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Cerebrovascular mechanisms of cognitive enhancement after periodized resistance training in older adults

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CEREBROVASCULAR MECHANISMS OF COGNITIVE ENHANCEMENT AFTER
PERIODIZED RESISTANCE TRAINING IN OLDER ADULTS

by

Timothy Ryan Macaulay

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
(BIOKINESIOLOGY)

August 2020

Copyright 2020 Timothy Ryan Macaulay

ii

DEDICATION

To all those who have smiled in the face of adversity and persevered in the
pursuit of their dreams.

To my family, friends, and mentors who have given me unconditional love
and unwavering support throughout my journeys.

iii

ACKNOWLEDGEMENTS
“The important thing is that your teammates have to know you're pulling for
them and you really want them to be successful.”

- Kobe Bryant (1978 – 2020)

I would like to thank all the people that believed in me and took a chance on me coming
straight out of undergrad. While I came in relatively untested, these last four years have
challenged me to grow and develop as a researcher, and everyone I have interacted with USC has
been interested in helping. Thank you, Dr. Gordon and all the Faculty in the Division, for
providing the resources and opportunities that allowed me to prosper. And special thanks go out
to the dissertation committee and co-investigators. As I write this, the COVID-19 crisis and
consequent lockdowns have created an unprecedented roadblock for this dissertation. Despite
this black swan event, we quickly developed online capabilities and transitioned to virtual
communications from home. I am proud of our adaptability and am grateful that our prior
planning and organization provided a buffer for the completion of this dissertation.
Dr. Jason Kutch, you are one of the clearest thinkers I have ever met. After every one of
our meetings, I came out with a breakthrough idea, wondering why I hadn’t thought of it sooner.
Thank you for your skillful use of analogies that made each of our conversations engaging and
fruitful. Dr. Dominique Duncan, you were always so easy to connect with and willing to help.
You, Dr. Marianna La Rocca, and Dr. Ryan Cabeen were so crucial for the structural MRI data
processing components of the study. Thank you all for being so patient with me as I banged my
head on the keyboard. Dr. Christi Lane, I always had a great time working with you. Your
expertise in statistics and chocolate were presented with an infectious confidence. Thank you for

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always making our analysis discussions fun. Dr. Lirong Yan, I am blown away by how
industrious you are. I know the ASL processing techniques that you performed for this study are
highly involved and time-consuming. Thank you for all your contributions to this project,
especially when it came down to crunch-time. Dr. Judy Pa, your knowledge of the field was a
key driver in this study’s success. Many of our strategic decisions were justified by your
previous experiences. Thank you for your inspiration and endless support for this project.
And to my advisor, Dr. E Todd Schroeder, you have shown me how to combine
productivity and entertainment, how to approach research with endless curiosity, and how to be
open to testing new ideas, even if just for the fun/challenge of it. When I had an interest in
exercise-induced neuroplasticity, a topic that you had no experience with, you constantly joked
that you were a “neck down” physiologist, but did not hesitate to allow me to pursue this
dissertation topic with a new team of neuroscientists. In addition, I knew little about industry
research before joining you, but you opened my eyes to the numerous ways our skills were
valued outside of academia. We tested celebrities, performed validation studies, and wrote
private white papers. We took it a step further with the backwards bike study, which started out
as just a fun experiment to mess with students, but turned into a rigorous and exciting study once
the motor learning experts joined in. Thank you for always putting your people first. You are the
reason the CERC has such a great reputation in research, teaching, and workplace fun.
In summary, I cannot emphasize enough how impactful that these last four years have
been. Thank you to my family and all the friends I have made for allowing me to make my own
decisions and supporting me no matter what. Although I am sad to be leaving everyone at USC, I
know that they will continue to conduct strong research and develop great students. Meanwhile, I
am prepared to lead my own research programs and make the Trojan family proud.

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TABLE OF CONTENTS

DEDICATION ................................................................................................................................ ii

ACKNOWLEDGEMENTS ........................................................................................................... iii

LIST OF TABLES ........................................................................................................................ vii

LIST OF FIGURES ..................................................................................................................... viii

ABSTRACT ................................................................................................................................... ix

CHAPTER I: AIMS ........................................................................................................................ 1

CHAPTER II: BACKGROUND & SIGNIFICANCE ................................................................... 4
Statement of the Problem ............................................................................................................ 4
Cognitive Benefits of Resistance Training ................................................................................. 4
Potential Mechanisms ................................................................................................................. 5
Preliminary Information ............................................................................................................. 6

CHAPTER III: LITERATURE REVIEW ...................................................................................... 9
Introduction ................................................................................................................................. 9
Cognitive Changes .................................................................................................................... 10
Physiological Factors ................................................................................................................ 13
Psychological Factors ............................................................................................................... 20
Conceptual Relationships ......................................................................................................... 23
Future Directions: ..................................................................................................................... 25
Conclusions ............................................................................................................................... 28

CHAPTER IV: 12 WEEKS OF PERIODIZED RESISTANCE TRAINING IMPROVES FLUID
COGNITION IN OLDER ADULTS ............................................................................................ 29
Abstract ..................................................................................................................................... 29
Introduction ............................................................................................................................... 30

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Methods .................................................................................................................................... 32
Results ....................................................................................................................................... 42
Discussion ................................................................................................................................. 50

CHAPTER V: IMPROVEMENTS IN CEREBROVASCULAR FUNCTION AFTER 12
WEEKS OF PERIODIZED RESISTANCE TRAINING IN OLDER ADULTS ........................ 60
Abstract ..................................................................................................................................... 60
Introduction ............................................................................................................................... 61
Methods .................................................................................................................................... 64
Results ....................................................................................................................................... 71
Discussion ................................................................................................................................. 84

CHAPTER VI: IMPROVEMENTS IN BRAIN STRUCTURE AND FUNCTION AFTER 12
WEEKS OF PERIODIZED RESISTANCE TRAINING IN OLDER ADULTS ........................ 90
Abstract ..................................................................................................................................... 90
Introduction ............................................................................................................................... 91
Methods .................................................................................................................................... 93
Results ....................................................................................................................................... 99
Discussion ............................................................................................................................... 106

CHAPTER VII: SUMMARY & CONCLUSIONS .................................................................... 111

REFERENCES ........................................................................................................................... 114

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LIST OF TABLES

Table 2.1 Case Study Vascular Compliance Results (N = 1).

8
Table 4.1 Participant Characteristics at Baseline (N = 20).

34
Table 4.2 Group means for cognitive outcomes, physiological outcomes, and
questionnaire responses at baseline, pre-intervention, and post-intervention
(N = 20).

42
Table 4.3 Correlation analyses between pre- to post-intervention changes in fluid
cognition composite score and other outcome variables of interest (N = 20).

49
Table 5.1 Group means for CV outcomes at baseline, pre-intervention, and post-
intervention (N = 20).

72
Table 5.2 Correlation analyses between pre- to post-intervention changes in fluid
cognition composite score and changes in CV outcomes (N = 20).

83
Table 6.1 Group means for MRI outcomes at baseline, pre-intervention, and post-
intervention (N = 20).

100
Table 6.2 Correlation analyses between pre- to post-intervention changes in fluid
cognition composite score and changes in MRI outcomes (N = 20).
106

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LIST OF FIGURES

Figure 2.1 Representative maps of cerebral blood volume (CBV).

7
Figure 2.2 Case study percent changes from pre- to post- intervention (N = 1).

8
Figure 3.1 Potential relationships between resistance training (RT) and cognitive
enhancement (Cog).

24
Figure 4.1 Flow diagram of participant recruitment and testing.

33
Figure 4.2 NIH Toolbox Cognition Battery uncorrected standard scores assessed at
baseline, pre-intervention and post-intervention.

46
Figure 5.1 Representative maps of relative cerebral blood volume (CBV).

69
Figure 5.2 Representative map of cerebral blood flow (CBF) from a single subject.

74
Figure 5.3 Global cerebral blood flow (CBF) at baseline, pre-intervention, and post-
intervention (N = 19).

75
Figure 5.4 Voxel-based statistical analyses of group cerebral blood flow changes.

75
Figures
5.5-5.8
Regional cerebral blood flow at baseline, pre-intervention, and post-
intervention (N = 19).

77-
78
Figure 5.9 Vascular compliance (VC) in A) large arteries and B) small arteries at
baseline, pre-intervention, and post-intervention (N = 17).

80
Figure 5.10 Representative image of white matter lesion volume segmentation.

81
Figure 5.11 White matter lesion (WML) volume at baseline, pre-intervention, and post-
intervention (N = 20).

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Figure 6.1 Hippocampal gray matter volume at baseline, pre-intervention, and post-
intervention (N = 20).

103
Figure 6.2 Posterior cingulate gray matter cortical thickness at baseline, pre-intervention,
and post-intervention (N = 19)

103
Figure 6.3 Example image of pyramidal tract position within the brain.

104
Figure 6.4 Example map of pyramidal tract fractional anisotropy (FA).

105

ix

ABSTRACT

The overall goal of this project was to investigate the effects of periodized RT on
cognition and overall brain health in older adults and to explore the potential mechanisms of
benefit. While there is ample evidence to suggest that long-term RT enhances cognitive function,
demonstrating rapid health benefits following higher-intensity RT may improve the clinical
utility of such interventions. In addition, further investigations are necessary to understand the
mechanisms of RT-induced cognitive enhancement that, for example, link skeletal muscle
physiology and function with brain morphology and neuroplasticity. We hypothesized that RT
improves cognitive function through physical and cerebrovascular adaptations. RT may be a vital
form of exercise to maintain both the physical and cognitive capacities necessary for functional
independence in older adults.
We applied standardized cognitive testing and advanced neuroimaging techniques to a
12-week periodized RT intervention in 20 healthy older adults. The NIH Toolbox® Cognition
Battery and multimodal 3T Magnetic Resonance Imaging were used along with physical
outcome measures such as muscular strength, body composition, and physical function. In this
one-group pilot study, all participants received supervised RT, thus participants served as their
own controls. Tests were administered at baseline, pre-intervention, and post-intervention.
Control periods took place before the RT intervention to ensure that results were not confounded
by detraining effects or long-term cognitive benefits of RT. The 12-week periodized RT
intervention involved 3 supervised training sessions per week, emphasizing muscular strength
development with linear increases in intensity.

x

This study demonstrated that 12 weeks of periodized RT improves fluid cognition in
older adults. Such changes in cognitive abilities have a critical yet often overlooked impact on
optimal performance in various populations. Detecting such rapid cognitive benefits could
improve the clinical utility of RT interventions by reducing time and costs and improving
adherence to the program. Exercise specialists will play a key role in implementing these
strength and conditioning programs according to sound scientific principles.
This study also generated effect sizes for changes in cerebrovascular function, brain
morphology, and functional connectivity. Our data will be used to design and justify future
studies that establish a neuroscientific foundation of RT’s benefit to the brain. Understanding the
specific cerebral outcomes of different training programs will allow fitness professionals to
design optimal exercise prescriptions, targeting fundamental processes of aging beyond physical
capacity.

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CHAPTER I:
AIMS

Exercise is a promising strategy to slow or prevent the progression of cognitive decline
and dementia (1). While resistance training (RT) is a popular form of exercise recommended for
older adults for its putative role in the protection against sarcopenia, its effects on brain health
have been less-well studied (2). Previous RT studies involving traditional protocols highlight
robust and lasting benefits to fluid cognition (3). In contrast, little is known about the effects of
high-intensity RT with advanced periodization techniques. Such protocols may produce similar
improvements in shorter durations, thus improving clinical utility (4). In addition, the
mechanisms of RT-induced cognitive enhancement are not well understood. Defining the
dominant neural and physiological processes that underlie these protective effects is essential to
explain the variance in cognitive responses and to design optimal treatment strategies for the
aging population.
The effectiveness of RT interventions for cognitive health may depend on maximizing
gains in muscular strength (5, 6). Therefore, we will apply strength and conditioning
principles and advanced neuroimaging techniques to a 12-week periodized RT intervention
(7) to determine neuroprotective contributions. Cerebrovascular (CV) function is a major
component of brain health and a primary mechanism of cognitive enhancement after aerobic
training (8, 9). However, the potential for RT to influence these pathways has not been
determined. Our study will test and extend previous cross-sectional evidence linking RT with CV
function (10). In addition, RT interventions have been demonstrated to improve systemic
vascular function (11, 12) and cognition (13-15) independently. Thus, we hypothesize that RT
improves cognitive function through muscular strength and CV adaptations. The overall goal of

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this dissertation is to investigate changes in cognition and CV function in 20 healthy older
adults 60-80 years of age, serving as their own controls, and explore the mechanisms that
mediate these effects. To address this goal, we propose the following aims:

Aim 1: To determine whether 12 weeks of periodized RT improves fluid cognition in older
adults. The NIH Toolbox® Cognition Battery (16) will be administered at baseline, pre-
intervention, and post-intervention to assess changes in executive function, attention, episodic
memory, processing speed, working memory, and language. Composite scores will be computed
for fluid, crystallized, and global cognitive function. Three test versions will be used to reduce
practice effects (17).
Hypothesis: A 12-week periodized RT program designed specifically to maximize strength
gains improves fluid cognition in healthy older adults.

Aim 2: To evaluate the effects of periodized RT on CV function in older adults. Arterial spin
labeling (ASL) MRI techniques will be used to assess changes in CV function by quantifying the
effects on resting cerebral blood flow, intracranial vascular compliance, and arterial transit time
(18). Mediation analyses will be performed to determine the mediating effects of CV function on
cognition.
Hypothesis: High-intensity RT can improve CV function in healthy older adults.

Aim 3: To explore associations between brain adaptations and potential mechanisms of
benefit, including: 1) physical capacity (e.g. muscular strength), body composition, and
functional mobility; 2) putative blood markers of exercise-induced brain plasticity (19); 3)

3

cardiovascular risk factors; and 4) brain morphometry and resting state brain activity. Significant
changes will be analyzed for mediation of cognitive function to identify the major pathways that
underlie the protective effects of RT.

This study will demonstrate the effects of a high-intensity RT intervention on
cognition and CV function in older adults. Detecting rapid cognitive improvements could
encourage the implementation of RT as a countermeasure against the rising prevalence of
cognitive dysfunction. Supportive evidence for CV plasticity will be used as preliminary data in
future definitive trials. Because of the proven value of cardiovascular risk factors for the
prevention of heart disease, measures of CV function are proposed to have a similarly positive
impact on the prevention of vascular forms of dementia (20). Finally, we expect to elucidate the
magnitude and mechanisms of RT-induced cognitive enhancement, knowledge that is critical to
design optimal treatment strategies targeting overall brain health during normal aging and aging
associated with dementia.

4

CHAPTER II:
BACKGROUND & SIGNIFICANCE

Statement of the Problem
Cognitive impairment is a major public health concern. Dementia is an age-related
disease caused by neurodegeneration and characterized by progressive cognitive decline that
interferes with independent living (21). The resulting loss of functional independence and
reduced quality of life places substantial monetary demands on the individual and the public
health system (22). Because irreversible brain damage often occurs before patients present to
clinic with demonstrable cognitive dysfunction (23, 24), successful treatment likely requires pre-
symptomatic neuroprotective therapies, as opposed to disease reversal. The most promising
strategy to reduce the incidence of disease may be to target modifiable risk factors in seemingly
healthy older adults (25).

Cognitive Benefits of Resistance Training
Resistance training (RT) enhances cognitive function in older adults. With aging,
even healthy older adults experience changes in fluid cognitive functions, such as executive
function, processing speed, and memory. However, there is strong evidence to suggest that
higher levels of physical activity reduce the rate of cognitive decline and risk of dementia (26).
While most studies have focused exclusively on aerobic training, recent findings highlight the
additional cognitive benefits of traditional RT (3). Randomized controlled trials demonstrate that
RT has similar, but independent effects on cognition compared to other forms of exercise (13-
15). One meta-analysis reported that RT has a positive effect (d = 0.71) on global fluid cognition

5

(27). However, most of these studies involved traditional training techniques and little is known
about the effects of higher-intensity RT. Demonstrating rapid health benefits with high-intensity
RT could encourage the implementation of RT as a countermeasure against the rising prevalence
of cognitive dysfunction.

Potential Mechanisms
RT may have additional benefits to overall brain health. Unlike aerobic training, RT-
induced cognitive enhancement is not associated with changes in aerobic capacity, but rather is
mediated by increases in muscular strength (5). A high-intensity RT program may optimize gains
in both strength and cognition. In addition, there is a robust link between physical and cognitive
aging that is likely not causal, but rather due to a shared neural or vascular substrate (28-30).
Therefore, further investigations are necessary to understand the mechanisms that aid the
protection of both. Cerebrovascular (CV) dysfunction is recognized as an important element that
may contribute to, and even precede, neuronal dysfunction and cognitive impairment (20, 31).
While aerobic training studies suggest that CV plasticity is a primary mechanism of cognitive
enhancement (8), this has not been tested after RT. RT is known to improve systemic vascular
function and cardiovascular health (11, 12), but whether a similar phenomenon occurs in the
brain is unclear. The many factors affecting CBF during intense exercise are complex (32, 33)
but seem to present an adaptive stimulus. Congruously, older women who engage in RT have
greater resting global CBF than women who do not (10).
Previous studies indicate potential indirect mechanisms of RT-induced cognitive
enhancement. Concomitant with improvements in cognition, imaging studies demonstrate
expanded gray matter in the posterior cingulate (34) reversed progression of white matter

6

hyperintensities (an imaging marker of cerebral small vessel disease) (34, 35), and positively
altered hemodynamic activity of response inhibition processes (36). Primary physiological
candidates are the peripheral factors that pass through the blood-brain barrier and influence brain
plasticity (37, 38). For example, brain-derived neurotrophic factor (BDNF) and insulin-like
growth factor (IGF-1) are putative markers of adult neurogenesis that have been demonstrated to
increase after RT (39, 40).

Preliminary Information
This study will combine proven strength and conditioning principles with validated
neuroimaging techniques. Recommendations for strength development include using high loads
(≥85% 1RM) for low repetitions (≤6) with progressive application of overload (41). This type of
high-intensity strength training effectively improves physical function in older adults (42).
Adding a periodized schedule of training and short rest intervals can help maximize gains in
muscle size and strength in healthy older adults (7, 43). To our knowledge, these types of
advanced training principles have not yet been applied to research on the brain. Our team at the
Laboratory of Neuro Imaging (LONI) has demonstrated the feasibility of conducting an exercise
intervention study that includes cognitive testing and MRI in older adults (44). Furthermore, they
have recently developed a dynamic ASL technique to assess intracranial vascular compliance
(18). By synchronizing ASL with the cardiac cycle to measure arterial cerebral blood volume
(CBV) at peak systole and end diastole, intracranial vascular compliance is calculated as the ratio
of the change in CBV between systole and diastole to the difference between systolic and
diastolic arterial blood pressure (Figure 2.1). These advanced imaging techniques were
developed for the comprehensive mapping of brain structure and function in health and disease.

7

This project will apply these novel approaches to an intervention study aimed at detecting CV
plasticity after strength training.

Figure 2.1 Representative maps of cerebral blood volume (CBV) at peak systole (A) and early
diastole (B), as well as the corresponding CBV difference between systole and diastole (C).

We performed a case study in one healthy older woman (68 years old). No control period
was used but our participant was tested with all training and testing techniques (February – May
2018). There were no adverse events. Results are presented below. As expected, we observed
improvements in body composition, physical function, and strength (Figure 2.2). For the NIH
Toolbox Cognition Battery, age-corrected scores are standardized from population data, with a
normative mean of 100 and standard deviation of 15 (16). Fluid cognition composite score
improved by 24 points, with increased scores in each of the subdomains. The largest
improvements were in executive function (+23.7%) and processing speed (+52.3%), consistent
with previous meta-analyses, and thus supports the cognitive specificity of exercise training (13).
We did not expect changes in crystallized cognition, as this domain represents accumulated
verbal knowledge and skills, and thus remains relatively stable in old age (45). Verbal learning is
a supplemental measure, and the raw score improved slightly. For vascular compliance, regions
of interest (ROIs) were classified into two categories – large arteries with fractional CBV > 5%

8

and small arteries and arterioles with fractional CBV of 1.5–5% (18). Improvements in both are
promising (Table 2.1).

Figure 2.2 Case study percent changes from pre- to post- intervention in physiological and
cognitive measures (N = 1). Fluid, Crystallized, and Total are age-corrected standard composite
scores from the NIH Toolbox Cognition Battery. The individual instruments of Fluid and
Crystallized composite scores are shown in boxes to the right.

Intracranial Vascular Compliance (mL/mmHg) Pre Post
Small Arteries (ROIs with CBV 1.5-5%) 2.58 – 2.65 2.71 – 3.11
Large Arteries (ROIs with CBV >5%) 8.69 – 9.12 8.75 – 9.41
Table 2.1 Case Study Vascular Compliance Results (N = 1). CBV = cerebral blood volume.

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CHAPTER III:
LITERATURE REVIEW

POTENTIAL INDIRECT MECHANISMS OF COGNITIVE ENHANCEMENT
AFTER PERIODIZED RESISTANCE TRAINING IN OLDER ADULTS

Introduction
Populations around the world are living longer and healthier lives. But with this change,
elderly individuals are increasingly susceptible to non-fatal progressive disorders. Dementia is
one such age-related disease caused by neurodegeneration and characterized by significant
cognitive decline that interferes with independent living. In the United States alone, the total
monetary cost of dementia care and treatment was estimated to be $277 billion in 2018 with an
additional family caregiver burden valued at more than $232 billion (21). The development and
severity of dementia is strongly influenced by factors occurring years to decades earlier (46).
Because irreversible brain damage often occurs before patients present to clinic with
demonstrable cognitive dysfunction, successful treatment likely requires pre-symptomatic
neuroprotective therapies, as opposed to disease reversal.
Exercise is one of the most promising strategies to slow or prevent the progression of
cognitive decline and dementia. However, the direct mechanisms remain relatively unknown.
While animal exercise studies have identified molecular pathways with overlapping or redundant
effects on brain function (37), human translational studies have been less definitive and generally
underpowered (47). But exercise also targets fundamental aging processes that share common
pathways with brain function. Thus, gaining knowledge of the underlying neural and
physiological processes of cognitive enhancement requires an awareness of the accompanying

10

adaptations related to cognitive changes. Understanding the factors that interact with exercise-
induced cognitive enhancement will help us design optimal treatment strategies for the aging
population.
There are two primary types of chronic exercise, aerobic training and resistance training
(RT), that have common and distinct physiological benefits (48, 49). Exercise interventions
involving one or both types improve fluid cognitive function in older adults (1, 27, 50). Previous
reviews of potential mechanisms have focused on physical activity in general or aerobic exercise
exclusively (51, 52). Meanwhile, there is growing interest in the distinct mechanisms of RT.
In this perspective, we will briefly discuss the positive changes in cognition after RT
interventions in older adults, then analyze the supramolecular physiological and psychological
benefits that may be related to cognitive enhancement. Potential mechanisms include
improvements in physical capacity, vascular function, brain structure and function, glucose
regulation, inflammation, mood, and sleep quality. These RT-induced adaptations may indirectly
or directly mediate benefits to brain function in older adults. We propose mechanistic studies to
investigate the contribution of each adaptation to overall cognitive performance. Understanding
these relationships will help establish a neuroscientific foundation for the effects of RT on
cognition and develop optimal intervention strategies to maintain cognitive health in the aging
population.

