Randomized Double-Blind Placebo-Controlled Trial: fMRI Assessment of Cranial Electrical Stimulation for Fibromyalgia in Veterans
Phase: NA
Conditions: Fibromyalgia
Sponsor: VA Office of Research and Development

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Title:  Randomized Double-Blind Placebo-Controlled Trial: fMRI Assessment of Cranial Electrical 
Stimulation for Fibromyalgia in Veterans
Principal Investigator:  Anna Woodbury, MD
Version Date: 10/20/2022
BACKGROUND AND SIGNIFICANCE
Fibromyalgia Prevalence, Function, and Impact
Chronic pain affects more individuals than heart disease, diabetes, and cancer combined, and the 
subjective nature of its assessment places it as a primary driver for the opioid epidemic, given the difficulty in 
discerning incompletely treated physical pain from substance use disorder and drug-seeking behavior.1  In 2016, 
63,632 drug overdose deaths occurred in the United States (a 21.5% increase from 2015), and opioids were 
found to be the main driver of these deaths (66.4% of all 2016 drug overdose deaths).3  Over 50% of veterans 
treated at VA hospitals suffer from chronic pain, and 63% of those who receive opioids for pain are also deemed 
high-risk patients with co-morbid psychiatric conditions.4  Thus, non-pharmacologic alternatives to opioids and 
other drugs are desperately needed for chronic pain treatment in order to slow the opioid epidemic, and this is 
particularly important in the veteran population where the risk of suicide and opioid use disorder is higher.  
Many chronic pain syndromes are difficult to treat, but one that is notoriously difficult and relies primarily 
on clinical judgement and diagnosis is fibromyalgia.  Fibromyalgia is a chronic pain syndrome that consists of 
chronic widespread pain, decreased physical function, fatigue, psychoemotional and sleep disturbances, and 
various somatic complaints affecting approximately 2-3% of the general population in the Americas (~8 million 
people in the U.S.).5  This syndrome is not only devastating to the patient, but also represents an economic 
burden to society due to disability and increased healthcare utilization.  It is estimated that fibromyalgia costs the 
U.S. population over $20 billion/year in lost wages and disability.6,7  Although the pathophysiologic mechanisms 
leading to development of the disease are not well-established, there is sufficient evidence to support the idea 
that fibromyalgia is a disorder of autonomic nervous system dysfunction8 and central (i.e. brain and spinal cord) 
pain processing mechanisms.9 A reliable clinical biomarker to guide fibromyalgia diagnosis and treatment is 
necessary to better serve these patients.  The current proposal addresses this gap by developing rs-fcMRI as a 
novel and reliable biomarker of 
pain severity and underlying 
neural circuitry in chronic pain.
Fibromyalgia 
affects 
women at a higher rate than 
men (estimated anywhere from 
7:1 to 9:1) in the general 
population.10  However, in the 
veteran population, a large 
number of males are also 
affected 
by 
fibromyalgia, 
according to data acquired at 
our institution (Fig 1).  In 
veterans deployed to Iraq and 
Afghanistan, the prevalence of 
fibromyalgia and other chronic 
multi-symptom 
illnesses 
is 
twice as high as in the general 
veteran population.11-15  This 
makes 
the 
syndrome 
particularly relevant to our 
contemporary 
veterans 
returning 
from 
Operation 
Enduring Freedom, Operation 
Iraqi Freedom, and Operation 
New 
Dawn.  
A 
better 
understanding of fibromyalgia 
and its treatments is necessary to prevent long-term morbidity and suffering in these young veterans. Currently 
Figure 1. Prevalence of Fibromyalgia in Atlanta VAHCS (2014-2015). A total of 
1,523 veterans with fibromyalgia were evaluated at the Atlanta VA in a one-year 
period.  Both male and female veterans are affected in relatively equal numbers, 
though the gender distribution varies with age.  A larger number of males are affected 
in the elderly veteran population, with a ~ 2:1 male to female ratio in the 20-50 age 
range at the Atlanta VA.  This figure demonstrates our ability to recruit both males 
and females aged 20-60 years from our underlying population.

available therapies often lead to intolerable side effects,16 and non-pharmacologic and complementary therapies 
are therefore often used as first-line treatments.17  
Innovation and Significance: The present proposal addresses several gaps in fibromyalgia research by 
1) investigating the clinical effect of cranial electrical stimulation (CES) using a FDA-cleared, non-invasive, non-
pharmacologic treatment for fibromyalgia and chronic pain 2) defining underlying neural substrates of 
fibromyalgia clinical pain and treatment response, and 3) applying rs-fcMRI as a predictor of treatment response.  
