Neuromodulation of Cognition in Older Adults: The Stimulated Brain Study
Phase: NA
Conditions: Healthy
Sponsor: University of Florida

Protocol IRB #201400328 
Page 1 of 53 
IRB version 03.09.04 
IRB version 03.09.04 
PI version 5/13/2019 
 
  
Official Title 
Neuromodulation of Cognition in Older Adults 
 
NCT number 
NCT02137122 
 
Document Date 
5/13/2019 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Protocol IRB #201400328 
Page 2 of 53 
IRB version 03.09.04 
IRB version 03.09.04 
PI version 5/13/2019 
 
 
 
Protocol # 201400328 
1. Project Title 
Neuromodulation of Cognition in Older Adults: The Stimulated Brain Study 
2. Investigator(s): 
Adam J. Woods, PhD 
Ron Cohen, PhD 
Michael Marsiske, PhD 
Samuel Wu, PhD 
Steve Anton, PhD 
Eric Porges, PhD 
Marco Pahor, MD 
Song Lai, PhD 
 
Andrew O’Shea M.Sc.: Study Coordinator 
Nicole Nissim, M.S. 
Keyanni Joy Johnson 
Roxanne Rezaei, B.A. 
Jessica Kraft, M.A. 
Aprinda Indahlastari, Ph.D 
Cindy Hernandez, B.S. 
Alexa Chen, B.A. 
Kathleen Frost, B.S. 
Kelsey Traeger, B.S. 
Kristin Calfee, B.A. 
Alexander Albizu, B.S. 
Steven DeKosky, M.D. 
Emanuel  Boutzoukas, B.S. 
Daniela Carballo 
Nicole Evangelista 
Hanna Hausman, B.A. 
Sam Vrinios 
Margaret Meldrum 
Sean Perryman, B.A. 
Vanessa Dominguez, B.S. 
Matt Fillingim 
3. Abstract: 
The current study will investigate methods for enhancing cognitive training (CT) effects 
in healthy older adults by employing a combination of interventions facilitating neural 

Protocol IRB #201400328 
Page 3 of 53 
IRB version 03.09.04 
IRB version 03.09.04 
PI version 5/13/2019 
 
plasticity and optimizing readiness for learning. Adults over the age of 65 represent the 
fastest growing group in the US population. As such, age-related cognitive decline 
represents a major concern for public health. Recent research suggests that cognitive 
training in older adults can improve cognitive performance, with effects lasting up to 10 
years. However, these effects are typically limited to the tasks trained, with little transfer 
to other cognitive abilities or everyday skills. A pilot randomized clinical trial will examine 
the individual and combined impact of pairing cognitive training with transcranial direct 
current stimulation (tDCS). tDCS is a method of non-invasive brain stimulation that 
directly stimulates brain regions involved in active cognitive function and enhances 
neural plasticity when paired with a training task. We will compare changes in cognitive 
and brain function resulting from CT combined with active tDCS versus CT combined 
with sham tDCS using a comprehensive neurocognitive, clinical, and multimodal 
neuroimaging assessment of brain structure, function, and metabolic state. Functional 
magnetic resonance imaging (FMRI) will be used to assess brain response during 
working memory, attention, and memory encoding; the active cognitive abilities trained 
by CT. Proton magnetic resonance spectroscopy (MRS) will assess cerebral 
metabolites, including GABA concentrations sensitive to neural plasticity.  
We hypothesize that:  
1) tDCS will enhance neurocognitive function, brain function, and functional 
outcomes from CT, with combined CT and tDCS providing the most benefit;  
2) Effects of tDCS on CT will be maintained up to 3 months following training  
3) Neuroimaging biomarkers of cerebral metabolism, neural plasticity (GABA 
concentrations) and functional brain response (FMRI) during resting vs. active 
cognitive tasks will predict individual response to tDCS.  
To date, no studies have examined combined intervention strategies using CT or 
optimization of learning and functional status through facilitation of active versus resting 
brain states in the elderly. The present study will provide a unique window into critical 
mechanisms for combating cognitive decline in a rapidly aging US population and novel 
methods for counteracting this looming public health crisis. 
4.  Background: 
4.1. Public Health / Clinical Significance: 1) Increased life expectancy has resulted in 
a marked increase of the older population. 2) Cognitive changes occur with advanced 
age that affect functional and health status. 3) While Alzheimer’s and related 
neurodegenerative diseases cause the most dramatic cognitive disturbances in the 
elderly, cognitive aging occurs even among people considered to be neurologically 
healthy. 4) Even mild neurocognitive disturbances affect people’s daily functioning, 
health status, and quality of life. 5) Alterations of brain structure and function occur as 
people reach advanced age, along with cerebral metabolic changes, that are associated 
with neurocognitive decline. 6) Our preliminary data suggests that baseline cerebral 
metabolite (MRS) and functional neuroimaging (FMRI) indices are associated with 
baseline neurocognitive functioning and predictive of subsequent age-related cognitive 
decline and brain disturbances. 7) There is a paucity of preventive and treatment 

Protocol IRB #201400328 
Page 4 of 53 
IRB version 03.09.04 
IRB version 03.09.04 
PI version 5/13/2019 
 
interventions for averting cognitive aging and enhancing cognitive function. 8) Certain 
cognitive training (CT) approaches improve specific areas of cognitive performance, 
although their relative efficacy and mechanisms of action are not well understood. 9) 
Most CT approaches do not generalize well to cognitive abilities beyond those being 
trained or to everyday functional abilities. Efforts are needed to improve the 
generalizability of CT. 10) Methods exist which could potentiate CT (e.g., tDCS), but 
they have not been rigorously tested in RCTs.  
4.2. Scientific significance. 1) While there is evidence that CT can improve cognitive 
functioning, the underlying mechanisms are not well understood. 2) The efficacy of CT 
likely is dependent on the plasticity of neural systems. 3) Evidence that certain types of 
neurochemical, electrical and behavioral stimulation potentiates synaptic plasticity and 
enhances learning has been demonstrated in laboratory animals. It is important to be 
able to measure these changes in humans during the course of learning such as that 
occurring with CT. Yet, in vivo human studies of these effects are difficult for obvious 
reasons. 4) Functional (FMRI) and cerebral metabolic (MRS) neuroimaging indirectly 
assess changes in neural plasticity during cognitive tasks. Recent developments in 
MRS enable Gama Amino butyric Acid (GABA), the primary inhibitory neurotransmitter 
and an essential neurotransmitter for synaptic communication and associative 
formation, to be measured from brain regions of interest (ROIs) 1 2. GABA 
concentrations predict attentional control 1 and sensory discrimination 3,4, decrease with 
age5 and yet demonstrate long-term increase with learning interventions.2,6. Functional 
connectivity measured by FMRI provides another potentially powerful approach for 
measuring these changes. Yet, these neuroimaging approaches have been employed 
to only a very limited extent in studies of the mechanisms of CT. Studies employing 
neuroimaging to assess CT outcome are needed. 5) Many open questions exist 
regarding the brain’s structural and functional connectivity in relationship to regional 
cerebral metabolites. Achieving better understanding of these relationships is important, 
since cerebral metabolic alterations may contribute neuropathology and perhaps even 
normal cognitive aging. 6) Extensive research exists for each of these neuroimaging 
modalities in isolation for various diseases, but multimodal studies employing these 
approaches simultaneously are less common, particularly in studies of normal aging or 
CT. 7) Various CT approaches exist, but only a few have been tested and shown to be 
effective in larger scale clinical trials (e.g., UFOV, dual N-back training). RCTs are 
needed to test the relative efficacy of these CT approaches, and whether there is value 
in using them in combination. 8) Brain stimulation may potentiate neural plasticity based 
on animal studies. Most of these approaches have yet to be tested in conjunction with 
CT in humans. 9) It is unclear whether optimal CT benefit is achieved by bolstering 
activation of brain regions necessary for the tasks to be performed. 10) Individual 
differences exist in the ability of people to benefit from CT. These differences are not 
well understood. We will examine neuroimaging and behavioral factors that predict CT 
outcome and that may account for these individual differences. 11) State-of-the-art 
neuroimaging analysis methods may yield insights into interactions among brain 
networks/systems, and ways to optimally integrate structural and functional connectivity 
with cerebral MRS and cognitive outcomes. 12) We will employ state-of-the-art 

Protocol IRB #201400328 
Page 5 of 53 
IRB version 03.09.04 
IRB version 03.09.04 
PI version 5/13/2019 
 
statistical methods, extending predictive modeling and causal inference approaches for 
neuroimaging.  
4.3. Clinical and scientific background.  
4.3.1. Cognitive aging and dysfunction affects health status, Quality of Life (QOL), 
and functional capacity. Brain dysfunction resulting from neurodegenerative disease 
or other medical condition adversely affects overall health status.7-11 Even mild cognitive 
deficits affect QOL, diet, physical activity and other health behaviors,10,12,13 and are 
often stronger predictors of health outcomes than other physical factors,7 but typically 
receive less clinical attention. Accordingly, cognitive aging has considerable functional 
relevance.  
4.3.2. Cognitive training. Various CT approaches enhance cognitive functioning in the 
elderly and remediate cognitive disorders. While improvements in cognitive 
performance are reported in many studies, this research suffers from a lack of well-
conducted RCTs designed to determine the specific factors contributing to cognitive 
improvements. However, several approaches are effective in improving cognitive 
performance in the context of large RCTs. The ACTIVE study showed that CT improved 
cognitive performance and resulted in some generalization to other functional abilities.  
4.3.3. Benefits of cognitive training. Various CT approaches exist. While improved 
cognitive performance is often reported, this research has suffered from a lack of well-
controlled RCTs, experimental designs that did not enable the basis for effects to be 
determined, and limited transfer of training.14-18 Yet, findings over the past decade (e.g., 
ACTIVE) suggest that certain CT approaches are effective for enhancing cognitive 
aging.17,19-36 Significant cognitive and functional improvements occur in laboratory and 
home-based CT studies.19,23,31,37-41 Effect sizes generally exceed d=1.0 immediately 
after CT, and even after 10 years (η2 >0.6.). In ACTIVE,22,24,32,36,42 people receiving CT 
outperformed those who were untrained, with normal cognitive aging attenuated. We 
considered and selected CT approaches based on consensus of our study team 
(Woods, Marsiske, Edwards, Czaja, et al.), and evidence supporting their effectiveness. 
Three types of CT training have been particularly effective in studies by our group and 
others: 1) UFOV; 2) N-back Working Memory; 3) Attention-arousal training, all available 
in the PositScience BrainHQ suite (See Miscellaneous Attachments for detailed 
descriptions/samples of each game.) 
Attention/Speed of Processing 
1. Hawk Eye- works on visual precision, which helps the brain perceive what you 
see quickly and accurately so that you can recall it better. 
2. Divided Attention- requires the brain to focus in on and react to particular 
details—matching colors, shapes, and/or fill patterns—while at the same time 
dismissing competing information. 
3. Target Tracker- is designed to help build divided attention by requiring you to 
track several items moving around your screen at the same time 
4. Double Decision - requires visual search and selective attention to peripheral 
objects among distractors.20 Difficulty gradually increases relative to object 
similarity, presentation rate, and distractor complexity and eccentricity. 

Protocol IRB #201400328 
Page 6 of 53 
IRB version 03.09.04 
IRB version 03.09.04 
PI version 5/13/2019 
 
 
 
Working Memory 
1. To Do List Training- the brain hears a set of instructions, then uses its memory of 
those instructions to follow them in order. The instructions get longer and more 
complex over time at the task, making greater demands of your working memory 
systems. 
2. Memory Grid - Auditory processing is one of the most important building blocks 
of memory. Only when you take in information with crystal clarity can the brain 
store it accurately and recall it clearly later. In Memory Grid, the task is to match 
cards representing syllables together. 
3. Auditory Aces- Participant will be presented with auditory information about 
playing cards. The information is presented one card at a time. The task is to 
decide if the current card information matches the card information presented a 
specific number of steps back in the sequence. 
4. Card Shark- N-back working memory task that varies on whether the current 
target matches stimuli presented 0-n steps before and presentation speed, 
leading to increased difficulty,44,45 and age-sensitivity46.  
     
4.3.4. Generalization and functional outcome. Training transfer has been most 
studied and shown on UFOV. In ACTIVE, ten-year maintenance of UFOV training 
effects occurred with evidence of substantial transfer at 5 and 10 years. UFOV training 
resulted in fewer self-reported limitations of everyday activities,24 higher locus of control 
30 and perceived health-related quality of life, 68 better subjective health,29 and less 
depression.27 68 At ten years, UFOV-trained people still reported less limitation in daily 
activities36. Self-reported driving cessation and archival accident records indicated lower 
odds of crashes and driving cessation for UFOV-trained elders at three,25 five,28 and 
ten-years post training.35 In other RCTs involving greater sustained adaptive CT 
dosages (similar to the currently proposed study) superior performance and reaction 
times were found on a driving-simulator and also for instrumental activities of daily living 
(look up phone numbers, read pill bottles, etc.).23 For the other two intervention 
components, near transfer to other cognitive tasks has been shown. N-back training 
transfers to matrix reasoning44 and to sustained attention and self-reported cognitive 
function in older adults for at least three months post training.48 Tonic/phasic attention 
training transfers to spatial selective attention and the temporal distribution of attention 
(attentional blink).66 
4.3.5. Brain stimulation to potentiate training. Since the pioneering work of Penfield, 
it has been recognized that sensory, motor and cognitive functions could be altered via 
electrical stimulation of specific brain regions. In laboratory animals, brain stimulation 
represented an alternative approach to experimental lesions, enabling both the 
potentiation and inhibition of neural activity depending on where in the brain stimulation 
was applied. Until recently, most human brain stimulation studies involved 
neurosurgically implanted electrodes, which has obvious limitations for general clinical 
use. Transcranial direct current stimulation (tDCS) is a non-invasive brain 

Protocol IRB #201400328 
Page 7 of 53 
IRB version 03.09.04 
IRB version 03.09.04 
PI version 5/13/2019 
 
stimulation method that alters the sub threshold membrane potential of neurons, 
facilitates neural plasticity and learning, and increases regional blood flow while 
modulating local GABA concentrations during stimulation.69-89 During tDCS, a weak 
electrical current is applied to the scalp that penetrates skin, bone, CSF and the 
meninges to stimulate underlying cortical and subcortical tissue.90-95 tDCS applied to 
dysfunctional cortical regions improves performance on a variety of cognitive tasks.96-99 
Bilateral tDCS to the frontal cortices improves decision-making, attention and working 
memory performance in older adults.100-103 Improvements from a single session of tDCS 
have been shown to last for up to five years in healthy adults.104-108 Small pilot RCTs 
(n=20/group) pairing CT with bilateral frontal tDCS show significant and lasting 
improvement in older adults experiencing declining cognitive function.108-112 
Maintenance of these tDCS and CT effects have been shown to last beyond one year. 
104,105,107,108,110 These studies demonstrate that CT combined with tDCS leads to lasting 
improvement in CT effectiveness for older adults and patients. Research suggests that 
increased regional blood flow and decreased GABA concentrations during tDCS 
facilitate the brain’s neural plastic response to paired training tasks.75,80,83,84,89,113-117 
Pairing CT with tDCS to combat age-related cognitive decline holds great promise for 
older adults.  
4.3.6. Age-associated brain changes. It is well known that with advanced age, 
humans are vulnerable to neurodegenerative diseases that cause brain pathology, 
usually evident on post-mortem autopsy.118-127 Though less pervasive, neuropathology 
is also relatively common in elderly adults without documented brain disease.128  
4.3.6.1. Age-associated brain change on structural neuroimaging. Changes in 
structural brain volume and morphometry on MRI, along with specific abnormalities, 
occur with advanced age, particularly when there is vascular co-morbidity.129-160  Raz et 
al. showed cortical and subcortical volume loss of .5 -4% per year across different 
cortical and subcortical regions in older adults without overt brain disease.129,133,141,143,161 
We have shown cortical and white matter volume loss across the lifespan in past large 
international studies.146,147,162-167 
4.3.6.3. Functional neuroimaging provides a potentially powerful method for 
assessing healthy and abnormal brain functioning (see Cohen and Sweet, for a 
review168). FMRI is noninvasive, can be used in conjunction with structural MRI and 
MRS, and is sensitive to functional brain abnormalities.153,157,169-179 It holds promise as a 
biomarker of cognitive aging, neural plasticity, and cognitive improvements following 
CT. Age-associated alterations in brain activation on FMRI during both rest state and 
active cognitive tasks have been demonstrated in many past studies. Unfortunately, the 
clinical potential of FMRI has yet to be fully realized, in part because many different 
paradigms have been employed across studies. Furthermore, longitudinal change in 
BOLD response as a function of aging has been examined in relatively few studies, and 
almost no large RCTs. Reduced cognitive reserve with aging has been linked to a 
number of FMRI effects, including HAROLD and PASA.180-183 Chang et al. showed that 
cognitive reserve influences FMRI activation, with a reduced “dynamic range” of BOLD 
response during tasks relative to rest explaining this effect.184-190 The concept of 
dynamic range is both important and useful, and will be discussed in greater detail when 
reviewing preliminary data (C4). ERP measures the speed at which the brain processes 

Protocol IRB #201400328 
Page 8 of 53 
IRB version 03.09.04 
IRB version 03.09.04 
PI version 5/13/2019 
 
cognitive information. These measures will provide markers of improvement in the 
temporal processing of information, a key element altered with age. 
4.3.6.4. Cerebral metabolites (MRS): Proton MRS, which is sensitive to chemical 
compounds containing hydrogen, useful for measuring brain metabolites, including N-
Acetyl Aspartate (NAA), choline (Cho), myo-inositol (MI), creatine (Cr), and glutamate-
glutamine complex (Glx). Our group and others have shown that MRS abnormalities 
occur among people with a variety of age-related brain disorders, including 
neurodegenerative disease, cerebrovascular disease, and HIV191-206, with reduced NAA 
and elevated MI associated with cognitive dysfunction and conversion to dementia. 
Elevated Cho and MI reflect inflammatory processes and glial and cell membrane 
disturbances, and are differentially associated with cognitive performance, clinical 
status, and also cortical, subcortical, and white matter volumes on MRI204-211. Thus, 
MRS is predictive of clinically significant neurocognitive dysfunction.206,208-210 
GABA, the brain’s principle inhibitory neurotransmitter,212 is essential for synaptic 
communication and regulation of neuronal excitability,213 and neural plasticity.214-218 It 
plays a key role in learning and memory219-235 and modulates other behavioral and 
affective functions, including executive control and attention.236-238 Decreased cerebral 
GABA occurs with advanced age,219,223,227,230 and GABA dysregulation occurs in 
neurological and psychiatric conditions.49,239-267 GABA delivered to the frontal cortex and 
hippocampus in animals facilitates cognitive and working memory performance. GABA 
can now be reliability measured using proton MRS,5,268-273 based on seminal work by 
Edden (consultant).274-278 GABA provides an in vivo biomarker of neural plasticity in 
brain ROIs important for the cognitive functions to be trained in our study. 276 84 
A.3.8. Summary and Conceptual 
Model. Age-associated functional, 
structural and metabolic brain 
changes occur, even in the absence of 
frank neurodegenerative disease. CT 
holds promise for reducing the 
adverse effects of cognitive aging, 
enhancing neural plasticity, cognitive 
efficiency, functional capacity, and 
quality of life. In theory, CT benefits 
could be augmented by coupling it 
with other interventions that either 
increase neural plasticity. Yet, relatively few of these approaches have been tested in 
RCTs, and the mechanisms underlying their effects are largely unknown. Even less is 
known about the combined effects of CT with tDCS. Our preliminary data provides 
strong support for CT to combat cognitive aging, and also for the effects of tDCS on 
cognition and brain function. We hypothesize that CT leads to improvements in neural 
plasticity (GABA MRS) and functional brain response (FMRI). In turn, this can lead to 
improved cerebral metabolic health and structural brain preservation. Coupling CT with 
tDCS will increase neural plasticity in brain areas important for working memory, 
focused attention, and executive control, improve effectiveness of CT, and ultimately 
cognitive health (see Figure 1 for conceptual model).  
Figure 1 