Cognitive Changes
There aging, even healthy older adults (i.e. without major medical or neuropsychiatric
disorders) experience declines in fluid cognitive functions (i.e. the capacity to reason and solve
novel problems). Decreased performance is typical in normal aging on tests involving processing
speed, attention, memory, and executive function. But there is strong evidence to suggest that

11

higher levels of physical activity reduce the rate of cognitive decline and risk of dementia (26).
Compared to general physical activity, specific forms of exercise like RT may have greater
implications for cognitive health in older adults (1).
Many clinical trials have investigated the effects of RT on fluid cognition in older adults.
Both a systematic review and meta-analysis have recently been published to summarize study
characteristics and provide a quantitative examination of RT efficacy (27, 50). We will discuss
the results of studies that have the greatest influence on this rapidly expanding line of research.
These well-designed randomized controlled trials (RCTs) have sufficient sample sizes to detect
improvements and active control groups to reduce the confounding effects of attention. At the
end of this article, we discuss future directions and recommend specific study characteristics to
produce new high-quality RCTs. Overall, RT appears to have positive effects on fluid cognition.
In healthy older adults with normal cognitive aging, the greatest improvements after RT
interventions are observed in fluid cognition composite scores and executive function (15).
Moderate improvements to working memory have also been observed after RT (14), but changes
to this subdomain are generally less common than after aerobic interventions (53). Long-term
benefits have also been demonstrated in healthy older adults. After one-year follow-up, twice-
weekly RT promoted executive function (d=0.31) and memory (d=0.45) relative to a balance-
and-toning control (54). Once weekly RT also promoted executive function (d=0.48), but not
memory. Thus, it appears that healthy older adults with normal age-related cognitive decline can
achieve positive and lasting cognitive benefits from RT interventions.
Recent attention has turned towards populations with mild cognitive impairment (MCI),
as these groups have the highest risk of dementia (21). People with MCI show cognitive decline
greater than expected for their age and education level but are still capable of performing

12

everyday activities. A study in older women with probable MCI demonstrated that twice-weekly
RT improved executive function and associative memory compared to a balance and toning
control group (55) despite the fact that exercise compliance was reported to be low. Similarly, a
RT trial in patients diagnosed with MCI had to reduce training from 3 days/week to 2 days/week
to minimize burden/transportation difficulties (34). The study still demonstrated improved global
cognition on the ADAS-cog and improved executive function subdomain. Notably, both studies
were 6 months in duration in contrast to the 12-month duration of the studies on which the
training protocols were based, suggesting that improvements may be detectable earlier in MCI.
However, clinicians should consider the lower compliance rates in these populations when
designing therapeutic interventions.
While more high-quality RCTs are still needed to accurately estimate the efficacy of RT
on cognition, the above findings provide supportive evidence for the positive effects both in
older adults with normal cognitive aging and in MCI. These cognitive changes could be a direct
response to practicing a goal-oriented motor skill that involves force production, muscle
coordination, kinesthetic memory, and sensory processing of a mechanical load. Thus, RT itself
could have independent effects. Cognitive changes could also be driven by molecular growth
factor cascades within the brain. But as mentioned earlier, these are difficult to measure in
humans. Because epidemiological studies link cognitive decline with other fundamental
processes of aging, and RT is known to benefit overall health, indirect mechanisms have been
proposed to explain how RT improves cognition. Evaluating the rationale of these potential
mechanisms can help justify the need for future mechanistic studies.

13

Physiological Factors
Physical Capacity:
Resistance training is well-known for its effects on muscle mass and physical capacity.
The expected strength increase for older adults participating in a RT program (mean duration of
17.6 weeks, and mean frequency of 2.7 days/week) is about 24-33%, with higher intensities
associated with greater improvement (56). Epidemiological studies link muscular strength with
rate of cognitive decline and incident dementia (57). However, RCTs are necessary to investigate
the causality of this relationship.
While improvements in cognition after aerobic training are associated with increases in
aerobic capacity (i.e. VO2max) (8), this has not been demonstrated after RT. Instead,
improvements in fluid cognition and executive function after RT appear to be mediated by
increases in muscular strength (5, 6). With the known dose-response relationship between RT
and muscular strength (56), a high-intensity RT program designed specifically to maximize
strength gains may also optimize improvements in cognition. However, studies have failed to
demonstrate greater cognitive improvements in subjects that undergo higher RT frequency or
intensity compared to those undergoing a lower ‘dose’ (14, 15). Determining how to maximize
cognitive improvements is an important consideration for future investigations to help therapists
make recommendations for exercise prescription. The link between strength and cognition may
be one source of RT’s unique benefits to brain function.
At the supraspinal level, neural mechanisms of muscular strength development remain
unclear to date, partly due to varying methodologies. Very short-term RT (<4 weeks), thought to
elicit neural adaptations without muscular hypertrophy, has been demonstrated to alter white
matter microstructure, reduce intracortical inhibition, and increase corticospinal excitability (58-

14

60). Because there is evidence to suggest that the balance between cortical inhibition and
excitation is related to cognitive performance (61), these neural adaptations may explain the
statistical mediation of cognitive improvements by muscular strength gains (5, 6). Thus, changes
in strength and cognition could share supraspinal neural pathways in response to RT.
Additional practical benefits of RT have been demonstrated. For example, even simple
home RT programs involving only body mass or elastic bands for resistance can improve
executive function and memory in older adults (62, 63). These types of interventions
demonstrate positive correlations between changes in global cognition and functional mobility
(64). Similarly with machine-based RT, enhancements in executive function are associated with
increased gait speed (15). Thus, RT can be a practical solution for both physical and cognitive
decline in older adults. And like the lasting benefits for executive function, changes in muscle
strength and muscle power can persist at one-year following 52 weeks of twice weekly RT (54,
65).
We have highlighted the positive link between physical and cognitive function in older
adults after training, but an equally strong association is present in their declines with aging.
Whether improving or worsening, physical and cognitive function are more likely to change in
concert than in different directions (28). Because changes in one do not precede changes in the
other, this association is likely not causal, but rather due to a shared neural or vascular substrate
(29). Therefore, having a mechanistic understanding may aid the protection of both. RT may be a
vital form of exercise to maintain both the physical and cognitive capacities necessary for
functional independence in older adults.

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Vascular Function:
The cerebrovascular (CV) system secures adequate blood supply to the brain in dynamic
and resting conditions. Disturbances to this system have long been suspected to play a role in the
development of dementia. In principle, CV dysfunction can involve any of the multiple
etiologies of stroke such as hypoperfusion, small vessel disease, or cardioembolism (66). Recent
evidence suggests that CV dysfunction may contribute to, and even precede, neuronal
dysfunction and cognitive impairment (67). For example, in a population-based study of older
adults in The Netherlands, cerebral hypoperfusion at baseline was associated with accelerated
cognitive decline and a higher risk of dementia at the median 6.9 year follow-up (68). CV
function is now recognized as a top research priority in all national plans to address dementia.
Epidemiological and aerobic intervention studies suggest that improved CV function is a
primary mechanism of exercise-induced cognitive enhancement (69). For example, resting
cerebral blood flow (CBF) is improved after only 12 weeks of aerobic training in healthy older
adults, and independently explains improvements in episodic memory (8). While this mechanism
has not been tested after RT directly, accumulating evidence supports the use of RT for vascular
adaptations.
Resistance training is known to improve systemic vascular function and cardiovascular
health (11). A recent meta-analysis of RCTs ≥4 weeks in duration reported a positive
relationship between RT frequency and improvements in endothelial function (12). These
observations are likely due to exercise-induced elevations in skeletal muscle blood flow,
providing a site-specific hemodynamic stimulus. It is likely that a similar phenomenon occurs in
the brain but is currently unclear. In healthy adults, global CBF increases during aerobic exercise
in an intensity-dependent manner, but fluctuates with arterial blood pressure at higher exercise

16

intensities and during resistance exercise (70). While this may serve as a protective response
against blood brain barrier disruption, the transient fluctuation between hyperperfusion and
hypoperfusion may also stimulate hypoxemia-related mechanisms for vascular adaptation. In
addition, regional CBF, which reflects the blood flow response to increased neural activity, is
augmented during muscle contraction and dependent on force production levels (33). The many
factors affecting CBF during intense exercise are complex but seem to present an adaptive
stimulus to the CV system. In support of this, healthy older women who engage in RT have
greater resting global cerebral perfusion than women who do not (10).
The promise of CV plasticity after RT can significantly impact how clinicians treat their
elderly patients. Because of the proven value of cardiovascular risk factors for the prevention of
heart disease, measures of CV function are proposed to have a similarly positive impact on the
prevention of vascular forms of dementia (20). Acute improvements in cognitive performance
after a single bout of exercise do not seem to be influenced by CBF (71). Instead, measuring the
relationship between CV function and cognition may be more informative in exercise
intervention studies due to long-term adaptations at rest. This supports the hypothesis that the
neural consequences of CV dysfunction is mediated by long-term hypoxia, which in turn
stimulates neurodegeneration, inflammation, and formation of plaques and neurofibrillary
tangles (72).

Brain Structure and Function:
Morphometric imaging results support RT-induced brain plasticity with concomitant
improvements in cognitive function. For example, after 6 months of RT in patients with probable
MCI, expanded gray matter in the posterior cingulate was associated with improvements in

17

cognitive function (34). Thus, RT may help improve cognition by reversing salient Alzheimer’s
Disease (AD) processes like gray matter atrophy. While reports following aerobic training
consistently demonstrate structural plasticity in the hippocampus, no such evidence has been
found for RT, suggesting that different training modalities produce distinct patterns of structural
plasticity in older adults (53, 73). White matter hyperintensities are structural markers of cerebral
small vessel disease that are common in older adults and strongly implicated in the pathogenesis
of vascular cognitive impairment and dementia (74). In studies of both healthy older adults and
patients with MCI, RT has been demonstrated to reverse the normal progression of white matter
hyperintensities (34, 35). However, it is unclear whether this reduction of white matter
hyperintensity progression aids cognitive enhancement. Demyelination and axonal damage may
persist despite the reversal of interstitial fluid accumulations represented by hyperintensities.
Regional neural activation during cognitive tasks is an important indicator of brain
function that can be assessed using changes in blood flow during functional MRI (fMRI). Twice-
weekly RT has been demonstrated to positively alter hemodynamic activity during task-based
fMRI after 12 months in healthy older women and after 6 months in older women with probable
MCI (36, 55). These hemodynamic changes occurred with improved performance in executive
function tasks. While this functional plasticity after RT is associated with response inhibition
processes, aerobic training appears to promote selective attention (75). Changes in resting state
fMRI have also been noted after RT with correlations between increased hippocampal-superior
frontal connectivity and improved memory (34). Previous reports indicate that MCI patients have
reduced hippocampal-superior frontal connectivity (76), and therefore strengthening this resting-
state network may represent a promising mechanism that counteracts cognitive dysfunction.

18

In sum, structural and functional brain plasticity may have direct influence on cognitive
improvements. However, narrowing down plasticity to a few specific structures may not fully
capture the complex nature of human brain function. Therefore, multimodal imaging would
improve our understanding of RT’s effect and help us determine how specific patient populations
could benefit.

Glucose Regulation:
Diabetes is considered a major risk factor for the development of dementia. The link
between these two increasingly-prevalent diseases is primarily attributed to hyperglycemia-
induced neuroinflammation via oxidative stress and mitochondrial production of free radicals
(77). Hyperglycemia often manifests in CV pathology (78), making the above discussion on
vascular function relevant to this section as well. Further damage by hyperglycemia can also lead
to insulin resistance in the brain, an early event in the development of dementia (79). Thus, the
profound evidence collected to date suggesting that RT is an effective strategy for the prevention
and treatment of diabetes may also provide support for its effectiveness in the prevention and
treatment of cognitive impairment.
RT improves glycemic control by decreasing visceral fat, lowering glycosylated
hemoglobin (HbA1c), and increasing insulin sensitivity (80). In addition, RT increases lean body
mass in older adults by about 1.1 kg (mean duration of 20.5 weeks), with higher training
volumes associated with greater hypertrophy (81). Gains in muscle mass raise resting
metabolism and provide additional capacity for glucose uptake and storage. However, there is
currently little evidence for a direct association between these changes and benefits to brain
function. Serum levels of insulin-like growth factor (IGF-1) play a major role in glucose

19

regulation and have been demonstrated to increase following RT intervention, associated with
improved cognitive performance (14). But IGF-1 is involved in numerous processes, including
insulin sensitivity, muscular hypertrophy, neurogenesis, and angiogenesis (37). The hypothesis
that improved glycemic control is associated with RT-induced cognitive enhancement is yet to
be tested. Overall, these adaptations could promote cognition by reversing cellular impairments
or CV dysfunction induced by poor glucose regulation.

Inflammation:
Aging in late adulthood significantly impairs immune function. The resulting systemic
inflammation has been described as an important contributor to cognitive impairment (82).
Increased inflammation is characterized by an increase in pro-inflammatory cytokines such as
interleukin-1 beta (IL-1β), interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis
factor-alpha (TNF-α), and/or a decrease in anti-inflammatory cytokines such as interleukin-4
(IL-4), interleukin-10 (IL-10), and interleukin-13 (IL-13). These circulating markers can induce a
neuroinflammatory response in the central nervous system via active transport across the blood
brain barrier. Higher levels of systemic inflammation are associated with lower cognitive
function and greater risk of cognitive decline and dementia (83). Even at midlife, higher levels of
CRP are associated with increased risk of dementia 25 years later (84).
General exercise is recognized as an effective countermeasure to chronic systemic
inflammation. In particular, RT has been found to reduce resting levels of the inflammatory
markers TNF-α and CRP (85, 86). In cognitively impaired older women, RT has been
demonstrated to increase anti-inflammatory cytokine concentrations with concomitant
improvements in global cognition (64). IL-10 increased in the RT group, whereas TNF-α and

20

CRP increased in the age-matched control group. Like pro-inflammatory cytokines, white blood
cell (WBC) counts also exhibit a negative relationship with cognitive function (87). Barring the
effect of acute infection, regular exercise can lower WBC counts in previously sedentary older
adults (88). A recent study in cognitively impaired older women demonstrated that RT decreased
the total number of leukocytes and lymphocytes in the blood (64). Furthermore, decreased
granulocyte counts were associated with increased cognition.
Taken together, the above evidence supports the hypothesis that RT can improve
cognitive function by creating a more anti-inflammatory environment. While the mechanisms are
still unclear, it is postulated that exercise induces acute IL-6 production which suppresses other
pro-inflammatory cytokines and stimulates the release of anti-inflammatory cytokines resulting
in lower chronic systemic inflammation over time (89). This may quell the activation of
microglia and ensuing cognitive damage caused by neuroinflammation (90). Ultimately, the
reduction in systemic inflammation can have a widespread effect, aiding in the protection against
dementia and various other chronic non-communicable diseases.

Psychological Factors
Mood:
Although rapid cognitive decline is the hallmark of dementia, behavioral and
psychological symptoms such as anxiety, depression, and apathy often dominate the presentation
of disease. These neuropsychiatric alterations can occur across all stages of dementia with
fluctuating prominence and are detectable even before the MCI stage. Because dementia is not a
specific disease but an overall term that describes a wide range of symptoms of cognitive
decline, there is no clear distinction between its causes and effects. There is strong evidence that

21

these psychological disorders act more as modifiable risk factors than symptoms of dementia.
For example, depression is not only a symptom but also among the top five most important risk
factors for dementia that can be reduced with clinical intervention (91). Thus, countermeasures
to these disorders are necessary for both the prevention and treatment of dementia.
Higher levels of late-life anxiety are associated with poorer cognitive function and
predictive of cognitive decline, especially in memory domains (92). Theories from performance
literature suggest that anxiety can affect cognition through over-arousal or by occupying storage
and preprocessing resources within the working memory system. In support of this, anxiety has
shown a curvilinear relationship with cognition in older adults (93). Specifically, mild anxiety
symptoms are associated with better cognitive performance, whereas severe anxiety symptoms
are negatively associated with specific cognitive domains such as learning and delayed recall. A
moderate effect (0.54) on anxiety reduction is often achieved with regular RT in healthy older
adults (94). This may in part be due to decreased self-attentiveness (65), which is known to
enhance psychological well-being, perhaps by freeing up cognitive resources. Some of these
findings occurred with concomitant improvements in cognitive function (14), but associations
were not tested.
In contrast to the curvilinear relationship above, symptoms of depression consistently
exhibit negative correlations with cognitive function in older adults (93). Furthermore, late-life
depressive symptoms can predict incident cognitive decline, especially in those that already have
MCI, while earlier-life depression is associated with over twofold increased risk of dementia
(95). Thus, depression, particularly at clinical levels, is considered a modifiable risk factor that
contributes to the onset and course of cognitive decline in old age, rather than a consequence of
cognitive impairment. Further studies are needed to determine whether treatment of depression

22

alone can delay or prevent dementia. The benefits of RT have been demonstrated, with large
improvements (effect size > 1.0) in patients diagnosed with depression, and small to moderate
effects on depressive symptoms in healthy older adults (94). At least one study has demonstrated
decreased symptoms of depression in older adults with concomitant improvements in cognitive
function (96). However, changes in fluid cognition were not associated with changes in
depression. Clearly, further investigations are necessary if we are to determine the efficacy of
targeting depression to prevent dementia. These countermeasures might need to be undertaken
sooner than what is commonly believed due to the association between earlier-life depression
and risk of dementia.

Sleep Quality:
Sleep has a well-known relationship with cognitive performance. Sleep deprivation can
result in daytime dysfunction and deficits in specific cognitive domains including executive
function, attention, and working memory (97). Even without the sensation of sleepiness, repeated
partial sleep deprivation has cognitive consequences akin to total sleep deprivation. Although
older adults are less susceptible than young adults to the acute cognitive effects of a poor night’s
sleep, age-related changes in habitual sleep patterns are apparent even in normal healthy aging.
The most prevalent changes include decreased sleep duration, worsening sleep efficiency, and
more frequent awakenings (98). These changes are associated with cognitive decline and greater
risk of cognitive impairment (99). Thus, improving sleep in older adults may enhance cognitive
performance and reduce the risk of dementia.
A recent systematic review concluded that chronic RT improves subjective assessments
of both sleep duration and sleep quality in older adults, with small-to-moderate and moderate-to-

23

large effect sizes, respectfully (100). These results have been reported in a range of populations,
from older patients diagnosed with depression to healthy older adults characterized as good
sleepers (101). Furthermore, the effect sizes on sleep quality tended to be larger in studies using
higher loads and/or higher frequencies (3 days per week) of RT. A high intensity RT program
may therefore be an important part of behavioral therapies targeting sleep quality. Unfortunately,
to the authors’ knowledge, no such studies have tested for associations between improvements in
sleep and cognition after RT. It is plausible that RT improves cognitive function partially
through improved sleep quality. Future studies are warranted to test this hypothesis.

Conceptual Relationships
We have highlighted the paucity of research directly establishing mechanisms of
cognitive enhancement after RT. We outlined potential factors and provided evidence to
illustrate how each of these factors could contribute to late-life cognitive improvements. The
overall theme involves a reversal of well-characterized, age-related changes – i.e. decreased
muscle mass and strength, vascular senescence, and sleep deprivation. All of which have been
shown to improve after RT. Resistance training, and exercise in general, affects fundamental
processes of aging that results in pleiotropic benefits to the individual.
There are different possibilities for the relationship between cognition and the factors
proposed above. RT could cause adaptations that subsequently promote brain health. For
example, improved CV function could promote neurovascular coupling and provide resources
for neuronal integrity, thus mediating the effects of RT on cognition (Figure 3.1A).
Alternatively, RT could directly stimulate mutually beneficial adaptations independently (Figure
3.1B). For example, RT could improve both mood and cognition. Then, lower anxiety allows for

24

increased cognitive performance, while clearer thinking brings about a positive attitude. Along
these lines, RT could also directly improve both cognition and mediating variables (Figure 3.1C).
For example, RT could directly improve both sleep and cognition, and the improved sleep
quality further benefits cognitive performance. Finally, RT could have multiple independent
effects, causing simultaneous changes with no ensuing interaction (Figure 3.1D). Thus, some
physiological or psychological benefits of RT may not necessarily be required for cognitive
enhancement.

Figure 3.1 Potential relationships between resistance training (RT) and cognitive enhancement
(Cog). Cerebrovascular (CV) function is used as an example but can be replaced with any of the
factors described above (i.e. muscular strength, brain structure and function, glucose regulation,
inflammation, mood, and sleep). A) RT could improve factors that subsequently promote brain
health. B) RT could directly improve mutually beneficial adaptations independently. C) RT
could also directly improve both cognition and a factor that mediates cognition. D) RT could
have multiple independent effects, causing simultaneous changes with no ensuing interaction.

The relationship between cognitive enhancement and what is actually changing at the
cellular and molecular levels remain unclear in humans (47). Improvements may be mediated by

25

neurophysiological changes that are best assessed at the micro-scale, such as by tissue biomarker
expression, electrophysiology in single neurons, or acute brain slice methods (37). These are not
measurable in humans in vivo using current technology. For example, serum levels of
neurotrophic factors are promising molecular signals of neuroplasticity, but there are numerous
variables that affect their concentrations in blood and cerebral spinal fluid. Thus, cross-species
studies still play a vital role in translational neuroscience, bridging molecular, systems, and
cognitive neuroscience research.

