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A virtual reality exergaming system to enhance brain health in older adults at risk for Alzheimer’s disease
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A virtual reality exergaming system to enhance brain health in older adults at risk for Alzheimer’s disease
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Content
Copyright 2021 Ashwin R. Sakhare
A Virtual Reality Exergaming System to Enhance Brain Health in
Older Adults at Risk for Alzheimer’s Disease
by
Ashwin R. Sakhare
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOMEDICAL ENGINEERING)
May 2021
ii
Acknowledgements
It’s been an unbelievable journey and a single acknowledgements section cannot do
justice to the immense amount of support and guidance I’ve received from so many people along
the way. To my fellow MS and PhD BME classmates and colleagues who shared this journey
with me over weekly coffee & tea hours, karaoke, USC sporting events, exploring LA, and long
grueling study sessions. To my PhD committee for their valued contributions and guidance. To
my HTE mentor, Dr. George Tolomiczenko, for his support, kindness, and exuberance in helping
me pursue my interests in healthcare. To Joy Stradford and Roshan Ravichandran for their
immense contributions to this research. To all the members of the Pa Lab for making me feel
welcome and proud to have called the lab home for the last 5 years. To my PhD advisor and
mentor, Dr. Judy Pa, who helped me grow as a scientist and researcher and was able to take my
ambitious goal of curing Alzheimer’s disease and provide a path for me to pursue those dreams.
To my family, including my mom and dad, for their love and support as I pursued a PhD far
away from home. Finally, to my grandfather, who inspired me to start this journey. It is my
sincere hope that this seminal work will provide the foundation for developing a therapeutic that
cures Alzheimer’s disease in the near future.
iii
Table of Contents
Acknowledgements ..................................................................................................................................... ii
List of Tables .............................................................................................................................................. iv
List of Figures .............................................................................................................................................. v
Abstract ....................................................................................................................................................... vi
Background & Significance........................................................................................................................ 1
Chapter Summaries .................................................................................................................................... 5
Chapter 1 Overview .................................................................................................................................... 7
Chapter I: Cycling and Spatial Navigation in an Enriched, Immersive 3D Virtual Park
Environment: A Phase 1 Feasibility Study in Younger and Older Adults ............................................ 9
Abstract ..................................................................................................................................................... 9
Introduction ............................................................................................................................................. 10
Methods ................................................................................................................................................... 12
Results ..................................................................................................................................................... 27
Discussion ............................................................................................................................................... 35
Conclusions ............................................................................................................................................. 41
Chapter II Overview ................................................................................................................................. 42
Chapter II: Assessing Test-Retest Reliability of Phase Contrast MRI for Measuring
Cerebrospinal Fluid and Cerebral Blood Flow Dynamics .................................................................... 44
Abstract ................................................................................................................................................... 44
Introduction ............................................................................................................................................. 45
Methods ................................................................................................................................................... 48
Results ..................................................................................................................................................... 57
Discussion ............................................................................................................................................... 62
Conclusion .............................................................................................................................................. 69
Chapter III Overview ............................................................................................................................... 70
Chapter III: Simultaneous Exercise and Cognitive Training in Virtual Reality Phase 2 Pilot
Study: Impact on Brain Health and Cognition in Older Adults ........................................................... 72
Abstract ................................................................................................................................................... 72
Introduction ............................................................................................................................................. 74
Methods ................................................................................................................................................... 75
Results ..................................................................................................................................................... 90
Discussion ............................................................................................................................................. 104
Conclusion ............................................................................................................................................ 113
Summary & Conclusions ........................................................................................................................ 115
References ................................................................................................................................................ 116
iv
List of Tables
Table 1.1: Participant demographics and performance in VR. .................................................................. 27
Table 1.2: Participant assessment of presence, mood, and adverse effects. ............................................... 28
Table 2.1: Test-retest reliability for all flow parameters. ........................................................................... 58
Table 2.2: Intra-rater and inter-rater reliability for subset of flow parameters. ......................................... 59
Table 3.1: Summary of aerobic exercise and cognitive training progression. ........................................... 79
Table 3.2: Subject demographics and physical characteristics. ................................................................. 90
Table 3.3: Descriptive statistics and effect sizes for all cognitive outcomes. ............................................ 91
Table 3.4: Descriptive statistics and effect sizes for all neuroimaging outcomes. ..................................... 94
Table 3.5: Descriptive statistics and effect sizes for all physical outcomes. .............................................. 97
Table 3.6: Summary of aerobic exercise and cognitive training performance in virtual reality. ............... 99
Table 3.7: VR intervention outcomes vs control group outcomes. .......................................................... 101
Table 3.8: CN vs MCI - Benefits from VR Intervention ......................................................................... 102
v
List of Figures
Figure 1.1: Cognitively Challenging Gameplay and Immersive Storylines in VR. ................................... 16
Figure 1.2: Novel and Engaging Virtual Environments. ........................................................................... 17
Figure 1.3: Custom-Built Stationary Exercise Bike................................................................................... 19
Figure 1.4: Visual cues in virtual environment during route learning. ...................................................... 24
Figure 1.5: Effects of VR exposure sickness symptoms in younger (HY) and older (HO) adults. ........... 30
Figure 1.6: Effects of VR exposure on stress and arousal states. .............................................................. 31
Figure 1.7: Sense of presence in younger and older adults for the ITC-SOPI questionnaire. ................... 32
Figure 1.8: Physical exertion and sickness symptoms over time. .............................................................. 34
Figure 2.1: Representative set of acquired MRI images for a participant. ................................................ 50
Figure 2.2: Single frame of an acquired flow image at cerebral aqueduct. ............................................... 51
Figure 2.3: Single frame of an acquired flow image at C2-C3 subarachnoid space. ................................. 52
Figure 2.4: Single frame of an acquired flow image at C2-C3 vascular region. ........................................ 52
Figure 2.5: Representative flow curves from one participant. ................................................................... 56
Figure 2.6: ICC and mean CV for the flow parameters calculated from the flow curves. ......................... 61
Figure 3.1: Flow diagram summarizing subject eligibility, participation, and attrition. ........................... 77
Figure 3.2: Timeline of assessments and VR training during study. ......................................................... 78
Figure 3.3: Subject cycling and navigating a virtual environment. ........................................................... 80
Figure 3.4: Executive Function – Improved cognitive flexibility and inhibitory control. ......................... 92
Figure 3.5: Memory – Improved visual discrimination due to pattern separation. .................................... 93
Figure 3.6: CSF and CBF Flow – Lower CBF pulsatility/resistivity and higher peak CSF flow. ............. 96
Figure 3.7: Brain Structure - Increased whole brain and superior parietal lobule volumes. ...................... 97
Figure 3.8: Aerobic Fitness – Improved balance. ...................................................................................... 98
Figure 3.9: Changes in HR and time spent cycling in VR during the intervention. ................................ 100
Figure 3.10: CN vs MCI - Visual Memory (MST) Improvements .......................................................... 103
vi
Abstract
Introduction: Alzheimer’s disease (AD) is a significant public health concern in older adults.
It is characterized by progressive memory loss, with deficits in spatial memory resulting in
difficulty navigating familiar spaces, ultimately compromising safety, autonomy, and quality of
life. Identifying new ways to maintain brain health is of utmost importance as there are currently
no effective disease-modifying treatments. Simultaneous exercise and cognitive enrichment have
been shown to enhance brain function in both animal and human studies. Virtual reality (VR) is a
promising tool for engaging in this combined activity. The goal of this dissertation was to 1)
assess safety and feasibility of a novel VR exergaming system that combines cycling-based
aerobic exercise and cognitive enrichment via targeted spatial navigation training, 2) develop and
test a novel MRI biomarker of brain health, and 3) examine the effects of a 12-week VR
intervention on proof-of-concept outcomes for brain health and cognition in healthy older adults.
Methods: The exergaming system consisted of a custom-built stationary exercise bike and
virtual reality game viewed through an immersive VR head-mounted display. The game was
designed with progressively challenging navigation tasks targeting spatial memory and attention.
Landmarks were strategically placed at every intersection to serve as visual cues. Prior the
intervention, a preliminary feasibility study was conducted to assess adverse effects and
enjoyment associated with cycling and spatial navigation in an immersive virtual environment.
An additional study was conducted to evaluate reliability of MRI for measuring cerebral blood
flow (CBF) and cerebrospinal fluid flow (CSF), potential biomarkers of brain health to be used
as an outcome measure for the intervention. Finally, a 12-week intervention consisting of
simultaneous exercise and spatial navigation training was conducted in older adults ages 50-85,
to assess impact on cognitive function, CBF and CSF flow, and brain morphometry.
vii
Results: For the feasibility study, exposure to virtual reality was associated with high
arousal and low stress levels in older adults. Symptoms of simulator sickness (adverse effects)
were enhanced but within an acceptable range. No association was found between physical
exertion levels and simulator sickness levels. For the reliability study, among the 26 CSF and
CBF flow parameters analyzed, 22 had excellent test-retest reliability (ICC>0.80), including
measurements of CBF arterial pulsatility index and resistivity index. All CSF and CBF flow
measurements had excellent inter-rater and intra-rater reliability. From the 12-week simultaneous
exercise and spatial navigation training intervention, improvements were found in CBF flow and
brain structure. Total grey matter and superior parietal lobule volumes showed improvements of
medium effect size, increasing 1.1% and 2.2%, respectively. Arterial blood flow pulsatility
decreased 12%, indicating a medium reduction in peripheral vascular resistance. Cognitive
benefits were also observed, with measurements of cognitive flexibility, response inhibition, and
pattern separation showing medium-to-large improvements of 13%, 26%, and 55%, respectively.
Conclusion: In this dissertation, we showed that it is feasible for older adults to engage in
simultaneous cycling and spatial navigation in an immersive virtual environment, and that the
experience is associated with low stress, high arousal, reports of enjoyment, and minimal adverse
effects. Moreover, we show that CSF and CBF flow, potential biomarkers of brain health, can be
measured with excellent reliability using MRI. Finally, we show that a 12-week intervention
engaging these simultaneous activities elicits positive changes in cognition and brain health, with
improvements in executive function, memory, brain volume, and CBF flow. Overall, this
dissertation provides supporting evidence for the benefits of combined exercise and cognitive
enrichment on brain health and represents a critical first step towards establishing our VR
exergaming system as a non-invasive technology for the primary prevention of AD.
1
Background & Significance
Alzheimer disease (AD) is a progressive, neurodegenerative disorder affecting more than
33 million people worldwide.
1
It is the 6
th
leading cause of death in the United States and the
leading cause of dementia in older adults, affecting one in ten individuals ages 65 and older.
137
Due to the gradual progression of the disease, a significant burden is placed on the healthcare
system, with costs approaching $300 billion a year.
137
A hallmark clinical symptom of AD is
memory dysfunction, with deficits observed in autobiographical, semantic, associative, episodic,
and working memory.
138,139,142
In addition to memory, deficits have also been observed in other
cognitive domains, including executive function, attention, perceptual speed, verbal ability,
visuospatial skill, and language.
138,140,141
The disease processes underlying AD are thought to begin more than 20 years prior to the
onset of clinical symptoms.
137
Existing pathophysiological models of disease progression suggest
a temporal sequence of amyloid deposition, synaptic dysfunction, tau deposition, and brain
volume atrophy prior to deterioration in cognitive function.
149
Deficits in episodic memory, the
most pronounced form of memory dysfunction in AD, have been observed in the preclinical
stage of the disease up to 6 years prior to diagnosis.
143
This includes spatial memory, a subtype of
episodic memory responsible for spatial location, spatial pattern, and object location recall.
142,144
Deficits in spatial memory have been shown to result in difficulty navigating familiar spaces,
ultimately compromising safety, autonomy, and quality of life.
145,146
Developing treatments that slow deterioration in spatial memory may allow individuals to
maintain functional independence for an extended period. However, ecologically valid
paradigms targeting spatial memory are currently limited.
142
Moreover, the etiology of AD
remains poorly understood making the effectiveness of any such paradigm unclear. Several
2
studies have hypothesized that synaptic, mitochondrial, metabolic, and inflammatory processes
precede amyloid deposition.
149
A few studies have theorized that amyloid deposition results from
dysfunction in the brain’s waste clearance system.
70
Indeed, cerebrospinal fluid (CSF) and
cerebral blood flow (CBF) have been shown to support waste removal through a network of
perivascular channels in the brain. This suggests that CSF and CBF flow dynamics may play
an important role as an early biomarker for disease progression.
The onset of the cascade of pathophysiological processes in AD, such as clearance
dysfunction, may be modulated by factors such as age, genetics, environment, and lifestyle.
149,150
According to one epidemiological study, approximately 50% of AD cases can be attributed to
seven lifestyle factors, including cognitive inactivity, physical inactivity, smoking, depression,
hypertension, obesity, and diabetes.
1
Among these lifestyle factors, physical inactivity has been
shown to contribute to the largest proportion of AD cases in the US, suggesting exercise
may significantly reduce disease prevalence.
1
This has been supported in several studies
showing a positive association between aerobic fitness and hippocampal volume, a key region of
deterioration in AD.
4,154
The temporal lag between the onset of pathophysiological processes and appearance of
clinical symptoms may be modulated by cognitive reserve, a theoretical construct defined by
resiliency to cognitive decline despite the presence of brain pathology.
149
The neural mechanisms
underlying this resiliency are believed to occur through neuroplasticity changes that promote the
use of more efficient and flexible cognitive paradigms as well as the use of alternative networks
to compensate for disrupted standard networks.
152
Studies have hypothesized that cognitive
reserve can be enhanced through cognitive enrichment.
151
This has been supported in several
studies, including one which found the time between onset of cognitive decline and clinical
3
diagnosis to be 7 years longer in highly educated older adults compared to those with low
education.
155
Exercise may also play an important role as an effect modifier in the relationship between
cognitive enrichment and cognitive reserve. Exercise has been shown to enhance hippocampal
neurogenesis, synaptic plasticity, and cell proliferation, while cognitive enrichment has been
shown to promote the survival of these newly formed cells.
153
This has been supported by recent
studies that have shown a synergistic effect where neurogenesis is enhanced if exercise and
cognitive enrichment are performed simultaneously.
153
Moreover, a systematic review on
cognitive interventions found improved general cognitive function in both cognitively normal
and impaired older adults when exercise and cognitive activity were combined.
156
This suggests
that simultaneous exercise and cognitive enrichment may play a critical role in the primary
prevention of AD.
A lack of effective treatments underlies the importance of primary prevention in AD.
Existing FDA-approved therapeutic drugs, such as cholinesterase inhibitors and NMDA receptor
antagonists, have elicited modest improvements in cognitive function.
157
However, these drugs
have only been shown to ameliorate symptoms and not slow disease progression, as their
mechanistic actions do not target the underlying etiology.
147,157
A number of investigational
drugs targeting amyloid-β, tau, neuroinflammation, and mitochondrial dysfunction have entered
the clinical phases of testing in recent years.
157
However, to date, drugs targeting these pathways
have struggled to establish efficacy during clinical testing.
148
As a result, the effectiveness of
these disease-modifying approaches in individuals with AD remains unclear. Consequently,
there has been a paradigm shift towards earlier intervention in cognitively normal older
4
adults at risk for AD, where treatment is initiated prior to the onset of clinically evident
symptoms.
149
Virtual reality (VR) has emerged as a promising tool for conducting non-pharmacological
interventions aimed at primary prevention in AD. Although still in its nascent stages as a
technology, VR has already been integrated into the field of medicine with applications in motor
rehabilitation, cognitive rehabilitation, pain management, and the treatment of psychological
disorders.
158,159,160,161,162
The utility of VR in medicine is driven by its ability provide a safe,
controlled, and adaptable environment for patients, allowing for studies that may otherwise not
be possible in the real world. VR has also been shown to be an ecologically valid tool for
assessing spatial navigation skills.
9,10,11,12,13
This has been supported by several studies, including
one which showed a high correlation between real-world and computerized spatial navigation in
a hospital environment (r=0.73).
10
Consequently, this makes virtual reality well-suited for
interventions that combine exercise and cognitive stimulation through spatial memory
engagement.
The series of studies described in the following chapters were designed to target effective
interventions and meaningful treatment monitoring biomarkers, in the form of 1) a Phase I safety
and feasibility study for a novel VR intervention, 2) a novel Alzheimer’s MRI biomarker of
cerebrospinal fluid flow and cerebral blood flow dynamics, and 3) a Phase 2 pilot study
examining the effects of the VR intervention on proof-of-concept outcomes for brain health and
cognition. These studies were designed in accordance with recently proposed guidelines for
empirically and systematically validating VR therapeutics for health.
42
Collectively, these studies
forge a new path forward in Alzheimer’s disease prevention.
5
Chapter Summaries
The overall goal of this dissertation is to investigate the impact of a simultaneous exercise and
spatial navigation training intervention in virtual reality on cognition and brain health in healthy
older adults 50-85 years of age, and to explore potential underlying neural mechanisms that
mediate these effects. This work is divided into 3 chapters and summarized below:
Chapter I: Cycling and Spatial Navigation in an Enriched, Immersive 3D Virtual Park
Environment: A Phase 1 Feasibility Study in Younger and Older Adults. This chapter
investigates adverse effects, mood, presence, and physical exertion levels associated with cycling
and spatial navigation in an immersive virtual environment while wearing a VR head-mounted
display. The results of this study were published in Frontiers in Aging Neuroscience (Sakhare et
al., 2019).
Chapter II: Test-Retest Reliability of Phase Contrast MRI for Measuring Cerebrospinal
Fluid and Cerebral Blood Flow Dynamics. This chapter assesses intra-rater, inter-rater, and
test-retest reliability of flow measurements acquired at the cerebral aqueduct, C2-C3
subarachnoid space, and C2-C3 vascular arteries and veins using PC-MRI. This chapter also
explores the potential for CSF and CBF flow measurements to be used as a non-invasive
biomarker of brain health. The results of this study were published in Journal of Magnetic
Resonance in Medicine (Sakhare et al., 2019).
6
Chapter III: Simultaneous Exercise and Cognitive Training in Virtual Reality Phase 2 Pilot
Study: Impact on Brain Health and Cognition in Older Adults. This chapter investigates the
impact of a 12-week simultaneous exercise and spatial navigation training intervention in VR on
cognition and brain health in older adults. Primary outcomes include assessments of memory,
executive function, and CSF and CBF dynamics. The results of this study are in preparation, with
an anticipated manuscript submission date of May 2021 (Sakhare et al., in prep).
7
Chapter 1 Overview
The potential of virtual reality as a tool for enhancing brain health is rooted in its usefulness
in other areas of medicine. VR has been used to assist in the rehabilitation of upper and lower
extremity function in post-stroke individuals and those with Parkinson’s disease and spinal cord
injuries.
159
It has also been used to attenuate anxiety associated with cancer chemotherapy and
other routine medical procedures, and has served as an analgesic for burn survivors and
individuals with chronic pain.
228
Furthermore, virtual reality has been used to treat PTSD and
other anxiety disorders using simulated environments that minimize avoidance and facilitating
emotional processing.
229
Finally, VR has been used as a tool for the assessment of cognitive
function in individuals with traumatic brain injury and dementia.
230,231
Virtual reality has several distinct advantages over conventional therapies. Primarily, VR
allows patients to safely interact with a realistic simulation of a real-world environment. This
allows patients to engage in therapies that may too dangerous or impractical to perform in a real-
world setting.
232
Moreover, VR allows clinicians full control over the presentation of stimuli
given to the patient. This promotes better generalization of learning as it allows clinicians to
easily modify therapies and create individualized environments based on a patient’s specific
needs.
158
VR has also been shown to positively impact mood and motivation through fun,
engaging, and interactive environments. In one such exercise study, subjects pedaling on a
stationary bike linked to a virtual scene were found to cycle for longer durations and distances
with higher energy consumption compared to subjects without the virtual scene.
159
Finally, VR
has been shown to be ecologically valid, particularly for neuropsychological assessments of
cognitive function.
230
8
The ability to create safe and controlled environments in virtual reality make it a promising
tool for engaging in simultaneous aerobic exercise and cognitive enrichment activities. In the
chapter that follows, we evaluate the feasibility of older adults engaging in this simultaneous
activity, by assessing adverse effects, mood, presence, and physical exertion levels associated
with cycling and navigating an enriched, immersive virtual reality environment. A novel
exergaming system, consisting of a custom-built stationary exercise bike and cognitively
challenging virtual reality game, was developed to support these simultaneous activities. The
results of this study were published in Frontiers in Aging Neuroscience (Sakhare et al., 2019).
9
Chapter I: Cycling and Spatial Navigation in an Enriched,
Immersive 3D Virtual Park Environment: A Phase 1 Feasibility
Study in Younger and Older Adults
Abstract
Background: Cognitive decline is a significant public health concern in older adults.