Protocol IRB #201400328 
Page 9 of 53 
IRB version 03.09.04 
IRB version 03.09.04 
PI version 5/13/2019 
 
5. Specific Aims: 
Age-related cognitive decline has become a major public health concern. As the 
population ages, the number of older adults experiencing cognitive and functional 
disturbances has increased. There is currently a paucity of effective interventions to 
prevent or treat cognitive decline or to enhance brain function in the elderly. The 
proposed study will recruit a cohort from the state with the highest growth of older 
adults. We will employ an adaptive randomized clinical trial (RCT) to test whether the 
benefits of cognitive training (CT) can be enhanced by a combined adjunctive 
intervention aimed at increasing neural plasticity and optimizing readiness for learning: 
transcranial direct current stimulation (tDCS).  
Current CT approaches have been shown to improve performance on trained tasks, 
with effects lasting up to ten years32,33,36,279,280. However, generalization of effects to 
other cognitive domains and everyday functioning has been a problem in the past. 
There is now compelling evidence that the elderly do experience functional 
improvements that persist long after initial training, though peoples’ ability to derive such 
benefits varies. Combined interventions have rarely been examined and methods aimed 
at increasing neural plasticity and optimizing readiness for learning are only now 
beginning to be explored. The proposed study will test one theoretical approach for 
facilitating and optimizing CT effects on functional outcome. We will use an adjunctive 
physiological method to further stimulate task-related brain regions, directly enhancing 
neural responsivity/plasticity and ultimately learning. While theoretical rationales exist 
for each of these approaches, no studies to date have shown whether adjunctive 
administration can optimize learning and functional status in the elderly.  
A randomized clinical trial will enroll 90 participants. Cognitively healthy elderly adults, 
age 65-90 will undergo either a CT intervention in combination with transcranial direct 
current brain stimulation (tDCS) or sham tDCS control. The CT intervention will employ 
a suite of adaptive training tasks from the POSIT Science Brain HQ281; a well validated 
CT method for enhancing cognitive functioning in the elderly. Participants will receive 
tDCS or sham/placebo prefrontal stimulation administered during training.  
Participants will be assessed at baseline, after CT (2 weeks), and at 3-month follow-up. 
At each time point, a comprehensive neurocognitive, clinical and multimodal 
neuroimaging assessment of brain function, metabolic state, and brain structure will be 
conducted.  
FMRI will be used to assess brain response during working memory, attention and 
memory encoding. Proton magnetic resonance spectroscopy (MRS) will assess 
cerebral metabolites, including gamma-aminobutyric acid (GABA) concentrations, 
sensitive to neural plasticity in task-associated brain regions.   
The central hypothesis of this proposal is that tDCS will increase neural plasticity 
in task associated brain regions, facilitating effectiveness of CT and transfer to 
everyday function.  

Protocol IRB #201400328 
Page 10 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
Aim 1. Determine whether neurocognitive improvement and longer-term functional 
outcome (as measured by “ecological assessment”) are better when CT is coupled with 
tDCS, an intervention that will increase neural plasticity and augment training effects.  
H1.1. Active tDCS combined with CT will amplify effects of CT on measures of 
attention, working memory, executive functioning, and learning efficiency, while 
sham tDCS will not. 
H1.2. Active tDCS will enhance near and far transfer of CT, but sham will not 
evidence near or far transfer. 
Aim 2. Determine whether CT combined with tDCS leads to greater functional and 
metabolic brain changes (FMRI, MRS). Effects will parallel Aim 1.  
H2.1. Combined CT + tDCS will potentiate decrease activation in working 
memory and attentional (dorsolateral prefrontal cortex, medial frontal cortex, 
inferior parietal lobe, supplementary motor association cortex) brain systems, 
reflecting increased neural efficiency, while CT + sham tDCS will not.  
H2.2 Cerebral metabolite alterations will occur secondary to CT and tDCS, with 
long-term increase in GABA and N-acetyl aspartate (NAA) concentrations, and 
decreased choline (Cho) and myoinositol (MI) concentrations in the frontal 
cortex and posterior parietal cortex. CT and tDCS will modulate MRS GABA in 
frontal areas. These effects will be specific to CT. 
Secondary aim. We will examine which baseline factors (e.g., clinical, demographic, 
neuroimaging, cognitive) best predict individual differences in outcome.  
6. Research Plan: 
6.1. Experimental design. This study employs a randomized trial design with 90 
participants. One factor with two levels [Factor 1 = stimulation type (active or sham) 
yields two cells. Thus, participants will be assigned to one of two conditions: 
1: Active tDCS + CT 
2: Sham tDCS + CT 
CT has previously been established with strong effects on cognitive and functional 
outcomes. Some small clinical trials showed small to medium effects of tDCS in 
conjunction with CT.  
Participants will be assessed at three primary time points during the study, plus one 
screening visit on the front end to be sure they meet all inclusion criteria for the study:  
1) Informed Consent and Screening Visit 
2) Assessment Visit #1- baseline pre-training;  
3) Assessment Visit #2- post-2 weeks  
4) Assessment Visit #3- three month follow-up after all training                                                      
(see Figure below for summary).  
This design will enable longitudinal analyses of CT and tDCS effects. We will examine 
their effects on cognitive performance, functional and metabolic neuroimaging 
measures, and everyday functional abilities. At each assessment, we will obtain clinical 
and medical history, neurocognitive measures, and neuroimaging (structural MRI, 

Protocol IRB #201400328 
Page 11 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
FMRI, MRS). All participants will undergo neuroimaging at baseline, following training, 
and at a three month follow-up.  
Stimulated Brain Study  
  
6.2. Study participants and randomization procedure. We will recruit 90 older adults 
(women = 45; age: 65-90 years). Study participants will consist of healthy individuals 
who have expressed an interest in taking part in an intervention aimed at optimizing and 
possibly preserving cognitive functioning and brain health. We will use web-based 
permuted block randomization to randomly divide participants into the four groups. 
People with pre-existing dementia, neurological brain disease, or meet criteria for a 
diagnosis of mild cognitive impairment (MCI) will be excluded, though people with 
subjective concerns about their cognitive functioning, who do not meet these criteria, 
may participate. Detailed inclusion/exclusion criteria can be found in Section 7. 
6.2.1. Experimental Design Considerations and Limitations: CT Approach. The 
POSITScience BrainHQ treatment program was selected because it 1) provides specific 
training tasks directed at three essential cognitive domains tied to our aims and 
hypotheses (attention, working memory, executive control) that correspond with the 
neurocognitive and functional neuroimaging measures to be studied; (2) is shown to 
produce significant cognitive and functional improvements with good effect sizes in past 
RCTs (ACTIVE) with up to 10 year durability and transfer of training to measures of self-
reported everyday functioning; 3) provides a “cognitive treatment engine282” which alone 
has a very highly likelihood of yielding significant cognitive and functional 
improvements, enabling us to test the augmenting effects of tDCS; and 4) is 
computerized, well standardized, and efficiently implemented.  
Screening 
ü 
ü 
ü 
ü 
 
 
ü 
 
 
Assessment 
Visits 1-3 
Activity 
Summary 
 
Informed 
Consent 
Form 
Medical 
History 
Hearing  
and Vision 
Tests 
Memory 
and 
Thinking 
Tasks 
Computerized 
Daily Activity 
Tasks, like 
banking etc. 
Physical 
and 
Medical 
Self 
Reports 
MRI 
Screen- 
Form 
NIH tool   
box 
MRI 
Brain 
Scan 
Appt.  
Visit #1  
Baseline                
 
 
 
ü 
ü 
ü 
ü 
ü 
ü 
Visit #2                 
After 2 weeks 
(10 days) of 
Training                 
 
 
 
ü 
ü 
ü 
ü 
ü 
ü 
Visit #3  
Three month 
followup 
 
 
 
ü 
ü 
ü 
ü 
ü 
ü 
Daily Lab 
Visits 
Brain 
Training 
Games  
Brain 
Stimulati
on/Sham 
tDCS 
Sensation 
Questions 
 
 
 
 
 
 
 
 
Days 1-10 
ü 
ü 
ü 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Protocol IRB #201400328 
Page 12 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
Combined CT. We selected a combined CT approach rather than testing one specific 
training task: 1) This would optimize CT treatment effects for this primary intervention, 
providing a strong and reliable engine of change with which to examine effects of tDCS; 
2) This approach enables us to affect several related cognitive functions that are 
strongly dependent on neural plasticity of the frontal cortex; and 3) This approach would 
maximize participants interest and motivation versus a single task that could become 
boring.  
tDCS. Brain stimulation provides a means of directly augmenting CT effects. tDCS was 
selected from possible alternatives (e.g., transcranial magnetic stimulation) based on 
research and data by Woods (PI), Hamilton, and Bikson (consultants), including its 
safety profile, ability to facilitate neural plasticity, and potential for application outside of 
research settings. Frontal stimulation was chosen based on prior and preliminary 
studies demonstrating significant impact on attention, working-memory, and other 
cognitive abilities to be trained during CT. 2mA tDCS was chosen based on prior 
research demonstrating that this parameter excites, rather than inhibits, activity in 
stimulated neurons. 283  
NIH Toolbox. A battery of neurocognitive tests was selected that could be completed in 
1.5 hrs for all participants. A battery was selected that would enable optimal 
assessment of attention, executive functions, and working memory, but would also 
include some measures of learning and memory, and to a lesser extent other cognitive 
functions. We use the NIH Toolbox-Cognitive as a core element of this assessment, as 
it: 1) Can be completed in 30 minutes; 2) Is computerized and well standardized with 
norms from a large national cohort of older adults; 3) Provides both accuracy and 
response time measures; 4) Emphasizes the cognitive domains of relevance to the 
study; and 5) Has been the subject of considerable focus and investment by NIA. This 
study provides an ideal vehicle for implementing this battery. We supplement the 
Toolbox with measures to provide more coverage of working memory, attention, 
learning and memory.  
 
 
 
 
 
 
 
 
 
 

Protocol IRB #201400328 
Page 13 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
 
 
 
Neuroimaging measures. We will focus on 
functional (FMRI) and cerebral metabolic (proton 
MRS) indices for two reasons: 1) These 
modalities are most linked to and likely sensitive 
to CT-associated neural plasticity and brain 
changes; and 2) Changes in these domains are 
likely to occur over the course of training 
compared to structural neuroimaging measures. 
We include active FMRI tasks related to the 
cognitive functions to be trained, as well as a 
passive resting state condition to examine the 
DMN. With respect to MRS, we use a single voxel 
method to achieve optimal sensitivity and will 
measure from two ROIs (frontal and posterior 
cingulate) corresponding to task and resting state 
associated brain areas. Along with Creatine (Cr), 
we will examine cerebral metabolites sensitive 
neuronal loss and membrane disturbances (NAA, 
GLx), and pro-inflammatory processes (Ch, MI). 
We will also measure cerebral GABA 
concentrations using a state of art MRS approach 
that will reflect neural plasticity in ROIs. The MRS 
indices will be examined for Aims 2. We will also 
collect FLAIR imaging data to assess white 
matter hyperintensity load, as a possible predictor 
of treatment response (secondary aim) 
6.3. Procedural sequence. The sequence and 
flow of the assessments to be conducted at 
baseline and each subsequent assessment is 
shown below. We describe participant recruitment 
and retention strategies in the Human Subjects 
section. We will inform potential participants 
about the study, and obtain their consent. We will 
then screen for inclusion/exclusion criteria and 
schedule them for baseline assessment visit #1 
evaluation. All following assessments are identical otherwise.  
6.4 Cognitive Training. CT will involve up to 10 hours of training over 2-weeks (10 
days). At the screening visit, post 2-week intervention and the 3-month follow up visit, 
participants will complete a 20-minute composite measure of the cognitive training 
Stimulated Brain                         
Surveys, Measures, Tasks and Activities 
Screening Visit: 
Informed Consent Process 
Inclusion/Exclusion Screeners 
NACC UDS, Words in Noise (NIH-TB), Visual Acuity (NIH-
TB), Colorblindness, MRI Screener, WTAR (optional: Mock 
MRI, MRI Measuring Device) Drug List review for 
inclusion/exclusion prescriptions 
Baseline Computerized Tasks 
Posit Science Composite Baseline 
Psychological Measures 
BDI, Computer experience survey  
Demographic Information 
Medical History Form 
Assessment Visits: BL, POST, F/U 
Neurocognitive Assessment 
HVLT-R, STROOP,  Trails A/B, BVMT-R, Digit Span, Timed 
Activities of Daily Living, COWA, PASAT, CALCAP  
Baseline Computerized Tasks 
Posit Science Baseline, NIH Toolbox, Functional Activities 
of Daily Living, 
Psychological Measures 
SF-36 Quality of Life, PSQ, STAI, BDI, AS, Computer 
experience survey, Expectations of brain training 
questionnaire, Expectations of brain stimulation 
questionnaire 
Physical Measures 
PROMIS Measure, tDCS Sensation Questionnaire, Grip 
Strength 
Mulitmodal Neuroimaging 
MRI Brain Scan  
 
Baseline, Post, Three Month Follow-up 
Training Interventions: 
Cognitive Training 5 hours per week            
(Posit Brain HQ 2 weeks) 
Posit Baseline Measure/weekly 
tDCS Brain Stimulation 
(once per day for 10 days) 
tDCS Stimulation Survey/daily 
Electrode Drift Measurement  

Protocol IRB #201400328 
Page 14 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
tasks. Training platform. CT employs a PositScience BrainHQ suite via its researcher 
portal. These tasks are web-based and multi-platform (i.e., Windows, Mac). Participants 
will be randomly assigned to training on 4 tasks focusing on working memory or 4 tasks 
focusing on attention/speed of processing. Participants will be required to have a 
specific viewing distances. Study interventionists will provide daily performance 
summaries. The rationale and task demands for each component were described earlier 
(A.3.3). These CT are commercially available (www.positscience.com), with well-
documented protocols/manuals (See Appendix 1) and thus not described in detail here. 
(See miscellaneous attachments for specific game descriptions.)  Hardware. 
Participants will complete CT on computers located at Clinical Translational Research 
Building. Support: Participants will have the opportunity to ask questions, and will be 
instructed on the various computer tasks. No special technical skills or experience are 
required A support phone number will be available if participants have questions arise 
between study visits.  
 
6.5. Transcranial Direct Current Stimulation: 
Bilateral Frontal tDCS: A Soterix Clinical Trials Direct Current Stimulator will apply 20 
minutes of 2.0mA direct current through two biocarbon rubber electrodes encased in 
saline soaked 5cm2 sponges (8cc of 0.9% saline solution) which then may be covered 
with electrode paste, and placed over the frontal cortices at F3 and F4 (10-20 system). 
Based on our well-established computational modeling workflow (C.4.1), F3/F4 
stimulation delivers a broad pattern of frontal stimulation (see C.4.1d). Current inflow will 
occur on the right (F4), and outflow on the left (F3). Impedance quality will be ≤10kΩ to 
insure proper stimulation of brain tissue.  
Sham tDCS: Sham stimulation is performed with the same device and all procedures 
will be identical except for the duration of stimulation. Participants will receive 30 
seconds of 2 mA of direct current stimulation at the beginning of the session. 
Participants habituate to the sensation of tDCS within 30-60 seconds of stimulation. 
This procedure provides the same sensation of tDCS without the full duration of 
stimulation, making it a highly effective sham procedure. Blinding: The device has built 
in RCT double blinding protocols. Soterix will communicate only with Dr. Wu (Co-
Mentor/statistician) to de-identify data for analyses. 
 
Physiological Recording: During stimulation sessions participants will be asked to wear 
a special wristband that will be used to record physiological information such as pulse.  
3D Head Models of Electrode Placement and Electrode Drift Measurement: We will take 
a brief set of images of the participant’s head after the electrodes are placed to make 
sure that the electrodes are in the correct location.  These images will be used to create 
a 3D model of the participant’s head that will give us accurate information about where 
the electrodes were placed. In addition, we will physically measure any change in the 
electrode positioning over the course of the stimulation session to allow for assessment 
of quality control changes due to electrode drift. This is done by placing a mark at the 
bottom corners of each electrode and using a tape measure to measure the difference 
in electrode location at the end of the session. Ideally, no drift should occur and thus 

Protocol IRB #201400328 
Page 15 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
measurement allows for use of a drift coefficient in statistical analyses of between 
participant differences in response.  
6.6. Neuroimaging Methods. We will conduct neuroimaging on a Philips Achieva 3.0 
Tesla research dedicated scanner with an existing research agreement. Scanning will 
take approximately 1 hour to acquire: 1) Structural MRI (T1, DTI), 2) FMRI (EPI-BOLD), 
3) Proton MR Spectroscopy (MRS).  
MRI Acclimation Protocol Options:  Participants may have the MRI Measurement Tool 
(See MRI Measurement Tool Appendix in Misc. Attachments) demonstrated for them to 
visually see the size of the inside of the chamber of the MRI Scanner.  Participants may 
be asked to try the device on themselves to gain a greater understanding of the size 
constraints related to the MRI Scan, and to give them a chance to experience the snug 
fit inside the core of the scanner.  If participants cannot fit in the measurement device, 
they will be excluded from the study. 
 