Future Directions:
We advocate the need for future studies to reproduce the effects of RT on fluid cognition
and investigate the potential mechanisms proposed here. While established guidelines for RCTs
should always be followed to reduce the risk of bias, we have additional recommendations for
this line of research specifically. We acknowledge that the following study characteristics
increase trial costs and complexity, but the benefits towards reproducibility have made them the
standard for key RCTs.
First, it is widely accepted that RT arms should be compared to active control arms to
accurately estimate the effects of training. Having the control arm perform light stretching or
low-intensity exercise with equal frequency and duration as the RT group reduces the
confounding effects of attention, social interaction, practice, scheduling, and study commitment
(102). A small but non-significant cognitive outcome effect size is generally expected in these
active control groups. Second, the variety of cognitive measures and effect sizes in the literature
highlights the need to use standardized performance-based measures of cognition (27).
Measurement standardization will ensure that assessment methods and results can be compared

26

across existing and future studies. One final characteristic for high quality RCTs is an
appropriate level of statistical power. With moderate effect sizes, larger samples are needed for
reliability. Null findings have typically been associated with small sample sizes (<20 participants
per group) (103) or high attrition rates (>25%) (104). In addition, studies have been published
with low adherence to training protocols (105), which may also provide only conservative
estimates of efficacy.
These issues highlight some of the challenges involved in conducting RT interventions.
For example, supervised RT, often used to maximize the effectiveness of RT for physical
performance (106), requires a high level of commitment from both participants and study
personnel. Trainers must be qualified to oversee proper exercise technique, ensure appropriate
adjustment of exercise loads, and monitor participant safety. Furthermore, attention provides key
motivational aspects of participation, including the perception of encouragement and expertise
from trainers, the feelings of exercise competence, and the recognition of personal positive
health benefits (107).
Over the last 15 years, the results of our RT studies with older adult populations have
benefited from high compliance rates (>98%). This might be due to random factors such as study
population or facility location, or to strategic factors such as hiring experienced trainers and
providing greater attention with one-on-one personal sessions. Compared to other forms of
exercise, RT offers the distinct advantage of allowing time for conversation during rest periods
between sets. This time can be used to inspire and educate participants. In addition, RT has a
goal-oriented skill component that requires concentration and facilitates learning and problem
solving. Investigators with physical therapy or strength and conditioning backgrounds are

27

therefore uniquely equipped with the knowledge and interpersonal skills necessary to improve
the reliability of these outcomes.
It is important to note that participants often perceive health benefits earlier than what is
supported by functional scores. Although these perceptions may not accurately reflect actual
abilities, these perceptions do drive behavior (107). We can anecdotally confirm that many
participants will comment on improvements in muscular strength and cognitive abilities in as
little as two weeks of training. Those participants that perceive the greatest benefits are more
inclined to challenge themselves in future training sessions. While some investigators
recommend blinding participants and trainers to study hypotheses, we have found that to be
impractical. Participants frequently ask about study hypotheses to justify their time and effort,
and there is enough media coverage of this research that participants and trainers will often
assume benefits to cognition. Over the course of our current intervention study we have received
several news articles from participants highlighting the benefits of exercise for brain function.
Therefore, we support the transparency of cognitive benefits in future mechanistic studies.
However, other safeguards should be taken to reduce bias during testing such as blinding
assessors to group assignments.
Innovations in neuroimaging technology, assessment techniques, and biomarker analyses
are frequently being developed, setting the stage for rapid advancements in dementia prevention
strategies. Future progress will require collaboration between neuroscientists and exercise
physiologists to develop optimal interventions for the aging population. Proof-of-concept clinical
trials are a critical first step toward accelerating this field, providing a practical approach to test
key hypotheses while minimizing the risks in conducting larger, more expensive, trials.

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Conclusions
While the importance of RT for overall health in older adults is well established, this new
and exciting evidence for cognitive improvements furthers the impact that therapists can have on
their patients. Our long-term goal is to help establish a neuroscientific foundation of RT’s benefit
to cognition to promote its therapeutic use in both treatment and prevention strategies. Fully
understanding the indirect and direct mechanisms will help clinicians develop comprehensive
interventions for their elderly patients. In doing so, we can make informed recommendations that
provide the most real-world benefit for the individual. At a time when social factors and longer
lifespans are raising the incidence of non-communicable progressive disorders like dementia, the
role of the physical therapist will necessarily involve treatment of the underlying factors related
to their patients’ cognitive challenges. RT may be the preferred prescription. The improvements
in physical capacity, vascular function, brain structure and function, glucose regulation,
inflammation, mood, and sleep quality offer significant health benefits in addition to those that
may contribute specifically to cognitive processes. Therefore, even with moderate benefit to a
specific disease like dementia, the global impact of increased RT prescription on healthcare
would be substantial.

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CHAPTER IV:
12 WEEKS OF PERIODIZED RESISTANCE TRAINING IMPROVES FLUID
COGNITION IN OLDER ADULTS

Abstract
Purpose: Resistance training (RT) is a promising strategy to slow or prevent fluid cognitive
decline during aging. However, the timeline of cognitive improvements and the underlying
processes have not been established. The purpose of this study was to determine whether 12-
weeks of periodized RT improves fluid cognition in healthy older adults and explore potential
physical and psychological mechanisms of benefit.

Methods: Twenty participants (69.1 ± 5.8 years, 14 women) completed this single group clinical
trial. Standard cognitive, physical, and psychological measures were administered at baseline,
pre-intervention, and post-intervention. During the 12-week control period between baseline and
pre-intervention, participants were encouraged to continue their normal activities of daily living
but were not monitored. During the 12-week RT intervention period, participants performed the
periodized program 3 days per week, supervised by an exercise specialist.

Results: Test-retest reliability over the control period was good for fluid composite cognition and
excellent for crystallized composite cognition. Fluid cognition composite scores significantly
increased from pre- to post-intervention (8.2 ± 6.1 %, p < 0.01, d = 1.27), while crystallized
cognition composite scores did not (-0.5 ± 2.8 %, p = 0.46, d = -0.34). Performance on all
individual fluid instruments, except episodic memory, significantly improved after the
intervention, including executive function, attention, working memory, and processing speed.

30

Changes in fluid cognition composite scores had small correlations with changes in diastolic
blood pressure, muscular strength, lower body power, and sleep quality.

Significance: Improvements in fluid cognitive function can be achieved in as little as 12 weeks
using a high intensity periodized RT program. Furthermore, the exploratory analyses performed
here provide promising preliminary data for future studies to investigate more defined
mechanisms of benefit.

Introduction
Cognitive benefits after exercise interventions are well documented and hold promise in
the effort to slow or prevent cognitive decline and dementia (1). While much of the previous
literature has focused on aerobic exercise, mounting evidence suggests that long-term resistance
training (RT) can play a unique role in treatment strategies for the aging population. A recent
meta-analysis reported positive effects (d = 0.71) of RT interventions on global fluid cognition
(27). Furthermore, select cognitive subdomains may be targeted and develop through distinct
mechanisms from other types of exercise (3). These findings emphasize the cognitive specificity
of training and need for further investigation.
Early implementation is an important consideration for RT because the decline towards
dementia is strongly influenced by factors occurring 10-15 years prior to the onset of cognitive
symptoms (46). While there is ample evidence to suggest that long-term RT enhances cognitive
function regardless of age or baseline abilities (108), demonstrating rapid health benefits
following higher-intensity RT may improve the clinical utility of such interventions.
Furthermore, a neuroscientific foundation for these benefits must be established before firm
recommendations can be made. There could possibly be direct effects from the cognitive

31

demands of RT – a goal-oriented skill that involves motor recruitment for force production,
attention to sensory stimuli for safety, and kinesthetic memory for body positioning and muscle
coordination. However, with the pleiotropic benefits of RT (109), more complex interactions
with related outcomes likely exist. Due to the long-standing spotlight on aerobic exercise, the
field is currently lacking this key mechanistic understanding of the effects of RT on cognition.
The most notable effects of RT are increased physical capacity and protection against
sarcopenia (56, 81), but improvements are also commonly observed in measures such as body
composition, mood, sleep quality, and cardiovascular health (11, 94, 110). These factors may
help us understand how cognitive enhancement arises. Even with only small associations, there
may be cumulative effects from each potential mechanism of benefit. This knowledge can be
used to optimize exercise prescriptions, depending on the desired outcome. For example,
improvements in global fluid cognition and executive function appear to be partially mediated by
increases in muscular strength (5). Thus, the efficacy of RT for cognitive health may depend on
maximizing gains in physical capacity, which can be accomplished by applying strength and
conditioning principles to the intervention (43).
The purpose of this study was to determine the effects of high-intensity RT on fluid
cognition in healthy older adults, and to explore potential physical and psychological
mechanisms of benefit. A healthy cohort was used because one of the most promising strategies
to reduce the prevalence of dementia is to develop interventions that promote healthy aging.
More rapid cognitive improvements could increase the clinical utility of such therapies.
Therefore, the intervention used here included linear periodization and emphasized high
intensities to uncover whether advanced training techniques could increase the cognitive
stimulus or rate of improvement. Our hypothesis was that a 12-week periodized RT program

32

performed 3 days per week and designed specifically to maximize strength gains improves fluid
cognition in older adults. As an exploratory analysis, the widespread benefits of RT were
analyzed for potential associations with cognitive enhancement. Many of these factors involve
fundamental processes of aging that share common pathways with brain function (109).

Methods
Participants and Study Design
Using a single arm pre-post design, the effects of a 12-week periodized RT intervention
on fluid cognition were compared to that of an initial control period. Thus, participants served as
their own controls and completed all assessments at three time points: baseline, pre-intervention,
and post-intervention. All control periods took place before the RT intervention to ensure that
results were not confounded by detraining effects or long-term cognitive benefits of RT (54). In
addition, a 12-week control period, equal in duration to the intervention, allowed direct within-
subjects statistical comparisons, accounting for each participants’ baseline values and individual
characteristics. Participants were asked to not change their eating or exercise habits outside of
the study and were encouraged to continue their normal activities.
All procedures of this single-group clinical trial were conducted at the University of
Southern California (USC, Los Angeles, CA) Health Sciences Campus. This study was approved
by the USC Health Sciences Review Board and registered with ClinicalTrials.gov (ID:
NCT03982550). A flow diagram of participant recruitment and testing is shown in Figure 4.1.
Written informed consent was obtained from all potential participants assessed for eligibility.
Primary considerations for inclusion/exclusion criteria were to ensure that participants were
healthy, interested, and available to participate, without any contraindications to RT.
Specifically, participants were required to meet the following criteria: 60-80 years of age; living

33

independently (without need of assistance); available for study visits; competent in English
sufficient for assessment and training; eligible to undergo magnetic resonance imaging (MRI);
answer ‘NO’ to all questions on the Physical Activity Readiness Questionnaire (PAR-Q) or
receive medical clearance from a physician; cognitively healthy (score higher than 23 on the
Mini Mental State Examination); have no known history of neurological disease, cerebral infarct,
or traumatic brain injury; have no known Type 1 or Type 2 Diabetes; have no changes in chronic
pharmacological treatment during the study period; do not self-report engaging in heavy RT (i.e.
“using a resistance heavy enough that you could not lift more than 15 times in a set”) in the last 6
months; and have no musculoskeletal injuries or medical conditions for which exercise is
contraindicated.

Figure 4.1 Flow diagram of participant recruitment and testing.

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This was a proof-of-concept study for potential mechanisms. To confirm the effects on
fluid cognition, we computed a preliminary sample size of 18 in an a priori power analysis using
the effect size (d = 0.71) of RT on global fluid cognition in a recently published meta-analysis
(27), alpha of 0.05, power of 0.80, and within-subjects pre-post design. We accounted for an
anticipated attrition rate of 10%, which is typically even lower in USC Clinical Exercise
Research Center (CERC) studies (7). All 20 participants (Table 4.1) that were included in the
study completed the required number of training sessions (>90%) and all assessments at baseline,
pre-intervention, and post-intervention. Brain MRIs were collected on all participants but are not
included in the present manuscript. There were no participant drop-outs or severe adverse events.

Table 4.1 Participant Characteristics at Baseline (N = 20).
Variable

Actual

Female 14 (70%)
Non-Hispanic:
- Asian
- Black or African American
- White
15 (75%)
- 2 (10%)
- 4 (20%)
- 9 (45%)
Hispanic:
- White
- More than one race
5 (25%)
- 1 (5%)
- 4 (20%)
Age (years) 69.1  5.8 (60.1,79.7)
Height (cm) 166.7  8.9 (154.4, 188.2)
Weight (kg) 72.3 ± 10.1 (58.3, 96.5)
Mini Mental State Examination^ 28.0  1.6 (26, 30)
N (%) or Mean  SD (range).
^Score < 24 indicates possible mild cognitive impairment. All participants were required to score
≥ 24 in order to be eligible for the study.

35

Resistance Training Intervention
Participants performed a periodized and progressive RT program emphasizing
development of total-body strength. All 36 training sessions (3 days per week for 12 weeks) were
performed in the CERC, supervised by an exercise specialist. Mesocycle I (weeks 1-4) was
designed for muscular hypertrophy to develop a base for more intense training in later phases.
Training bouts consisted of 4-6 resistance exercises with 3 sets per exercise and 8-10 repetitions
per set. Mesocycles II (weeks 5-8) and III (weeks 9-12) were designed to promote strength gains.
Training bouts consisted of 4-6 resistance exercises with 3-4 sets per exercise, 4-6 repetitions per
set, and linear increases in intensity over time.
These parameters were chosen to maximize performance on 4-6 repetition maximum
(RM) testing post-intervention, according to the principle of specificity (43). Training loads were
individually progressed in a safe and effective manner to employ a progressive overload to the
neuromuscular system and elicit the greatest training-induced neuromuscular adaptations.
Because older adults respond to training stimuli at different rates, autoregulatory progression was
used. While the sets and repetitions were fixed for each week, supervised autoregulation allowed
participants to progress loads at their own pace based on daily variations in performance (111).
Training logbooks were kept for each participant. Training sessions were approximately 1
hour in duration, beginning with a 5-minute general warm-up of light bodyweight exercises and
dynamic stretches and ending with a 5-minute cool-down of static stretching. A movement-
specific warm-up was performed for each exercise with a load lower than prescribed and done as
needed. The rest interval between sets was 60 seconds for all exercises, as in our previous study
(7). In the event a participant missed a training session during the 12-week intervention, she/he
could complete missed sessions during a 2-week buffer period. A maximum of 6 sessions (3 per

36

week) were allowed during this period. All post-intervention study procedures were conducted at
least 48 hours after the last training session and within 2 weeks after the last training session.

Primary Outcomes – Cognitive Function
All cognitive assessments were administered by the same trained investigator (TRM)
using the NIH Toolbox® Cognition Battery (NIHTB-CB, Version 1.21) application on an iPad
Pro 10.5-inch (Apple, Cupertino, CA). Each assessment lasted approximately 45 minutes in
duration. The NIHTB-CB provides a standard set of comprehensive assessment tools that have
been normed and validated in participants ages 3-85, and ensures that assessment methods and
results can be used for comparisons across existing and future studies (16). Three test versions
were used to reduce practice effects and were administered in random order for each participant.
The NIHTB-CB has seven individual instruments – Flanker Test, Picture Sequence
Memory Test, List Sorting Working Memory Test, Dimensional Change Card Sort Test, Pattern
Comparison Test, Picture Vocabulary Test, and Oral Reading Recognition Test – that test the
following cognitive subdomains: inhibitory control and attention, episodic memory, working
memory, executive function, and processing speed for fluid cognition and vocabulary and
reading recognition for crystallized cognition. In addition to scores for each individual
instrument, the battery yields a Fluid Cognition Composite Score and a Crystallized Cognition
Composite Score. The NIHTB-CB has good convergent validity with Gold Standard measures of
Crystallized (r = 0.90) and Fluid (r = 0.78) Cognition Composite Scores (45). The battery also
has excellent test-retest reliability (r = 0.92 and 0.86 for Crystallized and Fluid scores,
respectively), which is similar to that of the Gold Standard (r = 0.93 and 0.95) and with similar
2-week mean practice effects.

37

The NIHTB-CB outputs performance measures in three types of scores: uncorrected, age-
corrected, and fully corrected. Because our participants served as their own controls and there
were no correlations between participants’ age and uncorrected composite scores at baseline
(Crystallized Composite: R
2
= 0.002, Fluid Composite: R
2
= 0.004), only uncorrected scores
were analyzed. All scores are standardized to the population, with a normative mean of 100 and a
standard deviation of 15. All scores were withheld from participants until the end of the study.

Secondary Outcomes
Body Composition:
Body composition was assessed in morning fasted conditions. Weight was measured on
an InBody 770 (InBody, Seoul, South Korea). Percent body fat (PBF, %), lean body mass (LBM,
g), and fat mass (FM, g) were measured by dual energy x-ray absorptiometry (DXA) on a Lunar
iDXA (GE Healthcare, Waukesha, WI).

Muscular Strength:
Participants performed a familiarization session approximately one week prior to baseline
strength testing. They were instructed in proper technique, breathing and full range of motion on
all machine-based resistance exercises to be used. These exercises included the leg press, leg
extension, and leg curl for lower body, and the chest press, lat pulldown, seated row and seated
shoulder press for upper body. Muscular strength was assessed using 4-6RM tests. After general
and specific warmups, up to three attempts were performed to reach the final 4-6RM load with a
2-minute rest period between attempts.
The load and number of completed repetitions were used to estimate 1RM values
according to the standard equation: Predicted 1RM (lbs) = (weight lifted in lbs)/(1.0278 – (.0278

38

x reps)). The calculated 1RM represents a valid assessment of muscular strength in training-
specific exercises in older adults. Single measures for lower body strength, upper body strength,
and total body strength were calculated using the cumulative 1RMs of their respective exercises.

Physical Function:
Gait speed is an important marker of vitality that can predict the life expectancy of older
adults. Participants were instructed to walk on a marked 8-meter walkway at a normal,
comfortable speed. Time taken to walk the central 4 meters of the course was recorded. The
average of two measurements were used to calculate gait speed (m/sec).
The Timed Up and Go (TUG) test was used as a standard measure for mobility and fall
risk. Participants began seated in a chair with hands on the armrests and then were asked to rise,
walk to a line on the floor 3 meters from the chair, turn around, and return to the same seated
position as quickly and safe as possible. The average of three timed trials was recorded (sec).
Functional power was measured using a modified Margaria Stair Climb test for older
adults. Participants were instructed to ascend a flight of 10 stairs one step at a time as quickly as
possible without using the handrail. Timing began when one foot stepped on the 3rd stair and
ended when that foot reached the 9th stair. Power (W) was then calculated using the stair height,
body weight, and average time of three trials.
The Y-Balance test (Functional Movement Systems) was used as a dynamic assessment
of single-leg standing balance abilities. Participants stood with one foot on a platform from
which 3 pieces of polyvinylchloride pipe extend in the anterior, posteromedial, and posterolateral
directions. The participant was then instructed to push a target with the foot of the reaching limb
and return to a standing position without touching the ground. Maximal reach was recorded for

39

each foot, in each of the 3 directions. The six reach distances were summed and used for
analyses (cm). Four participants were not able to perform all six reach directions at baseline
and/or pre-intervention, so their data were excluded from analyses.

Questionnaires:
Physical activity levels outside of training may confound the effects of RT on cognition
(26). Therefore, the International Physical Activity Questionnaire (IPAQ) was used to assess
weekly physical activity outside of the study (112). The IPAQ aims to determine the average
volume of activity over 1 week in metabolic equivalents (MET*min/week). Participants were
asked not to include any activities of the present study in their IPAQ responses.
Sleep is also an important consideration for cognitive function in older adults, as poor
sleep is associated with impaired cognitive performance. There is evidence though that RT can
improve sleep quality (113). Therefore, the Pittsburgh Sleep Quality Index (PSQI) was
administered to evaluate overall sleep quality (114). Scores are inversely related to sleep quality.
A PSQI score of 5 or above is indicative of clinically poor sleep quality.
Social interaction and engagement can influence cognitive outcomes. To control for
perceptions of social support, the shortened Interpersonal Support Evaluation List (ISEL) was
administered (115). The ISEL has three dimensions of support: Appraisal support (availability of
someone to talk to about one’s problems), Belonging support (feeling accepted in social circles),
and Tangible support (instrumental aid). The three scores were averaged for an overall ISEL
score. A higher ISEL score indicates greater social support, up to a maximum of 16.
Quality-of-life is associated with both mental and physical health, making it a valuable
domain for research on exercise and the brain. General health-related quality of life status was

40

assessed using the Short Form-36 (SF-36). This survey yields an eight-scale profile of scores,
including physical functioning, role limitations due to physical health, role limitations due to
emotional problems, energy/fatigue, emotional well-being, social functioning, pain, and general
health (116). Higher scores indicate greater health status, up to a maximum of 100 for each scale.

Statistical Analyses
The following analyses were performed in SPSS Statistics V.25 (IBM, Chicago, IL). All
data were considered continuous, examined for normality using Shapiro-Wilk tests, and
described by either means and standard deviations or medians and interquartile range (if not
normally distributed). To confirm the reliability of our measures, the control period – differences
between baseline and pre-intervention – was evaluated using two-way mixed effects intraclass
correlation coefficients (ICCs) with absolute agreement. Estimates less than 0.50, between 0.50
to 0.75, between 0.75 and 0.90, and greater than 0.90 were classified as poor, moderate, good,
and excellent test-retest reliability, respectively (117). Effect sizes were calculated for all
outcomes to determine the magnitude of differences between control and intervention periods.
For normally distributed data, an adapted Cohen’s d effect size was calculated – mean changes
from pre- to post-intervention were subtracted by the mean changes from baseline to pre-
intervention, then divided by the average standard deviation of those changes (118, 119). For
non-normally distributed data, matched-pairs rank-biserial effect size was calculated – the
differences between control and intervention changes were ranked, then the sum of positive
ranks was subtracted by the sum of negative ranks and divided by the total sum of ranks (120).
For both adapted Cohen’s d and matched-pairs rank-biserial, effect sizes of 0.2, 0.5, 0.8, and 1.2
were classified as small, medium, large, and very large, respectively (121).

