REFINING NEUROIMAGING METHODOLOGY TO BETTER UNDERSTAND BRAIN RESTING-
STATE NETWORK FUNCTIONAL CONNECTIVITY IN OLDER ADULTS 

By 

Zachary Fernandez 

A DISSERTATION 

Submitted to 
Michigan State University 
in partial fulfillment of the requirements   
for the degree of 

Neuroscience – Doctor of Philosophy 

2024 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ABSTRACT 

The brain requires a consistent supply of oxygenated blood to meet its high metabolic 

energy demands. Through a process termed neurovascular coupling, neural activity increases 

local cerebral blood flow to meet regional metabolic energy demands. Functional magnetic 

resonance imaging (fMRI) is a non-invasive tool capable of indirectly quantifying brain activity 

through the blood oxygenation level dependent (BOLD) response. Interestingly, spatially distinct 

brain regions that are functionally connected while performing a task maintain their 

synchronized BOLD response time courses at baseline in the absence of a task. Regions that 

exhibit these highly correlated BOLD responses at rest are grouped together and referred to as 

a resting state network (RSN). Studies that focus on resting-state fMRI (rs-fMRI), a task-free 

adaptation of fMRI, have found RSNs to hold predictive power in a wide range of 

neurodevelopmental and neurodegenerative disorders. However, there are no set standards for 

conducting rs-fMRI research. Further investigation into optimizing rs-fMRI methodology is 

beneficial to ensure reproducibility of results, particularly those of clinical significance. To assist 

with this, this dissertation will examine the effects of various rest conditions used during rs-fMRI 

data acquisition on RSN functional connectivity and introduce a novel age-appropriate functional 

atlas for older adults.  

Rest conditions can vary between studies. This often includes entirely different sets of 

instructions to the subjects which can impact the data. Aim 1 of this dissertation investigates the 

effects of rest conditions with differing levels of cognitive load on a complete set of predefined 

RSNs. Rs-fMRI datasets were obtained from twenty-two healthy college students (22 ± 4 years 

old, 12 females) on a 3T MRI scanner. Each subject was scanned under four different 

conditions: (1) eyes open in dim light (Eyes-Open), (2) eyes closed and awake (Eyes-Closed), 

(3) eyes closed while remembering four numbers through the scan session (Eyes-Closed-

Number) and (4) asked to watch a movie (Movie). Overall, we found conditions with more 

 
 
 
 
 
 
 
external stimulation led to more global changes in functional connectivity during rs-fMRI. 

However, when considering each RSN of an existing functional atlas individually, there were 

differential changes between conditions. The results of this study can aid in future study 

interpretation and design. 

Next, we address the disproportion of functional atlases available for older adults. A 

functional atlas is a predefined set of recognizable RSNs that can be used to define regions in 

studies with subjects of a similar demographic. Most functional atlases that are currently 

available were derived from young and healthy populations. While often used to study RSNs in 

older adults, these atlases do not account for age-related brain atrophy and changes in RSN 

connectivity. Here, our goal was to use the baseline anatomical and rs-fMRI scans from the 

recently completed Risk Reduction for Alzheimer’s Disease (rrAD) clinical trial to create a robust 

functional atlas that is better-matched to study older populations. The rrAD sample is comprised 

of 420 hypertensive older adults (60 to 84 years, 68.8±5.9) scanned across five different 3T MRI 

scanners. Using a combination of seed-based and data-driven approaches, we created a 

functional atlas of recognizable RSNs that more adequately describes RSN functional 

connectivity of the rrAD cohort. We expect the rrAD420 rs-fMRI atlas to be directly applicable to 

study outcomes related to the functional connectivity of the rrAD population. However, we also 

believe the rrAD420 functional atlas would also be a suitable option for older populations in 

general. As such, our functional atlas can assist in the identification of RSN deviations with 

potential to serve as age-related biomarkers of disease in the future. 

 
 
 
 
 
 
 
 
 
 
 
To Manda Fernandez, Robert (Bobbo) Fernandez,  
Jarad Fernandez, Nichole Fernandez-Mulligan, and Mary Ann Shepherd 

iv 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ACKNOWLEDGEMENTS 

My project required a unique and diverse skillset, almost all of which I had to acquire while 

in graduate school (and during a pandemic - thank you COVID). This would have been 

impossible without a team that was dedicated to helping me develop into the scientist I hoped I 

could be. I’m so thankful to say that’s exactly the team I found. I would like to express my 

sincere gratitude to my mentor, Dr. David Zhu, for believing in me and allowing me to branch out 

of my comfort zone. Thank you for letting me be a part of something bigger than myself, and for 

challenging me to think in new ways. Also, a huge thank you to Dr. Norman Scheel for your 

infectious curiosity, willingness to help, and words of wisdom in and out of the lab. You set an 

excellent example for us to follow. Josh Hubert, thank you for asking the tough questions and 

for bringing a sense of playfulness to the lab. Dr. Josh Baker (and Dr. Sarah Tilden!), you made 

this quite the adventure and it would never have been the same without you, we did it buddy.  

Thank you to my dissertation committee, who contributed excellent ideas, time, and support 

and this accomplishment is largely attributed to their guidance. Thank you, Dr. Andrew Bender, 

for always advocating for me and staying after lab meeting to talk one on one. Thank you, Dr. 

Scott Counts, for your desire to try new things and work with others. You’ve taught me the 

importance of interdisciplinary collaboration and the value in combining multiple approaches to 

get the most out of a study.  

During my first year, I got excited about the idea of adding a translational element to my 

research project. I owe an enormous thank you to Dr. Anne Dorrance for hearing my ideas out 

and making my dream come true. Thank you to the Dorrance Lab, and Dr. Bill Jackson, for 

adopting a complete novice and showing me the ropes of animal work. I truly felt welcomed 

from the beginning as you allowed me to crash your lab meetings, included me in discussions, 

and gave me a chance to present the occasional neuroimaging paper at journal club. A special 

v 

 
 
 
 
 
 
 
thank you to Dr. Teri Lansdell and Dr. Laura Chambers for your patience and encouragement 

every step of the way. I really appreciate all the time and effort you put in.  

I am immensely grateful to Dr. Chunqi Qian, whose positivity and generosity really helped 

me to get across the finish line. I look forward to my continued work with Dr. Qian as a postdoc 

in his lab. In addition, thank you to Dr. Yi Chen who seemed to show up at the perfect time to 

take me under her wing. Her expertise and passion transformed the long nights in the scanner 

room into a fun learning experience. It was such an absolute privilege to work with all of you.  

Also, thank you to my program cohort Dr. Luis Martinetti Dr. Christine Kwiatkowski, and 

Hannah Rudolph for your instant friendship and being awesome people in general. Finally, 

thank you to my family who never doubted me. I wouldn’t be where I am today without you.  

vi 

 
 
 
 
 
 
 
 
 
 
 
  TABLE OF CONTENTS 

CHAPTER 1: INTRODUCTION .................................................................................................... 1 
REFERENCES ........................................................................................................................ 12 
APPENDIX .............................................................................................................................. 20 

CHAPTER 2: FUNCTIONAL CONNECTIVITY OF CORTICAL RESTING-STATE  
NETWORKS IS DIFFERENTIALLY AFFECTED BY REST CONDITIONS ................................ 21 
REFERENCES` ....................................................................................................................... 48 
APPENDIX .............................................................................................................................. 59 

CHAPTER 3: DEFINING A FUNCTIONAL ATLAS OF RESTING-STATE NETWORKS FOR 
OLDER HYPERTENSIVE ADULTS WITHIN THE rrAD420 ANATOMICAL SPACE .................. 64 
REFERENCES ........................................................................................................................ 91 
APPENDIX .............................................................................................................................. 96 

CHAPTER 4: FUTURE DIRECTIONS AND GENERAL CONCLUSIONS ................................ 132 
REFERENCES ...................................................................................................................... 142 
APPENDIX ............................................................................................................................ 145 

vii 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1.1 – Brain power  

CHAPTER 1: INTRODUCTION 

Although the brain only accounts for 2% of total body weight, it is the most metabolically 

active organ in the body as it utilizes roughly 20% of our total energy at rest (1). The relationship 

between the brain and cerebral vasculature is highly important because the brain, unlike other 

organs, does not possess a reserve that allows for the storage of nutrients for later use (2). 

Instead, it relies on an intricate cerebrovascular network for the constant delivery of oxygen-rich 

blood and nutrients in real-time, in which each individual neuron is positioned within 15 

micrometers of its own capillary (3). Oxygenated blood is preferentially directed to regions of the 

brain that show relatively higher levels of neural activation through functional hyperemia (4). 

Specifically, functional hyperemia, also described as neurovascular coupling, is the process by 

which neural activity increases local cerebral blood flow to meet regional metabolic energy 

demands. This hemodynamic response is achieved through specific coordinated vascular 

changes that induce arterial relaxation and direct cerebral blood flow to active regions, which 

can be noninvasively quantified through state-of-the-art neuroimaging techniques (4,5).  

1.2 – Introduction to functional magnetic resonance imaging  

Functional Magnetic Resonance Imaging (fMRI) leverages neurovascular coupling induced 

hyperoxia in active brain regions, together with the differential magnetic properties of 

oxygenated and deoxygenated hemoglobin, to measure the blood oxygenation level dependent 

(BOLD) response (6,7). The BOLD response is used to indirectly assess localized neural 

activation and functional connectivity of spatially distinct brain regions. Typically, fMRI is thought 

to include a task that assesses a particular cognitive domain while subjects are in the scanner. 

In this setting, brain regions that show synchronized increases in BOLD activity during active 

task periods are thought to be functionally connected to each other and work together to allow 

for the completion of the task at hand. To this end, traditional task-based fMRI has been crucial 

1 

 
 
 
 
to understanding the regional localization and differentiation of brain function by identifying brain 

regions that actively contribute to subjects performing specific tasks (8).  

Resting-state fMRI (rs-fMRI), a task-free adaptation of traditional fMRI, focuses on the low-

frequency fluctuations (traditionally <0.1 Hz) of BOLD signal in the brain and is considered to 

represent the innate functional connectivity of the brain at rest (9). Biswal and colleagues 

identified task-evoked connections between brain regions in the motor cortex that were 

associated with subjects performing a bilateral finger-tapping task. When examining 

spontaneous BOLD fluctuations at lower signal frequencies during rest periods, he found these 

connections were also maintained in a strikingly similar manner relative to active task periods. 

Furthermore, Biswal’s group related their findings to an animal electrophysiology study that 

presented correlations of electrical activity in the rat cerebral cortex with previously 

unrecognized spontaneous fluctuations in regional cerebral blood flow reported the year prior 

(10). Together, the results of these investigations highlight neurovascular coupling as an 

important feature to support baseline spontaneous neural activity, and that functionally 

connected brain regions show similar oscillations in their respective BOLD response time 

course even in a baseline resting state. Regions that show highly correlated BOLD response at 

rest throughout the time course are grouped together and referred to as a resting state network 

(RSN). Rs-fMRI studies have since made great strides in identifying distinct RSNs throughout 

the brain which have a consistent and reproducible organization (11–14). Collectively, all RSNs 

in a single parcellation can be considered as a functional atlas of the brain. Existing functional 

atlases are commonly used to perform node-based network modeling analyses and compare 

RSN functional connectivity between distinct subject groups or conditions. 

2 

 
 
 
 
 
 
1.3 – Lots of ways to do nothing - methodological choices of rest conditions in rs-fMRI  

There are pragmatic benefits to a task-free approach because there is relatively little set-up 

involved for the experimenter. Furthermore, rs-fMRI can be performed with any subject 

population, including children or individuals afflicted by neurodegenerative disorders that may 

inhibit their ability to perform complex tasks in the more conventional task-based fMRI setting. 

However, currently there is no set standard regarding the optimal conditions that should be 

adhered to when acquiring rs-fMRI data. For instance, some studies request subjects to keep 

their eyes open during the scan, while others ask that they close their eyes without falling 

asleep (13,15). Additionally, some studies ask subjects to focus on something specific, such as 

the sounds of the scanner to reach a somewhat meditative state, while others instruct subjects 

to let their minds wander or think of nothing in particular (16,17). Extant literature has already 

found the subtle differences in instructions between certain rest conditions to influence the 

functional connectivity of some RSNs (18,19) . For example, Patriat and colleagues compared 

RSN functional connectivity between three rest conditions, specifically eyes-closed, eyes-open, 

and eyes-open while focusing on a fixation cross (19). They observed changes in the auditory, 

default-mode, and attentional networks between rest conditions, and the most reliable 

reproducibility of these RSNs was captured when subjects were instructed to focus on a fixation 

cross. Likewise, Kawagoe and colleagues examined changes in RSN functional connectivity 

between two eyes-open rest conditions with different pre-scan instructions (18). One rest 

condition requested subjects to let their minds wander, while the other asked them to think of 

nothing. This slight difference in instruction subsequently led to significant differences in default-

mode network (DMN) functional connectivity between conditions. Previous work on this topic 

tends to focus on selected primary RSNs of interest or only test a few rest conditions at a time. 

Thus, it remains unclear how exactly each individual RSN responds to conditional changes 

between numerous rest conditions in a single study. The first aim of this dissertation was 

3 

 
 
 
 
designed with the intent to elucidate the effects of multiple rest conditions on all RSNs 

presented in the existing functional atlas defined by Yeo and colleagues (14). Accomplishing 

this aim offers a more comprehensive understanding of RSN deviations between rest conditions 

in healthy subjects, which will assist in better study design and interpretation of results in future 

clinically relevant rs-fMRI studies, such as in the case of Alzheimer’s Disease (AD). 

1.4 – Alzheimer’s Disease  

Dementia is a broad classification of neurological symptoms that includes several subtypes 

of pathological age-related cognitive decline that severely reduces qualify of life. Aside from the 

clear detrimental impact of dementia on healthy aging in the elderly, there is also a large 

economic burden that has been estimated at $200 billion per year (20). Given the growing 

concern of dementia due to the rapidly increasing elderly population, there is an urgent need for 

further investigation into better prevention and treatment options (21). AD has long been 

recognized as the most prominent dementia subtype, affecting an estimated 5.4 million 

Americans, as well as a leading cause of death in adults 65 and older (22,23). Patients with AD 

present with impaired memory, judgement, reasoning skills, visuospatial abilities, language 

function, and changes in personality or behavior that interfere with activities of daily living (24). 

The leading hypothesis for underlying pathophysiological mechanisms of AD suggests that 

aggregation of amyloid beta and tau proteins contribute to subsequent neurodegeneration and 

cognitive decline (25). In fact, while symptoms and biomarkers are useful in determining 

probable AD cases, a definite AD diagnosis can only be confirmed after both amyloid beta 

plaques and neurofibrillary tangles composed of tau proteins are identified in post-mortem brain 

tissue analysis (26). However, technological advancements have allowed for the identification of 

these key features in vivo through positron emission tomography (PET), which has led to a 

conceptual understanding that AD progresses along a continuum (27). Specifically, these 

findings showed that amyloid beta and tau accumulation precede AD-associated cognitive 

4 

 
 
 
 
deficits. This is further supported when considering patients with mild cognitive impairment 

(MCI), an intermediate phase between normal cognition and AD, as they also exhibit 

presymptomatic amyloid beta and tau accumulation, albeit to a lesser extent (27,28). Subjective 

cognitive impairment (SCI) has also been identified as a state that precedes MCI along the AD 

continuum (29,30). SCI is described as a subjective perception of cognitive decline, without 

observable deficits in neuropsychological testing. Older individuals with SCI are estimated to be 

twice as likely to progress to MCI or AD, and some have considered SCI as the first 

symptomatic sign of AD as they share some biomarkers with patients with MCI and AD (29,31).  

1.5 – Implications of RSN functional connectivity in Alzheimer’s Disease 

Clinical studies involving rs-fMRI have shown deviations in RSN connectivity to possess 

cognitive and clinical implications which have the potential to serve as viable biomarkers of 

disease states (32–34). In line with this, altered connectivity of RSNs such as the well-studied 

DMN has been associated with aging and aspects of cognitive decline (35,36). The DMN was 

discovered in task-based fMRI, presenting as negatively correlated activation (12,15,37). 

Specifically, the DMN includes brain regions such as the prefrontal cortex, precuneus, posterior 

cingulate cortex, hippocampus, inferior parietal lobule, and angular gyri (38,39). The DMN has 

since been associated with the internal processing of self-relevant information, inward thinking 

and preparation for future cognitive tasks (39,40). Prior work has shown the functional 

connectivity of the DMN is reduced in patients with AD and MCI which can occur years before 

the appearance of neuropsychological deficits (39,41–44). Interestingly, PET studies have 

revealed that hyperphosphorylated tau and amyloid beta, established hallmarks of AD, 

aggregate within primary DMN regions including the precuneus and posterior cingulate cortex 

(45,46). Recent work has also shown a similar pattern of amyloid beta deposition and 

diminished DMN connectivity in cases of MCI (47). Furthermore, rs-fMRI studies have shown 

early DMN connectivity disruptions in individuals with SCI (48). These findings suggest that 

5 

 
 
 
 
amyloid and tau deposition coincide with reduced DMN connectivity in AD patients, and these 

brain changes often occur before the manifestation of cognitive decline (27). In addition to DMN, 

other RSNs such as the frontoparietal network, salience network, dorsal attention network, and 

limbic network have also show deviations in functional connectivity that have been related to 

AD-associated pathology (49,50). Differences in RSN functional connectivity throughout AD 

progression can also be distinguished by machine learning algorithms, which have recently 

been used to automatically classify normal cognition, early MCI, late MCI, and AD patients 

based solely on rs-fMRI data (51,52). As such, measures of RSN functional connectivity can 

provide valuable insight for early AD detection and test the effectiveness of treatment strategies 

in preserving RSN integrity and cognition throughout the AD continuum (36,53,54).  

1.6 – Interplay between Hypertension and Alzheimer’s Disease  

Current treatments focus on mitigating AD symptoms and do not preserve cognitive function 

(55). Thus, it is necessary to gain a better understanding of modifiable risk factors of cognitive 

decline and the progression of AD. A recent postmortem study demonstrated that most AD 

cases have significant underlying vascular contributions, indicating a role of the cardiovascular 

system in AD pathophysiology (56). Furthermore, studies have observed the cortical vascular 

network to be disrupted in AD that can exacerbate AD-associated degeneration (57,58). Indeed, 

longitudinal evidence has provided more insight suggesting many modifiable risk factors 

associated with cardiovascular disease, such as obesity, smoking, diabetes, physical inactivity, 

and hypertension, to also be associated with the progression of AD (59–61). Of these risk 

factors hypertension is of particular interest due to its high prevalence, affecting more than 60% 

of individuals over the age of 65 (62,63). Chronic hypertension compromises neurovascular 

coupling, causing stiffening of cerebral arteries and a restructuring of vascular organization 

which can result in subsequent small vessel disease, white matter lesions, oxidative stress, and 

decreased beta-amyloid clearance (64–67). The indirect inference of neuronal activity through 

6 

 
 
 
 
regional changes in BOLD signal makes fMRI an optimal technique to detect changes in brain 

functional connectivity following challenges to vascular integrity (59). Hypertension has been 

shown to lead to a reduction in resting cerebral blood flow, which can in turn impact RSN 

connectivity (68). This claim is further supported by a recent near-infrared spectroscopy study 

showing that hypertension alone can elicit altered coupling strength in brain functional 

connectivity and worsened cognitive performance (69). Moreover, as mentioned previously, it is 

known that RSNs, including the DMN, are susceptible to alterations in patients with AD (39,70). 

These alterations can be exaggerated in hypertensive AD patients, as they exhibit even weaker 

DMN connectivity compared to their normotensive counterparts (71). While the study of the 

relationship between hypertension and AD has gained interest, there are still large gaps in 

knowledge regarding the effects of hypertension and blood pressure reduction on brain health 

and function.  

The potential cognitive benefits of lowering systolic blood pressure (SBP) through standard 

antihypertensive treatment (SBP target of £ 140 mmHg) are currently controversial and require 

further investigation (72–74). Recent trials involving a more intensive antihypertensive treatment 

option (SBP target of £ 125 mmHg) have shown the more aggressive option to be safe and yield 

a greater benefit in preventing cardiovascular disease over standard treatment (75,76). 

