI; and (iii) T2-fl uid
attenuated inversion recovery (FLAIR) MRI. First, a struc-
tural brain MRI was collected to obtain T1-weighted magnet-
ization prepared rapid gradient-echo (MP-RAGE) sequence
for high resolution anatomical images (TR = 2,300 ms;
TE = 2.98 ms; TI = 900 ms; slice thickness = 1.20 mm; fl ip
angle = 9°; fi eld of view = 256 mm). Next, whole brain ce-
rebral blood fl ow (CBF) was determined from cerebral
perfusion imaging using a pCASL method with back-
ground suppressed gradient and spin echo (GRASE)
readout, as previously described.
19,23
Th e following sequence
parameters were used for pCASL-MRI: TR = 5,000 ms;
TE = 36.3 ms (USC)/ 37.46 ms (UCI); FOV = 240 mm; res-
olution = 2.5 × 2.5 × 3.4 mm
3
; slice thickness = 3.42 mm;
number of slices = 24; labeling duration = 1517 ms; post-
labeling delay = 2000 ms; number of measurements = 1 M0
image + 1 dummy image + 15 pairs of tag-control images
(32 total acquisitions); total scan time = 5:25. As previously
described,
19
pCASL preprocessing included the following:
Motion correction, co-registration to structural T1-weighted
image, spatial smoothing with a 6 mm full-width at half-
maximum Gaussian kernel, tag-control subtraction. Finally,
participants also underwent T2-FLAIR MRI sequence
(TR = 10,000 ms; TE = 91 ms; TI = 2,500 ms; slice thick-
ness = 5.0 mm; fl ip angle = 150°; fi eld of view = 220 mm)
to determine white matter lesion burden as previously
described.
23
Severity of white matter lesions was estimated
by one rater blinded to other study measures as Fazekas
scores
24
(0–3).
Breathing protocols. Participants underwent three sep-
arate, sequential 5-minute breathing paradigms during brain
pCASL-MRI, as described elsewhere
19,23
: (i) resting condition
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American Journal of Hypertension 36(1) January 2023 65
Blood Pressure Variability and Vascular Reactivity
(breathe normally); (ii) paced breathing/hypocapnia (0.1 Hz
breathing); (iii) breath hold/hypercapnia (15-second breath
holds). To increase protocol compliance, participants were
instructed on each breathing paradigm fi rst during training
exercises outside of the scanner and then guided with visual
stimuli during each scan, as previously described.
19,23
For (i)
resting condition, participants were instructed verbally be-
fore the scan to breathe normally and were presented during
the scan with either a static green circle (USC) or a blank slide
(UCI).; For (ii) paced breathing/hypocapnia, participants
were shown a circle that was alternately fi lled with yellow for
5 seconds (“inhale”) and blue for 5 seconds (“exhale”).; and
For (iii) breath hold/hypercapnia, participants were shown
a circle that alternately fi lled with green for 25 seconds
(“breathe normally”) and red for 15 seconds (“hold breath”)
and were instructed to exhale before and aft er each breath
hold.
Capnography assessment. End-tidal CO
2
(etCO
2
) was
measured during each pCASL-MRI via a Phillips Medical
Systems MRI-compatible carbon dioxide device and nasal
cannula, as described elsewhere.
19
Briefl y, etCO
2
was deter-
mined at every expiration for the hypocapnia condition, and
the maximum etCO
2
per breath hold was used for the hyper-
capnia condition. Participants who failed to adhere to each
condition (e.g., breathing through the mouth as evidenced
by no discernable positive peaks in etCO
2
waveform) were
excluded from analyses.
CVR assessment. CVR was estimated as the percent
change in CBF per unit change in etCO
2
, based on estab-
lished methods.
14,19,25
Th e following was used to calcu-
late whole-brain CVR maps for each participant for each
breathing paradigm:
CVR =
100×(CBF
maximum
−CBF
minimum
)/ CBF
minimum
etCO2
maximum
−etCO2
minimum
BP assessment. BP was collected continuously using a
Biopac MRI-compatible BP monitoring device during the
5-minute resting pCASL-MRI scan, as previously described.
23