41

Only changes in cognition were tested for significance because fluid cognition was the
only outcome powered for a priori hypothesis testing. Paired t-tests were used, comparing data
from pre- and post-intervention, with statistical significance set at P < 0.05. The criteria for
outlier exclusion were set at any z-score greater than 3 or less than -3. One outlier was noted for
crystallized cognition. At post-intervention, this female participant had vocabulary and
crystallized cognition composite scores that were 4.3 and 4.1 standard deviations above the
sample mean, respectively. Therefore, all crystallized cognition data for this participant were
removed from further analyses.
Relationships between fluid cognition improvements and changes in other outcome
variables were analyzed in SPSS to explore potential mechanisms of benefit. Only raw changes
from pre- to post-intervention were used so that effects were not washed out by percent changes.
These raw changes often have high kurtosis when a small sample size (N = 20) is used.
Therefore, Spearman rank correlation coefficients were chosen to reduce effect variability (122).
This same technique was used to analyze relationships between fluid cognition improvements
and sample factors such as participant characteristics. Spearman’s rho values of 0.1, 0.3, and
greater than 0.5 were classified as small, medium, and large associations (118), respectively. A
priori power analyses (correlation: bivariate normal model) were performed using G*Power
3.1.9 to inform future trials of the required sample size to observe significant correlations in a
similar pre-post, one group design, based on our observed correlations, alpha = 0.05, two tails,
and power = 0.80.

42

Results
Table 4.2 presents the mean ± SD (or median ± IQR if data not normally distributed) for
all outcome measures of the study at baseline, pre-intervention, and post-intervention, test-retest
reliability of the 12-week control period, and effect sizes of the 12-week intervention period.

Table 4.2 Group means for cognitive outcomes, physiological outcomes, and questionnaire
responses at baseline, pre-intervention, and post-intervention (N = 20).
Measure

Baseline
(Week 0)
Pre-
Intervention
(Week 12)
Post-
Intervention
(Week 24)
ICC^
(3,1)
Effect
Size^^
(d)
Cognitive Outcomes:
NIHTB-CB Fluid Composite
Score
95.4 ± 8.2 95.9 ± 8.4 103.6 ± 8.7 0.77 1.27
- Flanker 95.8 ± 7.5 97.3 ± 6.2 101.4 ± 7.2 0.64 0.40
- List Sorting 96.5 ± 9.1 94.9 ± 10.2 102.9 ± 6.2 0.69 1.19
- Dimensional Change
Card Sort
101.5 ± 7.5 102.7 ± 7.4 105.9 ± 8.3 0.81 0.35
- Pattern Comparison 95.8 ± 12.1 93.90 ± 12.8 104.5 ± 12.8 0.57 1.15
- Picture Sequence 98.2 ± 12.1 100.5 ± 10.8 102.5 ± 12.4 0.47 -0.03
NIHTB-CB Crystallized
Composite Score
#

112.4 ± 7.2 112.8 ± 8.3 112.1 ± 7.0 0.91 -0.34
- Picture Vocabulary
#
113.5 ± 7.6 113.6 ± 9.0 112.6 ± 7.2 0.89 -0.26
- Oral Reading
#
110.4 ± 6.7 111.0 ± 6.9 110.8 ± 6.6 0.93 -0.35
Physiological Outcomes:
Body Weight (kg) 72.3 ± 10.1 72.0 ± 9.5 72.2 ± 9.8 0.98 -0.01
Body Fat Percentage (%) 38.8 ± 5.8 38.7 ± 5.9 37.8 ± 5.7 0.99 0.67
Lean Body Mass (kg) 42.8 ± 8.4 42.7 ± 7.9 43.5 ± 8.1 0.99 0.63
Fat Mass (kg) 26.8 ± 4.3 26.6 ± 4.5 26.2 ± 4.5 0.96 0.21
Systolic Blood Pressure
(mmHg)
121.6 ±
18.9
121.1 ± 15.0 117.2 ± 14.9 0.79 -0.04
Diastolic Blood Pressure
(mmHg)
63.4 ± 10.4 64.2 ± 10.0 64.5 ± 10.9 0.77 -0.14
Heart Rate (bpm) 65.7 ± 9.9 65.6 ± 11.1 64.5 ± 10.6 0.77 -0.01
Total Body Strength (kg) 307.1 ±
141.3
299.5 ± 140.6 452.2 ± 162.6 0.99 3.86
- Lower Body Strength
(kg)
207.6 ±
106.1
202.6 ± 106.1 319.9 ± 125.3 0.99 3.73
- Upper Body Strength
(kg)
99.5 ± 37.4 96.8 ± 36.5 132.3 ± 40.9 0.98 3.37
Margaria Power (W) 362.2 ±
103.2
366.8 ± 101.6 417.2 ± 112.0 0.97 1.59

43

Timed Up and Go (sec) 5.9 ± 0.5 6.1 ± 0.6 5.4 ± 0.5 0.67 -1.98
Y-Balance
##
(cm) 396.4 ±
38.7
407.7 ± 37.3 430.5 ± 38.3 0.59 0.55
Habitual Gait Speed (m/sec) 1.4 ± 0.2* 1.4 ± 0.2* 1.5 ± 0.2* 0.68 0.01*
Questionnaire Responses:
PSQI Score 4.0 ± 2.8* 5.0 ± 3.0* 3.5 ± 1.75* 0.84 -0.39*
ISEL Score 15.0 ± 2.0* 14.3 ± 3.5* 15.0 ± 2.3* 0.81 0.69*
SF-36 Physical Functioning 95.0 ±
13.8*
92.5 20.0* 95.0 10.0* 0.53 0.47*
SF-36 Limitations Due to
Physical Health
100.0 ±
18.8*
100.0 ± 25.0* 100.0 ± 0.0* 0.16 0.27*
SF-36 Limitations Due to
Emotional Problems
100.0 ±
24.9*
100.0 ± 24.8* 100.0 ± 0.0* 0.10 0.30*
SF-36 Energy/Fatigue 76.0 ± 9.8 76.8 ± 10.6 79.3 ± 10.4 0.56 0.19
SF-36 Emotional Well-Being 88.0 ± 8.0* 88.0 ± 15.0* 90.0 ± 8.0* 0.77 0.26*
SF-36 Social Functioning 100.0 ±
12.5*
100.0 ± 0.0* 100.0 ± 0.0* 0.66 -0.24*
SF-36 Pain 90.0 ±
28.1*
90.0 ± 30.0* 90.0 ± 17.4* 0.72 0.13*
SF-36 General Health 77.8 ± 12.3 80.3 ± 12.5 85.3 ± 10.1 0.46 0.26
The control period 12-week test-retest reliability was important since this study was a single
group clinical trial. No changes were observed from baseline to pre-intervention. The effects of
the 12-week periodized RT intervention are evident via changes from pre- to post-intervention
and calculated effect sizes. Mean ± SD.
^Two-way mixed intraclass correlation coefficient (ICC) with absolute agreement calculated
using baseline and pre-intervention data.
^^Adapted Cohen’s d effect size calculated by subtracting the mean changes from pre- to post-
intervention by the mean changes from baseline to pre-intervention and dividing the average
standard deviation of those changes.
*Median ± IQR and matched-pairs rank-biserial effect size calculated on the changes from pre-
to post-intervention versus changes from baseline to pre-intervention.
#
Data from one participant were considered outliers and therefore excluded from analyses (N =
19).
##
Four participants were not able to perform all six reach directions at baseline and/or pre-
intervention, so their data were excluded from analyses (N = 16).
NIHTB-CB = NIH Toolbox Cognition Battery. All scores presented as uncorrected standard
scores (population mean and SD = 100 ± 15).
PSQI = Pittsburgh Sleep Quality Index. Lower scores indicate better sleep quality, scores ≥ 5 are
classified as clinically poor sleep quality.
ISEL = International Support Evaluation List. Higher scores indicate greater social support, up to
a maximum of 16.
SF-36 = Short Form-36. Higher scores indicate greater health-related quality of life, up to a
maximum of 100.

44

Reliability:
Because participants were asked to maintain their normal eating and exercise habits
outside of the study, no changes in outcome measures were expected after the control period.
Reliability was moderate to good for fluid cognition measures, good to excellent for crystallized
cognition measures, excellent for body composition, good for blood pressure and heart rate,
excellent for muscular strength and power, moderate for physical function, and good for PSQI
and ISEL responses (Table 4.2). The eight SF-36 scales ranged from poor to good reliability.
Responses to the IPAQ were not normally distributed (Shapiro-Wilk P < 0.05). However,
there were no differences between baseline (median = 4012 MET*min/week, IQR = 5806
MET*min/week) and pre-intervention (median = 4318 MET*min/week, IQR = 5211
MET*min/week) responses, or between pre-intervention and post-intervention (median = 4439
MET*min/week, IQR = 4709 MET*min/week) responses. Therefore, participants appeared to
have followed instructions to not change their exercise habits outside of the study.

Cognitive Outcomes:
All participants completed the NIHTB-CB at baseline, pre-intervention, and post-
intervention. Composite score data are presented in Figure 4.2A. Fluid cognition composite
scores significantly increased from pre- to post-intervention (+7.7 ± 5.5 Standard Units, p <
0.01). Note that this mean 8.2% improvement is a very large effect size (d = 1.27) compared to
the control period and is about one half of the population standard deviation (population mean ±
SD = 100 ± 15). One outlier was removed from crystallized cognition analyses. In the remaining
19 participants, there was no change in crystallized cognition composite score from pre- to post-
intervention (-0.7 ± 3.3 Standard Units, p = 0.46).

45

Data from each individual instrument are presented in Figure 4.2B. For fluid cognition,
four of the five individual instrument scores significantly increased from pre- to post-
intervention: Dimensional Change Card Sort Test (+4.1 ± 6.8 Standard Units, p = 0.04), Flanker
Test (+3.2 ± 6.4 Standard Units, p = 0.02), List Sorting Test (+10.7 ± 10.2 Standard Units, p <
0.01), and Pattern Comparison Test (+2.0 ± 11.5 Standard Units, p < 0.01). The effect sizes were
small for the Dimensional Change Card Sort Test and the Flanker Test, and large for the List
Sorting Test and the Pattern Comparison Test (Table 4.2). The only fluid instrument that did not
improve from pre- to post-intervention was the Picture Sequence Test (+8.0 ± 8.5 Standard
Units, p = 0.45). As expected for crystallized cognition, there were no changes from pre- to post-
intervention in the Picture Vocabulary Test (-1.0 ± 4.5 Standard Units) or the Oral Reading
Recognition Test (-0.2 ± 2.2 Standard Units).

46

Figure 4.2 NIH Toolbox Cognition Battery uncorrected standard scores assessed at baseline,
pre-intervention and post-intervention. A) Crystallized (N = 19) and fluid (N = 20) composite
scores (Mean ± SD). B) Scores for each individual instrument (Means only, standard deviations
were removed for simplicity). Crystallized composite scores and their corresponding instruments
(Vocabulary and Reading) are shown in gray. Fluid composite scores and their corresponding
instruments (Flanker, Dimensional Change Card Sort, List Sorting, Pattern Comparison, and
Picture Sequence) are shown in black. * p < 0.05.

47

Physiological Outcomes:
Although there were no changes in weight from pre- to post-intervention (+0.4 ± 3.1 %),
body fat percentage decreased (-2.2 ± 2.5 %) with a medium effect size. Accordingly, lean body
mass increased (+1.9 ± 2.7 %) and fat mass trended downwards (-1.6 ± 4.9 %). There were no
changes in systolic blood pressure (-2.8 ± 9.4 %), diastolic blood pressure (+0.6 ± 9.7 %), or
heart rate (+0.5 ± 11.2 %).
As expected, very large effects were observed in muscular strength from pre- to post-
intervention. Total body strength increased by +61.5 ± 39.5 %, with the greatest gains coming
from lower body exercises (+72.2 ± 49.9 %) followed by upper body exercises (+42.1 ± 29.0 %).
Very large effects were also observed in Margaria power (+14.3 ± 9.1 %) and the TUG test (-
10.3 ± 5.9 %). Medium improvements were noted in Y-Balance performance (+5.2 ± 6.5 %), but
there were no changes in habitual gait speed (median = +3.5 %, IQR = 11.9 %).

Questionnaire Responses:
The intervention also showed positive benefits to questionnaire responses. A small
positive effect was observed on sleep quality, as measured by reduced PSQI score from pre- to
post-intervention (median = -20.0 %, IQR = 33.3 %). Overall perceptions of social support,
measured by the ISEL, also improved (median = +3.9 %, IQR = 9.2 %), primarily due to the
tangible dimension: appraisal support (median = +0.0 %, IQR = 16.7 %), belonging support
(median = +0.0 %, IQR = 5.0 %), and tangible support (median = +6.7 %, IQR = 14.7 %).
Finally, small positive effects were observed on the SF-36, including self-reported physical
functioning (median = +0.0 %, IQR = 13.1 %), role limitations due to physical health (median =
+0.0 %, IQR = 0.0 %), role limitations due to emotional problems (median = +0.0 %, IQR = 0.0

48

%), emotional well-being (median = +0.0 %, IQR = 12.4 %), and general health (+7.3 ± 9.8 %).
Other scales of the SF-36 had small and/or negative changes, including self-reported
energy/fatigue (+4.1 ± 12.5 %), social functioning (median = +0.0 %, IQR = 0.0 %), and pain
(median = +0.0 %, IQR = 24.7 %).

Exploratory Analyses:
Fluid cognition improvements were further explored for possible relationships with
physiological outcomes and questionnaire responses. The SF-36 was excluded from these
analyses because the median change for six of the eight domains was zero. First, spearman
correlations were computed between raw fluid cognition composite score changes and changes in
other outcome variables of interest. Table 4.3 shows the degrees of correlation between the
ranked data. Raw fluid cognition changes had small correlations with changes in fat mass, blood
pressure, heart rate, total body strength, Margaria power, Y-balance, and PSQI score, and weak
correlations with changes in body fat percentage, lean body mass, TUG time, and ISEL score.
Sample sizes computed in a priori power analyses are also presented in Table 4.3 to inform
future mechanistic trials.
Participant characteristics may also influence the observed changes in fluid cognition. For
example, there is some evidence that women acquire greater cognitive improvements from
exercise interventions than men (3). However, there was no difference in the pre- to post-
intervention changes between the 14 women (+7.4 ± 6.2 Standard Units) and 6 men (+8.5 ± 3.1
Standard Units). Similarly, regression to the mean is a well-known statistical tendency for
repeated measurements to move closer to average. However, there was no difference in the pre-
to post-intervention fluid cognition changes between the top ten performers at pre-intervention

49

(+8.8 ± 5.3 Standard Units) and the bottom 10 performers at pre-intervention (+6.6 ± 5.5
Standard Units). Finally, spearman correlations were computed between pre- to post-intervention
changes in fluid cognition composite score and pre-intervention values (Table 4.3). Of note,
there was a medium negative correlation with systolic blood pressure, and there were small
negative correlations with age, IPAQ response, and PSQI score. Sample sizes computed in a
priori power analyses may help future trials identify the individuals that benefit the most from
this type of intervention.

Table 4.3 Correlation analyses between pre- to post-intervention changes in fluid cognition
composite score and other outcome variables of interest (N = 20).
Changes from Pre- to Post-
Intervention
Pre-Intervention Values
Measure

Spearman’s rho

A Priori
Sample Size*
Spearman’s rho

A Priori
Sample Size*
Age - - -0.26 113
IPAQ - - -0.20 153
Body Fat Percentage -0.07 1599 -0.03 8718
Lean Body Mass 0.03 8718 -0.01 -
Fat Mass 0.17 269 0.11 646
Systolic Blood
Pressure
0.11 646 -0.33 69
Diastolic Blood
Pressure
-0.24 133 -0.08 1224
Heart Rate -0.17 269 -0.05 3137
Total Body Strength -0.24 133 0.18 239
- Lower Body -0.25 123 0.17 269
- Upper Body -0.11 646 0.22 159
Margaria Power 0.21 175 0.22 159
Timed Up and Go 0.07 1599 0.11 646
Y-Balance
#
0.23 146 0.36 58
Habitual Gait Speed 0.01 - 0.14 398
PSQI Score 0.27 105 -0.22 159
ISEL Score 0.01 - 0.15 346
These correlation analyses were used to explore factors that may influence fluid cognitive
enhancement after RT. Raw pre- to post-intervention changes in fluid cognition composite score
were used. For the other outcome variables, pre-intervention values and raw pre- to post-
intervention changes were used.
IPAQ = International Physical Activity Questionnaire.

50

PSQI = Pittsburgh Sleep Quality Index. Lower scores indicate better sleep quality, scores ≥ 5 are
classified as clinically poor sleep quality.
ISEL = International Support Evaluation List. Higher scores indicate greater social support, up to
a maximum of 16.
*A priori power analyses of correlation (bivariate normal model) in a similar pre-post, one group
design, with two tails, alpha = 0.05, and power = 0.80.
#
Four participants were not able to perform all six reach directions at baseline and/or pre-
intervention, so their data were excluded from analyses (N = 16).

Discussion
This study demonstrates that 12 weeks of periodized RT improves fluid cognition, but not
crystallized cognition, in healthy older adults. Significant improvements were observed in
NIHTB-CB fluid composite score and four of its five individual instruments: List Sorting Test,
Pattern Comparison Test, Flanker Test, and Dimensional Change Card Sort Test. However, no
changes were observed in the Picture Sequence Test or in crystallized composite score and its
instruments: Picture Vocabulary Test and Oral Reading Recognition Test. As expected with this
type of exercise intervention, there were also large increases in muscular strength and power,
with notable improvements in body composition and physical function. Although there were no
changes in blood pressure or heart rate, mental health benefits were observed in questionnaire
responses, including improved sleep quality, perceptions of social support, and health-related
quality of life. Together, these post-intervention improvements highlight the multidimensional
impact that high-intensity RT can have on overall health. Furthermore, the exploratory findings
contribute to our understanding of the underlying processes for fluid cognitive enhancement.

Participant Characteristics:
Participants were healthy and active individuals, physically and mentally capable of
performing the high-intensity training program. Their representativeness of the total U.S.

51

population is supported by their age-corrected NIHTB-CB fluid composite scores (106.3 ± 14.4
Age-Corrected Standard Units) at baseline, which only slightly outperforms the expected mean
(100 ± 15). Self-reported physical activity on the IPAQ (median = 4012 MET*min/week) was
higher than the worldwide median (3699 MET*min/week) for this questionnaire (112).
However, since there were no changes on the IPAQ at pre- or post-intervention, the study
outcomes were likely not confounded by changes in physical activity outside of the intervention.

Fluid Cognition Subdomains:
Of the individual instrument mean score improvements, large effects were recorded for
working memory and processing speed, and small effects were recorded for executive function
and attention. The only subdomain that did not improve was episodic memory. This appears to
both support and counter recent meta-analysis findings (27). Landrigan and colleagues reported
small to medium effects on executive functions (d = 0.39), and small non-significant effects on
working memory (d = 0.15). However, measures for specific subdomains often yield small and
noisy effects. In addition, there are a couple of key explanations for these findings.
One reason could be practice effects. Because effect sizes were calculated using mean
changes over the intervention period compared to mean changes over the control period, they
may be skewed by practice effects on the NIHTB-CB. For example, Table 4.2 shows that the
fluid instruments with slightly increased scores from baseline to pre-intervention (Flanker and
Dimensional Change Card) had smaller effect sizes than fluid instruments with slightly
decreased scores from baseline to pre-intervention (List Sorting and Pattern Comparison).

52

Therefore, differences in effect size between individual instruments may not only depend on the
cognitive constructs being measured, but also on test familiarity or learned strategies.
Small practice effects were expected (45). In a test-retest reliability study with a sample
representative of the population, the mean two-week (15.5 days) practice effect (d) for the
NIHTB-CB fluid composite score was 0.42. Comparatively, the fluid composite practice effect
in our study was much smaller (z = 0.06), likely due to our older cohort (mean age = 69.1 years)
and longer interval (12 weeks) between test-retest (123). It is worth noting too that even less
practice effects are expected for a third assessment (123), so changes from pre- to post-
intervention are likely primarily driven by intervention effects. While practice effects may
complicate interpretations of changes in individual cognitive instruments, using a fluid
composite score can wash out these effects and provide a single cognitive outcome upon which
the success of an intervention may be judged.
Another reason could be the heterogeneity of cognitive measures used in the literature.
Improvements in executive function have been commonly observed, with measures ranging from
Flanker tests, Stroop tests, Trail Making tests, and Reaction Time tests (15, 124, 125). Changes
in memory with RT have been less consistent and may depend on the test administered.
Improvements have been found in digit span tests (49, 63, 125), complex figure tests (49), and
verbal learning tests (54), but not in image recall tests (126) or auditory episodic memory tests
(127). Attributing the small effects of RT on memory to heterogeneity of measures is difficult
when the aerobic training literature appears to have a similar issue yet consistently demonstrate
larger effects (1). RT may in fact have little effect on laboratory-based memory performance.
Future studies should consider using more ecological assessments that provide a closer
replication of real-world functioning.

53

There could also be discrepancies in the literature as to how measures are classified into
fluid subdomains. Often measures will assess multiple subdomains. For example, working
memory is generally considered a component of executive function, but has dual service in
executive control and episodic memory. Therefore, the NIHTB-CB treats working memory as a
separate subdomain (16). The NIHTB-CB List Sorting Working Memory Test was designed
based on a paradigm emphasizing both holding and manipulation components of working
memory. Other investigators, however, do not separate working memory measures from episodic
memory measures. For example, in the recent meta-analysis (27), data from four studies that
used the Rey Auditory Verbal Learning Test (RAVLT) were included in the working memory
domain. But the RAVLT was used as a convergent validity measure (“Gold Standard”) to
validate the NIHTB-CB Picture Sequence Memory Test for episodic memory. Therefore,
subdomain analyses require a more systematic approach before definite conclusions can be made
about selective enhancement. One of the benefits of using comprehensive assessment measures
like the NIHTB-CB is that it standardizes assessment methods and results for comparisons across
existing and future studies.
At least one other RT study has used the NIHTB-CB for fluid cognitive outcomes;
however, they did not find any significant changes (128). Instead, Cherup and colleagues found
smaller effect sizes for fluid cognition composite score (d = 0.22) and individual instrument
scores (d = 0.03 – 0.51) compared to the current study, which may be in part due to differences
in exercise prescription. Their circuit training protocol involved lower loads to optimize the
production of muscular power for 12 repetitions per set, versus the periodized strength training
model in the current study. They did, however, find improvements in executive function through
improved inhibition (d = 1.49) and processing speed (d = 0.32) during ambulation (128). Last,

54

screening measures of cognitive impairment (e.g. Mini Mental State Examination) have been
used since they measure clinically meaningful levels of cognitive function. While these simple
tools might be advantageous for studies with MCI or dementia patients (129), they may not be
sensitive enough for studies with healthy older adults (130).