The evaluation of non-pharmacologic treatments for pain conditions such as fibromyalgia is of utmost importance 
in the current setting of the opioid epidemic and chronic pain as a risk factor for suicide,18,19 which is substantially 
increased among the veteran population.  The lack of adequate clinical biomarkers for pain is a recognized 
knowledge gap, leading to NIH-generated funding announcements to explore this area.  Using neuroimaging at 
multiple timepoints to assess treatment response is a novel approach to fibromyalgia evaluation, as is the 
utilization of auricular CES as a treatment.  Discoveries from this project related to fibromyalgia in veterans may 
have broader applications to the general chronic pain patient population. Further, this proposal directly fits in the 
Rehabilitation Research and Development (RR&D) Priority areas targeting “injuries, disorders and diseases with 
the potential to cause long term impairment and disability in the Veteran population; rehabilitation interventions 
and techniques designed to maximize motor, sensory, and psychological recovery; and endpoints that include 
functional outcomes of study subjects.”
Auricular CES as a Treatment
CES is an FDA cleared treatment for insomnia, depression, anxiety, and pain that consists of pulsed, 
alternating microcurrent applied via electrodes placed on the earlobes.  The mechanisms are not fully 
understood, though prior neuroimaging studies have explored the direct effect of CES on network 
connectivity.   Feusner et al. found that CES is associated with cortical deactivation for 0.5 Hz and 100 Hz 
frequencies in bilateral frontal, parietal and posterior midline regions and postulated that current intensity may 
be less critical than frequency of stimulation in relation to cortical deactivation.20 Their group found significant 
effects on some but not all nodes of the default mode network (Table 1). The authors suggest that based on this 
data CES may affect resting state functional connectivity and further exploration of the longer-term effects of 
daily treatment in relation to clinical improvement is needed, as well as how brain deactivation relates to 
previously observed decreases in electroencephalogram (EEG) frequencies21 to further understand the 
therapeutic mechanism of action.
Table 1. Regions of altered functional connectivity associated with CES stimulation at 100 Hz between the 
bilateral posterior cingulate gyrus (seed region) and other regions within the default mode network. 
Default mode network region
Z score
x, y, z
Increased connectivity
     Left planum temporale
3.87
−52, −34, 14
     Right postcentral gyrus 
3.56 
66, −14, 14
     Left supramarginal gyrus, anterior 
3.48 
−68, −26, 24
     Left postcentral gyrus 
3.4 
−68, −22, 24
     Right supramarginal gyrus, anterior 
2.89 
58, −26, 32
Decreased connectivity
     Left supramarginal gyrus, posterior 
3.34 
−42, −44, 34
     Left angular gyrus 
3.18 
−38, −58, 40
     Left lateral occipital cortex, superior
2.59 
−48, −62, 50
Z scores and MNI coordinates for local maxima (x, y, z) are given.  Table adapted from Feusner et al.20
There is some preliminary evidence to support the use of cranial electrical stimulation (CES) in the 
treatment of pain conditions including fibromyalgia.2,22  A 2001 study of 60 subjects randomized to active or sham 
CES for 3 weeks of daily 60 minute sessions revealed a 28% improvement in tender point scores, 27% 
improvement in general pain scores, and no placebo effect.23  In a 2013 study of 46 individuals with fibromyalgia 
primarily consisting of well-educated Caucasian females (>93% female), 17 participants were randomized to 
active CES, 14 to sham, and 15 controls to usual care.  Participants in the active CES and sham groups were 
instructed to use the Alpha-Stim CES device for 60 continuous minutes each day for 8 weeks. Participants in the 
CES device group received devices that were active and preset at the factory to provide maximum of 60 minutes 
of modified square-wave biphasic stimulation at 0.5 Hz and 100 μA, the lowest setting that has been used in 
previous studies with patients with FM and below the level of perception. Participants in the sham device group 

received sham devices that were identical to the active device, but did not deliver any electrical stimulation. 
Device use was monitored by asking participants to document at what time and for how long the device was 
used each day. Results of the study indicated that the active CES reduced average pain (p = .023), fatigue (p = 
.071), and sleep disturbance (p = .001) to a greater degree than the sham device or usual care alone over time. 