Briefl y, data were processed offl ine using a custom pipeline
scripted in AcqKnowledge.
23,26
Intraindividual BPV was cal-
culated as variation independent of mean (VIM), an increas-
ingly used index of BPV that is uncorrelated with average BP
levels.
10,23,27–31
We conducted a bivariate correlation between
BPV and average BP to confi rm that BPV was not signifi -
cantly correlated with average BP levels (systolic: r = −0.04,
P = 0.80; diastolic: r = 0.003, P = 0.99). VIM was calculated
as: VIM = standard deviation (SD)/mean
x
, where the power
x was derived from nonlinear curve fi tting of BP SD against
average BP using the nls package in R Project, as previously
described.
12,23,27,29,30
Th e present investigation focused on
systolic BPV based on prior work suggesting systolic, and
not diastolic, short-term BPV is related to simultaneous CBF
in older adults.
23
Other measurements. Blood samples from venipunc-
ture were used to determine APOE e4 carrier status (≥1
e4 allele), as previously prescribed.
32
Body mass index
(BMI [kg/m
2
]) was calculated from study screening body
measurements. Participants self-reported antihypertensive
medication use at study screening and participants were
categorized as those taking antihypertensive medication (all
classes) vs. those who were not.
Data availability statement. Study data and code are
available upon request.
STATISTICAL ANALYSIS
BPV and CVR data were screened for outliers (+/− 3 SD
from the mean), resulting in the removal of one participant’s
whole brain CVR during hypocapnia. Multiple linear regres-
sion was used to examine the relationship between BPV and
whole brain CVR during the hypocapnia and hypercapnia
breathing conditions separately. All models were controlled
for age and sex. Sensitivity analyses included the following
covariates (separate models tested to preserve statistical
power): (i) antihypertensive medication use; (ii) severity of
white matter hyperintensities (e.g., Fazekas score, 0–3); (iii)
BMI; (iv) average BP; and (v) MRI site (UCI and USC) (see
Supplementary Table S1). All analyses were 2-sided with sig-
nifi cance set at P < 0.05. All analyses were carried out in the
R Project.
RESULTS
Clinical and demographic information are summarized in
Table 1.
During hypocapnia, elevated systolic BPV was related to
signifi cantly lower whole brain CVR (ß = −0.43 [95% CI
−0.73, −0.12]; P = 0.008; adjusted R
2
= 0.11) (Figure 1A).
Higher systolic BPV was also associated with signifi cantly
lower whole brain CVR during hypercapnia (ß = −0.42 [95%
CI −0.77, −0.06]; P = 0.02; adjusted R
2
= 0.19) (Figure 1B).
Sensitivity analyses
All hypocapnia fi ndings remained signifi cant when con-
trolling for (i) antihypertensive medication use; (ii) se-
verity of white matter hyperintensities (e.g., Fazekas score,
0–3); (iii) BMI; (iv) average BP; and (v) MRI site (UCI
and USC) (see Supplementary Table S1). All sensitivity
analyses for hypercapnia fi ndings remained signifi cant, ex-
cept for antihypertensive medication use (P = 0.06) (see
Supplementary Table S1).
DISCUSSION
Findings indicate elevated short-term BPV is associ-
ated with lower CVR during hypocapnia and hypercapnia
breathing conditions in a sample of community-dwelling,
cognitively unimpaired older adults, independent of average
BP levels. A number of studies link higher BPV with greater
cerebrovascular disease burden on MRI and postmortem
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66 American Journal of Hypertension 36(1) January 2023
Sible et al.
evaluation.
9,12,13
Th e present fi ndings add to this work by
suggesting BPV may also be associated with prodromal cere-
brovascular dysfunction that could presage cerebrovascular
disease and related cognitive impairment.
BP is naturally highly dynamic, and fl uctuations occur
spontaneously and in response to internal (e.g., emotional)
and external (e.g., physical exertion) stimuli.
7
However,
these BP changes must be regulated to ensure adequate
pressure and fl ow of blood to the body’s organs. Over time,
autoregulatory forces such as barorefl ex function may
wane, leaving BP levels less regulated and more variable.
33

Th e brain is especially vulnerable to disruptions in CBF
given its high metabolic demand.
34
A number of studies
suggest the smaller vascular compartments (i.e., arterioles
and capillaries) are oft en where most age-related cerebral
arterial changes occur.
35
Consistently, elevated BPV over
the short-term and long-term has been linked with arte-
rial remodeling and stiff ening
36,37
as well as microvascular
damage.
9,12,13
Furthermore, it has been hypothesized that ar-
terial stiff ening may amplify BP fl uctuations, and that their
combined eff ect may be even more detrimental to arterial
health.
9
It is unclear whether elevated BPV is a cause or ef-
fect—or even an index—of arterial stiff ening
7,38
and longi-
tudinal and/or interventional studies may help clarify this
relationship. However, BPV is increasingly being considered
an important independent marker of vascular change that
may predate vascular brain injury and dementia risk.
Diminished CVR is predictive of stroke and white matter
hyperintensities and may refl ect prodromal cerebrovascular
dysfunction.
15,16
Just as age-related arterial stiff ening may exac-
erbate BPV , stiff er arteries may impact CVR by limiting the ce-
rebral vessels’ ability to mount a response to vasoactive stimuli.
39