The participant may also be offered to make a visit to the Mock MRI Scanner (See Mock 
MRI Appendix in Misc. Attachments), located in the UF Dental Tower Ground floor, DG-
73) to see if they are completely comfortable with the scanning process, and if they can 
realistically tolerate the size constraints. This can also be a time to discover if 
claustrophobia will be a limiting factor and exclusion for participation. If there is no 
concern regarding fit, or claustrophobia, this part may be skipped. 
6.6.1. FMRI paradigms. We will present the two FMRI tasks (2-Back, Spatial-Temporal 
Attention) using E-Prime 2 software (Psychology Software Tools, Inc., Pittsburgh, PA), 
with the video signal on a screen behind the participant’s head. The screen is viewed 
through a double-mirror attached to the head coil. An MR-compatible piano-key 
response box attached to the stimulus presentation computer will collect performance 
data. We will apply a cushioned-pillow head stabilizer to minimize head movement 
during scanning. 
2-Back. This task will measure brain changes due to our N-back training. We will 
assess verbal working memory on a 2-Back task, as in past studies153,157. Consonants 
are visually presented for 500ms with an ISI=2500ms. Participants determine if each 
stimulus is the same or different from previously stimuli, responding by binary button 
press (yes vs. no). Executive control, phonemic buffering, and sub-vocal phonemic 
rehearsal are required. 0-back and 2-Back conditions are alternated in a block design 
with two 5-minute runs of eight blocks (consonant lists), with four blocks of the 0-Back 
and four blocks of the 2-Back. 0-Back: Four blocks of nine consonants of random case 
and order (33% targets). Yes-no responses are made if targets that match stimuli 
occurring two earlier. 2-Back: Four blocks of 15 consonants (33% targets) will be 
pseudorandomly presented across the visual field. Accuracy and RT are recorded. 
The useful field of view (UFOV) fMRI task. Task will involve participants making 
decisions about where on the screen they saw a previously displayed object. 
Participants are presented two images simultaneously, one in a central box, and 
another outside the box. These images disappear very quickly and are replaced by a 
“noise” screen, similar to a scrambled television signal. Then participants are asked 
where they saw the object (a car) outside the box (e.g., up, down, left, right, etc.) and 

Protocol IRB #201400328 
Page 16 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
what object was inside the box (either a car or a truck). The participants will respond 
using buttons on a response box. After participants have answered they are presented 
a fixation cross and wait until the next trial begins. Time between trials is randomized 
and jittered in accordance with best practice event-related fMRI design. The task 
duration is approximately 17 minutes. Examples of the stimuli are below. 
 
1) Both objects are presented simultaneously 
 
 
2) Noise Screen 
 
 
Resting State. Participants will also be asked to rest for 6 minutes while functional data 
is being collected to assess resting state activation. 
6.6.2. Proton MRS: GABA-edited spectra will be acquired using the MEGA-PRESS 
experiment, from 1 voxel (medial frontal). 9 minutes duration. Spectra will be analyzed 
using the Gannet package for the batch analysis of GABA-edited MR spectra, 
quantifying GABA concentration relative to water, correcting for voxel CSF fraction. OFF 
spectra will also be analyzed using LCModel to give concentrations for additional 
metabolites NAA, Glx, Cho, MI, and Cr. Analyses. Cerebral MRS metabolite 
concentrations will be derived from spectral analysis295 With unsuppressed water FID at 
TE=30ms used for eddy-current correction. Time domain MRS ratios will be derived 
normalized for Cr. A double-exponential decay will be fitted to the water amplitudes at 
the 7 echo times. The signal amplitudes of brain tissue and CSF will be derived 
(corrected for T2 decay), with brain water signal used to correct for partial CSF volume. 
MRS Quality Assurance: GABA-edited MR spectra will be visually screened, and 
additionally spectra with a fitting residual of over 10% will be accepted for further 
analysis. The concentration measures for the additional metabolites will be accepted 
with an LCModel Cramer-Rao lower bounds (%SD) of less than 20%, a reliable 
estimate for a particular metabolite for group comparisons295. Scans resulting in spectra 
that do not meet quality criteria will be repeated. Past studies showed intra-subject 
variability of ~10% for GABA and 3-5% for other MRS peaks. Line width and signal-to-
noise resonance ratios are plotted over time (brain, phantom) to monitor spectral 

Protocol IRB #201400328 
Page 17 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
quality; inter- and intra-subject variability assessed by correlation and Bland and Altman 
plots.412,413 
6.6.3. Structural MRI. High-resolution whole brain axial gradient-echo MPRAGE 3-D 
T1-weighted images will be acquired for volumetric and cortical thickness analyses and 
FMRI. Analyses: Volumetric indices will be obtained for total gray and white matter, 
FreeSurfer ROIs296-298, and a priori ROIs (MRS, FMRI).  
6.6.4. FLAIR. Fluid attenuated inversion recovery (FLAIR) images will be acquired to 
assess white matter hyperintensity load (i.e., white matter damage). Volumetric and 
whole brain analyses of data will be used to look at region specific versus global levels 
of white matter load.  
6.7. Neurocognitive and Psychological Assessments: Assessments will include a 
neurocognitive battery (approximately 180 minutes in duration, see list attached in 
Miscellaneous documents, and listed below.  The battery consists of standardized, well-
established neurocognitive measures with strong reliability and validity 304. Each is 
widely used in clinical neuropsychology and in Dr. Cohen’s (Primary Mentor) research 
over the past 20 years. Our goal is to assess global cognitive ability (NIH-Toolbox: 
cognitive module), and specifically attention-executive functions, working memory, 
processing speed, and learning-memory. These are domains affected by 
aging24,31,146,305-314 and will also tap the domains assessed by FMRI (Aim 1).  
Psychological measures are those commonly used in cognitive training paradigms, such 
as to gain greater understanding of the relation of things like depression, anxiety and 
apathy to the cognitive training intervention, and the resulting effect on memory and 
thinking. The BDI-II has a high risk of identifying severe depression and suicidality.  The 
measure will be monitored carefully to identify and mitigate any risk to persons that are 
identified as at risk.  The PI will be informed immediately, and a referral to Clinical and 
Health Psychology Clinic will be made right away for the participant.  The PI or study 
staff will contact the participant directly to advise them of the concern, and assist with 
any intervention that may need to be referred.  The State-Trait Anxiety Inventory(STAI) 
has medium risk to identify anxiety disorders and the same protocol will be followed if a 
participant is identified to have severe anxiety disorders that require medical treatment 
or referral to a Clinical and Health Psychology appointment for treatment. The National 
Alzheimer’s Coordinating Center Uniform Data Set (NACC UDS), a comprehensive 
neuropsychological battery will be used to assess dementia/MCI in older adults. The 
NACC UDS is comprised of several neuropsychological tests including the Montreal 
Cognitive Assessment (MoCA), Craft Story Immediate and Delayed Recall (similar to 
the WMS Logical Memory), Benton Complex Figure Task Immediate and Delayed 
Recall, Number Span Forward and Backwards, Category Fluency, Trails A & B, The 
Multilingual Naming Test (similar to the Boston Naming Task), and Letter Fluency. The 
UDS takes approximately 45 minutes to administer. Score corrections are provided for 
age, sex and education. 
6.7.1. Physical activity, QOL, PROMIS self-reported health assessment. We will 
administer the, Medical Outcomes Study Short Form-36 (SF-36: v. 2.0, a widely used 
QOL measure), and the PROMIS self report measures at each assessment.  

Protocol IRB #201400328 
Page 18 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
The PROMIS measures assess change in self-reported cognitive and physical function. 
321 321. Change in self reported physical and mental health status correlate with QOL 
and mental and physical health status. 322,323 324 These measures will serve as important 
assessments of interventions influence on everyday life. 
 
Screening Visit 
Informed Consent 
MRI Screener 
Medical History/Prescription Drugs 
Drug Exclusion List Review 
NACC UDS 
Words in Noise (NIH-TB), Visual Acuity (NIH-TB), Color Vision 
WTAR 
Posit Composite Baseline Game Testing (0%-79% range) 
BDI 
Computer and Technology Use Questionnaire 
 
Assessment Visits #1,2,3 
NIH Toolbox 
HVLT-R 
BVMT 
Stroop Test 
Trails A & B 
COWA 
Apathy Scale 
SF 36 Quality of Life 
PROMIS self-report 
Digit Span 
Pittsburgh Sleep Questionnaire 
Pain Questionnaire 
BDI/STAI 
PASAT 
CALCAP 
Expectation of brain stimulation questionnaire 
Expectation of brain training questionnaire 
 
Daily Check In 
tDCS Stimulation sensation questionnaire 
7. Possible Discomforts and Risks: 
Potential Risks.  
There are minimal risks associated with participation in this study. The potential risks 
are as follows:   
Magnetic resonance imaging (MRI). MRI is a procedure that allows doctors to look 
inside the body by using a scanner that sends out a strong magnetic field and radio 

Protocol IRB #201400328 
Page 19 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
waves.  This procedure is used routinely for medical care and is very safe for most 
people, but you will be monitored during the entire MRI scan in case any problems 
occur. The risks of MRI as detailed in the Informed Consent are: 
• The MRI scanner contains a very strong magnet.  Therefore, you may not be 
able to have the MRI if you have any type of metal implanted in your body, for 
example, any pacing device (such as a heart pacer), any metal in your eyes, or 
certain types of heart valves or brain aneurysm clips.  Someone will ask you 
questions about this before you have the MRI. 
 
• There is not much room inside the MRI scanner.  You may be uncomfortable if 
you do not like to be in close spaces ("claustrophobia").  During the procedure, 
you will be able to talk with the MRI staff through a speaker system, and, in the 
event of an emergency, you can tell them to stop the scan 
 
• The MRI scanner produces a loud hammering noise, which has produced 
hearing loss in a very small number of patients.  You will be given earplugs to 
reduce this risk, and headphones for added protection. 
 
• If you are a woman of childbearing potential, there may be unknown risks to the 
fetus. Therefore, before you can have the MRI, you must have a pregnancy 
test. 
 
• If an obvious abnormality is discovered during your MRI scan, you will be 
informed about it by the research team, you will be provided with a copy of your 
MRI scan and we will encourage you to see your primary care physician. MRI 
will only be done for research purposes in this study. 
 
• You will be monitored very carefully while in the scanner, and repeatedly 
checked to ensure comfort. 
There is a risk of introducing metal into an MRI environment.  However, the MRI 
compatible transcranial direct current stimulator contains no materials that respond to 
the MRI environment.  Thus, the risks associated with undergoing MRI while receiving 
stimulation are the same as transcranial electrical stimulation outside the MRI 
scanner.   
Transcranial direct current stimulation. Transcranial direct current stimulation is 
considered safe but a small number of people do experience some side effects.  The 
most common side effects are itching and tingling or mild discomfort at the area of 
stimulation, and headache.  Other possible side effects include dizziness and nausea.  
Whenever an electrical stimulation is applied to the body, it could possibly cause a 
seizure or abnormal heartbeat, but this has never occurred with the transcranial direct 
current stimulation parameters used in this study.  
Although there is no known risk to pregnancy, there may be unknown risks and all 
women of child bearing potential must have a negative pregnancy test prior to 
stimulation. 

Protocol IRB #201400328 
Page 20 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
Cognitive Training. There is a risk participants will find cognitive training on the 
computer challenging, fatiguing, and/or boring.  Research staff will explain what to do 
and how to perform the training tasks tests during your initial study visit. Participants 
will also have access to a 24-hour help line should you have trouble working with the 
training computer. 
Neurocognitive and Functional tests. There is a risk that you will find cognitive and 
functional tests challenging, because it may be difficult to remember the things that 
you are asked to remember or have trouble hearing or seeing some of the sounds 
and pictures presented on the computer screen. You may skip any tests you do not 
wish to complete. Research staff will explain what to do and help you take the tests 
during your study visit. 
Questionnaires. There is a risk that you will find questions on the questionnaires 
uncomfortable to answer. You may skip any question you feel uncomfortable 
answering.  
Other possible risks to you may include fatigue due to the testing. Should this occur, 
you can take a rest-break at any time or you may discontinue the testing at any 
time. If requested by the participant, multiple test days are possible.  
When being tested some people may develop anxiety. If these tests make you 
anxious we can stop the testing.  
Researchers will take appropriate steps to protect any information they collect about 
you.  However, there is a slight risk that information about you could be revealed 
inappropriately or accidentally.  Depending on the nature of the information, such a 
release could upset or embarrass you, or possibly affect your insurability or 
employability.  Questions 17-21 in this form discuss what information about you will 
be collected, used, protected, and shared. 
This study may include risks that are unknown at this time. 
Participation in more than one research study or project may further increase the risks 
to you.  If you are already enrolled in another research study, please inform one of the 
research team members listed in question 3 of this form or the person reviewing this 
consent with you before enrolling in this or any other research study or project. If you 
are currently participating in another study using transcranial direct current stimulation 
or transcranial magnetic stimulation, you will be rescheduled so that your participation 
in the current study does not overlap with your participation in the other study. 
Adequacy of Protection against Risks. 
Recruitment and Informed Consent. All study participants will provide written informed 
consent. Persons will be recruited from the CAM-CTRP research registry, community 
outreach, or community agencies. Participants will also be recruited at community 
events with IRB-approved flyers, and participants will have the option to confidentially 
provide name, phone number, and email if they wish to be contacted by the study team 
to determine study eligibility. The contact information will be securely stored during the 

Protocol IRB #201400328 
Page 21 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
event and immediately stored in the Woods Lab per study data safety management 
plan. People interested in participating in the study will call the CAM-CTRP study 
recruitment coordinator. Potential participants interested in hearing more about the 
study will be provided information about the study. Persons will then indicate their 
agreement to participate by signing the informed consent document.  
 
Our inclusion and exclusion criteria are designed to minimize risks to participants.  
Inclusion criteria: 1) Men and women; 2) Age: 65 to 90 years; 3) English speaking; 4) 
Physically mobile; 5) working memory function between 0-75th percentile determined by 
screening results on the POSIT Baseline Cognitive Training computerized tasks. 
Rationale: (1) The age range is selected to include a higher proportion of persons with 
possible concern about cognitive deficits (2) The language requirement will avoid 
difficulties with interpretation of cognitive results; (3) the mobility requirement is required 
because participants will need to be able to participate in neurocognitive testing and 
MRI testing at different locations; 4) the working memory function requirement is 
needed to avoid randomized groups from being biased by over-representation of 
extremely high (e.g., super agers) working memory function in a particular group and to 
include a representative sample consistent with the “typical” cognitive aging profile in 
US.  
Exclusion criteria: 1) Neurological disorders (e.g., dementia, stroke, seizures, 
traumatic brain injury). 2) Evidence of dementia (NACC UDS scores of 1.5 standard 
deviations below the mean for age, sex and education adjusted norms in a single 
cognitive domain on the task). 3) Past opportunistic brain infection 4) Major psychiatric 
illness (schizophrenia, intractable affective disorder, current substance dependence 
diagnosis or severe major depression and/or suicidality. 5) Unstable (e.g., cancer other 
than basal cell skin) and chronic (e.g, severe diabetes) medical conditions. 6) MRI 
contraindications (e.g., pregnancy, claustrophobia, metal implants that are 
contraindicated for MRI). 7) Physical impairment precluding motor response or lying still 
for 1 hr and inability to walk two blocks without stopping.8) Certain prescription 
medications may possibly reduce effects otherwise induced by the tDCS stimulation 
protocol, and are listed on a Drug Exclusion List attached to the Appendices in 
Miscellaneous attachments of this study. 9) Hearing or vision deficits that will not allow 
for standardized cognitive training stimulation; ie colorblindness, inability to hear through 
headphones (with or without hearing aids), macular degeration or other significant 
diseases that cause severe loss of vision.  If vision is corrected with lenses to 
appropriate levels, then participant will be eligible. 10) Left handedness, as those with 
left-handedness have a higher percentage rate of atypical functional lateralization for 
brain functions, which would significantly interfere with interpretability of brain data.  
Rationale: (1) neurological diseases affecting the brain create obvious confounds that 
would obscure the study’s findings or increase risk from non-invasive brain stimulation 
(i.e., seizures). (2) The NACC UDS will be used to exclude people meeting criteria for 
severe dementia, as this study is focused on more mild cognitive deficits; (3) past 
opportunistic brain infection, and (4) a history of severe psychiatric illness 

Protocol IRB #201400328 
Page 22 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
(schizophrenia, chronic intractable unipolar or bipolar depression) also would directly 
affect neurocognitive test performance and thus would confound study findings. (5) 
Unstable (e.g., cancer) and certain chronic medical conditions (e.g., severe obesity) 
may also confound findings and increase study attrition; (6) Given that this study 
requires MRI imaging to address all aims, factors that make MRI imaging unsafe or 
infeasible for particular study candidates will serve as a basis for exclusion; and (7) 
Physical limitations are a basis for exclusion based on inability to participate in all study 
procedures. Excluding people who cannot walk or sit for an hour will reduce problems in 
the scanner that could confound study findings.  
Protection against Risk.  
Protection against Risk of confidentiality. Information pertaining to research subjects will 
be obtained from (1) interviews with subjects and (2) procedures described in the 
"research design and methods" section. All data will be considered confidential 
according to HIPPA guidelines for personal health information. All participants will sign a 
combined consent to participate in research and HIPPA compliant confidentiality 
document approved by the IRB overseeing the clinical recruitment setting (i.e. the 
University of Florida IRB, and the Florida Department of Health IRB).  
 
Precautions will be taken to ensure that all research materials are inaccessible to 
anyone other than the investigators, and by ensuring that only qualified and trained 
individuals conduct the study research procedures. Prior to study initiation, procedures 
for protecting the confidential nature of participant data collected will be reviewed and all 
questions or concerns will be clarified at this time. These procedures will be reviewed 
throughout the study. Staff will be trained and certified in handling human subject 
information to maintain privacy and confidentiality. Procedures for allowing access to 
investigators to use this information for research will be under the authority of the PI and 
will follow HIPPA compliant guidelines for the release of PHI.  
 
Contact information for study participants will be kept in separate files and 
databases from the research data. This information will be used by the research 
assistants to send reminders about follow-up times and appointments via phone, email 
or mail correspondence. The information will only be kept on computers or devices that 
are both password protected and encrypted. Any written forms will be kept in locked file 
cabinets or locked briefcases. None of the research data in the central data base will 
have participant identity information. No results will ever be reported in a personally 
identifiable manner. All research data will be entered directly into a web-based survey 
that is maintained by the University of Florida CTSI (REDCap), and the data are 
encrypted as soon as they are sent in a wireless format. The data will be transferred 
and stored on secure servers at the University of Florida, with no identifying information.  
 