Subsequently, evidence from the Systolic Blood Pressure Intervention Trial (SPRINT) study 

served as the primary basis for the American Heart Association’s decision to update their 

recommended treatment goal to be less than 130 mmHg (77). In addition, a follow-up study of 

the SPRINT cohort revealed a lower incidence of MCI and AD following intensive blood 

pressure reduction compared to standard treatment (78). However, since the SPRINT study 

was cut short after the primary aims of the study regarding cardiovascular disease were 

achieved, the effects of intensive hypertensive treatment on preserving cognitive faculties 

remain unclear. In addition, fMRI was not included in the SPRINT study as it was unnecessary 

7 

 
 
 
 
to achieve those aims, thus the effects of intensive hypertensive treatment on preserving brain 

connectivity also remains unclear.  

To fill this gap, the Risk Reduction for AD (rrAD) clinical trial was designed to investigate the 

effects of intensive blood pressure lowering and aerobic exercise on neurocognitive function 

and brain connectivity in a sample entirely comprised of hypertensive individuals with a high 

familial risk of AD. The rrAD trial enrolled 640 cognitively normal older subjects aged 60 to 85 

years old with hypertension and a family history of AD. Although these subjects showed no 

deficits in cognition, individuals with a family history of AD are more likely to experience 

neurodegenerative symptoms and cognitive decline at an earlier onset (79). However, apart 

from the family history and the hypertension diagnosis, all subjects enrolled in the trial were 

otherwise healthy. As stated in the recently published rationale and methods of the rrAD trial, 

subjects were randomly assigned to treatment groups: standard treatment, intensive reduction 

of vascular risk factors (ISVR), exercise training, and ISVR in combination with exercise training 

(80). Groups that were designated to receive ISVR had a blood pressure target of SBP £ 125 

mmHg that was achieved through a stepwise approach (Figure 1.1) under the supervision of a 

clinician. Groups that were designated to receive exercise training performed a structured 

moderate to vigorous aerobic exercise training supervised by exercise professionals at local 

facilities. They were taught basic exercise techniques in weeks 1-4 three times a week by their 

trainers. Additionally, they each wore heart rate monitors and were taught how to stay within a 

specific target heartrate zone during exercise sessions. The exercise intensity was increased 

over the first three months, and those unable to exercise continuously performed intermittent 

bouts until the heart rate target zone was reached. After week 13 the subjects were allowed to 

exercise at home, however, to remain compliant with the study they were required to keep an 

exercise log. Physical exercise is highly recommended to reduce blood pressure in hypertensive 

individuals to prevent cardiovascular disease, especially in cases where hypertensive 

8 

 
 
 
 
medications are not enough to manage hypertension on their own (81,82). Exercise is also an 

important factor to consider with respect to preserving cognition and brain health because 

studies have associated physical inactivity with decreased Brain Derived Neurotrophic Factor 

(BDNF) expression, hippocampal atrophy, cognitive decline, and higher incidence of dementia 

(83–85). To provide a control for participation in a structured exercise program, the ISVR and 

standard treatment groups without the aerobic exercise component were enrolled into a 

stretching and balance program that focused on range of motion and flexibility (86). In order to 

assess the effectiveness of each treatment group in preserving RSN connectivity, rs-fMRI data 

collected from the rrAD will be used to compare RSN connectivity between groups from 

anatomical and rs-fMRI scans obtained at baseline and at 24 months following treatment. 

However, to accomplish this primary aim of the rrAD trial it is important to accurately define 

RSNs that are pertinent to the population demographics of the study. 

1.7 – Age-appropriate functional atlas for older adults 

A variety of methods have been used to define RSNs. This includes the hypothesis-driven 

seed-based correlation approach, which was the voxel-based approach originally used by 

Biswal and colleagues (9). The seed-based correlation strategy requires the experimenter to 

select a region of interest (ROI) to generate the RSN that will be used in the study. The BOLD 

time course is then extracted from the chosen ROI and compared to the BOLD time course of 

all other regions throughout the entire brain to obtain R-coefficients. Brain regions that follow a 

similar BOLD time course relative to the selected ROI are included in the RSN generated in the 

following analysis. The seed-based correlation approach is limited to defining one RSN at a time 

and requires an in-depth understanding of the RSN from existing literature to select the optimal 

ROI for each specific RSN of interest. This can present an issue when the goal of a study is to 

compare multiple RSNs or to examine RSN dynamics between groups.  

9 

 
 
 
 
Data-driven approaches such as group independent component analysis (GICA) and 

probabilistic functional mode decomposition (PROFUMO) can also be used to identify RSNs 

within a rs-fMRI dataset (87,88). GICA and PROFUMO are considered exploratory model-free 

methods of blind source separation that can summarize the entire dataset without the need for 

experimenter input. GICA is regarded as the current standard data-driven blind source 

separation technique to decompose raw rs-fMRI data into components that are maximally 

spatially independent from one another, such that each individual piece of the data belongs to a 

single component (87). Recently, PROFUMO has also emerged as a data-driven tool to extract 

RSNs in groups and individuals with some advantages over GICA without the requirement for 

spatial independence (88). This advantage allows PROFUMO to capture multimodal brain 

regions in multiple spatial maps, which results in combinatory RSNs and representative RSN 

subnetworks that GICA could otherwise miss.  

Another common approach in rs-fMRI functional connectivity analysis is to use an already 

established functional atlas to perform node-based network modeling. In this technique, the 

experimenter identifies regions of each RSN from an existing functional atlas within their own 

sample and examines the connections between them. When using a network modeling 

approach, it is important to consider the population demographics in which the functional atlas 

was created and determine if it is well-matched to the demographics of the current study. The 

majority of current functional atlases were defined based on young and healthy populations and 

are aligned a standard space that is also based on younger adults, such as MNI152 

(11,13,14,89). While often used to study RSNs in older adults, these atlases do not account for 

age-related brain atrophy and changes in RSN connectivity (90–93). The second aim of this 

dissertation sought to provide a more age-appropriate solution to investigate the primary 

outcomes of the rrAD by creating a functional atlas that adequately describes RSN functional 

connectivity of older adults. To accomplish this, we performed a combination of hypothesis-

10 

 
 
 
 
driven and data-driven approaches on the baseline rs-fMRI scans from the rrAD study in order 

to define a comprehensive set of RSNs. The RSN spatial maps of our functional atlas were 

defined in rrAD420 standard space, an MNI152-adjacent template that captures features of an 

aging brain such as ventricular enlargement and atrophy patterns. The rrAD420 space was 

created through SPM12’s segmentation and non-linear DARTEL registration and derived from 

420 rrAD subjects with good quality neuroimaging data (94). 

This dissertation expands on current rs-fMRI methodology and presents a solution to better 

enable the study of hypertension and AD interplay on RSN functional connectivity. First, in 

Chapter 2, we examine the effects of multiple rest conditions with varying levels of cognitive 

load on RSN functional connectivity using the 17-network parcellation solution presented by Yeo 

and colleagues (14). Chapter 3 introduces the novel rrAD420 functional atlas and offers a 

detailed description of the methods used in its creation as well as the RSNs that were identified. 

Finally, chapter 4 discusses the future directions and direct applications of our contributions. 

11 

 
 
 
 
 
 
 
 
 
 
 
 
 
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FIGURES 

APPENDIX 

Figure 1.1: Schematic outlining intensive treatment steps to achieve target blood pressure of 
SBP £ 125 mmHg (Adapted from funded HIPAC parent grant). 

Intensive Hypertension Treatment Protocol 

24-hour systolic Blood Pressure = 140 mmHg
Add or change patient medication to 20 mg nicardipine and 12.5 mg losartan 

2 – 4 week check

If target blood pressure ≤125 mmHg, and no observed side effects - encourage adherence to medication.
--------------------------------------------------------------------------------------------------------------------------------------------------------------------
Else - increase to 30 mg nicardipine and 25 mg losartan

2 – 4 week check

If target blood pressure ≤125 mmHg, and no observed side effects - encourage adherence to medication.
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
Else - optimize dosage and/or discuss compliance in adhering to medication with participant. Consider referral 
to hypertension specialist for additional steps in lowering blood pressure

20 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
CHAPTER 2: FUNCTIONAL CONNECTIVITY OF CORTICAL RESTING-STATE NETWORKS 
IS DIFFERENTIALLY AFFECTED BY REST CONDITIONS 

This chapter was adapted from: Fernandez, Z., Scheel, N., Baker, J. H., & Zhu, D. C. (2022). 

Functional connectivity of cortical resting-state networks is differentially affected by rest 

conditions. Brain Research, 1796, 148081. https://doi.org/10.1016/j.brainres.2022.148081 

2.1 – Abstract 

Optimal conditions for resting-state functional magnetic resonance imaging (rs-fMRI) are still 

highly debated. Here, we comprehensively assessed the effects of various rest conditions on all 

cortical resting-state networks (RSNs) defined by an established atlas. Twenty-two healthy 

college students (22 ± 4 years old, 12 females) were scanned on a GE 3T MRI scanner. Rs-

fMRI datasets were collected under four different conditions for each subject: (1) eyes open in 

dim light (Eyes-Open), (2) eyes closed and awake (Eyes-Closed), (3) eyes closed while 

remembering four numbers through the scan session (Eyes-Closed-Number) and (4) asked to 

watch a movie (Movie). We completed a thorough examination of the 17 functional RSNs 

defined by Yeo and colleagues. Importantly, the movie led to changes in global connectivity and 

should be avoided as a rest condition. Conversely, there were no significant connectivity 

differences between conditions within the frontoparietal control and limbic networks and the 

following subnetworks as defined by Yeo et al.: default-B, dorsal-attention-B and 

salience/ventral-attention-B. These were not even significant when compared to the highly 

stimulative Movie condition. A significant difference was not found between Eyes-Closed and 

Eyes-Closed-Number conditions in whole-brain, within-network, and between-network 

comparisons. When considering other rest conditions, however, we observed connectivity 

changes in some subnetworks, including those of the default-mode-network. Overall, we found 

conditions with more external stimulation led to more changes in functional connectivity during 

rs-fMRI. In conclusion, the comprehensive results of our study can aid in choosing rest 

conditions for the study of overall and specific functional networks.  

21 

 
 
 
 
2.2 – Introduction  

Resting-state functional MRI (rs-fMRI) measures low-frequency fluctuations (<0.1 Hz) of 

Blood-Oxygen-Level-Dependent (BOLD) signals in the brain in the absence of a task and is 

considered to capture the innate functional connectivity (functional connectivity) of the brain at 

rest (9). Rs-fMRI studies have revealed a consistent organization of distinct resting-state 

networks (RSNs) within the brain (12–14,94). Therefore, changes in RSN functional connectivity 

may be a measurable pathophysiologic change that could serve as a biomarker of various 

neurological disorders (33,95–99). Furthermore, rs-fMRI is instrumental for understanding brain 

functions in those who cannot perform specific tasks in task-based fMRI studies, such as 

pediatric populations or patients with Alzheimer’s Disease (99,100).  

While rs-fMRI is widely used, factors such as rest condition, scan length, and subject 

cardiovascular variability have been shown to impact the reliability of measures, including RSN 

connectivity (19,101,102). In this study, we specifically aim to address the effects of rest 

conditions on the functional connectivity of cortical RSNs. Examples of various conditions used 

in rs-fMRI studies include: (1) Ask subjects to keep their eyes open and look at a target (e.g., a 

fixation cross) (15,103). (2) Ask subjects to “close your eyes, think of nothing in particular, and 

do not fall asleep” or to simply close their eyes during scanning (13,16). (3) Request subjects to 

focus on the sounds of the scanner with their eyes closed to achieve a meditative state (17). (4) 

Watch a movie during scan acquisition for the benefit of minimizing subject motion, especially in 

children (104). With all the various approaches to rest conditions applied in prior studies, 

deciding on the most appropriate condition becomes challenging.  

When choosing a rest condition, it is important to consider how condition-specific 

environmental stimulation, or the introduction of subtle tasks, could violate a true “resting state”.  

For example, watching a movie may help to minimize subject motion, but it also introduces a 

continuously changing stimulus that can lead to dynamic alterations in RSN functional 

22 

 
 
 
 
connectivity (105–107). Similarly, requesting participants to intently focus on scanner noises 

with their eyes closed could affect the functional connectivity of the highly responsive auditory 

network (108). 

Though variations in environmental stimulation between rest conditions are more subtle, 

compared to explicitly performing a task, previous work has shown that these differences may 

still invoke significant changes in observed RSN functional connectivity patterns (19,106,109). 

For example, Patriat and colleagues compared the rest conditions: eyes-closed, eyes-open, and 

eyes focused on a fixation cross. They demonstrated that the auditory, default-mode, and 

attentional networks were most reliably reproduced when subjects were instructed to focus on a 

fixation cross (19). Yan and colleagues also investigated changes between eyes-closed, eyes-

open, and eyes-fixated rest conditions. They specifically assessed the functional connectivity of 

the default-mode network and found significant connectivity differences between rest conditions. 

Their results suggested that eyes-open rest conditions lead to more reproducible results of the 

default-mode network when compared to the eyes-closed rest condition (109). Kim and 

colleagues further expanded on these findings by comparing a movie-watching condition to an 

eyes-open rest condition. They found significant resting-state functional connectivity alterations 

at the global level as well as within the visual and attention networks of the Gordon-Lauman 

parcellation (106,110).  

Prior studies, like those above, focused on only a few selected networks or conditions, 

leaving a need for a more comprehensive examination. Here, we investigated the effects of four 

rest conditions with varying levels of stimulation on functional connectivity of all cortical RSNs 

using the parcellation of Yeo and colleagues, in a demographically similar sample of young 

adults (14). Based on the rs-fMRI data from 1000 healthy young subjects, Yeo et al. divided the 

cerebral cortex into highly reproducible organizations of 7 “coarse” and 17 “fine-resolution” 

networks. Choosing the latter, Yeo’s 17 network parcellation, decreases the risk of averaging 

23 

 
 
 
 
time courses of large regions that may be significantly different, thereby reducing the risk of 

committing a Type II error (14,95). In this study, rs-fMRI datasets were acquired while subjects 

were asked to adhere to the following conditions:  

(1)  eyes open in a dim light (Eyes-Open condition),   

(2)  eyes closed and asked to stay awake (Eyes-Closed condition),  

(3)  eyes closed and asked to remember four numbers before the scan started (Eyes-

Closed-Number condition); the subjects were also informed that they would be quizzed 
on the numbers after this sequence by responding on a keypad, and   

(4)  eyes open and asked to watch a movie clip from Finding Nemo (Movie condition). 

We mainly evaluated the effects of different rest conditions on 1) the global average within-

network functional connectivity, 2) each of Yeo’s 17 distinct RSNs, and 3) the individual node-to-

node connections within each RSN. We conducted the investigation following a top-down 

systematic approach. Specifically, we first analyzed if the different rest conditions could drive a 

significant global connectivity difference. After we found a difference at the global level, we 

investigated which networks drove the global differences. After a network was found to be 

affected by the rest conditions, we further elucidated the connection(s) that drove the 

connectivity differences within that network. To supplement this main evaluation, we also 

investigated the whole-brain and between-network functional connectivity. With these 

comprehensive evaluations, we aimed to gain a more complete understanding of the effect of 

different conditions on functional connectivity stability throughout the cortex. We hypothesize 

that the functional connectivity of all cortical RSNs is differentially susceptible to varying levels 

of environmental stimulation. The RSNs that will likely experience connectivity alterations are 

those that play a role in the attentional, auditory, or visual information processing (14,19,106). 

For the readers’ reference the 17 RSNs from Yeo’s work, as well as the regions of each RSN, 

are listed in Figure 2.1. Additionally, the reader will find a brief overview of these 17 networks 

and their function below. 

24 

 
 
 
 
 
2.2.1 – Frontoparietal Control Network (FPN) 

Control-A, -B, and -C resemble subnetworks of the FPN. The FPN spans frontal, parietal, 

and cingulate regions of the brain that have been associated with executive functions (95,111). 

Prior work has shown that this network is most active while performing a wide range of novel 

tasks (112). In addition, Cole and colleagues show that FPN regions further accommodate 

ongoing tasks by adjusting global functional connectivity to better meet current cognitive 

demands.  

2.2.2 – Default-Mode Network (DMN) 

Default-A, -B, and -C resemble subnetworks of the DMN. Of these three subnetworks, 

Default-A contains the classically labeled DMN regions such as the prefrontal cortex, posterior 

cingulate cortex, and angular gyri (38,39). The DMN was discovered in task-based fMRI, 

presenting as negatively correlated activation (12,15). The DMN has since been associated with 

the internal processing of self-relevant information and preparation for future cognitive tasks 

(39).  

2.2.3 – Dorsal Attention Network (DAN) 

Dorsal-attention-A and -B describe subnetworks of the DAN. The DAN is comprised of 

frontal and parietal regions involved in goal-directed processing related to visual attention (113). 

Specifically, the DAN includes the frontal eye fields, superior parietal lobule, and intraparietal 

sulcus (14,113,114).  

2.2.4 – Limbic Network 

Limbic-orbitofrontal and limbic-temporal-pole resemble subnetworks of the limbic network. 

The limbic network is a key component in emotional processing (115–117). The limbic-

orbitofrontal subnetwork is comprised of two contralateral orbitofrontal regions, while the limbic-

25 

 
 
 
 
  
 
temporal-pole subnetwork is composed of two contralateral temporal pole regions. The 

orbitofrontal cortex is suggested as a crucial region for limbic valence by associating an 

emotional value with experiences and reinforcing beneficial behaviors (118–120). Neuroimaging 

studies have further supported this claim, revealing deactivation of the medial orbitofrontal 

cortex in subjects that experienced both a psychological stressor and a subsequent significant 

increase in the stress hormone cortisol (121). In contrast, a comprehensive examination of the 

temporal pole region by Olson et al. (2007) suggests that its role involves facilitating the pairing 

of emotional responses to visual and auditory sensory information that has already been 

thoroughly processed by the brain. 

2.2.5 – Salience/Ventral Attention Network (VAN) 

The subnetworks labeled as salience/ventral-attention-A and -B closely resemble the 

salience network and VAN respectively (122,123). Subcortical regions were not included in 

Yeo’s network parcellation, explaining this grouping of subnetworks (14). Regions of the 

salience network and VAN that were included overlap, and thus were grouped in this 

parcellation.  

The salience network, mostly represented by salience/ventral-attention-A, partially overlaps 

with the VAN but also includes multiple subcortical and limbic regions such as the amygdala, 

medial thalamus, hypothalamus, and ventral tegmental area (122). Seeley proposes that the 

salience network is involved in the integration of emotion with highly processed sensory data to 

assist in goal-oriented decision-making.  

The VAN, resembled by salience/ventral-attention-B, is functionally complementary to the 

DAN and is comprised of the temporoparietal junction and regions of the ventral, frontal, and 

parietal cortex (123). These regions are collectively associated with directing attention to stimuli 

outside the scope of the task at hand (Corbetta and Shulman, 2002; Farrant and Uddin, 2015; 

26 

 
 
 
 
 
Shulman et al., 2002). Shulman’s study demonstrates its activity is highly dependent on the 

specific nature of the task at hand. The importance of considering the VAN independently from 

the DAN has been highlighted in other previous works due to the suspected differences in 

resting-state functional connectivity of these two networks (124).  

2.2.6 – Somatomotor Network  

Somatomotor-A and -B are subnetworks of the somatomotor network (125). The 

somatomotor network includes the primary and secondary motor areas, as well as the insula. It 

functions in the planning and execution of body movements (125–127).  

2.2.7 – Auditory Network 

  Within the 17 networks, the temporal-parietal network resembles a subnetwork of the 

auditory network, which includes cortical auditory regions that are crucial for processing 

language and other auditory information (128). The left and right temporal-parietal regions 

encompass the auditory cortices and the single bilateral connection between these two regions 

makes up the temporal-parietal network (14). Furthermore, this network has been implicated in 

directing auditory attention when selective listening is required (129). Hence, its connectivity 

could be influenced by acoustic scanner noises (130).  

2.2.8 – Visual Network 

Visual-central and visual-peripheral are subnetworks of the visual network. Task-based fMRI 

studies have shown the visual cortex to be functionally organized into the visual central network, 

associated with the visual processing of detailed objects (e.g., faces), and the visual-peripheral 

network, associated with processing less detailed images of larger objects (e.g., buildings) 

(131). As visual RSN connectivity fluctuations occur even in complete darkness, slight changes 

in environmental stimulation could also show an influence on the visual network functional 

connectivity (132).  