Figure 1. Elevated short-term systolic BPV is associated with CVR during hypocapnia and hypercapnia. Scatterplots display the relationship be-
tween short-term systolic BPV and whole brain CVR during (A) hypocapnia and (B) hypercapnia. Lines are shaded with 95% CI. Abbreviations: BPV = blood
pressure variability.
Table 1. Demographic and clinical information.
Total sample (N = 41)
Age (years) 69.6 (8.7)
Sex (male/female) 14/27
Education (years) 16.2 (2.7)
APOE-ϵ4 carriers (n, %) 19 (46.3%)
DRS-2 total (scaled score)* 11.7 (2.4)
Body mass index (kg/m
2
) 25.5 (4.8)
Fazekas score (n, %)
0 3 (7.3%)
1 24 (58.5%)
2 11 (26.8%)
3 3 (7.3%)
Antihypertensive use (n, %) 13 (31.7%)
Systolic BP(mmHg)
Average 132.7 (21.0)
VIM 3.4 (2.0)
Diastolic BP(mmHg)
Average 76.2 (12.4)
VIM 5.2 (2.9)
Means and SDs has shown unless otherwise indicated.
*DRS-2 total scaled scores are age- and education-adjusted.
Abbreviations: APOE e4 = apolipoprotein e4; DRS-2 = Dementia
Rating Scale – second edition; BP = blood pressure; VIM = varia-
bility independent of mean
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American Journal of Hypertension 36(1) January 2023 67
Blood Pressure Variability and Vascular Reactivity
Specifi cally, arterial stiff ening may dampen dilation and constric-
tion of the vessel walls and lead to a less robust CVR response.
Hypertension may additionally attenuate CVR by shift ing the
cerebral autoregulatory curve, which could in turn establish
more opportunities for hypoperfusion, microvascular damage,
and cerebrovascular disease.
20,21
Our fi ndings add to this liter-
ature by suggesting that BPV , independent of average BP levels,
may be a risk indicator for emerging cerebrovascular dysfunc-
tion and disease. Importantly, results are in line with prior work
linking BPV to frank cerebrovascular disease burden detectable
on MRI
9
and autopsy,
12,13
and may elucidate relationships with
even earlier markers of cerebrovascular dysfunction.
Recent BPV research has highlighted that aspects of
antihypertensive treatment other than lowering average BP
levels may be important for brain health outcomes. For ex-
ample, some studies suggest that certain antihypertensive
classes, or a combination of classes, may reduce BPV and
the risk of stroke.
40
Due to the relatively small sample size, it
was not possible to assess diff erential antihypertensive class
eff ects on BPV and CVR. However, this remains an impor-
tant area for future research.
To the best of our knowledge, no studies to date have
examined the relationship between BPV and CVR. While
most studies on CVR have focused on either hypocapnia
or hypercapnia,
14
the present investigation included
both breathing conditions. Th is allowed us to appreciate
relationships with periods of vasoconstriction and vasodi-
lation. Th e study is limited by the small sample size with rel-
atively minimal cerebrovascular risk (e.g., 66% had Fazekas
scores ≤ 1). Future work with larger samples and varying
degrees of vascular disease may help to further elucidate ce-
rebrovascular risk associated with BPV and CVR.
Elevated BPV is associated with lower CVR in community-
dwelling older adults without history of dementia or stroke.
Findings add to prior work linking high BPV to cerebrovas-
cular disease burden on MRI and at postmortem evaluation
and suggest BPV may be an understudied vascular risk in-
dicator associated with prodromal cerebrovascular disease.
SUPPLEMENTARY MATERIAL
Supplementary data are available at American Journal of
Hypertension online.
FUNDING
Th is work was supported by the National Institute
of Health/National Institute of Aging (R01AG064228,
R01AG060049, P30AG066519, and P01AG052350), the
National Science Foundation (DGE1418060), and the
Alzheimer’s Association (AARG-17-532905).
ACKNOWLEDGMENTS
W e would like to thank the study participants and their
families.
DISCLOSURE
None.
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TITLE PAGE
Title: Short-term blood pressure variability is inversely related to regional amplitude of low
frequency fluctuations in older and younger adults
Running Title: Blood pressure variability and amplitude of low frequency fluctuations
Isabel J. Sible, MA
a

Hyun Joo Yoo, PhD
b

Jungwon Min, MS
b

Kaoru Nashiro, PhD
b

Catie Chang, PhD
c

Daniel A. Nation, PhD
d,e

Mara Mather, PhD
a,b,f*

a
Department of Psychology, University of Southern California, Los Angeles, CA 90089, USA
b
Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
c
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
37235, USA
d
Institute for Memory Impairments and Neurological Disorders, University of California, Irvine,
Irvine, CA 92697, USA
e
Department of Psychological Science, University of California Irvine, Irvine, CA 92697, USA
f
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA
90089, USA
*
Corresponding Author
Mara Mather, Ph.D.
Professor
University of Southern California
Davis School of Gerontology
3715 McClintock Ave
Los Angeles, CA 90089
Phone: (213) 821-1868
mara.mather@usc.edu
1

Manuscript word count: 2692

1
Abbreviations: BPV = blodd pressure variability; BP = blood pressure; ALFF = amplitude of low frequency
fluctuations; BOLD = blood-oxygen-level-dependent signal; USC = University of Southern California; rsfMRI =
resting-state functional MRI; VIM = variation independent of mean; HC = hippocampus; PHG = parahippocampal
gyrus; EC = entorhinal cortex; PC = perirhinal cortex
ABSTRACT
Blood pressure variability (BPV), independent of mean blood pressure levels, is associated with
cerebrovascular disease burden on MRI and postmortem evaluation. However, less is known
about relationships with markers of cerebrovascular dysfunction, such as diminished
spontaneous brain activity as measured by the amplitude of low frequency fluctuations (ALFF).
We investigated the relationship between short-term BPV and concurrent regional ALFF from
resting state fMRI in a sample of community-dwelling older adults (n = 44) and healthy younger
adults (n = 49). We found that elevated BPV in older adults was associated with lower ALFF in
medial temporal regions. Younger adults showed similar relationships that were limited to the
hippocampus and parahippocampal gyrus. Findings suggest a possible vulnerability of medial
temporal regions to cerebrovascular dysfunction and short-term fluctuations in blood pressure.
BPV may be an understudied risk factor for cerebrovascular changes in aging.
KEY WORDS: blood pressure variability; amplitude of low frequency fluctuations; aging;
medial temporal lobes

1. INTRODUCTION
Blood pressure (BP) control remains a promising therapeutic target for reducing risk of stroke,
cerebrovascular disease, and dementia (Iadecola et al., 2016; Yaffe, 2019). In addition to the goal
of lowering mean BP levels, newer evidence suggests BP variation over seconds, days, months,
and years is also associated with deleterious cognitive and brain health outcomes, especially in
older adults (Nagai et al., 2017). Interestingly, some studies indicate the predictive value of BP
variability (BPV) exceeds that of mean BP levels for cerebrovascular disease burden, cognitive
impairment, and dementia (de Heus et al., 2021; Ma et al., 2021).