No survey data will be labeled with the participant’s name or other identifying 
information, but will instead be labeled with a study ID number. Documents linking study 
ID numbers to identifying information (e.g., name, address, etc) will be stored 
electronically in a password-protected file. All paper-data with identifying information will 
be stored in locked file drawers, separate from coded data. Documents linking study ID 

Protocol IRB #201400328 
Page 23 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
numbers to identifying information will be destroyed at the end of the study. Documents 
containing data collected on un-consented individuals (i.e., screening logs used to avoid 
approaching the same individual for study enrollment twice) will be shredded daily. All 
electronic data will be secured and encrypted. Identifying information will not be 
reported.  
Protections of risks related to study questionnaires. To minimize any risks related to 
emotional responses to questionnaires, persons will be informed about the types of 
questions included in the surveys, which are similar to the types of questions persons 
might be asked by their doctor in a clinical setting.  
Protection of risks related to tDCS. To minimize risk associated with tDCS, participants 
will be monitored throughout stimulation sessions and asked to report any discomfort. If 
scalp sensation is uncomfortable, stimulation levels will be decreased to a comfortable 
level or will be stopped. In the event of a headache, stimulation will be decreased to a 
comfortable level (where the headache or nausea is no longer present) or will be 
stopped. All tDCS sessions will be administered and continually supervise by a trained 
experimenter. The above symptoms have only been reported when participants are 
actively being stimulated. However, to assess for any symptoms occurring during the 24 
hour interval between stimulation sessions, we will administer a brief symptom 
screening questionnaire at the beginning (symptoms in the past 24 hours) and end of 
each session (symptoms during stimulation). tDCS has not been shown to cause 
seizures nor lower the seizure threshold in animals. There are no reports of seizure 
induced by tDCS in human participants in the literature. However, this may not be true 
for epilepsy patients, whose seizure threshold rates are likely abnormal. Prior history of 
neurological disorders is an exclusionary criterion for our study and thus no participants 
will have a history of seizure. 
Protection against risks associated with neuroimaging. MRI is widely regarded as a 
safe, noninvasive procedure for visualization of brain tissue in both adults and children. 
Prior fMRI studies by our group and by other groups document the innocuous nature of 
these procedures. Prior to study participation, all participants will be informed of the MRI 
procedure during the informed consent/assent process. The proposed study will be 
performed on an FDA approved Phillips 3 Tesla scanner located at the Advanced 
Magnetic Resonance Imaging and Spectroscopy (AMRIS) research facility at UF. There 
are no known long-term effects of MRI procedures on the body. The FDA Information 
Sheets, Food and Drug Administration, October 1995, p. 79, lists as a non-significant 
risk device, “Magnetic Resonance Imaging (MRI) Devices within FDA-specified 
parameters.”  This study satisfies those parameters. The 3.0 tesla MR scanners meets 
FDA parameters for field strength, gradient switching, and RF power deposition for all 
FDA-approved acquisition schemes including echo-planar imaging. In addition, this 
research protocol involves the use of an FDA-approved acquisition scheme, or the 
power deposition of experimental acquisition scheme proposed meets FDA parameters 
as verified on a phantom using the power monitoring system installed on the MR 
scanner. Both Dr. Woods and trained MRI staff will check for exclusion criteria. The 
main MRI-related risks include: (a) sensitivity to the loudness of the MRI machine - all 
subjects will be given and must wear ear plugs; a squeeze-ball and microphone will be 

Protocol IRB #201400328 
Page 24 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
provided so that they may stop the testing if they become uncomfortable or anxious at 
any time; (b) claustrophobia - subjects will have the opportunity to practice in a 
simulator and to become as familiar and comfortable as possible before commencing 
the experiment. In addition, they will be given the opportunity to examine the scanner 
before the tasks starts. The study will be ended early if the space is a problem for them; 
no medications (e.g., benzodiazepines or tranquilizers) will be offered to them; (c) 
lightheadedness when sitting up after lying in the MRI machine - this feeling sometimes 
occurs but has always gone away in a few minutes. Participants will thus be assisted in 
getting up to make sure they do not fall; (d) risks associated with having an MRI in 
pregnant women - while risks to pregnant women have not been conclusively 
documented, reports of potential risk to unborn fetuses have prompted the exclusion of 
these individuals from research. Given the age range of our sample, the probability that 
one of our female subjects will be pregnant is relatively low. Nevertheless, as a 
standard procedure, all female participants will undergo a test to determine pregnancy 
prior to entering the MRI. In sum, the MRI neuroimaging procedures pose no 
radiological or medical risk, given that participants with metal implants susceptible to 
magnetic heating will be excluded based on standard scanner policies. A small number 
of people may become anxious in the small space of the scanner. These individuals will 
have the opportunity to terminate the scan session. Furthermore, all recruits will be 
screened for phobias prior to enrollment. FMRI procedures will be supervised by Dr. 
Woods, a close collaborator with Dr. Song Lai, the MR physicist in charge of the facility. 
There is a medical technologist at the imaging site at all times to insure scanner safety, 
and neuroradiologists on call as needed. If an abnormality is noted on the structural 
MRIs by study staff, the PI will provide the participant with a copy of the scan and 
encourage them to follow up with a neurologist. 
Protection against risks associated with neurocognitive tests. The neurocognitive 
assessments have minimal risk associated with them. Some participants experience 
stress associated with being tested, though this tends to be quite limited. Breaks will be 
given in those cases. Research staff that collect data have been trained in the conduct 
of all cognitive function tests by other senior staff members. Research staff members 
will be certified in the conduct of the cognitive function tests before they work with study 
participants.  
Data and Safety Monitoring Plan: 
A data and safety monitoring plan (DSMP) will be implemented to ensure the safety of 
all participants involved in the study and to ensure the validity and integrity of the data. 
The PI will be responsible for coordinating activities of the DSMP, including: arranging 
meetings and communications, and identifying and reviewing relevant participant 
materials. The data and safety information obtained on each study participant will be 
reviewed at weekly meetings. The primary goal of the DSMP will be to monitor the 
progress of the study and safety of participants and if necessary, recommend modifying 
the study or terminating the study as appropriate. 
Concerns that might dictate modification or termination of the study include: 

Protocol IRB #201400328 
Page 25 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
• Participant safety 
• Outcome data 
• Data quality 
• Integrity 
• Intervention efficacy 
• Recruitment 
• Performance 
Both the PI and the study staff will review the study weekly and examine reports of 
adverse incidents and reports of study participant recruitment and follow-up. Throughout 
the course of the study, information regarding issues deemed critical to the study or to 
the safety of research participants will be provided to the PI and primary mentors. As a 
result of receiving this critical information, a meeting to discuss this information may be 
convened. Information deemed critical would include: 
• Serious and non-serious adverse events that may occur 
• Suspicion of scientific fraud or misconduct 
• Any other issues which may warrant protocol changes or modifications. 
Because this is an exploratory study with minimal risk associated with the proposed 
intervention and limited financial resources, there will not be an external data safety and 
monitoring board for this study. The research team, however, will follow the procedures 
for data safety and monitoring as required by the Institutional Review Board at the 
University of Florida (UF IRB). 
The research team will monitor participants for any potential adverse events, and all 
reported events will be forwarded by the PI to the UF IRB. 
Procedure for collection and storage of data. A number of quality control procedures 
will be used to ensure the validity and integrity of the data and the safety of all 
participants involved in the study. Relevant data and safety information obtained on 
each study participant will be verified against the original source documents by the 
primary study coordinator and any identified discrepancies will be reviewed at these 
weekly meetings. The primary goal of these meetings will be to monitor the progress of 
the study and safety of participants and if necessary, recommend modifying the study or 
terminating the study as appropriate.  
All identifying information will be archived in Dr. Woods’ neuroimaging laboratory within 
the Center for Cognitive Aging and Memory at UF. Imaging data will undergo several 
levels of processing, and all raw and processed data will be archived on a password-
protected server in password-protected folders and files. Only study staff will have 
access to these files. The self-report data will be double entered using the SPSS Data 
Entry system. This system signals the user when an out-of-range value is entered. All 
data entry is then verified via double-entry, with the program signaling mismatches with 
the original entry. Next, computer-generated reports of variable frequencies and subject 
lists will be reviewed, leading to possible corrections to coding or entry. After checking 
for accuracy of data within a given group, data will be stored in the password-protected 
folders along with the imaging data. 

Protocol IRB #201400328 
Page 26 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
Location and logistics of data collection. All procedures involving human subjects will be 
performed at facilities of the UF Health Care System. Neurocognitive testing and 
training will be performed at either the Clinical and Translational Research Institute or 
the AMRIS/MBI UF facility. Several clinical research examination rooms are equipped 
and dedicated to neurocognitive and functional assessment, and contain all necessary 
computers and test materials. Blood for serum biomarker analysis will also be collected 
at this site. The Clinical and Translational Research Institute (CTSI) which contains 
laboratories, freezers, and main frame computers. Storage of neuroimaging data 
backup will occur there. Neuroimaging will take place at the AMRIS facility of the 
McKnight Brain Institute. 
Storage of collected data. All electronic data are stored in password protected, secured 
computer systems. All paper data will be stored in a locked file cabinet. Data will only be 
removed when coded, entered, or audited. Only the participant’s study identification 
number will appear on any data forms. Only the PI, the Co-Is, and the RAs will have 
access to the completed data forms and electronically stored data. All data are 
considered part of the participant’s confidential record. Data collected from research 
participants will be stored in a secured, password protected computer file that is 
separate from network systems. All paper data (e.g., subject contact information, 
consent forms, etc.) will be placed in a locked file cabinet within 24 hours of their 
acquisition as designated by the study's RA . All data will remain confidential. A file will 
be maintained that associates the participant’s name with that participant’s study 
identification number. This file will be kept in a locked cabinet separate from the study 
data 
Data entry requirements. The data entry system will require a login identification and 
password in order to gain access to the data. Where appropriate, validation and range 
rules will be applied to the actual entry fields. Only the PI and Co-Is will be able to view 
the data in its raw state.   
Audit/verification of entered data. All data designated as primary outcome data will be 
subject to a 100% cross-referencing between electronic and paper forms. This audit 
must have an error rate less than 1%. If the verification fails the audit, all data will be re-
entered, the original computer files discarded, and the newly re-entered data audited. 
This process will continue until the audit no longer exceeds the maximum allowable 
error rate. All audits will be supervised and documented by the PI.  
Data management and analysis. Our research team has substantial experience in the 
design and implementation of data management procedures that provide accurate 
recording and storage of data, participant confidentiality, and timely analysis. Based on 
our past experience, we believe that our major data management and analysis needs 
for the proposed project can be met by using a high-end PC, equipped with the latest 
version of SPSS for Windows and appropriate spreadsheet programs. All data files are 
automatically backed-up daily.  
Data quality control. All staff involved in data collection will be trained and certified to 
ensure their competence, and re-certified periodically throughout the study as we have 
done in similar trials. Data will be collected and numerically coded using pre-tested 
electronic entry forms. At the time of collection, there will be initial clerical review of all 

Protocol IRB #201400328 
Page 27 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
data for accuracy and completeness. Every effort will be made to ensure that missing 
data are kept to a minimum. Data entry programs with range checking and response 
validation will be used for all data entered. Under supervision from the PI, the data 
manager will conduct error checking procedures and preliminary analyses on all data to 
ensure their accuracy. The RAs will be trained to avoid omissions in data entry and 
computer entry protocols will be programmed to avoid accidental skipping of question 
items. We believe that the quality control system to be used will ensure a complete and 
accurate database, and maximize the likelihood that the intervention will be delivered 
correctly and efficiently. As we have done in prior studies, a manual of procedures will 
be developed during the initial study start-up period that explicitly describes the specific 
procedures related to intervention delivery, data collection, and quality assurance.   
Frequency of data review. Relevant data and safety information obtained on each study 
participant will be verified against the original source documents by the primary study 
coordinator on a bi-weekly basis. As noted above, any identified discrepancies will be 
discussed with the Principal Investigator and reviewed at weekly meetings.  
Measurement and reporting of participant accrual and adherence to eligibility criteria. 
Review of the rate of participant accrual, adherence to inclusion/exclusion criteria will 
occur weekly during the recruitment phase and then every month to assure that 
participants meet eligibility criteria and ethnic diversity goals outlined in the grant 
proposal.  
Designation of an independent monitoring committee. The Independent Monitoring 
Committee for this study will consist of an established board which has reviewed all 
studies conducted within the CAM-CTRP during bi-annual conference calls for the past 
six years.  
Safety Review Plan Study progress and safety will be reviewed monthly (and more 
frequently if needed) by the principal investigator. Progress reports, including participant 
recruitment, retention/attrition, and AEs will be provided the IoA Independent Monitoring 
committee for bi-annual reviews. An annual report will be compiled and will include a list 
and summary of AEs. In addition, the annual report will address (1) whether adverse 
event rates are consistent with pre-study assumptions; (2) reason for dropouts from the 
study; (3) whether all participants met entry criteria; (4) whether continuation of the 
study is justified on the basis that additional data are needed to accomplish the stated 
aims of the study; and (5) conditions whereby the study might be terminated 
prematurely. The annual report will be signed by the chair of the Independent 
Monitoring Committee and may be forwarded to the IRB, NIH, & CTSI. The IRB and 
other applicable recipients will review progress of this study annually. 
Final storage of paper data. All paper data (e.g., consent forms) will be housed at a 
facility that specializes in the storage of medical/ research information. The destruction 
date of these files will be at least 7 years from the termination of the study and will be 
authorized by the Principal Investigator of the research study. 
Access to cleaned computer data. Once the study is complete, and all data have been 
collected, entered and passed the audit process, the data will be available to the 
Principal Investigator and his designates for analysis. Only the Principal Investigator can 

Protocol IRB #201400328 
Page 28 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
give permission for the release of aggregated study data. No confidential information 
may be released without the express written consent of the study participants. Only 
copies of the finalized data will be released. The original data file will remain in its 
pristine state. 
Monitoring physical health and safety. All assessment visits will be conducted at a 
central location and all testing sessions will be conducted and supervised by a trained 
and certified research staff that will monitor potential adverse experiences and 
symptoms. At each visit, participants will be asked to report any adverse events they 
have experienced since their last visit. Immediate medical treatment will be provided for 
any illness or injury resulting from this study. Trained nursing staff members are present 
in the research center at all times, and a physician will also be available to evaluate the 
participant if needed. 
In addition to the assessments conducted at in-person study visits, participants will be 
contacted by phone on a weekly basis between the end of training and the 12-week 
follow-up visit and will be asked to report any adverse events that have occurred since 
their previous phone contact or study visit. Specifically, they will be asked about any 
adverse events, including itching, pain, nausea, headache, etc. they have had. 
Participants will also be asked about their mood, including depressive symptomatology, 
and other health related activities (i.e., physical activity), as well as general health 
status. Based on reported symptoms, participants may be asked to come in for an 
additional follow-up assessment visit with one of the healthcare professionals on the 
study team (i.e., physician or psychologist) for further evaluation of their symptoms. 
Based on information obtained at this visit, participants will then be referred for follow-up 
assessment and/or treatment with the appropriate healthcare provider. 
Monitoring mental health and safety. At each assessment visit, participants will 
complete the BDI to assess for depressive symptom severity. Any participant who 
endorses clinical levels of any psychiatric disorder or who endorses suicidality will be 
referred to Dr. Cohen, study psychologist, for further follow-up assessment. The 
psychologist will then provide a recommendation regarding the appropriate course of 
follow-up and also advise on whether it is safe for the participant to continue in the 
study. If a participant is found to be actively suicidal with a plan and/or intent, they will 
be escorted immediately to the emergency room at the Shands Hospital at the 
University of Florida. If safety is an issue, UF security or local authorities will be called 
for assistance. If a participant endorses suicidal thoughts or significant 
psychopathology, participants will be referred for further by evaluation with Dr. 
Whitehead (Licensed psychologist and co-investigator). Based on this evaluation, she 
will recommend follow-up assessment and treatment appropriate to the situation 
including treatment at the Psychology and/or Psychiatry Clinics within Shands Hospital 
at the University of Florida. 
Adverse Event Reporting. An adverse event (AE) is defined as any unfavorable and 
unintended sign (including an abnormal laboratory finding), symptom, or disease 
temporally associated with the intervention irrespective of whether it is considered 
related to the intervention.  

Protocol IRB #201400328 
Page 29 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
Non serious adverse events. Defined as conditions that may be unpleasant and 
bothersome to the participant that does not require discontinuing the study.  
Serious adverse events (SAEs). Defined as events that may be harmful to the 
participant and/or may be serious enough to warrant either temporary or permanent 
discontinuation of the study intervention, either because they are intolerable or because 
they are judged to be potentially harmful. All serious adverse events require immediate 
reporting and an assessment of the implications for the continuation of the study and/or 
modification of the consent form. The following are considered serious events: 
It is acute or life threatening; 
It results in prolonged, permanent or severe disability; 
It is another severe illness including worsening of a pre-existing condition, injury or 
accidents; 
It is an inpatient hospitalization or surgical procedure, or a treatment to prevent a 
serious event; 
It results in death; 
It is a clinically significant abnormal laboratory or diagnostic test. 
 
 
 
 
 
 
 
 
Classification of 
AE severity. The 
principal 
investigator will 
evaluate adverse events for seriousness, expectedness, severity, and relationship to 
study intervention at each study visit. The primary mechanism for ensuring participant 
safety will be clinical observation of symptoms. The study will be conducted and 
supervised by trained study staff that are certified in CPR and will monitor potential 
adverse experiences and symptoms. During each visit, participants will log any health-
related problems or symptoms they are experiencing. These sheets will be reviewed by 
study staff before allowing the participant to continue. Community health services will be 
contacted immediately if warranted by the participants’ symptoms. 
 
All adverse events (AEs) will also be categorized according to the likelihood that 
they are related to the study intervention. Specifically, they will be labeled either 
definitely, probably, possibly or unrelated to the study intervention. The classification of 
potential relationship to the intervention is shown in Table 3. 
 
Effective screening will exclude individuals who would be at increased medical 
risk as a result of participation in this research. FMRI contraindications, such as 
claustrophobia or some metal implants will be assessed by telephone screening and 
again on the day of the scan. Dr. Woods, who has completed MRI safety training and 

Protocol IRB #201400328 
Page 30 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
has over 4 years experience screening FMRI participants, will supervise these 
procedures. The RA who will conduct neuroimaging analyses will be trained in UF 
AMRIS safety procedures and will also be responsible insuring for participant safety. 
Adverse events, although unlikely, may occur as a result of neuroimaging.. Such events 
will immediately be reported by Dr. Woods (PI) to the local IRB, with serious adverse 
events reported to both the IRB and NIH. 
SAE reporting. Serious adverse events (SAEs) that are unanticipated, serious, and 
possibly related to the study intervention will be reported within 24 hours of study’s 
knowledge of the SAE to the Independent Monitor, IRB, and CTSA in accordance with 
the regulatory requirements. Anticipated SAEs or those unrelated to the study 
intervention will be reported to the same individuals/entities in accordance with 
regulatory requirements. 
 
Minor events will be reported to the IRB at the time of annual review, as well as 
to the oversight committee on a bi-annual basis. Both the principal investigator and the 
study staff will review the study weekly and examine reports of adverse incidents and 
reports of study participant recruitment and follow-up. Throughout the course of the 
study, information regarding issues deemed critical to the study or to the safety of 
research participants will be provided to the principal investigator and other study 
investigators as needed. As a result of receiving this critical information, a meeting to 
discuss this information may be convened. Information deemed critical would include: 
• serious and non-serious adverse events that may occur;  
• suspicion of scientific fraud or misconduct;  
• any other issues which may warrant protocol changes or modifications.  
Adverse event (AE) and serious adverse event (SAE) determination and monitoring will 
be achieved via administration of structured questionnaires. Standardized forms for 
referring and treating study participants who experience adverse events will be in place.  
Documentation of Training on Protection of Human Participants. All key personnel 
on the study have successfully completed and obtained certification from their 
respective institutions for Mandatory Education in Human Research Subjects 
Protection. The Mandatory Education is described as follows:  
On August 23, 2000, an institutional policy was enacted requiring all clinical 
investigators and their key personnel to undergo mandatory education in human 
research subject protection. The program was initiated on September 1, 2000 and 
includes all clinical researchers regardless of source of research funding. At the core of 
the self-directed training program is the tutorial manual entitled, "Protecting Study 
Volunteers in Research" by Dunn and Chadwick. Each researcher receives a copy of 
the manual and, in a timely fashion, completes the test included in the manual. Each 
researcher must obtain a score of >86 to complete the certification requirements to 
conduct clinical research at this institution. In all cases, no researcher will be allowed to 
conduct clinical research without proof of human research subject protection education 
after September 30, 2001.  