Identifying new ways to maintain cognitive and brain health throughout the lifespan is of utmost
importance. Simultaneous exercise and cognitive engagement has been shown to enhance brain
function in animal and human studies. Virtual reality (VR) may be a promising approach for
conducting simultaneous exercise and cognitive studies. In this study, we evaluated the
feasibility of cycling in a cognitively enriched and immersive spatial navigation VR environment
in younger and older adults.
Methods: 20 younger (25.9±3.7 years) and 20 older (63.6±5.6 years) adults participated in
this study. Participants completed four trials (2 learning, 2 recall) of cycling while wearing a
head-mounted device and navigating a VR park environment. Questionnaires were administered
to assess adverse effects, mood, presence, and physical exertion levels associated with cycling in
the VR environment.
Results: 4 subjects withdrew from the study due to adverse effects, yielding a 90%
completion rate. Simulator sickness levels were enhanced in both age groups with exposure to
the VR environment but were within an acceptable range. Exposure to the virtual environment
was associated with high arousal and low stress levels, suggesting a state of excitement, and
10
most participants reported enjoyment of the spatial navigation task and VR environment. No
association was found between physical exertion levels and simulator sickness levels.
Conclusions: This study demonstrates that spatial navigation while cycling is feasible and
that older adults report similar experiences to younger adults. VR may be a powerful tool for
engaging physical and cognitive activity in older adults with acceptable adverse effects and with
reports of enjoyment. Future studies are needed to assess the efficacy of a combined exercise and
cognitive VR program as an intervention for promoting healthy brain aging, especially in older
adults with increased risk of age-related cognitive decline.
Introduction
Cognitive decline in older adults is a significant public health issue. However, recent
studies have shown that individuals with a lifestyle rich in cognitive and physical stimulation
experience less age-related cognitive decline.
1
Exercise and cognitive enrichment are two
lifestyle factors that have been associated with a reduced risk of dementia.
1
Exercise enhances
hippocampal neurogenesis, synaptic plasticity, and cell proliferation,
2,3,4
while cognitive
enrichment promotes the survival of these newly formed cells.
5
Recent animal studies have
shown that neurogenesis can be enhanced if exercise is combined with cognitive enrichment.
5
This has been supported in human studies in which higher cognitive performance was reported
after combined physical and cognitive activity compared to either one alone.
6
Taken together,
this suggests that the greatest improvements in cognitive function may be achieved when
exercise and cognitive stimulation are performed simultaneously.
Spatial navigation is a key cognitive process that enables daily exploration of the world.
Declines in spatial navigation have been shown with age and may result from changes in neural
11
function and structure.
7
Virtual reality (VR) has emerged as a promising technology for
combined exercise and spatial navigation studies as it provides a safe and controlled environment
to monitor physical activity, manipulate experimental parameters, and interact with the user.
8
Previous studies have shown VR to be an ecologically valid tool for assessing spatial navigation
deficits in healthy adults and individuals with neurological disorders, including those with
Alzheimer’s disease.
9,10,11,12,13
However, only a limited number of studies have utilized VR for
exercise, and specifically for cycling.
14,15
Only one study, to our knowledge, has combined
cycling and spatial navigation in VR, which was conducted in a group of younger adults
16
; thus
its application to older adults is unknown. Furthermore, many of these studies have been
conducted on non-immersive desktop monitors and projector screens instead of immersive head-
mounted (HMD) displays.
9,10,11,12,13,14,15
Recent technological advances have made HMDs an affordable option for immersive VR.
HMDs can couple head movement to the position and orientation of the user’s field of view,
creating a sense of presence and engagement in the virtual environment.
17
Previous studies have
shown presence to be important for performance on spatial navigation tasks in VR.
18
However,
increased presence with HMDs can often introduce adverse effects, commonly termed simulator
sickness in VR, particularly when coupled with locomotion due to incongruence between
perceived and actual self-motion.
19,20,21
There is also a concern that older adults are more likely
to experience simulator sickness than younger adults due to age-associated deterioration in
sensory processing
22
, possibly exacerbating the severity of sensory conflict present during
locomotion in immersive VR.
Thus, immersive HMDs may have higher ecological validity than desktop monitors and
projector screens for cycling and spatial navigation in VR, but with the potential for enhanced
12
adverse effects. Moreover, the presence of these adverse effects can significantly impact
enjoyment and performance on cycling and spatial navigation tasks in VR. Concerns of
enjoyment and performance are enhanced in the older adult population, as older adults typically
have less exposure to technology and digital gaming than younger adults. Therefore, the
objective of this study was to evaluate the feasibility of cycling and spatial navigation in VR
using immersive HMDs in older adults with younger adults serving as a reference group for
assessing adverse effects, mood, enjoyment, presence, and performance.
Methods
Participants
A total of forty adults, including 20 younger adults (25.9±3.7 years old; 21-33 years; 9
females) and 20 older adults (63.6±5.6 years old; 52-70 years; 10 females) who were physically
capable of cycling participated in this study. All subjects provided written consent to participate
in this study, which was approved by the institutional review board and performed in accordance
with the 1964 Declaration of Helsinki. Subjects were selected from a convenience sample of
local students, staff, and community-dwelling adults. Subjects with known medical conditions
contradicting exercise or neurological disorders were excluded from the study.
Assessments
Performance on the spatial navigation tasks was assessed by total cycling time, mean
cycling speed, and percentage of correct decisions in the virtual environment. Self-reported
measures of mood, presence, physical exertion, and simulator sickness were collected and
13
described below. All questionnaires have reported good reliability and internal
consistency.
23,24,25,26,27
Mood
Mood was assessed using the Stress Arousal Checklist (SAC)
28
, which provides a
differential measurement of situational stress and arousal. High stress and arousal levels for
younger and older adults were defined by cutoff scores of 6.2 and 6.0, and 5.1 and 6.4,
respectively based on normative values for each age group.
30
Simulator Sickness
Adverse effects were measured using 3 different questionnaires that assessed pre-post VR
simulator sickness, simulator sickness during VR exposure, and historical motion sickness as a
child and adult. Simulator sickness after VR exposure was assessed using the Simulator Sickness
Questionnaire (SSQ).
31
The SSQ was also administered before VR exposure to measure baseline
levels of pre-existing symptoms including difficulty focusing, headache, eyestrain, and general
discomfort. A total sickness cutoff score of 15 based on previous work
22
was used to determine if
participants experienced notable simulator sickness after VR exposure and to split participants
into two adverse effect groups: minimal and notable. This score also represents the 75
th
percentile of sickness scores reported on a variety of flight simulators, as well as the midpoint for
the part of the population that experienced adverse effects when exposed to these flight
simulators.
Simulator sickness was also evaluated after each trial using the Short Symptom Checklist
(SSC).
32
The SSC is a shortened version of the SSQ containing a subset of 6 symptoms: nausea,
14
eye strain, dizziness with eyes closed, stomach awareness, difficulty focusing, and general
discomfort.
Motion sickness was assessed using the Motion Sickness Susceptibility Questionnaire
(MSSQ).
33
The global MSSQ was used to evaluate a participant’s susceptibility to motion
sickness in nine different modes of transportation (i.e. cars, trains, ships) as a child (MSSQ-C)
and as an adult (MSSQ-A). A table of normative values was used to convert the global MSSQ
score to a percentile with higher scores indicating higher susceptibility to motion sickness.
Presence
The participant’s subjective experience of presence in the virtual environment was
assessed using the ITC sense of presence inventory (ITC-SOPI).
26
The ITC-SOPI measures
presence based on 4 principal factors: spatial presence, engagement, ecological validity, and
negative effects. The negative effects factor provides a measure of adverse physiological
reactions including dizziness, nausea, headache, and eyestrain.
Physical Exertion
Physical exertion levels were assessed using the Borg Rate of Perceived Exertion (RPE)
scale.
34
The Borg RPE is a graded scale (6 – no exertion, 20 – maximal exertion) that has been
shown to correlate highly with heart rate and exercise intensity on a cycle ergometer.
34
A peak
RPE cutoff score of 12 was used to assess whether younger and older adult participants achieved
moderate exercise intensity levels while cycling in the virtual environment.
35,36
15
Game Design
The virtual reality game was developed using Unity 3D (v2018.1.1f1). The game was
designed to enhance enjoyment and motivation in older adults engaging in aerobic exercise and
cognitively challenging tasks. This involved creating immersive storylines and gameplay and
integrating it into a cognitive training paradigm (Figure 1.1). Briefly, the adventure begins as a
rookie ranger at Sal’s Sanctuary, a nature reserve for animals set in an urban environment
(Figure 1.2). The animals have escaped the sanctuary and subjects are tasked with locating and
returning them safely. Subjects are assigned one route each day and are required to make at least
4 search and rescue trips along this route. Once an animal is located, the subject is required to
transport the animal to the gates of the sanctuary before starting the next search and rescue.
Subjects are also required to collect food along the route that is associated with the diet of the
animal to be rescued.
16
Figure 1.1: Cognitively Challenging Gameplay and Immersive Storylines in VR.
(top left) Award system allowing user to select dog as companion for mission; (top right) Strategic landmarks placed
at each intersection and guiding arrow for learning route; (bottom left) Rescue an animal that has escaped the
sanctuary; (bottom right) Fight fires that appear in the wildlife enclosure. The bottom-left and top-right figures
illustrate locations within the urban environment at Sal’s Sanctuary.
After rescuing all animals at Sal’s Sanctuary, the adventure continues as a chief ranger at
Sal’s Wildlife Enclosure, a nature reserve for animals set in a savannah, jungle, safari, and aviary
(Figure 1.2). Subjects are tasked with maintaining the wildlife enclosure through missions
consisting of feeding animals, treating injured animals at the vet clinic, putting out fires, and
repairing broken equipment. Subjects are required to collect items along the route that can be
used to complete the given mission. Promotions and rewards are awarded throughout the game to
enhance motivation and interest.
17
Figure 1.2: Novel and Engaging Virtual Environments.
Virtual environments consisted of a desert (top left), aviary (top right), savannah (middle left), jungle (middle right),
and urban park (bottom).
18
Cognitive training consists of two primary tasks: selective attention and spatial
navigation. The selective attention task requires collecting specific roadside objects while
ignoring distractors and the spatial navigation tasks consist of encoding, immediate recall, and
long-delay recall of routes within the environment. Each environment contained a unique set of
landmarks strategically placed at every road intersection to serve as visual cues during
navigation. The sequence of navigation tasks within a game session are as follows: long-delay
recall, encoding, and immediate recall. The long-delay recall requires subjects to remember the
route from a previous visit. This is followed by two encoding trials where arrows are provided at
each intersection to serve as guidance along a new route. The last two immediate recall trials
require subjects to navigate the new route without the assistance of arrows, relying only on
environmental cues. As the game progresses, the routes get longer and the number of
intersections increases, requiring subjects to remember more landmarks during navigation.
Bike Design
The bike was designed and fabricated in-house at the machine shop at USC. It was designed
to mimic the pedaling, turning, and braking mechanisms of a typical bike, but modified for an
upright seated posture and fit with a wider, gel-padded saddle to enhance comfort in older adults
(Figure 1.3). The bike was also configured with the ability to control pedal resistance as well as
provide speed, turning, and braking feedback to a PC. A custom circuit board was constructed in-
house to condition the sensor signals and an Arduino microcontroller was used to facilitate
communication between the sensors and PC.
Mechanical Design: The drivetrain, handlebar, and seat post frames were welded and
constructed out of aluminum to reduce weight and cost. All other components, including the leg
19
stabilizers and gearing mechanism within the drive train were bolt-mounted for ease of
assembly/disassembly. The bike was comprised of adjustable angled telescopic seat and
handlebar posts. The handlebar was mounted to a ball-bearing to allow approximately 150
degrees rotation. A brass set screw was used to manually adjust the resistance to turning. The
bike consisted of a double gear reduction such that the output torque was approximately 11.5%
of the input torque. A freewheel sprocket was used to allow for coasting. Radially oriented
electromagnets were mounted concentrically inside a flywheel to allow for varying of pedal
resistance using the principles of eddy current braking.
Figure 1.3: Custom-Built Stationary Exercise Bike.
(top left) 3D CAD model of bike; (top right) fabricated bike; (bottom) circuit schematic diagram.
20
Electrical Design: Speed detection was achieved using a custom laser-cut acrylic encoder
wheel and Omron EE-SV3-D transmissive optical sensor. The optical sensor consisted of a
photodiode (light emitter) and phototransistor (light detector). A standard 220 Ω resistor was
connected in series between the 5V supply voltage and anode to keep the forward current within
its specified current rating. The phototransistor was configured as a common collector (CC)
phototransistor circuit. A 10 kΩ load resistor was added between ground and the emitter to
configure the phototransistor for switch mode, where the output was either OFF or ON based on
the absence or presence of light, respectively. It should be noted that a 5 kΩ load resistor is
typically adequate for switch mode and will have a faster response time to changes in light
intensity compared to a 10 kΩ load resistor. The output signal from the collector was connected
to a digital pin on the Arduino.
A linear, rotatory 10 kΩ potentiometer and dual-axis XY joystick module were used for
turning and brake sensing, respectively. Both sensors were configured as voltage dividers,
outputting a voltage between 0V and the 5V supply voltage based on the rotation of the knob or
the deflection of the joystick. A TMP36 semi-conductor temperature sensor was used to monitor
heat dissipation at the electromagnet coils. A 100 nF capacitor was added between the output pin
and ground for each sensor to dampen signal fluctuations due to mechanical bounce, electrical
noise, and electromagnetic interference. The output pin on each sensor was connected to a
unique analog pin on the Arduino.
Pulse width modulation (PWM) and a RFP30N06LE MOSFET transistor were used to
manipulate pedal resistance. An HCPL-7710 opto-isolator was placed between the Arduino’s
digital PWM signal and the MOSFET to electrically isolate the MOSFET from the Arduino. This
was done because the high PWM signal frequency (490 Hz) resulted in frequent switching of the
21
MOSFET causing undesirable noise during signal sampling. The PWM signal was connected to
pin 2 of the opto-isolator. Arduino’s 5V power supply and ground were connected to pins 1 and
4 of the opto-isolator. 12V from an external variable power supply was regulated down to 5V via
a L7805ABV linear voltage regulator and connected to pin 5 of the opto-isolator. Pin 8 of the
opto-isolator was connected to ground of the external power supply. A 100 nF capacitor was
connected between pins 1 and 4 and 5 and 8 of the opto-isolator in accordance with datasheet
recommendations. Similarly, a 330 nF and 100 nF capacitor were connected between ground and
the voltage regulator input and output pins, respectively.
The opto-isolated PWM signal at pin 6 of the opto-isolator was connected to the gate pin
on the MOSFET. A 47 Ω gate resistor was placed in series with pin 6 of the opto-isolator and the
MOSFET gate to limited ringing and electrical noise due to parasitic inductance. A 10 kΩ pull-
down resistor was connected in series between ground and the MOSFET gate to pull the voltage
down and turn the MOSFET off in the absence of a PWM input signal. The MOSFET source
was connected to the source of the MOSFET. One electromagnet lead was connected to the
source of the MOSFET, while the other was connected to 12V power from the external power
supply. An 1N5817 Schottky diode was connected in parallel with the leads of the electromagnet
to prevent flyback when current to the electromagnet is interrupted. A 2000 µF electrolytic
capacitor and 100 nF capacitor were connected in parallel across 12V power and ground from
the external power supply to smooth out the input voltage to the electromagnet.
The temperature, turning, and braking sensors were connected to analog input pins, the
speed sensor was connected to a digital input pin, and pedal resistance was provided by a PWM
output pin on the Arduino Uno. The Arduino Uno connected to the PC via a USB A-Male to B-
Male cable. A standard Arduino Uno driver was used to establish the connection as a COM port
22
on the PC. The baud rate was set to 1,000,000 for serial port communication. An external library
was used to reduce noise associated with the analog input signals for the brake, turning, and
temperature sensors.
248
All analog sensor values were updated within the main loop. For the
speed sensor, a digital interrupt trigger was used to track when the state of the optical sensor
changed. A digital pin reads HIGH(1) when the voltage signal is > 3.0V and LOW(0) when the
voltage is < 3.0V on a 5V Arduino board.
To calculate speed, the Arduino code was structured to count the number of times the
digital interrupt was triggered over a specified sampling period of 50ms. A digital interrupt is
triggered when the encoder wheel spoke enters and leaves the light path. The number of
interrupts over the 50 ms period was then converted to a speed using the following formula:
Interrupt Count (counts/s) = Interrupt Count * (1 / 50 ms) * (1 / 1000s)
Speed (m/s) = (2 * pi * Flywheel Radius (m)) / (Spokes per Revolution) * (Interrupt
Count)
The encoder wheel was mounted to the output shaft of the flywheel and centered within
the gap of the optical sensor. For the encoder wheels, we laser cut 6 different variations out of
acrylic. These variations included 2, 4, 8, 16, 32, 64, and 128 spokes. For each wheel, we
evaluated 2 conditions: aliasing and a ceiling effect. Aliasing occurs when the encoder wheel
rotates faster than the optical sensor can react to the light change or the Arduino can trigger the
digital interrupt pin. With a 10K load resistor, the response time of the phototransistor is
approximately 100 µs. With the Arduino, the interrupt service routine theoretically executes
every .625 µS based on a clock speed of 16 MHz. However, in practice, this is often much
23
slower due to factors such as code overhead and often results in an interrupt frequency on the
scale of milliseconds, which likely makes it the rate limiting step. The ceiling effect occurs when
the resolution of the encoder wheel is too small to capture the actual pedal speed. In other words,
after a certain pedal speed, the number of spokes that pass through the optical sensor gets capped
over a 50 ms period. During testing, we found that the 2,4,8, and 16 spoke encoder wheels
experienced the ceiling effect while the 128 spoke encoder wheel had significant aliasing at
normal pedal speeds. We found the 64 spoke encoder wheel to be the best option as we found no
aliasing and no ceiling effect, even at very high pedal speeds.
Virtual Environment Study Configuration
Participants viewed the environment through an HTC Vive Pro headset with 110-degree
field of view (FOV) and a 90 Hz refresh rate. The environment was run on an Alienware Aurora
R7 PC (core i7-7700 CPU, 16 GB RAM, 1080 Ti graphics card). The study environment
consisted of a nature park setting comprised of natural landmarks and animals. Locomotion in
the park was achieved by cycling on a custom-built stationary exercise bike, where the handlebar
angle and pedal speed were proportional to the movement of a virtual bike. Participants cycled
along a network of connected roads with salient landmarks at each intersection to serve as
navigational cues. Participants had no avatar to embody but had a stable helmet and nose tip
within their FOV. To reduce the likelihood of nausea during locomotion, a technique known as
tunneling was employed each time the virtual bike encountered an intersection requiring a turn
(Figure 1.4).
37,38,39
With this technique, the visual field in the periphery of the headset was
cropped and replaced with a static black background with white lines, restricting the participant’s
FOV and the amount of optical flow to the periphery of the eye.
37,38,39
24
Figure 1.4: Visual cues in virtual environment during route learning.
(top left) beam of light serves as a visual cue to orient the participant in the direction of the destination; (top right)
landmark located at intersection to serve as a visual cue for encoding the correct route in memory; (bottom left)
tunneling effect at intersection to mitigate adverse effects due to sensory conflict when turning; (bottom right)
directional arrows on ground and at intersection guide participant to destination on the first two trials
Experimental Protocol
The experimental protocol consisted of a 1-minute practice trial followed by four 2-3
minute task trials of cycling in the park, and a set of pre/post assessments. Prior to training in the
VR environment, participants stated their current RPE and completed SAC and SSQ
questionnaires to establish a baseline of physical exertion, mood, and simulator sickness. Blood
pressure (BP) was collected on all older adults. Those with a systolic BP >170 or diastolic BP
>100 were not allowed to participate in the study following ACSM guidelines for exercise in
older adults.
40
Inter-pupillary distance (IPD) was measured for each participant and adjusted
accordingly on the HTC Vive headset to increase visual acuity.
25
Practice Trial: Participants sat on the stationary bike while the HMD was placed on their
head. Participants then followed the instructions displayed within the game. The first task was to
bike around an enclosed oval track for one minute (30 seconds in each direction) to adapt to
turning and pedaling the stationary bike in the virtual environment.
Task Trials: After the practice trial, participants appeared in the park and were instructed
to bike to a fountain landmark located approximately 0.5 miles from the start position. This task
was completed 4 times under the following trial conditions: learning (2), cued recall, and free
recall (Figure 1.4). In the learning condition, the correct route was identified by yellow arrows
located on the surface of the road, as well as a directional blinking arrow at each intersection.