While BPV has been associated with cerebrovascular disease on MRI (Ma et al., 2020a; Tully et
al., 2020) and postmortem evaluation (Ma et al., 2021; Sible et al., 2021a), less is known about
relationships with cerebrovascular dysfunction that may precede frank markers of
cerebrovascular disease. One recent study examined BPV in older adults during hypocapnia and
hypercapnia challenge during perfusion imaging to estimate cerebrovascular reactivity (Sible et
al., 2022a), which reflects the ability of the brain’s blood vessels to dilate and constrict in
response to vasoactive stimuli and may represent early cerebrovascular dysfunction relevant to
cognitive function (Markus and Cullinane, 2001; Reinhard et al., 2014; Sur et al., 2020). In this
study, higher BPV was associated with lower cerebrovascular reactivity, even in a sample of
community-dwelling older adults with minimal cerebrovascular disease (Sible et al., 2022a).
Cerebrovascular reactivity reflects cerebrovascular function in response to stimuli, but
relationships with cerebrovascular function at rest are understudied and could provide
information about baseline physiological processes underlying the link between BPV and
cerebrovascular disease. Amplitude of low frequency fluctuations (ALFF) is an fMRI-based
measure of oscillations of the blood-oxygen-level-dependent (BOLD) signal and is thought to
reflect regional spontaneous vascular and neuronal brain activity (Biswal et al., 1995; Vigneau-
Roy et al., 2014; Zou et al., 2008). A growing number of studies suggest reductions in ALFF,
especially in regions vulnerable to Alzheimer’s disease and microvascular insult, are related to
cerebrovascular disease burden (e.g., white matter hyperintensities) and cognitive impairment
(Ni et al., 2022; Wang et al., 2011; Zhang et al., 2021). Additionally, ALFF may decline with
age, representing less efficient neurovascular unit function at rest (Hu et al., 2014). However,
little is known about relationships between BPV and ALFF, and whether differences may exist
for older and younger adults. Moreover, the medial temporal lobes were recently shown to be
vulnerable to elevated BPV and associated lower cerebral perfusion in older adults but not
younger adults (Sible et al., 2022c), but relationships with ALFF in these regions are unclear.
Findings could help elucidate potential mechanisms underlying the strong link between BPV,
cerebrovascular disease, and dementia risk. To investigate these possibilities, we examined the
relationship between BPV and concurrent regional ALFF in a sample of community-dwelling
older adults and healthy younger adults.

2. METHODS
2.1 Participants
Study data were from the baseline neuroimaging sessions from participants enrolled in a clinical
trial (Heart Rate Variability and Emotion Regulation or “HRV-ER” NCT03458910 at
ClinicalTrials.gov) at the Emotion and Cognition Lab at the University of Southern California
(USC). Participants were recruited via USC’s Healthy Minds and undergraduate subject pools,
USC’s online bulletin board, social media, direct mail and flyers. Participants with medical,
neurological, or psychiatric conditions were excluded from the study; however, participants
taking antidepressant or antianxiety medication were not excluded unless they anticipated a
change in treatment during the study. The study was approved by the Institutional Review Board
at USC and all participants provided their written informed consent.

The present study included 44 older adult participants (aged 55-80) and 49 younger adult
participants (aged 18-31) who underwent continuous BP monitoring over a 7-minute period
during resting-state fMRI (rsfMRI).

2.2 Measures
2.2.1 ALFF assessment
Participants underwent brain MRI at the USC’s Dana and David Dornsife Cognitive
Neuroimaging Center using a 3T Siemens® MAGNETOM Prisma MRI scanner and 32-channel
head coil, as previously described (Min et al., 2022). T1-weighted magnetization prepared rapid
gradient-echo (MP-RAGE) was acquired for high resolution anatomical images (TR = 2300 ms;
TE = 2.26 ms; slice thickness = 1.0 mm; flip angle = 9°; field of view = 256 mm, voxel size =
1.0 mm isotropic). RsfMRI data were acquired using multi-echo-planar imaging sequence with
TR = 2400 mm; TE = 18/35/53ms; slice thickness = 3.0 mm; flip angle = 75°, FOV = 240 mm;
voxel size = 3.0 x 3.0 x 3.0 mm. We acquired 175 volumes (7 min). Participants were instructed
to rest, breathe normally, and look at the central white cross on the screen.

To minimize the effects of motion, we employed multi-echo sequences during our rsfMRI scans.
Previous work indicates that BOLD T2* signal is linearly dependent on echo time, whereas non-
BOLD signal is not echo-time dependent (Kundu et al., 2012). We applied a multi-echo
independent component analysis (ME-ICA) denoising step to distinguish BOLD fluctuations
from non-BOLD artifacts including motion and physiology (Kundu et al., 2013), as previously
described (Min et al., 2022).

After preprocessing by ME-ICA, we performed additional preprocessing steps using FSL,
Analysis of Functional NeuroImaging (AFNI), and custom code written in MATLAB (Biswal et
al., 2010). The additional preprocessing steps consisted of: (1) temporal despiking; (2) linear
detrending; (3) spatial smoothing (full width at half maximum [FWHM] = 6 mm) and (4) global
intensity normalization. We converted the preprocessed images into ASCII files for processing
by custom MATLAB code. To remove very low frequencies in the BOLD signal, we applied
smoothness priors detrending with parameter lambda = 50, which corresponds to the cutoff
frequency 0.01 Hz (Tarvainen et al., 2002). We estimated voxel-wise power spectral density
(PSD) using the autoregressive (AR) Burg method for each individual scan to capitalize on the
improved accuracy of autoregressive approaches relative to the conventional fast Fourier
approach (Kay and Marple, 1981). To determine the model order, we first obtained estimates of
the best model order using SPSS forecasting ARIMA’s Bayesian information criterion (BIC),
autocorrelation function (ACF) and partial-autocorrelation function (PACF) for each participant.
Once the best model order for each individual was determined, the modal score across
participants (model order = 3) was selected. After voxel-wise PSD estimation, the individual
output files were converted into nifti files and the PSD map images were normalized to the
MNI152 2-mm template using the transformation matrix from the individual preprocessed
images.