Participants using active CES also had improved functional status versus the other groups over time (p = .028).22  
The effects of alpha-stim are thought to be mediated through a direct action on the brain, and therefore 
this same research group examined fMRI outcomes in 6 subjects from the active and sham groups and found 
decreased BOLD signal in the posterior cingulate gyrus (p = 0.034), cingulate gyrus (p = 0.001), anterior cingulate 
(p = 0.0056) and thalamus (p = 0.031) from baseline to week 8 in the active vs. sham CES group (Fig 2).  An 
increase was observed in the insula (p = 0.044) and the prefrontal cortex (p = 0.0003) from baseline to week 8 
in the sham vs. active CES group.  No significant differences were found in the somatosensory cortices or 
amygdala between the two groups.2
While the study by Taylor et al. has evaluated neuroimaging in a small sample (6 subjects in 
active and sham groups) of primarily female fibromyalgia patients and has shown a positive effect of CES for 
clinical outcomes as well, CES has not been evaluated in a veteran population nor has it been adequately 
evaluated in males with fibromyalgia.  A VA-funded systematic review of CES published in 2018 concluded that 
Figure 2. fMRI data analysis (Excerpt from Taylor et al.) Mean representative images of changes in BOLD activation 
between the active device group versus the sham device group from baseline (pre-intervention) to week 8 (post-
intervention), showing increases in activity (orange) and decreases in activity (blue) in the (A–D) cingulate gyrus, (E and 
F) insula, and (G and H) prefrontal cortex. 2

evidence is insufficient for CES to have clinically important effects on fibromyalgia given that most trials had 
small sample sizes, short durations, and high risk of bias due to inadequate blinding.24  However, the review 
suggests that CES does not cause serious side effects and there is low-strength evidence to suggest modest 
benefit in patients with anxiety and depression.  Therefore, further research is warranted regarding the use of 
this low-risk device, particularly in fibromyalgia. Using the Taylor et al. study as preliminary data, we propose a 
study to evaluate the effects of CES in a double-blind, randomized, sham-controlled trial of active vs. sham CES 
in 50 male and female veterans diagnosed with fibromyalgia and aim to explore the neural substrates of CES-
induced analgesia using rs-fcMRI in all 50 subjects. 
Neuroimaging as a Biomarker 
Demonstrating neural changes as they relate to pain treatment is important to the field of fibromyalgia 
research, as these changes can provide a biomarker against which treatment success can be bench-marked 
and lead to the discovery of new treatment targets.   Currently, validated tools for analgesic response are 
primarily subjective.  The visual analogues scale, numerical rating scale, and DVPRS (Defense and Veterans 
Pain Rating Scale) are all validated, but subjective measures based on patient-reported outcome measures that 
can be affected by malingering and secondary gain.25  The subjective nature of pain and its assessment is a 
likely driver for the current opioid epidemic, in which physicians are unable to distinguish opioid-seeking related 
to poorly controlled pain from opioid-seeking related to substance use disorder.  Chronic pain is distinguished 
from acute pain primarily by the duration of disease, but is in reality a much more complex state that involves 
not only physical pain but also psychological distress, despair and anxiety.26 Because chronic pain is complex 
and often hard to objectively define, neuroimaging has been used in research to more objectively assess pain 
states, and has even been employed in litigation cases regarding pain.27,28  
Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and other 
neuroimaging techniques are now increasingly used to study the neural bases of pain.29,30  Several regions in 
the human brain have been identified as important for the perception of pain (Fig 3).  The perception of pain 
includes 
activation, 
at 
times 
bilaterally, 
of: 
1) 
the primary 
and 
secondary 
somatosensory 
cortices 2) insula, anterior cingulate cortex (ACC) and hippocampus and 3) prefrontal cortices (PFCs), as well 
as alterations in: subcortical areas (brainstem and periaqueductal grey [PAG], hypothalamus, amygdala and 
cerebellum) 
during 
painful 
experiences.29  Brain 
activity in response to 
pain can be localized 
using 
a 
technology 
called Blood Oxygen 
Level 
Dependent 
(BOLD) 
fMRI. 
The 
BOLD 
fMRI 
signal 
relies on local blood 
flow 
and 
metabolic 
changes in response 
to changes in neural 
activation. 
These 
localized changes in 
neural activation are 
then spatially mapped 
on the patient’s brain, 
and 
anatomically 
identified 
by 
an 
experienced neuroimaging scientist, such as the applicant’s primary mentor, Dr. Crosson. “Seeds,” which are 
pre-determined neuroanatomical areas of importance or regions of interest (ROIs) for the study can be localized 
and BOLD signal from these seeds correlated with the whole brain analysis to identify temporal correlations with 
other potentially important regions. 