The fountain located at the destination was highlighted by a narrow beam of light that vertically
spanned the entire FOV. In the cued recall condition, the arrows were removed and only the
beam of light remained for navigational guidance. In the free recall condition, no arrows or beam
of light were provided, requiring the participant to rely only on park landmarks for navigation.
At the end of each trial, the participant’s headset was removed and their responses to the
RPE scale and SSC were recorded. To optimize engagement and motivation, participants were
asked to select a reward after the 1
st
, 2
nd
, and 3
rd
conditions. This included the selection of a
basket to go on the virtual bike, an animal companion to ride in the basket, and a song genre to
listen to while riding. At the end of the training, participants completed the SAC and SSQ to
evaluate mood and simulator sickness and the ITC-SOPI and MSSQ to evaluate presence in the
VR environment and general susceptibility to motion sickness.
26
Statistical Analysis
All analysis was performed in SPSS (IBM v24, 2016).
41
The primary outcomes for this
analysis were adverse effects, mood, and presence as measured by the SSQ, SAC, and ITC-
SOPI, respectively. The SAC and SSQ were measured pre-post VR exposure. An exploratory
analysis was also performed to assess physical exertion (Borg RPE), sickness symptoms per trial
(SSC), motion sickness susceptibility (MSSQ), and spatial navigation performance. A two-way,
repeated measures ANOVA was performed to evaluate group (HY, HO) by time (VR exposure)
interactions and group and time main effects on the SAC, SSC, and RPE. The SSC was
measured after each of the four trials. The RPE was measured at baseline and after each of the
four trials. A one-way ANOVA was performed to evaluate group differences on the ITC-SOPI
and MSSQ, as well as performance on the navigation tasks, as these measures were only
collected once. A three-way, repeated measures ANOVA was performed to evaluate group, time,
or adverse effect level (minimal, notable) differences on the SSC scores reported for each trial.
When a significant (p < 0.05) interaction was found, post-hoc comparisons were performed using
a paired two-sample t-test for time or an independent two-sample t-test for group. The Wilcoxon
Sign and Mann Whitney U tests were used to evaluate the effects of time and group,
respectively, on the SSQ. The SSQ was analyzed using non-parametric tests using the change
score defined as post-pre, as the SSQ data were not normally distributed. Bonferroni corrections
for multiple comparisons were applied based on the number of dependent variables for a given
questionnaire.
27
Results
Mean, standard deviation, and p-values associated with participant demographics and
performance in the VR environment are displayed in Table 1.1. Study outcomes are displayed in
Table 1.2.
Table 1.1: Participant demographics and performance in VR.
Mean, standard deviation, and significance values are shown.
28
Table 1.2: Participant assessment of presence, mood, and adverse effects.
Mean, standard deviation, and significance values are shown. P-values for questionnaires with pre/post responses
denote the significance level associated with the interaction between time (pre/post) and group (young/old).
29
Primary outcomes of adverse effects, mood, and presence
Simulator Sickness
Simulator sickness as measured by the SSQ (Figure 1.5) was significantly affected by
time (z=-3.43, p=0.004, r=0.38), such that symptoms were higher after VR exposure. A total of
four adults (1 younger, 3 older, 10% of study sample) withdrew from the study due to severe
symptoms. Analysis of the SSQ subscales showed significant effects of time on symptoms within
the subdomains of nausea (z=-4.32, p<0.001, r=0.48) and disorientation (z=-2.58, p=0.04,
r=0.29), such that symptoms were higher after VR exposure (Figure 1.5). The oculomotor (z=-
1.92, p=0.06, r=0.21) subdomain was not significantly affected by time. Total sickness (z=0.00,
p=1.00, r=0.00), nausea (z=-0.07, p=0.95, r=0.00), oculomotor (z=-0.14, p=0.89, r=0.02), and
disorientation (z=-0.48, p=0.64, r=0.05) change scores were not significantly affected by group.
Overall, we found that symptoms associated with simulator sickness were enhanced after
exposure to the virtual environment. However, total sickness levels were below the cutoff score
of 15 for both age groups, suggesting overall adverse effects were acceptable.
30
Figure 1.5: Effects of VR exposure sickness symptoms in younger (HY) and older (HO) adults.
While symptoms associated each subdomain were enhanced after VR exposure, total sickness levels were less than
15 (threshold denoted by dotted horizontal line) for both age groups, suggesting that overall adverse effects were
minimal. No group differences were observed on the changes scores. * indicates that a significant difference in
symptom severity was found pre-post within an age group.
Mood
Stress levels as measured by the SAC (Figure 1.6) were not significantly affected by time
(F(1,38)=0.34, p=0.57, ηp
2
=0.01) or group (F(1,38)=2.74, p=0.11, ηp
2
=0.07), and there was no
interaction between time and group (F(1,38)=2.88, p=0.47, ηp
2
=0.01). Arousal levels (Figure 1.6)
were also not significantly affected by time (F(1,38)=0.10, p=0.75, ηp
2
=0.00) or group
(F(1,38)=2.84, p=0.10, ηp
2
=0.07), and there was no significant interaction between time and
group (F(1,38)=5.05, p=0.06, ηp
2
=0.12). Mean arousal levels for both age groups were higher
31
than their respective cutoff scores (HY–6.0, HO–6.4) prior to and after VR exposure (Table 1.2).
Mean stress levels for both age groups were also lower than their respective cutoff scores (HY–
6.2, HO–5.1) prior to and after VR exposure. Therefore, exposure to the virtual environment did
not negatively affect pre-existing high arousal and low stress levels, indicating a state of
excitement.
24
On a subjective measurement of pleasure, participants rated the statement “I
enjoyed myself.” on a 5-point scale of ‘strongly disagree’ to ‘strongly agree’. Mean enjoyment
levels (Table 1.2) for the younger and older adults were 4.0±0.9 and 3.8±1.1, respectively,
suggesting that participants enjoyed the VR experience.
Figure 1.6: Effects of VR exposure on stress and arousal states.
No significant differences were found for group or time.
Presence
Analysis of the ITC-SOPI (Figure 1.7) showed that group differences did not
significantly affect spatial presence (F(1,38)=0.37, p=0.55, ηp
2
=0.01), engagement
(F(1,38)=0.02, p=0.89, ηp
2
=0.00), or ecological validity (F(1,38)=0.14, p=0.28, ηp
2
=0.00).
However, negative effects scores were significantly affected by group (F(1,38)=7.84, p=0.04,
32
ηp
2
=0.17), such that younger adults had higher negative effects than older adults. Taken together,
this suggests that sense of presence in the virtual environment was not significantly different
between younger and older adults.
Figure 1.7: Sense of presence in younger and older adults for the ITC-SOPI questionnaire.
Younger adults experienced higher levels of negative effects than older adults.
* indicates that a significant difference was found between younger and older adults with negative effects.
33
Secondary analysis of physical exertion, sickness symptoms, and motion sickness
Physical Activity
RPE levels (Figure 1.8) were significantly affected by time (F(1,69)=95.84, p<0.001,
ηp
2
=0.74) and group (F(1,33)=7.02, p=0.01, ηp
2
=0.18), such that younger adults reported
significantly higher RPE levels than older adults. There was no interaction between time and
group (F(1,69)=1.37, p=0.26, ηp
2
=0.04). Post hoc analysis for time showed that RPE levels
increased with each successive trial, suggesting that the participants appropriately perceived an
increase in physical exertion with time spent pedaling in the virtual environment. Pairwise
comparisons showed significant RPE level differences across all trials (p<0.001), except for the
third and fourth trial (p=0.10). Post hoc analysis for group showed that mean RPE levels were
higher for younger adults (12.2±.53) than older adults (10.1±0.6). However, peak RPE levels
were lower for younger adults (12±3.2) than older adults (13±2.9). A sub-analysis on the
relationship between simulator sickness and peak RPE levels found that RPE levels were not
significantly associated with SSQ total sickness levels (F(1,37)=0.12, p=0.73, ηp
2
=0.00),
suggesting that physical exertion does not enhance symptoms of simulator sickness in the virtual
environment.
Sickness symptoms
The SSC (Figure 1.8) showed that total sickness levels were affected by group
(F(1,33)=4.92, p=0.04, ηp
2
=0.13), such that younger adults (1.9±0.4) reported higher scores than
older adults (0.8±0.4). There was no interaction between time and group (F(1,81)=1.12, p=0.34,
ηp
2
=0.03). Total sickness levels were not significantly affected by time (F(1,81)=2.09, p=0.14,
ηp
2
=0.06). While total sickness levels were not assessed at baseline, this finding suggests that
34
symptom severity only increased during the 1
st
trial and not with additional time spent in the
virtual environment.
Figure 1.8: Physical exertion and sickness symptoms over time.
(A) Perceived physical exertion levels increase appropriately with time spent in the virtual environment. Younger
adults were within the target exercise zone while older adults were approaching it. (B) Shows SSC total sickness
levels after each trial. No association was found between duration of exposure and symptom severity.
In a separate sub-analysis, participants were categorized into two adverse effect groups,
minimal or notable, based on an SSQ total sickness cutoff score of 15. From this analysis, we
found SSC total sickness levels were significantly affected by adverse effect group for trial 1
(F(1,36)=13.1, p=0.001, ηp
2
=0.27), trial 2 (F(1,34)=19.25, p<0.001, ηp
2
=0.36), trial 3
(F(1,33)=20.64, p<0.001, ηp
2
=0.39), and trial 4 (F(1,31)=12.02, p=0.002, ηp
2
=0.28). The mean
scores for the minimal and notable adverse effect groups for each trial are as follows: trial 1
(0.8±0.3 and 2.9±0.4), trial 2 (0.7±0.3 and 3.3±0.5), trial 3 (0.8±0.3 and 3.4±0.5), trial 4 (0.9±0.4
and 3.5±0.6). This suggests that participants with notable adverse effects experienced an onset of
symptoms prior to the end of trial 1.
35
History of motion sickness
Motion sickness susceptibility was not significantly affected by group for the MSSQ-C
(F(1,38)=0.13, p=0.72, ηp
2
=0.00), MSSQ-A (F(1,38)=1.18, p=0.28, ηp
2
=0.03), or global MSSQ
scores (F(1,38)=0.44, p=0.59, ηp
2
=0.02). In a sub-analysis comparing global MSSQ percentile
scores to SSQ total sickness scores, it was found that total sickness levels were positively
associated with percentile scores (F(1,37)=5.64, p=0.02, ηp
2
=0.13), suggesting that participants
who are more susceptible to motion sickness are likely to experience higher sickness levels in
our VR environment.
Spatial Navigation
No significant differences between younger and older adults were found in total cycling times
(F(1,33)=3.08, p=0.09, ηp
2
=0.09), mean cycling speeds (F(1,38)=1.66, p=0.21, ηp
2
=0.04), or the
percentage of correct decisions made while navigating in the virtual environment (F(1,38)=3.54,
p=0.07, ηp
2
=0.09) (Table 1.1).
Discussion
Recently proposed guidelines suggest a scientific framework for empirically and
systematically validating VR therapeutics for health. Consistent with these guidelines, our
“VR1” feasibility study is based on patient-reported outcomes for adverse effects, mood, and
presence, which is analogous to a traditional Phase 1 clinical trial.
42
Overall, the findings from
our study support that cycling and spatial navigation using a head-mounted display (HMD) in
immersive VR is feasible and enjoyable in both younger and older adults. Younger adults were
used as a reference group to ascertain whether significant group differences arise due to the
36
younger adults having more environmental exposure to or feeling more comfortable with
technology and digital gaming. The findings of our study suggest that age was not a significant
factor in the feasibility of VR in older adults.
Spatial presence, engagement, and ecological validity levels were higher in our study
than in similar navigation studies using non-immersive displays.
43
In one such study, participants
were seated on a racing bicycle and tasked with cycling along a virtual rural landscape displayed
on a wall-mounted projector screen.
43
Participant’s cycled under two conditions: high and low
immersion. In the low immersion condition, a moving dot placed on a top-down view of the
racetrack was used to represent the participant’s position.
43
In the high immersion condition, a
computer-generated cyclist was displayed on the projector screen and controlled by the
participants pedaling speed and handlebar rotation.
43
Spatial presence (2.73/1.95), ecological
validity (2.98/1.81), and engagement (3.33/2.30) levels reported for both the high and low
immersion conditions, respectively, were lower than the values reported in our study. This
suggests that immersive HMDs elicit greater psychological involvement, a more natural
perception of the environment, and a stronger sense of being physically present in the virtual
space than non-immersive displays. HMDs are stereoscopic, providing depth perception for
understanding the relative size and position of objects in a virtual environment, which is
important for allocentric and egocentric spatial navigation.
45
Moreover, HMDs provide a high
level of fidelity, such that the differences in interactions or experiences between the real world
and virtual environment are minimized in comparison to desktop monitors and projector
screens.
46
A high level of fidelity has been shown to enhance the transfer of spatial navigation
skills from virtual to real world environments.
46
37
When using immersive VR, simulator sickness is often a concern. Simulator sickness is
theorized to be due to postural instability and sensory conflict.
49
Postural instability occurs when
an environment or stimuli affects the body’s ability to maintain postural control.
50
It is theorized
that motion sickness occurs after prolonged maladaptation to the conditions causing postural
instability.
50
It is also theorized that the severity of motion sickness scales directly with the
duration and severity of postural instability.
50
Sensory conflict occurs when sensory input to the
eyes is incongruent with the vestibular, proprioceptive, and somatosensory systems, causing a
mismatch between perceived and expected sensory stimulation in the body.
49
The principal cause
of sensory conflict during locomotion is vection
51,52
, defined as visually induced perception of
self-motion. Two locomotion techniques used in VR are treadmill walking and cycling and
usually involve gait-training
22,53,54
and exercise, respectively.
49,55,56
Previous studies assessing
simulator sickness with treadmill walking have generally reported good tolerability.
22,57
To our
knowledge, only one study has assessed simulator sickness while cycling in an immersive virtual
environment. That study, consisting of healthy younger adult participants, reported a significant
increase in adverse effects after cycling on a virtual island.
16
In our study, younger and older adults also reported an increase in simulator sickness
symptoms after cycling in the virtual park, including higher nausea and disorientation levels.
However, 90% of our sample successfully completed the study, and total sickness levels for both
age groups were within an acceptable range based on cutoff scores established in validation
studies on flight simulators.
31
Total sickness levels in our study were also lower than the total
sickness levels reported in the virtual island cycling study on younger adults. This can possibly
be attributed to the implementation of a stable nose tip and helmet within the FOV, as well as
tunneling during turning, which is supported by previous studies that have used these techniques
38
to minimize simulator sickness during locomotion.
37,38,39
We observed a significant group
difference on the SSC and negative effects subscale of the ITC-SOPI, such that younger adults
reported higher adverse effects than older adults. However, these findings are likely due to
younger adults reporting higher sickness symptoms at baseline. Indeed, the pre-post change
scores on the SSQ were not significantly different between age groups, suggesting that younger
and older adults were similarly affected by VR exposure relative to their baseline symptoms.
In addition to acceptable total sickness levels, we found no association between duration
of VR exposure and symptom severity in younger and older adults, suggesting that both age
groups acclimated quickly to cycling in the virtual environment. Moreover, our study revealed
that exercise did not enhance adverse effects, as we found no association between physical
exertion levels and symptoms related to simulator sickness. In fact, peak rates of physical
exertion were at high enough intensity levels to be within the recommended range of exercise
intensity for health-based and rehabilitative cardiovascular fitness
35,36
, suggesting that exercising
in VR at moderate aerobic intensity levels is tolerable in younger and older adults.
In addition to assessing adverse effects of VR while engaging in physical activity and
locomotion, enjoyment is also critical, as approximately 50% of sedentary adults discontinue
exercise programs within the first 6 months.
58
Indeed exercise adherence in the older adult
population can be a challenge due to lack of motivation, health conditions, and physical
discomfort from exercise.
58
In our study, we attempted to enhance enjoyment and motivation by
allowing participants to select higher value rewards as more challenging navigation tasks were
completed. Prior to the last task, a choice of music was provided, as music has been shown to be
the most important factor associated with enjoyment of exercise.
59
Our findings support this
game design methodology, as ITC-SOPI analysis revealed that most participants enjoyed the
39
virtual experience. Furthermore, according to the SAC, both younger and older adults maintained
high levels of arousal and low levels of stress, indicating there was no evidence of an unpleasant
experience or a negative shift in hedonic tone that would detract from the overall experience.
Taken together, this suggests that younger and older adults enjoyed performing the virtual
navigation tasks, even while under increasing levels of physical exertion. This also supports the
findings of other studies which have shown higher adherence to exercise when using VR,
60
including one in which cycling motivation in VR in older adult cardiopulmonary patients was
enhanced and associated with increased cycling times, distance, and total caloric expenditure
compared to a non-VR environment.
15
Here we have established the feasibility of cycling and spatial navigation in a virtual
environment in both younger and older adults. Both age groups were able to navigate the virtual
park environment with 99% accuracy at intersections, with 95% of younger adults and 75% of
older adults able to complete the navigation tasks without error. This suggests that our spatial
navigation training paradigm based on cued learning is accessible. Moreover, we have shown
that cycling in an enriched virtual environment is enjoyable and tolerable for both age groups,
with only 10% of participants discontinuing due to adverse effects. This makes VR a viable tool
for interventions that combine exercise and spatial navigation, as well as a safe alternative to
cycling in the real world, which requires individuals to continuously maneuver obstacles, such as
pedestrians, other bicycles, cars, and environmental barriers. These safety concerns are higher in
the older adult population, particularly in individuals with cognitive impairment, as age-
associated deteriorations in sensory processing and reaction times can increase the risk of falls
and accidents. Moreover, this risk is enhanced when the individual is required to engage in
40
simultaneous cycling and cognitive training, as cognitive resources are now divided between
cycling and completing the cognitive task.
However, our study is not without limitations. First, adverse effects in VR may be
mediated by gender, as studies have shown that women are more susceptible than men to motion
sickness.
61,62
In one such study in which participants utilized a handheld controller to navigate a
virtual hallway, it was found that over twice as many women reported motion sickness compared
to men.
61
Taken together, this suggests that the discontinuation rate may vary from the 10%
reported in our study, particularly if the study sample is skewed towards one gender. Second,
participants in our study were only exposed to the virtual environment for 10-12 minutes, which
is less time than many exercise interventions that typically last at least 30 minutes.
63,64
While we
found no association between VR exposure time and adverse effects over a 10-12 minute period,
sickness levels have been shown to increase with prolonged exposure
65
, and may be enhanced
with physical exertion. However, it has also been shown that many individuals can adapt to VR
through brief, repeated exposures over time.
65
Therefore, future studies should consider utilizing
an adaptation period prior to engaging participants in interventions requiring prolonged VR
exposure. Another option is to assess whether the degree of tunneling can be manipulated to
make cycling in VR tolerable for individuals that experienced significant adverse effects.
However, these studies should also consider that tunneling restricts the user’s FOV, which can
reduce immersion and presence, and negatively impact performance on spatial navigation tasks.
One potential tool to screen individuals susceptible to simulator sickness is the MSSQ,
which creates a global percentile score based on an individual’s self-reported sickness as a child
and adult for nine common modes of transportation. Our study revealed a significant association
between global MSSQ motion sickness percentile and total SSQ sickness levels, which has also
41
been reported in other studies.
16
Another option is to measure postural kinematics, as studies
have shown that postural instability precedes the onset of motion sickness.
66
This could be
particularly useful for the older adult population, as older adults may already have a baseline
level of impaired postural stability due to age-associated deteriorations in the vestibular,
proprioceptive, and cognitive systems responsible for maintaining balance.
66
Finally, it may be
possible to screen participants by assessing their adverse effects after cycling in a virtual
environment for 10-12 minutes, as all affected participants in our study experienced symptoms
early within this timeframe.
Conclusions
Establishing the feasibility of cycling and spatial navigation in immersive virtual
environments has clinical importance for both younger and older adults. VR provides clinicians
and researchers with a safe and controlled environment for combining spatial navigation with
exercise, as well as monitoring cognitive and physical performance. It also provides flexibility to
manipulate spatial navigation task difficulty based on one’s fitness level, cognitive status, and
age. Moreover, rewards and achievements can easily be incorporated into a virtual environment
to enhance enjoyment and increase the likelihood of younger and older adult participation and
adherence to an intervention. These benefits make VR a promising tool for interventions aimed
at improving cognitive and physical health, especially in older adults at risk for cognitive
decline.