ALFF analysis was performed on the obtained power spectrum using the AR method. Since the
power at a given frequency is proportional to the square of the amplitude at that frequency
component, we calculated the square root of power at each frequency and obtained the sum of
the square root of power within the 0.01~0.1 Hz frequency range at each voxel to make
individual ALFF spatial maps. Z-ALFF is a normalized ALFF relative to the mean amplitude of
low frequency fluctuations across voxels. For Z-ALFF spatial maps, the ALFF value at each
voxel was transformed to Z score (i.e., minus the global mean value and then divided by the
standard deviation) (Zou et al., 2008; Zuo et al., 2010).

We then determined right and left hemispheric ALFF and Z-ALFF for several a priori regions-
of-interest in the medial temporal lobe recently implicated in studies of BPV and regional
cerebral perfusion (Sible et al., 2022c, 2021b), tau accumulation (Sible et al., 2022b), and gray
matter atrophy (Sible and Nation, 2021): hippocampus, parahippocampal gyrus, entorhinal
cortex, and perirhinal cortex. Regional ALFF and Z-ALFF were then further subdivided into the
following frequency bands: slow2 (.198 - .25 Hz), slow3 (.073 - .198 Hz), slow4 (.027 - .073),
slow5 (.01 - .027 Hz).

2.2.2 BP assessment
BP was collected continuously using a Biopac® MRI-compatible BP monitoring device during
the 7-minute rsfMRI scan. As previously described (Sible et al., 2022c, 2022a; Sturm et al.,
2018b), data were processed offline using a custom pipeline scripted in AcqKnowledge®.
Intraindividual BPV was calculated as variation independent of mean (VIM), an index of BPV
that is not significantly correlated with mean BP levels (de Heus et al., 2019; Rothwell et al.,
2010b; Rouch et al., 2020; Sible et al., 2022c, 2021b; Sible and Nation, 2020; Xia et al., 2017).
In our sample, we confirmed BPV was not significantly correlated with mean BP levels
(bivariate correlation: r = -.04, p = .72). VIM was calculated as: VIM = standard deviation
(SD)/mean
x
, where the power x was derived from non-linear curve fitting of BP SD against mean
BP using the nls package in R Project, as previously described (Rothwell et al., 2010b; Sible et
al., 2022c, 2021b, 2021a; Sible and Nation, 2020). We focused our investigation on systolic
BPV, given recent findings that systolic, and not diastolic, short-term BPV is related to
concurrent cerebral blood flow in older adults (Sible et al., 2022c). We also calculated mean BP
over the scan.

2.2.3 Data availability statement
Study data are available in the public repository, OpenNeuro, “HRV-ER”
(https://openneuro.org/datasets/ds003823).

3. STATISTICAL ANALYSIS
Multiple linear regression was used to examine the relationship between BPV and regional Z-
ALFF (all frequency ranges) in older adults and younger adults separately. BPV elevation has
been hypothesized to reflect sympathetic nervous system overactivation (Imai et al., 1997;
Mancia et al., 1997; Parati et al., 2013). Some studies of older adults suggest the sympathetic
nervous system is largely lateralized to the brain’s right hemisphere (Guo et al., 2016; Sturm et
al., 2018a). Therefore, we examined relationships between BPV and regional Z-ALFF in the
right and left hemisphere separately. Exploratory analyses examined associations between mean
BP and regional Z-ALFF in order to directly compare potential effects with BPV (see
Supplementary Materials). All models controlled for age and sex. All analyses were 2-sided with
significance set at p < .05. All analyses were carried out in R Project (R Core Team, 2020).

4. RESULTS
Table 1 summarizes clinical and demographic information.

4.1 BPV
4.1.1 Older adults
As reported in Table 2 and shown in Figure 1, elevated BPV in older adults was associated with
significantly lower Z-ALFF in all medial temporal regions (i.e., hippocampus, parahippocampal
gyrus, entorhinal cortex, perirhinal cortex). Associations were more robust in the right
hemisphere relative to the left hemisphere, and in the faster frequency ranges (i.e., slow2, slow3)
when compared to the slower frequency ranges (i.e., slow4, slow5).

4.1.2 Younger adults
As summarized in Table 3 and shown in Figure 2, elevated BPV in younger adults was related to
significantly lower Z-ALFF in hippocampus and parahippocampal gyrus only. BPV was not
significantly associated with Z-ALFF in entorhinal cortex or perirhinal cortex. Findings were
present in both hemispheres and mostly in the slow3 and slow4 frequency ranges (vs slow2 and
slow5).

4.2 Mean BP
Mean BP was not significantly associated with Z-ALFF in any region in older adults or younger
adults (see Supplementary Tables 1-2).

5. DISCUSSION
Study findings suggest higher BPV, independent of mean BP, is associated with lower ALFF in
medial temporal regions, especially in older adults. Several studies indicate BPV elevation is
associated with frank cerebrovascular disease burden observable on MRI and postmortem
evaluation (Ma et al., 2020a; Sible et al., 2021a; Tully et al., 2020), and the present results add to
this literature by suggesting BPV may also be related to early cerebrovascular dysfunction in
regions highly vulnerable to Alzheimer’s disease.