Protocol IRB #201400328 
Page 31 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
Additional and continuing education opportunities for clinical researchers include the 
Office of Research Administration newsletter that is circulated to approximately 900 
recipients every 6 weeks. Relevant information concerning research review is available 
on the UF ORA web page. In addition to standard institutional research information, the 
web page contains links to other sites such as CenterWatch, NIH.  
8. Possible Benefits: 
There are well-documented benefits for older adults who participate in cognitive training, 
including improvement in working memory, attention, and executive functions. Deficits in 
these cognitive abilities significantly impact quality of life, financial capacity, medication 
adherence, and mortality in older adults. All participants undergoing CT may benefit 
from these effects. Those participants randomly assigned to the active tDCS+CT 
treatment group may experience further benefit beyond that of cognitive training alone. 
Benefits to society will include the contribution of novel data regarding the efficacy of 
neuromodulation methods for enhancing cognitive training effects in older adults. This 
may help inform treatment protocols in a growing segment of the US population (i.e., 
those 65 years and older). Data collected from this study will serve as a foundation for 
larger clinical studies that examine optimized methods for treating aging-related 
cognitive decline using cognitive training and tDCS. Some participants may not benefit 
from the study at all. 
9. Conflict of Interest: 
None 
References: 
1. 
Sumner P, Edden RA, Bompas A, Evans CJ, Singh KD. More GABA, less 
distraction: a neurochemical predictor of motor decision speed. Nat Neurosci. Jul 
2010;13(7):825-827. 
2. 
Streeter CC, Jensen JE, Perlmutter RM, et al. Yoga Asana sessions increase 
brain GABA levels: a pilot study. J Altern Complement Med. May 2007;13(4):419-
426. 
3. 
Edden RA, Muthukumaraswamy SD, Freeman TC, Singh KD. Orientation 
discrimination performance is predicted by GABA concentration and gamma 
oscillation frequency in human primary visual cortex. J Neurosci. Dec 16 
2009;29(50):15721-15726. 
4. 
Puts NA, Edden RA, Evans CJ, McGlone F, McGonigle DJ. Regionally specific 
human GABA concentration correlates with tactile discrimination thresholds. J 
Neurosci. Nov 16 2011;31(46):16556-16560. 
5. 
Gao F, Edden RA, Li M, et al. Edited magnetic resonance spectroscopy detects 
an age-related decline in brain GABA levels. Neuroimage. Sep 2013;78:75-82. 
6. 
Streeter CC, Whitfield TH, Owen L, et al. Effects of yoga versus walking on 
mood, anxiety, and brain GABA levels: a randomized controlled MRS study. J 
Altern Complement Med. Nov 2010;16(11):1145-1152. 

Protocol IRB #201400328 
Page 32 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
7. 
Cohen RA, Moser DJ, Clark MM, et al. Neurocognitive functioning and 
improvement in quality of life following participation in cardiac rehabilitation. Am J 
Cardiol. May 1 1999;83(9):1374-1378. 
8. 
Osowiecki DM, Cohen RA, Morrow KM, et al. Neurocognitive and psychological 
contributions to quality of life in HIV-1-infected women. AIDS. Jul 7 
2000;14(10):1327-1332. 
9. 
Al-Khindi T, Macdonald RL, Schweizer TA. Cognitive and functional outcome 
after aneurysmal subarachnoid hemorrhage. Stroke; a journal of cerebral 
circulation. Aug;41(8):e519-536. 
10. 
Wadley VG, Crowe M, Marsiske M, et al. Changes in everyday function in 
individuals with psychometrically defined mild cognitive impairment in the 
Advanced Cognitive Training for Independent and Vital Elderly Study. J Am 
Geriatr Soc. Aug 2007;55(8):1192-1198. 
11. 
Soderqvist A, Miedel R, Ponzer S, Tidermark J. The influence of cognitive 
function on outcome after a hip fracture. The Journal of bone and joint surgery. 
Oct 2006;88(10):2115-2123. 
12. 
Ettenhofer ML, Hinkin CH, Castellon SA, et al. Aging, neurocognition, and 
medication adherence in HIV infection. Am J Geriatr Psychiatry. Apr 
2009;17(4):281-290. 
13. 
Ochner CN, Green D, van Steenburgh JJ, Kounios J, Lowe MR. Asymmetric 
prefrontal cortex activation in relation to markers of overeating in obese humans. 
Appetite. Aug 2009;53(1):44-49. 
14. 
Reijnders J, van Heugten C, van Boxtel M. Cognitive interventions in healthy 
older adults and people with mild cognitive impairment: a systematic review. 
Ageing Res Rev. Jan 2013;12(1):263-275. 
15. 
Li H, Li J, Li N, Li B, Wang P, Zhou T. Cognitive intervention for persons with mild 
cognitive impairment: A meta-analysis. Ageing Res Rev. Apr 2011;10(2):285-
296. 
16. 
Papp KV, Walsh SJ, Snyder PJ. Immediate and delayed effects of cognitive 
interventions in healthy elderly: a review of current literature and future 
directions. Alzheimers Dement. Jan 2009;5(1):50-60. 
17. 
Ball K, Edwards JD, Ross LA. The impact of speed of processing training on 
cognitive and everyday functions. The journals of gerontology. Series B, 
Psychological sciences and social sciences. Jun 2007;62 Spec No 1:19-31. 
18. 
Cicerone KD, Dahlberg C, Kalmar K, et al. Evidence-based cognitive 
rehabilitation: recommendations for clinical practice. Arch Phys Med Rehabil. 
Dec 2000;81(12):1596-1615. 
19. 
Ball KK, Beard BL, Roenker DL, Miller RL, Griggs DS. Age and visual search: 
expanding the useful field of view. Journal of the Optical Society of America. A, 
Optics and image science. Dec 1988;5(12):2210-2219. 
20. 
Ball K, Owsley C. The useful field of view test: a new technique for evaluating 
age-related declines in visual function. Journal of the American Optometric 
Association. Jan 1993;64(1):71-79. 
21. 
Jobe JB, Smith DM, Ball K, et al. ACTIVE: a cognitive intervention trial to 
promote independence in older adults. Control Clin Trials. Aug 2001;22(4):453-
479. 

Protocol IRB #201400328 
Page 33 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
22. 
Ball K, Berch DB, Helmers KF, et al. Effects of cognitive training interventions 
with older adults: a randomized controlled trial. JAMA. Nov 13 
2002;288(18):2271-2281. 
23. 
Edwards JD, Wadley VG, Vance DE, Wood K, Roenker DL, Ball KK. The impact 
of speed of processing training on cognitive and everyday performance. Aging & 
mental health. May 2005;9(3):262-271. 
24. 
Willis SL, Tennstedt SL, Marsiske M, et al. Long-term effects of cognitive training 
on everyday functional outcomes in older adults. JAMA. Dec 20 
2006;296(23):2805-2814. 
25. 
Edwards JD, Ross LA, Ackerman ML, et al. Longitudinal predictors of driving 
cessation among older adults from the ACTIVE clinical trial. The journals of 
gerontology. Series B, Psychological sciences and social sciences. Jan 
2008;63(1):P6-12. 
26. 
Edwards JD, Myers C, Ross LA, et al. The longitudinal impact of cognitive speed 
of processing training on driving mobility. The Gerontologist. Aug 
2009;49(4):485-494. 
27. 
Wolinsky FD, Vander Weg MW, Martin R, et al. The effect of speed-of-
processing training on depressive symptoms in ACTIVE. The journals of 
gerontology. Series A, Biological sciences and medical sciences. Apr 
2009;64(4):468-472. 
28. 
Ball K, Edwards JD, Ross LA, McGwin G, Jr. Cognitive training decreases motor 
vehicle collision involvement of older drivers. J Am Geriatr Soc. Nov 
2010;58(11):2107-2113. 
29. 
Wolinsky FD, Mahncke H, Vander Weg MW, et al. Speed of processing training 
protects self-rated health in older adults: enduring effects observed in the multi-
site ACTIVE randomized controlled trial. International psychogeriatrics / IPA. May 
2010;22(3):470-478. 
30. 
Wolinsky FD, Vander Weg MW, Martin R, et al. Does cognitive training improve 
internal locus of control among older adults? The journals of gerontology. Series 
B, Psychological sciences and social sciences. Sep 2010;65(5):591-598. 
31. 
Belchior P, Marsiske M, Sisco SM, et al. Video game training to improve 
selective visual attention in older adults. Computers in human behavior. Jul 1 
2013;29(4):1318-1324. 
32. 
Jones RN, Marsiske M, Ball K, et al. The ACTIVE cognitive training interventions 
and trajectories of performance among older adults. J Aging Health. Dec 
2013;25(8 Suppl):186S-208S. 
33. 
Marsiske M, Dzierzewski JM, Thomas KR, et al. Race-related disparities in 5-
year cognitive level and change in untrained active participants. J Aging Health. 
Dec 2013;25(8 Suppl):103S-127S. 
34. 
Ross L, Edwards, JD, Ball, K. Mobility Outcomes in ACTIVE. Paper presented at: 
Gerontological Society of America Meetings 2013; New Orleans, LA. 
35. 
Ross LA, Edwards, J. D., & Ball, K. . Mobility Outcomes in ACTIVE. 
Gerontological Society of America Meetings; 2013; New Orleans, LA. 
36. 
Rebok GW, Ball K, Guey LT, et al. Ten-Year Effects of the Advanced Cognitive 
Training for Independent and Vital Elderly Cognitive Training Trial on Cognition 
and Everyday Functioning in Older Adults. J Am Geriatr Soc. Jan 13 2014. 

Protocol IRB #201400328 
Page 34 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
37. 
Edwards JD, Ruva CL, O'Brien JL, Haley CB, Lister JJ. An examination of 
mediators of the transfer of cognitive speed of processing training to everyday 
functional performance. Psychology and aging. Jun 2013;28(2):314-321. 
38. 
Edwards JD, Hauser RA, O'Connor ML, Valdes EG, Zesiewicz TA, Uc EY. 
Randomized trial of cognitive speed of processing training in Parkinson disease. 
Neurology. Oct 8 2013;81(15):1284-1290. 
39. 
Edwards JD, Valdes EG, Peronto C, et al. The Efficacy of InSight Cognitive 
Training to Improve Useful Field of View Performance: A Brief Report. The 
journals of gerontology. Series B, Psychological sciences and social sciences. 
Nov 8 2013. 
40. 
Wolinsky FD, Unverzagt FW, Smith DM, Jones R, Wright E, Tennstedt SL. The 
effects of the ACTIVE cognitive training trial on clinically relevant declines in 
health-related quality of life. The journals of gerontology. Series B, Psychological 
sciences and social sciences. Sep 2006;61(5):S281-287. 
41. 
Wolinsky FD, Vander Weg MW, Howren MB, Jones MP, Dotson MM. A 
randomized controlled trial of cognitive training using a visual speed of 
processing intervention in middle aged and older adults. PloS one. 
2013;8(5):e61624. 
42. 
Singer T, Verhaeghen P, Ghisletta P, Lindenberger U, Baltes PB. The fate of 
cognition in very old age: six-year longitudinal findings in the Berlin Aging Study 
(BASE). Psychology and aging. Jun 2003;18(2):318-331. 
43. 
Berry AS, Zanto TP, Clapp WC, et al. The influence of perceptual training on 
working memory in older adults. PloS one. 2010;5(7):e11537. 
44. 
Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ. Improving fluid intelligence with 
training on working memory. Proceedings of the National Academy of Sciences 
of the United States of America. May 13 2008;105(19):6829-6833. 
45. 
Jaeggi SM, Buschkuehl M, Perrig WJ, Meier B. The concurrent validity of the N-
back task as a working memory measure. Memory. May 2010;18(4):394-412. 
46. 
Schneider-Garces NJ, Gordon BA, Brumback-Peltz CR, et al. Span, CRUNCH, 
and beyond: working memory capacity and the aging brain. Journal of cognitive 
neuroscience. Apr 2010;22(4):655-669. 
47. 
Brehmer Y, Rieckmann A, Bellander M, Westerberg H, Fischer H, Backman L. 
Neural correlates of training-related working-memory gains in old age. 
Neuroimage. Oct 15 2011;58(4):1110-1120. 
48. 
Brehmer Y, Westerberg H, Backman L. Working-memory training in younger and 
older adults: training gains, transfer, and maintenance. Frontiers in human 
neuroscience. 2012;6:63. 
49. 
Harrison PJ. The neuropathology of schizophrenia. A critical review of the data 
and their interpretation. Brain. Apr 1999;122 ( Pt 4):593-624. 
50. 
Jaeggi SM, Studer-Luethi, B., Buschkuehl, M., Su, Y. F., Jonides, J., & Perrig, W. 
J. . The relationship between n-back performance and matrix reasoning—
Implications for training and transfer. Intelligence. 2010;38(6):625-635. 
51. 
Jaeggi SM, Buschkuehl, M., Jonides, J., & Shah, P. Cogmed and working 
memory training—Current challenges and the search for underlying mechanisms. 
J Appl Res Mem Cogn. 2012;1(3):211-213. 

Protocol IRB #201400328 
Page 35 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
52. 
Klingberg T. Training Working Memory. The ADHD Report: Special Issue—
Focus on Assessment. 2006;14(1):6-8. 
53. 
Coull JT, Nobre AC, Frith CD. The noradrenergic alpha2 agonist clonidine 
modulates behavioural and neuroanatomical correlates of human attentional 
orienting and alerting. Cerebral cortex. Jan 2001;11(1):73-84. 
54. 
Heilman KM, Schwartz HD, Watson RT. Hypoarousal in patients with the neglect 
syndrome and emotional indifference. Neurology. Mar 1978;28(3):229-232. 
55. 
Hjaltason H, Tegner R, Tham K, Levander M, Ericson K. Sustained attention and 
awareness of disability in chronic neglect. Neuropsychologia. Dec 
1996;34(12):1229-1233. 
56. 
Husain M, Shapiro K, Martin J, Kennard C. Abnormal temporal dynamics of 
visual attention in spatial neglect patients. Nature. Jan 9 1997;385(6612):154-
156. 
57. 
Malhotra P, Coulthard EJ, Husain M. Role of right posterior parietal cortex in 
maintaining attention to spatial locations over time. Brain. Mar 2009;132(Pt 
3):645-660. 
58. 
Posner MI. Measuring alertness. Annals of the New York Academy of Sciences. 
2008;1129:193-199. 
59. 
Robertson IH, Mattingley JB, Rorden C, Driver J. Phasic alerting of neglect 
patients overcomes their spatial deficit in visual awareness. Nature. Sep 10 
1998;395(6698):169-172. 
60. 
Sturm W, Willmes K. On the functional neuroanatomy of intrinsic and phasic 
alertness. Neuroimage. Jul 2001;14(1 Pt 2):S76-84. 
61. 
Thiel CM, Zilles K, Fink GR. Cerebral correlates of alerting, orienting and 
reorienting of visuospatial attention: an event-related fMRI study. Neuroimage. 
Jan 2004;21(1):318-328. 
62. 
Thimm M, Fink GR, Kust J, Karbe H, Sturm W. Impact of alertness training on 
spatial neglect: a behavioural and fMRI study. Neuropsychologia. 
2006;44(7):1230-1246. 
63. 
Woods AJ, Mennemeier M, Garcia-Rill E, et al. Improvement in arousal, visual 
neglect, and perception of stimulus intensity following cold pressor stimulation. 
Neurocase. 2012;18(2):115-122. 
64. 
Woods AJ, Philbeck JW, Chelette K, Skinner RD, Garcia-Rill E, Mennemeier M. 
Cold pressor stimulation diminishes P50 amplitude in normal subjects. Acta 
neurobiologiae experimentalis. 2011;71(3):348-358. 
65. 
Woods AJ, Philbeck JW, Wirtz P. Hyper-arousal decreases human visual 
thresholds. PloS one. 2013;8(4):e61415. 
66. 
Degutis JM, Van Vleet TM. Tonic and phasic alertness training: a novel 
behavioral therapy to improve spatial and non-spatial attention in patients with 
hemispatial neglect. Frontiers in human neuroscience. 2010;4. 
67. 
Van Vleet TM, Hoang-duc AK, DeGutis J, Robertson LC. Modulation of non-
spatial attention and the global/local processing bias. Neuropsychologia. Feb 
2011;49(3):352-359. 
68. 
Wolinsky FD, Unverzagt FW, Smith DM, Jones R, Stoddard A, Tennstedt SL. 
The ACTIVE cognitive training trial and health-related quality of life: protection 

Protocol IRB #201400328 
Page 36 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
that lasts for 5 years. The journals of gerontology. Series A, Biological sciences 
and medical sciences. Dec 2006;61(12):1324-1329. 
69. 
Antal A, Fischer T, Saiote C, et al. Transcranial electrical stimulation modifies the 
neuronal response to psychosocial stress exposure. Human brain mapping. Dec 
31 2013. 
70. 
Arlotti M, Rahman A, Minhas P, Bikson M. Axon terminal polarization induced by 
weak uniform DC electric fields: a modeling study. Conference proceedings : ... 
Annual International Conference of the IEEE Engineering in Medicine and 
Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 
2012;2012:4575-4578. 
71. 
Chrysikou EG, Hamilton RH. Noninvasive brain stimulation in the treatment of 
aphasia: exploring interhemispheric relationships and their implications for 
neurorehabilitation. Restorative neurology and neuroscience. 2011;29(6):375-
394. 
72. 
Clark VP, Coffman BA, Mayer AR, et al. TDCS guided using fMRI significantly 
accelerates learning to identify concealed objects. Neuroimage. Jan 2 
2012;59(1):117-128. 
73. 
da Silva MC, Conti CL, Klauss J, et al. Behavioral effects of transcranial Direct 
Current Stimulation (tDCS) induced dorsolateral prefrontal cortex plasticity in 
alcohol dependence. Journal of physiology, Paris. Dec 2013;107(6):493-502. 
74. 
Moreno-Duarte I, Morse LR, Alam M, Bikson M, Zafonte R, Fregni F. Targeted 
therapies using electrical and magnetic neural stimulation for the treatment of 
chronic pain in spinal cord injury. Neuroimage. Jan 15 2014;85 Pt 3:1003-1013. 
75. 
Nawani H, Kalmady SV, Bose A, et al. Neural Basis of tDCS Effects on Auditory 
Verbal Hallucinations in Schizophrenia: A Case Report Evidence for Cortical 
Neuroplasticity Modulation. The journal of ECT. Mar 2014;30(1):e2-4. 
76. 
Nitsche MA, Niehaus L, Hoffmann KT, et al. MRI study of human brain exposed 
to weak direct current stimulation of the frontal cortex. Clinical neurophysiology : 
official journal of the International Federation of Clinical Neurophysiology. Oct 
2004;115(10):2419-2423. 
77. 
Nord CL, Lally N, Charpentier CJ. Harnessing electric potential: DLPFC tDCS 
induces widespread brain perfusion changes. Frontiers in systems neuroscience. 
2013;7:99. 
78. 
Okano AH, Fontes EB, Montenegro RA, et al. Brain stimulation modulates the 
autonomic nervous system, rating of perceived exertion and performance during 
maximal exercise. British journal of sports medicine. Feb 27 2013. 
79. 
Radman T, Datta A, Ramos RL, Brumberg JC, Bikson M. One-dimensional 
representation of a neuron in a uniform electric field. Conference proceedings : ... 
Annual International Conference of the IEEE Engineering in Medicine and 
Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 
2009;2009:6481-6484. 
80. 
Rahman A, Reato D, Arlotti M, et al. Cellular effects of acute direct current 
stimulation: somatic and synaptic terminal effects. The Journal of physiology. 
May 15 2013;591(Pt 10):2563-2578. 