27 

 
 
 
 
 
2.3 – Results  

In this study, Eyes-Open, Eyes-Closed, Eyes-Closed-Number, and Movie conditions were 

compared in 22 healthy college students. For all statistical analyses, the threshold of 

significance was set at p ≤ 0.05. Unless indicated otherwise, the p values shown in the Results 

section are after Bonferroni correction for multiple comparison. Repeated-measures ANOVA 

(RM-ANOVA) revealed that the global average of the absolute between-node connectivity 

(representing whole-brain connectivity), the global average within-network connectivity, as well 

as the connectivity of 9 individual RSNs, were significantly affected by rest conditions (Table 

2.1). Furthermore, RM-ANOVA confirmed that our findings were not driven by the differences in 

motion between conditions. The Movie condition invoked a significant decrease in average 

within-network connectivity when compared to all other conditions. The paired t-tests further 

revealed significant differences in connectivity between conditions in the 9 RSNs found 

significantly affected by the rest conditions based on RM-ANOVA. No significant differences 

were found between conditions that required the subjects to keep their eyes closed (Eyes-

Closed vs Eyes-Closed-Number) in any pairwise comparison. Connections between regions of 

significant networks were visualized for each condition using the BrainNet Viewer software 

package, except for the somatomotor-A and temporal-parietal subnetworks which each only 

contains one connection (133) (Figures 2.2, 2.3, and 2.4). For detailed information on significant 

connections of each network refer to Table S2.1-S2.6 in the Supplementary Materials. RM-

ANOVA and the paired t-tests also revealed significant effect of the rest conditions on the 

between-network connectivity. Interesting, no significant difference was found between Eyes-

Closed and Eyes-Closed-Number conditions. Detailed information regarding the between-

network analyzes can be referred to Figure S2.1 in the Supplementary Materials. Below the 

reader will find our results specific to each network.  

28 

 
 
 
 
  
 
2.3.1 – Frontoparietal Control Network (FPN) 

Analyzing the FPN subnetworks, RM-ANOVA did not reveal significant functional 

connectivity differences, in control-A, -B, or -C subnetworks (Table 2.1). However, there was a 

trend level difference (p = 0.008 before Bonferroni correction) in control-A subnetwork functional 

connectivity (Table 2.1). 

2.3.2 – Default-Mode Network (DMN) 

A significant difference in functional connectivity was revealed for the default-A subnetwork 

shown in Table 2.1 (p ≤ 0.001). Network-level pairwise comparisons revealed a significant 

decrease in functional connectivity when the Movie condition was compared to Eyes-Closed (p 

= 0.013), Eyes-Open (p ≤ 0.001), and Eyes-Closed-Number conditions (p ≤ 0.001). We also 

observed a significant decrease when the Eyes-Closed condition was compared to the Eyes-

Open condition (p = 0.012). We further observed differences in eight connections when the 

Eyes-Open condition was compared to the Movie condition and identified differences in five 

connections when the Eyes-Closed-Number condition was compared to the Movie condition 

(Figure 2.2; Table S2.1). The RM-ANOVA did not show a difference in default-B subnetwork 

connectivity between conditions (Table 2.1). However, statistical significance was reached in the 

RM-ANOVA for the default-C subnetwork (p < 0.001), with between-condition pairwise 

comparisons identifying significant connectivity differences when the Movie condition was 

compared to Eyes-Open (p = 0.042) and Eyes-Closed-Number (p = 0.019) conditions (Table 

2.1).  

2.3.3 – Dorsal Attention Network (DAN) 

  RM-ANOVA revealed a significant difference between conditions in the connectivity of the 

dorsal-attention-A subnetwork, see Table 2.1 (p < 0.001). In this subnetwork, a significant 

difference in pairwise comparisons was found in the Eyes-Open condition versus both Eyes-

29 

 
 
 
 
 
 
Closed-Number (p < 0.001) and Eyes-Closed conditions (p = 0.005). Additionally, our results 

show that the Movie condition did not significantly differ from any other condition for this network 

(Table 2.1). We observed three connections exhibiting a difference in the comparison between 

Eyes-Open and Eyes-Closed, as well as five connections driving the connectivity changes seen 

between Eyes-Open and Eyes-Closed-Number conditions (Figure 2.2; Table S2.2). However, 

RM-ANOVA revealed significant changes in functional connectivity of the dorsal-attention-B 

subnetwork across conditions (Table 2.1).  

2.3.4 – Limbic Network 

  RM-ANOVAs did not reveal significant differences in either the limbic-temporal-pole or 

limbic-orbitofrontal subnetworks between conditions (Table 2.1).  

2.3.5 – Salience/Ventral Attention Network 

Significant differences (p ≤ 0.001) in connectivity in the salience/ventral-attention-A 

subnetwork were observed between conditions (Table 2.1). Subsequent pairwise comparisons 

further revealed significantly lower connectivity when watching a movie compared to the Eyes-

Open (p < 0.001), Eyes-Closed (p < 0.001), and Eyes-Closed-Number (p ≤ 0.002) conditions 

(Figure 2.3; Table S2.3). Functional connectivity in Eyes-Open, Eyes-Closed, and Eyes-Closed-

Number conditions did not differ significantly for this subnetwork (Table 2.1). Of the three, Eyes-

Closed-Number had the fewest connections that differed from the Movie condition, with five 

connections driving the observed differences (Table S2.3).  Moreover, Eyes-Open vs Movie and 

Eyes-Closed vs Movie had 11 and 13 significant connections respectively (Table S2.3). 

Additionally, RM-ANOVA showed a trend level difference (p = 0.005 before Bonferroni 

correction) in salience/ventral-attention-B subnetwork functional connectivity (Table 2.1).  

30 

 
 
 
 
 
 
 
2.3.6 – Somatomotor Network 

Functional connectivity was significantly different in the somatomotor-A subnetwork between 

conditions (p = 0.002). We observed significantly decreased subnetwork connectivity in the 

Movie condition compared to the Eyes-Open (p = 0.02), Eyes-Closed (p = 0.005) and Eyes-

Closed-Number (p = 0.002) conditions (Table 2.1).  RM-ANOVA also revealed a similar effect in 

the somatomotor-B subnetwork throughout conditions (p ≤ 0.001). The somatomotor-B 

subnetwork shows a significant decrease in connectivity in the Movie condition compared to the 

Eyes-Closed and Eyes-Closed-Number conditions (p ≤ 0.001) (Table 2.1). We observed 15 

connections that significantly drove differences between the Movie and Eyes-Closed conditions, 

and 14 that drove the differences observed when comparing the Movie and Eyes-Closed-

Number conditions (Figure 2.3; Table S2.4). Additionally, we found significantly decreased 

connectivity in the somatomotor-B subnetwork in the Eyes-Open condition compared to the 

Eyes-Closed condition (p = 0.004). As shown in Table S2.4, this was mostly driven by the 

connection between the left central sulcus and right auditory region (p = 0.014), however, there 

was a trend level effect between the left auditory and left central sulcus (p = 0.09). 

2.3.7 – Auditory Network 

  We observed significant differences in temporal-parietal subnetwork connectivity across 

conditions (p = 0.002). Pairwise comparisons of network connectivity revealed a significant 

increase in the Movie condition compared to the Eyes-Open condition (p ≤ 0.001) (Table 2.1). 

We also observed a slight increase in connectivity in both Eyes-Closed and Eyes-Closed-

Number conditions relative to Eyes-Open. However, these comparisons did not reach 

significance following Bonferroni correction.  

31 

 
 
 
 
 
 
 
2.3.8 – Visual Network 

  RM-ANOVA revealed a significant difference in the visual-central subnetwork across 

conditions (p ≤ 0.001). Pairwise comparisons showed its connectivity to be significantly higher in 

the Eyes-Open condition compared to the Movie (p = 0.001) and Eyes-Closed (p = 0.007) 

conditions (Table 2.1). One connection, between the right striate and right extrastriate, was 

found to drive the significant differences observed in the visual-central subnetwork between the 

Eyes-Open and Eyes-Closed conditions (Figure 2.4; Table S2.5). Comparing Eyes-Open to the 

Movie condition, we observed increased subnetwork functional connectivity of the Eyes-Open 

condition, which was driven by three individual connections (Table S2.5). Of these, two bilateral 

connections of the visual-central subnetwork were shown to significantly decline in connectivity 

in the Movie condition compared to Eyes-Open; these included the connections between the left 

and right striate cortices (p ≤ 0.001) and between the left and right extrastriate regions (p ≤ 

0.001).  

Finally, our analyses demonstrated a significant difference in the visual-peripheral 

subnetwork between conditions (p = 0.001). Pairwise comparisons revealed a significant 

decrease in average subnetwork functional connectivity in the Movie condition compared to 

Eyes-Open conditions (p = 0.004), Eyes-Closed (p = 0.021), and Eyes-Closed-Number (p = 

0.001) (Table 2.1). Comparing the Eyes-Open and Movie conditions revealed the most 

connections with significant changes in functional connectivity (Figure 2.4; Table S2.6). The 

bilateral connection between the left and right striate of the visual-peripheral subnetwork was 

especially susceptible to changes in resting-state connectivity, showing significance in all 

comparisons other than Eyes-Closes versus Eyes-Closed-Number (Table S2.6). Connections 

between left and right inferior extrastriate (p ≤ 0.001), as well as left superior extrastriate and 

right superior extrastriate (p ≤ 0.001), were significantly increased during the Eyes-Closed-

Number condition exclusively when compared to the Movie condition (Table S2.6).  

32 

 
 
 
 
 
2.4 – Discussion 

In this study, we compared the effect of four different rest conditions (Eyes-Closed, Eyes-

Closed-Number, Eyes-Open, and Movie) on functional connectivity measures. Each condition 

carried varying levels of cognitive load, which allowed for the investigation of network-specific 

susceptibility to environmental stimuli. We carried out comprehensive analyses on all cortical 

RSNs using Yeo’s 17 network parcellation, and below we discuss the observed changes in 

global connectivity as well as each RSN (14).  

2.4.1 – Overall Connectivity 

The global average of absolute between-node connectivity, global average of within-network 

connectivity and between-network connectivity were all significantly affected by rest conditions. 

The Movie condition in general showed more effect on functional connectivity than other 

conditions. The Movie condition showed a significant average within-network decrease in 

functional connectivity relative to Eyes-Open, Eyes-Closed, and Eyes-Closed-Number 

conditions. This finding is consistent with a recent study showing that movie-watching can 

change the functional organization of whole-brain connectivity (106). Kim and colleagues 

concluded that in a movie condition, functional connections of RSNs are reorganized to form new 

connections involving brain regions that are more adherent to the cognitive demands of movie-

watching. None of the other three conditions elicited a significant change in global average of 

absolute between-node connectivity and global average within-network connectivity when 

compared to one another. This suggests that whole-brain and global average within-network 

resting-state connectivity is relatively stable throughout rest conditions unless excessive 

stimulation is present. Based on this result, watching a movie during rs-fMRI can lead to large 

variations in RSN connectivity overall, which violates the pretense of a “rest” condition.   

33 

 
 
 
 
 
 
 
2.4.2 – Frontoparietal Control Network (FPN) 

 Although it did not reach significance, control-A connectivity was slightly decreased while 

movie-watching, which could be explained by the FPN’s role in mediating activity between the 

dorsal attention network and the DMN in the presence of excess external stimulation (134). 

However, control-B and -C subnetworks were shown to be relatively unaffected by changes in 

rest conditions, as their connectivity pattern did not seem to be significantly influenced by the 

condition. 

2.4.3 – Default-Mode Network (DMN) 

In Yeo’s network parcellation, the default-A subnetwork is most like the commonly 

recognized DMN. A decrease in connectivity was expected in this subnetwork as the subject's 

focus was diverted from self to the surrounding external stimuli. Indeed, we show several 

connections drive lower subnetwork connectivity in the Movie condition compared to the Eyes-

Closed, Eyes-Closed-Number, and Eyes-Open conditions. The changes in default-A connectivity 

appear to be mainly attributed to the long-range anterior-posterior connections, with the largest 

decrease occurring when the Movie was compared to every other condition (Figure 2.2; Table 

S2.1). This finding is consistent with previous studies regarding event-related, task-induced 

deactivation of this network (15,39). Additionally, we show a slight increase in default-A 

subnetwork functional connectivity in the Eyes-Open condition compared to the Eyes-Closed 

condition (Table 2.1). This finding would suggest our observations are similar to existing 

literature, which showed an Eyes-Open condition to be well-suited to capture the default-mode 

connectivity (19,109). Looking at the functional connectivity of Yeo’s default-B subnetwork, it 

seems to be largely unaffected by the differences between conditions tested here. Whereas 

Yeo’s default-C subnetwork displays connectivity changes in the Movie condition compared to 

the Eyes-Open and Eyes-Closed-Number conditions. The changes in subnetwork connectivity 

across conditions suggest some level of susceptibility of default-C to connectivity alterations and 

34 

 
 
 
 
 
 
should warrant caution for future studies on the amount of external stimulation present in the rest 

condition.  

2.4.4 – Dorsal Attention Network (DAN) 

The changes in dorsal-attention-A connectivity between conditions appear to be mainly 

attributed to the interhemispheric connections, with the largest decrease occurring in both Eyes-

Closed and Eyes-Closed-Number when compared to the Eyes-Open condition (Figure 2.2; 

Table S2.2). Our data demonstrated that connectivity was impacted mostly by whether subjects 

had their eyes open, with both eyes-closed conditions eliciting a significant decrease in network 

connectivity. There also appeared to be a slight increase in connectivity in the Movie condition 

relative to both the Eyes-Closed and Eyes-Closed-Number conditions, though not reaching 

significance. Overall, our results support previous findings that show DAN resting-state 

functional connectivity differences between Eyes-Open and both eyes-closed conditions (19).  

  However, there was no significant difference in functional connectivity of the dorsal-

attention-B subnetwork between conditions. This may be due to the lack of an engaging task 

resulting in stable connections between regions of this subnetwork throughout conditions (113). 

Here, the Movie condition did not elicit significant connectivity alterations when compared to any 

other condition in either of the DAN subnetworks. This differs from recent work which showed 

the DAN to functionally reorganize to accommodate movie-watching and could be due to 

differences in parcellation strategies (106). 

2.4.5 – Limbic Network 

The differences in environmental stimulation between conditions were not found to drive 

significant alterations in the limbic subnetworks. This finding could be explained by the fact that 

the current study did not include conditions with a task that would invoke emotion strong enough 

to drive changes in RSN connectivity within our demographic (115,121,135). It is also important 

35 

 
 
 
 
 
 
to note that each limbic subnetwork only contains a single bilateral connection in Yeo’s 

parcellation, which limits a fine-grained analysis of this network and thus may have impacted the 

results. 

2.4.6 – Salience/Ventral Attention Network (VAN) 

 The changes in salience/ventral-attention-A connectivity between conditions appear to be 

mainly attributed to the interhemispheric connections, with the largest decrease occurring when 

the Movie was compared to every other condition (Figure 2.3; Table S2.3). This is consistent 

with previous findings, which show salience network connectivity to be highly impacted by 

movie-watching (136).  

Although comparisons did not reach significance, the salience/ventral-attention-B 

subnetwork presented a similar trend (p = 0.005 before Bonferroni correction) in functional 

connectivity as salience/ventral-attention-A between conditions. This could be explained by the 

partially overlapping functions between these subnetworks (122). The trend-level decrease in 

functional connectivity of salience/ventral-attention-B in the Movie condition compared to other 

conditions may suggest that movie-watching violated the “rest” requirement.  A follow-up 

investigation of the interaction between VAN functional connectivity and eye movements could 

bring more clarity(137). Overall, our findings warrant caution on the effects of movie-watching 

on RSN functional connectivity of both the salience network and the VAN.  

2.4.7 – Somatomotor Network 

 The somatomotor-A subnetwork exhibits decreased connectivity in the Movie condition 

relative to Eyes-Open, Eyes-Closed, and Eyes-Closed-Number conditions. While somatomotor-A 

is comprised of only a single connection between the left and right central sulci, its connectivity 

appears susceptible to alterations in conditions with higher levels of environmental stimulation. In 

the Movie condition, subjects seemed to be more focused on the movie, rather than their body 

36 

 
 
 
 
 
 
position or action planning, which presumably lead to decreased somatomotor subnetwork 

functional connectivity (127,138). These connectivity differences can be further explained by a 

restructuring of functional network organization to better suit the processing of actions presented 

on the screen in the Movie condition (106).  

The functional connectivity alterations we observed in the somatomotor-B subnetwork can 

be explained by a similar rationale. This subnetwork exhibited decreased connectivity in most 

connections in the Movie condition compared to both the Eyes-Closed and Eyes-Closed-Number 

conditions, as well as relatively higher connectivity when the Eyes-Closed was compared to the 

Eyes-Open condition (Figure 2.3; Table S2.4). Previous work has shown that when subjects 

close their eyes, they are likely to imagine voluntary movements, or plan to make movements, 

which can stimulate regions of the somatomotor-B subnetwork, such as the secondary 

somatosensory cortical region (139). We observe decreased somatomotor-B subnetwork 

connectivity in the Movie condition compared to the Eyes-Closed and Eyes-Closed-Number 

conditions. Additionally, the somatomotor-B subnetwork had the most connections of any 

network that were significantly affected by differences between conditions. 

However, one difference between the somatomotor-A and -B subnetworks was that 

connectivity in the Eyes-Open condition compared to Eyes-Closed was significantly reduced for 

somatomotor-B (Table 2.1). This difference was driven by increased connectivity between the left 

central sulcus and right auditory regions in the Eyes-Closed condition (Figure 2.3; Table S2.4). 

Overall, our findings confirm and expand on previous research that shows the somatomotor 

subnetwork’s susceptibility to decreased connectivity in the Movie condition compared to rest 

conditions with closed eyes, as well as lower connectivity in somatomotor-B in Eyes-Open 

relative to Eyes-Closed conditions (19,106). 

37 

 
 
 
 
 
 
 
2.4.8 – Auditory Network 

In concordance with current literature, it was expected that the audio presented during the 

movie would invoke increased connectivity of auditory regions compared to the other rest 

conditions (106,108). Congruently, we observed significantly stronger connectivity of the 

temporal-parietal subnetwork in the Movie condition compared to the Eyes-Open condition. This 

recapitulates the findings of Kim et al. comparing RSN connectivity while movie-watching to an 

eyes-open rest condition (106). However, the same effect was not revealed comparing the Movie 

condition with the Eyes-Closed and Eyes-Closed-Number conditions. The observed higher 

auditory connectivity in both eyes-closed conditions compared to Eyes-Open was consistent with 

the previously reported by Patriat and colleagues (2013). While this difference was not significant 

in the current study, the slight increase could be attributed to subjects more intently focusing on 

the sounds of the scanner when their eyes were closed (17,130). Nonetheless, the slightly 

increased connectivity in both eyes-closed conditions lacks significance in pairwise comparisons 

involving the high-connectivity levels seen in the Movie condition. Overall, our results expand on 

the findings of Kim et al., showing a significant effect of movie-watching on the auditory network 

compared to the Eyes-Open condition, however not reaching significance compared to Eyes-

Closed and Eyes-Closed-Number conditions (106).  

2.4.9 – Visual Network 

There were two significant differences in functional connectivity of Yeo’s visual-central 

subnetwork comparing the different conditions. First, in line with Patriat’s findings on the visual 

RSN, there was an increase in visual-central subnetwork connectivity, mainly due to the stronger 

anterior-posterior connections (Figure 2.4; Table S2.5), in the Eyes-Open condition compared to 

the Eyes-Closed condition (19). Second, we identified a significant decrease in this subnetwork 

connectivity in the Movie condition compared to the Eyes-Open condition. This corroborates the 

findings of Kim et al., as the decreased visual-central subnetwork connectivity in the Movie 

38 

 
 
 
 
 
 
condition could be indicative of network adaptations to better meet the cognitive demands of 

natural vision (106).   