42
Chapter II Overview
Conducting a Phase 1 safety and feasibility trial of simultaneous aerobic exercise and
cognitive training in virtual reality was an important first step towards evaluating our VR
exergaming system as a potential tool for the primary prevention of AD. The next step was to
develop a reliable biomarker of brain health to be used as an outcome measure for an
intervention engaging these simultaneous activities.
Several commonly used fluid and imaging biomarkers have been developed over the years to
track the progression of AD. Magnetic resonance imaging (MRI) has been used to measure brain
volume atrophy due to neurodegeneration, while positron emission tomography (PET) has been
used to measure brain metabolism, an indicator of synaptic dysfunction.
233
PET imaging has also
been utilized to measure amyloid plaque and tau tangle deposition in the brain, while lumbar
punctures have been performed to provide similar measurements in the CSF. However, each
biomarker has distinct drawbacks. PET imaging measurements require the use of invasive,
radioactive tracers, lumbar punctures are invasive and uncomfortable, and structural MRI
measurements of brain volume atrophy are not disease specific and only detectable in the later
stages once significant disease pathology is already present.
149
Therefore, there is a need to
develop an early, non-invasive biomarker of disease progression.
The onset of Alzheimer’s disease is theorized to result from dysfunction in the brain’s waste
clearance system, leading to amyloid plaque deposition and a cascade of pathophysiological
processes.
3
Studies have elucidated several clearance mechanisms that facilitate the removal of
amyloid plaques in the brain.
3
These include enzymatic degradation, transport across the blood-
brain barrier, and drainage via perivascular pathways.
3
Although the relative contributions of
each mechanism are still not known, it is believed that the drainage pathways account for up to
43
55-60% of amyloid removal.
3
These pathways are characterized by the flow of cerebrospinal
fluid (CSF) through a network of perivascular channels that allow for elimination of waste
contained in the interstitial space.
5,29,30,34,35
All drainage pathways are driven by arterial
pulsatility which is dynamically linked to CSF flow.
3,6,31,32,40
As a result, CSF and cerebral blood
flow (CBF) dynamics provide a unique way of evaluating dysfunction in these clearance
pathways.
Phase-contrast MRI is a technique that can be utilized to measure CSF and CBF flow in the
brain. However, the reliability of MRI for measuring cerebral flow has not been extensively
explored. In the chapter that follows, we assess the intra-rater, inter-rater, and test-retest
reliability of PC-MRI for measuring a comprehensive set of CSF and CBF flow parameters
theorized to be associated with clearance of amyloid in the brain. The results of this study
were published in Journal of Magnetic Resonance in Medicine (Sakhare et al., 2019).
44
Chapter II: Assessing Test-Retest Reliability of Phase Contrast MRI
for Measuring Cerebrospinal Fluid and Cerebral Blood Flow
Dynamics
Abstract
Purpose: Pathological states occur when cerebrospinal fluid (CSF) and cerebral blood
flow (CBF) dynamics become dysregulated in the brain. PC-MRI is a non-invasive imaging
technique that enables quantitative measurements of CSF and CBF flow. While studies have
validated PC-MRI as an imaging technique for flow, few studies have evaluated its reliability for
CSF and CBF flow parameters commonly associated with neurological disease. The purpose of
this study was to evaluate test-retest reliability at the cerebral aqueduct (CA) and C2-C3 area
using PC-MRI to assess the feasibility of investigating CSF and CBF flow dynamics.
Methods: This study was performed on 27 cognitively normal young adults (ages 20-35).
Flow data was acquired on a 3T Siemens Prisma using a 2D cine-PC pulse sequence. Three
consecutive flow measurements were acquired at the CA and C2-C3 area. Intraclass correlation
(ICC) and coefficient of variance (CV) were used to evaluate intra-rater, inter-rater, and test-
retest reliability.
Results: Among the 26 flow parameters analyzed, 22 had excellent reliability
(ICC>0.80), including measurements of CSF stroke volume, flush peak, and fill peak, and 4
parameters had good reliability (ICC 0.60–0.79). 16 flow parameters had a mean CV<10%, 7
had a CV<15%, and 3 had a CV<30%. All CSF and CBF flow measurements had excellent inter-
rater and intra-rater reliability (ICC>0.80).
45
Conclusion: This study shows that CSF and CBF flow can be reliably measured at the CA and
C2-C3 area using PC-MRI, making it a promising tool for studying flow dynamics in the central
nervous system.
Introduction
The continuous circulation of cerebrospinal fluid (CSF) and cerebral blood flow (CBF) is
key to the health of our central nervous system. CBF provides oxygen and glucose to support the
high metabolic demands of the brain and plays a key role in sustaining localized neuronal
activity through neurovascular coupling.
68
CSF provides buoyancy to cushion the brain from
injury, distributes neurotrophic factors, and stabilizes pH and chemical gradients.
69
CSF and
CBF also support waste removal from the CNS through the blood brain barrier, glymphatic, and
perivascular clearance pathways.
70
Indeed, when CSF and CBF dynamics in the brain become dysregulated,
pathophysiological states occur that have been associated with communicating and normal
pressure hydrocephalus
71,72,73,74,75
, syringomyelia
76,77
, and Chiari malformations.
78,79,80,81
For
example, individuals with Chiari malformations have decreased CSF velocity and shorter periods
of caudal CSF flow at the cranio-cervical junction compared to normal controls.
82
Other diseases
have recently shown evidence of CSF and CBF dysregulation, such that mild cognitive
impairment (MCI) patients have shown higher arterial pulsatility, pulse volume, and CBF flow
84
,
and Alzheimer’s disease (AD) patients have shown lower arterio-venous delays
84
when
compared to age-matched normal controls. Taken together, these studies suggest that brain flow
46
dynamics may be an important biomarker for identifying meaningful alterations in neurological
diseases.
Using phase-contrast MRI (PC-MRI), a validated, non-invasive imaging technique, rapid
measurements of CSF and CBF flow in the brain can be quantitatively assessed.
85,86,87
Furthermore, by coupling the measurement with a peripheral pulse transducer, CSF and CBF
flow acquisition can be synchronized to the cardiac cycle, allowing for more accurate
physiological interpretations.
71
However, while PC-MRI has shown a lot of utility in measuring
flow, there are several potential sources of error that can significantly affect the accuracy and
precision of this technique, including slice orientation, the velocity encoding value, complexity
of the ROI, partial volume effects, and intra-voxel dephasing.
85,88,89,90,91
Gradient non-linearities,
concomitant gradients (Maxwell terms), and eddy-current field errors are also major sources of
inaccuracies in flow measurements.
85,90,91,92,93
Gradient non-linearities are caused by geometric limitations in the gradient coils, which
result in unwanted higher order, nonlinear encoding gradients.
96
Concomitant gradients are
nonlinear, spatially-dependent magnetic fields that are generated when a linear magnetic field
gradient is activated.
94,95,96
Gradient non-linearities and concomitant gradients both cause phase
errors but can be corrected for and eliminated during image reconstruction without user
intervention.
94,97
Eddy currents are created from rapidly switching the velocity encoding gradient
during acquisition. This switching causes a change in the magnetic flux at the gradient coils,
leading to spatially varying phase errors on the image.
95,98
The magnitude of these eddy currents
are based on the flow velocity at the region of interest, as regions with slow flow require stronger
encoding gradients resulting in larger field distortions.
95
Eddy currents that cannot be
compensated for by the pre-emphasis system will result in a velocity offset in the background
47
(stationary tissue) pixels on the image.
99
Regions with CSF flow are most susceptible to these
effects as peak velocities are usually less than 20 cm/s, and often on the order of 1-2 cm/s, while
blood flow velocities are typically between 50-300 cm/s.
100
Previous studies evaluating PC-MRI flow measurement accuracy and precision on
phantoms have shown variable results based on vessel diameter and flow rate. In one study,
phantom flow velocities between 0.8 cm/s and 25.4 cm/s resulted in an average measurement
error for accuracy of 21% and CV of 3%. However, at the low end of this velocity range (<12
cm/s), PC-MRI significantly overestimated flow velocities and had reduced reproducibility.
100
In
another study, it was shown that PC-MRI had good reproducibility but overestimated peak
systolic and diastolic flow by approximately 35% when the phantom vessel diameter was 2 mm,
a physiologically normal size for the CA.
101
This was attributed to partial volume effects at the
boundaries.
101
At larger diameters of 4 mm and 6 mm, such as those often seen in individuals
with NPH, the measured flow rates were similar to the true phantom flow rates.
101
In the same
study, it was also shown that flow could reliably be measured in vivo at the CA with CVs <
9%.
101
Overall, this suggests that accuracy, and to a lesser extent precision, is significantly
affected by vessel diameter and flow rate, likely due to a combination of the sources of error
described above.
Several of the studies described above have validated PC-MRI as a reliable imaging
technique for flow in phantoms
91
as well as CSF and CBF flow in the human brain.
93,102,103
However, many of these studies have not comprehensively evaluated test-retest, intra-rater, and
inter-rater reliability for biologically-relevant parameters related to CSF and CBF time, flow rate,
pulsatility, and volume, which have been measured in research studies of neurological
diseases.
83,84,104,105
Therefore, in this study, we evaluated test-retest, intra-rater, and inter-rater
48
reliability for 26 comprehensive measurements of flow at the cerebral aqueduct and cervical C2-
C3 area using PC-MRI in a cohort of cognitively normal young adults, to determine the
feasibility of investigating CSF and CBF flow dynamics.
Methods
Participants
Twenty-seven healthy, cognitively normal young adults (age 24.8±3.9 years; range 20-35
years of age; 14 females) provided written consent to participate in this study, which was
approved by the institutional review board and performed in accordance with the 1964
Declaration of Helsinki. Participants were selected from a convenience sample of local students
and staff. Subjects with MRI contraindications, psychiatric illness, and neurological disorders
were excluded from the study.
Image Acquisition
Flow measurements were acquired on a 3T Siemens Prisma MRI machine using a 2D cine-
PC pulse sequence with flow compensation. MRI parameters, derived from previous studies
assessing CSF and CBF flow
84
, were optimized and set as follows: 32 frames per cardiac cycle;
140x140 mm
2
field of view; 25° flip angle; 60% phase oversampling; 336x336 matrix with
interpolation and fractional echo readout; 5 mm slice thickness; 201 Hz/Px receiver bandwidth; 1
NEX; TR/TE – 27.06/8.55 ms (cerebral aqueduct, C2-C3 vascular vessels), 28.02/9.03 ms (C2-
C3 subarachnoid space). The TR duration represented a combination of flow compensation +
flow encoding. All gradients were played at full strength (40 mT/m). For each segment, a single
k-space line was collected. The encoding velocity was set to 10 cm/s for the cerebral aqueduct, 5
49
cm/s for the C2-C3 subarachnoid space, and 80 cm/s for the C2-C3 vascular vessels. A sagittal
scout image was used as a localizer, and a T1 MP-RAGE image was acquired to select
anatomical views for flow quantification. Acquisition planes were perpendicular to the direction
of flow, with the cerebral aqueduct and C2-C3 area each being acquired in separate orthogonal
planes. A peripheral pulse transducer was placed on the participant’s finger for retrospective
cardiac gating during acquisition. For every participant, three consecutive measurements were
acquired at each region of interest to be included in the test-retest reliability assessment. PC-MRI
data was acquired when the participant was at rest with a steady-state heart rate.
Regions of Interest
Two regions of interest were assessed for test-retest reliability (Figure 2.1): the cerebral
aqueduct (CA) and C2-C3 area. Within the C2-C3 area, flow was measured at the left and right
internal carotid arteries (ICA), vertebral arteries (VA), internal jugular veins (IJV), and
subarachnoid space (SS). These ROIs were selected due to their utilization in several prior
studies of CSF and CBF flow dynamics
84,104,106,107
, suggesting these areas are potentially
sensitive to the pathological changes that occur in neurological disease.
50
Figure 2.1: Representative set of acquired MRI images for a participant.
(A) Structural T1 MPRAGE. The yellow dashed lines represent the acquisition planes, which were perpendicular to
the direction of flow. (B) PC-MRI flow phase image at the cerebral aqueduct (CA), (C) subarachnoid space (SS),
and (D) internal carotid artery (ICA), internal jugular vein (IJV), and vertebral artery (VA).
ROI Segmentation
All post-processing for test-retest reliability was performed by a biomedical engineering
PhD student with 18 months of experience in PC-MRI and ROI segmentation. For evaluating
inter-rater reliability, an MD / PhD student was trained for 2 hours over the course of one day on
segmenting the ROIs at the CA and C2-C3 area. Segmentation for analysis of test-retest
reliability was performed on a rolling basis as participants were imaged over the course of 18
months. Segmentation for intra-rater and inter-rater reliability analysis was performed over the
course of 2 weeks. Post-processing took approximately 1.5 hours per participant and included
ROI segmentation, generation of flow curves, extraction of flow parameters, and statistical
analysis of reliability.
Flow curves were generated for the CA, SS, ICA, VA, and IJV regions using BioFlow
v3.1.2, a free medical imaging analysis software with semi-automated segmentation
capabilities.
108
However for this study, each region of interest was identified and manually traced
51
as it was quicker and yielded similar ROI masks to the semi-automated segmentation routine,
which often required altering the thresholding settings to avoid inclusion of pixels outside the
practical boundaries of the ROI. A single ROI mask was applied across each of the 32
timeframes. The type of image used for segmentation varied based on the ROI and was set as
follows: CA – complex difference (Figure 2.2), C2-C3 SS – phase (Figure 2.3), C2-C3 vascular
vessels – magnitude (Figure 2.4). The image types for each ROI were chosen based on which
provided the best pixel intensity contrast between flow and zero-flow pixels upon visual
inspection.
Figure 2.2: Single frame of an acquired flow image at cerebral aqueduct.
(Left) Original complex-difference image. (Right) Background correction (BG) and cerebral aqueduct (CA) masks
applied.
52
Figure 2.3: Single frame of an acquired flow image at C2-C3 subarachnoid space.
(Left) Phase image with an applied mask at the C2-C3 SS. (Right) Circular background correction (BG) mask
applied at the C2 posterior spinous process.
Figure 2.4: Single frame of an acquired flow image at C2-C3 vascular region.
(A) Example of a circular mask applied at the C2-C3 vascular ROIs, specifically at the right ICA. (B) Circular
background correction (BG) mask applied at the C2 posterior spinous process. (C) Magnitude image of the C2-C3
vascular area containing the ICA, IJV, and VA
53
Background Correction
To account for null background offsets due to imperfect suppression of eddy currents
during acquisition, a background correction was applied to all flow curves generated at the CA
and C2-C3 area. For the CA, a C-shaped mask with an inner radius twice the ROI diameter and
an outer radius three times the ROI diameter was applied at the midbrain just above the ROI
(Figure 2.2).
101,109
A C-shaped mask located above the ROI was chosen instead of a circular
mask as this method has been shown in previous studies to be more reproducible.
109
Once the
mask was applied, a background flow curve was generated and a point-by-point subtraction was
performed on the CA flow curve. This was done to account for any time-based changes in
background noise, as the mask was applied proximal to the ROI in an area with tissue and non-
zero flow. For the C2-C3 area, a circular mask with a diameter approximately equivalent to that
of the IJV ROI was applied at the center of the C2 posterior spinous process (Figure 2.3, Figure
2.4). As this is a known region of zero-flow, the resulting background flow curve was averaged,
and the mean value was subtracted from each of the SS, ICA, VA, and IJV flow curves.
Calculation of Flow Parameters
The flow parameters analyzed in this study were derived from previous studies of CSF and
CBF flow dynamics. All temporal delay parameters were calculated in %CC, a measure
indicating the time in the cardiac cycle in which the flow measurement occurred relative to the
total time of the heartbeat. For each flow curve, there were 32 flow measurements representing
one cardiac cycle.
The ICA and VA flow curves were summed to create an arterial flow curve, while the
venous flow curve was represented as the IJV flow curve. While the IJV is the primary pathway
54
for venous outflow in the supine position, there are also contributions from the vertebral,
epidural, and deep cervical veins.
110
As such, the IJV is only responsible for a percentage of the
total venous outflow. To ensure that the arterial and venous flow curves were of the same scale, a
correction factor was applied in which the ratio of average arterial flow to average venous flow
was multiplied by each of the 32 flow values in the venous flow curve
84
. The arterio-venous flow
curve (Figure 2.5E) was constructed as the summation of the arterial (Figure 2.5C) and corrected
venous (Figure 2.5D) flow curves.
The zero-time reference for each flow curve represented the time in which the cardiac
cycle was at peak systole as observed by the peripheral pulse transducer. However, due to transit
time differences for blood between the heart, finger, and carotid artery, the arterial flow curve’s
zero-time point did not reflect the actual time in which cardiac peak systole occurred.
The CA (Figure 2.5A) and SS (Figure 2.5B) flow curves were integrated to yield stroke
volume (mm
3
), which represents the total displacement of CSF in the rostral and caudal direction
over the cardiac cycle.
72,75,108,111,112
The maximum and minimum points on the flow curve were
used to represent the flush and fill peak (mm
3
/s), respectively.
107,111,113,114
The delay between the
beginning of the flow curve and the maximum and minimum points were represented as the
time-to-flush and time-to-fill peak (%CC), respectively.
108,113,114
Arterial pulsatility (mm
3
/s
2
) was calculated as the slope of the arterial flow curve during
systole.
84
Pulsatility index was calculated as the difference in systolic and diastolic peak flow
over the mean flow rate.
98,115,116
Resistivity index was calculated as the difference in systolic and
diastolic peak flow over systolic peak flow.
98,116
Pulse volume (mm
3
) was calculated as the
integration of the arterial flow curve over the period in which systole occurred.
84
The minimal
and maximal points along the arterial and venous flow curve were represented as the systolic and
55
diastolic peak (mm
3
/s), respectively.
107,117
The delay between the beginning of the flow curves
and the minimum and maximum points were represented as the time-to-systolic and time-to-
diastolic peak (%CC), respectively.
107
Finally, the average of all 32 flow values on the arterial
and venous flow curves was used to represent the average arterial flow and average venous flow,
respectively.
104
The arterio-venous flow curve was integrated to yield stroke volume (mm
3
), which
represents the total amount of blood displaced in the caudal direction of the cardiac cycle.
72,84
The time difference between the minimum arterial and maximum venous flow curve values was
used to represent the arterio-venous delay (ms).
72,84
56
Figure 2.5: Representative flow curves from one participant.
(A) CA flow curve – 1. fill peak, 2. flush peak, 3. time-to-flush peak, 4. stroke volume, 5. time-to-fill peak; (B) SS
flow curve – same flows parameters as CA flow curve; (C) arterial flow curve – 1. arterial pulsatility, 2. arterial
pulse volume, 3. systolic peak, 4. time-to-systolic peak, 5. diastolic peak, 6. time-to-diastolic peak, 7. average flow,
(D) venous flow curve – same flow parameters as arterial flow curve excluding arterial pulsatility and pulse volume,
(E) arterio-venous flow curve – 1. stroke volume, 2. arterio-venous delay.
57
Reliability Measurements
Test-retest reliability for each flow parameter was evaluated using the intraclass
correlation coefficient (ICC) and coefficient of variance.
118,119
The ICC (3,1) two-way mixed
ANOVA model in SPSS (IBM v24, 2016)
120
was used in this analysis to estimate the correlation
between measurements for each flow parameter.
118
The participants and PC-MRI were both
treated as fixed effects when assessing reliability. The reliability of each flow parameter was
characterized as excellent (ICC > 0.80), good (ICC 0.60 – 0.79), moderate (ICC 0.40 – 0.59), fair
(ICC 0.20 – 0.39), or poor (ICC < 0.20).
121
Test-retest reliability was also assessed using
coefficient of variation (CV), defined as the standard deviation of a group of measurements
normalized to the mean of the group. The CVs for all participants were averaged for each flow
parameter.
Intra-rater and inter-rater reliability of ROI segmentation during post-processing was also
assessed for both CSF and CBF flow in a subset of 10 randomly selected participants. The
cerebral aqueduct and all C2-C3 vascular ROIs were selected and manually traced three times for
each participant in a randomized manner over multiple sessions. ICC was used to evaluate the
intra-rater and inter-rater reliability for the 5 aqueductal and 14 vascular flow parameters.
Results
Mean, standard deviation and test-retest reliability (ICC, CV%) values for all parameters
analyzed at the CA and C2-C3 ROIs are shown in Table 2.1. Intra-rater and inter-rater reliability
(ICC) values for the CA and C2-C3 vascular parameters analyzed are shown in Table 2.2.
58
Table 2.1: Test-retest reliability for all flow parameters.
59
Table 2.2: Intra-rater and inter-rater reliability for subset of flow parameters.