Resting functional connectivity magnetic resonance imaging (rs-fcMRI) is a type of BOLD fMRI that 
examines the intrinsic connections in the brain when no external stimuli are applied.  The “resting state” evaluates 
the brain while the patient is lying at rest, but still awake, thus enabling researchers to investigate chronic, clinical 
Figure 3.  Areas Associated with Pain in Neuroimaging 
Research. Neurosynth is an on-line platform for automatically 
synthesizing the results of many different neuroimaging 
studies.  In a Neurosynth search of the term “pain” on 
November 26, 2018, an automated meta-analysis of 516 
studies mapped signals for brain regions of importance onto 
this standardized brain image.  This figure depicts the regions 
of interest associated with pain based on available 
neuroimaging literature.

pain at rest, as opposed to evoked, experimentally-applied pain.  The applicant’s co-mentor, Dr. Napadow, 
and others have observed altered cross-network connectivity between seeds of the somatosensory, salience, 
and default mode network(s) in chronic pain patients, including those with fibromyalgia, using rs-fcMRI.31-34  
Activity within the cingulo-frontal cortex, thalamus and PAG has been shown to be involved in distraction from 
pain, as well.35,36  With changes in clinical pain and pain processing, rs-fcMRI may identify unexpected regions 
that respond to CES in fibromyalgia patients. Thus, identifying neural plasticity related to clinical pain and 
treatment outcomes provides valuable information in both the assessment of pain as well as targeting treatment. 
Furthermore, evaluating brain reorganization during different stages of recovery following treatment (short and 
long-term) could also provide valuable clinical information for enhancing the effectiveness of treatment outcome. 
Several studies have brought attention to the potential for neuroimaging to be used as a biomarker for 
pain treatment outcomes and serve as the basis for the current proposal’s use of rs-fcMRI to evaluate aberrant 
network connectivity in fibromyalgia.  In a study by Harris, Napadow, et al., a reduction in clinical fibromyalgia 
pain was found to be associated with reductions in brain connectivity from the posterior insula to DMN regions 
during treatment with pregabalin but not placebo, and baseline neuroimaging values were able to predict 
analgesic response to pregabalin but not placebo.37 Another pharmacological treatment study revealed 
reductions in clinical pain associated with decreased functional connectivity between pain regions such as the 
rostral part of the ACC and the insular cortex, as well as the PAG and the insular cortex for fibromyalgia patients 
treated with milnacipran, but not placebo.38  Importantly, those with lower baseline functional connectivity had 
the greatest reduction in clinical pain, indicating the potential predictive value of rs-fcMRI as a biomarker.38 More 
recently, Napadow and colleagues have published a paper using rs-fcMRI as one of 3 multimodal metrics to 
predict clinical pain intensity with greater than 90% success.39    
Napadow and colleagues also explored the ability of a non-pharmacologic treatment (acupuncture) to 
modulate resting state connectivity in DMN and SMN, showing that true acupuncture but not sham increased 
DMN connectivity with pain, affective, and memory-related brain regions, and that increased DMN connectivity 
with the hippocampal formation was negatively correlated with increased sympathetic related heart rate 
variability while positively correlated with the parasympathetic related metric; the authors theorize that this 
modulation and sympathovagal response may help to explain acupuncture’s therapeutic effects.40  Another study 
by Napadow et al. correlated reductions in clinical pain related to reductions in intrinsic DMN to insula connectivity 
in 17 fibromyalgia patients undergoing nonpharmacologic acupuncture treatment, suggesting that intrinsic brain 
connectivity can be used as a biomarker of spontaneous chronic pain in fibromyalgia.41  These studies utilizing 
a non-pharmacologic treatment to alter rs-fcMRI through a possible sympathetically-mediated pathway serve as 
a basis for the present investigation. Thus, the experience of pain and its treatment involves complex interactions 
and diverse brain regions, necessitating further study.  
As evidenced by some of the already cited studies above, neuroimaging and rs-fcMRI can be applied 
more broadly to pain syndromes other than fibromyalgia, as well  The use of rs-fcMRI is superior to assessing 
clinical pain scores alone; while patient reported outcomes are important, they are subject to malingering and 
secondary gain.  Furthermore, rs-fcMRI can evaluate the neural correlates and mechanisms for the development 
of chronic pain.  Though this field of research is still evolving, rs-fcMRI research can lead to an improved 
understanding of how brain changes reflect or maintain pain.  Understanding mechanisms for the development 
of chronic pain and identifying neural correlates of pain and pain relief through the proposed study may help to 
differentiate between pain syndromes such as fibromyalgia, target treatment response in a personalized way, 
and lead to a set of reliable neuroimaging biomarkers to assess pain and treatment.42 
PRELIMINARY DATA & FEASIBILITY
In our CDA-1 feasibility study, were able to obtain baseline and follow-up rs-fcMRI as well as multiple 
functional outcomes at baseline, 4, 8 and 12 weeks following the completion of treatment in 21 subjects over the 
course of 2 years in an open-label trial evaluating an auricular stimulator for pain.  All subjects assigned to the 
“treatment” group completed the study, suggesting that the addition of a sham control will increase participant 
follow-through and study completion.  