In addition to processing images of the human body, it has been shown that regions of the 

extrastriate cortex may be involved in the planning of body movements as part of dorsal 

visuomotor processing (138,140). Here, this effect seemed to be evident in the visual-peripheral 

subnetwork, as its connectivity alteration between conditions was similar to connectivity 

changes observed with the somatomotor-A subnetwork (Table 2.1). As such, visuomotor 

processing may have been impacted as the extrastriate cortex regions appeared to be the 

primary driver for changes seen in the visual-peripheral subnetwork (Table S2.6). Specifically, 

we found significantly decreased connectivity in the Movie condition, mainly due to the weaker 

interhemispheric connections (Figure 2.4; Table S2.6), compared to Eyes-Open, Eyes-Closed, 

and Eyes-Closed-Number conditions. No other comparison between Eyes-Open, Eyes-Closed, 

and Eyes-Closed-Number conditions revealed a difference in visual network connectivity, which 

further resembled the results of the somatomotor-A subnetwork. This could potentially be a 

result of an attentional shift to the movie rather than planning body movements in the Movie 

condition and should be addressed in further studies. Overall, given the observed connectivity 

differences between the visual-peripheral and the visual-central subnetworks between 

conditions, visual subnetworks should be investigated separately (131,140).  

2.4.10 – General Discussion/Summary 

  Overall, whole-brain and within-network functional connectivity was affected most by the 

Movie condition compared to every other condition. Importantly, each of our scans was 

completed in a single session which allowed for our investigation of individual RSN stability 

between conditions. Some subnetworks (control-A, control-B, control-C, default-B, dorsal-

attention-B, limbic-orbitofrontal, limbic-temporal-pole, and salience/ventral-attention-B) 

39 

 
 
 
 
 
demonstrate connections that are less susceptible to changes between rest conditions, 

including the Movie condition.  

The remaining networks (default-A, default-C, dorsal-attention-A, salience/ventral-attention-

A, somatomotor-A, somatomotor-B, temporal-parietal, visual-central, and visual-peripheral) 

were significantly altered between conditions in unique ways that may be explained by their 

specific functions. We confirm and expand upon previous findings by utilizing the complete 

cortical parcellation presented in Yeo’s work (14,19,106). We corroborate that Eyes-Open and 

Eyes-Closed conditions yield functional connectivity profiles that differ in subnetworks of DMN, 

DAN and visual RSNs (19). We show the only subnetworks to elicit higher levels of connectivity 

in either eyes-closed condition relative to the Eyes-Open condition were the somatomotor-B 

subnetwork, as well as the temporal-parietal subnetwork, although only at the trend level 

(Tables 2.1 and S2.5). Both subnetworks contained auditory regions (Figure 2.1), which 

reproduces prior reports that auditory RSN exhibit relatively higher levels of connectivity in eyes-

closed rest conditions (19).  

There can be a general concern that differential mental processing occurs during the full rs-

fMRI scan, which may lead to differential functional connectivity effects. To address this 

concern, we equally split the time course of each node, and conducted paired t-tests between 

the first and second halves of the resting-state scans for each network. We found no significant 

functional connectivity differences in any of the Yeo’s 17 networks in any condition. While these 

divided time courses are rather short for reliable functional connectivity estimations, these non-

significant findings strengthen our primary results. There also can be a concern of possible 

cross-over effects from a prior scanning condition. This possibility is much reduced with our 

experimental procedure. There were 1-min breaks before and between the 7-min rs-fMRI scans, 

with each scan at one rest condition. During the breaks, the MR operator verbally asked the 

subject to relax and press the button if he/she was ready for the new scan. This intervention 

40 

 
 
 
 
 
 
from the MR operator reduced the possible cross-over effects of movie-watching or the number 

remembering task. 

A highlight of the findings is that no significant differences were found between Eyes-

Closed-Number and Eyes-Closed conditions in whole-brain functional connectivity, global 

average within-network functional connectivity, and functional connectivity in all individual 17 

networks as well as between-network functional connectivity. These non-significant findings 

suggest that internal cognitive process, including random and specific thoughts and memory 

rehearsal, will not affect functional connectivity. A follow-up study with a longer string of 

numbers can be interesting to understand the effect of memory rehearsal. On the other hand, 

the 4-number string has already placed a reasonable demand on memory rehearsal. Based on 

subject responses, all engaged in memory rehearsal. The non-significant findings in all 

comparisons let us speculate that a longer string of numbers will not significantly change the 

functional connectivity. 

In our supplementary analyses, some individual between-network functional connectivity 

was also found significantly affected by rest conditions, suggesting a change of relationship 

between networks. Specific functional connectivity differences have been shown while watching 

a movie compared to an eyes-open rest condition (106). Moreover, a recent study has found 

that the affective experience of movie-watching can evoke altered connectivity patterns that are 

influenced by subject partiality toward the movie (141). Thus, between-subject variability in RSN 

connectivity is expected in movie conditions, although this was not explored in the present 

study.  We did observe functional connectivity patterns that could be indicative of subnetwork 

interaction in some comparisons between conditions. Indeed, we observed a slight increase in 

mean connectivity of dorsal-attention-A along with a decrease in default-A when comparing the 

movie condition with both Eyes-Closed and Eyes-Closed Number conditions, a phenomenon 

41 

 
 
 
 
 
 
similarly reported by Fox et al., 2005 (Table 2.1). This finding may highlight the opposing roles 

of DAN and DMN when processing external stimulation. 

Additionally, the visual-central subnetwork appears to operate in contrast to the temporal-

parietal subnetwork. To illustrate, in comparisons with the Movie condition with the Eyes-Open 

condition we observe opposing changes of functional connectivity in the temporal-parietal and 

visual-central subnetworks. This seemingly represents a compensatory decrease in visual-

central subnetwork connectivity to accommodate an attentional shift to the movie dialogue (Table 

2.1). Finally, we observed slightly increased temporal-parietal subnetwork connectivity in both 

the Eyes-Closed and Eyes-Closed-Number conditions relative to Eyes-Open, which may 

represent the shift in subject focus to acoustic scanner noise in eyes-closed conditions. Similarly, 

the increase in visual-central subnetwork functional connectivity in Eyes-Open versus the Eyes-

Closed condition may represent shifts in attention from visual input (e.g., fixation cross) to 

background auditory input in the Eyes-Open condition. In future studies, it may be beneficial to 

ask subjects to recall what they focused on to disentangle these subnetwork relationships.  

2.4.11 – Study Limitations 

  While we attempted a comprehensive evaluation of all cortical networks based on the 

reliable cerebral cortical parcellation by Yeo and colleagues (14), Yeo’s networks excluded 

subcortical regions and the cerebellum. The inclusion of subcortical regions could provide a 

more thorough understanding of RSN functional connectivity between rest conditions. Another 

limitation is the potential effect of rest condition sequence on functional connectivity. Future 

studies may want to compare the effects of condition order on functional connectivity.  Finally, 

the number of conditions to evaluate in the current study was limited by potential subject fatigue. 

The comparison between Eyes-Open-Number and Eyes-Open conditions could also provide an 

interesting insight. However, to reduce subject fatigue, we decided to only compare the Eyes-

Closed-Number with the Eyes-Closed condition. This choice also reduced the potential variation 

42 

 
 
 
 
 
of external visual stimulation that could not be controlled when the eyes were open. We 

speculate that internal processes, such as random thoughts and specific memory rehearsal, 

would not affect the functional connectivity, regardless of if subjects have their eyes open or 

closed.  

2.4.12 – Conclusions 

Overall, conditions that provide more external stimulation during resting-state fMRI will lead 

to more changes in connectivity. Watching a movie can lead to global changes in fMRI 

connectivity and should be avoided as a rest condition. The other conditions (Eyes-Open, Eyes-

Closed, and Eyes-Closed-Number) did not show significant differences between one another at 

the average within-network level, but some variation did exist at the individual network level. We 

did not find significant differences in the frontoparietal-control-B and -C, limbic, and ventral 

attention subnetwork connectivity profiles between conditions, not even when comparing to the 

highly stimulative Movie condition. Finally, significant differences were not found between Eyes-

Closed and Eyes-Closed-Number conditions in the functional connectivity between-nodes 

globally, within-network globally, between networks, or within the individual RSNs. This 

suggests cognitive engagement, if it is internal, and not related to external stimulation, does not 

significantly alter resting-state connectivity measurements. This suggests careful instruction on 

what to think during rs-fMRI is not necessary. However, we could not fully conclude whether the 

subjects should have their eyes open or closed during rs-fMRI. In sum, the presented 

comprehensive analyses will aid in designing rest conditions suited for the study of global and 

specific networks.   

43 

 
 
 
 
 
 
 
 
2.5 – Experimental Procedures  

2.5.1 – Participants 

Twenty-two healthy college students (12 females, 22 ± 4 years old) participated in this study. 

All participants were screened for neurological disorders of which none were reported. This 

study was approved by the Michigan State University Institutional Review Board.  All subjects 

signed consent forms approved by the Michigan State University Institutional Review Board 

before participation.   

2.5.2 – Imaging Acquisition 

This experiment was conducted on a GE 3T Signa® HDx MR scanner (GE Healthcare, 

Waukesha, WI) with an 8-channel head coil. During each session, the first and higher-order 

shimming procedures were carried out to improve magnetic field homogeneity. To study resting-

state brain function, echo-planar images, starting from inferior to superior regions of the brain, 

were acquired for 7 minutes using the following parameters: 36 contiguous 3-mm axial slices in 

an interleaved order, time of echo (TE) = 27.7 ms, time of repetition (TR) = 2500 ms, flip angle = 

80°, field of view (FOV) = 22 cm, matrix size = 64 × 64, and ramp sampling. The first four data 

points were discarded to achieve a steady image signal. Each volume was acquired 164 times 

while the subjects were asked to adhere to each of the four previously described rest conditions 

(Eyes-Open, Eyes-Closed, Eyes-Closed-Number, and Movie). High-resolution isotropic (1 mm3) 

T1-weighted inversion recovery fast spoiled gradient-recalled (IR FSPGR) anatomical images 

with cerebrospinal fluid suppressed were obtained to cover the whole brain. These images were 

used within FreeSurfer to parcellate RSN regions.  

44 

 
 
 
 
 
 
 
 
2.5.3 – Resting-State fMRI Analysis 

Individual Subject Pre-Processing: The “afni_proc.py” routine in AFNI (142) was used to 

generate the scripts to preprocess the rs-fMRI data. For each subject, signal time course spikes 

were first detected and removed. Data points with excess motion were identified (normalized 

motion derivative > 0.5 or voxel outliers > 10%) and modeled as regressors in subsequent 

processing. Then, acquisition timing differences across slice locations were corrected. The 

functional images were aligned to T1-weighted high-resolution anatomical images, using the 

third volume as reference. Rigid-body motion correction was applied in three translational and 

three rotational directions. Translational and rotational estimates, as well as their derivatives, 

were modeled as regressors for the subsequent noise regression step. For each subject, spatial 

blurring with a full-width half-maximum of 4 mm was used to reduce random noise.  

For the time course at each voxel location, the “3dDeconvolve” routine in AFNI (142) was 

used to remove temporal noise contributions due to motion, baseline, and system-induced 

signal trends (up to the 4th order). The mean signal time courses of CSF and white matter 

regions as well as a temporal band-pass filter with a range of 0.009 Hz – 0.08 Hz were also 

modeled as regressors during the “3dDeconvolve” step. The pre-processed voxel time courses 

were then used in subsequent correlation analyses. 

Subject-Level rs-fMRI Network Analyses: For each subject, T1-weighted images were 

segmented via the FreeSurfer standard processing pipeline “recon-all” (143). The cortical nodes 

of the 17 networks derived by Yeo and colleagues (14) were identified in the subject native 

space using FreeSurfer segmentation (143). Pearson correlation between two rs-fMRI time 

courses of each pair of nodes in each network was performed in Matlab (MATLAB Version 

R2020a). The mean correlation was calculated for all node-pairs in each network to represent 

the connectivity of each network. The overall average within-network connectivity (correlation) is 

the mean of the connectivity values (correlations) of all 17 networks. In assessing the whole-

45 

 
 
 
 
 
brain connectivity, due to the cancellation effect of positive and negative correlations, notably 

between networks that are known to be anticorrelated (i.e. task-negative vs. task-positive 

networks), we took the global average of the absolute r values between all nodes to represent 

the whole-brain connectivity. In this perspective, we consider a connection with a high negative r 

value to be highly connected, although in a negative way. To further investigate between-

network connectivity, we first calculated the mean time course for each network across its 

nodes, then we calculated the pairwise correlations between networks to represent between-

network connectivity. 

  Group Analyses on functional connectivity: To prepare for statistical analyses, all correlation 

coefficients of each subject were first Fisher's Z-transformed. Using SPSS (IBM SPSS Statistics 

for Windows, Version 26.0), RM-ANOVAs were conducted on these Fisher Z-transformed r-

value distributions across the rest conditions of these subjects on the global average of absolute 

between-node connectivity, global average of within-network connectivity, the connectivity of the 

17 RSNs, as well as between-network connectivity, followed by paired t-tests between different 

conditions. We also explored the differences of specific connections via paired t-tests within 

each network between conditions, to investigate connections driving overall changes in each 

individual RSN that was found significantly affected by the rest conditions based on RM-

ANOVA. Correlation coefficients were obtained between each pair of nodes in each network to 

construct a correlation matrix for all RSNs in each condition. The correlation matrix of each RSN 

was compared between conditions to locate the connections that showed significant differences 

following paired t-tests. Statistical significance was set at p ≤ 0.05 for all statistical analyses. 

RM-ANOVAs at the global level did not require multiple comparison correction. Bonferroni 

correction was applied in all multiple comparison corrections. Bonferroni correction was applied 

to the RM-ANOVAs on the 17 networks by setting a p ≤ 0.00294 to obtain a corrected p ≤ 0.05. 

After a network was found significantly affected by the rest conditions, Bonferroni correction was 

46 

 
 
 
 
applied to the six paired t-tests on the rest conditions by setting p ≤ 0.00833 to obtain a 

corrected p ≤ 0.05. Bonferroni correction was applied to a between-node connection by setting a 

p threshold less than 0.05 divided by the total number of between-node connections in a 

network to obtain a corrected p ≤ 0.05.   

47 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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FIGURES 

APPENDIX 

Figure 2.1: (Top Row) AFNI software depiction using an underlay MNI152 standardized brain 
with a color coded overlay of Yeo’s 17 Networks (available at 
http://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation_ Yeo2011). (Below Top Row) 
Complete listing of the names of Yeo’s 17 networks along with corresponding common network 
names and the cortical ROIs that belong to each network. 

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Figure 2.2: Brain Net Viewer graphical visualization of connectivity within RSNs that showed a 
significant difference in functional connectivity between conditions following repeated-measures 
ANOVAs (p ≤ 0.05). The strength of each connection is denoted by both its size (larger diameter 
denoting stronger connection) and color (scale). Each node is labeled with a number coding for 
each region of interest. Default-Mode Network -A (Top): (1) IPL, (2) IPL, (3) PCC, (4) PCC, (5) 
PFCd, (6) PFCd, (7) PFCm, (8) PFCm, and (9) Temp. Dorsal-Attention Network -A (Bottom): 
(1) TempOcc, (2) TempOcc, (3) ParOcc, (4) ParOcc, (5) SPL, and (6) SPL (For abbreviations, 
see Figure 2.1). 

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Figure 2.3: Brain Net Viewer graphical visualization of connectivity within RSNs that showed a 
significant difference in functional connectivity between conditions following repeated-measures 
ANOVAs (p ≤ 0.05). The strength of each connection is denoted by both its size (larger diameter 
denoting stronger connection) and color (scale). Each node is labeled with a number coding for 
each region of interest. Salience/Ventral-Attention Network -A (Top): (1) Ins, (2) Ins, (3) PrCv, 
(4) PrCv, (5) ParOper, (6) ParOper, (7) FrMed, (8) FrMed, (9) ParMed, (10) ParMed, and (11) 
PrC. Somatomotor Network -B (Bottom): (1) Aud, (2) Aud, (3) Ins, (4) Ins, (5) S2, (6) S2, (7) 
Cent, and (8) Cent (For abbreviations, see Figure 2.1). 

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Figure 2.4: Brain Net Viewer graphical visualization of connectivity within RSNs that showed a 
significant difference in functional connectivity between conditions following repeated-measures 
ANOVAs (p ≤ 0.05). The strength of each connection is denoted by both its size (larger diameter 
denoting stronger connection) and color (scale). Each node is labeled with a number coding for 
each region of interest. Central Visual Network (Top): (1) Striate, (2) Striate, (3) ExStr, and (4) 
ExStr. (Bottom) Peripheral Visual Network (Bottom): (1) ExStrInf, (2) ExStrInf, (3) Striate, (4) 
Striate, (5) ExStrSup, (6) ExStrSup (For abbreviations, see Figure 2.1). 

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TABLES 
Table  2.1.  Resting-State  Functional  Connectivity  at  Different  Conditions:  *Significant 
comparisons after Bonferroni correction are indicated in bold. The first two rows in the “ANOVA” 
column did not require correction for multiple comparisons, as it is a global measurement. The 
“ANOVA” column (excluding the first two rows) shows uncorrected p-values with significance set 
at p < 0.00294. The “Pairwise t-test” columns show corrected p-values with significance set at p 
≤  0.05.  The  p-values  greater  than  1  following  Bonferroni  correction  are  shown  as  “1”. 
Abbreviations: Eyes-Open (EO); Eyes-Closed (EC); Eyes-Closed-Number (ECN); Movie (M).  

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CHAPTER 3: DEFINING A FUNCTIONAL ATLAS OF RESTING-STATE NETWORKS FOR 
OLDER HYPERTENSIVE ADULTS WITHIN THE rrAD420 ANATOMICAL SPACE 

This chapter was adapted from: Fernandez, Z.*, Scheel, N.*, Keller,J, Binder, E., Vidoni, E., 

Burns, J., Stowe, A., Kerwin, D., Vongpatanasin., W., Cullum, M., Zhang, R., & Zhu, D. C. 

(2022). Introducing rrAD420, an anatomical template and multi-modal atlas for older adults. (In 

Preparation) 

3.1 – Abstract 

Risk Reduction for Alzheimer’s Disease (rrAD) is a recently completed randomized clinical 

trial designed to investigate the effects of improved cardiovascular health on neurocognitive 

function as well as brain structure and connectivity ((1)). The rrAD sample is comprised of 420 

hypertensive older adults (60 to 84 years, 68.8±5.9) with a family history of dementia or 

subjective cognitive decline that underwent aerobic exercise training and/or intensive 

pharmacological interventions. This trial collected anatomical and resting-state functional MRI 

(rs-fMRI) scans at baseline and two years following intervention for all subjects across five 

different 3T MRI scanners.  

Current rs-fMRI atlases, such as those developed by Yeo (2), Shirer (3), and Damoiseaux 

(4), were based on young and healthy populations. While being used to study resting-state 

networks (RSNs) in older adults, these atlases do not account for age-related brain atrophy and 

changes in RSN connectivity. Here, our goal is to use the rrAD baseline rs-fMRI scans to create 

a robust rs-fMRI atlas that is better-suited to study older populations. Using SPM12’s DARTEL 

registration (5) we created a cohort-specific MNI-adjacent anatomical template space, namely 

rrAD420. Using the data-driven approaches of temporally concatenated group independent 

component analysis (GICA) (6) and probabilistic functional mode decomposition (PROFUMO) 

(7), we created an rs-fMRI atlas that more adequately describes RSN functional connectivity of 

our cohort, including different modes of major networks. We expect the rrAD420 rs-fMRI atlas to 

64 

 
 
 
 
be applicable to study the functional connectivity of older populations in general, which could 

lead to more reliable biomarker detection. 

3.2 – Introduction 

Functional magnetic resonance imaging (fMRI) offers a noninvasive measurement of 

hemodynamic changes in the blood oxygenation level-dependent (BOLD) response to infer 

brain function (8). Until recently, fMRI studies have mostly focused on the task-evoked BOLD 

signals of specific brain regions to gauge regional neuronal activation while subjects are 

presented with a specific stimulus or task compared to baseline. However, there is an ever-

growing interest towards resting-state fMRI (rs-fMRI), a model-free approach that does not 

require a specific task or stimulus. Rs-fMRI concentrates on the spontaneous fluctuations of 

BOLD signals at low frequencies (0.01 - 0.1 Hz) at rest. These fluctuations have been shown to 

preserve the synchronized configurations of functionally connected brain regions found using 

task-based fMRI (9). Spatially, distinct brain regions that share a common BOLD signal time-

course are believed to be functionally connected. They are typically grouped together and 

collectively referred to as a resting-state network (RSN). Previous work has revealed a 

consistent organization of RSNs, and the complete collection of all RSNs in a single parcellation 

can be described as a functional atlas (2,4,10,11).  