Arterial stiffness is one mechanism thought to underly relationships between BPV,
cerebrovascular disease, and dementia risk. As arteries stiffen, their ability to dampen pulse wave
dynamics tends to decrease, which could allow BP levels to fluctuate more widely (Imai et al.,
1997; Zhou et al., 2018). Chronic large oscillations in BP levels could have a “tsunami effect”
(Saji et al., 2016) on vessel walls, promoting arterial remodeling, changes in the blood-brain
barrier, and resulting disruptions in neurovascular functioning, which could be related to the
present findings of reduced spontaneous brain activity. Therefore, it is possible that higher BPV
may indirectly alter neurovascular coupling as captured by MRI measures such as lower ALFF.

Reductions in ALFF were observed in medial temporal lobes, which support memory function
and are known to be highly sensitive to disruptions in cerebral blood flow and an early site for
neurofibrillary changes in Alzheimer’s disease (Braak and Braak, 1991; Schmidt-Kastner and
Freund, 1991). Interestingly, the present findings between higher BPV and reduced ALFF are
strikingly similar to recent findings between higher BPV and reduced cerebral blood flow in
these same regions in older adults (Sible et al., 2022c). Moreover, other BPV studies in older
adults implicate gray matter atrophy (Ma et al., 2020b; Sible and Nation, 2021), tau
accumulation (Ma et al., 2021; Sible et al., 2021a), and cerebral perfusion decline in the medial
temporal lobes (Sible et al., 2021b). Together these findings suggest BPV may be an
understudied vascular factor related to both vascular and neuronal brain changes in aging,
especially in highly vulnerable regions critical for cognitive function. The present study is both
cross-sectional and observational and future work with longitudinal and/or interventional designs
will help clarify the role of BPV in brain health.

Associations between BPV and ALFF in older adults were more robust in the right hemisphere,
while younger adults showed associations similarly across hemispheres, albeit in fewer brain
regions. BPV elevation has been hypothesized to reflect overactivation of the sympathetic
nervous system (Mancia et al., 1997), which in some studies has been lateralized to the right
hemisphere (Guo et al., 2016; Sturm et al., 2018a). Our findings support this hypothesis and may
be relevant to therapeutic intervention. For example, some studies indicate that different classes
of antihypertensive agents have differential effects on BPV and risk of stroke, independent of
mean BP levels (Rothwell et al., 2010a; Webb et al., 2010). Although the present study was not
adequately powered to test this possibility as it relates to ALFF, this remains an important area
for future research. Additionally, mean BP, which is a more traditionally studied index of BP and
often the target in intervention studies (Williamson et al., 2019), was not significantly associated
with ALFF in older adults or younger adults. This highlights the specific contribution of BPV,
and not mean BP, to ALFF and provides new information on the relationship between BPV and
cerebrovascular dysfunction and disease.

The study used a novel approach of collecting BP continuously during rsfMRI, which allowed us
to examine the relationship between BPV and concurrent spontaneous vascular and neuronal
brain activity. Prior work has shown that BPV elevation is associated with cerebrovascular
lesions on structural MRI (Ma et al., 2020a; Tully et al., 2020), and one recent study suggests
high BPV is related to reduced cerebrovascular reactivity during hypocapnia and hypercapnia
challenge during perfusion MRI (Sible et al., 2022a). The present study adds to this work by
using functional MRI to delineate relationships with early markers of cerebrovascular
dysfunction at rest. Additionally, our sample included both older and younger adults. Higher
BPV was associated with lower ALFF in both older and younger adults, but associations were
more widespread and robust in older adults, suggesting a possible age-related vulnerability of BP
fluctuations to spontaneous brain activity. There are several limitations worth noting. First, the
study sample was relatively small. Relatedly, we were not able to examine relationships with
Alzheimer’s disease risk gene apolipoprotein e4. Several recent studies indicate BPV may be
associated with important markers of Alzheimer’s disease, especially in apolipoprotein e4
carriers (Sible et al., 2022b; Sible and Nation, 2022, 2021) increasingly appreciated to have
vulnerability to vascular factors (Bangen et al., 2013; Montagne et al., 2020). Studies with larger
samples will be better able to investigate this possibility as it relates to ALFF. Also, older adults
in the study were living independently in the community, but other characteristics of cognitive
function were not used as inclusion/exclusion criteria. Future work exploring relationships in
well-characterized older adult samples has the potential to add to our understanding of BPV as
an understudied vascular risk factor for dementia.

6. CONCLUSIONS
Elevated BPV was associated with lower ALFF in medial temporal regions, especially in older
adults. Findings add to ongoing work detailing relationships between BPV, cerebrovascular
disease, and dementia by exploring associations with early markers of cerebrovascular
dysfunction at rest. BPV may be an understudied vascular factor for cerebrovascular changes in
aging relevant to cognitive function.

ACKNOWLEDGEMENTS
We would like to thank the study participants.

Funding sources
This work was supported by the NIH/NIA (grant numbers: R01AG057184, R01AG025340,
R01AG064228, R01AG060049, P30AG066519, P01AG052350) and Alzheimer’s Association
(grant number: AARG-17-532905).