Protocol IRB #201400328 
Page 37 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
81. 
Reato D, Rahman A, Bikson M, Parra LC. Effects of weak transcranial alternating 
current stimulation on brain activity-a review of known mechanisms from animal 
studies. Frontiers in human neuroscience. 2013;7:687. 
82. 
Sehm B, Schafer A, Kipping J, et al. Dynamic modulation of intrinsic functional 
connectivity by transcranial direct current stimulation. Journal of neurophysiology. 
Dec 2012;108(12):3253-3263. 
83. 
Shah PP, Szaflarski JP, Allendorfer J, Hamilton RH. Induction of neuroplasticity 
and recovery in post-stroke aphasia by non-invasive brain stimulation. Frontiers 
in human neuroscience. 2013;7:888. 
84. 
Stagg CJ, Bachtiar V, Johansen-Berg H. The role of GABA in human motor 
learning. Current biology : CB. Mar 22 2011;21(6):480-484. 
85. 
Stagg CJ, Lin RL, Mezue M, et al. Widespread modulation of cerebral perfusion 
induced during and after transcranial direct current stimulation applied to the left 
dorsolateral prefrontal cortex. J Neurosci. Jul 10 2013;33(28):11425-11431. 
86. 
Stagg CJ, Nitsche MA. Physiological basis of transcranial direct current 
stimulation. The Neuroscientist : a review journal bringing neurobiology, 
neurology and psychiatry. Feb 2011;17(1):37-53. 
87. 
Turkeltaub PE, Benson J, Hamilton RH, Datta A, Bikson M, Coslett HB. Left 
lateralizing transcranial direct current stimulation improves reading efficiency. 
Brain stimulation. Jul 2012;5(3):201-207. 
88. 
Woods AJ, Hamilton RH, Kranjec A, et al. Space, Time, and Causality in the 
Human Brain. Neuroimage. Feb 18 2014. 
89. 
Zheng X, Alsop DC, Schlaug G. Effects of transcranial direct current stimulation 
(tDCS) on human regional cerebral blood flow. Neuroimage. Sep 1 
2011;58(1):26-33. 
90. 
Fregni F, Boggio PS, Santos MC, et al. Noninvasive cortical stimulation with 
transcranial direct current stimulation in Parkinson's disease. Movement 
disorders : official journal of the Movement Disorder Society. Oct 
2006;21(10):1693-1702. 
91. 
Kessler SK, Minhas P, Woods AJ, Rosen A, Gorman C, Bikson M. Dosage 
considerations for transcranial direct current stimulation in children: a 
computational modeling study. PloS one. 2013;8(9):e76112. 
92. 
Lang N, Nitsche MA, Paulus W, Rothwell JC, Lemon RN. Effects of transcranial 
direct current stimulation over the human motor cortex on corticospinal and 
transcallosal excitability. Experimental brain research. Jun 2004;156(4):439-443. 
93. 
Lang N, Siebner HR, Ward NS, et al. How does transcranial DC stimulation of 
the primary motor cortex alter regional neuronal activity in the human brain? The 
European journal of neuroscience. Jul 2005;22(2):495-504. 
94. 
Liebetanz D, Nitsche MA, Tergau F, Paulus W. Pharmacological approach to the 
mechanisms of transcranial DC-stimulation-induced after-effects of human motor 
cortex excitability. Brain. Oct 2002;125(Pt 10):2238-2247. 
95. 
Minhas P, Bikson M, Woods AJ, Rosen AR, Kessler SK. Transcranial direct 
current stimulation in pediatric brain: a computational modeling study. 
Conference proceedings : ... Annual International Conference of the IEEE 
Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and 
Biology Society. Conference. 2012;2012:859-862. 

Protocol IRB #201400328 
Page 38 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
96. 
Feng WW, Bowden MG, Kautz S. Review of transcranial direct current 
stimulation in poststroke recovery. Topics in stroke rehabilitation. Jan-Feb 
2013;20(1):68-77. 
97. 
Polanowska K, Seniow J. [Influence of transcranial direct current stimulation on 
cognitive functioning of patients with brain injury]. Neurologia i neurochirurgia 
polska. Nov-Dec 2010;44(6):580-590. 
98. 
Schlaug G, Renga V. Transcranial direct current stimulation: a noninvasive tool 
to facilitate stroke recovery. Expert review of medical devices. Nov 
2008;5(6):759-768. 
99. 
Schlaug G, Renga V, Nair D. Transcranial direct current stimulation in stroke 
recovery. Archives of neurology. Dec 2008;65(12):1571-1576. 
100. Berryhill ME, Jones KT. tDCS selectively improves working memory in older 
adults with more education. Neuroscience letters. Jul 19 2012;521(2):148-151. 
101. Cerruti C, Schlaug G. Anodal transcranial direct current stimulation of the 
prefrontal cortex enhances complex verbal associative thought. Journal of 
cognitive neuroscience. Oct 2009;21(10):1980-1987. 
102. Dell'Osso B, Zanoni S, Ferrucci R, et al. Transcranial direct current stimulation 
for the outpatient treatment of poor-responder depressed patients. European 
psychiatry : the journal of the Association of European Psychiatrists. Oct 
2012;27(7):513-517. 
103. Javadi AH, Walsh V. Transcranial direct current stimulation (tDCS) of the left 
dorsolateral prefrontal cortex modulates declarative memory. Brain stimulation. 
Jul 2012;5(3):231-241. 
104. Brunoni AR, Nitsche MA, Bolognini N, et al. Clinical research with transcranial 
direct current stimulation (tDCS): challenges and future directions. Brain 
stimulation. Jul 2012;5(3):175-195. 
105. Cohen Kadosh R, Soskic S, Iuculano T, Kanai R, Walsh V. Modulating neuronal 
activity produces specific and long-lasting changes in numerical competence. 
Current biology : CB. Nov 23 2010;20(22):2016-2020. 
106. Dockery CA, Hueckel-Weng R, Birbaumer N, Plewnia C. Enhancement of 
planning ability by transcranial direct current stimulation. J Neurosci. Jun 3 
2009;29(22):7271-7277. 
107. Hamilton RH, Chrysikou EG, Coslett B. Mechanisms of aphasia recovery after 
stroke and the role of noninvasive brain stimulation. Brain and language. Jul 
2011;118(1-2):40-50. 
108. Park SH, Seo JH, Kim YH, Ko MH. Long-term effects of transcranial direct 
current stimulation combined with computer-assisted cognitive training in healthy 
older adults. Neuroreport. Jan 22 2014;25(2):122-126. 
109. Ditye T, Jacobson L, Walsh V, Lavidor M. Modulating behavioral inhibition by 
tDCS combined with cognitive training. Experimental brain research. Jun 
2012;219(3):363-368. 
110. Lesniak M, Polanowska K, Seniow J, Czlonkowska A. Effects of Repeated 
Anodal tDCS Coupled With Cognitive Training for Patients With Severe 
Traumatic Brain Injury: A Pilot Randomized Controlled Trial. The Journal of head 
trauma rehabilitation. Jun 10 2013. 

Protocol IRB #201400328 
Page 39 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
111. Li SC, Schmiedek F, Huxhold O, Rocke C, Smith J, Lindenberger U. Working 
memory plasticity in old age: practice gain, transfer, and maintenance. 
Psychology and aging. Dec 2008;23(4):731-742. 
112. Martin DM, Liu R, Alonzo A, et al. Can transcranial direct current stimulation 
enhance outcomes from cognitive training? A randomized controlled trial in 
healthy participants. The international journal of neuropsychopharmacology / 
official scientific journal of the Collegium Internationale 
Neuropsychopharmacologicum. Oct 2013;16(9):1927-1936. 
113. Kim GW, Ko MH. Facilitation of corticospinal tract excitability by transcranial 
direct current stimulation combined with voluntary grip exercise. Neuroscience 
letters. Aug 26 2013;548:181-184. 
114. Krause B, Marquez-Ruiz J, Kadosh RC. The effect of transcranial direct current 
stimulation: a role for cortical excitation/inhibition balance? Frontiers in human 
neuroscience. 2013;7:602. 
115. Nitsche MA, Liebetanz D, Schlitterlau A, et al. GABAergic modulation of DC 
stimulation-induced motor cortex excitability shifts in humans. The European 
journal of neuroscience. May 2004;19(10):2720-2726. 
116. Ranieri F, Podda MV, Riccardi E, et al. Modulation of LTP at rat hippocampal 
CA3-CA1 synapses by direct current stimulation. Journal of neurophysiology. Apr 
2012;107(7):1868-1880. 
117. Tremblay S, Beaule V, Lepage JF, Theoret H. Anodal transcranial direct current 
stimulation modulates GABAB-related intracortical inhibition in the M1 of healthy 
individuals. Neuroreport. Jan 9 2013;24(1):46-50. 
118. Stopa EG, Butala P, Salloway S, et al. Cerebral cortical arteriolar angiopathy, 
vascular beta-amyloid, smooth muscle actin, Braak stage, and APOE genotype. 
Stroke; a journal of cerebral circulation. Mar 2008;39(3):814-821. 
119. Rapp MA, Schnaider-Beeri M, Purohit DP, Perl DP, Haroutunian V, Sano M. 
Increased neurofibrillary tangles in patients with Alzheimer disease with comorbid 
depression. Am J Geriatr Psychiatry. Feb 2008;16(2):168-174. 
120. Mortimer JA, Gosche KM, Riley KP, Markesbery WR, Snowdon DA. Delayed 
recall, hippocampal volume and Alzheimer neuropathology: findings from the 
Nun Study. Neurology. Feb 10 2004;62(3):428-432. 
121. Braak E, Griffing K, Arai K, Bohl J, Bratzke H, Braak H. Neuropathology of 
Alzheimer's disease: what is new since A. Alzheimer? Eur Arch Psychiatry Clin 
Neurosci. 1999;249 Suppl 3:14-22. 
122. Thal DR, Arendt T, Waldmann G, et al. Progression of neurofibrillary changes 
and PHF-tau in end-stage Alzheimer's disease is different from plaque and 
cortical microglial pathology. Neurobiol Aging. Nov-Dec 1998;19(6):517-525. 
123. Thal DR, Hartig W, Schober R. Stage-correlated distribution of type 1 and 2 
dystrophic neurites in cortical and hippocampal plaques in Alzheimer's disease. J 
Hirnforsch. 1998;39(2):175-181. 
124. Mizukami K, Grayson DR, Ikonomovic MD, Sheffield R, Armstrong DM. GABAA 
receptor beta 2 and beta 3 subunits mRNA in the hippocampal formation of aged 
human brain with Alzheimer-related neuropathology. Brain Res Mol Brain Res. 
May 1998;56(1-2):268-272. 

Protocol IRB #201400328 
Page 40 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
125. Jellinger KA, Bancher C. Senile dementia with tangles (tangle predominant form 
of senile dementia). Brain Pathol. Apr 1998;8(2):367-376. 
126. Nagy Z, Vatter-Bittner B, Braak H, et al. Staging of Alzheimer-type pathology: an 
interrater-intrarater study. Dement Geriatr Cogn Disord. Jul-Aug 1997;8(4):248-
251. 
127. Braak H, Braak E. Evolution of the neuropathology of Alzheimer's disease. Acta 
Neurol Scand Suppl. 1996;165:3-12. 
128. Knopman DS, Parisi JE, Salviati A, et al. Neuropathology of cognitively normal 
elderly. J Neuropathol Exp Neurol. Nov 2003;62(11):1087-1095. 
129. Raz N, Schmiedek F, Rodrigue KM, Kennedy KM, Lindenberger U, Lovden M. 
Differential brain shrinkage over 6 months shows limited association with 
cognitive practice. Brain Cogn. Jul 2013;82(2):171-180. 
130. Fjell AM, Westlye LT, Grydeland H, et al. Critical ages in the life course of the 
adult brain: nonlinear subcortical aging. Neurobiol Aging. Oct 2013;34(10):2239-
2247. 
131. Raz N, Yang YQ, Rodrigue KM, Kennedy KM, Lindenberger U, Ghisletta P. 
White matter deterioration in 15 months: latent growth curve models in healthy 
adults. Neurobiol Aging. Feb 2012;33(2):429 e421-425. 
132. Rodrigue KM, Haacke EM, Raz N. Differential effects of age and history of 
hypertension on regional brain volumes and iron. Neuroimage. Jan 15 
2011;54(2):750-759. 
133. Raz N, Ghisletta P, Rodrigue KM, Kennedy KM, Lindenberger U. Trajectories of 
brain aging in middle-aged and older adults: regional and individual differences. 
Neuroimage. Jun 2010;51(2):501-511. 
134. Burgmans S, van Boxtel MP, Gronenschild EH, et al. Multiple indicators of age-
related differences in cerebral white matter and the modifying effects of 
hypertension. Neuroimage. Feb 1 2010;49(3):2083-2093. 
135. Kennedy KM, Rodrigue KM, Head D, Gunning-Dixon F, Raz N. Neuroanatomical 
and cognitive mediators of age-related differences in perceptual priming and 
learning. Neuropsychology. Jul 2009;23(4):475-491. 
136. Head D, Rodrigue KM, Kennedy KM, Raz N. Neuroanatomical and cognitive 
mediators of age-related differences in episodic memory. Neuropsychology. Jul 
2008;22(4):491-507. 
137. Kennedy KM, Erickson KI, Rodrigue KM, et al. Age-related differences in regional 
brain volumes: a comparison of optimized voxel-based morphometry to manual 
volumetry. Neurobiol Aging. Oct 2009;30(10):1657-1676. 
138. Raz N, Rodrigue KM, Haacke EM. Brain aging and its modifiers: insights from in 
vivo neuromorphometry and susceptibility weighted imaging. Annals of the New 
York Academy of Sciences. Feb 2007;1097:84-93. 
139. Erickson KI, Colcombe SJ, Raz N, et al. Selective sparing of brain tissue in 
postmenopausal women receiving hormone replacement therapy. Neurobiol 
Aging. Aug-Sep 2005;26(8):1205-1213. 
140. Raz N, Lindenberger U, Rodrigue KM, et al. Regional brain changes in aging 
healthy adults: general trends, individual differences and modifiers. Cerebral 
cortex. Nov 2005;15(11):1676-1689. 

Protocol IRB #201400328 
Page 41 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
141. Raz N, Gunning-Dixon FM, Head D, Dupuis JH, Acker JD. Neuroanatomical 
correlates of cognitive aging: evidence from structural magnetic resonance 
imaging. Neuropsychology. Jan 1998;12(1):95-114. 
142. Raz N, Gunning FM, Head D, et al. Selective aging of the human cerebral cortex 
observed in vivo: differential vulnerability of the prefrontal gray matter. Cerebral 
cortex. Apr-May 1997;7(3):268-282. 
143. Raz N, Torres IJ, Acker JD. Age, gender, and hemispheric differences in human 
striatum: a quantitative review and new data from in vivo MRI morphometry. 
Neurobiol Learn Mem. Mar 1995;63(2):133-142. 
144. Raz N, Torres IJ, Briggs SD, et al. Selective neuroanatomic abnormalities in 
Down's syndrome and their cognitive correlates: evidence from MRI 
morphometry. Neurology. Feb 1995;45(2):356-366. 
145. Brickman AM, Provenzano FA, Muraskin J, et al. Regional white matter 
hyperintensity volume, not hippocampal atrophy, predicts incident Alzheimer 
disease in the community. Archives of neurology. Dec 2012;69(12):1621-1627. 
146. Zimmerman ME, Brickman AM, Paul RH, et al. The relationship between frontal 
gray matter volume and cognition varies across the healthy adult lifespan. Am J 
Geriatr Psychiatry. Oct 2006;14(10):823-833. 
147. Brickman AM, Habeck C, Zarahn E, Flynn J, Stern Y. Structural MRI covariance 
patterns associated with normal aging and neuropsychological functioning. 
Neurobiol Aging. Feb 2007;28(2):284-295. 
148. Paul RH, Haque O, Gunstad J, et al. Subcortical hyperintensities impact 
cognitive function among a select subset of healthy elderly. Arch Clin 
Neuropsychol. Aug 2005;20(6):697-704. 
149. Tate DF, Jefferson AL, Brickman AM, et al. Regional White Matter Signal 
Abnormalities and Cognitive Correlates Among Geriatric Patients with Treated 
Cardiovascular Disease. Brain Imaging Behav. Sep 1 2008;2(3):200-206. 
150. Jefferson AL, Tate DF, Poppas A, et al. Lower cardiac output is associated with 
greater white matter hyperintensities in older adults with cardiovascular disease. 
J Am Geriatr Soc. Jul 2007;55(7):1044-1048. 
151. Miller LA, Gunstad J, Spitznagel MB, et al. CAMTA1 T polymorphism is 
associated with neuropsychological test performance in older adults with 
cardiovascular disease. Psychogeriatrics. Sep 2011;11(3):135-140. 
152. Cohen RA, Poppas A, Forman DE, et al. Vascular and cognitive functions 
associated with cardiovascular disease in the elderly. J Clin Exp Neuropsychol. 
Jan 2009;31(1):96-110. 
153. Haley AP, Sweet LH, Gunstad J, et al. Verbal working memory and 
atherosclerosis in patients with cardiovascular disease: an fMRI study. J 
Neuroimaging. Jul 2007;17(3):227-233. 
154. Hoth KF, Tate DF, Poppas A, et al. Endothelial function and white matter 
hyperintensities in older adults with cardiovascular disease. Stroke; a journal of 
cerebral circulation. Feb 2007;38(2):308-312. 
155. Garrett KD, Browndyke JN, Whelihan W, et al. The neuropsychological profile of 
vascular cognitive impairment--no dementia: comparisons to patients at risk for 
cerebrovascular disease and vascular dementia. Arch Clin Neuropsychol. Sep 
2004;19(6):745-757. 