Typically, rs-fMRI research is conducted by characterizing RSNs through either seed-based 

or data-driven approaches, or by using existing atlases that are well-matched for the 

demographics of the population of interest for a given study. Seed-based approaches can be 

considered hypothesis-driven, as the experimenter must choose each optimal seed region in 

order to generate the corresponding RSNs of interest one at a time (9,12,13). This method 

cross-correlates the BOLD time course of voxels within the chosen seed region with all other 

voxels throughout the rest of the brain and estimates the spatial map of regions that share 

similar spontaneous BOLD signal fluctuations throughout the time course. Whereas data-driven 

65 

 
 
 
 
methods are thought to be more exploratory, as this type of approach attempts to summarize 

the entire dataset between a set number of separate components, or modes, and provide 

multiple spatial configurations that resemble known RSNs simultaneously (6,7,14–16) 

Independent component analysis (ICA) is regarded as the current standard data-driven blind 

source separation technique to decompose raw rs-fMRI data into components that are 

maximally spatially independent from one another, such that each individual piece of the data 

belongs to a single component (6). Recently, probabilistic functional modes (PROFUMO) also 

emerged as a data-driven tool to extract RSNs in groups and individuals without the 

requirement of spatial independence (7,16). Regardless of the strategy used to characterize 

RSNs, the general concept is the same: brain regions that are integrated together within the 

same RSN are thought to be more functionally connected to each other than regions that do not 

belong to the same RSN. Currently, there is no consensus on the exact number of 

distinguishable RSNs present in the brain, and RSN configuration can differ between functional 

atlases depending on the strategies used to define RSNs. Although these slight variations exist 

between functional atlases, major RSNs such as the default mode network (DMN), executive 

control network (ECN), salience network, visual network, and somatomotor network (SMN) have 

been represented throughout multiple functional atlases (9,10,17–23).  

The demographics of the individuals which functional atlases are based on can also lead to 

differences in RSN configuration. Most of the existing functional atlases are focused on healthy 

and often younger populations, and the same can be said about the anatomical standard 

images used to normalize group-based results (24). However, the brain experiences age-related 

structural and functional changes that should be accounted for when using a functional atlas in 

older populations (25). For example, brain atrophy and ventricular enlargement are common in 

aged populations, leading to substantial deviations from the standard MNI152 and MNI305 

coordinates which are based on much younger subjects, 25.02 ± 4.9 and 23.4 ± 4.1 years of 

66 

 
 
 
 
age respectively (26,27). These anatomical differences are problematic as they might cause 

severe artifacts when transforming non-normative brains into MNI space via volumetric non-

linear normalization pipelines (28). Furthermore, fMRI studies have demonstrated functional 

connectivity to change throughout the lifespan, even in normal nonpathological aging (24,29). 

Indeed, a study by Doucet and colleagues found that major RSNs, including DMN, ECN, SMN, 

visual and salience networks, had a different spatial composition in older adults compared to 

younger adults (24). Their results highlight the importance of age-appropriate functional atlases 

and present the first functional atlas derived from older adults, namely the Atlas55+. While the 

Atlas55+ provides a viable option to examine major RSNs in older adults through a network 

modeling approach, there are currently limited alternatives for other commonly studied RSNs 

that capture the expected anatomical and functional distinctions from younger populations. 

Examples of such other common RSNs include the language network, limbic network, and 

visuospatial network (2,11). The current study sought to provide a functional atlas for older 

adults that includes a comprehensive RSN parcellation, as well as an age-appropriate standard 

anatomical space, using the rs-fMRI and anatomical T1-weighted scan data from the Risk 

Reduction for Alzheimer’s Disease (rrAD) study.   

The rrAD study is a recently completed randomized controlled trial designed to assess the 

effects of aerobic exercise training and intensive pharmacological cardiovascular interventions 

on neurocognitive function in hypertensive older adults with a family history of dementia or 

subjective cognitive decline (1). The in-depth neuroimaging protocol included anatomical, 

functional, and physiological MRI scans, obtained at baseline and again after two years of 

intervention. 420 older subjects (60 to 84 years, 68.8±5.9) had baseline scans on five different 

3T MRI scanners (two Siemens, two GE, and one Philips). Each scan underwent rigorous 

quality control directed by Dr. David Zhu (a trained MRI physicist). 

67 

 
 
 
 
Here, we utilize the data-driven group independent component analysis (GICA) (6) and 

probabilistic functional mode decomposition (PROFUMO) (7), together with seed-based 

connectivity maps of the pre-processed rs-fMRI data, to create a functional atlas tailored to 

older hypertensive adults. This sample provides a good number of older subjects with imaging 

data collected for each subject in a standardized manner across all testing locations. 

Additionally, because all subjects are demographically similar, our anatomical space and 

functional atlas will be optimized for future studies that wish to perform RSN modeling within 

hypertensive older adults. However, we also believe the provided templates and flow fields of 

our rrAD420 space will extend to better representing the RSNs of older adults in general. Using 

the high-quality baseline data of this study, we aim to integrate images from multiple modalities 

to create an accurate brain template and atlas that is better suited to represent older 

populations and can be used by researchers studying comparable cohorts.  

3.3 – Methods 

3.3.1 – Participants 

Four-hundred-twenty hypertensive older adults (261 females, 68.8 ± 5.9 years old) with a 

family history of dementia or subjective memory complaints and a sedentary lifestyle 

participated in the rrAD study. They did not have significant cognitive impairment. All subjects 

were recruited from the Dallas, Baton Rouge, Kansas City, and St. Louis areas. This study was 

approved by the Human Subjects Committee at Pennington Biomedical Research Center 

(PBRC; n = 128), The University of Texas Southwestern Medical Center and Texas Health 

Presbyterian Hospital Dallas (UTSW/IEEM; GE scanner n = 56; Phillips scanner n = 44), 

University of Kansas Medical Center (KUMC; n = 103), and Washington University School of 

Medicine (WashU; n = 89). All participants met the strict inclusion criteria detailed in the recent 

rrAD rationale and methods paper and signed the necessary consent forms before participation 

(1).  

68 

 
 
 
 
 
3.3.2 – Imaging Acquisition 

This experiment was conducted across five different 3T MRI scanners (UTSW using a 

Philips Achieva and a GE Discovery MR 750 W scanner, KUMC a Siemens Skyra scanner, 

WashU a Siemens Prisma scanner, and PBRC a GE Discovery MR 750 W scanner). To 

accommodate inter-scanner variability and harmonize the data collected between sites, each 

protocol was individually calibrated. All scanners utilized a 32-channel head coil, except for the 

UTSW GE system which used a 48-channel coil. For this current study, we focused on the rrAD 

neuroimaging data and specifically utilized the baseline rs-fMRI and T1-weighted anatomical 

images. The rs-fMRI scans were acquired with 2.5 s TR (time of repetition), 28 ms TE (time of 

echo), and a 64 x 64 matrix size with 3.4mm x 3.4mm pixels. A 3 mm slice thickness was used 

on all but the UTSW GE system, which instead used thicker slices of 3.4 mm to account for the 

reduced signal-to-noise ratio on the 48-channel coil. Each rs-fMRI scan lasted 12-minutes, 

during which subjects were asked to focus on a fixation cross to ensure a consistent rest 

condition across subjects, as our lab and others have shown RSNs are influenced by the 

condition subjects are exposed to during scan acquisition (30,31). For all subjects, the 

anatomical 3D 1-mm isotropic T1-weighted MPRAGE scans with cerebrospinal fluid suppressed 

were collected in accordance with the rrAD neuroimaging protocol using the following 

parameters: 176 sagittal slices, TE = 3.8–4 ms, TR of acquisition ≈ 8.6 ms, time of inversion (TI) 

= 830 ms, TR of inversion = 2,330 ms, flip angle = 8°, FOV (field of view) = 25.6 cm x 25.6 cm, 

matrix size = 256 x 256, slice thickness = 1 mm.   

3.3.3 – Data Preprocessing 

For processing the rs-fMRI data in subject space, we implemented the “afni_proc.py” routine 

in AFNI (32) to generate the script for a standard preprocessing pipeline. For each subject, 

spikes in the signal time course were first detected and removed. Data points with excess 

normalized motion derivatives or voxel outliers were identified and modeled as regressors in 

69 

 
 
 
 
 
subsequent processing. Slice-timing corrections were applied to account for acquisition time 

differences across slice locations. The functional images were co-registered to the T1-weighted 

high-resolution anatomical images, using the third volume as reference. Then, we applied rigid-

body motion correction in three translational and three rotational directions. Translational and 

rotational estimates, and their derivatives, were modeled as regressors for the following noise 

regression step. Spatial blurring with a full-width half-maximum of 4 mm was also applied to 

each subject to reduce random noise and improve signal-to-noise ratios. Using the output from 

these initial preprocessing steps, we transformed the motion parameters from AFNI’s motion 

correction into an FSL-compatible format to perform an aggressive ICA-AROMA to remove 

noise, such as subject motion, from the rs-fMRI data (33,34). We ran ICA-AROMA version 0.4 

beta in MATLAB on the data in order to improve the resolution of smaller structures within the 

brain. First, we extracted the noise components from the rs-fMRI data that was blurred with a 6-

mm full-width half-maximum kernel as suggested in previous work (36), then the time courses of 

these noise components were regressed out of the rs-fMRI data that was blurred with a 4-mm 

kernel (33,35). A comprehensive explanation for the rationale behind choosing aggressive ICA-

AROMA for the removal of nuisance components on rrAD rs-fMRI data over other 

preprocessing techniques, such as standard censoring, censoring with global signal removal, 

non-aggressive ICA-AROMA, or the spatially organized component klassifikator (SOCK) has 

recently been published (36). In line with previous research, our lab found that while the other 

aforementioned techniques performed reasonably well aggressive ICA-AROMA showed the 

greatest level of data reproducibility (33,37). As a final preprocessing step, we applied a 

temporal band-pass filter in the range of 0.009 Hz – 0.08 Hz as part of the regression model. 

After the T1-weighted high-resolution anatomical images were properly co-registered with 

the rs-fMRI images, the origin was manually reoriented to the anterior commissure for each 

subject to stabilize the following spatial normalization steps. Then, in order to create a cohort-

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specific MNI-adjacent standard template, the reoriented images were used as inputs for 

SPM12’s segmentation and non-linear DARTEL registration (5). We transformed the rs-fMRI 

images from the subjects’ native space into the newly created standard space, referred to as the 

rrAD420 space. The rrAD420 normalized anatomical space captures the characteristics specific 

to the rrAD cohort and provides an excellent template to display the functional connectivity 

profiles extracted from the rs-fMRI data and later compare them between treatment groups 

through network modeling methods (Figure 3.1).  

3.3.4 – Functional Data Decomposition 

  Using hypothesis-driven seed-based connectivity maps (9,12,38) in combination with data-

driven approaches, temporally concatenated group independent component analysis (GICA) (6) 

and probabilistic functional mode decomposition (PROFUMO) (7), on the pre-processed rs-fMRI 

data, we created a functional atlas that describes the functional connectivity for the older adults 

in the rrAD cohort. Data-driven methods for blind source separation have the advantage of 

distinguishing sources of noise and signal, and later identify RSNs without having to define the 

model beforehand (6,7,38). One example of these techniques is GICA, which is the most 

frequently used temporal decomposition method in functional neuroimaging research.  

  GICA is a matrix factorization tool that unmixes the signals from rs-fMRI data, and groups 

spatially distinct brain regions of interest (ROIs) that share a common BOLD signal time-course 

into single components. Here, we employed FSL’s MELODIC software to perform GICA on the 

entire sample by temporal concatenation (39). Through this approach, GICA reorganized the 

four-dimensional rs-fMRI data for each subject into a two-dimensional time x space matrix, with 

each row representing an individual voxel and each column representing an individual time 

point. The time x space matrices for all subjects were then concatenated in the temporal 

direction in order to identify components that relate to the entire group. GICA decomposes this 

large, group time x space matrix into two matrices; a spatial matrix that contains a topological 

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map for each group component that was identified and a temporal matrix with a time-course 

corresponding to each spatial component that was identified. Afterwards, MELODIC takes each 

row of the group spatial matrix and converts it back into a three-dimensional image to visualize 

each spatial component. GICA attempts to maximize spatial independence between each 

component based on the non-gaussianity of the data, and each component explains parts of the 

original input data by providing a map that represents either neural signals, noise, or a mixture 

of both (40). MELODIC uses negative entropy as a measure for non-gaussianity, and by 

maximizing the non-gaussianity of the unmixed signals it decreases the chance of identifying 

random noise as a component (41). Furthermore, these group spatial components were 

backtransformed to the individual subjects by performing the dual regression in FSL. This allows 

for the group spatial maps to be mapped to the subject time courses for later comparison 

between groups. Due to this step, GICA is considered unidirectional as it always builds the 

group spatial maps first and then applies them to the individual subjects. This unidirectionality in 

turn can limit the amount of inter-subject variation in functional connectivity that GICA can 

incorporate into the group spatial maps (42). In addition, because MELODIC does not allow 

spatial overlap between components, it does not represent the functional networks well when 

networks are in-fact overlapping (43). This may pose some issues when assigning an ROI that 

is potentially involved multiple RSNs to a single component. Recently, FSL released a new 

data-driven matrix factorization approach called Probabilistic Functional Mode decomposition 

(PROFUMO) to define RSNs within the brain. PROFUMO offers some advantage over GICA 

(7,16). In contrast to GICA, PROFUMO offers a bidirectional approach to simultaneously model 

the population and the individuals. This is achieved by using the group spatial maps to 

normalize the subject spatial maps through a top-down framework, while also using the subject 

spatial maps to continuously update the group spatial maps through a bottom-up framework. 

This process repeats iteratively until the group spatial maps best agree with the subject spatial 

maps. In doing so, these updated group spatial maps can better capture inter-subject variability 

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(44). Another difference to GICA is that PROFUMO does not assume spatial independence 

between modes, with modes being analogous to “components” in GICA. This allows for spatial 

repeats across modes, so a single multifunctional ROI can be included across multiple modes. 

Here, similar to GICA, we applied the approach using PROFUMO to unmix the preprocessed rs-

fMRI data into individual modes comprised of ROIs that share a common BOLD signal time-

course. PROFUMO does this by estimating a probability distribution for the spatial and temporal 

elements of the group and each individual subject and optimizing the estimated distribution 

through Bayesian inference (7). This probability distribution optimization is reached by using the 

initial estimates, referred to as priors, from the group modes to normalize the subject modes. 

The priors are then optimized for each subject until the final distribution estimates, referred to as 

posteriors, are obtained. These subject mode posteriors are subsequently used to update the 

group modes, and the process repeats until there is a convergence between the group and 

subject spatial maps.  

3.3.5 – Atlas Creation 

To create a hypothesis-driven map of the default mode network (DMN) for our functional 

atlas through traditional seed-based correlation analysis, we chose the isthmus of the posterior 

cingulate cortex as our seed region (45). We used Freesurfer segmentation output for each 

subject to identify and select the region to correlate BOLD time course of the seed region with 

every other voxel time course throughout the brain (46). We then transformed the structural 

images from each subject into rrAD420 standard space using the DARTEL transformation (5). 

Finally, we obtained our group-level DMN spatial maps through averaging all subject 

connectivity maps and thresholding through gaussian mixture modeling and ROI extraction (9).  

Although the described data-driven techniques are powerful, it is difficult to determine how 

many true signal sources are present in the mixed signal. In addition, fMRI data is inherently 

noisy, and we must allow extra components to account for nuisance signals, and while ICA can 

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separate some of these noise components, it is inherently difficult to label some components as 

neuronal signals or noise (47). Therefore, we performed multiple iterations of both MELODIC 

and PROFUMO in order to find the best-fitting parcellation to use for our functional atlas. 

Specifically, we ran seven instances of GICA with the dimensionality hyperparameter set at 21, 

35, 49, 63, 77, 91, and 105, and three instances of PROFUMO with 30, 50, and 80 dimensions. 

We chose to start with a 21-component parcellation for GICA as suggested by previous 

literature and incrementally increased the number of components with each iteration (6). We 

followed a similar reasoning when selecting the number of parcellations to use for PROFUMO. 

However, we had to limit the number of iterations due to the immense computational demand 

required to run PROFUMO. We then manually checked the output of each GICA and 

PROFUMO iteration separately and labeled each component as true signal or noise following 

the ICA hand classification protocol presented by Griffanti et al. (48). This protocol provides 

useful examples of spatial patterns observed in individual components to look for and assist in 

identifying the components with true neuronal signals versus those that capture signals of other 

origins such as subject motion, vessels, or artefacts. Also included in their protocol is the 

“innocent until proven guilty” flowchart which summarized their procedure and proved to be 

instrumental in labeling components and modes. Components and modes determined to be true 

signals were assigned a network designation based on the RSN each component or mode best 

resembled. To facilitate the labeling phase, we implemented an anatomical dual-regression 

approach, combining FreeSurfer segmentation and DARTEL normalization to create 

probabilistic representations of commonly used brain atlases, such as the automatic anatomical 

labeling (AAL) (49), Brodmann (50), Desikan (51), Destrieux (52), Yeo (2), and Shirer (11) from 

MNI into rrAD420 space, allowing conversions between rrAD420 and other atlases. We used 

these atlases in rrAD420 space to cross-reference with our own data and accurately label 

components and modes as the RSN they best aligned with through a manual two-rater system. 

This procedure required both raters to agree before assigning an RSN designation (Zac 

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Fernandez and Dr. Norman Scheel). Once all components were appropriately designated as 

signal or noise, we used metrics of RSN representation, RSN splitting, and RSN grouping as 

criteria to select the parcellation dimension with optimal separation of our data. RSN 

representation was decided based on the percentage of RSNs present relative to the 14 

networks from the Shirer atlas (11). RSN splitting was quantified by tallying the number of 

networks present in each component, while RSN grouping was quantified by tallying the number 

of components that represented the same RSN. RSN splitting and RSN grouping describe 

issues associated with the problem of overfitting and underfitting the data, respectively (Figure 

3.2). If the dimensionality hyperparameter is greater than the actual number of RSNs present in 

the data, then overfitting occurs and a single RSN can be split into subnetworks between 

multiple components or modes. Conversely, if the dimensionality hyperparameter is less than 

the actual number of RSNs present in the data, then underfitting occurs and multiple RSNs will 

be improperly grouped together within a single component or mode. After considering these 

metrics, we concluded the 49-component GICA parcellation and the 50-mode PROFUMO 

parcellation provided the best RSN separations for our functional atlas. 

3.4 – Results 

The rrAD420 template and functional atlas integrate multimodal neuroimaging data of older 

populations into a common space, accounting for cohort-specific distinctions, such as cortical 

atrophy and enlarged ventricles. It provides references to commonly used brain parcellations 

and completes these with functional and structural atlases created from the rrAD cohort, forming 

a comprehensive multimodal reference brain template. 

3.4.1 – Seed-Based Correlation Analysis 

  Here, we provide a hypothesis-driven spatial map of DMN that was generated through a 

traditional seed-based approach (Figure 3.3). The results of our analysis found that an optimal 

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DMN spatial map comprised 24 ROIs, including the right posterior cingulum, left posterior 

cingulum, right precuneus/angular gyrus, left precuneus/angular gyrus, right medial frontal 

gyrus/medial orbital gyrus, left medial frontal gyrus/medial orbital gyrus, right superior frontal 

gyrus, left superior frontal gyrus, right anterior superior temporal sulcus, left anterior superior 

temporal sulcus, right medial prefrontal thalamus, left medial prefrontal thalamus, right 

hippocampus subiculum, left hippocampus subiculum, right parahippocampal place area, left 

parahippocampal place area, right inferior frontal gyrus (BA11), left inferior frontal gyrus (BA11), 

right medial cerebellum, left medial cerebellum, right cerebellar tonsil, left cerebellar tonsil, right 

posterior cerebellum, left posterior cerebellum.  