Exercise Prescription:
To our knowledge, this is the first study to examine cognitive outcomes in older adults
after a high intensity periodized strength training intervention. The loads were individually
progressed based on each participants’ performance through a pre-established program for sets
and repetitions, and all sessions were supervised by an exercise specialist. These considerations
might explain the greater increase in global fluid cognition compared to that found in a recent
meta-analysis (d = 1.27 versus d = 0.71, respectively), despite the duration (12 weeks) being
shorter than the median duration (16 weeks, range 4 – 96 weeks) of the 24 studies in the meta-
analysis (27).
Most of the previous RT studies in healthy older adults involved low-load circuits (125,
128), elastic band work (63), or a fixed number of repetitions per set, progressing load via
repeated max testing (14, 126, 130) or a pre-defined achievement metric (15, 54, 124). In
addition, most interventions were conducted through classes (15, 54, 124, 125), small group
training (14), or instructions for independent exercise (63, 126, 130), as opposed to one-on-one
personal training . We believe that one-on-one personal training offers a distinct advantage. On
top of safety and protocol supervision, rest periods allow time for conversations that keep
participants engaged. This time between sets can be used to connect with participants, inspire
maximal effort, and educate them about the benefits of exercise.

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Only one study (127) used a linear periodization model similar to what was used in the
current study. However, the current study cycled down to lower reps (4 reps/set versus 6
reps/set), corresponding to higher loads (90 % of 1RM versus 85 % of 1RM), and used a shorter
rest period between sets (1 min versus 3 min). Furthermore, the fluid composite effect size in the
current study was much higher (d = 1.27 versus d = 0.28). Although the influence of different
training parameters (i.e. frequency, duration, load, sets, and repetitions) on cognition is still
unclear (14, 15), the large effect observed in the current study supports the need for further
investigations to determine optimal exercise prescription.

Physiological Outcomes and Questionnaire Responses:
Gains in muscular strength, lean mass, and physical function are the prominent benefits
of RT for older adults. The expected increases in muscular strength and lean mass in untrained
older adults are about 24-33 % (mean duration 17.6 weeks and mean frequency 2.7 days/week)
and 1.1 kg (mean duration 20.5 weeks and mean frequency 2.8 days/week), respectively (56, 81).
Our RT intervention in the current study was designed to maximize strength gains using high
intensities and linear periodization techniques (43, 56). Therefore, the relatively large increase in
strength (61.5 %) was expected. Conversely, the increase in lean mass we observed (0.8 kg) was
slightly below average. Higher volumes could have been used to design a RT program for
hypertrophy (81). Other exercise parameters and factors outside of training, such as diet, could
also be manipulated to alter the effects on fat mass as well as other outcomes tested (i.e. blood
pressure, and heart rate) (11).
While mental health issues have notoriously negative consequences to cognitive function,
RT is recognized as an effective strategy to promote many important aspects of mental health

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(94). We observed improvements in sleep quality, perceptions of social support, and certain
scales of health-related quality of life. Perhaps the most important of which to cognition is sleep
quality. Age-related worsening of habitual sleep patterns is apparent even during healthy aging,
and is associated with increased cognitive decline and greater risk of cognitive impairment (99).
But RT improves sleep quality simultaneously with cognitive enhancement (110), with greater
effects observed when higher loads (i.e. > 70 % 1RM) and/or higher frequencies (i.e. 3 days per
week) are used (100). Participants in our study had a median sleep quality score (PSQI score =
5.0) at pre-intervention that is considered to be the cutoff for clinically poor sleep quality (114).
But since our RT intervention involved both high intensities and high frequency, it is no surprise
that sleep quality was improved at post-intervention.
It is important to note that, for the ISEL and SF-36, participants were asked to disregard
study-related activities in their responses. Nonetheless, the high median scores at baseline and
pre-intervention indicate a possible ceiling effect. Perceptions of social support did improve,
which may serve as a buffer against the effects of stress (115). In addition, there were small to
moderate improvements in self-reported physical functioning, role limitations due to physical
health, role limitations due to emotional problems, energy/fatigue, emotional well-being, pain,
and general health. These widespread improvements may represent an overall mood
enhancement of RT, since it’s known to benefit many aspects of mental health (94).

Exploratory Analyses:
Simultaneous improvements in cognition and other outcomes are commonly observed but
confirming associations between changes requires larger sample sizes. A priori power analyses
were performed to help future trials determine those sample sizes. A small correlation was

57

observed between changes in fluid composite score and changes in sleep quality. The positive
correlation indicates that smaller decreases in PSQI score (less improved sleep) were associated
with larger increases in fluid composite score. However, the negative correlation between PSQI
score at pre-intervention and changes in fluid cognition indicates that good sleepers before the
intervention had larger increases in cognitive improvements. These novel findings are small but
interesting given the improvement in sleep quality from pre- to post-intervention. As described
above, habitual and acute sleep patterns play a vital role in long-term cognitive abilities and daily
cognitive performance, respectively (99). Therefore, baseline sleep may influence the capacity
for positive neuroplasticity, while improvements in sleep may be independent of cognitive
enhancement. Future studies can refine our understanding of the role of improved sleep in RT-
induced cognitive enhancement by designing combined interventions involving both RT and
manipulated sleep patterns.
The negative correlations between changes in fluid composite score and changes in
strength were unexpected. Previous studies have found the opposite – positive associations
between changes in strength and changes in cognition. More specifically, increases in lower
body strength mediated the positive effects of RT on executive function and fluid cognition (5).
Perhaps these differences are due to assessment techniques. While previous studies assessed
strength via isokinetic dynamometry for maximal knee torque or pneumatic resistance machines
for 1RMs, we used pulley- and plate-loaded resistance exercise machines for 1RMs. These
machines have initial resistances that are not calculated as part of the load (e.g. the sled on the
leg press). But since we used spearman correlations, the wide range of strength gains in our study
(17.6 – 210.7 %) should have been ranked out. Another difference in our study was the
magnitude of strength gains. The effect size we observed was much larger than the previous

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controlled study that demonstrated mediation (d = 3.86 versus d = 0.84, respectively) (5).
Therefore, there may be an unknown factor or interaction effect. Despite this unexpectedly
negative correlation with strength, the positive correlation between changes in fluid composite
score and changes in muscular power does help extend the epidemiological link between
physical and cognitive functions (109). Future studies are warranted to investigate the
supraspinal neural pathways that connect gains in physical and cognitive function.
Although it is currently unclear which individuals gain the most from these types of
interventions, our exploratory analyses suggest that healthy older adults can expect similar
cognitive benefits from RT regardless of their baseline characteristics. Despite previous studies
suggesting that women benefit more than men in cognitive outcomes (3), we did not find any
evidence of an interaction with sex, perhaps due to our small sample size. Similarly, previous
studies suggest that patients with mild cognitive impairment receive greater cognitive benefits or
faster rates of improvement than healthy populations after a RT intervention (55). While that
might exist in mild cognitive impairment, our exploratory findings and that of others (124)
suggest that lower baseline cognition scores do not predict greater potential improvements in
cognitively healthy populations (108). Last, although younger and less active participants had
slightly greater increases in fluid cognition, these associations were small. Perhaps the most
important consideration for efficacy then is adherence (128). Clearly, future studies are needed to
match specific populations with the most optimal interventions, while being mindful of
participant preferences for maximum adherence.

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Limitations:
One limitation of this study was the use of a single group. Because this was a proof-of-
concept study for potential mechanisms (neuroimaging outcomes will be published elsewhere),
the design was determined based on feasibility and study funding. Having a single group allowed
us to address practicality while maintaining a thorough design. A randomized crossover was
opposed so that results were not confounded by detraining effects or long-term cognitive benefits
of RT (54). A separate active control group is needed in future studies to accurately estimate the
effects of training and establish causal relationships. Furthermore, while blinding participants to
study hypotheses is impractical, other safeguards can be used to reduce bias during testing such
as blinding assessors to group assignment (109).

Conclusion:
In conclusion, we have demonstrated that RT-induced fluid cognitive enhancement can
occur in 12 weeks in older adults using a high-intensity periodized program. These relatively
rapid improvements occurred simultaneously with well-characterized benefits to body
composition, muscular strength, physical function, sleep, and quality of life. Our exploratory
analyses provide promising preliminary data for future studies to investigate more defined
mechanisms of benefit. This critical next step will help establish the neuroscientific foundation
needed before firm recommendations can be made and specific training programs be developed
for cognitive function. With RT’s robust benefits to healthy aging and general well-being, even if
the value to a specific condition like cognitive impairment is only moderate, the overall benefit
of increased implementation to healthcare would be substantial.

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CHAPTER V:
IMPROVEMENTS IN CEREBROVASCULAR FUNCTION AFTER 12 WEEKS OF
PERIODIZED RESISTANCE TRAINING IN OLDER ADULTS

Abstract
Purpose: Resistance training (RT) is a promising strategy to promote healthy cognitive aging.
However, the brain mechanisms through which RT improves cognition have yet to be
determined. Here, we examined the effects of 12-weeks of RT on cerebrovascular (CV) function
in healthy 60-80-year-old adults.

Methods: Twenty healthy older adults (14 females, mean age 69.1 years) participated in this
single group clinical trial. The NIH Toolbox Cognition Battery and multimodal 3T magnetic
resonance imaging (MRI) were performed at three time points: baseline, pre-intervention, and
post-intervention. The period between baseline and pre-intervention served as a control of equal
duration as the high-intensity RT intervention. Both dynamic and structural CV measures were
analyzed, including cerebral blood flow (CBF), arterial transit time (ATT), vascular compliance
(VC), and white matter lesion (WML) volume.

Results: Fluid cognition composite score significantly increased from pre- to post-intervention
(+8.2 ± 6.1 %, p < 0.01). Effect sizes were generated for MRI outcomes. Small beneficial effects
were observed in global measures, including whole brain CBF (d = 0.12), gray matter CBF (d =
0.22), and WML volume (d = -0.22). Medium effects were observed in regional measures,
including hippocampal CBF (d = 0.40), anterior cingulate CBF (d = 0.38), posterior cingulate
CBF (d = 0.61), putamen CBF (d = 0.56), insula CBF (d = 0.69), occipital lobe CBF (d = 0.45),

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and temporal lobe CBF (d = 0.50). Only changes in WML volume were associated with changes
in fluid cognition (spearman’s rho = 0.44). Mixed effects were observed in ATT and VC
measures, but these outcomes also had moderate and poor reliability from baseline to pre-
intervention, respectively.

Conclusion: The effect sizes generated here will provide investigators with preliminary data to
help design and justify future clinical trials. Our findings are the first to suggest that RT can
improve CV function in older adults through CBF and WML volume.

Introduction
Resistance training (RT) interventions can improve fluid cognition in both healthy older
adults and patients with mild cognitive impairment (27). These improvements hold promise in
the multifaceted effort to delay or even prevent cognitive decline and dementia. However, a clear
understanding of the neurophysiological processes that underlie these benefits must be
established before explicit exercise recommendations can be made. While most of the magnetic
resonance imaging literature has focused on aerobic exercise interventions (131), RT likely has
distinct brain mechanisms of benefit that have yet to be fully determined (34).
Cerebrovascular (CV) function is a major component of brain health, responsible for
securing adequate blood supply to the brain in resting and dynamic conditions. Mounting
evidence suggests that CV dysfunction may precede, and even cause, neuronal dysfunction and
cognitive impairment (20). For example, reductions in global cerebral blood flow (CBF) are
associated with accelerated cognitive decline and increased risk of dementia (68). While aerobic
training studies suggest that CV plasticity is a primary mechanism of cognitive enhancement (9),

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this has not been tested after RT. Specifically, regional CBF is increased after only 12 weeks of
aerobic training in healthy older adults, and these changes are associated with improvements in
episodic memory (4, 8). RT is known to improve systemic vascular function and cardiovascular
health (11, 12), so improvements in CV function are promising. Furthermore, there are changes
in CBF patterns during resistance exercise that, although complex, do seem to present an
adaptive stimulus (109). Intervention studies are thus warranted. Specifically, investigations are
needed to test and extend previous cross-sectional evidence linking RT with greater global CBF
in older women (10).
Arterial spin labeling (ASL) is a noninvasive magnetic resonance imaging (MRI)
technique that uses arterial blood water as an endogenous tracer for quantitative assessment of
global and regional CBF (132). Various ASL methodology have been developed that are
sensitive to aging, disease progression, and intervention (133). For example, arterial cerebral
blood volume (CBV) can be estimated by combining dynamic ASL with a cine segmented multi-
phase balanced steady-state free precession (bSSFP) sequence (134). Lower CBV is associated
with cognitive decline (135). Measures that are more microvascular in nature such as arterial
transit time (ATT), i.e. the duration for blood to travel from large arteries to small capillaries, can
be estimated from multi-delay pseudo-continuous ASL (pCASL) (136). Shorter ATT is
associated with poorer cognitive function (137). Finally, by synchronizing dynamic ASL with
the cardiac cycle, vascular compliance (VC) can be calculated as the ratio of the change in CBV
between systole and diastole to the change in arterial blood pressure (BP) between systole and
diastole (18). Intracranial VC provides an essential buffering mechanism for continuous blood
flow to the brain, whereas the opposite, e.g. increased pulsatility and resistance, is associated

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with decreased cognitive function and believed to cause long-term brain damage (138). None of
these ASL techniques have been used to investigate the effects of RT on CV function.
Another relevant marker of CV dysfunction, specifically of small-vessel disease, is white
matter lesion (WML) volume. WMLs are structural markers of ischemic and inflammatory brain
tissue that are visualized as hyperintense signals on T2-weighted FLAIR (Fluid-attenuated
inversion recovery) MRI (66) and strongly implicated in the pathogenesis of vascular cognitive
impairment and dementia (74). Furthermore, the presence of WMLs interacts with resting CBF,
such that hypoperfusion-related rate of cognitive decline and risk of dementia increase with
greater severity of WML volume (68). A similar interaction effect may occur in reverse with
exercise. RT is an effective countermeasure against the normal progression of WMLs in both
healthy older adults and patients with MCI (34, 35). Whether these structural benefits,
concomitant with improved cognition, are related to changes in dynamic CV function has not
been determined.
The purpose of this investigation was to estimate the effects of 12-weeks of high-
intensity RT on CV function (measured via 3T ASL and FLAIR) and to explore potential
associations between these CV adaptations and cognitive improvements. We hypothesized that
12-weeks of periodized RT performed 3 days per week can improve resting CBF, ATT, VC, and
WML volume in older adults 60-80 years of age. Because this is a mechanistic proof of concept
clinical trial, the main outcomes were assessed using the estimated effect sizes in each of these
difference measures.

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Methods
Participants and Study Design
Using a single arm pre-post design, the effects of a 12-week periodized RT intervention
on fluid cognition and CV function were compared to that of an initial control period. Thus,
participants served as their own controls and completed all assessments at three time points:
baseline, pre-intervention, and post-intervention. All control periods took place before the RT
intervention to ensure that results were not confounded by detraining effects or long-term
cognitive benefits of RT (54). The full protocol for this mechanistic proof-of-concept clinical
trial has been described (see chapter 4).
This simple yet rigorous and well-designed study was approved by the USC Health
Sciences Review Board, registered with ClinicalTrials.gov (ID: NCT03982550), and all
procedures were conducted at the University of Southern California (USC, Los Angeles, CA)
Health Sciences Campus. Written informed consent was obtained from all potential participants
assessed for eligibility. Primary considerations for inclusion/exclusion criteria were to ensure
that participants were healthy, interested, and available to participate, without any
contraindications to RT or MRI. Twenty healthy older adults were enrolled in the study and all
completed the required number of training sessions (>90%) and assessments at baseline, pre-
intervention, and post-intervention (Table 4.1). There were no participant drop-outs or severe
adverse events. A flow diagram of participant recruitment and testing is shown in Figure 4.1.

Intervention
Participants performed a periodized and progressive RT program emphasizing
development of total-body strength. All 36 training sessions (3 days per week for 12 weeks) were
performed in the CERC, supervised by an exercise specialist. Mesocycle I (weeks 1-4) was

65

designed for muscular hypertrophy to develop a base for more intense training in later phases.
Training bouts consisted of 4-6 resistance exercises with 3 sets per exercise and 8-10 repetitions
per set. Mesocycles II (weeks 5-8) and III (weeks 9-12) were designed to promote strength gains.
Training bouts consisted of 4-6 resistance exercises with 3-4 sets per exercise, 4-6 repetitions per
set, and linear increases in intensity over time.

Cognitive Testing
All cognitive assessments were administered by the same trained investigator (TRM)
using the NIH Toolbox® Cognition Battery (NIHTB-CB, Version 1.21) application on an iPad
Pro 10.5-inch (Apple, Cupertino, CA). Each assessment lasted approximately 45 minutes in
duration. The NIHTB-CB provides a standard set of comprehensive assessment tools that have
been normed and validated in participants ages 3-85, and ensures that assessment methods and
results can be used for comparisons across existing and future studies (16). Three test versions
were used to reduce practice effects and were administered in random order for each participant.
The NIHTB-CB has seven individual instruments – Flanker Test, Picture Sequence
Memory Test, List Sorting Working Memory Test, Dimensional Change Card Sort Test, Pattern
Comparison Test, Picture Vocabulary Test, and Oral Reading Recognition Test – that test the
following cognitive subdomains: inhibitory control and attention, episodic memory, working
memory, executive function, and processing speed for fluid cognition and vocabulary and
reading recognition for crystallized cognition. In addition to scores for each individual
instrument, the battery yields a Fluid Cognition Composite Score and a Crystallized Cognition
Composite Score (45). We analyzed uncorrected scores, standardized to the population with a
normative mean of 100 and a standard deviation of 15.

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MRI Data Acquisition
MRI data were acquired on a Siemens 3T Magnetom Prisma (Erlangen, Germany) using
a 20-channel head coil. Padding was placed around the head to minimize head motion during
scans. Electrocardiogram (ECG) leads were placed on the participants’ chest prior to scans.
Before and after the VC scans, brachial blood pressure was recorded using an MR compatible
cuff sphygmomanometer. Participants were asked to stay as still as possible without falling
asleep.
Following standard scout and structural MRI, an ECG-triggered time resolved phase
contrast (PC) MRI was performed to measure the blood flow velocity in the internal carotid
arteries (ICA). A single axial slice of 5.0 mm thickness at the level of C1/C2 was imaged with
the following parameters: TR/TE = 49.25/4.86 ms, FOV read = 200mm, flip angle = 15°, matrix
= 192 x 192, Velocity encoding = 100 cm/s, 23 phases with an interval of 50 ms, scan time of 2
min. The time delays at peak systole and early diastole were identified in each individual subject
(on average 150 ms and 400 ms following the trigger, respectively). These time delays were used
as the ECG trigger to synchronize two separate multi-phase bSSFP ASL scans (pulsed spin
labeling) with the peak systolic and early diastolic phases (18). The flow-sensitive alternating
inversion recovery (FAIR) scheme was implemented for spin labeling, which was immediately
followed by a cine bSSFP readout train with the following parameters: TR/TE = 73.2/1.83 ms,
FOV read = 220 mm, flip angle = 40°, matrix = 96 x 96, slice thickness = 8.0 mm, centric
ordering k-space acquisition with 20 lines per segment, and 29 phases from 150 to 2250 ms with
an interval of 75 ms. The interval between inversion pulses was approximately 3 s covering 3 to
4 heartbeats. An oblique axial slice of 5-mm thickness (Parallel to AC–PC) at the level of
internal capsule was imaged. In each cine bSSFP ASL scan, 8 pairs of label/control acquisitions

67

were collected which took approximately 3 min. The two bSSFP ASL scans at systole and
diastole took approximately 6 min.
Following the two ECG-triggered dynamic bSSFP ASL scans, a 5-min resting-state
perfusion MRI scan was performed using a pCASL sequence with background suppressed 3D
gradient and spin echo readout (139). Fifty-four 2.5 mm transverse slices were acquired to cover
the whole brain with the following imaging parameters: TR/TE = 4100ms/36.76 ms, FOV read =
240 mm, flip angle = 120°, matrix = 64 x 64. The tagging plane was positioned 90 mm inferior
to the center of the imaging slab with a labeling duration of 700 ms and post-labeling delay of
1800 ms. Thirty pairs of label and control acquisitions were performed.
A T-2 weighted sagittal 3D FLAIR (TR/TE: 5000/388 ms; TI: 1800 ms) was acquired
with an interleaved series and anterior to posterior phase encoding. Each slab had 176 slices (1.0
mm thickness) and a field of view read of 256 mm

MRI Data Processing
Perfusion images were generated from 3D pCASL data by pairwise subtraction between
the control and label pairs, after head motion correction. Quantitative CBF maps were calculated
based on a standard one compartment model (140), assuming a labeling efficiency of 0.77 by
taking into account the loss of efficiency by two background suppression pulses (139), and blood
T1 of 1650 ms at 3T (141). ATT was calculated as the time for labeled blood to flow from the
labeling plane to the microvascular perfusion site. A structural 3D MPRAGE (Magnetization-
Prepared Rapid Acquisition with Gradient Echo) scan was segmented into white matter, gray
matter (including cortical and subcortical segmentations), and cerebrospinal fluid using SPM8
(https://www.fil.ion.ucl.ac.uk/spm/). Mean CBF and ATT were extracted from these gray matter
and white matter masks, thresholded for probabilities above 80% for each subject. A voxel-based

68

analysis was also performed on group CBF changes from pre- to post-intervention. A
postprocessing package in SPM8 was used to perform a longitudinal general linear model with
the main factor of time. Whole-brain cluster-level false discovery rate correction was used with
an initial threshold at P < 0.05 and cluster size k > 100.
Arterial CBV maps were generated from dynamic bSSFP ASL sequences after image
outlier rejection (134). Motion control with image outlier rejection was essential for reliable CV
measurements because the dynamic ASL techniques employed single slice 2D acquisition. Both
within-scan and inter-scan outlier rejection was performed instead of motion correction to
preserve the original signal intensity. Perfusion-weighted image series were first generated by
pair-wise subtraction of control and label acquisitions at each inversion time (TI), and the time
course of perfusion-weighted signals of the entire slice were extracted at each TI for each
measurement. The mean perfusion-weighted signal and the standard deviation were calculated at
each TI across the measurements. If the time course of the perfusion weighted signal of all the
time points (TIs) in one measurement was beyond one standard deviation from the mean, the
ASL data at all time points of that measurement were excluded.
The calculation of CBV involves the ratio of the sum concentrations of labeled spins
between the artery and each pixel, as a function of T1 relaxation time of arterial blood (134). The
arterial input function was derived from one or two pixels within the large arteries that showed
the earliest peak. Two regions of interest were defined: large arteries with CBV > 5% total slice
volume and small arteries/arterioles with CBV of 1.5-5% of the total slice volume. Vascular
compliance was then calculated as the difference between relative CBV (% of total volume) at
systole and relative CBV at diastole divided by the difference between systolic and diastolic
blood pressure (18). An example is shown in Figure 5.1.