The proposed CDA-2, a randomized, sham-controlled trial of auricular CES, will evaluate 1) the clinical 
utility of CES for fibromyalgia as compared to placebo control, 2) long-term CES-related neural changes 
visualized on rs-fcMRI and 3) the ability of rs-fcMRI to predict CES treatment response.  Should CES prove an 
effective treatment for fibromyalgia, the device could easily be implemented in the VA pain clinic through an 
existing VA contract, providing a safe and viable alternative to opioids and other pharmacologic therapies.  
Results from this study will also help to develop and refine rs-fcMRI as a clinical biomarker for pain assessment.

Tests of a Priori Hypotheses 
Aim 1. Primary outcome (improvements in clinical pain and function). 
We will investigate whether pain and functional improvements occur with the application of CES, as the 
ultimate goal of reducing pain is to improve function. Clinical pain, as measured by the DVPRS, will be our 
primary outcome measure of interest.  Dependent variables for the evaluation of functional improvements with 
CES include PROMIS measures, the arm curl, 30-s chair stand and handgrip strength tests at 1 and 12 weeks 
follow-up after the completion of the 4-week treatment period. Patient-reported changes in pain will be evaluated 
using 1) DVPRS severity scores and 2) analgesic consumption before and after treatment (1 week) and at long-
term follow-up (12 weeks) following completion of sham or active CES for each group.  The primary hypothesis 
is that active CES will show significant improvements post-treatment relative to pre-treatment in multiple domains 
as compared to sham CES. To test this hypothesis, pairwise repeated measures comparisons between post- 
and pre-treatment DVPRS, analgesic consumption and functional assessments will be performed within each 
group, FWE corrected to p<0.05. Similar analyses will be conducted at 12 weeks follow-up. Data from each time 
point can be considered its own family of comparisons for this purpose. Further, analysis of sample 
characteristics for the groups (active vs. sham CES) will be conducted to assess comparability of the samples. 
Categorical variables such as sex and biobehavioral data will be assessed using Fisher’s exact test, but 
continuous variables such as age will be assessed using two-tailed t-tests. All reported p-values will be 2-tailed 
and considered significant at the 0.05 level, FWE corrected. Data collected and analyzed regarding functional 
changes related to CES treatment will be assessed for new hypotheses.
We will use a 2-groups (active vs. sham CES) x 3 times (pre- and post- treatment at 1, and 12 weeks 
follow-up) analysis of the variance to compare outcome for the groups over time. We will employ a linear mixed 
effects (LME) modeling framework to fit the ANOVA model to the data. LME’s are more robust than traditional 
ANOVA methods when there is a possibility for imbalance in the effects due to attrition. Missing data resulting 
from dropout and other losses to follow-up can be addressed more easily in LMEs and give a stronger estimate 
of the fixed group effect while controlling for time as a random effect in the model. 
Aim 2. Secondary outcome (rs-fcMRI as a biomarker of treatment outcomes) 
The secondary outcome measure is connectivity between SMN and DMN, though we will also explore 
connectivity to the SN based on our preliminary data.  We hypothesize that active CES will increase connectivity  
between the seed and areas of the DMN based on prior mechanistic research using CES (Table 1). 20 We also 
expect a decrease in BOLD signal in the PCC, insula, thalamus, and other areas related to pain based on prior 
data.2  We expect that these changes will also be present 12 weeks following treatment, suggesting a mechanism 
for long-term treatment effects.  We do not expect to see similarly significant changes for the sham CES placebo 
control group. Each of the two correlations will be FWE corrected to p<.05 for two comparisons for each group. 