Cerebral Aqueduct
Test-retest reliability for the CSF flow parameters analyzed in the CA ROI are shown in
Figure 2.6A. All CSF flow parameters in this region reported an ICC>0.93, which is considered
excellent reliability, as well as a CV<10%, except for flush peak (CV=11%). Intra-rater
reliability and inter-rater reliability were excellent with an ICC=0.99 for all CSF flow
parameters.
60
C2-C3 Area
Test-retest reliability for the CSF flow parameters analyzed in the C2-C3 SS ROI are
shown in Figure 2.6B. Time-to-fill peak had an ICC=0.75, which is considered good reliability,
and a CV=16%. The remaining CSF flow parameters in this region reported an ICC>0.94 and a
CV<10%, except for fill peak, with a CV=12%.
Figure 2.6C and Figure 2.6D show the CBF flow parameters analyzed in the C2-C3
vascular ROI. Diastolic peak had good reliability with an ICC=0.70 and a CV=12%. Average
arterial flow also had good reliability with an ICC=.79, but with a lower CV=9%. Pulsatility,
pulsatility index, and pulse volume each had excellent reliability with an ICC=0.80, ICC=0.85,
and ICC=0.88, respectively. However, the CVs for these parameters were moderate at 22%,
14%, and 14%, respectively. All remaining arterial flow parameters had excellent reliability and
a CV<10%. All venous flow parameters had excellent reliability with an ICC>0.80 and a
corresponding CV<10%, except for systolic peak which had good reliability at an ICC=0.78.
Intra-rater reliability and Inter-rater reliability was excellent with ICC>0.93 for all arterial and
venous CBF flow parameters, except for pulsatility with an intra-rater ICC=0.87.
Figure 2.6E shows the arterio-venous flow parameters analyzed in the C2-C3 vascular
ROI. From this flow curve, both stroke volume and arterio-venous delay had excellent reliability
with an ICC=0.89. However, arterio-venous delay had a much higher CV=27% compared to
stroke volume with a CV=14%.
61
Figure 2.6: ICC and mean CV for the flow parameters calculated from the flow curves.
CA, SS, arterial, venous, and arterio-venous flow curves shown. Among the CSF flow parameters, CA stroke
volume and time-to-fill peak had the highest ICC. Arterial time-to-systolic peak and time-to-diastolic peak had the
highest ICC of the CBF flow parameters.
62
Discussion
The CSF and CBF flow parameters analyzed in this study generally had excellent test-
retest reliability. The CSF flow parameters that measured flow rate (flush peak, fill peak),
temporal delays (time-to-flush peak, time-to-fill peak), and volume (stroke volume) all had
excellent reliability (ICC>0.93). The single exception was time-to-fill peak for the SS which had
good reliability (ICC=0.75). Similarly, CBF flow rate (systolic peak, diastolic peak, average
venous flow, average arterial flow), temporal delays (time-to-systolic peak, time-to-diastolic
peak), pulsatility (pulsatility index, resistivity index, pulsatility), and volume (arterio-venous
stroke volume, pulse volume) generally had excellent reliability. The exceptions were arterial
diastolic peak, average arterial flow, and venous systolic peak, which all had good reliability
(ICC 0.70-0.79). Overall, the flow rate, temporal, pulsatility, and volume parameters all showed
low variability.
Test-retest reliability, on a subset of the CSF and CBF flow parameters analyzed in our
study, has been reported in other studies. One such study used PC-MRI to evaluate test-retest
reliability of stroke volume and mean flow rate at the CA and C2-C3 region and reported
excellent reliability similar to our study, but did not analyze temporal parameters, which are
important measures for understanding the dynamic interplay between CSF and CBF flow in the
brain.
93
In a similar study, good reliability was found for CSF flow rate and temporal parameters,
but were only evaluated for the CA.
102
In a study of CBF dynamics, short-term, long-term, and
post-processing reliability of total cerebral blood flow measurements showed excellent
reliability.
103
The flow parameters described in our study have been used to evaluate the effects of
pathological changes in the brain’s drainage pathways and circulatory system. The brain is
63
contained within a rigid skull and composed of tissue, CSF, and blood. According to the Monro-
Kellie doctrine, any volume change in one intracranial component requires a compensatory
change in another.
105
During systole, there is arterial inflow into the brain which initiates a
cascade of outflow through the subarachnoid space, veins, and ventricles to maintain homeostatic
pressure in the cranium.
122
The first step in the cascade is cranio-caudal CSF flush through the
cervical subarachnoid space, followed by venous outflow, and finally cranio-caudal CSF flush
through the cerebral aqueduct.
105
The temporal parameters described in this study can be used to
measure the time-course of flow through the brain and potentially detect any abnormalities in the
relationship between CSF and CBF flow in this cascade. Many studies have utilized these
temporal flow parameters to study CSF and CBF flow dynamics in conditions of
hydrocephalus
106,123
and normal aging
107
.
Flow rate and volume parameters have also shown great utility in past studies. In one such
study, significantly higher arterial pulsatility, pulse volume, and arterio-venous stroke volume
were found in individuals with MCI when compared to normal older adults.
84
The study posited
that the hyperdynamic arterial flow in MCI was due to degeneration of the arterial walls leading
to changes in the regulation of arterial input.
84
In another study, decreased stroke volumes were
reported in healthy older adults at the CA and cervical SS when compared to healthy young
adults.
107
The study theorized that the lower flow volumes were due to abnormal changes in the
resistance and compliance of the cervical and aqueductal compartments.
107
Aqueductal stroke
volume has also been shown to have prognostic value in symptom progression and
responsiveness to shunting in normal pressure hydrocephalus.
75,124
One related study proposed
the ratio of aqueductal to subarachnoid stroke volume, measuring the distribution of flow
64
between the CA and C2-C3 spaces, to be a strong indicator of flow dysfunction in patients with
hydrocephalus.
125
While our study and many of the previous studies described above utilized a 2D cine-PC
pulse sequence to measure flow dynamics, PC-MRI has recently been expanded to measure flow
in 3D as well. This technique, known as 4D flow MRI, encodes velocity along the 3-orthogonal
spatial axis (x, y, z) allowing for a volumetric reconstruction of flow within a vessel.
97
Though
4D flow MRI is a relatively new technique, it has been utilized in several studies to evaluate flow
dynamics in patients with clinical pathologies. In one such study, 4D flow MRI was used to
evaluate group differences in mean blood flow, pulsatility, and mean transit time at the internal
carotid artery, middle cerebral artery, superior sagittal sinus, and transverse sinus among
individuals with AD, MCI, and those who were cognitively normal.
126
In another study, this
technique was used to measure peak CSF flow at the cranio-cervical junction and cervical spinal
canal in healthy individuals and those with Chiari malformations.
113
However, while 4D flow
MRI has shown great promise in understanding flow dynamics, long scan times (5-20 minutes)
and poor spatial resolution need to be addressed before it can be used as a routine clinical tool.
127
Our study reports a comprehensive evaluation of the test-retest reliability of the different
types of flow parameters which have been shown to be biologically relevant for understanding
health and disease. However, our study does have a few notable limitations. First, the
participants in this study are all healthy, young adults. It is possible that a similar study in older
individuals with pathological conditions would yield greater variability in the flow parameters
analyzed at the CA and C2-C3 area. Second, while we visually ensured that the acquisition plane
was perpendicular to the presumed direction of flow at the cerebral aqueduct and C2-C3 area,
physiological variations in positioning of the vessels through these regions make it impossible to
65
capture all flow directions. However, while this could lead to reduced accuracy of the flow
measurements, the precision should remain relatively unaffected. Third, using a peripheral pulse
transducer to synchronize data acquisition to the cardiac cycle is known to be less reliable than
ECG, as the ECG’s sharp R-wave peak can be more reliably identified than the broad peak from
an O2 saturation signal. It is possible this could result in lower reliability in temporal-based flow
parameters. However, this did not affect our temporal parameters, which generally had excellent
test-retest reliability.
An additional limitation is that our study only assessed test-retest reliability during a single
session with each repeat scan utilizing the same localizer. Additional variability could be
introduced if repeat measurements are taken after acquiring a new localizer scan following
repositioning of participants on the scanner. This variability could also increase if the
measurements are taken across multiple days. However, in one study on PC-MRI reproducibility,
total cerebral blood flow measurements reported a CV < 11% regardless of whether the repeat
scan was acquired after subject repositioning or on a different day.
103
In another study by
Luetmer et al.
101
, mean flow rate at the CA reported a CV < 9% for three repeated measurements
taken on different scanners over 2 weeks. Since the goal of our study was to evaluate reliability
over time within a single session, additional studies are needed to investigate the effects of day-
to-day variability and subject repositioning on the flow parameters described in this study.
While the limitations described above can affect test-retest reliability, accurate and
consistent ROI segmentation during post-processing is also a significant factor. In our study,
manually tracing the ROIs required visual inspection and subjective determinations of the lumen
boundaries which were sometimes blurred by motion artifacts and partial volume effects. The
aqueduct was particularly challenging, as any inaccuracies in boundary identification can
66
significantly affect the flow measurements due to its relatively small size. However, inter-rater
and intra-rater reliability was excellent at this region, suggesting that these sources of error did
not substantially affect the reliability of boundary identification.
While segmentation of the CA was challenging due to its small size, the C2-C3 SS was
also challenging because of its complex ring shape and low intensity contrast between flow and
zero-flow pixels. However, because of its large surface area, small errors in boundary
identification likely had a minimal effect on the results. This is further confirmed by the
excellent inter-rater and intra-rater reliability in this region.
The C2-C3 vascular vessels were the most challenging region to segment due to the
vessel’s movement throughout the cardiac cycle. This required the ROI mask to be applied such
that it encapsulated the entire boundary of the vessel across all image slices. However, the
proximity of the ICA and IJV vessels made it difficult to ensure that the mask for one ROI did
not overlap onto another. Due to these challenges, the flow parameters in this region had the
lowest test-retest, intra-rater and inter-rater reliability. Moreover, since the flow parameters in
this region were based on summed arterial and venous flow curves, boundary identification
errors at each vessel accumulated.
Another approach to the post-processing analysis is using time-resolved segmentation
instead of applying a single ROI mask across all slices. Time-resolved segmentation is a
technique in which a separate ROI mask is applied across each image slice, reducing the
probability of including pixels outside the vessel boundary, such as those within an adjacent ROI
or stationary tissue. This could improve accuracy and reliability in the CBF flow measurements
but will also result in significantly higher post-processing times which might not be practical in
many settings such as the clinic. It may also not be as necessary with the vessels in the brain,
67
which have a smaller amount of displacement over the cardiac cycle than the larger
cardiovascular vessels proximal to the heart.
Despite the post-processing challenges described above, all parameters analyzed in our
study showed excellent intra-rater and inter-rater reliability. These findings are supported by
previous studies looking at intra-rater and inter-rater reliability in a subset of CSF and CBF flow
parameters. In one study looking at repeatability of aqueductal CSF flow measurements, both
intra-rater and inter-rater variability were shown to be lower than inter-trial variability.
101
This
was confirmed in another study on CSF flow, where aqueductal stroke volume, peak mean
velocity, and peak systolic velocity, parameters commonly used in the literature, all had high
intra-rater and inter-rater reliability (ICC > .88).
111
In a similar study looking at CBF flow
measurement reliability at the carotid and vertebral arteries, inter-rater variability was again
shown to be lower than inter-trial variability.
128
In another study looking at repeatability of CSF
and CBF flow at the C2 cervical level, high intra-rater and inter-rater reliability was reported and
it was also shown that experience level of the rater had a negligible influence on variability.
129
Overall, this suggests that inter-rater and intra-rater variance are not significant contributors to
the overall variance between trials.
In addition to ROI segmentation during post-processing, there are also other
physiological factors that can influence CSF and CBF flow dynamics and subsequently, test-
retest reliability. These include physiological factors such as heart rate
15
, respiration
130,131
, and
diurnal variations due to the body’s circadian system.
132
Dietary intake of caffeine has also been
shown to influence cerebral blood flow.
133
In a study on test-retest reliability of CBF flow, it was
shown that venous flow at the internal jugular veins had greater variability than carotid arterial
flow due to its sensitivity to body position, head position, hydration levels, and respiratory
68
rate.
134
This variability can also be attributed to the higher flexibility and collapsibility of the
internal jugular veins relative to the internal carotid artery.
110
Furthermore, when the participant
is in a supine position, the left brachiocephalic vein can undergo severe narrowing resulting in
temporary retrograde flow of the left jugular vein.
135,135
Taken together, these physiological
effects can often result in the appearance of partial or missing jugular vein lumens, as well as bi-
directional flow on an image acquired with PC-MRI.
110
These possibilities need to be taken into
consideration when evaluating flow at the internal jugular vein in healthy individuals and those
with pathological conditions.
In this study we have shown that flow rate, volume, pulsatility, and temporal parameters
can be measured and calculated with good repeatability at the CA and cervical C2-C3 area,
regions commonly studied in health and disease, using PC-MRI. However, despite technological
advances in the suppression of eddy currents, concomitant gradients, and gradient non-linearities,
as well as improvements in the post-processing pipeline, there are still systematic sources of
error that cannot be accounted for which can affect the accuracy of these flow measurements.
Moreover, since accuracy has also shown to be dependent on vessel size and flow rate, these
systematic errors can affect each flow region of the brain differently.
100
Furthermore, as there is
no non-invasive gold standard for measuring cerebrovascular flow, it is difficult to evaluate the
magnitude of the flow bias relative to the true physiological flow rates.
These accuracy issues need to be addressed to establish PC-MRI as a clinically useful
tool for measuring CSF and CBF flow dynamics. One option is to establish normative values for
the CSF and CBF flow parameters based on criteria such as age, ethnicity, gender, and
pathology, to be used as a standard for comparison. In a clinical setting, a patient’s flow
measurements can then be compared to normative values to assist in diagnosis. Another option is
69
to establish a baseline measurement of flow dynamics in a patient which can then be used to
evaluate treatment effects or disease progression over time.
Conclusion
Overall, identifying a non-invasive biomarker of flow dynamics has broad implications
across several diseases that are associated with CSF and CBF dysfunction, such as Alzheimer’s
disease, MCI, and communicating hydrocephalus. Future steps in establishing flow dynamics as
a non-invasive biomarker include standardizing post-processing analysis to reduce complexity
and processing time, educating practitioners on the clinical utility of measuring CSF and CBF
flow dynamics, and continuing to identify the appropriate clinical populations who may benefit.
70
Chapter III Overview
In the first chapter of this dissertation, feasibility of engaging in simultaneous exercise and
cognitive enrichment in virtual reality was evaluated and established in the form of a Phase 1
trial. Older adults were able to cycle and navigate an enriched, immersive virtual environment
with reports of enjoyment and minimal adverse effects. In the second chapter, reliability of MRI
for measuring CSF and CBF flow dynamics, potential biomarkers of brain health and early
indicators of Alzheimer’s disease progression was assessed. In the final chapter of this
dissertation, a Phase 2 trial consisting of a 12-week simultaneous exercise and cognitive
enrichment VR intervention in older adults will be presented.
The recognition of simultaneous exercise and cognitive enrichment as an approach for the
primary prevention of Alzheimer’s disease is driven by previous studies that have shown an
association between modifiable lifestyle factors and Alzheimer’s disease risk. Among these
lifestyle factors, physical inactivity and low education have been shown to contribute to the
largest proportion of AD cases in the US and worldwide, respectively.
1
Physical activity also has
been shown to have positive effects on cardiovascular function and metabolism, reducing the risk
of developing hypertension, diabetes, and obesity, risk factors for AD.
234,235,236
Taken together,
this suggests that physical activity and cognitive enrichment may play a significant role in AD
risk reduction.
The underlying mechanisms associated with exercise’s impact on brain health have been
extensively explored. Exercise enhances cerebrovascular function by inducing neurovascular
remodeling through the upregulation of BDNF, IGF, and VEGF growth factors.
238,239
,241,242,243,244,245
Exercise also increases perfusion, lowers levels of inflammation, and reduces
arterial stiffness.
237,240
Moreover, exercise may promote learning and memory through
71
hippocampal neurogenesis, synaptic plasticity, and cell proliferation.
153
In addition to exercise,
cognitive enrichment has also been associated with improved brain health. It is theorized that
cognitive enrichment leads to neuroplasticity changes in the brain which builds cognitive
reserve, a construct defined by the brain’s resilience to cognitive decline despite the presence of
neurodegeneration.
1
The benefits of combined aerobic exercise and cognitive enrichment on cognition have
been shown in several studies. In a systematic review by Lauenroth et al, 18 of 20 studies found
improvements in cognitive performance after engaging in these combined activities. Emerging
evidence has also shown that there may be a synergy between exercise and cognitive stimulation,
such that the benefits of engaging in both simultaneously are higher than either one individually.
The underlying neural mechanisms that drive this synergistic effect have been shown in animal
studies, where cognitive enrichment has been shown promote the survival of new cells formed
through exercise-induced neurogeneis.
153
This has been supported in a few human studies,
including one in which older adults who engaged in 12-weeks of cycling and cognitive
enrichment showed higher improvements in psychomotor speed compared to exercise-only
controls.
63
Overall, only a limited number of studies have combined exercise and cognitive enrichment.
Even fewer studies have assessed this combined activity in virtual reality. To our knowledge, no
studies have assessed the impact of simultaneous aerobic exercise and targeted spatial memory
engagement in virtual reality on cognition and brain health. Therefore, the aim of this chapter
is to conduct a 12-week intervention engaging these simultaneous activities and to assess
impact on brain health. The results of this study are in preparation, with an anticipated
manuscript submission date of May 2021 (Sakhare et al., in prep).
72
Chapter III: Simultaneous Exercise and Cognitive Training in
Virtual Reality Phase 2 Pilot Study: Impact on Brain Health and
Cognition in Older Adults
Abstract
Background: Cognitive decline is a significant public health concern in older adults.
Aerobic exercise and environmental enrichment have been shown to enhance brain function.
Virtual reality (VR) is a promising method for combining these activities in a meaningful and
ecologically valid way. The purpose of this Phase 2 pilot study was to calculate relative change
and effect sizes to assess the impact of simultaneous exercise and cognitive training in VR on
brain health and cognition in older adults.
Methods: Eleven cognitively normal older adults (age: 64.3±9.1 years, 7 females,
education: 16.9±2.3 years) participated in a 12-week intervention study. Participants cycled on a
custom-built stationary exercise bike while wearing a VR head-mounted display and navigating
novel virtual environments to assess spatial memory. Exercise frequency was 3 sessions per
week for 25-50 minutes per session at 50-80% HRmax. Cognitive training consisted of 5
navigation trials per session (1 previous session long-delay recall, 2 encoding trials, and 2
retrieval trials). Brain and cognitive function changes were assessed using MRI imaging and a
cognitive battery.
Results: Medium effect size improvements in cerebral flow and brain structure were
observed. Pulsatility, a measure of peripheral vascular resistance, decreased 13% (pre:
1.28±0.40; post: 1.12±0.40; d=0.47). Total grey matter volume increased 1.2% (pre: 623.9±35.6;
73
post: 631.2±55.0 cm
3
; r=0.32), while the volume of the superior parietal lobule, a region
associated with spatial orientation, increased 2.3% (pre: 24.7±2.9; post: 25.2±3.5 cm
3
; r=0.32).
Cognitive benefits were also observed. Visual memory discrimination related to pattern
separation showed a large improvement of 59% (pre: 0.22±0.20; post: 0.35±0.19; ηp
2
=0.33).
Cognitive flexibility (Trail Making Test B) (pre: 80±44; post:70±44; r=0.41) and response
inhibition (pre: 119.9±49.3; post: 88.6±38.8 ms; W=0.50) showed medium improvements of
13% and 26%, respectively.
Conclusions: Our study found that 12 weeks of simultaneous exercise and cognitive
training in VR elicits positive changes in brain volume, vascular resistance, memory, and
executive function with moderate-to-large effect sizes in our pilot study. These results suggest
that this cycling and spatial navigation intervention may improve brain health and cognition in
older adults at risk for cognitive decline and should be evaluated in a larger, randomized
controlled trial.
74
Introduction
Alzheimer’s disease (AD) is the leading cause of dementia in older adults, with numbers
expected to triple over the next 30 years due to an aging population and longer lifespans.
1
Due to
the gradual progression of the disease, a significant burden is placed on the healthcare system,
with costs approaching $300 billion a year.
137
AD is characterized by progressive memory loss,
with deficits in episodic memory observed in the preclinical stage of the disease up to 6 years
prior to diagnosis.
143
This includes spatial memory, a subtype of episodic memory responsible for
spatial location, spatial pattern, and object location recall.
142,144
Deficits in spatial memory have
been shown to result in difficulty navigating familiar spaces, ultimately compromising safety,
autonomy, and quality of life.