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As surrogates of VC, pulsatility index and resistance index were calculated, representing
the pulsatility of CBF velocity waveforms and the resistance of small vessels, respectively (138).
Whole brain CBF was used. The pulsatility index is calculated as the difference between systolic
and diastolic flow velocities divided by the mean flow velocity (142). The resistance index is
calculated by dividing the mean arterial pressure by the CBF (138).

Figure 5.1 Representative maps of relative cerebral blood volume (CBV) at peak systole (A) and
early diastole (B), as well as the corresponding CBV difference between systolic and diastolic
blood pressure (C).

WMLs were extracted from FLAIR images using the lesion prediction algorithm (143) as
implemented in the Lesion Segmentation Toolbox (V3.0.0, https://www.applied-
statistics.de/lst.html) for SPM12. This algorithm consists of a binary classifier in the form of a
logistic regression model trained on data from patients with multiple sclerosis. A lesion belief
map is used as covariates for the model, as well as spatial covariates that account for voxel
specific changes in lesion probability. Each participant’s corresponding MPRAGE image was
added as a reference for coregistration before lesion segmentation. Parameters of the model
provide an estimate for the lesion probability of each voxel in the image. The main output of this
segmentation process was total WML volume across the whole brain.

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Statistical Analyses
The following analyses were performed in SPSS Statistics V.25 (IBM, Chicago, IL). All
data were considered continuous, examined for normality using Shapiro-Wilk tests, and
described by either means and standard deviations or medians and interquartile range (if not
normally distributed). To confirm the reliability of our measures, the control period – differences
between baseline and pre-intervention – was evaluated using two-way mixed effects intraclass
correlation coefficients (ICCs) with absolute agreement. Estimates less than 0.50, between 0.50
to 0.75, between 0.75 and 0.90, and greater than 0.90 were classified as poor, moderate, good,
and excellent test-retest reliability, respectively (117). Effect sizes were calculated for all
outcomes to determine the magnitude of differences between control and intervention periods.
For normally distributed data, an adapted Cohen’s d effect size was calculated – mean changes
from pre- to post-intervention were subtracted by the mean changes from baseline to pre-
intervention, then divided by the average standard deviation of those changes (118, 119). For
non-normally distributed data, matched-pairs rank-biserial effect size was calculated – the
differences between control and intervention changes were ranked, then the sum of positive
ranks was subtracted by the sum of negative ranks and divided by the total sum of ranks (120).
For both adapted Cohen’s d and matched-pairs rank-biserial, effect sizes of 0.2, 0.5, 0.8, and 1.2
were classified as small, medium, large, and very large, respectively (121). Only changes in fluid
cognition were tested for significance because fluid cognition was the only outcome powered for
a priori. Paired t-tests were used, comparing data from pre- and post-intervention, with statistical
significance set at P < 0.05.
Relationships between fluid cognition improvements and changes in other outcome
variables were analyzed in SPSS to explore potential mechanisms of benefit. Only raw changes

71

from pre- to post-intervention were used so that effects were not washed out by percent changes.
These raw changes often have high kurtosis when a small sample size (N = 20) is used.
Therefore, Spearman rank correlation coefficients were chosen to reduce effect variability (122).
This same technique was used to analyze relationships between fluid cognition improvements
and sampling factors such as participant characteristics. Spearman’s rho values of 0.1, 0.3, and
greater than 0.5 were classified as small, medium, and large associations (118), respectively. A
priori power analyses (correlation: bivariate normal model) were performed using G*Power
3.1.9.2 to inform future trials of the required sample size to observe significant correlations in a
similar pre-post, one group design, based on our observed correlations, alpha = 0.05, two tails,
and power = 0.80.

Results
Cognitive Outcomes:
Cognitive outcomes have been described in detail previously (see chapter 4). Briefly,
Fluid cognition composite scores had good reliability and significantly increased from pre- to
post-intervention (+7.7 ± 5.5 Standard Units, p < 0.01). Four of the five individual instrument
scores significantly increased from pre- to post-intervention: Dimensional Change Card Sort Test
(+4.1 ± 6.8 Standard Units, p = 0.04), Flanker Test (+3.2 ± 6.4 Standard Units, p = 0.02), List
Sorting Test (+10.7 ± 10.2 Standard Units, p < 0.01), and Pattern Comparison Test (+2.0 ± 11.5
Standard Units, p < 0.01). One outlier was removed from crystallized cognition analyses. In the
remaining 19 participants, there was no change in crystallized cognition composite score from
pre- to post-intervention or the individual instruments: Picture Vocabulary Test and the Oral
Reading Recognition Test.

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Table 5.1 presents the mean ± SD (or median ± IQR if data not normally distributed) for
all MRI outcome measures of the study at baseline, pre-intervention, and post-intervention, test-
retest reliability of the 12-week control period, and effect sizes of the 12-week intervention
period. Because participants were asked to maintain their normal eating and exercise habits
outside of the study, no changes in outcome measures were expected after the control period.
Reliability was moderate for global CBF, moderate for ATT, very poor for VC, moderate for
pulsatility index and resistance index, and excellent for WML volume. Most regional CBF
measures had moderate reliability (hippocampus, anterior cingulate, posterior cingulate,
putamen, amygdala, insula, occipital lobe, and temporal lobe), though a few (caudate nucleus,
frontal lobe, and parietal lobe) had poor reliability (Table 5.1).

Table 5.1 Group means for CV outcomes at baseline, pre-intervention, and post-intervention (N
= 20)
Measure

Baseline Pre-
Intervention
Post-
Intervention
ICC^
(3,1)
Effect Size^^
(d)
Global Perfusion Outcomes
#
:
Whole Brain CBF
(ml/100g/min)
37.3 ± 6.6 36.6 ± 7.4 36.7 ± 7.6 0.69 0.12
Gray Matter CBF
(ml/100g/min)
40.1 ± 7.6 38.8 ± 8.5 39.5 ± 8.7 0.61 0.22
White Matter CBF
(ml/100g/min)
34.1 ± 5.7 34.1 ± 6.3 33.8 ± 6.3 0.73 -0.04
Whole Brain ATT (ms) 1128 ± 92 1155 ± 117 1158 ± 114 0.61 -0.13
Gray Matter ATT (ms) 1095 ± 97 1116 ± 122 1126 ± 121 0.69 -0.07
White Matter ATT
(ms)
1148 ± 86 1178 ± 121 1172 ± 107 0.54 -0.20
Regional Perfusion Outcomes
#
:
Hippocampal CBF
(ml/100g/min)
37.4 ± 7.7 35.1 ± 8.0 35.3 ± 9.9 0.75 0.40
Anterior Cingulate
CBF (ml/100g/min)
44.1 ± 8.3 41.9 ± 9.1 42.7 ± 9.3 0.61 0.38
Posterior Cingulate
CBF (ml/100g/min)
43.8 ± 9.9 40.7 ± 9.6 42.2 ± 8.3 0.57 0.61
Putamen CBF
(ml/100g/min)
41.6 ± 7.2 39.2 ± 9.8 41.3 ± 11.1 0.58 0.56

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Amygdala CBF
(ml/100g/min)
37.7 ± 7.6 36.4 ± 8.3 36.3 ± 11.1 0.64 0.18
Caudate Nucleus CBF
(ml/100g/min)
35.7 ± 7.9 34.7 ± 8.6 34.1 ± 8.5 0.35 0.03
Insula CBF
(ml/100g/min)
45.8 ± 7.2 42.3 ± 8.8 43.6 ± 10.3 0.51 0.69
Frontal Lobe CBF
(ml/100g/min)
37.2 ± 6.7 37.6 ± 7.3 38.3 ± 7.9 0.36 0.03
Occipital Lobe CBF
(ml/100g/min)
39.1 ± 11.1 36.4 ± 13.9 38.9 ± 13.2 0.54 0.45
Parietal Lobe CBF
(ml/100g/min)
35.9 ± 9.3 36.8 ± 10.6 38.2 ± 10.6 0.30 0.05
Temporal Lobe CBF
(ml/100g/min)
40.5 ± 7.4 37.7 ± 9.6 39.1 ± 9.6 0.51 0.50
Vascular Compliance Outcomes:
Large Artery VC
(% total volume /
mmHg)
##

(7.2 ± 6.8)
x 10
-3
*
(5.0 ± 6.9)
x 10
-3
*
(4.1 ± 5.8)
x 10
-3
*
0.46 -0.08*
Small Artery VC
(% total volume /
mmHg)
##

(4.0 ± 2.8)
x 10
-3
*
(2.6 ± 2.9)
x 10
-3
*
(1.7 ± 3.1)
x 10
-3
*
-0.01 0.08*
Whole Brain
Pulsatility Index
0.99 ±
0.23*
1.01 ± 0.25* 1.01 ± 0.35* 0.73 0.15*
Whole Brain
Resistance Index
0.64 ±
0.06*
0.65 ± 0.10* 0.65 ± 0.12* 0.77 0.16*
Structural Outcomes:
Whole Brain WML
Volume (ml)
0.8 ± 1.6* 0.9 ± 1.6* 0.7 ± 1.7* 0.98 -0.22*
The control period 12-week test-retest reliability was important since this study was a single
group clinical trial. No changes were observed from baseline to pre-intervention. The effects of
the 12-week periodized RT intervention are evident via changes from pre- to post-intervention
and calculated effect sizes. Mean ± SD.
^Two-way mixed intraclass correlation coefficient (ICC) with absolute agreement calculated
using baseline and pre-intervention data.
^^ Adapted Cohen’s d effect size calculated by subtracting the mean changes from pre- to post-
intervention by the mean changes from baseline to pre-intervention and dividing by the average
standard deviation of those changes.
*Median ± IQR and matched-pairs rank-biserial effect size calculated on the changes from pre-
to post-intervention versus changes from baseline to pre-intervention.
#
Data from one participant were removed because of excess noise, likely due to head movement
during the MRI (N = 19).
##
Data from three participants were removed because two had offset imaging planes and one had
excess noise, likely due to head movement during the MRI (N = 17).
CBF = Cerebral Blood Flow. ATT = Arterial Transit Time. VC = Vascular Compliance. WML =
White Matter Lesion.

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Global Cerebral Blood Flow:
Global CBF data from one participant were removed because of excess noise, likely due
to head movement during the MRI. The results of the remaining 19 participants are shown in
Table 5.1 and an example CBF map is shown in Figure 5.2. The changes in CBF (ml/100g/min)
and ATT (ms) were mixed. Whole brain CBF slightly increased from pre- to post-intervention
(+1.9 ± 17.9 %), with a small increase in gray matter CBF (+3.4 ± 19.8 %) but not white matter
CBF (-0.2 ± 16.7 %) after the intervention (Figure 5.3). Because effect sizes were calculated
comparing the intervention period to the control period, and CBF slightly decreased from
baseline to pre-intervention (e.g. whole brain: -1.4 ± 14.0 %), the estimates generated show even
more pronounced positive effects (Table 5.1). Using a separate voxel-based approach to group
changes in whole brain CBF from pre- to post-intervention, small significant clusters were
identified in the right inferior frontal gyrus (Figure 5.4).

Figure 5.2 Representative map of cerebral blood flow (CBF) from a single subject. CBF was
labeled via pseudo-continuous arterial spin labeling (pCASL) and maps were generated by
pairwise subtraction between the control image and it’s labeled pair. Bright colors (e.g.
yellow/red) indicate greater CBF.

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Figure 5.3 Global cerebral blood flow (CBF) measured via pseudo-continuous arterial spin
labeling (pCASL) at baseline, pre-intervention, and post-intervention (N = 19). Structural gray
matter and white matter masks were used to segment CBF in these tissue types. Mean ± SD.

Figure 5.4 Voxel-based statistical analyses of group cerebral blood flow (CBF) changes from
pre- to post-intervention. Significant clusters (d > 3.0) are shown in white.

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Regional Cerebral Blood Flow:
Regional CBF data from one participant were removed because of excess noise, likely
due to head movement during the MRI. The results of the remaining 19 participants are shown in
Table 5.1. Promising small to medium positive effect sizes were observed in the hippocampus,
anterior cingulate, posterior cingulate, putamen, insula, occipital lobe, and temporal lobe,
primarily due to an attenuation or reversal of declines over the control period. For example,
hippocampal CBF only increased an average +1.9 ± 22.9 % from pre- to post-intervention, but
that contrasts the average -5.8 ± 14.1 % change from baseline to pre-intervention. Note however
the variability of these changes. Other regions had similar variability in changes from pre- to
post-intervention; for example, anterior cingulate CBF (+3.3 ± 17.6 %), posterior cingulate CBF
(+5.6 ± 15.1 %), and putamen CBF (+6.6 ± 20.7 %). To illustrate, individual data for
hippocampal CBF, anterior cingulate CBF, posterior cingulate CBF, and putamen CBF are
shown in Figures 5.5-5.8, respectively. The remaining regions examined (the frontal lobe,
caudate nucleus, and parietal lobe) had negligible effect sizes due to similar changes over the
control and intervention periods. Frontal lobe CBF and parietal lobe CBF continued slight
increases over control to intervention periods, and caudate nucleus CBF continued slight declines
over control to intervention periods.

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Figure 5.5 Regional cerebral blood flow in the hippocampus at baseline, pre-intervention, and
post-intervention (N = 19). The bold line shows the mean for 19 participants, while the
remaining lines show individual participant data.

Figure 5.6 Regional cerebral blood flow in the anterior cingulate at baseline, pre-intervention,
and post-intervention (N = 19). The bold line shows the mean for 19 participants, while the
remaining lines show individual participant data.

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Figure 5.7 Regional cerebral blood flow in the posterior cingulate at baseline, pre-intervention,
and post-intervention (N = 19). The bold line shows the mean for 19 participants, while the
remaining lines show individual participant data.

Figure 5.8 Regional cerebral blood flow in the putamen at baseline, pre-intervention, and post-
intervention (N = 19). The bold line shows the mean for 19 participants, while the remaining
lines show individual participant data.

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Arterial Transit Time:
Whole brain ATT did not change from pre- to post-intervention (+0.6 ± 7.3 %), but the
effect size came out negative because whole brain ATT slightly increased from baseline to pre-
intervention (+2.4 ± 7.5 %). While white matter ATT slightly decreased after the intervention (-
0.2 ± 8.2 %), gray matter ATT slightly increased (+1.1 ± 7.0 %).

Vascular Compliance:
Intracranial VC data from three participants were removed because two had offset
imaging planes and one had excess noise, likely due to head movement during the MRI (N = 17).
The results of the remaining 17 participants are shown in Table 5.1 and an example relative CBV
(% of total volume) map is shown in Figure 5.1. Data were not normally distributed (Shapiro-
Wilk P < 0.05). In addition, caution should be taken with interpretations because of the poor
measurement reliability.
Figure 5.9 shows that large and small artery VC decreased from pre- to post-intervention
(median = -1.6 %, IQR = 175.6 %; and median = -3.0 %, IQR = 166.7 %, respectively).
However, there were discrepancies with the effect sizes calculated in Table 5.1. Since the
matched pairs rank biserial effect size only considers the proportion of positive and negative
ranks, values may not align with the median change when there is high variability in those
changes. Similarly, the surrogates of VC, pulsatility index and resistance index, did not change
from pre- to post-intervention (median = -1.3 %, IQR = 14.6 %; and median = -0.1 %, IQR = 8.7
%, respectively), but had discrepancies with calculated effect sizes (Table 5.1). Together, the
changes in intracranial VC, pulsatility index, and resistance index each had high variability and
negligible effect sizes.

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Figure 5.9 Vascular compliance (VC) in A) large arteries and B) small arteries at baseline, pre-
intervention, and post-intervention (N = 17). Large arteries were defined as regions with cerebral
blood volume (CBV, measured via balanced steady-state free precession arterial spin labeling
sequence) greater than 5% of total volume and small arteries were defined as regions with CBV
of 1.5-5% of total volume. VC was then calculated as the change in CBV between peak systole
and end diastole divided by the systolic-diastolic blood pressure difference.

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White Matter Lesion Volume:
An example of WML volume segmentation is shown in Figure 5.10. Whole brain WML
volume slightly decreased from pre- to post-intervention (median = -3.7 %, IQR = 22.4 %). The
promising effect size indicated an attenuation of the WML volume progression from baseline to
pre-intervention (median = +5.3 %, IQR = 26.3 %, Figure 5.11).
Figure 5.10 Representative image of white matter lesion (WML) volume segmentation in a
transverse brain slice from a single subject. Left: Transverse slice of a co-registered T2-weighted
FLAIR (Fluid-attenuated inversion recovery) image. Right: Segmentations of WML volume via
the lesion prediction algorithm are shown on the same slice in yellow, orange, and red.

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Figure 5.11 White matter lesion (WML) volume in 20 participants at baseline, pre-intervention,
and post-intervention. Boxes show Q1, median, and Q3, respectively, and each participants’ data
are plotted as connected dots.

Correlation Analyses:
MRI outcomes were further explored for possible relationships with pre- to post-
intervention changes in fluid cognition. Table 5.2 shows the degrees of correlation between the
ranked data. Raw fluid cognition changes had small negative correlations with changes in whole
brain CBF, gray matter CBF (and most regional CBF), white matter CBF, large artery VC, small
artery VC, and whole brain pulsatility index; a medium positive correlation with changes in
whole brain WML volume; and small positive correlations with all ATT measures.

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Table 5.2 Correlation analyses between pre- to post-intervention changes in fluid cognition
composite score and changes in other outcome variables of interest (N = 20).
Measure

Spearman’s rho

A Priori Sample Size*
Global Perfusion Outcomes
#
:
Whole Brain CBF -0.27 105
Gray Matter CBF -0.17 269
White Matter CBF -0.28 97
Regional Perfusion Outcomes
#
:
Frontal Lobe CBF -0.35 61
Hippocampal CBF -0.26 113
Anterior Cingulate CBF -0.06 2177
Posterior Cingulate CBF -0.04 4903
Putamen CBF -0.17 269
Amygdala CBF -0.26 113
Caudate Nucleus CBF -0.11 646
Insula CBF -0.26 113
Occipital Lobe CBF -0.18 239
Parietal Lobe CBF -0.33 69
Temporal Lobe CBF -0.28 97
Arterial Transit Time
#
:
Whole Brain ATT 0.14 398
Gray Matter ATT 0.12 542
White Matter ATT -0.01 -
Vascular Compliance Outcomes:
Large Artery VC
##
-0.12 542
Small Artery VC
##
-0.25 123
Whole Brain Pulsatility Index -0.27 105
Whole Brain Resistance
Index
-0.09 966
Structural Outcomes:
Whole Brain WML Volume
(ml)
0.44 38
These correlation analyses were used to explore potential indirect mechanisms of fluid cognitive
enhancement after RT.
*A priori power analyses of correlation (bivariate normal model) in a similar pre-post, one group
design, with two tails, alpha = 0.05, and power = 0.80.
#
Data from one participant were removed because of excess noise, likely due to head movement
during the MRI (N = 19).
##
Data from three participants were removed because two had offset imaging planes and one had
excess noise, likely due to head movement during the MRI (N = 17).
CBF = Cerebral Blood Flow. ATT = Arterial Transit Time. WML = White Matter Lesion.

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Discussion
In this mechanistic proof of concept clinical trial, we report the first estimates of changes
in CV function after high-intensity RT in older adults. Along with significant improvements in
fluid cognition, we found medium positive effects on posterior cingulate CBF, putamen CBF,
insula CBF, and temporal lobe CBF, and small positive effects on global CBF, hippocampal
CBF, anterior cingulate CBF, occipital lobe CBF, and whole brain WML volume. These data
provide promising evidence for CV plasticity after RT interventions, and support future
randomized clinical trials with larger sample sizes.
The most clinically relevant change was the improvement in CBF. This measure is
strongly supported in the literature as a marker of neurodegeneration related to Alzheimer’s
disease and vascular dementia (68, 144). Multiple studies have demonstrated significantly
increased CBF after aerobic training in both healthy older adults (4, 8, 145) and patients with
mild cognitive impairment (146). Therefore, we hypothesized that RT would also improve CBF,
as outlined previously (109): RT is known to improve systemic vascular function and
cardiovascular health (11, 12); global CBF increases in an intensity-dependent manner and
fluctuates at higher intensities like RT (147); transient hypoperfusion may stimulate hypoxemia-
related mechanisms for vascular adaptation (32); regional CBF is augmented during muscle
contraction and dependent on force production levels (33); and finally, demonstrating improved
global CBF would confirm previous cross-sectional evidence in older women (10).
Small positive effects were observed in global CBF, a good marker for aging and
neurodegenerative diseases (68, 144), but regional quantifications are likely more sensitive for
understanding the effects on cognition – specific regions like the hippocampus, precuneus,
superior temporal lobe, and orbitofrontal regions have shown associations with aging and

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cognition (133). None of the aerobic training studies mentioned previously found significant
changes at the whole brain level (4, 8, 145). Instead, the regional CBF increases were found
primarily in the hippocampus and anterior cingulate, associated with improvements in memory
(4, 8) and executive function (145). Similarly we observed positive CBF effects in regions
involved in neurodegeneration processes (148). The positive effect sizes consistently reflected a
small increase in regional CBF from pre- to post-intervention that appeared to be an attenuation,
or even reversal, of normal age-related declines over the control period. These included posterior
cingulate CBF, putamen CBF, insula CBF, temporal lobe CBF, hippocampal CBF, anterior
cingulate CBF, and occipital lobe CBF. These data provide the first indication for dynamic CV
plasticity after RT.
It is important to note that the changes in regional CBF all had negative (or zero)
correlations with changes in fluid cognition. Although this was unexpected, it may suggest that
changes in regional CBF do not directly contribute to cognitive improvements. Instead RT-
induced cognitive enhancement may be mediated by other factors. Since CV plasticity is an
important mechanism of aerobic training induced cognitive enhancement (4, 8), this may be a
distinct mechanism between exercise modalities. Regardless, improvements in regional CBF as a
result of RT would likely promote overall brain health.
Another clinically relevant change was observed in WML volume, a marker of small
vessel disease with high prevalences in older adults and strong implications in the pathogenesis
of vascular cognitive impairment and dementia (149). While WML volume is expected to
steadily increase with normal aging, we only observed a median increase after the 12-week
control period and not after the 12-week intervention. These promising results support previous
findings and suggest that RT has a countermeasure effect on WML volume progression (34, 35).