Generally, we expect no group differences at pre-treatment, but expect that between-group differences will 
emerge for networks specified a priori as well as exploratory analyses at the post treatment scans.43
Seed-voxel Functional Connectivity Approach (Hypothesis-Driven Analysis): Seed-based resting 
connectivity analyses between relevant areas based on our a priori hypotheses will be performed. The seeds for 
this analysis were chosen based on our preliminary data, as well as the literature on fibromyalgia, pain, and 
CES. 2,20,30-34,41,44,45 These seeds come from three networks: DMN, SMN, and SN. Table 2 identifies the seeds 
by network. Each seed will be correlated with all other seeds at the individual subject level using a Pearson 
correlation. Correlations will be converted to Z scores to perform for group analyses.  Changes in network 
connectivity will be tested using a 2 groups (active vs sham) by 2 times (pre-treatment vs post-treatment) analysis 
of variance (ANOVA). For each analysis, we will first test for the expected interaction term (α < .05, false 
discovery rate (FDR) corrected for number of tests within or between networks), followed by pre- vs post-
treatment pairwise comparisons for each group to confirm the expected pattern of change (α < 0.05, FDR 
corrected); we will also test baseline vs. follow-up to determine persistence of change. 
Table 2. DMN, SMN, and SN seeds chosen for analysis based on a priori hypotheses.
DMN seeds (x,y,z)
SMN seeds (x,y,z)
SN seeds (x,y,z)
mPFC31,46
Right Putamen34,41      
Right dlPFC31
Right PCC44
Left M144                  
Left anterior insula31
Left PCC20,46
Right M144               
Right anterior insula31
Precuneus2
Right S1-Hand47,48   
Left posterior insula34

Left S1-Hand47,48      
Right posterior insula32
Thalamus2
dACC49
Right TPJ31
Each seed is presented with references to prior literature supporting its testing in pain syndromes.
The seeds will be spherical, 1-cm diameter and centered on the MNI peak coordinates of regions of 
activity defined from prior published studies and preliminary data. The same seeds will be eroded to include only 
gray matter voxels using the Johns Hopkins University-International Consortium of Brain Mapping white-matter 
atlas.50 We will then correlate the averaged time series from the seed regions using AFNI. The resultant cross-
correlation coefficients will be converted to z-scores, which will act as the dependent variables for the primary 
outcome analysis with rs-fcMRI. The average time series from these distinct ROIs is used as a regressor in a 
whole brain GLM to find which other regions contain correlated time series. Thus, a limited number of seeds has 
been carefully chosen based on our preliminary data, previous studies, and known pain pathways.  All group 
analyses will use AFNI. We will further perform a linear regression with z-scores from rs-fcMRI correlations from 
seed voxels and baseline pain levels (DVPRS) in order to evaluate links between baseline resting connectivity 
and base-line individual differences in pain sensitivity. 
Hierarchical covariate-adjusted ICA (hc-ICA) Approach (Exploratory Analysis): The above approach is a 
granular comparison of hypothesized changes in node pairs. Hence, it is complemented by the following 
exploratory network-based approach that will allow us both to assess network level changes in function. The hc-
ICA, implemented by the HINT matlab toolbox,51 is a hierarchical model where the first-level model of hc-ICA 
decomposes a subject's fMRI signals into a product of subject-specific functional networks and subject-specific 
temporal mixing matrix to capture between-subject variabilities in the spatio-temporal processes in the functional 
networks. The second-level model of hc-ICA models subject-specific networks in terms of population-level 
networks, effects from subjects’ covariates and between-subject random variabilities. Compared to alternative 
methods such as dual regression ICA, hc-ICA has been shown to provide more accurate estimates of the brain 
functional networks on both the population- and individual-level and provide more reliable and powerful statistical 
tests for evaluating group differences in brain functional networks by directly modeling covariate effects in ICA 
decomposition.43 We plan to apply the hc-ICA to fMRI data to extract brain functional networks. We will include 
treatment group and DVPRS as the primary covariates in the hc-ICA and include age, sex, ethnicity, and other 
potential confounding variables. This will allow us to more accurately assess treatment group differences in the 
networks while controlling for potential confounding factors. Among the ICA-extracted brain networks, we will 
focus on DMN as our primary network and evaluate medial visual network (MVN) as a negative control, as we 
expect CES to have no effect on this primary visual sensory network. Based on hypothesis testing in hc-ICA, we 
will evaluate whether the brain intrinsic connectivity/network covaries with clinical pain and how networks change 
following CES in active and sham groups. The results will be threshold at p <0.05, cluster-corrected for multiple 
comparisons. We will also conduct exploratory analyses on other brain functional networks extracted by hc-ICA 
(corrected for multiple comparisons), which will provide an unbiased data-driven analysis to generate new 
hypotheses for future studies and grant submissions. 