Declarations of interest: none.
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2008. An improved approach to detection of amplitude of low-frequency fluctuation
(ALFF) for resting-state fMRI: fractional ALFF. J. Neurosci. Methods 172, 137–41.
https://doi.org/10.1016/j.jneumeth.2008.04.012
Zuo, X.-N., Di Martino, A., Kelly, C., Shehzad, Z.E., Gee, D.G., Klein, D.F., Castellanos, F.X.,
Biswal, B.B., Milham, M.P., 2010. The oscillating brain: Complex and reliable.
Neuroimage 49, 1432–1445. https://doi.org/10.1016/j.neuroimage.2009.09.037
Figure 1. Elevated BPV is associated with lower concurrent Z-ALFF in all medial temporal
regions in older adults
Scatterplots display the relationship between BPV and slow2 Z-ALFF in A) hippocampus B)
parahippocampal gyrus C) entorhinal cortex and D) perirhinal cortex in older adults. Right
hemisphere values of Z-ALFF are in red; left hemisphere values of Z-ALFF are in blue.
Abbreviations: BPV = blood pressure variability

Figure 2. Elevated BPV is associated with lower concurrent Z-ALFF in hippocampus and
parahippocampal gyrus in younger adults
Scatterplots display the relationship between BPV and slow4 Z-ALFF in A) hippocampus and B)
parahippocampal gyrus in younger adults. Right hemisphere values of Z-ALFF are in red; left
hemisphere values of Z-ALFF are in blue.
Abbreviations: BPV = blood pressure variability

Table 1.
Demographic information.
Older adults (n = 44) Younger adults (n = 49)
Age (years) 65.1 (6.6) 23.3 (3.0)
Sex (M/F) 16/28 23/26
Education (years) 16.6 (2.1) 16.5 (2.3)
Systolic BP
Mean 131.8 (24.3) 117.0 (25.7)
BPV 4.3 (1.7) 3.2 (1.7)
Mean (SD) reported unless otherwise indicated.
Abbreviations: BP = blood pressure; BPV = blood pressure variability

Table 2.
Model estimates of BPV predicting regional Z-ALFF in older adults.
ALFF
(.01 – .10 Hz)
Slow2
(.198 - .25 Hz)
Slow3
(.073 - .198 Hz)
Slow4
(.027 - .073)
Slow5
(.01 - .027 Hz)
Region
L HC -.11 [-.41, .19] -.12 [-.42, .18] -.12 [-.43, .19] -.11 [-.42, .19] -.004 [-.30,
.29]
R HC -.38 [-.65, -.11] -.29 [-.54, -.03] -.34 [-.63, -.05] -.37 [-.65, -.09] -.27 [-.56, .03]
L PHG -.13 [-.41, .15] -.21 [-.50, .08] -.18 [-.45, .11] -.13 [-.41, .15] -.06 [-.34, .21]
R PHG -.33 [-.64, -.02] -.34 [-.63, -.04] -.30 [-.64, .04] -.33 [-.64, -.03] -.25 [-.56, .06]
L EC -.28 [-.60, .04] -.32 [-.65, -.002] -.25 [-.57, .07] -.32 [-.64, .002] -.30 [-.62, .02]
R EC -.27 [-.57, .03] -.29 [-.56, -.02] -.36 [-.64, -.09] -.25 [-.56, .06] -.19 [-.50, .40]
L PC -.21 [-.51, .09] -.26 [-.58, .06] -.20 [-.50, .09] -.25 [-.55, .05] -.21 [-.51, .08]
R PC -.24 [-.51, .03] -.29 [-.56, -.03] -.34 [-.62, -.05] -.23 [-.49, .04] -.19 [-.46, .09]
Standardized beta (ß) and 95% confidence intervals shown unless otherwise indicated.
Bolded items indicate BPV is significantly associated with regional Z-ALFF.
Abbreviations: L = left hemisphere: R = right hemisphere; HC = hippocampus; PHG =
parahippocampal gyrus; EC = entorhinal cortex; PC = perirhinal cortex; ALFF = amplitude of
low frequency fluctuations

Table 3.
Model estimates of BPV predicting regional Z-ALFF in younger adults.
ALFF
(.01 – .10 Hz)
Slow2
(.198 - .25 Hz)
Slow3
(.073 - .198 Hz)
Slow4
(.027 - .073)
Slow5
(.01 - .027 Hz)
Region
L HC -.28 [-.54, -.01] -.19 [-.46, .07] -.27 [-.53, -.01] -.28 [-.54, -.02] -.20 [-.47, .07]
R HC -.31 [-.61, -.01] -.24 [-.50, .02] -.30 [-.58, -.03] -.31 [-.62, -.003] -.21 [-.52, .10]
L PHG -.24 [-.47, -.01] -.15 [-.42, .13] -.22 [-.46, .03] -.24 [-.47, -.01] -.19 [-.43, .04]
R PHG -.12 [-.41, .16] -.13 [-.40, .13] -.16 [-.45, .14] -.12 [-.40, .16] -.06 [-.34, .21]
L EC -.15 [-.46, .16] -.09 [-.37, .19] -.13 [-.42, .16] -.16 [-.47, .15] -.10 [-.40, .20]
R EC -.16 [-.45, .12] -.06 [-.30, .19] -.11 [-.38, .16] -.18 [-.47, .11] -.17 [-.47, .13]
L PC -.22 [-.50, .07] -.20 [-.48, .07] -.24 [-.52, .03] -.21 [-.50, .08] -.15 [-.44, .15]
R PC -.14 [-.44, .16] -.17 [-.42, .07] -.17 [-.45, .11] -.14 [-.44, .16] -.14 [-.43, .16]
Standardized beta (ß) and 95% confidence intervals shown unless otherwise indicated.
Bolded items indicate BPV is significantly associated with regional Z-ALFF.
Abbreviations: L = left hemisphere: R = right hemisphere; HC = hippocampus; PHG =
parahippocampal gyrus; EC = entorhinal cortex; PC = perirhinal cortex; ALFF = amplitude of
low frequency fluctuations