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The mechanisms of this protective effect and relevance for cognition are currently unclear (74).
The underlying physiology of the imaging marker would suggest a reversal of interstitial fluid
accumulations that are represented by hyperintensities, thus halting long-term demyelination and
axonal damage (150).
Another unexpected association was found between WML volume and changes in fluid
cognition. The positive correlation indicates that greater improvements in fluid cognition
occurred with smaller benefits to WML volume. Again, this may suggest that CV adaptations
occur independently from cognitive enhancement after RT intervention. However, a previous
study reported associations between attenuated WML volume progression and maintained gait
speed (35). Therefore, benefits to WML volume may still contribute to other widespread benefits
of RT and to overall brain health.
Less convincing are the changes in ATT and VC, the results of which were mixed. First,
most VC measures suggested that VC was unchanged or decreased after the intervention. Since
VC is necessary to buffer pulsatile pressures for continuous blood flow to the brain (151), this
decrease could mean that RT leads to arterial stiffening, thus causing increased pulsatility and
resistance (138). However, we find that unlikely given the positive changes in CBF and WML
volume. Could this then be an adaptation to or a continuation of the high pulsatile pressures that
occur during RT (147)? Also unlikely because a minimum of 48 hours buffer was used between
the last training session and the first testing session. The high pulsatility during exercise might
even be beneficial, driving neurotrophic factors into the brain for neuroplasticity (152). Why
then did we find a detrimental change in VC? Perhaps RT was unable to attenuate the normal
decreases in VC with aging (153). Alternatively, our VC measures may not be sensitive enough
for repeated testing. As can be seen in Table 5.1, some IQRs were greater than their medians and

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reliability was poor. Since heart rate can affect CBF pulsatility (142), participants’ nervousness
in the MRI may have also confounded the results, but there were no changes in resting heart rate
during the MRI (see chapter 4).
Although shorter ATT is associated with poorer cognitive function (137), interpretations
are complicated, which may explain why it has not been used in previous aerobic training
studies. Shorter ATT is thought to be attributed to higher pulsatile pressures (154), meaning that
longer ATT indicates smooth continuous flow, i.e. increased VC. However, ischemic diseases
like stroke and steno-occlusive large-artery disease prolong ATT due to narrowed arteries and
increased tortuosity, resulting in collateral blood delivery (155, 156). These mechanisms
prolonging ATT can be present in healthy individuals (157), albeit to a lesser extent, thus
improvements would shorten ATT. The small effect sizes generated here combined with the
complicated interpretations do not encourage the use of ATT measures in future RT trials.

Limitations:
One limitation of this study was the use of a single group. Because this was a proof-of-
concept study for potential mechanisms, the design was determined based on feasibility and
study funding. Having a single group allowed us to address practicality while maintaining a
thorough design. A randomized crossover was opposed so that results were not confounded by
detraining effects or long-term cognitive benefits of RT (54). A separate active control group is
needed in future studies to accurately estimate the effects of training and establish causal
relationships.
Another limitation was the high variability of our data. WML volume was the only
measure with excellent reliability over the control period. This is likely due to the structural

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nature of the scan. On the other hand, dynamic ASL measures are affected by natural
physiological variability as well as potential modifiers such as nervousness (158). Most of the
dynamic ASL measures (specifically CBF, ATT, and pulsatility and resistance indices) had only
moderate reliability over the control period, so their results should be interpreted with some
caution. This is especially true for VC data, which had very low reliability. The bSSFP ASL
scans were only performed using single 2D slices. To improve the accuracy of measurements,
multiple repetitions needed to be acquired, which add up to a relatively long scan time. This
raises the importance of motion control with image outlier rejection, especially since older adults
tend to move more during the scan. Three dimensional acquisitions are being developed for
future studies to improve imaging efficiency and motion correction in volumetric measurements.

Conclusion:
Although our results are mixed, we found novel supportive evidence for positive RT-
induced CV plasticity through resting CBF and WML volume changes. These are both strong
measures supported by previous evidence. The effect sizes generated here are the first to suggest
that RT can improve CBF, and they support previous findings that RT attenuates the progression
of WMLs. However, larger studies are needed to confirm these findings and re-test associations
with fluid cognition. We provide sample size justification for these future investigations. In
contrast, changes in ATT and VC were mixed or negative, but are not reliable enough measures
for use in future studies.
Unfortunately, we did not find supportive evidence for CV mechanisms of cognitive
enhancement. Improvements in regional CBF and WML volume had negative or zero
correlations with changes in fluid cognition. Larger studies are needed to confirm this. Whether

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cognitive improvements are partially mediated by CV plasticity, or RT-induced cognitive
enhancement occurs through entirely different mechanisms, these findings still support the
positive benefits of RT to overall brain health and fluid cognition. Understanding the
neurophysiological mechanisms through which RT protects and improves cognitive health is
vital for developing optimal interventions to promote healthy aging. A major benefit of
conducting these small proof of concept clinical trials is generating these preliminary signals of
efficacy that can redirect investigators towards the most promising mechanisms, thus saving
resources and ensuring that larger trials are fully justified.

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CHAPTER VI:
IMPROVEMENTS IN BRAIN STRUCTURE AND FUNCTION AFTER 12 WEEKS OF
PERIODIZED RESISTANCE TRAINING IN OLDER ADULTS

Abstract
Purpose: Resistance training (RT) is a promising strategy to promote healthy cognitive aging.
However, the brain mechanisms through which RT improves cognition have yet to be
determined. Here, we examined the effects of 12-weeks of RT on brain structure and function in
healthy 60-80-year-old adults.

Methods: Twenty healthy older adults (14 females, mean age 69.1 years) participated in this
single group clinical trial. The NIH Toolbox Cognition Battery and multimodal 3T magnetic
resonance imaging (MRI) were performed at three time points: baseline, pre-intervention, and
post-intervention. The period between baseline and pre-intervention served as a control of equal
duration as the high-intensity RT intervention.

Results: Fluid cognition composite score significantly increased from pre- to post-intervention
(+8.2 ± 6.1 %, p < 0.01). Effect sizes were generated for MRI outcomes. Although gray matter in
the hippocampus and posterior cingulate did not change after the intervention, there is some
indication for attenuated age-related losses when compared to the control period (d = 0.33 and d
= 0.31, respectively). White matter microstructure improved in the pyramidal tracts (d = 0.5) but
had high variability in regions linked to age-related deterioration. Resting state functional
connectivity is currently being analyzed and will be presented in future versions of this
manuscript.

91

Conclusion: The effect sizes generated here will provide investigators with preliminary data to
help design and justify future clinical trials. Our findings suggest that there are several possible
related brain mechanisms through which RT-induced cognitive enhancement may be achieved,
including changes in GM volume, WM microstructure, and resting-state functional connectivity.

Introduction
Resistance training (RT) interventions can improve fluid cognition in both healthy older
adults and patients with mild cognitive impairment (27). These improvements hold promise in
the multifaceted effort to delay or even prevent cognitive decline and dementia. However, a clear
understanding of the neurophysiological processes that underlie these benefits must be
established before explicit exercise recommendations can be made. While most of the magnetic
resonance imaging literature has focused on aerobic exercise interventions (131), RT likely has
distinct brain mechanisms of benefit that have yet to be fully determined (34).
Gray matter (GM) volume is tightly linked with dementia. Typically, early Alzheimer’s
disease involves atrophy in the entorhinal and hippocampal areas and then progresses to include
the posterior cingulate, cortical temporal lobe, and eventually total GM volume (159). Despite
extensive research on GM plasticity after aerobic training, specifically in the hippocampus (160),
there has been little evidence for the effects of RT on GM volume (15, 54). Most recently, Suo
and colleagues (34) found expanded GM thickness in the posterior cingulate after 6 months of
RT in MCI patients, with associated improvements in global fluid cognition. However, there
were no changes in hippocampal GM volume. These salient biomarkers of Alzheimer’s disease
are not our only regions of interest (ROIs). Other areas related to motor behavior, like the

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putamen and caudate nucleus (58), may provide a muscular strength-specific pathway that
explains the statistical mediation of cognitive improvement after RT by increases in lower body
strength (5).
White matter (WM) also plays an essential role in cognitive function, mediating
connectivity between GM regions. Age-related deterioration in local WM microstructure is
associated with cognitive decline and increased risk of dementia (161), often corresponding to
adjacent GM atrophy (162). While observational studies promote physical exercise as a potential
countermeasure to WM deterioration (163), aerobic training studies have failed to demonstrate
group-level improvements in measures of WM microstructure or associations with cognitive
enhancement (164, 165). To date, no studies have investigated the effects of RT on WM
microstructure. However, there is promising evidence for RT as a countermeasure to WM
atrophy (54) and WM lesions (34). Furthermore, short-term RT of the plantar flexors have
reportedly improved the corresponding corticospinal tract microstructure (58), perhaps
representing a supraspinal neural mechanism of muscular strength gains. It remains to be
determined whether RT can have more widespread effects on WM microstructure for cognition.
Functional connectivity analysis is another technique to underline brain mechanisms
associated with cognition (166). Specifically, resting state connectivity patterns with the frontal
lobe and within the default mode network (DMN) decline with aging and predict neurocognitive
disorders (167). While there is some evidence for functional plasticity after aerobic training and
balance training (168), the effects of RT remain unclear (34).
The purpose of this investigation was to estimate the effects of 12-weeks of high-
intensity RT on brain structure and function, measured via multimodal 3T magnetic resonance
imaging (MRI), and to explore potential associations between these brain adaptations and

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cognitive improvements. Specifically, we sought to determine whether RT could improve GM
volume, diffusion tensor imaging (DTI) metrics of WM microstructure, and resting-state
functional connectivity. We hypothesized that 12-weeks of periodized RT performed 3 days per
week can improve GM volume in the hippocampus, putamen and caudate nucleus, GM cortical
thickness in posterior cingulate and precuneus, WM microstructure throughout the major tracts,
and resting state functional connectivity through salient aging networks in older adults. Because
this is a mechanistic proof of concept clinical trial, the main outcomes will be the estimated
effect sizes, which can be used to design and justify larger future trials.

Methods
Study Design
Using a single arm pre-post design, the effects of a 12-week periodized RT intervention
on fluid cognition and brain structure and function were compared to that of an initial control
period. Thus, participants served as their own controls and completed all assessments at three
time points: baseline, pre-intervention, and post-intervention. All control periods took place
before the RT intervention to ensure that results were not confounded by detraining effects or
long-term cognitive benefits of RT (54). The full protocol for this mechanistic proof-of-concept
clinical trial has been described (see chapter 4).
This simple yet rigorous and well-designed study was approved by the USC Health
Sciences Review Board, registered with ClinicalTrials.gov (ID: NCT03982550), and all
procedures were conducted at the University of Southern California (USC, Los Angeles, CA)
Health Sciences Campus. Written informed consent was obtained from all potential participants
assessed for eligibility. Primary considerations for inclusion/exclusion criteria were to ensure
that participants were healthy, interested, and available to participate, without any

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contraindications to RT or MRI. Twenty healthy older adults were enrolled in the study (Table
4.1) and all completed the required number of training sessions (>90%) and assessments at
baseline, pre-intervention, and post-intervention. There were no participant drop-outs or severe
adverse events. A flow diagram of participant recruitment and testing is shown in Figure 4.1.

Intervention
Participants performed a periodized and progressive RT program emphasizing
development of total-body strength. All 36 training sessions (3 days per week for 12 weeks) were
performed in the CERC, supervised by an exercise specialist. Mesocycle I (weeks 1-4) was
designed for muscular hypertrophy to develop a base for more intense training in later phases.
Training bouts consisted of 4-6 resistance exercises with 3 sets per exercise and 8-10 repetitions
per set. Mesocycles II (weeks 5-8) and III (weeks 9-12) were designed to promote strength gains.
Training bouts consisted of 4-6 resistance exercises with 3-4 sets per exercise, 4-6 repetitions per
set, and linear increases in intensity over time.

Cognitive Testing
All cognitive assessments were administered by the same trained investigator (TRM)
using the NIH Toolbox® Cognition Battery (NIHTB-CB, Version 1.21) application on an iPad
Pro 10.5-inch (Apple, Cupertino, CA). Each assessment lasted approximately 45 minutes in
duration. The NIHTB-CB provides a standard set of comprehensive assessment tools that have
been normed and validated in participants ages 3-85, and ensures that assessment methods and
results can be used for comparisons across existing and future studies (16). Three test versions
were used to reduce practice effects and were administered in random order for each participant.

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The NIHTB-CB has seven individual instruments – Flanker Test, Picture Sequence
Memory Test, List Sorting Working Memory Test, Dimensional Change Card Sort Test, Pattern
Comparison Test, Picture Vocabulary Test, and Oral Reading Recognition Test – that test the
following cognitive subdomains: inhibitory control and attention, episodic memory, working
memory, executive function, and processing speed for fluid cognition and vocabulary and
reading recognition for crystallized cognition. In addition to scores for each individual
instrument, the battery yields a Fluid Cognition Composite Score and a Crystallized Cognition
Composite Score (45). We analyzed uncorrected scores, standardized to the population with a
normative mean of 100 and a standard deviation of 15.

MRI Data Acquisition
MRI data were acquired on a Siemens 3T Magnetom Prisma (Erlangen, Germany) using
a 20-channel head coil. Padding was placed around the head to minimize head motion during
scans. Participants were asked to stay as still as possible without falling asleep.
A sagittal scout image was used as a localizer, and brain structure was assessed using a
T1-weighted accelerated sagittal 3D MPRAGE (Magnetization-Prepared Rapid Acquisition with
Gradient Echo) scan (TR/TE: 2300/2.95 ms; TI: 900 ms; Flip Angle: 9 deg; slice thickness 1.2
mm without gap; field of view read: 270 mm; resolution 256). Diffusion weighted MRI (dMRI)
data were acquired using a multishell diffusion scheme (TR/TE: 3400/71 ms) with b-values of
500, 1000, and 2000 s/mm
2
. The number of diffusion sampling directions were 6, 48, and 60,
respectively. Full brain coverage was accomplished by acquiring 126 axial slices. The in-plane
resolution was 1 mm with a slice thickness of 2 mm. Last, a resting-state functional MRI (fMRI)
scan was conducted using T2* echo-planar imaging with an interleaved BOLD sequence (TR/TE
= 800/37 ms, flip angle = 52 degrees, slice thickness = 2.0 mm, 72 slices, 5.75 min). The field of

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view read was 208 mm with each voxel size 2.0 x 2.0 x 2.0 mm. Participants were asked to keep
their eyes open for the duration of the fMRI scan.

MRI Data Processing
The cortical reconstruction and volumetric segmentation process in FreeSurfer (6.0.0,
https://surfer.nmr.mgh.harvard.edu/) was applied to MPRAGE images. Briefly, this process
involves several automated processing steps, including motion correction, intensity
normalization, skull stripping, Talairach transformation, and atlas registration (169). Pial
surfaces were improved using T2-weighted FLAIR image contrast. Segmentation of GM
volumetric structures further involve tessellation of the boundaries between GM and white
matter, automated topology correction, and surface deformation following intensity gradients
(170). Cortical thicknesses are parcellated using surface inflation and registration to a spherical
atlas. Both intensity and continuity information used from the entire three-dimensional volume in
segmentation and deformation procedures. The maps are created using spatial intensity gradients
across tissue classes and are therefore not reliant on absolute signal intensity (171). Furthermore,
the maps are not restricted to the voxel resolution of the original data, and thus can detect
submillimeter differences. Specific ROIs for this study included total intracranial volume,
hippocampal GM volume, putamen GM volume, caudate nucleus GM volume, posterior
cingulate cortical thickness, and precuneus cortical thickness.
The Quantitative Imaging Toolkit (QIT, http://cabeen.io/qitwiki) was used to process
dMRI data (172). A hybrid DTI tractography algorithm was implemented to segment complex
fiber bundles. This approach first explores an array of possible pathways probabilistically to
determine the possible paths that form the bundle being queried. The following tracking
parameters are used: a large angle threshold of 85 degrees; dense seeding with 10 seeds per

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voxel; a minimum volume fraction of 0.075; random sampling of fiber orientations around peak
directions with a fixed standard deviation of 0.15; and track filtering to meet the fiber bundle
query criteria (173). A tract orientation map is computed to summarize the most likely
orientations of the probabilistic tracks passing through each voxel. Then the algorithm exploits
this information by estimating the optimal fiber compartment for each voxel that best represents
the queried bundle. The compartment assignment problem is framed as inference on a global
Bayesian graphical model with priors based on the probabilistic tract orientation map and spatial
smoothness. Finally, streamline tractography is performed and the final bundle is obtained (174).
Voxelwise maps of fractional anisotropy (FA) and mean diffusivity (MD) were
calculated and used to compute average DTI metrics for each bundle. FA and MD are scalar
measures that describe the directional dependence of diffusion and the total diffusion within
voxels, respectively (175). The FA value is thought to reflect WM fiber density, axonal diameter,
and myelination, while the MD value reflects the strength of white matter tracts. Specific ROIs
for this study include the pyramidal tracts, anterior thalamic radiations, corona radiata, inferior
fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus,
superior thalamic radiations, and the uncinate fasciculus.
Data from resting state fMRI scans are currently being processed to generate seed-based
functional connectivity maps using familiar methods (176). While the full procedures are still
being determined, they generally involve slice time correction, motion correction, spatial
smoothing using a gaussian kernel of full width half-maximum of 5mm and nonlinear high-pass
temporal filtering (150 s). Images are registered to the Montreal Neurological Institute (MNI)
standard template using the flirt function in the FSL software package. Then, an average signal is
extracted from each region of 165 anatomically-defined regions of the Destrieux atlas (177).

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Functional connectivity between each pair of regions is calculated using a general linear model
that controls for 9 confounds of no interest: the global signal, CSF signal, white matter signal,
and the 6 parameters of rigid body head motion. For each participant, this analysis produces
13,530 unique functional connectivity values based on correlations between the mean signal time
courses within each region.

Statistical Analyses
The following analyses were performed in SPSS Statistics V.25 (IBM, Chicago, IL). All
data were considered continuous, examined for normality using Shapiro-Wilk tests, and
described by either means and standard deviations or medians and interquartile range (if not
normally distributed). To confirm the reliability of our measures, the control period – differences
between baseline and pre-intervention – was evaluated using two-way mixed effects intraclass
correlation coefficients (ICCs) with absolute agreement. Estimates less than 0.50, between 0.50
to 0.75, between 0.75 and 0.90, and greater than 0.90 were classified as poor, moderate, good,
and excellent test-retest reliability, respectively (117). Effect sizes were calculated for all
outcomes to determine the magnitude of differences between control and intervention periods.
For normally distributed data, an adapted Cohen’s d effect size was calculated – mean changes
from pre- to post-intervention were subtracted by the mean changes from baseline to pre-
intervention, then divided by the average standard deviation of those changes (118, 119). For
non-normally distributed data, matched-pairs rank-biserial effect size was calculated – the
differences between control and intervention changes were ranked, then the sum of positive
ranks was subtracted by the sum of negative ranks and divided by the total sum of ranks (120).
For both adapted Cohen’s d and matched-pairs rank-biserial, effect sizes of 0.2, 0.5, 0.8, and 1.2
were classified as small, medium, large, and very large, respectively (121). Only changes in fluid

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cognition were tested for significance because fluid cognition was the only outcome powered for
a priori. Paired t-tests were used, comparing data from pre- and post-intervention, with statistical
significance set at P < 0.05.
Relationships between fluid cognition improvements and changes in other outcome
variables were analyzed in SPSS to explore potential mechanisms of benefit. Only raw changes
from pre- to post-intervention were used so that effects were not washed out by percent changes.
These raw changes often have high kurtosis when a small sample size (N = 20) is used.
Therefore, Spearman rank correlation coefficients were chosen to reduce effect variability (122).
This same technique was used to analyze relationships between fluid cognition improvements
and sampling factors such as participant characteristics. Spearman’s rho values of 0.1, 0.3, and
greater than 0.5 were classified as small, medium, and large associations (118), respectively. A
priori power analyses (correlation: bivariate normal model) were performed using G*Power
3.1.9 to inform future trials of the required sample size to observe significant correlations in a
similar pre-post, one group design, based on our observed correlations, alpha = 0.05, two tails,
and power = 0.80.