145,146
There are currently no effective disease-modifying treatments for AD, as existing FDA-
approved drugs have only been shown to ameliorate symptoms and not target the underlying
etiology.
147,157
This has prompted a need to initiate interventions prior to the onset of clinically
evident symptoms.
148
Exercise and cognitive enrichment are two modifiable lifestyle factors that
have been associated with a reduced risk of dementia.
1
Indeed, studies have shown that
individuals with a lifestyle rich in mental and physical stimulation experience less age-related
cognitive decline.
1
A limited number of studies have also shown that cognitive function may be
enhanced if exercise and cognitive enrichment are performed simultaneously. In one such study,
older adults with dementia engaged in a 12-week intervention consisting of cycling and cognitive
enrichment comprised of tasks assessing response inhibition, task switching, and processing
speed.
63
The study found moderate improvements in psychomotor speed compared to exercise-
only controls.
75
Virtual reality (VR) is a promising technology for engaging in simultaneous exercise and
cognitive enrichment. VR provides a safe and controlled environment to monitor physical
activity, manipulate experimental parameters, and interact with the user.
8
Moreover, previous
studies have shown virtual reality to be an ecologically valid tool for assessing spatial navigation
deficits in healthy older adults and individuals with Alzheimer’s disease.
9,10,11,12,13
Furthermore,
immersive VR head-mounted displays create a sense of presence and engagement that has been
shown to be important for performance on spatial navigation tasks.
17,18
To our knowledge, no
studies have assessed the impact of simultaneous exercise and spatial navigation training in
immersive virtual reality on cognition and brain health. Therefore, in this study, we conducted a
Phase 2 12-week intervention in older adults that engages these simultaneous activities and
assessed changes in neuroimaging and neuropsychological measures of brain health, including
memory, executive function, cerebral blood flow, and brain morphometry.
Methods
Study Population
Fifteen healthy older adults (64.8±8.1 years old; 52-80 years; 9 females) provided written
consent and participated in this pilot study. Study procedures were approved by the Institutional
Review Board at the University of Southern California and performed in accordance with the
Declaration of Helsinki. Subjects were selected from a convenience sample of community-
dwelling older adults using the LEARNit clinical trial (NCT02000622) database, flyers, and
online advertisements. Subjects were included in the study if they were 50-85 years of age and
were physically capable of cycling and engaging in moderate-to-vigorous physical activity.
76
Exclusion criteria included dementia, depression, stroke, neurological disorders, history of
traumatic brain injury, or contraindications to MRI.
A flow diagram summarizing subject eligibility, participation, and attrition throughout
the study is shown in Figure 3.1. Briefly, 51 older adults were contacted via phone to assess
study eligibility in accordance with the inclusion and exclusion criteria described above. Thirty-
six eligible older adults came in for a 1-hour on-campus visit to assess adverse effects associated
with cycling and navigating a virtual park environment while wearing a VR head-mounted
display. Adverse effects were assessed using the Simulator Sickness Questionnaire (SSQ), with a
total score of greater than 15 used as guidance for discontinuing further participation in the
study.
31,163
Twenty subjects enrolled in the study, with 15 completing baseline data collection.
Eleven subjects completed the entire study, including VR training and all data collection
timepoints.
77
Figure 3.1: Flow diagram summarizing subject eligibility, participation, and attrition.
Study Design
Figure 3.2 summarizes the timeline of events for the study. First, all enrolled subjects
were assigned and required to wear a GENEActiv watch for 14 days. This run-in period was
used to measure baseline levels of physical fitness and to evaluate subject compliance. After the
run-in period, subjects participated in a full-day baseline visit consisting of neuroimaging,
cognitive, and physical performance assessments. Following the baseline visit, subjects started a
12-week intervention consisting of 36 VR training visits. Subjects were advised not to make any
lifestyle changes during the intervention. If subjects had planned vacations or absences, the
78
intervention was extended until all VR training visits were completed. Cognitive assessments,
Flanker and MST, were administered at baseline, 4 weeks, 8 weeks, and 12 weeks. Physical
assessments, Astrand-Rhyming and InBody, were administered at baseline, 6 weeks, and 12
weeks. At the end of the intervention, subjects participated in another full-day visit consisting of
the same neuroimaging, cognitive, and physical performance assessments performed at baseline.
The order of assessments was kept constant between the baseline and follow-up visits when
possible.
Figure 3.2: Timeline of assessments and VR training during study.
Intervention
Subjects engaged in simultaneous exercise and spatial navigation training in VR 3 times
per week, alternating between training day and rest day to allow for exercise recovery. Each visit
was comprised of VR training and a set of pre-post questionnaires. VR training consisted of
cycling on a custom-built stationary exercise bike at moderate-to-vigorous intensity levels for
25-50 minutes while navigating a cognitively challenging virtual environment. The Short
Symptom Checklist (SSC) questionnaire was administered to check for adverse effects that are
commonly associated with immersive VR, including nausea, eye strain, dizziness with eyes
closed, and stomach awareness.
32
The Borg Rate of Perceived Exertion Scale (RPE) was utilized
79
to obtain an additional surrogate measurement of exertion to be used when heart rate readings
were unreliable or impacted by medication.
34
Blood pressure was measured at the beginning of
each week to track changes associated with the intervention.
Table 3.1: Summary of aerobic exercise and cognitive training progression.
* Indicates that the specified training week contains trials requiring subjects to navigate in the reverse direction (end
point to starting point).
Aerobic Exercise
Aerobic exercise was prescribed using a linear periodized schedule (Table 3.1), with
cycling intensity levels increasing from 50% HR max at the onset of the intervention to 80% HR
max at 12 weeks. Maximal HR was estimated according to a regression equation established by
Tanaka et al.
205
Periodization consisted of two 6-week phases, moderate and vigorous, with
intensity levels increasing by 5% every two weeks within each phase. In the first 6 weeks,
intensity levels increased from 50% to 60% HR max with a corresponding increase in volume
from 120 to 150 minutes per week. In the last 6 weeks, intensity levels increased from 70% to
Cognitive Training
Week Intensity (% HR Max) Duration (minutes) Difficulty (Avg. # Turns)
1 50% 40 4
2 50% 45 8
3 55% 50 10
4 55% 50 11
5 60% 50 10*
6 60% 50 7
7 70% 40 12
8 70% 35 13
9 75% 30 22
10 75% 25 22
11 80% 25 19*
12 80% 25 16*
Aerobic Cycling
80
80% HR max with volumes decreasing from 120 to 75 minutes per week. Cycling intensity and
associated volumes were calculated according to the Surgeon General recommendations for
physical activity in older adults and the American College of Sports Medicine.
249
Heart rate was
tracked using a Polar H7 chest strap monitor and cycling resistance levels were adjusted at the
beginning of each visit to ensure subjects were within their target heart range.
Figure 3.3: Subject cycling and navigating a virtual environment.
(Left) Subject cycling on custom-built stationary exercise bike. (Middle) Navigating a virtual environment and
rescuing an animal. (Right) Top-down miniature map of the road network.
Cognitive Training
Cognitive training was conducted in an immersive virtual reality environment viewed
through an HTC Vive Pro head mounted display (HMD). Subjects were tasked with navigating
along a network of roads in the environment, via pedaling on the stationary exercise bike, and
completing a set of cognitive tasks targeting spatial memory and attention. Cognitive tasks were
administered in five unique virtual environments, including an urban park, aviary, savannah,
desert, and jungle, to facilitate learning under different conditions. Each environment contained a
unique set of landmarks strategically placed at every road intersection to serve as visual cues
during navigation.
Spatial memory was targeted using a cued route-learning paradigm consisting of three
types of trials: cued learning, immediate recall, and delayed recall. In the cued learning trial,
81
arrows were placed at each road intersection to guide the subject while they learned a new route.
In the immediate recall trial, subjects were tasked with navigating that same route, but without
the assistance of arrows. In the delayed recall trial, subjects were required to navigate a route
they learned from a previous visit without the assistance of arrows. At each visit, participants
completed five trials consisting of a delayed recall, two cued learning, and two immediate recall
trials. The difficulty of the routes, defined by the number of decision points (intersections),
progressively increased each week for the first 6 weeks (Table 3.1). The difficulty was reset at
week 7 as subjects transitioned from the urban park environment to the remaining four
environments before progressively increasing again.
Attention was targeted by requiring subjects to collect specific items located along the
left and right side of the road using their corresponding handlebar brakes. Briefly, each
environment had a specific mission, including rescuing, feeding, and healing animals, as well as
fighting fires and repairing power plants. Each route contained items associated with that
environment’s mission as well as distractor items. While navigating a route, subjects were tasked
with collecting items specific to that environment and ignoring distractors.
Neuroimaging
All subjects received a 3T MRI scan at baseline and at 12-week follow-up after
completing the intervention. MRI scans were acquired on a 3T Siemens Prisma using a 32-
channel head coil (Siemens Medical Solutions, Erlangen, Germany). A localizer and scout scan
were acquired at the beginning of the session to locate and align the subject’s head for
subsequent scan sequences.
82
Cerebral Flow: Cerebrospinal fluid flow (CSF) and cerebral blood flow (CBF)
measurements were acquired using a 2D cine-PC (PC-MRI) pulse sequence with retrospective
cardiac gating. CSF flow was measured at the cerebral aqueduct (CA) and the subarachnoid
space (SS) at the level between the second and third cervical vertebrae (C2-C3). CBF flow was
measured at the C2-C3 level at the internal carotid arteries (ICA) and vertebral arteries (VA).
PC-MRI data was acquired with the following parameters: frames, 32; TR/TE, 27-28/ 8-9 ms;
flip angle, 25°; field of view (FOV), 140 x 140 x 5 mm; matrix, 336 x 336; velocity encoding, 10
(CA), 5 (SS), 80 (ICA/VA) cm/s.
PC-MRI data was processed using BioFlow v3.1.2, a free medical imaging analysis
software.
108
Each anatomical region of interest (ROI), including the CA, SS, and left and right
ICA and VA, was manually traced and segmented to generate a waveform consisting of 32 flow
rate (mm
3
/s) measurements over a cardiac cycle. The ICA and VA waveforms were summed to
create a single arterial flow waveform. Background correction was applied to the CA and SS /
arterial waveforms by subtracting out signal from stationary tissue located at the midbrain and
C2 posterior spinous process, respectively.
Stroke volume (SV) and flush peak (FshP) were calculated from the CA and SS CSF
waveforms. Stroke volume represents the total volume of CSF displaced in the caudal and
cranial directions, while flush peak represents the peak flow rate in the caudal direction.
107,164
Pulsatility index (PI) and resistivity index (RI), two surrogate measures of peripheral vascular
resistance, were calculated from the arterial waveform.
186,187
PI represents the difference in peak
systolic and diastolic flow divided by mean arterial flow, while RI represents the difference in
peak systolic and diastolic flow divided by peak systolic flow.
98
83
Perfusion: Whole-brain CBF perfusion measurements were acquired using a Pseudo-
Continuous Arterial Spin Labeling (PCASL) sequence. PCASL images were acquired with
background suppression and the following parameters: TR/TE/post-label decay, 4300/38.3/2000
ms; resolution, 2.5 mm isotropic; label duration, 700 ms. PCASL data was processed using an
automated in-house processing pipeline involving motion correction of the label and control
images, generation of perfusion-weighted images, and principal component analysis-based
denoising.
199
An established kinetic model for the ASL signal was utilized to generate CBF time
series images which were then averaged to create a mean CBF image.
198
Structural Volumes: Structural volume measurements were acquired using three
sequences: (1) T1-weighted (T1w) magnetization-prepared rapid acquisition gradient echo (MP-
RAGE); (2) T2-weighted SPACE; and (3) T2-weighted (T2w) turbo spin echo (TSE). Three-
dimensional images of the entire brain were obtained from the T1w MP-RAGE and T1w SPACE
sequences, both acquired in the sagittal plane. T1w MP-RAGE images were acquired with the
following parameters: TR/TE, 2400/2.22 ms; FOV, 256 x 256 x 208 mm; resolution, 0.8 mm
3
isotropic. T2w SPACE images were acquired with the following parameters: TR/TE, 3200/563
ms; FOV, 256 x 256 x 208 mm; resolution, 0.8 mm
3
isotropic. A high-resolution image of the
hippocampal subfields was obtained using a T2w TSE acquired at an angle perpendicular to the
long axis of the hippocampus with the following parameters: TR/TE, 8020/50 ms; FOV, 175 x
175 x 28 mm; resolution, 0.4 x 0.7 x 2.0 mm.
T1w MP-RAGE data was processed using FreeSurfer v6, a software package for
segmenting and labeling neuroanatomical structures.
167,168
Grey matter volumes were obtained
using the recon-all script, which performed motion correction, intensity normalization, Talairach
84
coordinate transformation, skull-stripping, registration, and white and grey matter
segmentation.
190,191
Segmented regions of interest (ROI) included the superior parietal lobule,
hippocampus, and middle frontal gyrus due to their association with spatial orientation, working
memory, and attention and executive function, respectively.
188,189,192
Total grey matter volume
was also measured to provide a global assessment of brain structure.
T2w TSE data was processed using ASHS, a free open-source software for automated
segmentation of the medial temporal lobe.
166
T2w TSE, T1w MP-RAGE, and an atlas package
available online through the NITRC image repository, were provided as inputs to the ASHS
pipeline.
169,170
The atlas package consisted of manually segmented hippocampal images of older
adult subjects obtained from a research study on aging conducted at the Penn Memory Center at
the University of Pennsylvania.
166
ASHS performed registration, multi-atlas segmentation using
joint label fusion, and correction of the consensus segmentation using corrective machine
learning classifiers.
166
CA1, dentate gyrus (DG), and entorhinal cortex (ERC) volumes were
obtained from the output of the segmentation pipeline.
Cognitive Battery
Six neuropsychological tests were used to assess global cognition, executive function,
and memory. All tests were administered at baseline and 12 weeks, with Flanker and MST
administered at 2 additional timepoints of 4 weeks and 8 weeks.
Global Cognition: Montreal-Cognitive Assessment (MOCA) is a screening instrument
for assessing global cognition and detecting mild cognitive impairment.
185,193
The MOCA was
85
scored on a 30-point scale and consisted of tasks assessing short-term memory, visuospatial
ability, executive function, language, phonemic fluency, working memory, and attention.
185
Executive Function: Three tests were selected to assess core components of executive
function: (1) D-KEFS Trail Making Test (cognitive flexibility)
171,173
; (2) Eriksen Flanker Task
(inhibition)
172
; and (3) Digit Symbol Substitution Test (attention, processing speed)
174
. The Trail
Making Test (TMT) is comprised of a part A and B. TMT-A requires subjects to draw lines
connecting a series of numbers in numerical order, while TMT-B is a switching task requiring
subjects to sequentially alternate between numbers and letters.
175
Both parts were scored
according to the time required to complete the task.
176
TMT-A provides a baseline measure of
visual and motor processing speed, while TMT-B assesses set-shifting, a measure of cognitive
flexibility.
176
The difference between the 2 scores (TMT-B-A) was calculated to assess cognitive
flexibility independent of processing speed (TMT-A).
177
The Flanker task consists of two primary conditions, both requiring subjects to respond to
a central target arrow flanked by distractors.
178,179
The distractors point in the same direction as
the target arrow in the congruent condition, but the opposite direction in the incongruent
condition. An interference score, measuring inhibitory control, was calculated by subtracting the
mean reaction time for the congruent trials from the mean reaction time for the incongruent
trials.
180
The Digit Symbol Substitution Test (DSST), a test of processing speed and attention,
requires subjects to match symbols to numbers based on a key containing a set of numbers 1-9,
each paired with a unique symbol.
182
The DSST was scored based on the total number of correct
pairings within 90 seconds.
86
Memory: Memory was assessed using the Rey Auditory Verbal Learning Test (RAVLT)
and Mnemonic Similarity Task (MST). RAVLT is a word-list test of verbal memory consisting
of 5 consecutive learning trials (List A) and a distractor trial (List B).
183
Each trial involves the
verbal presentation and recall of a 15-item word list. Immediate recall was calculated as the total
number of recalled words from list A across the 5 learning trials. Delayed recall was calculated
as the number of words recalled from List A after a 30-minute delay following the presentation
of List B.
MST is a pattern separation task that is highly sensitive to hippocampal function.
184
MST
consists of a study phase and a test phase. In the study phase, subjects are required to label
images as indoor or outdoor objects. In the test phase, subjects are required to label a set of
images as old, similar, or new. The set consists of images that are repeats from the study phase
(foil), perceptually similar to the study phase (lure), and previously unseen (target). Lure
discrimination index (LDI) was calculated as the probability of similar responses given to lures
(Similar | Lure) minus the probability of similar responses given to foils (Similar | Foil).
Physical Performance Battery
Three physical performance tests were administered at baseline and 12 weeks, with
Astrand-Rhyming and InBody also administered at 6 weeks. Aerobic physical fitness was
assessed using the Astrand-Rhyming protocol, a submaximal cycle ergometer test for estimating
VO2max.
194
The protocol requires subjects to pedal at a constant cadence (60-85 rpm) and
workload, set according to reference values based on age and gender, for approximately 8-10
minutes. VO2max was calculated based on a nomogram accounting for age, gender, final heart
rate, and workload.
195
VO2max testing was performed primarily on a Keiser M3i stationary
87
exercise bike. However, the testing instrument was changed to the Wahoo Kickr Snap at the
onset of COVID to better conform to safety guidelines regarding social distancing. This
transition occurred after 7 subjects had completed the study, such that the remaining 4 subjects
were tested on the Wahoo Kickr Snap for all 3 data collection timepoints. As testing was
completed on two different instruments, an additional sub-analysis was performed on each
instrument.
Body fat was measured using the InBody 770, a commercially available analyzer
utilizing bioelectric impedance to estimate body composition.
196
Balance was assessed using the
Y-Balance protocol, which evaluates dynamic stability in the anterior, posteromedial, and
posterolateral directions. The protocol requires the subject to plant one leg and reach with the
opposite leg as far as possible in each of the three directions. A composite score was calculated
as the average reach in the three directions, normalized by limb length of the right leg, across
both plant legs.
Primary Outcomes
Proof-of-concept primary outcome measures were selected for brain function and
cognition. This included executive function, memory, and cerebral blood flow. Cerebral blood
flow measurements included arterial pulsatility index (PI) and resistivity index (RI), surrogate
measures of peripheral vascular resistance which has been shown to be associated with cognitive
dysfunction in individuals with Alzheimer’s disease.
202
Executive function and memory
measurements included the Flanker interference control and MST lure discrimination index,
measures of inhibitory control and pattern separation, respectively. These measures were
88
selected as they were hypothesized to be the most sensitive to the changes elicited by the
prescribed intervention over a 12-week period.
Secondary Outcomes
The following measures were secondary outcomes. This included CA and SS CSF flow
measurements, stroke volume and flush peak, and whole brain mean CBF perfusion. Structural
measurements included total grey matter, superior parietal lobule, hippocampal, and middle
frontal gyrus volumes. Cognitive measurements included the MOCA, TMT-B, and Digit Symbol
scores. Finally, physical measurements included VO2max aerobic fitness, body fat %, and
balance via the Astrand-Rhyming, InBody 770 and Y-Balance protocol, respectively.
Statistical Analysis
All analysis was performed in SPSS (IBM v27, 2020).
201
Demographic data was
summarized using descriptive statistics. Paired t-tests were performed to examine changes
following the intervention in all outcome measures collected at two timepoints (baseline, 12
weeks). This included all neuroimaging, cognitive, and physical measures except for Flanker,
MST, Astrand, and InBody. One subject was excluded from the perfusion, CSF, and CBF flow
analysis as they wore a facemask during MRI image acquisition which has been shown to impact
respiration and cerebral blood flow.
130,131,247
Normality assumptions were assessed through
visual inspection of the distribution of pre-post difference scores on a histogram and QQ plot.
Effect sizes were reported using Cohen’s D (d), with values of .2, .5, and .8 interpreted as small,
medium, and large, respectively.
204
Outcome measures that did not satisfy the assumptions for a
89
paired t-test were assessed using the Wilcoxon matched-pairs signed-ranks test, with effect size
(r) values of .1, .3, and .5 interpreted as small, medium, and large, respectively.
204
A one-way repeated measures ANOVA was performed to evaluate time main effects on
the Astrand, InBody, MST, and Flanker outcomes. Two subjects were excluded from the Flanker
and MST analysis due to missing data at the 12-week timepoint. Normality assumptions were
assessed through visual inspection of the distribution of the residuals on a histogram and QQ
plot. Homoscedasticity was assessed using Mauchly’s test for sphericity. Effect sizes were
reported using partial eta squared (ηp
2
), with values of .01, .06, and .14 interpreted as small,
medium, and large, respectively.