Aim 3. Exploratory outcome (rs-fcMRI as a predictor of treatment response)
Our exploratory outcome measure utilizes rs-fcMRI as a predictor of treatment response.  Following 
completion of treatment and follow-up, we will separate treatment responders and non-responders into two 
groups. A positive response will be defined by a decrease in clinical pain of at least 2 points (defined by prior 
studies as the minimally clinically important difference)52 at 12 weeks.  Using the hc-ICA exploratory analysis 
described above, we will extract brain functional networks from baseline rs-fcMRI data in the active CES 
treatment group.  We will include treatment response and DVPRS as the primary covariates.  Based on 
hypothesis testing in hc-ICA, we will evaluate whether treatment response covaries with baseline brain 
connectivity and how baseline connectivity may differentiate CES responders from non-responders.  We will 
similarly analyze sham placebo responders and non-responders, and compare the two analyses for differences, 
as baseline connectivity in individuals with fibromyalgia may also be predictive of a placebo response.  The 
results will be threshold at p <0.05, cluster-corrected for multiple comparisons.  
After all analyses have been performed, we will use correlation coefficients to investigate the predictive 
ability of baseline resting SMN-DMN connectivity to predict post-CES changes in pain levels. Baseline 
connectivity will be extracted as z-scores from imaging data.  Correlations will be assessed using DensParCorr, 
a statistical package developed by collaborator, Dr. Ying Guo, that is designed to better eliminate false positive 

correlations.53 We will also use a linear regression model to explore any association between treatment-
modulated clinical pain and the change in resting brain connectivity.
Thus, should active CES show no significant clinical improvement relative to sham CES in group 
analyses, but clinically significant improvements among a sub-group of responders (decrease in pain score by 2 
points at 12 weeks), we can determine the neural correlates that separate responders from non-responders and 
evaluate the predictive value of baseline rs-fcMRI for treatment response.  This is distinguished from Aim 2, 
which evaluates correlations between changes in clinical pain and changes in rs-fcMRI network connectivity 
between groups.  Thus, the aims are independent of each-other.
RESEARCH DESIGN AND METHODS
Overview 
This project is designed as a double-blind, randomized, sham placebo-controlled trial to determine the clinical 
utility of auricular CES in veterans with fibromyalgia, the short and long-term effects of CES on fibromyalgia-
related altered network connectivity using rs-fcMRI as a biomarker, and the ability of baseline rs-fcMRI to predict 
treatment outcomes.  Eligibility criteria include:
•
Age 20-60 years old (limit set to minimize brain structural changes due to aging). 
•
Diagnosis of fibromyalgia by the American College of Rheumatology 2016 criteria.54 
•
Right-handedness, in order to provide consistency in brain structure and function.
•
Pain score of 4 or greater on DVPRS in the 3 months prior to enrollment.
•
Stable medication use related to FM for at least 4 weeks prior to enrollment.
•
Ability to safely tolerate MRI
Exclusion criteria include pregnancy; history of seizures or neurologic conditions that alter the brain; 
claustrophobia, MRI-incompatible implants, or other conditions incompatible with MRI; and history of 
uncontrolled psychiatric illness or autoimmune disease that leads to pain and can better explain symptoms. 
Subjects will be randomized to sham or active CES in addition to standard therapy (n=25 per group, 50 
total subjects, see “Sample Size” below).  Subjects will be block randomized, stratified for sex.  Baseline 
assessments and rs-fcMRI will be obtained prior to initiation of the intervention.  The intervention (both sham 
and active CES) will occur over the course of 12 weeks, and subjects will be assessed at 4 and 12 weeks post-
treatment initiation.  Follow-up rs-fcMRI will also be obtained at the 4 and 12 week timepoints during the 12 week 
course of treatment, to assess for short- and long-term changes in connectivity.  Each participant will receive 
$100 on completion of the study. Text messages may be used by the study team to communicate with subjects 
who have consented to participate. All text messages will be sent using an encrypted, VA-issued study iPhone. 
A summary of study phases is presented in Table 3.  
Table 3. Research Design
Time
Research Activity
Phase 1
Week 1
Recruitment, Screening, Enrollment
Phase 2
Week 2-3
Baseline Assessments, rs-fcMRI
Phase 3
Weeks 4-7
Intervention
Phase 4
Week 8-9
Acute follow-up, rs-fcMRI (1 wk post)
Phase 5
Week 20-21
Long-term follow-up, rs-fcMRI (12 wks post)
Each Cohort (1-5), consisting of 10 subjects randomized to either inactive sham or active CES, will proceed 
through Phases 1-5 as described in Table 2 over the course of the 5-year study period.