Protocol IRB #201400328 
Page 42 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
156. Alosco ML, Gunstad J, Jerskey BA, et al. The adverse effects of reduced 
cerebral perfusion on cognition and brain structure in older adults with 
cardiovascular disease. Brain Behav. Nov 2013;3(6):626-636. 
157. Irani F, Sweet LH, Haley AP, et al. A fMRI Study of Verbal Working Memory, 
Cardiac Output, and Ejection Fraction in Elderly Patients with Cardiovascular 
Disease. Brain Imaging Behav. Dec 2009;3(4):350-357. 
158. Haley AP, Hoth KF, Gunstad J, et al. Subjective cognitive complaints relate to 
white matter hyperintensities and future cognitive decline in patients with 
cardiovascular disease. Am J Geriatr Psychiatry. Nov 2009;17(11):976-985. 
159. Gunstad J, Bausserman L, Paul RH, et al. C-reactive protein, but not 
homocysteine, is related to cognitive dysfunction in older adults with 
cardiovascular disease. J Clin Neurosci. Jun 2006;13(5):540-546. 
160. Gunstad J, Cohen RA, Tate DF, et al. Blood pressure variability and white matter 
hyperintensities in older adults with cardiovascular disease. Blood Press. 
2005;14(6):353-358. 
161. Raz N, Williamson A, Gunning-Dixon F, Head D, Acker JD. Neuroanatomical and 
cognitive correlates of adult age differences in acquisition of a perceptual-motor 
skill. Microsc Res Tech. Oct 1 2000;51(1):85-93. 
162. Brickman AM, Zimmerman ME, Paul RH, et al. Regional white matter and 
neuropsychological functioning across the adult lifespan. Biol Psychiatry. Sep 1 
2006;60(5):444-453. 
163. Brickman AM. Contemplating Alzheimer's disease and the contribution of white 
matter hyperintensities. Curr Neurol Neurosci Rep. Dec 2013;13(12):415. 
164. Brickman AM, Buchsbaum MS, Shihabuddin L, Hazlett EA, Borod JC, Mohs RC. 
Striatal size, glucose metabolic rate, and verbal learning in normal aging. Brain 
Res Cogn Brain Res. Jun 2003;17(1):106-116. 
165. Brickman AM, Paul RH, Cohen RA, et al. Category and letter verbal fluency 
across the adult lifespan: relationship to EEG theta power. Arch Clin 
Neuropsychol. Jul 2005;20(5):561-573. 
166. Brickman AM, Schupf N, Manly JJ, et al. Brain morphology in older African 
Americans, Caribbean Hispanics, and whites from northern Manhattan. Archives 
of neurology. Aug 2008;65(8):1053-1061. 
167. Brickman AM, Sneed JR, Provenzano FA, et al. Quantitative approaches for 
assessment of white matter hyperintensities in elderly populations. Psychiatry 
Res. Aug 30 2011;193(2):101-106. 
168. Cohen R, Sweet LH. Neuroimaging in Behavioral Medicine and Clinical 
Neuroscience. New York, NY: Springer Publishing; 2012. 
169. Clark US, Cohen RA, Sweet LH, et al. Effects of HIV and early life stress on 
amygdala morphometry and neurocognitive function. J Int Neuropsychol Soc. Jul 
2012;18(4):657-668. 
170. Sweet LH, Hassenstab JJ, McCaffery JM, et al. Brain response to food 
stimulation in obese, normal weight, and successful weight loss maintainers. 
Obesity (Silver Spring). Nov 2012;20(11):2220-2225. 
171. Sweet LH, Mulligan RC, Finnerty CE, et al. Effects of nicotine withdrawal on 
verbal working memory and associated brain response. Psychiatry Res. Jul 30 
2010;183(1):69-74. 

Protocol IRB #201400328 
Page 43 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
172. McCaffery JM, Haley AP, Sweet LH, et al. Differential functional magnetic 
resonance imaging response to food pictures in successful weight-loss 
maintainers relative to normal-weight and obese controls. Am J Clin Nutr. Oct 
2009;90(4):928-934. 
173. Browndyke JN, Paskavitz J, Sweet LH, et al. Neuroanatomical correlates of 
malingered memory impairment: event-related fMRI of deception on a recognition 
memory task. Brain Inj. Jun 2008;22(6):481-489. 
174. David SP, Munafo MR, Johansen-Berg H, et al. Effects of Acute Nicotine 
Abstinence on Cue-elicited Ventral Striatum/Nucleus Accumbens Activation in 
Female Cigarette Smokers: A Functional Magnetic Resonance Imaging Study. 
Brain Imaging Behav. Dec 2007;1(3-4):43-57. 
175. Sweet LH, Paskavitz JF, Haley AP, et al. Imaging phonological similarity effects 
on verbal working memory. Neuropsychologia. Mar 7 2008;46(4):1114-1123. 
176. Sweet LH, Paskavitz JF, O'Connor MJ, Browndyke JN, Wellen JW, Cohen RA. 
FMRI correlates of the WAIS-III symbol search subtest. J Int Neuropsychol Soc. 
Jul 2005;11(4):471-476. 
177. Sweet LH, Rao SM, Primeau M, Durgerian S, Cohen RA. Functional magnetic 
resonance imaging response to increased verbal working memory demands 
among patients with multiple sclerosis. Human brain mapping. Jan 
2006;27(1):28-36. 
178. Sweet LH, Rao SM, Primeau M, Mayer AR, Cohen RA. Functional magnetic 
resonance imaging of working memory among multiple sclerosis patients. J 
Neuroimaging. Apr 2004;14(2):150-157. 
179. Sweet LH, Paul RH, Cohen RA, et al. Neuroimaging correlates of dementia rating 
scale performance at baseline and 12-month follow-up among patients with 
vascular dementia. J Geriatr Psychiatry Neurol. Dec 2003;16(4):240-244. 
180. Davis SW, Kragel JE, Madden DJ, Cabeza R. The architecture of cross-
hemispheric communication in the aging brain: linking behavior to functional and 
structural connectivity. Cerebral cortex. Jan 2012;22(1):232-242. 
181. Madden DJ, Costello MC, Dennis NA, et al. Adult age differences in functional 
connectivity during executive control. Neuroimage. Aug 15 2010;52(2):643-657. 
182. Davis SW, Dennis NA, Buchler NG, White LE, Madden DJ, Cabeza R. Assessing 
the effects of age on long white matter tracts using diffusion tensor tractography. 
Neuroimage. Jun 2009;46(2):530-541. 
183. Davis SW, Dennis NA, Daselaar SM, Fleck MS, Cabeza R. Que PASA? The 
posterior-anterior shift in aging. Cerebral cortex. May 2008;18(5):1201-1209. 
184. Chang L, Holt JL, Yakupov R, Jiang CS, Ernst T. Lower cognitive reserve in the 
aging human immunodeficiency virus-infected brain. Neurobiol Aging. Apr 
2013;34(4):1240-1253. 
185. Chang L, Lee P, Yiannoutsos C, et al. A multicenter In Vivo Proton MRS study of 
HIV-associated brain injury and the effects of aging. HIV MRS 
Consortium;Submitted. 
186. Chang L, Speck O, Miller EN, et al. Neural correlates of attention and working 
memory deficits in HIV patients. Neurology. Sep 25 2001;57(6):1001-1007. 

Protocol IRB #201400328 
Page 44 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
187. Chang L, Wong V, Nakama H, et al. Greater than age-related changes in brain 
diffusion of HIV patients after 1 year. J Neuroimmune Pharmacol. Dec 
2008;3(4):265-274. 
188. Ernst T, Chang L, Jovicich J, Ames N, Arnold S. Abnormal brain activation on 
functional MRI in cognitively asymptomatic HIV patients. Neurology. Nov 12 
2002;59(9):1343-1349. 
189. Ernst T, Itti E, Itti L, Chang L. Changes in cerebral metabolism are detected prior 
to perfusion changes in early HIV-CMC: A coregistered (1)H MRS and SPECT 
study. J Magn Reson Imaging. Dec 2000;12(6):859-865. 
190. Ernst T, Yakupov R, Nakama H, et al. Declined neural efficiency in cognitively 
stable human immunodeficiency virus patients. Ann Neurol. Mar 2009;65(3):316-
325. 
191. Watanabe T, Shiino A, Akiguchi I. Absolute quantification in proton magnetic 
resonance spectroscopy is useful to differentiate amnesic mild cognitive 
impairment from Alzheimer's disease and healthy aging. Dement Geriatr Cogn 
Disord. 2010;30(1):71-77. 
192. Ding B, Chen KM, Ling HW, et al. Diffusion tensor imaging correlates with proton 
magnetic resonance spectroscopy in posterior cingulate region of patients with 
Alzheimer's disease. Dement Geriatr Cogn Disord. 2008;25(3):218-225. 
193. Metastasio A, Rinaldi P, Tarducci R, et al. Conversion of MCI to dementia: Role 
of proton magnetic resonance spectroscopy. Neurobiol Aging. Jul 
2006;27(7):926-932. 
194. Ackl N, Ising M, Schreiber YA, Atiya M, Sonntag A, Auer DP. Hippocampal 
metabolic abnormalities in mild cognitive impairment and Alzheimer's disease. 
Neuroscience letters. Aug 12-19 2005;384(1-2):23-28. 
195. Modrego PJ, Fayed N, Pina MA. Conversion from mild cognitive impairment to 
probable Alzheimer's disease predicted by brain magnetic resonance 
spectroscopy. Am J Psychiatry. Apr 2005;162(4):667-675. 
196. Frederick BD, Lyoo IK, Satlin A, et al. In vivo proton magnetic resonance 
spectroscopy of the temporal lobe in Alzheimer's disease. Prog 
Neuropsychopharmacol Biol Psychiatry. Dec 2004;28(8):1313-1322. 
197. Pilatus U, Lais C, Rochmont Adu M, et al. Conversion to dementia in mild 
cognitive impairment is associated with decline of N-actylaspartate and creatine 
as revealed by magnetic resonance spectroscopy. Psychiatry Res. Jul 15 
2009;173(1):1-7. 
198. Waldman AD, Rai GS. The relationship between cognitive impairment and in vivo 
metabolite ratios in patients with clinical Alzheimer's disease and vascular 
dementia: a proton magnetic resonance spectroscopy study. Neuroradiology. 
Aug 2003;45(8):507-512. 
199. Pagonabarraga J, Gomez-Anson B, Rotger R, et al. Spectroscopic changes 
associated with mild cognitive impairment and dementia in Parkinson's disease. 
Dement Geriatr Cogn Disord. 2012;34(5-6):312-318. 
200. Gasparovic C, Prestopnik J, Thompson J, et al. 1H-MR spectroscopy metabolite 
levels correlate with executive function in vascular cognitive impairment. J Neurol 
Neurosurg Psychiatry. Jul 2013;84(7):715-721. 

Protocol IRB #201400328 
Page 45 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
201. Wang S, Yuan J, Guo X, et al. Neurochemical correlates of cognitive dysfunction 
in patients with leukoaraiosis: a proton magnetic resonance spectroscopy study. 
Neurol Res. Dec 2012;34(10):989-997. 
202. Griffith HR, Stewart CC, den Hollander JA. Proton magnetic resonance 
spectroscopy in dementias and mild cognitive impairment. Int Rev Neurobiol. 
2009;84:105-131. 
203. Ross AJ, Sachdev PS, Wen W, Valenzuela MJ, Brodaty H. 1H MRS in stroke 
patients with and without cognitive impairment. Neurobiol Aging. Jun 
2005;26(6):873-882. 
204. Chang L, Lee PL, Yiannoutsos CT, et al. A multicenter in vivo proton-MRS study 
of HIV-associated dementia and its relationship to age. Neuroimage. Dec 
2004;23(4):1336-1347. 
205. Lee PL, Yiannoutsos CT, Ernst T, et al. A multi-center 1H MRS study of the AIDS 
dementia complex: validation and preliminary analysis. J Magn Reson Imaging. 
Jun 2003;17(6):625-633. 
206. Yiannoutsos CT, Ernst T, Chang L, et al. Regional patterns of brain metabolites 
in AIDS dementia complex. Neuroimage. Nov 2004;23(3):928-935. 
207. Hua X, Boyle CP, Harezlak J, et al. Disrupted cerebral metabolite levels and 
lower nadir CD4 + counts are linked to brain volume deficits in 210 HIV-infected 
patients on stable treatment. Neuroimage Clin. 2013;3:132-142. 
208. Harezlak J, Buchthal S, Taylor M, et al. Persistence of HIV-associated cognitive 
impairment, inflammation, and neuronal injury in era of highly active antiretroviral 
treatment. AIDS. Mar 13 2011;25(5):625-633. 
209. Cohen RA, Harezlak J, Gongvatana A, et al. Cerebral metabolite abnormalities in 
human immunodeficiency virus are associated with cortical and subcortical 
volumes. Journal of neurovirology. Nov 2010;16(6):435-444. 
210. Paul RH, Ernst T, Brickman AM, et al. Relative sensitivity of magnetic resonance 
spectroscopy and quantitative magnetic resonance imaging to cognitive function 
among nondemented individuals infected with HIV. J Int Neuropsychol Soc. Sep 
2008;14(5):725-733. 
211. Paul RH, Gunstad J, Cooper N, et al. Cross-cultural assessment of 
neuropsychological performance and electrical brain function measures: 
additional validation of an international brain database. Int J Neurosci. Apr 
2007;117(4):549-568. 
212. Watanabe M, Maemura K, Kanbara K, Tamayama T, Hayasaki H. GABA and 
GABA receptors in the central nervous system and other organs. Int Rev Cytol. 
2002;213:1-47. 
213. Szabadics J, Varga C, Molnar G, Olah S, Barzo P, Tamas G. Excitatory effect of 
GABAergic axo-axonic cells in cortical microcircuits. Science. Jan 13 
2006;311(5758):233-235. 
214. Foster TC, Sharrow KM, Kumar A, Masse J. Interaction of age and chronic 
estradiol replacement on memory and markers of brain aging. Neurobiol Aging. 
Oct 2003;24(6):839-852. 
215. Foster TC, Kumar A. Calcium dysregulation in the aging brain. The 
Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry. 
Aug 2002;8(4):297-301. 

Protocol IRB #201400328 
Page 46 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
216. Foster TC, Sharrow KM, Masse JR, Norris CM, Kumar A. Calcineurin links Ca2+ 
dysregulation with brain aging. J Neurosci. Jun 1 2001;21(11):4066-4073. 
217. Foster TC. Involvement of hippocampal synaptic plasticity in age-related memory 
decline. Brain Res Brain Res Rev. Nov 1999;30(3):236-249. 
218. Foster TC, Norris CM. Age-associated changes in Ca(2+)-dependent processes: 
relation to hippocampal synaptic plasticity. Hippocampus. 1997;7(6):602-612. 
219. Baumgartner BJ, Harvey RJ, Darlison MG, Barnes EM, Jr. Developmental up-
regulation and agonist-dependent down-regulation of GABAA receptor subunit 
mRNAs in chick cortical neurons. Brain Res Mol Brain Res. Oct 1994;26(1-2):9-
17. 
220. Green EJ, Barnes CA, McNaughton BL. Behavioral state dependence of homo- 
and hetero-synaptic modulation of dentate gyrus excitability. Experimental brain 
research. 1993;93(1):55-65. 
221. Mizumori SJ, Barnes CA, McNaughton BL. Differential effects of age on 
subpopulations of hippocampal theta cells. Neurobiol Aging. Nov-Dec 
1992;13(6):673-679. 
222. Burke SN, Barnes CA. Senescent synapses and hippocampal circuit dynamics. 
Trends Neurosci. Mar 2010;33(3):153-161. 
223. Burke SN, Maurer AP, Yang Z, Navratilova Z, Barnes CA. Glutamate receptor-
mediated restoration of experience-dependent place field expansion plasticity in 
aged rats. Behav Neurosci. Jun 2008;122(3):535-548. 
224. Barnes J, Hinkley L, Masters S, Boubert L. Visual memory transformations in 
dyslexia. Percept Mot Skills. Jun 2007;104(3 Pt 1):881-891. 
225. Barnes P, Good M. Impaired Pavlovian cued fear conditioning in Tg2576 mice 
expressing a human mutant amyloid precursor protein gene. Behav Brain Res. 
Feb 10 2005;157(1):107-117. 
226. Barnes DE, Tager IB, Satariano WA, Yaffe K. The relationship between literacy 
and cognition in well-educated elders. The journals of gerontology. Series A, 
Biological sciences and medical sciences. Apr 2004;59(4):390-395. 
227. Barnes CA. Long-term potentiation and the ageing brain. Philos Trans R Soc 
Lond B Biol Sci. Apr 29 2003;358(1432):765-772. 
228. Barnes CA, Rao G, Orr G. Age-related decrease in the Schaffer collateral-
evoked EPSP in awake, freely behaving rats. Neural Plast. 2000;7(3):167-178. 
229. Barnes CA, Rao G, Houston FP. LTP induction threshold change in old rats at 
the perforant path--granule cell synapse. Neurobiol Aging. Sep-Oct 
2000;21(5):613-620. 
230. Barnes CA, Rao G, Shen J. Age-related decrease in the N-methyl-D-aspartateR-
mediated excitatory postsynaptic potential in hippocampal region CA1. Neurobiol 
Aging. Jul-Aug 1997;18(4):445-452. 
231. Rao G, Barnes CA, McNaughton BL. Effects of age on L-glutamate-induced 
depolarization in three hippocampal subfields. Neurobiol Aging. Jan-Feb 
1993;14(1):27-33. 
232. Nelson HE, Pantelis C, Carruthers K, Speller J, Baxendale S, Barnes TR. 
Cognitive functioning and symptomatology in chronic schizophrenia. Psychol 
Med. May 1990;20(2):357-365. 

Protocol IRB #201400328 
Page 47 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
233. Barnes CA. Effects of aging on the dynamics of information processing and 
synaptic weight changes in the mammalian hippocampus. Prog Brain Res. 
1990;86:89-104. 
234. Barnes CA. Aging and the physiology of spatial memory. Neurobiol Aging. Sep-
Dec 1988;9(5-6):563-568. 
235. Barnes CA. Memory deficits associated with senescence: a neurophysiological 
and behavioral study in the rat. J Comp Physiol Psychol. Feb 1979;93(1):74-104. 
236. Alexander GE, Ryan L, Bowers D, et al. Characterizing cognitive aging in 
humans with links to animal models. Front Aging Neurosci. 2012;4:21. 
237. Bizon JL, Foster TC, Alexander GE, Glisky EL. Characterizing cognitive aging of 
working memory and executive function in animal models. Front Aging Neurosci. 
2012;4:19. 
238. Roberson ED, Defazio RA, Barnes CA, et al. Challenges and opportunities for 
characterizing cognitive aging across species. Front Aging Neurosci. 2012;4:6. 
239. Nilsen LH, Melo TM, Saether O, Witter MP, Sonnewald U. Altered neurochemical 
profile in the McGill-R-Thy1-APP rat model of Alzheimer's disease: a longitudinal 
in vivo 1 H MRS study. J Neurochem. Nov 2012;123(4):532-541. 
240. Martin WR. MR spectroscopy in neurodegenerative disease. Mol Imaging Biol. 
Jul-Aug 2007;9(4):196-203. 
241. Broccardo C, Nieoullon V, Amin R, et al. ABCA2 is a marker of neural 
progenitors and neuronal subsets in the adult rodent brain. J Neurochem. Apr 
2006;97(2):345-355. 
242. Wang H, Lian K, Han B, et al. Age-Related Alterations in the Metabolic Profile in 
the Hippocampus of the Senescence-Accelerated Mouse Prone 8: A 
Spontaneous Alzheimer's Disease Mouse Model. J Alzheimers Dis. Nov 27 2013. 
243. Paul SM, Doherty JJ, Robichaud AJ, et al. The major brain cholesterol metabolite 
24(S)-hydroxycholesterol is a potent allosteric modulator of N-methyl-D-aspartate 
receptors. J Neurosci. Oct 30 2013;33(44):17290-17300. 
244. Nilsen LH, Rae C, Ittner LM, Gotz J, Sonnewald U. Glutamate metabolism is 
impaired in transgenic mice with tau hyperphosphorylation. J Cereb Blood Flow 
Metab. May 2013;33(5):684-691. 
245. Ferchmin PA, Perez D, Castro Alvarez W, Penzo MA, Maldonado HM, Eterovic 
VA. gamma-Aminobutyric acid type A receptor inhibition triggers a nicotinic 
neuroprotective mechanism. J Neurosci Res. Mar 2013;91(3):416-425. 
246. Koh MT, Rosenzweig-Lipson S, Gallagher M. Selective GABA(A) alpha5 positive 
allosteric modulators improve cognitive function in aged rats with memory 
impairment. Neuropharmacology. Jan 2013;64:145-152. 
247. Leon-Espinosa G, DeFelipe J, Munoz A. Effects of amyloid-beta plaque proximity 
on the axon initial segment of pyramidal cells. J Alzheimers Dis. 2012;29(4):841-
852. 
248. Krantic S, Isorce N, Mechawar N, et al. Hippocampal GABAergic neurons are 
susceptible to amyloid-beta toxicity in vitro and are decreased in number in the 
Alzheimer's disease TgCRND8 mouse model. J Alzheimers Dis. 2012;29(2):293-
308. 