3.4.2 – Group Independent Component Analysis 

We examined the FSL MELODIC output of each of the seven GICA iterations with 

hyperparameters for the total number of components allowed set at 21, 35, 49, 63, 77, 91, and 

105. Adhering to the hand-classification guide provided by Griffanti and colleagues, we 

categorized each component as either signal or noise (48). We found the 21-component 

parcellation yielded 9 components that represent true signal, and the remaining 12 noise 

components were of non-neuronal origin. Likewise, the 35-component parcellation yielded 15 

components that were true signal and 20 noise components. The 49-component parcellation 

yielded 19 true signal components and 30 noise components. While the remaining 63-

component 77-component, 91-component and 105-component parcellations respectively 

yielded 20, 27, 31 and 32 components that were derived from true signal. We then designated 

each component that was classified as true signal as the RSN it best represented for each 

iteration, using the 14 Shirer networks as a reference atlas (11). In instances where spatial 

maps of a component represented the true signal of an RSN that was not included in the Shirer 

atlas, such as the limbic network, we used additional functional atlases to supplement the 

labeling phase of the process. In order to decide which parcellation strategy was best for our 

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functional atlas, we assessed RSN representation, RSN splitting, and RSN grouping as defined 

in the methods section.  

We found that all iterations represented all 14 of Shirer’s networks in some capacity, either 

as a single network in a single component or as a secondary RSN in a component that groups 

one or more RSNs together (Figure 3.4). However, when only considering primary RSN 

representation in components, we found variance between parcellations. Specifically, the 21-

component and 35-component parcellations did not identify as many primary network 

components as the other iterations. The 49-component parcellation was able to capture a 

primary representation of most RSNs, and there was a plateau effect as the 63-component, 77-

component, and 91-component parcellations performed at the same level. The 105-component 

parcellation was the only iteration to show primary RSN representation for all RSNs in single 

components (Figure 3.5). The next metric we considered in our decision was RSN splitting. We 

found that although the 105-component parcellation captured all primary RSNs from the 

reference atlas, it also had the highest occurrence of splitting single RSNs between multiple 

components (Figure 3.6). Although this issue was not as extreme in other parcellations, there 

was a trend that as the hyperparameters allowed for more components, network splitting would 

become more common. Lastly, we considered RSN grouping and found the inverse of RSN 

splitting to be true. The 21-component and 35-component parcellations were shown to group 

more RSNs together within single components, which lowered the primary network 

representation for these iterations (Figure 3.7). Overall, we found the 49-component parcellation 

provided the best RSN separation in terms of network representation and the balance between 

RSN splitting and grouping. Therefore, we chose this parcellation solution to use as the GICA 

portion of our rrAD420 functional atlas. 

Within the 49-component GICA parcellation solution of the rrAD420 functional atlas, we 

identified fifteen recognizable RSNs including: primary visual, sensorimotor (SMN), left 

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executive control (LECN), precuneus, dorsal attention/visuospatial (DAN/Vis), ventral default 

mode (vDMN), high visual, posterior salience, anterior salience, language, dorsal default mode 

(dDMN), right executive control (RECN), auditory, basal ganglia, and limbic networks. These 

RSNs were ordered in terms of spatial reproducibility with the primary visual network being the 

most reproducible and the limbic network being the most difficult to reproduce (33,34). A listing 

of the number and names of regions included for each RSN (Table 3.1) and visual 

representations for each RSN spatial map and a list of corresponding brain regions have been 

provided (Figure 3.8).  

For the GICA portion of the rrAD420 functional atlas, the primary visual network spanned 

the medial occipital lobe and was comprised of the right superior middle occipital gyrus and left 

superior middle occipital gyrus. SMN was represented across two components which were 

combined for the purposes of the functional atlas to create a single SMN spatial map. The 

regions included in the finalized SMN spatial map were the right superior pre/postcentral gyrus, 

left superior pre/postcentral gyrus; right inferior precentral gyrus, and left inferior precentral 

gyrus. LECN included the left inferior parietal lobe, left middle/inferior frontal gyrus, left superior 

medial gyrus, left posterior middle cingulate cortex, left inferior/middle temporal gyrus, and right 

posterior cerebellum. The precuneus network included the right precuneus, left precuneus, right 

posterior/middle cingulate cortex, and left posterior/middle cingulate cortex. The DAN/Vis 

network contained a mixture of regions from the visuospatial network as presented in the Shirer 

atlas, as well as regions that belong to the DAN presented by Yeo and colleagues (2,11). 

Specifically, the DAN/Vis network included the right superior parietal cortex, right precuneus, left 

superior parietal cortex, left precuneus, right superior frontal cortex, left superior frontal cortex, 

right inferior frontal gyrus, left inferior frontal gyrus, right middle temporal gyrus, left middle 

temporal gyrus, and right inferior temporal gyrus. The vDMN contained the right precuneus, left 

precuneus, right frontal eye field, left frontal eye field, right middle occipital gyrus, left middle 

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occipital gyrus, right retrosplenial cortex, left retrosplenial cortex, right parahippocampal place 

area, and left parahippocampal place area. The high visual network covered regions of the 

lateral occipital lobe which consisted of a single bilateral connection between the right prestriate 

cortex and the left prestriate cortex. The posterior salience network was comprised of the right 

supramarginal gyrus, left supramarginal gyrus; right precuneus/middle cingulate cortex, left 

precuneus/middle cingulate cortex, right thalamus, left thalamus, right insula, left insula, right 

superior cerebellum, and left superior cerebellum. Regions of the anterior salience network were 

identified across four components and were subsequently combined into a single spatial map to 

capture the RSN in its entirety. The final anterior salience network map included the right 

superior frontal gyrus, left superior frontal gyrus, right middle frontal gyrus, left middle frontal 

gyrus, right dorsal anterior cingulate cortex, left dorsal anterior cingulate cortex, right superior 

frontal gyrus/pre-supplemental motor area, left superior frontal gyrus/pre-supplemental motor 

area, right insula, left insula, right superior cerebellum, left superior cerebellum, right inferior 

cerebellum, and left inferior cerebellum. We also observed the language network across two 

separate components, which were merged to create a finalized language network spatial map. 

The brain regions of the final language network spatial map included the right angular gyrus, left 

angular gyrus, right superior temporal gyrus, left superior temporal gyrus, right superior/middle 

temporal gyrus, left superior/middle temporal gyrus, right pars triangularis, left pars triangularis, 

right middle temporal gyrus, and left middle temporal gyrus. Two components of our GICA 

parcellation resembled the dDMN, these components were combined to create a single dDMN 

spatial map. The final dDMN spatial map contained the most brain regions of any RSN in the 

rrAD420 functional atlas. These regions included the right posterior cingulate cortex, left 

posterior cingulate cortex, right superior frontal gyrus, left superior frontal/medial gyrus, right 

angular gyrus, left angular gyrus, right superior medial gyrus, right basal ganglia, left basal 

ganglia, right anterior cingulate cortex, left anterior cingulate cortex, right inferior frontal gyrus, 

left inferior frontal gyrus, right hippocampus, left hippocampus, right middle temporal gyrus, left 

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middle temporal gyrus, right superior cerebellum, left superior cerebellum, right inferior 

cerebellum, left inferior cerebellum. The RECN also presented across two components that 

were combined to form a single spatial map to represent the complete RSN. The finalized 

RECN spatial map was comprised of the right inferior parietal lobe/angular gyrus, right 

middle/inferior frontal gyrus, right superior medial gyrus, right posterior middle cingulate cortex, 

right pars opercularis/inferior frontal gyrus, right middle frontal gyrus, right inferior/middle 

temporal gyrus, and left posterior cerebellum. The auditory network spanned the auditory 

cortex, which included a single bilateral connection between the right superior temporal 

gyrus/operculum and left superior temporal gyrus/operculum. The basal ganglia network was a 

simple RSN that contained the right basal ganglia and left basal ganglia. The final network for 

the GICA portion of our atlas was the limbic network, which involved the right temporal pole, left 

temporal pole, right pallidum, and left pallidum. 

3.4.3 – Probabilistic Functional Modes  

Following the same process detailed in the above GICA section, we examined the FSL 

PROFUMO output of each of the three PROFUMO iterations with the hyperparameters for the 

number of network modes set at 30, 50, and 80. Following the same hand-classification 

guidelines detailed above, we categorized each mode as either signal or noise (48). We found 

the 30-mode parcellation yielded 24 modes that represent true signal, and the remaining 6 

modes were of non-neuronal origin and considered as noise. The 50-mode parcellation yielded 

42 true signal modes and 30 noise modes, and the 80-mode parcellation had 65 modes derived 

from true signal and 15 modes from noise. Again, we assessed RSN representation, RSN 

splitting, and RSN grouping to determine which parcellation solution would provide the best 

RSN separation. In terms of RSN representation, we found that each iteration captured all 

RSNs of the reference functional atlas presented by Shirer and colleagues (11) (Figure 3.5). 

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However, the 50-mode parcellation showed the most promise as the basis for our functional 

atlas when considering RSN splitting and grouping (Figure 3.6; Figure 3.7).  

Within the 50-mode PROFUMO parcellation solution of the rrAD420 functional atlas, we 

identified thirteen “pure” RSNs including the precuneus, primary visual, LECN, SMN, high 

visual, RECN, vDMN, dDMN, posterior salience, visuospatial, anterior salience, limbic, and 

basal ganglia networks. In addition, some modes integrated elements of multiple RSNs together 

into a single spatial map. This occurred primarily due to PROFUMO’s allowance for multimodal 

brain regions to be represented across multiple modes, whereas GICA seeks to maximize 

spatial independence for each region between components. These modes were kept in our 

functional atlas as “combinatory” networks. We identified six RSNs of this type including the 

anterior ECN/dDMN (aECN/dDMN), language/dDMN, anterior salience/dorsal attention network 

(aSalience/DAN), language/temporoparietal, language/auditory/temporoparietal, and 

language/auditory networks. Also, in contrast to the GICA portion of our functional atlas, the 

PROFUMO parcellation yielded some cases in which multiple modes captured elements of the 

same RSN. In these instances, all modes were kept as subnetworks for their respective RSNs. 

Nine RSNs of our functional atlas include subnetworks, namely the primary visual, SMN, high 

visual, vDMN, dDMN, posterior salience, visuospatial, anterior salience, and limbic networks, as 

well as the language/DMN combinatory network. We assigned the mode that best captured the 

reference spatial map of its respective RSN as the primary representative subnetwork. As such, 

the primary representative subnetwork was listed first for each RSN with subnetworks in our 

functional atlas (i.e. “PROFUMO X.1” in Table 3.2 and Figure 3.9). 

For the “pure” networks in the PROFUMO portion of the rrAD420 functional atlas, we 

identified the precuneus network in a single mode with regions composed of the right 

precuneus, left precuneus, right posterior/middle cingulate cortex, and left posterior/middle 

cingulate cortex. The primary visual network was represented by three modes which were kept 

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separately as subnetworks because each mode captured distinct features of the primary visual 

network. The first subnetwork included the right and left superior middle occipital gyri. This 

closely resembles the analogous primary visual network spatial map from the GICA section. The 

second primary visual subnetwork was comprised of the right inferior occipital gyrus and left 

inferior occipital gyrus. While the third subnetwork was comprised of the right middle occipital 

gyrus and left middle occipital gyrus, with some anticorrelated regions that survived 

thresholding, the right superior occipital gyrus and left superior occipital gyrus. The LECN 

spanned the frontal and parietal cortices which included the left inferior parietal lobe/angular 

gyrus, left medial/inferior frontal cortex, left superior medial frontal cortex, left inferior/medial 

temporal cortex, left posterior middle cingulate cortex, right inferior semilunar lobule of the 

cerebellum, and right posterior cerebellum. Five modes each resembled portions of the 

sensorimotor network and were defined as subnetworks. The first sensorimotor subnetwork 

included the right superior pre/postcentral gyrus and left superior pre/postcentral gyrus. The 

second subnetwork was comprised of the right inferior precentral gyrus and left inferior 

precentral gyrus. Next, the third subnetwork was comprised of the right middle precentral gyrus 

and left middle precentral gyrus. Cerebellar regions of the sensorimotor network were identified 

in the fourth subnetwork, and specifically included the bilateral anterior portions of the 

cerebellum. The final sensorimotor subnetwork involved the medial portions of the right 

superior pre/postcentral gyrus and left superior pre/postcentral gyrus. Subnetworks of the high 

visual network were identified between three modes. The first subnetwork best resembled the 

GICA high visual network and included the right and left prestriate cortex. The second 

subnetwork covered the right and left inferior occipital gyrus, while the final high visual 

subnetwork contained the right and left superior occipital gyrus. For the RECN, a single mode 

resembled the network in its entirety, consisting of the right inferior parietal lobe/angular gyrus, 

right medial/inferior frontal cortex, right superior medial frontal cortex, right inferior/medial 

temporal cortex, right posterior middle cingulate cortex, left inferior posterior cerebellum, and left 

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posterior cerebellum. Two subnetworks of the vDMN were identified between two modes. The 

first subnetwork was most consistent with the vDMN spatial map in the GICA portion, as it 

contained the right precuneus, left precuneus, right frontal eye field, left frontal eye field, right 

middle occipital gyrus, left middle occipital gyrus, right retrosplenial cortex, left retrosplenial 

cortex, right parahippocampal place area, and left parahippocampal place area. While the 

second vDMN subnetwork contained a single connection between the left and right precuneus. 

For our atlas, we identified four modes that were labeled as dDMN subnetworks. The first 

subnetwork best maintained the typical configuration expected of the dDMN, which included 

the right posterior cingulate cortex, left posterior cingulate cortex, right angular gyrus, left 

angular gyrus, right superior frontal gyrus, left superior frontal gyrus, right medial prefrontal 

cortex, left medial prefrontal cortex, right middle temporal lobe, and left middle temporal lobe. 

Cerebellar regions of the dDMN were included in the second subnetwork, namely the right 

posterior cerebellum and left posterior cerebellum. The third dDMN subnetwork included a 

single connection between the left and right prefrontal cortex. While the fourth dDMN 

subnetwork captured the right anterior cingulate cortex, left anterior cingulate cortex, right 

middle cingulate cortex and left middle cingulate cortex. The posterior salience network was 

represented by two modes, which were both included in our atlas as subnetworks. The first 

posterior salience subnetwork was comprised of the right supramarginal gyrus, left 

supramarginal gyrus, right precuneus/middle cingulate cortex, left precuneus/middle cingulate 

cortex, right superior middle frontal gyrus, left middle frontal gyrus, right inferior frontal gyrus, left 

inferior frontal gyrus, right inferior middle frontal gyrus, left inferior middle frontal gyrus, and right 

middle temporal gyrus. The second subnetwork contained only two regions, the right superior 

parietal lobule and left superior parietal lobule. Regions of the visuospatial network were 

identified across six modes, which were all included as individual subnetworks. The primary 

representative subnetwork included the right superior parietal cortex/precuneus, left superior 

parietal cortex/precuneus, right superior frontal cortex, left superior frontal cortex, right inferior 

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frontal gyrus, left inferior frontal gyrus, right inferior temporal gyrus, and left inferior temporal 

gyrus. The second visuospatial subnetwork contained two large, mirrored regions that bilaterally 

spanned the inferior parietal cortex and superior parietal cortex/precuneus. Third, we identified a 

visuospatial subnetwork comprised of the right inferior parietal lobule, right pars opercularis, and 

right middle frontal gyrus. The fourth visuospatial subnetwork included the right inferior frontal 

gyrus and left inferior frontal gyrus. The fifth subnetwork included the right intraparietal sulcus 

and left intraparietal sulcus. While the sixth and final visuospatial subnetwork contained regions 

of the right superior parietal lobule and left superior parietal lobule. Three modes represented 

subnetworks of the anterior salience network. The primary subnetwork best captured features of 

the classic anterior salience network, which included the right superior/middle frontal gyrus, left 

middle frontal gyrus, right dorsal anterior cingulate cortex, left dorsal anterior cingulate cortex, 

right insula, and left insula. The second subnetwork contained the right middle frontal gyrus and 

left middle frontal gyrus. While the third subnetwork consisted of a single connection between 

the right and left secondary motor area. For our atlas, two modes of the rrAD420 PROFUMO 

parcellation resembled subnetworks of the limbic network. First, we identified a limbic 

subnetwork that contained the right and left orbitofrontal cortex. Our second limbic subnetwork 

included the right temporal pole and the left temporal pole. A single mode represented the basal 

ganglia network in its entirety, and this network included the left and right basal ganglia.  

In addition to the individual “pure” networks listed above, the PROFUMO portion of the 

rrAD420 atlas also yielded six “combinatory” networks because multimodal regions were 

allowed to belong to more than one mode. The first combinatory network merged anterior 

regions of the ECN (aECN) and portions of the dDMN. Specifically, the aECN/dDMN network 

contained the right superior frontal sulcus, left superior frontal sulcus, right medial superior 

frontal gyrus and left medial superior frontal gyrus. Three modes represented subnetworks 

which combined regions of the language network and dDMN together in individual spatial maps. 

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The first language/dDMN subnetwork was comprised of the right angular gyrus, left angular 

gyrus, right middle temporal gyrus, left posterior cerebellum, right pars triangularis, right middle 

frontal gyrus, right dorsomedial prefrontal cortex, and right precuneus. The second 

language/dDMN subnetwork spanned the right posterior cerebellum, left angular gyrus, left 

middle temporal gyrus, left pars triangularis, left middle frontal gyrus, left dorsomedial prefrontal 

cortex, and left precuneus. While the final language/dDMN subnetwork contained a single 

connection between the right pars triangularis and left pars triangularis. Another combinatory 

network that emerged was the aSalience/DAN. This network consisted of two large regions that 

covered the right and left superior precentral gyrus. The PROFUMO portion of the rrAD420 atlas 

concludes with three combinatory networks that involve the language network. The first was the 

language network combined with the temporoparietal network presented by Yeo and colleagues 

(2). This network included the right and left middle temporal gyrus. Next, we found a mode that 

contained regions shared by the language network, auditory network and temporoparietal 

network. Specifically, this included two regions that span the right and left posterior middle 

temporal gyrus, and each contained some portions of the posterior superior temporal and 

posterior superior temporal gyri as well. Finally, we identified a mode that spanned over both the 

language and auditory networks, namely the right and left superior temporal gyrus. A listing of 

the number of modes for each network as well as the number and names of regions included for 

each mode (Table 3.2) and visual representations for each RSN spatial map, and a list of 

corresponding brain regions have been provided (Figure 3.9). 

3.5 – Discussion  

Changes in neuroplasticity and functional connectivity occurs with life experiences and age-

related variables. Functional atlases based on younger adults may not necessarily be reliable in 

older adults as they do not account for these changes. The high number of subjects with a 

similar demographic background in the rrAD trial presented an opportunity to establish a 

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standard anatomical template for older adults. In addition, the harmonized rs-fMRI scan 

protocols between scanners used for the study allowed us to define a complete listing of RSNs 

identified within this standard space. Specifically, the rrAD420 template and functional atlas 

integrate multimodal neuroimaging data of older populations into a common space accounting 

for cohort-specific distinctions, such as cortical atrophy and enlarged ventricles. It provides 

references to commonly used brain parcellations and completes these with functional and 

structural atlases created from the rrAD cohort, forming a comprehensive multimodal reference 

brain template. Age-appropriate baseline functional atlases of late adulthood could offer more 

predictive power regarding age-related disease progression, prevention, and treatment 

effectiveness. Within our rrAD420 functional atlas, we supply multiple options for spatial maps 

that can be chosen based on the goals or data quality of an individual study. Major RSNs of 

interest such as the DMN, salience network, primary visual network, sensorimotor network, and 

ECN have shown potential as biomarkers of neurodegenerative disease (53). Both the GICA 

and PROFUMO portions of the rrAD420 functional atlas provide spatial distributions 

representing each major RSN to assist in the investigation of brain functional connectivity in late 

adulthood. In addition, RSNs that are not typically counted among the major RSNs such as the 

visuospatial network, basal ganglia network, and limbic network are also included to facilitate 

further identification of relevant imaging biomarkers for aging and age-related disease in future 

studies. 

3.5.1 – Seed-based Correlation Analysis 

The DMN is considered to be a task-negative RSN that shows higher activation in the 

absence of a task, and presents as anticorrelations when subjects are performing a task 

(54,55). Typically, the DMN includes brain regions such as the prefrontal cortex, precuneus, 

posterior cingulate cortex, hippocampus, inferior parietal lobule, and angular gyri (56). The role 

of DMN has since been associated with mind wandering, internally driven processing and 

86 

 
 
 
 
preparation for future tasks (57). Prior work has shown DMN functional connectivity to be 

reduced in older individuals relative to younger adults, and it is selectively vulnerable in early 

Alzheimer’s Disease (29,56,58,59). In addition, PET studies have revealed that 

hyperphosphorylated tau and amyloid beta, established hallmarks of AD, aggregate within 

primary DMN regions including the precuneus and posterior cingulate cortex (60,61).  