204
Outcome measures that failed the assumptions for the
ANOVA were assessed using the Friedman’s test with Kendall’s W effect size values of .2, .5,
and .8 interpreted as small, medium, and large, respectively.
204
Percent mean and standard deviation change scores, defined as the difference in scores pre-
to post-intervention divided by the pre-intervention score, were reported for all outcome
measures that were analyzed using parametric tests. Percent median and IQR change scores were
reported for all measures analyzed using non-parametric tests.
90
Results
Table 3.2: Subject demographics and physical characteristics.
Mean, standard deviation, minimum, and maximum values are shown.
Physical activity measurements were recorded using a GENEActiv accelerometer. Light, moderate, and vigorous
physical activity levels were defined according to 30, 100, and 200 mg threshold levels, respectively. The reported
values represent the average number of minutes in which accelerometer measurements were greater than the
light/moderate/vigorous activity thresholds for at least 80% of a 1-minute bout.
250
a
One subject excluded due to missing accelerometer data.
91
Cognitive Battery
Table 3.3: Descriptive statistics and effect sizes for all cognitive outcomes.
Descriptive statistics are reported as median ± IQR and effect sizes reported using r or Kendall W for data collected
at 2 timepoints and 3+ timepoints, respectively, unless otherwise specified.
a
Data normally distributed so statistics reported as mean ± SD and effect sizes reported using Cohen’s d.
b
Data normally distributed so statistics reported as mean ± SD and effect sizes reported as using η p
2
.
c
Two subjects excluded due to missing data at 12-week timepoint.
* Medium effect sizes associated with pre- to post-intervention changes.
** Large effect sizes associated with pre- to post-intervention changes.
Global Cognition: No differences in global cognition, as measured by the MOCA, were
found pre- (26.1 ± 2.81) to post-intervention (25.5 ± 3.6).
Executive Function: Two core aspects of executive function improved during the
intervention: cognitive flexibility and inhibitory control (Figure 3.4). Cognitive flexibility as
measured by TMT-B showed improvement of medium effect size, with completion times
decreasing 12.5% pre- to post-intervention (median = -12.5%, IQR = 0.0%, r = 0.41). This
improvement was slightly diminished but remained moderate even after controlling for
processing speed by subtracting out TMT A completion time (TMT B-A) (median = -25.5%,
IQR = 29.4%, r = 0.36). Inhibitory control as measured by Flanker task reaction times also
Measure Baseline 4 Weeks 8 Weeks 12 Weeks Effect Size
MOCA
a
26.1 ± 2.8 - - 25.5 ± 3.6 0.27
DKEFS TMT
Part B 80.0 ± 44.0 - - 70.0 ± 44.0 0.41*
Part A 28.0 ± 24.0 - - 29.0 ± 20.0 0.16
Part B - A 55.0 ± 34.0 - - 41.0 ± 44.0 0.36*
Digit Symbol Substitution
a
68.7 ± 15.0 71.4 ± 14.2 0.37
Flanker
Incongruent 705.8 ± 127.5 645.0 ± 183.5 691.0 ± 244.7 604.7 ± 124.2 0.31
Congruent 571.7 ± 52.0 552.1 ± 154.8 598.2 ± 179.9 542.7 ± 113.5 0.03
Interference Control 119.9 ± 49.3 102.93 ± 47.5 92.9 ± 42.4 88.6 ± 38.8 0.50*
RAVLT
Immediate Recall 49.0 ± 14.0 - - 50.0 ± 14.0 0.21
Delayed Recall 9.6 ± 4.0 - - 9.5 ± 3.9 0.05
MST
c
Lure Discrimination Index
b
0.22 ± 0.20 0.28 ± 0.18 0.38 ± 0.11 0.35 ± 0.19 0.33**
92
showed medium effect size improvement. Pairwise comparisons showed a decrease in reaction
time across all timepoints, including baseline to 4 weeks (median = -14.2%, IQR = -3.6%), 4
weeks to 8 weeks (median = -9.8%, IQR = -10.8%), and 8 weeks to 12 weeks (median = -4.6%,
IQR = -8.3%). Overall, reaction times decreased 26.1% pre- to post-intervention (median = -
26.1%, IQR = -21.2%, W = 0.50). No differences in processing speed and attention, as measured
by the Digit Symbol Substitution Test, were found pre- (68.73 ± 15.0) to post-intervention (71.4
± 14.2).
Figure 3.4: Executive Function – Improved cognitive flexibility and inhibitory control.
(A) Improvements in cognitive flexibility, as measured by TMT-B, seen pre- to post-intervention. (B) Inhibitory
control, as measured by Flanker, improved across each of the 4 timepoints.
Memory: Visual memory discrimination related to pattern separation, as measured by the
MST lure discrimination index (LDI), showed large effect size improvement with scores
increasing 59% pre- to post-intervention (+59% ± -5.0%, η
2
= 0.33) (Figure 3.5). Pairwise
comparisons showed an increase in scores across the first three timepoints, including baseline to
4 weeks (+23.8% ± -10.4%) and 4 weeks to 8 weeks (+38.4% ± -35.6%). The increase in scores
after only 4 weeks suggests that the intervention elicited immediate improvements in pattern
separation. No differences in verbal memory, as measured by RAVLT, were found pre- to post-
93
intervention on either the immediate (pre: median = 49.0, IQR = 14.0; post: median = 50.0, IQR
= 14.0) or 30-minute delayed (pre: 9.6 ± 4.0; post: 9.5 ± 3.9) word-list recalls.
Figure 3.5: Memory – Improved visual discrimination due to pattern separation.
94
Neuroimaging
Table 3.4: Descriptive statistics and effect sizes for all neuroimaging outcomes.
Descriptive statistics are reported as mean ± SD and effect sizes reported using Cohen’s d unless otherwise
specified.
a
Data not normally distributed so statistics reported as median ± IQR and effect sizes reported using test statistic
from non-parametric Wilcoxon signed rank test.
b One subject was excluded due to wearing a facemask while in MRI scanner.
c PCASL sequence not acquired in one subject.
* Medium effect sizes associated with pre- to post-intervention changes.
95
Cerebral Flow: Changes in PC-MRI measurements of both cerebral blood flow and
cerebrospinal fluid flow were found pre to post-intervention (Figure 3.6). CBF flow at the
internal carotid and vertebral arteries showed changes of medium effect size, with arterial
pulsatility decreasing 12.5% (-12.5% ± 0.5%, d = 0.58) and arterial resistivity decreasing 5.4% (-
5.4% ± -3.5%, d = 0.47), indicating a reduction in the peripheral resistance of the cerebral
arteries. CSF flow at the C2-C3 subarachnoid space showed changes of medium effect size, with
flush peak (craniocaudal) flow rates increasing 13.1% pre- to post-intervention (13.1% ± 32.6%,
d = 0.59). However, no differences were found in stroke volume (pre: 482.6 ± 173.7 mm
3
; post:
539.0 ± 212.1 mm
3
). For CSF flow at the cerebral aqueduct, no differences were found for both
stroke volume (pre: median = 50.7, IQR = 48.9 mm
3
; post: 57.8, IQR = 35.2 mm
3
) and flush
peak (pre: 210.8 ± 78.5 mm
3
/s; post: 220.7 ± 61.3 mm
3
/s). No differences were found in PCASL
whole brain cerebral blood perfusion pre- (median = 37.4, IQR = 14.4 ml/100g/min) to post-
intervention (median = 39.5, IQR = 8.6 ml/100g/min).
96
Figure 3.6: CSF and CBF Flow – Lower CBF pulsatility/resistivity and higher peak CSF flow.
(A-B) Lower arterial pulsatility and resistivity suggests reduced peripheral vascular resistance. (C) Higher cranio-
caudal peak CSF flow through C2-C3 SS suggests improved clearance.
Structural Volumes: Analysis of whole brain volumes showed changes of medium effect
size, with total grey matter volume increasing 1.2% pre- to post-intervention (median = +1.2%,
IQR = 54.6%, r = 0.32) (Figure 3.7). Within the cerebral cortex, the superior parietal lobule
showed a 2.3% increase in volume pre- to-post intervention (median = +2.3%, IQR = 18.8%,
r=0.32) of medium effect size (Figure 3.7). No changes were found in the middle frontal gyrus
(pre: 41.8 ± 3.5 cm
3
; post: 42.1 ± 3.8 cm
3
) or hippocampus (pre: median = 7.6, IQR = 0.8 cm
3
;
post: median = 7.7, IQR = 0.7 cm
3
) pre to post-intervention. Analysis of the hippocampal
substructures found no changes pre- to post-intervention at the dentate gyrus (pre: 1.64 ± 0.15
97
cm
3
; post: 1.62 ± 0.15 cm
3
), entorhinal cortex (pre: 0.93 ± 0.10 cm
3
; post: 0.93 ± 0.10 cm
3
), or
CA1 (pre: 2.68 ± 0.28 cm
3
; post: 2.67 ± 0.30 cm
3
).
Figure 3.7: Brain Structure - Increased whole brain and superior parietal lobule volumes.
(A-B) whole brain gray matter and superior parietal lobule volumes increased pre- to post-intervention.
Physical Performance
Table 3.5: Descriptive statistics and effect sizes for all physical outcomes.
Descriptive statistics are reported as mean ± SD and effect sizes reported using Cohen’s d or η p
2
for data collected 2
timepoints and 3+ timepoints, respectively, unless otherwise specified.
a
Data not normally distributed so statistics reported as median ± IQR and effect sizes reported using test statistic
from non-parametric Wilcoxon signed rank test.
b
Data not normally distributed so statistics reported as median ± IQR and effect sizes reported as using Kendall W.
* Medium effect sizes associated with pre- to post-intervention changes.
** Large effect sizes associated with pre- to post-intervention changes.
Aerobic fitness as measured by VO2max showed no differences pre- to post-intervention
(pre: median = 22.6, IQR = 20.9; post: median = 26.0, IQR = 17.7) when all subjects were
analyzed together. However, differences were found in the sub-analysis on subjects grouped by
98
exercise bike (Figure 3.8). Subjects analyzed on the Keiser M3i (n=7) showed a 2.1% overall
decrease in VO2max pre- to post-intervention (-2.1% ± -33.4%, ηp
2
= 0.12). Pairwise
comparisons showed a 14% decrease from baseline to 6 weeks (-14.1 ± -42.2%) and 14.5%
increase from 6 weeks to 12 weeks (+14.5% ± 16.8%). Subjects analyzed on the Wahoo Kickr
Snap (n=4) showed a 35.5% overall increase in VO2max pre- to post-intervention (+35.5% ± -
38.5%, ηp
2
= 0.12). Pairwise comparisons showed a 65% increase from baseline to 6 weeks
(+64.7% ± 1.8%) and 17.8% decrease from 6 weeks to 12 weeks (-17.8% ± -39.6%).
Body composition as measured by body fat % showed no differences pre- to post-
intervention (pre: median = 33.1, IQR = 9.7; post: median = 33.0, IQR = 11.3). Balance,
measured according to lower body reach using the Y-Balance test, showed medium effect size
improvements with scores increasing 6.6% pre- to post-intervention (+6.6% ± -23.7%, d = 0.47)
(Figure 3.8).
Figure 3.8: Aerobic Fitness – Improved balance.
99
Aerobic Exercise and Cognitive Training
Table 3.6: Summary of aerobic exercise and cognitive training performance in virtual reality.
a
Speed and distance traveled were estimated based on the wheelbase and gear ratio of the stationary exercise bike.
Subjects were within or above their target heart-rate zone while cycling in VR 73% of the
time. Subjects were below their target heart-rate zone 27% of the time. On a per-week basis,
subjects cycled well above their target HR for the first 8 weeks and slightly below it the last 4
weeks (Figure 3.9). Subjects spent an average of 118 minutes per week cycling in VR. On a per-
week basis, average time spent cycling closely tracked weekly target times, with subjects cycling
slightly longer the first 5 weeks and slightly shorter the last 7 weeks (Figure 3.9). Overall,
subjects cycled for approximately the prescribed amount of time, but at higher than
recommended exertion levels in the moderate phase and lower than recommended exertion levels
in the vigorous phase according to the exercise periodization protocol shown in Table 3.1.
Performance Mean ± SD (Min, Max)
Avg. Velocity (mph)
a
11.5 ± 2.0 (0, 17.5)
RPE
Baseline 6.9 ± 1.5 (6, 12)
Final 11.7 ± 2.5 (6, 17)
Peak 13.2 ± 2.7 (6, 20)
Avg. HR (bpm) 111 ± 13 (68, 155)
Time In or Above Target HR Zone (%) 73 ± 36 (0, 100)
Less than Target (%) 27 ± 36 (0, 100)
Correct Decisions (%)
Forward 93 ± 8 (52, 100)
Reverse 82 ± 16 (36, 100)
Immediate Recall 94 ± 8 (50, 100)
Delayed Recall 89.9 ± 12.1 (33, 100)
Distance Traveled (miles)
a
269 ± 13 (235, 283)
Time in VR
Total (hours) 23.7 ± 4.0 (18.7, 31.7)
Week (mins) 118.4 ± 19.9 (93.5, 158.5)
Visit (mins) 39.6 ± 6.5 (31.2, 52.8)
100
Figure 3.9: Changes in HR and time spent cycling in VR during the intervention.
(A) Exertion levels, defined as percentage of HR max, per week. Solid line indicates target exertion based on
aerobic exercise schedule. (B) Time spent cycling per week. Solid line indicates target volume based on aerobic
exercise schedule.
VR Intervention Outcomes vs. Control Group Outcomes
The design of this proof-of-concept pilot study did not include a formal control group. As
an alternative method to account for possible practice effects or natural changes in brain health
that occur over time, control groups from previously published randomized controlled exercise
and cognitive training interventions were used (Table 3.7).
252,253,254,255
These reference studies
had similar subject demographics to our VR intervention, as they were conducted in cognitively
normal older adults with mean ages ranging from 62 to 72 years old. Moreover, all but one
reference study was 12 weeks in duration. The control group was non-active in three of the five
reference studies and consisted of light stretching activities in the other two. Table 3.7 shows a
comparison between our VR intervention and the reference studies control groups for the
outcome measures that had medium-to-large effect sizes. For each outcome measure, the relative
change from baseline to 12 weeks was larger for our VR intervention compared to the control
group, supporting our findings on the positive impact of simultaneous cycling and spatial
navigation on brain health and cognition.
101
Table 3.7: VR intervention outcomes vs control group outcomes.
a
Pulsatility and resistivity calculations for the reference control and intervention were based on two different MRI
acquisition sequences. The reference control measurements were acquired using an in-house vascular compliance
sequence, while the intervention measurements were acquired using a PC-MRI sequence.
b
Y-balance scores for the control were calculated as a summation of the standing reach scores in the left and right
anterior, posteromedial, and posterolateral directions. The y-balance scores for the VR intervention were re-
calculated using the same formula as the control to provide a more meaningful comparison.
c
Reference interference control values calculated as the difference in mean incongruent (Incon) and congruent
(Con) reaction times at baseline and 8- weeks reported in the reference study as follows: baseline (Incon: 790.6 ±
137.3, Con: 603.9 ± 81.3). 8-weeks (Incon: 770.4 ± 182.2, Con: 591.3 ± 86.4)
d
Reference control values are shown for a 12-week control period between baseline and the start of a periodized
aerobic exercise intervention conducted in older adults by Dr. Tim McCauley at the CERC at USC. Participants
were advised to continue their normal daily activities during this control period.
Measure Study Characteristics Baseline 12 Weeks Baseline 12 Weeks
Structural Volume (cm3)
Total Grey Matter 548.1 ± 46.2 548.2 ± 49.3 623.9 ± 35.6 631.2 ± 55.0
Superior Parietal Lobule 24.7 ± 2.9 25.2 ± 3.5
CSF Flush Peak (mm
3
/s)
1986 ± 601 2247 ± 797
CBF Flow
a
Pulsatility Index 0.99 ± 0.23 1.01 ± 0.25 1.28 ± 0.40 1.12 ± 0.40
Resistivity Index 0.64 ± 0.06 0.65 ± 0.10 0.68 ± 0.11 0.65 ± 0.11
Y-Balance
b
396.4 ± 38.7 407.7 ± 37.3 385.9 ± 91.3 411.2 ± 69.6
MST - Lure Discrimination Index
Kovacevic et al.
252
n = 23
Age: 72 ± 6.6
Sex: 65% F
Length: 12 weeks
Activity: Stretching
0.12 ± 0.12 0.17 ± 0.17 0.22 ± 0.20 0.35 ± 0.19
TMT - Part B
Juliano et al.
253
n = 20
Age: 67 ± 11.7
Sex: 60% F
Length: 12 weeks
Activity: None
78.1 ± 29.0 79.6 ± 35.2 80.0 ± 44.0 70.0 ± 44.0
TMT - Part B - A
Nishiguchi et al.
254
n = 24
Age: 74 ± 5.6
Sex: 46% F
Length: 12 weeks
Activity: None
37.9 ± 20.7 41.5 ± 30.7 55.0 ± 34.0 41.0 ± 44.0
Flanker - Interference Control
c
Gothe et al.
255
n = 57
Age: 62 ± 5.6
Sex: 75% F
Length: 8 weeks
Activity: Stretching
186.7 179.1 119.9 ± 49.3 88.6 ± 38.8
McCauley et al.
d
n = 20
Age: 69 ± 5.8
Sex: 70% F
Length: 12 weeks
Activity: None
VR Intervention Control Groups from Other Studies
102
Benefits of VR Intervention - Cognitively Normal vs. MCI
The brain and cognitive outcomes reported in this study were based on statistical analysis
conducted with all subjects grouped together, regardless of cognitive status. This was done to
maximize power for calculating effect sizes. To understand if the benefits of our VR intervention
were differentially impacted by cognitive status, we conducted an additional exploratory
subgroup analysis. In this subgroup analysis, subjects were categorized as either cognitively
normal (CN) or mild cognitive impairment (MCI). Subjects were considered MCI if their
baseline MOCA global cognition score was < 26.
257
Table 3.8 shows baseline and change scores
for both groups for the brain and cognitive outcome measures that had medium-large effect sizes.
Table 3.8: CN vs MCI - Benefits from VR Intervention
Mean ± SD are reported for baseline and change values for each measure in both CN and MCI groups
The MCI group had poorer baseline characteristics compared to the CN group on 4 of the
5 brain outcome measures. This included lower superior parietal lobule volumes, CSF flow, and
higher CBF pulsatility and resistivity. However, improvements in these outcome measures from
baseline to 12 weeks appeared to be higher in the MCI group compared to the CN group. This
included larger relative improvements in CSF flow, CBF resistivity, total grey matter volume,
Measure Baseline Change (Pre-Post) Baseline Change (Pre-Post)
Structural Volume (cm3)
Total Grey Matter 623.8 ± 24.8 2.15 ± 10.3 630.2 ± 34.8 5.0 ± 4.3
Superior Parietal Lobule 24.6 ± 1.9 0.1 ± 1.1 24.1 ± 2.6 0.7 ± 0.9
CSF Flush Peak (mm
3
/s)
2044.7 ± 520.4 251.5 ± 526.1 1898.9 ± 784.6 274.8 ± 367.8
CBF Flow
Pulsatility Index 1.23 ± 0.47 -0.20 ± 0.28 1.35 ± 0.30 -0.09 ± 0.30
Resistivity Index 0.66 ± 0.13 -0.04 ± 0.09 0.72 ± 0.08 -0.04 ± 0.07
MST - Lure Discrimination Index 0.31 ± 0.19 0.07 ± 0.13 0.06 ± 0.08 0.23 ± 0.16
TMT - Part B 69.4 ± 19.4 -3.1 ± 19.0 120.5 ± 62.4 -23.0 ± 18.4
TMT - Part B - A 41.4 ± 21.5 -5.3 ± 20.0 77.3 ± 50.2 -17.5 ± 23.5
Flanker - Interference Control 124.4 ± 44.4 -48.1 ± 22.6 123.7 ± 30.2 -38.0 ± 16.1
CN (N=7) MCI (N=4)
103
and superior parietal lobule volume. Similar findings were also seen in the cognitive outcomes,
where the MCI group had poorer baseline characteristics but a larger improvement on 3 of the 4
measures compared to the CN group. This included larger improvements in executive function
(TMT- Part B, TMT - Part B-A) and visual memory discrimination related to pattern separation
(MST). MST performance had the highest differential, with lure discrimination scores increasing
0.22 in the MCI group compared to 0.07 in the CN group (Figure 3.10). Overall, this suggests
that the VR intervention elicited larger benefits to brain health and cognition in the MCI subjects
compared to the CN subjects.