Protocol IRB #201400328 
Page 48 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
249. Poil SS, Jansen R, van Aerde K, et al. Fast network oscillations in vitro exhibit a 
slow decay of temporal auto-correlations. The European journal of neuroscience. 
Aug 2011;34(3):394-403. 
250. Li G, Bien-Ly N, Andrews-Zwilling Y, et al. GABAergic interneuron dysfunction 
impairs hippocampal neurogenesis in adult apolipoprotein E4 knockin mice. Cell 
Stem Cell. Dec 4 2009;5(6):634-645. 
251. Rangel A, Madronal N, Gruart A, et al. Regulation of GABA(A) and glutamate 
receptor expression, synaptic facilitation and long-term potentiation in the 
hippocampus of prion mutant mice. PloS one. 2009;4(10):e7592. 
252. Molinaro G, Battaglia G, Riozzi B, et al. Memantine treatment reduces the 
expression of the K(+)/Cl(-) cotransporter KCC2 in the hippocampus and cerebral 
cortex, and attenuates behavioural responses mediated by GABA(A) receptor 
activation in mice. Brain Res. Apr 10 2009;1265:75-79. 
253. Matsuyama S, Taniguchi T, Kadoyama K, Matsumoto A. Long-term potentiation-
like facilitation through GABAA receptor blockade in the mouse dentate gyrus in 
vivo. Neuroreport. Dec 3 2008;19(18):1809-1813. 
254. Yuk DY, Lee YK, Nam SY, et al. Reduced anxiety in the mice expressing mutant 
(N141I) presenilin 2. J Neurosci Res. Feb 2009;87(2):522-531. 
255. Albuquerque EX, Pereira EF, Mike A, Eisenberg HM, Maelicke A, Alkondon M. 
Neuronal nicotinic receptors in synaptic functions in humans and rats: 
physiological and clinical relevance. Behav Brain Res. Aug 2000;113(1-2):131-
141. 
256. Alkondon M, Pereira EF, Yu P, et al. Targeted deletion of the kynurenine 
aminotransferase ii gene reveals a critical role of endogenous kynurenic acid in 
the regulation of synaptic transmission via alpha7 nicotinic receptors in the 
hippocampus. J Neurosci. May 12 2004;24(19):4635-4648. 
257. Edgar PF, Schonberger SJ, Dean B, Faull RL, Kydd R, Cooper GJ. A 
comparative proteome analysis of hippocampal tissue from schizophrenic and 
Alzheimer's disease individuals. Mol Psychiatry. Mar 1999;4(2):173-178. 
258. Griguoli M, Cherubini E. Regulation of hippocampal inhibitory circuits by nicotinic 
acetylcholine receptors. The Journal of physiology. Feb 15 2012;590(Pt 4):655-
666. 
259. Uehara T, Sumiyoshi T, Hattori H, et al. T-817MA, a novel neurotrophic agent, 
ameliorates loss of GABAergic parvalbumin-positive neurons and sensorimotor 
gating deficits in rats transiently exposed to MK-801 in the neonatal period. J 
Psychiatr Res. May 2012;46(5):622-629. 
260. Tao R, Li C, Newburn EN, et al. Transcript-specific associations of SLC12A5 
(KCC2) in human prefrontal cortex with development, schizophrenia, and 
affective disorders. J Neurosci. Apr 11 2012;32(15):5216-5222. 
261. Pattwell SS, Bath KG, Perez-Castro R, Lee FS, Chao MV, Ninan I. The BDNF 
Val66Met polymorphism impairs synaptic transmission and plasticity in the 
infralimbic medial prefrontal cortex. J Neurosci. Feb 15 2012;32(7):2410-2421. 
262. Vinkers CH, Oosting RS, van Bogaert MJ, Olivier B, Groenink L. Early-life 
blockade of 5-HT(1A) receptors alters adult anxiety behavior and benzodiazepine 
sensitivity. Biol Psychiatry. Feb 15 2010;67(4):309-316. 

Protocol IRB #201400328 
Page 49 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
263. Miczek KA, Yap JJ, Covington HE, 3rd. Social stress, therapeutics and drug 
abuse: preclinical models of escalated and depressed intake. Pharmacol Ther. 
Nov 2008;120(2):102-128. 
264. Hoschl C, Hajek T. Hippocampal damage mediated by corticosteroids--a 
neuropsychiatric research challenge. Eur Arch Psychiatry Clin Neurosci. 
2001;251 Suppl 2:II81-88. 
265. Motohashi N, Ikawa K, Kariya T. GABAB receptors are up-regulated by chronic 
treatment with lithium or carbamazepine. GABA hypothesis of affective 
disorders? Eur J Pharmacol. Jul 4 1989;166(1):95-99. 
266. Martin P, Pichat P, Massol J, Soubrie P, Lloyd KG, Puech AJ. Decreased GABA 
B receptors in helpless rats: reversal by tricyclic antidepressants. 
Neuropsychobiology. 1989;22(4):220-224. 
267. Lloyd KG, Morselli PL, Bartholini G. GABA and affective disorders. Med Biol. 
1987;65(2-3):159-165. 
268. Long Z, Li XR, Xu J, et al. Thalamic GABA Predicts Fine Motor Performance in 
Manganese-Exposed Smelter Workers. PloS one. 2014;9(2):e88220. 
269. Mullins PG, McGonigle DJ, O'Gorman RL, et al. Current practice in the use of 
MEGA-PRESS spectroscopy for the detection of GABA. Neuroimage. Feb 1 
2014;86:43-52. 
270. Shaw A, Brealy J, Richardson H, et al. Marked reductions in visual evoked 
responses but not gamma-aminobutyric acid concentrations or gamma-band 
measures in remitted depression. Biol Psychiatry. Apr 1 2013;73(7):691-698. 
271. Foerster BR, Callaghan BC, Petrou M, Edden RA, Chenevert TL, Feldman EL. 
Decreased motor cortex gamma-aminobutyric acid in amyotrophic lateral 
sclerosis. Neurology. May 15 2012;78(20):1596-1600. 
272. Stone JM, Dietrich C, Edden R, et al. Ketamine effects on brain GABA and 
glutamate levels with 1H-MRS: relationship to ketamine-induced 
psychopathology. Mol Psychiatry. Jul 2012;17(7):664-665. 
273. Dydak U, Jiang YM, Long LL, et al. In vivo measurement of brain GABA 
concentrations by magnetic resonance spectroscopy in smelters occupationally 
exposed to manganese. Environ Health Perspect. Feb 2011;119(2):219-224. 
274. Edden RA, Puts NA, Barker PB. Macromolecule-suppressed GABA-edited 
magnetic resonance spectroscopy at 3T. Magn Reson Med. Sep 2012;68(3):657-
661. 
275. Edden RA, Crocetti D, Zhu H, Gilbert DL, Mostofsky SH. Reduced GABA 
concentration in attention-deficit/hyperactivity disorder. Arch Gen Psychiatry. Jul 
2012;69(7):750-753. 
276. Michels L, Martin E, Klaver P, et al. Frontal GABA levels change during working 
memory. PloS one. 2012;7(4):e31933. 
277. Edden RA, Intrapiromkul J, Zhu H, Cheng Y, Barker PB. Measuring T2 in vivo 
with J-difference editing: application to GABA at 3 Tesla. J Magn Reson Imaging. 
Jan 2012;35(1):229-234. 
278. Edden RA, Barker PB. If J doesn't evolve, it won't J-resolve: J-PRESS with 
bandwidth-limited refocusing pulses. Magn Reson Med. Jun 2011;65(6):1509-
1514. 

Protocol IRB #201400328 
Page 50 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
279. Sisco SM, Marsiske M, Gross AL, Rebok GW. The influence of cognitive training 
on older adults' recall for short stories. J Aging Health. Dec 2013;25(8 
Suppl):230S-248S. 
280. Payne BR, Gross AL, Parisi JM, et al. Modelling longitudinal changes in older 
adults' memory for spoken discourse: Findings from the ACTIVE cohort. Memory. 
Dec 4 2013. 
281. Merzenich M. POSIT Science. 2014; http://www.positscience.com. 
282. Nadeau SE, Wu SS. CIMT as a behavioral engine in research on physiological 
adjuvants to neurorehabilitation: the challenge of merging animal and human 
research. NeuroRehabilitation. 2006;21(2):107-130. 
283. Batsikadze G, Moliadze V, Paulus W, Kuo MF, Nitsche MA. Partially non-linear 
stimulation intensity-dependent effects of direct current stimulation on motor 
cortex excitability in humans. The Journal of physiology. Apr 1 2013;591(Pt 
7):1987-2000. 
284. Trahan DE, Larrabee GJ, Quintana JW. Visual recognition memory in normal 
adults and patients with unilateral vascular lesions. J Clin Exp Neuropsychol. Dec 
1990;12(6):857-872. 
285. Larrabee GJ, Trahan DE, Curtiss G. Construct validity of the Continuous Visual 
Memory Test. Arch Clin Neuropsychol. Oct 1992;7(5):395-405. 
286. Paolo AM, Troster AI, Ryan JJ. Test-retest stability of the Continuous Visual 
Memory Test in elderly persons. Arch Clin Neuropsychol. Oct 1998;13(7):617-
621. 
287. Paolo AM, Troster AI, Ryan JJ. Continuous Visual Memory Test performance in 
healthy persons 60 to 94 years of age. Arch Clin Neuropsychol. May 
1998;13(4):333-337. 
288. Snitz BE, Roman DD, Beniak TE. Efficacy of the Continuous Visual Memory Test 
in lateralizing temporal lobe dysfunction in chronic complex-partial epilepsy. J 
Clin Exp Neuropsychol. Oct 1996;18(5):747-754. 
289. Hall S, Pinkston SL, Szalda-Petree AC, Coronis AR. The performance of healthy 
older adults on the Continuous Visual Memory Test and the Visual-Motor 
Integration Test: preliminary findings. J Clin Psychol. Jul 1996;52(4):449-454. 
290. Harker KT, Connolly JF. Assessment of visual working memory using event-
related potentials. Clinical neurophysiology : official journal of the International 
Federation of Clinical Neurophysiology. Nov 2007;118(11):2479-2488. 
291. Banos JH, Dickson AL, Greer T. A computer-assisted administration of the 
Continuous Visual Memory Test. Clin Neuropsychol. Dec 2001;15(4):551-555. 
292. Retzlaff PD, Morris GL. Event-related potentials during the Continuous Visual 
Memory Test. J Clin Psychol. Jan 1996;52(1):43-47. 
293. Woods AJ, Hamilton, et al., . Space, time, and causaltiy in the human brain. 
Neuroimage. 2014;in press. 
294. Woods AJ, Lehet M, Chatterjee A. Context modulates the contribution of time 
and space in causal inference. Front Psychol. 2012;3:371. 
295. Provencher SW. Automatic quantitation of localized in vivo 1H spectra with 
LCModel. NMR in biomedicine. Jun 2001;14(4):260-264. 

Protocol IRB #201400328 
Page 51 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
296. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from 
magnetic resonance images. Proceedings of the National Academy of Sciences 
of the United States of America. Sep 26 2000;97(20):11050-11055. 
297. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, 
flattening, and a surface-based coordinate system. Neuroimage. Feb 
1999;9(2):195-207. 
298. Fischl B, Sereno MI, Tootell RB, Dale AM. High-resolution intersubject averaging 
and a coordinate system for the cortical surface. Human brain mapping. 
1999;8(4):272-284. 
299. Andrade KC, Pontes-Neto OM, Leite JP, Santos AC, Baffa O, de Araujo DB. 
Quantitative aspects of brain perfusion dynamic induced by BOLD fMRI. Arq 
Neuropsiquiatr. Dec 2006;64(4):895-898. 
300. Leoni RF, Mazzeto-Betti KC, Andrade KC, de Araujo DB. Quantitative evaluation 
of hemodynamic response after hypercapnia among different brain territories by 
fMRI. Neuroimage. Jul 15 2008;41(4):1192-1198. 
301. Kim SG, Rostrup E, Larsson HB, Ogawa S, Paulson OB. Determination of 
relative CMRO2 from CBF and BOLD changes: significant increase of oxygen 
consumption rate during visual stimulation. Magn Reson Med. Jun 
1999;41(6):1152-1161. 
302. Kastrup A, Kruger G, Glover GH, Neumann-Haefelin T, Moseley ME. Regional 
variability of cerebral blood oxygenation response to hypercapnia. Neuroimage. 
Dec 1999;10(6):675-681. 
303. Posse S, Kemna LJ, Elghahwagi B, Wiese S, Kiselev VG. Effect of graded hypo- 
and hypercapnia on fMRI contrast in visual cortex: quantification of T(*)(2) 
changes by multiecho EPI. Magn Reson Med. Aug 2001;46(2):264-271. 
304. Lezak MD. Neuropsychological assessment in behavioral toxicology--developing 
techniques and interpretative issues. Scandinavian journal of work, environment 
& health. 1984;10 Suppl 1:25-29. 
305. DiGirolamo GJ, Kramer AF, Barad V, et al. General and task-specific frontal lobe 
recruitment in older adults during executive processes: a fMRI investigation of 
task-switching. Neuroreport. Jul 3 2001;12(9):2065-2071. 
306. Gross AL, Rebok GW, Brandt J, Tommet D, Marsiske M, Jones RN. Modeling 
learning and memory using verbal learning tests: results from ACTIVE. The 
journals of gerontology. Series B, Psychological sciences and social sciences. 
Mar 2013;68(2):153-167. 
307. Gunstad J, Cohen RA, Paul RH, Luyster FS, Gordon E. Age effects in time 
estimation: relationship to frontal brain morphometry. Journal of integrative 
neuroscience. Mar 2006;5(1):75-87. 
308. Jones RN, Rosenberg AL, Morris JN, et al. A growth curve model of learning 
acquisition among cognitively normal older adults. Experimental aging research. 
Jul-Sep 2005;31(3):291-312. 
309. Okonkwo OC, Cohen RA, Gunstad J, Tremont G, Alosco ML, Poppas A. 
Longitudinal trajectories of cognitive decline among older adults with 
cardiovascular disease. Cerebrovasc Dis. 2010;30(4):362-373. 

Protocol IRB #201400328 
Page 52 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
310. Paul RH, Brickman AM, Cohen RA, et al. Cognitive status of young and older 
cigarette smokers: data from the international brain database. J Clin Neurosci. 
May 2006;13(4):457-465. 
311. Roller CA, Cohen HS, Kimball KT, Bloomberg JJ. Effects of normal aging on 
visuo-motor plasticity. Neurobiol Aging. Jan-Feb 2002;23(1):117-123. 
312. Woods AJ, Mark VW, Pitts AC, Mennemeier M. Pervasive cognitive impairment 
in acute rehabilitation inpatients without brain injury. PM & R : the journal of 
injury, function, and rehabilitation. May 2011;3(5):426-432; quiz 432. 
313. Zimerman M, Nitsch M, Giraux P, Gerloff C, Cohen LG, Hummel FC. 
Neuroenhancement of the aging brain: restoring skill acquisition in old subjects. 
Ann Neurol. Jan 2013;73(1):10-15. 
314. Zlatar ZZ, Towler S, McGregor KM, et al. Functional language networks in 
sedentary and physically active older adults. J Int Neuropsychol Soc. Jul 
2013;19(6):625-634. 
315. Taha J, Czaja SJ, Sharit J, Morrow DG. Factors affecting usage of a personal 
health record (PHR) to manage health. Psychology and aging. Dec 
2013;28(4):1124-1139. 
316. Sharit J, Czaja SJ, Nair S, Lee CC. Effects of age, speech rate, and 
environmental support in using telephone voice menu systems. Hum Factors. 
Summer 2003;45(2):234-251. 
317. Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of 
habitual physical activity in epidemiological studies. Am J Clin Nutr. Nov 
1982;36(5):936-942. 
318. Richardson MT, Ainsworth BE, Wu HC, Jacobs DR, Jr., Leon AS. Ability of the 
Atherosclerosis Risk in Communities (ARIC)/Baecke Questionnaire to assess 
leisure-time physical activity. International journal of epidemiology. Aug 
1995;24(4):685-693. 
319. Philippaerts RM, Westerterp KR, Lefevre J. Doubly labelled water validation of 
three physical activity questionnaires. International journal of sports medicine. Jul 
1999;20(5):284-289. 
320. Philippaerts RM, Lefevre J. Reliability and validity of three physical activity 
questionnaires in Flemish males. American journal of epidemiology. May 15 
1998;147(10):982-990. 
321. Cella D, Riley W, Stone A, et al. The Patient-Reported Outcomes Measurement 
Information System (PROMIS) developed and tested its first wave of adult self-
reported health outcome item banks: 2005-2008. Journal of clinical epidemiology. 
Nov 2010;63(11):1179-1194. 
322. Magasi S, Ryan G, Revicki D, et al. Content validity of patient-reported outcome 
measures: perspectives from a PROMIS meeting. Quality of life research : an 
international journal of quality of life aspects of treatment, care and rehabilitation. 
Jun 2012;21(5):739-746. 
323. Tucker CA, Cieza A, Riley AW, et al. Concept Analysis of the Patient Reported 
Outcomes Measurement Information System (PROMIS) and the International 
Classification of Functioning, Disability and Health (ICF). Quality of life research : 
an international journal of quality of life aspects of treatment, care and 
rehabilitation. Feb 6 2014. 

Protocol IRB #201400328 
Page 53 of 53 
IRB version 03.09.04 
PI version 5/13/2019 
 
324. Reeve BB, Hays RD, Bjorner JB, et al. Psychometric evaluation and calibration 
of health-related quality of life item banks: plans for the Patient-Reported 
Outcomes Measurement Information System (PROMIS). Medical care. May 
2007;45(5 Suppl 1):S22-31.