In the rrAD420 atlas, we provide a high-resolution seed-based spatial map of DMN 

containing twenty-four regions, the so-called DMN24. The DMN24 is tailored to older adults and 

will benefit researchers primarily concerned with the DMN in aging. In this map, the first 

eighteen regions listed are cortical, and the last six are defined in the cerebellum. The 

cerebellum often excluded in some studies because it falls outside the FOV due to factors such 

as head position or brain size. Our sample had enough subjects that included the full 

cerebellum to increase the power of our findings, as the inclusion of these regions in our spatial 

map was clear and substantiated by the existing literature (56).  Although these six regions 

showed strong functional connectivity, the last six regions of the DMN24 can be excluded from 

the analysis if studies choose to ignore cerebellar regions.  

3.5.2 – Group Independent Component Analysis 

The GICA portion of the rrAD420 functional atlas provides a spatial distribution for all 15 

RSNs identified across the whole brain. This method represents the current standard for data-

driven RSN definition, and will be useful to investigate multiple independent RSNs 

simultaneously in older populations. The GICA parcellation would benefit studies that wish to 

identify changes in mean functional connectivity for the entire RSN(s) of interest. There are 

some drawbacks to this technique such as the unidirectionality of the method which does not 

explicitly account for inter-subject differences, and the limited ability to distinguish between 

overlapping RSNs. 

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3.5.3 – Probabilistic Functional Modes  

The PROFUMO portion of the rrAD420 functional atlas expands on GICA by simultaneously 

modeling RSNs at the group and subject levels through Bayesian inference (7). The group-level 

modes are used to normalize subject-specific modes from top-down, and the subject-specific 

modes are used to update and normalize group-level modes from the bottom-up. This 

introduces a bidirectionality to the model that better accounts for subject variability. Compared 

to GICA, the PROFUMO portion of the rrAD420 functional atlas found a higher proportion of 

modes derived of true signal origin. This resulted in less modes being discarded as they 

represent meaningful information rather than random noise or artifact. PROFUMO also allowed 

for the grouping of multimodal regions into various subnetworks and combinatory networks. 

These factors may hold some advantage over GICA in some cases as it accommodates the 

study of specific RSN subdivisions that can vary in their susceptibility to connectivity disruptions 

of age-related disease (62,63). Namely, the primary visual, SMN, high visual, vDMN, dDMN, 

posterior salience, visuospatial, anterior salience, and limbic networks of the PROFUMO 

parcellation contained subnetworks. The subnetwork that best captured the primary 

representation of each RSN was listed first for each respective network. The primary 

representative subnetwork was well-matched to the GICA counterparts; however, they were not 

identical. Therefore, in future studies comparing mean functional connectivity that require all 

regions of a given RSN, the GICA portion may be favorable. In addition, the combinatory RSNs 

that were identified in the PROFUMO section include: the aECN/dDMN, language/dDMN, 

anterior salience/DAN, language/temporoparietal, language/auditory/temporoparietal, and 

language/auditory networks. These represent regions of overlapping RSNs that were highly 

correlated to each other and could warrant further study as potential biomarkers. For instance, 

the DMN has been shown to have interconnections with the language network (64). The DMN 

recruitment of language network regions such as the middle temporal gyrus and middle frontal 

88 

 
 
 
 
gyrus have been shown to be associated with inner speech, or internal, verbal thinking (65). 

Therefore, the language/dDMN RSNs of the PROFUMO parcellation may present an interesting 

opportunity to study these connections as biomarkers. While PROFUMO is a powerful tool that 

addresses some of the drawbacks of GICA, PROFUMO itself also has some downside. 

Primarily, PROFUMO is computationally expensive. Each iteration required an allocation of 4TB 

of RAM working memory, so it had to be run on a high-performance computation center 

(HPCC). However, the current version of PROFUMO employs additional dimensionality 

reduction algorithms to make it more computationally efficient (66). 

3.5.4 – Limitations 

While we attempted to use state-of-the-art neuroimaging techniques to define an age-

appropriate structural template and functional atlas, some limitations of the current study are 

worth noting. All rrAD subjects were hypertensive which could alter the connectivity of some 

RSNs. However, hypertension is highly prevalent in older adults which could be useful in 

representing the population. Furthermore, all subjects had measures in place to control their 

blood pressure and they scored within the normal range for cognitive testing. Therefore, we 

predict our functional atlas will still be preferable over existing options for older subjects in 

general beyond the scope of the rrAD trial.   

3.5.5 – Conclusion  

Here, we present a functional atlas for older adults containing RSN spatial maps derived 

from both seed-based and data-driven approaches. While these spatial maps are intended for 

the direct assessment of rrAD trial outcomes, we expect our functional atlas will also be 

applicable options for the study of RSNs in older adults in future studies. We provide a seed-

based map of the DMN that can be directly applied in aging studies and for biomarker detection 

in multiple neurodegenerative diseases. The data-driven GICA and PROFUMO sections 

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identified all group-level RSNs simultaneously. The standard GICA section presents each RSN 

with maximal spatial independence. While the PROFUMO section presents relevant 

subnetworks of RSNs, when applicable, and additional combinatory/overlapping RSNs that may 

be of interest to future studies investigating biomarkers of aging or neurodegenerative disease.  

In addition to rs-fMRI data, the rrAD study included several other imaging modalities 

including T2 FLAIR images for white matter hyperintensity (WMH) quantification, arterial spin 

labeling (ASL) to estimate regional cerebral blood flow (CBF), and diffusion imaging for fiber 

tracking and structural connectivity analyses. These other modalities will later be transformed 

into rrAD420 space as well for future studies. The averages across subjects from the complete 

imaging dataset will incorporate cohort- and modality-specific templates for T1, T2 FLAIR, WMH 

distributions, CBF, and DTI fractional anisotropy, mean diffusivity, and free water. Through the 

rrAD420 template, longitudinal multi-modal image analyses can now be carried out using a 

template unique to this rrAD population. Our rrAD420 should also apply to older populations in 

general, facilitating analyses across different imaging modalities, data integration, and 

biomarker detection. 

3.5.6 – Acknowledgements  

This work has been supported by NIH grants (R01AG057571 and R01AG049749), the 

computational resources provided by the Institute for Cyber-Enabled Research at Michigan 

State University 

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FIGURES 

APPENDIX 

Figure 3.1: Comparison of standard MNI152 template (TOP) based on younger adults 
(25.02 ± 4.9) and the standard rrAD420 template based on 420 older adults (68.8 ± 5.9) in 
the rrAD study.  

MNI152

rrAD420

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Figure 3.2: Basic schematic to explain the concept of overfitting and underfitting. The 
“ground truth” (LEFT) represents the desired grouping of connected regions that we want to 
capture using our model. Overfitting (TOP) occurs when the hyperparameters are set too 
high in the blind source separation model and leads to splitting a true network between 
multiple components. Underfitting (BOTTOM) occurs when the hyperparameters are set too 
low in the model and leads to grouping multiple networks into a single component. 

97 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.3: Representative images of the seed-based group spatial map of Default Mode 
Network (DMN24) based on 420 hypertensive older adults with a high familial risk of dementia 
or subjective cognitive decline that participated in the rrAD trial. The first 18 regions listed are 
cortical regions, while the remaining 6 are cerebellar regions. 

DMN24: Right posterior cingulum, left posterior cingulum, right precuneus/angular gyrus, left 
precuneus/angular gyrus, right medial frontal gyrus/medial orbital gyrus, left medial frontal 
gyrus/medial orbital gyrus, right superior frontal gyrus, left superior frontal gyrus, right anterior 
superior temporal sulcus, left anterior superior temporal sulcus, right medial prefrontal thalamus, 
left medial prefrontal thalamus, right hippocampus subiculum, left hippocampus subiculum, right 
parahippocampal place area, left parahippocampal place area, right inferior frontal gyrus 
(BA11), left inferior frontal gyrus (BA11), right medial cerebellum, left medial cerebellum, right 
cerebellar tonsil, left cerebellar tonsil, right posterior cerebellum, left posterior cerebellum. 

98 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.4: Percentage of the 14 RSNs in the Shirer functional atlas, which served as our 
reference atlas, represented by each GICA and PROFUMO parcellation regardless of the 
designation of primary or secondary. 

99 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.5: Percentage of the 14 RSNs in the Shirer functional atlas, which served as our 
reference atlas, represented by each GICA and PROFUMO parcellation that presented as a 
primary network. 

100 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.6: Percentage of the 14 RSNs in the Shirer functional atlas, which served as our 
reference atlas, dispersed between multiple components for each GICA and PROFUMO 
parcellation. 

101 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.7: Percentage of the 14 RSNs in the Shirer functional atlas, which served as our 
reference atlas, grouped within a single component for each GICA and PROFUMO 
parcellation. 

102 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.8: Representative images and list of brain regions for each group resting-state network 
based on 420 hypertensive older adults with a high familial risk of dementia or subjective 
cognitive decline that participated in the rrAD trial. We performed group independent component 
analysis (GICA) on rrAD subject resting-state fMRI data through FSL’s MELODIC software. 
Here, we display resting-state networks from the 49-component parcellation, which showed the 
most ideal network separation from those tested.  

Primary Visual Network: Right superior middle occipital gyrus (striate); Left superior middle 
occipital gyrus (striate cortex). 

Sensorimotor Network: Right superior pre/postcentral gyrus (Sensorimotor cortex); Left 
superior pre/postcentral gyrus (Sensorimotor cortex); Right inferior precentral gyrus 
(Sensorimotor cortex); Left inferior precentral gyrus (Sensorimotor cortex). 

103 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.8 (cont’d):  

Left Executive Control Network (LECN): Left inferior parietal lobe/angular gyrus; Left 
middle/inferior frontal gyrus; Left superior medial gyrus; Left posterior middle cingulate cortex; 
Left inferior/middle temporal gyrus; Right posterior cerebellum. 

Precuneus Network: Right precuneus; Left precuneus; Right posterior/middle cingulate cortex; 
Left posterior/middle cingulate cortex. 

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Figure 3.8 (cont’d):  

Dorsal Attention/Visuospatial Network: Right superior parietal cortex/precuneus; Left 
superior parietal cortex/precuneus; Right superior frontal cortex; Left superior frontal cortex; 
Right inferior frontal gyrus; Left inferior frontal gyrus; Right middle temporal gyrus; Left middle 
temporal gyrus; Right inferior temporal gyrus. 

Ventral Default Mode Network (vDMN): Right precuneus; Left precuneus; Right frontal eye 
field; Left frontal eye field; Right middle occipital gyrus; Left middle occipital gyrus; Right 
retrosplenial cortex; Left retrosplenial cortex; Right parahippocampal place area; Left 
parahippocampal place area. 

High Visual Network: Right prestriate; Left prestriate. 

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Figure 3.8 (cont’d):  

Posterior Salience Network: Right supramarginal gyrus; Left supramarginal gyrus; Right 
precuneus/middle cingulate cortex; Left precuneus/middle cingulate cortex; Right thalamus; Left 
thalamus; Right insula; Left insula; Right superior cerebellum; Left superior cerebellum. 

Anterior Salience Network: Right superior frontal gyrus; Left superior frontal gyrus; Right 
middle frontal gyrus; Left middle frontal gyrus; Right dorsal anterior cingulate cortex; Left dorsal 
anterior cingulate cortex; Right superior frontal gyrus/pre-supplemental motor area; Left superior 
frontal gyrus/pre-supplemental motor area; Right insula; Left insula; Right superior cerebellum; 
Left superior cerebellum; Right inferior cerebellum; Left inferior cerebellum. 

106 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.8 (cont’d):  

Language Network: Right angular gyrus; Left angular gyrus; Right superior temporal gyrus; 
Left superior temporal gyrus; Right superior/middle temporal gyrus; Left superior/middle 
temporal gyrus; Right pars triangularis; Left pars triangularis; Right middle temporal gyrus; Left 
middle temporal gyrus.  

Dorsal Default Mode Network (dDMN): Right posterior cingulate cortex; Left posterior 
cingulate cortex; Right superior frontal gyrus; Left superior frontal/medial gyrus; Right angular 
gyrus; Left angular gyrus; Right superior medial gyrus; Right basal ganglia; Left basal ganglia; 
Right anterior cingulate cortex; Left anterior cingulate cortex; Right inferior frontal gyrus; Left 
inferior frontal gyrus; Right hippocampus; Left hippocampus; Right middle temporal gyrus; Left 
middle temporal gyrus; Right superior cerebellum; Left superior cerebellum; Right inferior 
cerebellum; Left inferior cerebellum.  

107 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.8 (cont’d):  

Right Executive Control Network (RECN): Right inferior parietal lobe/angular gyrus; Right 
middle/inferior frontal gyrus; Right superior medial gyrus; Right posterior middle cingulate 
cortex; Right pars opercularis/inferior frontal gyrus; Right middle frontal gyrus; Right 
inferior/middle temporal gyrus; Left posterior cerebellum. 

Auditory Network: Right superior temporal gyrus/operculum; Left superior temporal 
gyrus/operculum. 

108 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.8 (cont’d):  

Basal Ganglia Network: Right basal ganglia; Left basal ganglia.  

Limbic Network: Right temporal pole; Left temporal pole; Right pallidum; Left pallidum.  

Figure 3.9: Representative images and list of brain regions for each group resting-state network 
based on 420 hypertensive older adults with a high risk of dementia that participated in the rrAD 

109 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
trial. We performed Probabilistic Functional Modes (PROFUMO) on rrAD subject resting-state 
fMRI data through FSL’s newly integrated PROFUMO software. Here, we display resting-state 
networks from the 50-mode parcellation, which showed the most ideal network separation from 
those tested. 

Single Networks 

Precuneus Network: Right precuneus; Left precuneus; Right posterior/middle cingulate cortex; 
Left posterior/middle cingulate cortex. 

Figure 3.9 (cont’d):  

110 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Primary Visual Network (2.1): Right superior middle occipital gyrus (striate); Left superior 
middle occipital gyrus (striate cortex). 

Primary Visual Network (2.2): Right inferior occipital gyrus; Left inferior occipital gyrus. 

Primary Visual Network (2.3): Right superior occipital gyrus; Left superior occipital gyrus; 
Right middle occipital gyrus; Left middle occipital gyrus.  

Figure 3.9 (cont’d):  

111 

 
 
 
 
 
 
 
 
 
 
 
 
Left Executive Control Network (LECN): Left inferior parietal lobe/angular gyrus; Left 
medial/inferior frontal cortex; Left superior medial frontal cortex; Left inferior/medial temporal 
cortex; Left posterior middle cingulate cortex; Right inferior semilunar lobule of the cerebellum; 
Right posterior lobule of the cerebellum. 

Figure 3.9 (cont’d):  

112 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Sensorimotor Network (4.1): Right superior pre/postcentral gyrus (Sensorimotor cortex); Left 
superior pre/postcentral gyrus (Sensorimotor cortex). 

Sensorimotor Network (4.2): Right inferior precentral gyrus (Sensorimotor cortex); Left inferior 
precentral gyrus (Sensorimotor cortex). 

Figure 3.9 (cont’d):  

113 

 
 
 
 
 
Sensorimotor Network (4.3): Right middle precentral gyrus (Sensorimotor cortex); Left middle 
precentral gyrus (Sensorimotor cortex). 

Sensorimotor Network (4.4): Right anterior cerebellum; Left anterior cerebellum. 

Sensorimotor Network (4.5): Right superior pre/postcentral gyrus (Sensorimotor cortex); Left 
superior pre/postcentral gyrus (Sensorimotor cortex).  

High Visual Network (5.1): Right prestriate cortex; Left prestriate cortex. 

High Visual Network (5.2): Right inferior occipital lobe; Left inferior occipital lobe. 

High Visual Network (5.3): Right superior occipital lobe; Left superior occipital lobe. 

Figure 3.9 (cont’d): 

114 

 
 
 
 
 
 
 
 
 
Right Executive Control Network (RECN): Right inferior parietal lobe/angular gyrus; Right 
medial/inferior frontal cortex; Right superior medial frontal cortex; Right inferior/medial temporal 
cortex; Right posterior middle cingulate cortex; Left inferior posterior cerebellum; Left posterior 
cerebellum. 

Ventral Default Mode Network (7.1): Right precuneus; Left precuneus; Right frontal eye field; 
Left frontal eye field; Right middle occipital gyrus; Left middle occipital gyrus; Right retrosplenial 
cortex; Left retrosplenial cortex; Right parahippocampal place area; Left parahippocampal place 
area. 

Ventral Default Mode Network (7.2): Right precuneus; Left precuneus. 

Figure 3.9 (cont’d):  

115 

 
 
 
 
 
 
 
 
 
 
Dorsal Default Mode Network (8.1): Right posterior cingulate cortex; Left posterior cingulate 
cortex; Right angular gyrus; Left angular gyrus; Right superior frontal gyrus; Left superior frontal 
gyrus; Right medial prefrontal cortex; Left medial prefrontal cortex; Right middle temporal lobe; 
Left middle temporal lobe. 

Dorsal Default Mode Network (8.2): Right posterior cerebellum; Left posterior cerebellum. 

Dorsal Default Mode Network (8.3): Right prefrontal cortex; Left prefrontal cortex. 

Dorsal Default Mode Network (8.4): Right anterior cingulate cortex; Left anterior cingulate 
cortex; Right cingulate cortex; Left middle cingulate cortex. 

Figure 3.9 (cont’d):  

116 

 
 
 
 
 
Posterior Salience Network (9.1): Right supramarginal gyrus; Left supramarginal gyrus; Right 
precuneus/middle cingulate cortex; Left precuneus/middle cingulate cortex; Right superior 
middle frontal gyrus; Left middle frontal gyrus; Right inferior frontal gyrus; Left inferior frontal 
gyrus; Right inferior middle frontal gyrus; Left inferior middle frontal gyrus; Right middle temporal 
gyrus.  

Posterior Salience Network (9.2): Right superior parietal cortex; Left superior parietal cortex. 

117 

 
 
 
 
 
 
 
Figure 3.9 (cont’d):  

Visuospatial Network (10.1): Right superior parietal cortex/precuneus; Left superior parietal 
cortex/precuneus; Right superior frontal cortex; Left superior frontal cortex; Right inferior frontal 
gyrus; Left inferior frontal gyrus; Right inferior temporal gyrus; Left inferior temporal gyrus. 

118 

 
 
 
 
 
 
Figure 3.9 (cont’d):  

Visuospatial Network (10.2): Right inferior parietal cortex and superior parietal 
cortex/precuneus; Left inferior parietal cortex and superior parietal cortex/precuneus. 

Visuospatial Network (10.3): Right inferior parietal lobule; Right pars opercularis; Right middle 
frontal gyrus. 

Visuospatial Network (10.4): Right inferior frontal gyrus; Left inferior frontal gyrus. 

Visuospatial Network (10.5): Right intraparietal sulcus; Left intraparietal sulcus. 

Visuospatial Network (10.6): Right superior parietal lobule; Left superior parietal lobule. 

Anterior Salience Network (11.1): Right superior/middle frontal gyrus; Left middle frontal 
gyrus; Right dorsal anterior cingulate cortex; Left dorsal anterior cingulate cortex; Right insula; 
Left insula.  

Anterior Salience Network (11.2): Right middle frontal gyrus; Left middle frontal gyrus. 

Anterior Salience Network (11.3): Right secondary motor area (SMA); Left secondary motor 
area (SMA). 

119 

 
 
 
 
 
Figure 3.9 (cont’d):  

Limbic Network (12.1): Right orbitofrontal cortex; Left orbitofrontal cortex.  

Limbic Network (12.2): Right temporal pole; Left temporal pole.  

Basal Ganglia Network: Right basal ganglia; Left basal ganglia.  

120 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.9 (cont’d):  

Combinatory Networks 

Anterior Executive Control Network (aECN)/Dorsal Default Mode Network (dDMN): Right 
superior frontal sulcus; Left superior frontal sulcus; Right medial superior frontal gyrus; Left 
medial superior frontal gyrus. 