Figure 3.10: CN vs MCI - Visual Memory (MST) Improvements
104
Discussion
In this pilot study we showed that virtual reality can be utilized for combined exercise
and cognitive enrichment activities in older adults. Moreover, we showed that a 12-week
intervention consisting of simultaneous cycling and spatial navigation elicits positive changes in
neuroimaging and neuropsychological measures of brain health. We also showed that the
benefits of this intervention may be mediated by cognitive status, as MCI individuals showed
larger improvements than CN individuals on 7 of 9 brain and cognitive outcome measures.
Cognitive Performance
Among the neuropsychological measures, we found improvements in key aspects of
memory and executive function. Improvements in pattern separation, a hallmark feature of
episodic memory and a sensitive measure of hippocampal function, were found with a dose-
dependent effect over 4 timepoints.
210
This is a key finding as pattern separation has been shown
to decline across the lifespan, with older adults performing poorly compared to younger adults
on recognition memory tasks requiring visual pattern separation due to age-related alterations in
hippocampal integrity.
211,212
Our findings support previous studies which have shown that pattern
separation can be improved in older adults through exercise and environmental enrichment. In
one such environmental enrichment study, older adults showed significant improvement in MST
lure discrimination performance after a 4-week intervention involving spatial exploration and
learning in the real world.
213
In another exercise-only study, a 30% improvement in MST lure
discrimination performance was found in older adults after a 12-week intervention involving
high-intensity interval training.
214
105
Executive function is a heterogenous concept consisting of several subcomponents that
are differentially affected by age.
215
In this study, we assessed three core subcomponents:
attention, cognitive flexibility, and inhibitory control. Improvements in both cognitive flexibility
and inhibitory control, but not attention, were observed. These improvements in executive
function are consistent with previous studies, including a meta-analysis assessing the impact of
aerobic exercise on executive function in older adults.
216
In the meta-analysis, aerobic exercise
interventions were associated with modest, but significant improvements on a battery of
assessments measuring executive function.
216
This included cognitive flexibility, in which 5
studies, 6 to 17 weeks in duration, showed significant improvement in TMT-B completion
times.
216
Cerebral Flow
Among the neuroimaging measures, we found positive changes in cerebral blood flow
(CBF) pulsatility and resistivity at the arteries in the cervical C2-C3 region, but no improvements
in whole brain perfusion. Pulsatility and resistivity are important measures of cardiovascular
health and are strong related to arterial elasticity.
217
The elastic property of arteries serves to
dampen harmful pulsations in blood flow throughout the brain. Aging is associated with a
gradual stiffening of the arteries, resulting in higher pulsatility due to increased peripheral
vascular resistance.
218
This dysregulation of cerebral blood flow can cause structural damage in
the brain as well as hypertension, a risk factor for developing Alzheimer’s disease.
1
As a result,
pulsatility may be a potential early biomarker for AD. This is supported in several studies,
including one which showed that individuals with MCI and AD had higher arterial pulsatility
relative to healthy older adults.
71
106
The improvements in CBF flow found in our study are likely driven by exercise which
has been shown to lower arterial stiffness and may play a key role in reversing cerebrovascular
dysfunction. Moreover, exercise is a modifiable risk factor for AD, suggesting that interventions
that incorporate aerobic exercise may be useful for preventing cognitive decline. This has been
supported in several studies, including a cross-sectional study on middle-aged adults, in which
higher aerobic fitness was associated with lower arterial stiffness and better cognitive
performance.
220
In another 12-week study in older adults, it was found that the aerobic exercise
group had significantly lower pulsatility and resistivity compared to the control group at the
basilar, vertebral, posterior, anterior, and middle cerebral arteries.
219
This supports the findings
from our study, which show lower pulsatility and resistivity at the vertebral and carotid arteries.
As an exploratory analysis, we also assessed cerebrospinal fluid flow (CSF) at the
cerebral aqueduct and C2-C3 region. CSF flow dysfunction in these two regions is commonly
associated with normal pressure hydrocephalus
71
, syringomyelia
76
, and Chiari malformations.
78
However, recent studies have also shown that CSF flow alterations occur with normal aging. In
one such study, it was shown that stroke volumes and flush peak flow rates at the C2-C3 region,
and stroke volumes at the cerebral aqueduct, were lower in older adults compared to younger
adults.
107
While studies assessing CSF flow in AD are limited, there is evidence to suggest it
plays an important role in the clearance of amyloid plaques from the brain. This clearance
pathway is characterized by arterial pulsatility which drives CSF flow through a network of
perivascular channels, facilitating elimination of waste from the interstitial space.
5,29,30,34,35
Taken
together, this suggests that low CSF flow may be associated with impaired clearance.
Studies assessing the effects of exercise on CSF flow are limited. However, as exercise
has been shown to improve CBF flow, it is expected that changes in CSF flow would occur as
107
well due to the dynamic link between CBF and CSF flow in the brain established by the Monroe-
Kellie doctrine.
105
While we did not find any differences in CSF flow at the aqueduct, we did
find CSF flow changes at the C2-C3 region. Specifically, we found improvements in peak
cranio-caudal CSF flow rates. Taken together, this suggests that exercise induces alterations in
CSF and CBF flow that may enhance waste clearance from the brain.
Structural Volumes
Structural changes in the brain, including increases in total grey matter and superior
parietal lobule volumes, were observed. This a key finding as normal aging is associated with
brain volume loss, with estimates ranging from 0.32% to 0.55% per year in healthy adults 70
years of age or older.
221
Moreover, regions of brain volume loss are differentially impacted by
age, with larger losses occurring in the frontal, parietal, and temporal lobe.
223
Improvements in
brain volume have been shown in several exercise studies, including one which found significant
increases in grey matter volume in older adults who engaged in 6-months of moderate aerobic
exercise.
223
In another study, it was shown that 82% of grey matter volume in the brain was
associated with physical activity, including aerobic exercise, weight lifting, running, martial arts,
and sports.
222
Taken together, this supports the findings in our study and suggests that brain
volume in older adults may be preserved and enhanced after only 3 months of aerobic exercise at
moderate-to-vigorous intensity levels. In addition to exercise, cognitive enrichment may also
play an important role in enhancing brain volume. This is supported by our findings at the
superior parietal lobule, an important region for forming egocentric representations of space in
spatial memory.
256
Improvements observed specifically at this region suggests that our cognitive
108
training paradigm targeting spatial memory engagement may have subserved the impact of
aerobic exercise on brain volume.
Interestingly, we did not find any changes in hippocampal volumes. This included the
hippocampal subfields CA1, dentate gyrus, and entorhinal cortex. Preserving hippocampal
volumes is of critical importance as the dentate gyrus is the primary site of neurogenesis while
the CA1 and the entorhinal cortex are important regions for spatial memory. Moreover, the
hippocampus is highly sensitive to the effects of aging, with volume loss estimated to be as high
as 1.1% per year in older adults.
224
Previous studies assessing the impact of aerobic exercise on
hippocampal volumes have yielded results with significant heterogeneity. In one such study,
hippocampal volumes increased 2% after 1 year of aerobic exercise consisting of walking at
moderate intensities.
4
However, in another study in which older adults exercised at moderate-to-
vigorous intensities over 16 weeks, it was shown that hippocampal volumes were preserved but
not enhanced.
225
This was supported in a meta-analysis, which showed that exercise prevented
decreases in hippocampal volume over time.
226
Overall, the impact of exercise on hippocampal
volumes remains unclear. Moreover, exercise did not seem to be subserved by cognitive
enrichment, as we found no changes to hippocampal volumes despite placing specific demands
on the hippocampal subfields involved with spatial memory using spatial navigation tasks in
virtual reality. It is also possible that our 3-month intervention was not long enough to elicit
significant changes to hippocampal volumes.
Aerobic Fitness and Body Composition
Among the physical measures, no differences were found in body fat. This may be due to
the duration of the study, with 3 months not being long enough to elicit changes in body
109
composition in older adults. It is also possible that body fat readings, as measured by the
InBody770, were confounded by other factors such as hydration. In addition to body fat, we also
found no changes in VO2max pre- to post-intervention. This may be due to changing the testing
instrument from a Keiser M3i to a Wahoo Kickr Snap halfway through the study due to the onset
of COVID. In the subgroup analysis looking at only the Keiser M3i (n=7), VO2max increased
from baseline to 6 weeks but decreased from 6 weeks to 12 weeks. This could indicate the
subjects in this subgroup started the study at high aerobic fitness levels and experienced
deconditioning while engaging in moderate aerobic exercise the first 6 weeks.
Participant Adherence to the Intervention
Another objective of this pilot study was to assess how well older adults could adhere to
an aerobic exercise protocol while engaging in cognitive training in VR. Our periodized aerobic
exercise routine was designed for subjects to cycle at moderate (50%-60% HR max) intensity
levels the first 6 weeks and vigorous (70%-80% HR max) intensity levels the last 6 weeks. We
found that subjects cycled well above their moderate target exertion levels the first 6 weeks. This
may be due to age, as older adults have a lower estimated HR max which yields a target HR that
is close to their resting HR at low prescribed intensity levels. Subjects cycled approximately
within their target exertion levels in weeks 7 through 10, but slightly below in weeks 11 and 12.
This may be due to the design of the cognitive training paradigm, where subjects do not have
enough time to get up to the required heart rate before the end of the trial. Overall, however,
subjects were successfully able to reach target exertion levels, as they cycled within or above
their target HR zone 73% of the time. Moreover, subjects were also able to cycle for the
prescribed duration, as the average cycling time for subjects tracked closely with the target times
110
each week. Taken together, this suggests that it is possible to incorporate an aerobic exercise
routine that adheres to the Surgeon General guidelines for exercise in older adults while
simultaneously engaging in a cognitive training paradigm in virtual reality.
Cognitive Training
The cognitive training paradigm was designed to progressively increase in difficulty
throughout the intervention. Overall, all subjects performed well on the navigation tasks, making
correct decisions at 93% ± 8% of the intersections (min: 52%, max 100%). The high
performance on these tasks indicates that the older adult subjects were successfully able to
navigate and learn new routes in virtual reality. This suggests that the visual cues in the virtual
environments, including the landmarks at each intersection, were sufficient for spatial learning.
Due to the high performance on the navigation tasks, subjects may benefit from a higher level of
route difficulty. This can be achieved by adding more intersections or reducing the number of
cued learning trials.
Participant Retention and Attrition
A total of fifty-one older adults were contacted and screened for eligibility to participate
in this study. Thirty-six eligible older adults completed an additional VR screen, with 27% (n =
10) excluded from further participation due to self-reported symptoms of simulator sickness
associated with cycling in a virtual environment. This was expected, as the exclusion rate was
comparable to our previously published feasibility study in which 25% of younger and older
adults experienced adverse effects that exceeded acceptable threshold levels.
163
Among the 26
subjects that passed the VR screen, fifteen completed baseline data collection with eleven
111
completing the entire 12-week intervention and all data collection timepoints, yielding a 26%
attrition rate. While the attrition rate appears relatively high, it was confounded by the onset of
COVID. Indeed, two subjects discontinued due to safety concerns regarding COVID and not the
intervention itself. After excluding for this unprecedented pandemic, the attrition rate dropped to
13%, which is well within previously established guidelines for clinical trials.
207
Moreover, in a
systematic review on exercise studies in sedentary adults, it was found that the average rate of
attrition for prescribed weightlifting and aerobic exercise periodization routines was 9.5%.
206
In
this study, only 6% of the study sample (n = 1) discontinued due to non-compliance with the
prescribed intervention, which is lower than the attrition rates seen in other exercise studies.
Adverse Effects in Virtual Reality
The primary concern with coupling locomotion and virtual reality while wearing an
immersive HMD is adverse effects due to sensory incongruence induced by a mismatch between
perceived motion in VR and actual motion in the real world.
19,20,21
In this study, only one subject
discontinued due to significant adverse effects, suggesting the VR screen was successful in
preemptively excluding individuals with high sensitivity to simulator sickness. A few subjects
did experience mild adverse effects, including one subject that experienced slight nausea and
stomach awareness sporadically throughout the intervention. Two subjects also experienced
slight nausea within the first week, but quickly acclimated to VR and reported no adverse effects
on any of the subsequent visits afterwards. Overall, symptoms were temporary and self-resolved
in all cases. A preliminary acclimation period that gradually increases the time spent in VR may
help prevent these initial transient symptoms.
112
Aside from adverse effects, there was also a concern that prolonged exposure to VR may
elicit maladaptive changes to balance. Previous studies have shown that immersive virtual
environments can cause postural instability.
208
One such study found an association between
duration of exposure and center of pressure excursion, such that postural control was
significantly reduced after 3 hours of immersion in VR.
209
While these effects were typically
transient, the concerns were enhanced in our study as subjects spent an average of 40 minutes per
visit, 118 minutes per week, and 23 total hours in VR. Therefore, the y-balance test was
administered at baseline and at the end of the intervention to evaluate changes in postural
stability. Analysis of the y-balance showed an improvement in balance pre to-post intervention,
suggesting there were no persistent deleterious effects of VR on postural stability.
Limitations
There were several notable limitations to this pilot study. The primary limitation was a
small sample size of 11 subjects, which is insufficient for assessing statistical significance.
Instead, effect sizes and percent changes were reported for each measure. The lack of a control
group also makes it difficult to assess practice effects that could occur with repeated
neuropsychological testing, or to assess natural change that occurs in the brain over time.
Instead, control groups from previously published randomized controlled studies with similar
subject demographics were utilized to approximate natural changes in brain health and cognition
over 12 weeks. Control group comparisons showed a lack of practice effects for the
neuropsychological tests, including the Flanker, DKEFS TMT-B, and MST. This is supported by
a few studies, including one in which MST was empirically validated to show no practice effects
113
with repeated short-term testing.
184
Finally, because this study was designed to be a proof-of-
concept pilot study with a small sample size, the inclusion of a control group is not required.
251
An additional limitation of this study was the heterogeneity of the subject demographics.
Our study included all adults between 50 and 85 years of age, spanning almost the entire range of
older adulthood. This may have confounded our findings as age differentially impacts brain
structure and cognitive function. Similarly, our sample was imbalanced by gender, with almost
twice as many women participating in the study as men. Furthermore, the subjects in our study
were physically active and highly educated. Self-reported measures of physical activity showed
that most subjects engaged in weightlifting or aerobic exercise, including walking and cycling,
prior to the start of the intervention. As a result, the positive changes in brain health associated
with aerobic exercise and cognitive training in this active, well-educated cohort may
underestimate the true changes observed in a more typical, sedentary older adult population.
Finally, while the benefits of aerobic exercise and cognitive training have been shown to appear
within 12 weeks, longer intervention durations may be needed to elicit changes in key regions
such as the hippocampus.
Conclusion
In this pilot study we showed that a 12-week intervention consisting of simultaneous
aerobic exercise and cognitive training in virtual reality elicits positive changes in executive
function, memory, brain structure, and cerebral blood flow. We also showed that this
intervention can be conducted in older adults, ages 50-85, with high compliance and low
attrition. Future steps include conducting this study in a larger, randomized controlled clinical
trial. This clinical trial should be structured to have at least 2 control arms, including aerobic
114
exercise-only and cognitive training-only. This will allow for understanding the relative benefits
of engaging in combined activities over each one individually.
115
Summary & Conclusions
Alzheimer’s disease is arguably the most serious healthcare issue of our generation. It is the
6
th
leading cause of death in the United States and the leading cause of dementia in older adults,
affecting one in ten individuals ages 65 and older.
137
As there are currently no effective disease
modifying-treatments, identifying new ways to promote brain health is of critical importance. In
this dissertation, we presented findings regarding the use of a novel virtual reality exergaming
system to promote brain health in older adults at risk for Alzheimer’s disease. Specifically, we
showed that older adults can safely use this exergaming system to engage in simultaneous
cycling and spatial navigation in virtual reality with reports of enjoyment and minimal adverse
effects. We also showed that a 12-week intervention engaging these simultaneous activities
elicits positive changes in brain health, including improvements in memory, executive function,
and brain volume. Finally, we show that CSF and CBF flow dynamics, potential biomarkers of
brain health, can be reliability measured using MRI. Overall, this work provides supporting
evidence for the benefits of simultaneous aerobic exercise and cognitive enrichment on brain
health and represents a critical first step towards establishing our VR exergaming system as a
non-invasive technology for the primary prevention of Alzheimer’s disease.
116
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Abstract (if available)
Abstract
Introduction: Alzheimer’s disease (AD) is a significant public health concern in older adults. It is characterized by progressive memory loss, with deficits in spatial memory resulting in difficulty navigating familiar spaces, ultimately compromising safety, autonomy, and quality of life. Identifying new ways to maintain brain health is of utmost importance as there are currently no effective disease-modifying treatments. Simultaneous exercise and cognitive enrichment have been shown to enhance brain function in both animal and human studies. Virtual reality (VR) is a promising tool for engaging in this combined activity. The goal of this dissertation was to 1) assess safety and feasibility of a novel VR exergaming system that combines cycling-based aerobic exercise and cognitive enrichment via targeted spatial navigation training, 2) develop and test a novel MRI biomarker of brain health, and 3) examine the effects of a 12-week VR intervention on proof-of-concept outcomes for brain health and cognition in healthy older adults. ❧ Methods: The exergaming system consisted of a custom-built stationary exercise bike and virtual reality game viewed through an immersive VR head-mounted display. The game was designed with progressively challenging navigation tasks targeting spatial memory and attention. Landmarks were strategically placed at every intersection to serve as visual cues. Prior the intervention, a preliminary feasibility study was conducted to assess adverse effects and enjoyment associated with cycling and spatial navigation in an immersive virtual environment. An additional study was conducted to evaluate reliability of MRI for measuring cerebral blood flow (CBF) and cerebrospinal fluid flow (CSF), potential biomarkers of brain health to be used as an outcome measure for the intervention. Finally, a 12-week intervention consisting of simultaneous exercise and spatial navigation training was conducted in older adults ages 50-85, to assess impact on cognitive function, CBF and CSF flow, and brain morphometry. ❧ Results: For the feasibility study, exposure to virtual reality was associated with high arousal and low stress levels in older adults. Symptoms of simulator sickness (adverse effects) were enhanced but within an acceptable range. No association was found between physical exertion levels and simulator sickness levels. For the reliability study, among the 26 CSF and CBF flow parameters analyzed, 22 had excellent test-retest reliability (ICC>0.80), including measurements of CBF arterial pulsatility index and resistivity index. All CSF and CBF flow measurements had excellent inter-rater and intra-rater reliability. From the 12-week simultaneous exercise and spatial navigation training intervention, improvements were found in CBF flow and brain structure. Total grey matter and superior parietal lobule volumes showed improvements of medium effect size, increasing 1.1% and 2.2%, respectively. Arterial blood flow pulsatility decreased 12%, indicating a medium reduction in peripheral vascular resistance. Cognitive benefits were also observed, with measurements of cognitive flexibility, response inhibition, and pattern separation showing medium-to-large improvements of 13%, 26%, and 55%, respectively. ❧ Conclusion: In this dissertation, we showed that it is feasible for older adults to engage in simultaneous cycling and spatial navigation in an immersive virtual environment, and that the experience is associated with low stress, high arousal, reports of enjoyment, and minimal adverse effects. Moreover, we show that CSF and CBF flow, potential biomarkers of brain health, can be measured with excellent reliability using MRI. Finally, we show that a 12-week intervention engaging these simultaneous activities elicits positive changes in cognition and brain health, with improvements in executive function, memory, brain volume, and CBF flow. Overall, this dissertation provides supporting evidence for the benefits of combined exercise and cognitive enrichment on brain health and represents a critical first step towards establishing our VR exergaming system as a non-invasive technology for the primary prevention of AD.
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Asset Metadata
Creator
Sakhare, Ashwin Rajan
(author)
Core Title
A virtual reality exergaming system to enhance brain health in older adults at risk for Alzheimer’s disease
School
Viterbi School of Engineering
Degree
Doctor of Philosophy
Degree Program
Biomedical Engineering
Publication Date
03/26/2021
Defense Date
03/04/2021
Publisher
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Tag
aging,Alzheimer's disease,brain health,cerebral blood flow dynamics,cerebrospinal fluid flow dynamics,cognitive decline,exergaming,OAI-PMH Harvest,serious games,virtual reality
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English
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Pa, Judy (
committee chair
), Khoo, Michael (
committee member
), Valero-Cuevas, Francisco (
committee member
), Wang, Danny (
committee member
)
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arsakhare87@gmail.com,sakhare@usc.edu
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Tags
Alzheimer's disease
brain health
cerebral blood flow dynamics
cerebrospinal fluid flow dynamics
cognitive decline
exergaming
serious games
virtual reality