Results
Cognitive Outcomes:
Cognitive outcomes have been described in detail previously (see chapter 4). Briefly,
Fluid cognition composite scores had good reliability and significantly increased from pre- to
post-intervention (+7.7 ± 5.5 Standard Units, p < 0.01). Four of the five individual instrument
scores significantly increased from pre- to post-intervention: Dimensional Change Card Sort Test
(+4.1 ± 6.8 Standard Units, p = 0.04), Flanker Test (+3.2 ± 6.4 Standard Units, p = 0.02), List

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Sorting Test (+10.7 ± 10.2 Standard Units, p < 0.01), and Pattern Comparison Test (+2.0 ± 11.5
Standard Units, p < 0.01). One outlier was removed from crystallized cognition analyses. In the
remaining 19 participants, there was no change in crystallized cognition composite score from
pre- to post-intervention or the individual instruments: Picture Vocabulary Test and the Oral
Reading Recognition Test.
Table 6.1 presents the mean ± SD (or median ± IQR if data not normally distributed) for
all MRI outcome measures of the study at baseline, pre-intervention, and post-intervention, test-
retest reliability of the 12-week control period, and effect sizes of the 12-week intervention
period. Because participants were asked to maintain their normal eating and exercise habits
outside of the study, no changes in outcome measures were expected after the control period.
Reliability was excellent for MPRAGE outcomes, and good to excellent for DTI outcomes.
Caudate nucleus and posterior cingulate data from one participant were excluded because of
outlier values over 3.5 standard deviations from the group mean.

Table 6.1 Group means for MRI outcomes at baseline, pre-intervention, and post-intervention (N
= 20).
Measure

Baseline Pre-
Intervention
Post-
Intervention
ICC^
(3,1)
Effect
Size^^
(d)
MPRAGE Outcomes:
Total Intracranial
Volume (cm
3
)
1470.4 ± 144.8 1471.1 ±
146.0
1471.4 ±
143.3
0.99 0.04
Hippocampus GM
Volume (mm
3
)
8157 ± 915 8068 ± 976 8052 ± 946 0.98 0.33
Putamen GM Volume
(mm
3
)
10168 ± 1204 10248 ± 1021 10201 ± 1192 0.93 -0.29
Caudate Nucleus GM
Volume (mm
3
)
#

6722 ± 841 6751 ± 805 6789 ± 817 0.98 0.06
Posterior Cingulate
Cortical Thickness
(mm)
#

2.4 ± 0.1 2.4 ± 0.1 2.4 ± 0.1 0.97 0.31
Precuneus Cortical 2.3 ± 0.1 2.3 ± 0.1 2.3 ± 0.1 0.92 -0.04

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Thickness (mm)
DTI Outcomes:
Pyramidal Tracts FA 0.694 ± 0.026* 0.688 ±
0.036*
0.690 ±
0.034*
0.98 0.05*
Pyramidal Tracts MD (162.1 ± 5.7)
x 10
-5

(162.4 ± 5.9)
x 10
-5

(164.3 ± 6.9)
x 10
-5

0.89 0.50
Overall ROIs Mean
FA
0.289 ± 0.013 0.288 ± 0.013 0.287 ± 0.014 0.96 0.08
Overall ROIs Mean
MD
(83.7 ± 2.4)
x 10
-5

(83.9 ± 2.5)
x 10
-5

(8.4 ± 2.4)
x 10
-5

0.96 0.22
- Anterior Thalamic
Radiations FA
0.254 ± 0.028 0.252 ± 0.022 0.253 ± 0.027 0.95 0.29
- Anterior Thalamic
Radiations MD
(91.2 ± 3.9)
x 10
-5

(91.3 ± 3.4)
x 10
-5

(9.2 ± 4.2)
x 10
-5

0.85 0.25
- Corona Radiata FA 0.351 ± 0.017 0.352 ± 0.019 0.349 ± 0.020 0.97 -0.57
- Corona Radiata MD (90.6 ± 3.6)
x 10
-5

(90.5 ± 3.5)
x 10
-5

(91.0 ± 3.4)
x 10
-5

0.93 0.45
- Inferior Fronto-
Occipital Fasciculus
FA
0.277 ± 0.017 0.274 ± 0.015 0.274 ± 0.017 0.89 0.49
- Inferior Fronto-
Occipital Fasciculus
MD
(81.2 ± 2.8)
x 10
-5

(81.4 ± 2.7)
x 10
-5

(81.7 ± 2.8)
x 10
-5

0.96 0.01
- Inferior Longitudinal
Fasciculus FA
0.273 ± 0.012 0.272 ± 0.013 0.272 ± 0.012 0.84 0.09
- Inferior Longitudinal
Fasciculus MD
(80.7 ± 2.8)
x 10
-5

(80.7 ± 2.7)
x 10
-5

(80.6 ± 2.8)
x 10
-5

0.94 -0.08
- Superior
Longitudinal
Fasciculus FA
0.260 ± 0.011 0.259 ± 0.011 0.260 ± 0.012 0.96 0.28
- Superior
Longitudinal
Fasciculus MD
(82.5 ± 3.2)
x 10
-5

(82.8 ± 3.3)
x 10
-5

(83.2 ± 3.4)
x 10
-5

0.93 0.15
- Superior Thalamic
Radiations FA
0.362 ± 0.025 0.361 ± 0.027 0.360 ± 0.026 0.97 -0.07
- Superior Thalamic
Radiations MD
(78.1 ± 2.3)
x 10
-5

(78.3 ± 2.3)
x 10
-5

(78.7 ± 2.0)
x 10
-5

0.83 0.07
- Uncinate Fasciculus
FA
0.242 ± 0.012 0.244 ± 0.012 0.243 ± 0.014 0.86 -0.29
- Uncinate Fasciculus
MD
(81.9 ± 2.8)
x 10
-5

(82.1 ± 2.8)
x 10
-5

(82.1 ± 2.8)
x 10
-5

0.97 -0.15
The control period 12-week test-retest reliability was important since this study was a single
group clinical trial. No changes were observed from baseline to pre-intervention. The effects of
the 12-week periodized RT intervention are evident via changes from pre- to post-intervention
and calculated effect sizes. Mean ± SD.

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^Two-way mixed intraclass correlation coefficient (ICC) with absolute agreement calculated
using baseline and pre-intervention data.
^^Adapted Cohen’s d effect size calculated by subtracting the mean changes from pre- to post-
intervention by the mean changes from baseline to pre-intervention and dividing the average
standard deviation of those changes.
*Median ± IQR and matched-pairs rank-biserial effect size calculated on the changes from pre-
to post-intervention versus changes from baseline to pre-intervention.
#
Data from one participant were excluded, with outlier values over 3.5 standard deviations from
the group mean (N = 19).
GM = Gray Matter. MD = Mean Diffusivity. FA = Fractional Anisotropy.

MPRAGE Outcomes:
Starting with putative markers of aging and neurocognitive disorders, there we no
changes from pre- to post-intervention in total intracranial volume (-0.1 ± 0.8 %), hippocampal
GM volume (+0.1 ± 3.2 %), posterior cingulate cortical thickness (+0.6 ± 3.1 %), or precuneus
cortical thickness (-0.1 ± 3.2 %). Instead, the positive effect sizes noted in Table 6.1 for
hippocampal GM volume and posterior cingulate cortical thickness indicate an attenuation, or
even reversal, of age-related losses (Figures 6.1 and 6.2, respectively). For example, mean GM
deterioration was observed from baseline to pre-intervention in hippocampal GM volume (-1.2 ±
2.4 %) and posterior cingulate cortical thickness (-0.3 ± 2.9 %). Therefore, RT may have a small
protective effect that counters neurodegeneration. As for our motor behavior ROIs, the expected
small negative effect size was observed in putamen GM volume but not caudate nucleus GM
volume after the intervention.

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Figure 6.1 Hippocampal gray matter (GM) volume at baseline, pre-intervention, and post-
intervention (N = 20). The columns show means for 20 participants and the marked lines show
individual participant data.

Figure 6.2 Posterior cingulate gray matter (GM) cortical thickness at baseline, pre-intervention,
and post-intervention (N = 19). The columns show means for 19 participants and the marked
lines show individual participant data.

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DTI Outcomes:
Example images of the pyramidal tracts are shown in Figures 6.3 and 6.4. As expected,
white matter microstructure improved in the pyramidal tracts. MD had a medium effect size
(Table 6.1) increasing from pre- to post-intervention by 1.1 ± 2.1 %. However, FA did not
change from pre- to post-intervention (median = +0.5 %, IQR = 1.5 %) and the effect size was
very small. The remaining ROIs, which collectively represent WM tracts susceptible to age-
related deterioration, had small positive changes. Despite some variability, the overall mean
effect size (adapted Cohen’s d) for combined FA and MD were 0.08 and 0.22, respectively.
Individual effect sizes for each of the WM tracts are shown in Table 6.1.

Figure 6.3 Example image of pyramidal tract position within the brain. An anterior view of the
pyramidal tracts of a single subject are shown inside a transparent brain with coloring added by
orientation and position.

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Figure 6.4 Example map of pyramidal tract fractional anisotropy (FA). The right pyramidal tract
of a single subject is shown with darker colors representing higher FA.

Resting State Functional Connectivity:
Seed-based functional connectivity maps are currently being generated and will be
presented in future manuscripts.

Correlation Analyses:
MRI outcomes were further explored for possible relationships with pre- to post-
intervention changes in fluid cognition. Total intracranial volume and individual age-related WM
tracts were excluded for conciseness. Spearman correlations were computed between raw
changes in MRI outcomes and raw changes in fluid cognition composite score. Table 6.2 shows
the degrees of correlation between the ranked data. Raw fluid cognition changes had small
positive correlations with changes in hippocampal GM volume, putamen GM volume, posterior
cingulate cortical thickness, pyramidal tracts FA/MD, and overall WM tract ROIs mean MD, but

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small negative correlations with caudate nucleus GM volume, precuneus cortical thickness, and
overall WM tract ROIs mean FA.

Table 6.2 Correlation analyses between pre- to post-intervention changes in fluid cognition
composite score and changes in MRI outcomes (N = 20)
Measure

Spearman’s rho

A Priori Sample Size*
MPRAGE Outcomes:
Hippocampus GM Volume 0.24 133
Putamen GM Volume 0.24 133
Caudate Nucleus GM
Volume
-0.17 269
Posterior Cingulate Cortical
Thickness
0.20 193
Precuneus Cortical Thickness -0.17 269
DTI Outcomes:
Pyramidal Tracts FA 0.12 542
Pyramidal Tracts MD 0.18 239
Overall ROIs Mean FA -0.19 215
Overall ROIs Mean MD 0.29 91
These correlation analyses were used to explore potential indirect mechanisms of fluid cognitive
enhancement after RT.
*A priori power analyses of correlation (bivariate normal model) in a similar pre-post, one group
design, with two tails, alpha = 0.05, and power = 0.80.
GM = Gray Matter. FA = Fractional Anisotropy. MD = Mean Diffusivity.

Discussion
This study presents new exploratory findings of the effects of a 12 week periodized RT
intervention on GM volume, WM microstructure, and functional connectivity in older adults.
While the effects were small, an overall theme of protection was evident in putative imaging
markers of neurodegeneration. These occurred concomitantly with significant improvements in
fluid cognitive function. Furthermore, there were small correlations between this fluid cognitive
enhancement and changes in MRI outcomes. Accordingly, our data support and extend previous

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findings, suggesting that RT can help improve fluid cognition in older adults through structural
and functional brain mechanisms.
In line with Suo and colleagues (34), we found attenuated losses in posterior cingulate
cortical thickness, despite no changes in total intracranial volume (15). Thus, RT appears to have
more regional effects than global. In addition, we replicate the positive correlation between
changes in posterior cingulate cortical thickness and changes in fluid cognition (34). Since loss
of cortical thickness in the posterior cingulate is a biomarker of Alzheimer’s disease processes
(148), the countermeasure effects of RT strengthen the therapeutic relevance of real cognitive
benefits. While no studies have found evidence for hippocampal plasticity after RT (34, 54), we
report the first indications for potential improvements. We found attenuated losses in
hippocampal GM volume and a positive correlation between changes in hippocampal GM
volume and changes in fluid cognition. This is an important finding because decreased
hippocampal volume is associated with declines in multiple NIHTB-CB subdomains, including
episodic memory, working memory, processing speed and executive function (178). Caution
should be taken with these exploratory findings. To date, aerobic training is the primary form of
exercise to induce hippocampal plasticity with specific implications for memory (160). However,
episodic memory was the only fluid cognition domain that did not improve in the current study.
There appears to be a cognitive specificity of exercise such that RT has a greater effect on
executive function while aerobic training has a greater effect on memory (27) (see chapter 4).
Furthermore, distinct molecular mechanisms of training are supported by animal studies (49).
Therefore, more direct studies are needed to resolve these differences and determine optimal
exercise prescriptions.

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Our DTI findings are consistent with the few exercise intervention studies that have
investigated WM microstructure. First, we found increased MD and attenuated FA decline in the
pyramidal tracts. Palmer and colleagues also reported changes in the corticospinal tracts, the
primary fibers of the pyramidal tracts, associated with strength gains after only 4 weeks of
training (58). From a task-specific motor learning perspective, this is evidence of a supraspinal
neural pathway for muscular strength gains because the corticospinal tracts are the WM of upper
motor neurons that control muscular contraction and force production (59). We also replicate
their findings of reduced putamen GM volume after training (58), and extend it to the caudate
nucleus. Together, the putamen and the caudate nucleus form the dorsal striatum and are thought
to be involved with movement and learning (179). Plasticity of these structures appears to be the
response to changes in motor behavior, from both cross-sectional evidence in dancers (180), and
exercise intervention in rats (181). Interestingly, the changes in pyramidal tract MD, pyramidal
tract FA, and putamen GM volume each had small positive associations with changes in fluid
cognition. These structural brain changes may provide a pathway for the statistical mediation of
cognitive enhancement by increases in muscular strength after RT (5). Furthermore, we’ve also
found correlations between changes in muscular power and changes in fluid cognition in our
previous reports of the current study (see chapter 4). Future studies are warranted to elucidate
these mechanisms.
For the rest of the brain WM microstructure, we provide the first documentation of
overall FA and MD after RT intervention. While overall mean FA did not change and had a
small negative association with fluid cognition changes, RT had a small positive effect on overall
mean MD with a small positive correlation with fluid cognition changes. These results are
promising and clinically relevant for the protection against dementia (161). However, caution

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should be taken due to the high variability between individual tracts. For example, the corona
radiata had a medium negative effect size for FA and an almost equally large positive effect size
for MD. The strongest changes were improvements in the anterior thalamic radiations, inferior
fronto-occipital fasciculus, and superior longitudinal fasciculus, and declines in the uncinate
fasciculus. This is consistent with aerobic training studies, suggesting that exercise has no whole
brain effect on WM microstructure (165). Instead, small regional indications of benefit may
depend on the magnitude of fitness improvements (164) or the use of more complex and socially
engaging tasks (182).
Changes in resting state functional connectivity will be discussed, including their
significance to cognitive function (34, 183) and how they compare to changes after aerobic
training (168, 184), cognitive training (185), and combined aerobic and cognitive training (186).
We hypothesize that RT improves functional connectivity in salient networks of age-related
neurodegeneration.

Limitations:
Our results must be considered in the context of limitations. One limitation of this study
was the use of a single group. Because this was a proof-of-concept study for potential
mechanisms, the design was determined based on feasibility and study funding. Having a single
group allowed us to address practicality while maintaining a thorough design. Another limitation
was the ROI-based analyses of GM and WM, as opposed to whole-brain analyses corrected by
false discovery rate. While this provided a focused examination of RT’s effects in the context of
aging, future studies will need to characterize the multidimensional nature of age-related changes
(159, 162). Because our findings are exploratory, they do not conclusively prove that RT causes

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structural or functional plasticity or that these changes are mechanisms of cognitive
enhancement. Instead, this line of work reduces the time and costs associated with larger trials,
and the results are complementary to future examinations of efficacy.

Conclusion:
The effect sizes generated here will provide investigators with preliminary data to help
design and justify future clinical trials. Our findings suggest that there are several possible
related brain mechanisms through which RT-induced cognitive enhancement may be achieved,
including changes in GM volume (specifically hippocampal GM volume and posterior cingulate
cortical thickness), WM microstructure, and resting-state functional connectivity. Hence,
multimodal imaging is needed to thoroughly elucidate the underlying processes. In further
support of this, these changes all appear to be directly linked to cerebrovascular function (8, 168,
187). The inclusion of association analyses between changes in the brain and cognition can help
explain large individual variations in training responsiveness. This will help us design optimal
individualized exercise prescriptions, supporting its implementation as a preventative
countermeasure.

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CHAPTER VII:
SUMMARY & CONCLUSIONS

This simple, yet rigorous and well-designed study has demonstrated that 12-weeks of
periodized resistance training (RT) improves fluid cognition in healthy older adults. These
findings extend our understanding of the training parameters required to observe cognitive
improvements. In addition, brain magnetic resonance imaging outcomes suggest that RT can
improve measures of cerebrovascular function and gray matter structure, attenuating or even
reversing salient neurodegenerative processes. Thus, RT may have considerably greater
implications for healthy brain aging than previously expected.
Despite our relatively short intervention, the large cognitive effects we observed may be
attributed to the high intensity periodized training program. Furthermore, one-on-one personal
training was used to replicate the conditions that would be used in standard clinical practice, thus
providing greater attention and feedback to participants. These findings strengthen the now
extensive evidence supporting RT as an effective strategy to promote healthy cognitive aging
and suggest that further mechanistic and applied research studies be conducted.
The fluid cognition subdomains that appear to gain the greatest benefits from RT are
executive functions and processing speed. Because other forms of exercise, such as aerobic
training, seem to have different effects than RT on specific subdomains like episodic memory,
there may be distinct targetable cognitive benefits of each. Furthermore, there are unique
physiological changes and mental health benefits with each exercise modality. For example,
greater improvements in physical function and protection against sarcopenia occur with RT, but
greater cardiovascular adaptations are seen with aerobic training. Therefore, our purpose is not to

112

advocate for the exclusive use of RT, but rather encourage the addition of RT as part of a
comprehensive neuroprotective plan.
We have also thoroughly discussed the potential mechanisms of these cognitive benefits,
which have yet to be verified. Improvements may result directly from RT, but with the
simultaneous benefits to related fundamental processes of aging (i.e. physical function, body
composition, and mental health), we contend that some improvements in cognition likely
accumulate through various mediating factors, or they improve through overlapping molecular
mechanisms. Determining which outcomes have changes that are associated with changes in
fluid cognition can help us develop optimal treatment strategies for the aging population.
Although our sample size was not powered to test for statistically significant changes in
outcomes other than cognition, or for correlations with cognition, it did provide a practical
approach to explore the related factors. We found some evidence for associations with changes
in physical function but did not find evidence for associations with changes in sleep quality. It
seems that baseline sleep quality may influence potential cognitive changes, but changes in sleep
quality do not. Other changes, like body composition and mood, only had small correlations but
can still help investigators make decisions in the design of future studies.
The most novel aspect of this study was the exploration of complex brain imaging
outcomes. In this mechanistic proof of concept clinical trial, we generated effect sizes for
changes in cerebrovascular function, gray matter structure, white matter microstructure, and
functional connectivity to determine whether RT could potentially improve these outcomes. In
line with robust preliminary information in the literature, the effect sizes we generated here
suggest that RT can improve common regional measures of cerebrovascular function and gray
matter structure. This was observed despite normal age-related declines over the initial control

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period. Thus, RT may attenuate or even reverse salient neurodegenerative processes. Future
studies are thus warranted to extend the efficacy of RT for improvements in these specific brain
imaging outcomes and determine whether they are major processes underlying cognitive
enhancement. Fully understanding these mechanisms is vital for establishing a neuroscientific
foundation that underpins RT prescription. With the extensive benefits of RT, the impact of
increased implementation would be substantial.

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Abstract (if available)

Abstract The overall goal of this project was to investigate the effects of periodized RT on cognition and overall brain health in older adults and to explore the potential mechanisms of benefit. While there is ample evidence to suggest that long-term RT enhances cognitive function, demonstrating rapid health benefits following higher-intensity RT may improve the clinical utility of such interventions. In addition, further investigations are necessary to understand the mechanisms of RT-induced cognitive enhancement that, for example, link skeletal muscle physiology and function with brain morphology and neuroplasticity. We hypothesized that RT improves cognitive function through physical and cerebrovascular adaptations. RT may be a vital form of exercise to maintain both the physical and cognitive capacities necessary for functional independence in older adults. ❧ We applied standardized cognitive testing and advanced neuroimaging techniques to a 12-week periodized RT intervention in 20 healthy older adults. The NIH Toolbox® Cognition Battery and multimodal 3T Magnetic Resonance Imaging were used along with physical outcome measures such as muscular strength, body composition, and physical function. In this one-group pilot study, all participants received supervised RT, thus participants served as their own controls. Tests were administered at baseline, pre-intervention, and post-intervention. Control periods took place before the RT intervention to ensure that results were not confounded by detraining effects or long-term cognitive benefits of RT. The 12-week periodized RT intervention involved 3 supervised training sessions per week, emphasizing muscular strength development with linear increases in intensity. ❧ This study demonstrated that 12 weeks of periodized RT improves fluid cognition in older adults. Such changes in cognitive abilities have a critical yet often overlooked impact on optimal performance in various populations. Detecting such rapid cognitive benefits could improve the clinical utility of RT interventions by reducing time and costs and improving adherence to the program. Exercise specialists will play a key role in implementing these strength and conditioning programs according to sound scientific principles. ❧ This study also generated effect sizes for changes in cerebrovascular function, brain morphology, and functional connectivity. Our data will be used to design and justify future studies that establish a neuroscientific foundation of RT’s benefit to the brain. Understanding the specific cerebral outcomes of different training programs will allow fitness professionals to design optimal exercise prescriptions, targeting fundamental processes of aging beyond physical capacity.

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Asset Metadata

Creator Macaulay, Timothy Ryan (author)

Core Title Cerebrovascular mechanisms of cognitive enhancement after periodized resistance training in older adults

School School of Dentistry

Degree Doctor of Philosophy

Degree Program Biokinesiology

Publication Date 06/22/2020

Defense Date 04/17/2020

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

Tag arterial spin labeling,attention,dementia,executive function,fluid cognition,magnetic resonance imaging,memory,muscular strength,OAI-PMH Harvest,processing speed

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Schroeder, E. Todd (committee chair), Duncan, Dominique (committee member), Kutch, Jason (committee member), Lane, Christianne (committee member), Pa, Judy (committee member)

Creator Email tmacaula@usc.edu,trmacaulay@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-320857

Unique identifier UC11666068

Identifier etd-MacaulayTi-8615.pdf (filename),usctheses-c89-320857 (legacy record id)

Legacy Identifier etd-MacaulayTi-8615.pdf

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Document Type Dissertation

Rights Macaulay, Timothy Ryan

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

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