Language/Dorsal Default Mode Network (dDMN) (15.1): Right angular gyrus; Left angular 
gyrus; Right middle temporal gyrus; Left posterior cerebellum; Right pars triangularis; Right 
middle frontal gyrus; Right dorsomedial prefrontal cortex; Right precuneus. 

Language/Dorsal Default Mode Network (dDMN) (15.2): Right posterior cerebellum; Left 
angular gyrus; Left middle temporal gyrus; Left pars triangularis; Left middle frontal gyrus; Left 
dorsomedial prefrontal cortex; Left precuneus.  

Language Network/Dorsal Default Mode Network (dDMN) (15.3): Right pars triangularis; Left 
pars triangularis. 

121 

 
 
 
 
 
 
Figure 3.9 (cont’d):  

Anterior Salience Network (aSalience)/Dorsal Attention Network (DAN): Right superior 
precentral gyrus; Left superior precentral gyrus. 

Language Network/Temporoparietal (TempParietal) Network: Right middle temporal gyrus; 
Left middle temporal gyrus. 

122 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.9 (cont’d):  

Language Network/Auditory (Aud) Network/Temporoparietal (TempParietal) Network: 
Right posterior middle temporal gyrus; Left posterior middle temporal gyrus. 

Language Network/Auditory Network: Right superior temporal gyrus; Left superior temporal 
gyrus. 

TABLES 

123 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3.1: Listing of each RSN from the 49-Component GICA Parcellation including the number 
of ROIs that comprise each RSN, and the names of each of those ROIs.  

Table 3.1 (cont’d): 

124 

 
 
 
 
 
 
 
125 

 
 
 
 
 
 
 
 
 
 
Table 3.2: Listing of each RSN from the 50-Mode PROFUMO parcellation including the number 
of modes that comprise each network, the number of ROIs within each mode, and the names of 
each of those ROIs. 

126 

 
 
 
 
 
  
Table 3.2 (cont’d): 

127 

 
 
 
 
 
Table 3.2 (cont’d): 

128 

 
 
 
 
 
Table 3.2 (cont’d): 

129 

 
 
 
 
 
 
Table 3.2 (cont’d): 

130 

 
 
 
 
 
Table 3.2 (cont’d): 

131 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
CHAPTER 4: FUTURE DIRECTIONS AND GENERAL CONCLUSIONS 

4.1 – Application of the rrAD240 functional atlas 

The rrAD420 functional atlas defines the RSNs from baseline resting-state functional 

magnetic resonance imaging (rs-fMRI) scans of the rrAD cohort. Specifically, the rrAD trial 

seeks to investigate the effects of lowering blood pressure on cognition and brain functional 

connectivity in a cohort entirely comprised of hypertensive patients with an increased risk of 

developing Alzheimer’s Disease (AD). The treatment groups that will be tested include standard 

treatment, intensive reduction of vascular risk factors (ISVR), exercise training, and ISVR in 

combination with exercise training.  

The age-appropriate and cohort-specific RSNs presented in the rrAD420 functional atlas will 

be compared between rrAD treatment groups to gauge the effectiveness of each intervention in 

preserving brain functional connectivity. Correlation analyses of rs-fMRI time courses between 

nodes of each RSN will be performed using Analysis of Functional NeuroImages software 

(AFNI, NIH, USA) “3dfim+” (1). The mean correlation will be calculated for all node-pairs in each 

RSN. For group statistical analyses, correlation coefficients will be standardized through 

Fisher's Z-transformation. To compare RSN mean functional connectivity between treatment 

groups, we will calculate the average Z value for each RSN of interest for all subjects in each 

group. ANOVAs will be conducted on these Z value distributions across subjects, followed by 

Student’s t-tests of RSNs between treatment groups. We will also explore the differences 

between specific connections via paired Student’s t-tests within each RSN to investigate 

connections that could be driving overall changes in each RSN. Correlation coefficients will be 

obtained between each pair of nodes in each RSN to construct a correlation matrix for each 

RSN. We will then compare the connectivity of each RSN between both time points for each 

group to determine whether there was a chance in functional connectivity at the end of the study 

relative to baseline. The correlation matrix of each RSN will be then compared between groups 

132 

 
 
 
 
to locate the specific connections that show a group difference between treatment effects 

following Student’s t-tests. The significance level will be set at p ≤ 0.05 after Bonferroni 

correction in all statistical analyses. 

To compare the effects of standard and intensive blood pressure reduction on AD-

associated alterations in the default mode network (DMN), executive control network (ECN), 

and salience network as defined in the rrAD40 functional atlas. The DMN, ECN and salience 

network have been shown to be associated with each other and are collectively referred to as 

the triple network system (2). The DMN is responsible for inward thinking, while it the ECN is 

involved in external thinking involving an outward attention. There is evidence that suggests the 

salience network to be responsible for switching between these opposing states. Studies have 

shown this triple network system to be compromised along the AD continuum. Therefore, we 

can compare both mean within-network connectivity and between-network connectivity between 

the rrAD timepoints. If a difference is found, we can then compare between treatment groups to 

determine if there is an effect of the strategy used to reduce blood pressure. The triple network 

system has shown differences along the AD continuum, and this disrupted connectivity has 

exhibited predictive power in classifying AD progression (3–5). Here, we predict there will be no 

differences at baseline; however, we expect the ISVR group to prevent the disruption of RSNs 

relative to the standard treatment group after the two-year treatment.  

Our findings on RSN functional connectivity can also be correlated to cognitive performance 

as inferred by established tests that were administered to all rrAD participants. The 

neuropsychological testing used in the rrAD study has been validated in prior studies and is 

widely used to effectively capture age-related cognitive decline, namely the Alzheimer’s Disease 

Cooperative Study Preclinical Alzheimer’s Cognitive Composite (ADCS-PACC) and NIH-

Toolbox cognition battery (6,7).  

133 

 
 
 
 
Similarly, we will use the rrAD420 atlas determine the combined effects of physical exercise 

and hypertensive treatment on AD-associated disruptions in neural network connectivity and 

cognitive decline. We will investigate the effects of exercise training alone, and combined with 

intensive blood pressure reduction, on RSN functional connectivity and cognitive performance 

on the ADCS-PACC and NIH-Toolbox cognition battery. Once again, we will focus on the triple 

network system comprised of the DMN, ECN, and salience network as defined in the rrAD420 

functional atlas.  

Overall, we do not expect to see differences in RSN organization between groups at 

baseline. However, following treatment we expect the ISVR group to offer more benefit than 

standard treatment in protecting against cognitive decline and altered functional connectivity 

profiles of the DMN, ECN, and salience network. We further anticipate that exercise combined 

with ISVR confers more benefit on these measures than either exercise or ISVR alone. If 

successful, the rrAD trial would underscore the importance of an integrated blood pressure 

reduction approach in maintaining brain health and provide an intervention to control modifiable 

risk factors associated with cognitive decline and AD in hypertensive individuals with increased 

risk of AD development. While the rrAD420 functional atlas will clearly be instrumental in 

accomplishing the specific primary aims of the rrAD trial described here, we also believe that the 

age-related nuisances captured by our atlas could be extended to older adults in general.  

We could validate the rrAD420 functional atlas with a direct comparison to legacy atlases, 

such as the Shirer functional atlas, using the rrAD dataset (8). This could allow us to determine 

if the rrAD420 functional atlas can capture functional connectivity differences between rrAD 

treatment groups potentially missed by existing alternatives. We could further test the predictive 

value of the rrAD420 atlas by comparing its performance to an existing functional atlas using 

data from an open-access database like the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 

http://adni.loni.usc.edu; (9). The ADNI study includes scans from subjects at each stage of the 

134 

 
 
 
 
AD continuum (healthy controls, subjective cognitive impairment, mild cognitive impairment, and 

AD). They also maintain longitudinal documentation on some groups within the dataset, which 

allows for the tracking of some characteristics of disease progression to better predict cases 

that will develop into a full AD diagnosis. From this dataset, RSNs like the DMN have been 

shown to have a gradual degradation pattern along the continuum (10,11). Therefore, we could 

test how this progression is captured by the rrAD420 functional atlas and compare our results to 

existing functional atlas options used in already published work (12). 

4.2 – Translational Potential of RSNs in a novel rat model of Alzheimer’s Disease with 

hypertension  

The triple network system defined above is not unique to humans. In fact, it has been found 

in both rodents and nonhuman primates (13). This system has already shown a similar pattern 

of disruption in AD rats compared to controls (14,15). Through collaborative efforts with the labs 

of Dr. Anne Dorrance, Dr. Scott Counts, and Dr. Chunqi Qian, we have rs-fMRI data from a 

novel transgenic Frankenrat model designed to explore the combined effects of hypertension 

and AD (16). The Frankenrat model was created by crossbreeding TgF344-AD rats onto the 

SHRSP background. TgF344-AD rats offer a model that captures hallmarks of AD pathology 

including amyloid-beta plaque accumulation, neurofibrillary tangles, frank neuronal cell loss, 

cognitive impairment, and the expression of human amyloid precursor protein (APPswe) and 

presenilin 1 (PS1DE9) genes (17,18). Additionally, SHRSP rats provide an excellent model for 

hypertension that develops by 4 months of age (19). The cross between TgF344-AD and 

SHRSP rats yields a novel hypertensive model that also expresses familial AD risk genes, 

APPswe and PS1DE9.  In addition, we will have comparable scans from age-matched control 

groups of normotensive AD, hypertensive rats with no AD, and normotensive rats with no AD 

available for comparison. Using the rs-fMRI scans from these animals we will determine 

135 

 
 
 
 
whether AD-associated disruptions of the triple network system are exacerbated by 

hypertension, and if hypertension alone can impact RSN functional connectivity.  

In our preliminary analysis, we used a total of 9 Transgene-positive or Transgene-negative 

Frankentats rats (4 male, 5 females). Animals were first anesthetized with 5% isoflurane in a 

chamber and baby cream (Meijer) was injected into the ear to recover the signal loss and to 

avoid noise contamination as described in our recently published article (16). The animals then 

received an initial subcutaneous injection of dexmedetomidine (0.1 mg/kg). Following the 

injection, isoflurane was discontinued, and the animal was transferred to the scanner. While in 

the scanner, dexmedetomidine was delivered subcutaneously at a constant rate to maintain the 

anesthesia (0.1 mg/kg/h). The animal’s body temperature, arterial oxygen saturation level, and 

respiration rates were monitored and maintained within normal ranges when the animal was 

inside the scanner. Spontaneous respiration rate ranged from 50 to 70 breaths per min during 

rs-fMRI image acquisition. All images were acquired with a 7 T/16 cm aperture bore small-

animal scanner (Bruker BioSpin). Functional images were acquired with a 3D gradient-echo EPI 

(GE-EPI) sequence with the following parameters: time of echo (TE) = 20 ms, time of repetition 

(TR) = 1 s, field of view (FOV) = 2.6 cm × 2.6 cm × 1.6 cm, matrix size = 52 × 52 × 32, voxel 

size = 0.5 mm × 0.5 mm × 0.5 mm. Each rs-fMRI scan acquired 900 time points over 15 mins. 

All signal processing and analyses were implemented in MATLAB software (Mathworks, Natick, 

MA), FMRIB Software Library (FSL) and AFNI. The preprocessing procedures followed the 

commonly used AFNI protocol for rat rs-fMRI data, including motion correction, despike, spatial 

blurring with a kernel of 4-mm full width at half maximum, and bandpass filtering from 0.001-0.4 

Hz (20,21). Then, we conducted ICA analysis with 60 components using MELODIC in FSL to 

identify and remove non-neural artefacts. We manually identified each component as real signal 

or noise based on their spatial, temporal, and spectral features. After denoising the components 

defined as noise, fMRI data from each rat were aligned to the averaged anatomical RARE 

136 

 
 
 
 
template. We then performed a see-based correlation analysis using the posterior cingulate 

cortex set as the seed region to create a rat DMN spatial map (Figure 4.1). Specifically, the rat 

DMN is comprised of the entorhinal cortex, prelimbic cingulate, rostral dorsal prelimbic cortex, 

retrosplenial granular and dysgranular cortex, globus pallidus, hypothalamus, and hippocampus 

(21,22). Next, we will identify the remaining two RSNs of the triple network system, the ECN and 

salience network, in order to assess functional connectivity differences between groups. 

However, DMN itself has shown decreased connectivity in healthy aging across species, 

including rodents (21–23). Furthermore, studies have found disruptions in TgF344-AD rat DMN 

functional connectivity relative to age-matched controls. Our initial DMN spatial map can already 

enable us to elaborate on whether hypertension exacerbates this deteriorative effect as the 

mean DMN functional connectivity can be compared between Frankenrats, and each control 

group. 

Once our rs-fMRI analysis is complete, our results can be combined with behavioral 

assessments to investigate the effect of AD and hypertension interplay on functional 

connectivity of the triple network system and cognition. Specifically, age-matched SHRSPs and 

Frankenrats underwent behavioral testing including the open-field task, spontaneous alternation 

test in the Y-maze, and the novel object task prior to scanning in order to compare 

hippocampus-related memory function. To test short-term spatial reference memory function, 

we performed the spontaneous alternation test in the Y-maze. Rats were allowed to run the 

maze for a 5-minute period. We recorded sessions and implemented Ethovision XT 11.5 

software from Nodulus to detect the sequence in which they visit each arm of the maze (Noldus, 

Nijmegen Area, The Netherlands). We then used Noldulus to quantify the number of correct 

triads and divide that number by the total entries to ascertain the percentage of spontaneous 

alternation. Rats have an innate desire to explore novel environments. As such, they tend to 

visit each arm of the maze in an alternating fashion by entering the arm which they have least 

137 

 
 
 
 
recently visited. However, if the rat does not remember the arms, they have already entered, 

then it will make more errors in its alternating behavior. We also tested hippocampus-

dependent, nonspatial memory using the novel object task. Rats were placed in the arena and 

allowed to explore two identical “familiar” objects for 10 minutes. After initial exposure, rats were 

removed from the arena and placed into separate holding cages. After 90 minutes, one of the 

familiar objects was replaced with a novel object. The rat was placed back into the arena and, 

again, allowed to explore the arena for 5 minutes. We recorded sessions and implemented 

Ethovision XT 11.5 software to track the amount of time spent exploring each object. Videos 

were checked for quality to ensure tracking software was correct, and errors were manually 

corrected in the program’s track editor. Cumulative duration was calculated by dividing the time 

spent with one object by the combined time spent exploring both objects. Cognitively normal 

rats are more interested in the novel object and spend more time exploring it than the familiar 

object. However, cognitively impaired rats are expected to spend an equal amount of time 

exploring both objects, as they may not remember the familiar object. We also employed the 

open-field task to ensure that our findings in the Y-maze and novel object task are not 

influenced by differences in locomotor activity. Rats were placed into a Plexiglas box equipped 

with motion sensors and allowed to freely move around for 30 minutes while being recorded by 

Fusion Open Field Activity Monitoring System (Omnitech Electronics, Inc. Columbus Ohio, 

USA). After completion, time spent in each zone, total distance traveled, and average velocity 

will be calculated by the software. Student’s t-tests will be used to compare behavioral findings 

between SHRSP and Frankenrat groups. We anticipate no difference in locomotive behavior 

assessed by the open field task between groups. However, we do expect Frankenrats to display 

worse hippocampus-related spatial and nonspatial memory function as inferred by spontaneous 

alternating behavior and novel object task. 

138 

 
 
 
 
In addition, preclinical animal studies allow for the investigation of important factors that 

cannot be directly measured in humans. For instance, binding of BDNF to tropomyosin receptor 

kinase B (TrkB) mediates trophic coupling between neurons and cerebral endothelial cells and 

yields a neuroprotective effect (24). Studies have shown BDNF to be compromised in cases of 

both hypertension and AD (25,26). Similarly, a study by Raz and colleagues investigated the 

effects of both (1) hypertension and (2) genetic variants in several single nucleotide 

polymorphisms on age-related 7 cognitive performance (27). They found that processing speed 

and episodic (association) memory performance were negatively affected by the BDNF 

Val66Met polymorphism, and this effect was exacerbated by hypertension. This finding was 

consistent with extant literature that shows BDNF Methionine allele carriers Val66Met to be 

associated with a significant decrease in BDNF expression relative to Valine allele homozygotes 

(28). However, further investigation requires preclinical research that supplements findings of 

this nature in humans to uncover the location where BDNF expression is mostly reduced. While 

previous studies have noted hypertension-related differences in total BDNF, it is possible that 

decreased BDNF may be more specific to certain BDNF variants (29). As such, there may be a 

subsequent compensatory increase in other BDNF RNA isoforms which we would not detect in 

the quantification of total BDNF (30). Therefore, it is possible to use primers specific to exon-1 

and exon-4 containing BDNF as an alternative to investigate region specific changes in BDNF 

expression between Frankenrats and SHRSPs, as these isoforms are expressed primarily in the 

hippocampus and cortex respectively. Indeed, animal studies have shown BDNF to be down 

regulated in hypertensive rats, specifically in the cortex and hippocampus (31), which can be 

rescued through antihypertensive pharmacological intervention (32,33). Another recent study 

indicates the BDNF system as a potential therapeutic target to rescue hippocampus-related 

cognitive deficiencies in an AD mouse model using a TrkB agonist, AS86 (34). This study shows 

promise particularly because previous treatments have not been able to rescue AD-associated 

cognitive decline (35). Together, these findings implicate BDNF in the relationship between 

139 

 
 
 
 
hypertension and AD-associated cognitive decline and brain changes. However, more evidence 

is needed on a model that captures both hypertension and AD pathology to validate this claim. 

After each scan, the animals were sacrificed, and the brains were extracted. They were 

subsequently micro-dissected and the bilateral samples of hippocampus, caudate, and cortex 

were collected. We intend to quantify the amount of BDNF expression in these regions to 

determine if reduced hippocampal and cortical BDNF expression is related to disruptions in 

triple network system functional connectivity.  

4.3 – General Conclusions 

In conclusion, this dissertation has examined methodologies of rs-fMRI in a variety of ways. 

Firstly, we show RSN behavior differentially changes throughout rest conditions. To do so, we 

completed a comprehensive examination of seventeen cortical RSNs presented in an existing 

functional atlas through four rest conditions with varying levels of cognitive load (36). We 

showed that each RSN responded differentially to changes in rest condition, with some 

networks being more resilient than others even with the relatively high stimulation of the movie 

condition. However, for the most part we showed that more external stimulation led to more 

overall changes in resting-state functional connectivity. We also show some RSNs are more 

susceptible to deviations in functional connectivity as subtle changes in rest condition are 

introduced. For example, DMN showed a difference in functional connectivity between eyes-

open and eyes-closed conditions. Furthermore, we showed that internal cognitive processing 

was not observed to affect functional connectivity at any level for any RSN.  

Next, we establish a functional atlas for older adults that will immediately benefit the recently 

concluded rrAD clinical trial, as well as future studies that are interested in network modeling 

approaches that assess RSN functional connectivity within a similar late-adulthood 

demographic. Our rrAD420 functional atlas provides multiple options for the study of RSN 

functional connectivity in older adults. Specifically, we include a high-resolution seed-based 

140 

 
 
 
 
spatial map of DMN, a complete parcellation of maximally independent RSNs derived through 

traditional group independent component analysis, and a complete parcellation of RSNs that 

allows for overlap between each RSN as derived from a probabilistic functional mode 

decomposition. 

Finally, we assist in the validation of a novel hypertensive AD animal model by providing a 

RSN spatial map for the model derived from a seed-based correlation approach using the 

posterior cingulate cortex as the seed region. This spatial map contains regions of the 

previously reported DMN, which were captured using novel ICE ear bars. This novel preparation 

reduced susceptibility weighted artifacts and increased signal in relatively deeper regions of the 

brain, such as the entorhinal cortex. The enhanced signal allows for a reliable DMN of this 

model that can be compared healthy controls and other types of assays that are difficult to 

obtain in human studies, such as region-specific BDNF expression. With this well-matched 

translational model that is comparable to participants of the rrAD studies, we can further expand 

on the underlying mechanisms that contribute to observed differences in RSN connectivity 

following a reduction in systolic blood pressure.  

141 

 
 
 
 
 
 
 
 
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FIGURES 

APPENDIX 

Figure 4.1: Default-mode network (DMN) shown in color as constructed from PCC seed-based 
analysis of rs-fMRI data (n =5) overlaid on RARE structural images (adapted from Chen, 2023).  

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