SUPPLEMENTARY MATERIALS
Supplementary Table 1.
Model estimates of mean BP predicting regional Z-ALFF in older adults.
ALFF
(.01 – .10 Hz)
Slow2
(.198 - .25 Hz)
Slow3
(.073 - .198 Hz)
Slow4
(.027 - .073)
Slow5
(.01 - .027 Hz)
Region
L HC .15 [-.15, .45] .002 [-.30, .30] -.01 [-.32, .30] .16 [-.14, .46] .07 [-.22, .36]
R HC .17 [-.12, .46] -.06 [-.33, .22] -.13 [-.44, .17] .20 [-.10, .50] .12 [-.18, .43]
L PHG .03 [-.25, .31] -.10 [-.40, .19] -.27 [-.54, .005] .05 [-.23, .34] .02 [-.25, .29]
R PHG -.10 [-.42, .23] -.21 [-.52, .09] -.33 [-.66, .007] -.05 [-.37, .27] -.13 [-.45, .18]
L EC .10 [-.23, .43] -.08 [-.41, .26] -.10 [-.42, .23] .12 [-.22, .45] .14 [-.19, .47]
R EC .10 [-.21, .40] -.01 [-.29, .27] -.08 [-.37, .22] .09 [-.22, .41] .07 [-.24, .38]
L PC .05 [-.25, .36] -.03 [-.36, .29] -.13 [-.43, .16] .07 [-.24, .38] .02 [-.28, .32]
R PC .07 [-.21, .34] -.02 [-.31, .26] -.11 [-.41, .19] .07 [-.21, .34] .03 [-.25, .31]
Standardized beta (ß) and 95% confidence intervals shown unless otherwise indicated.
Abbreviations: L = left hemisphere: R = right hemisphere; HC = hippocampus; PHG =
parahippocampal gyrus; EC = entorhinal cortex; PC = perirhinal cortex; ALFF = amplitude of
low frequency fluctuations

Supplementary Table 2.
Model estimates of mean BP predicting regional Z-ALFF in younger adults.
ALFF
(.01 – .10 Hz)
Slow2
(.198 - .25 Hz)
Slow3
(.073 - .198 Hz)
Slow4
(.027 - .073)
Slow5
(.01 - .027 Hz)
Region
L HC -.03 [-.30, .24] .05 [-.22, .32] -.007 [-.28, .26] -.01 [-.28, .26] -.06 [-.33, .21]
R HC -.20 [-.51, .10] -.06 [-.33, .20] -.13 [-.41, .15] -.19 [-.50, .12] -.27 [-.57, .03]
L PHG .11 [-.13, .34] .11 [-.17, .38] .08 [-.17, .33] .12 [-.12, .35] .08 [-.16, .31]
R PHG .04 [-.25, .32] .03 [-.24, .29] .04 [-.25, .34] .04 [-.24, .31] -.05 [-.33, .22]
L EC -.04 [-.35, .27] .04 [-.24, .31] .003 [-.28, .29] -.04 [-.35, .27] -.007 [-.31, .29]
R EC -.11 [-.39, .18] -.06 [-.30, .18] -.11 [-.37, .15] -.08 [-.37, .20] -.10 [-.40, .19]
L PC -.10 [-.39, .19] .08 [-.20, .36] .02 [-.26, .30] -.11 [-.40, .19] -.15 [-.44, .15]
R PC -.21 [-.50, .09] -.11 [-.36, .13] -.15 [-.42, .13] -.20 [-.49, .10] -.24 [-.52, .05]
Standardized beta (ß) and 95% confidence intervals shown unless otherwise indicated.
Abbreviations: L = left hemisphere: R = right hemisphere; HC = hippocampus; PHG =
parahippocampal gyrus; EC = entorhinal cortex; PC = perirhinal cortex; ALFF = amplitude of
low frequency fluctuations

Abstract (if available)

Abstract To address these gaps in the aging literature, we aimed to investigate the hypothesis that elevated BPV may convey susceptibility to dementia through links with cerebral microvascular hypoperfusion (Study 1) and cerebrovascular dysfunction in response to stimuli (Study 2) and at rest (Study 3) in brain regions vulnerable to aging and AD. All studies examined cross-sectional relationships between continuous beat-to-beat BPV and concurrent functional neuroimaging markers in community-dwelling older adults (aged 55-90 years) without history of dementia or clinical stroke living in Los Angeles County and Orange County. Study 1 and Study 3 also examined relationships in younger adult controls (aged 18-31 years) living in Los Angeles County. Study 1 used pseudo-continuous arterial spin labelling (pCASL)-MRI to capture regional cerebral blood flow during a 5-minute period of rest. Study 2 used pCASL-MRI during 5-minute visually guided hypercapnia and hypocapnia challenge to capture CVR response to stimuli. Study 3 used resting state functional MRI (rsfMRI) to capture oscillations in regional brain activity during a 7-minute period of rest. We studied functional activity in a priori selected brain regions known to convey susceptibility to cerebrovascular insult and AD (Iadecola, 2004; Vikner et al., 2021; Zlokovic, 2011), including the medial temporal lobe. We also examined relationships between mean BP and functional neuroimaging markers to directly compare potential effects with BPV.

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Asset Metadata

Creator Sible, Isabel (author)

Core Title The role of blood pressure variability in cognitive decline, cerebrovascular disease and Alzheimer’s disease

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Degree Conferral Date 2023-05

Publication Date 11/04/2023

Defense Date 03/21/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

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Advisor Margolin, Gayla (committee chair), Nation, Daniel (committee chair), Beam, Chris (committee member), Khoo, Michael (committee member), Mather, Mara (committee member)

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