Sample Size
Three outcomes are used in our sample size calculations: DVPRS (Aim 1, clinical pain), 30 second 
chair stand test (Aim 1, function), and DMN-SMN connectivity (Aim 2, rs-fcMRI).  All power calculations are 
based on preliminary data presented above.  Clinical pain changes using DVPRS is chosen as our primary 
outcome of interest.  The 30-second chair stand test (30sCST) is chosen as our representative functional 
outcome, since it exhibited the smallest between group change.  DMN-SMN connectivity is chosen as our 
secondary outcome of interest as a neuroimaging biomarker for clinical pain and treatment response.  Sample 
size analyses were conducted assuming a significance of 1% and 80% power (2-sample, 1-sided).  
The primary hypothesis (Aim 1) is that there will be improvements in clinical pain and function for the 
active CES group as compared to sham placebo CES treatment.  The secondary hypothesis (Aim 2) is that 
increased DMN-SMN connectivity (L-PCC to L-S1M1) will be observed for the CES treatment group relative to 
the placebo control group.  For the PI’s CDA-1, we recruited 20 subjects over the period of 1.5 years without 

offering compensation, out of over 1,500 veterans with fibromyalgia seen annually at the Atlanta VAHCS (Fig 
1), thus demonstrating the feasibility of recruitment of veterans with fibromyalgia for a CES/rs-fcMRI study.  
Additionally, 12 subjects completed treatment in the 1.5 year period.  Ten subjects completed the study.  Two 
subjects were lost to follow-up (17%), and both were in the control group.  All active treatment subjects completed 
12-week follow-up.  Thus, we would expect a more evenly distributed and decreased drop-out rate when a sham, 
placebo device is used and compensation is offered.  Sample sizes required for our clinical outcomes are shown 
in Table 4, but we will power our study to our connectivity outcome, which requires a larger sample.
Based on our preliminary data, the mean (SD) of the post-treatment change in L-S1M1 to L-PCC 
connectivity is 0.041 (0.079) for the treatment group and -0.026 (0.049) for the standard treatment group; the 
observed between-group difference effect size is 1.03. We would need 20 subjects in each group to achieve 
80% power to detect the difference between the CES group and the standard treatment group in their post-
treatment change in connectivity for L-S1M1 to L-PCC at the significance level of 0.01 using a two-sided t-test, 
assuming the between-group difference effect size is 1.03 as observed in the pilot data. Though we observed a 
17% attrition rate for 12 subjects in the CDA-1 (all completed their follow-up MRI, but two were lost to follow-up 
at 8 weeks and 12 weeks), in order to maintain a conservative estimate for sample size calculations, we will 
assume a 20% attrition rate.  If we expect a 20% attrition at post-treatment visit, we will then need to recruit 
20/0.8 = 25 subjects per group. 
Table 3.  Sample Size Calculations
Control
Intervention
N per group
Mean 
Change
SD
Mean 
Change
SD
Clinical pain (DVPRS)
0.375
1.493
-1.833
2.229
10
Function (30sCST)
-0.250
1.500
3.000
4.980
14
Rs-fcMRI (S1M1 to PCC)
-0.026
0.049
0.041
0.079
20
Procedures
All study protocols will be approved by the Emory University/VA institutional review board (IRB) and VA 
R&D committee. A summary of the study protocol and timeline is provided in Figure 9. Patients will be pre-
screened using chart review of patients at the Atlanta VAMC and then invited via a phone call for a face-to-face 
screening session. At the screening session, an informed consent will be signed in accordance with ethical 
principles from the Declaration of Helsinki and the Ethical Committee at Karolinska Institutet. Then, the study 
physician (A.W.) will make assessments according to the stated inclusion and exclusion criteria. Subjects who 
meet study criteria will return for baseline assessments including rs-fcMRI, collection of biobehavioural 
information such as cognitive and psychological assessments, eating, sleeping and drinking habits, PROMIS 
measures including “physical function,” “anxiety,” “depression,” “fatigue,” “sleep disturbance,” “social function,” 
“pain interference,” “global health”, arm curl, 30s chair stand, handgrip strength tests, Defense and Veterans 
Pain Rating Scale (DVPRS) and documented baseline analgesic consumption.
To decrease the amount of face-to-face interaction with our participants, the study will be using 
a VA approved audio (telephone) or video communication (insert type here) to screen and consent 
participants. Recordings of the audio or visual communication will not be permitted.  If the participant 
meets initial eligibility criteria, we will mail or email through VA Outlook using Azure encryption two 
unsigned copies of the ICF/HIPAA.   We will ensure the individual has enough time after receiving the 
document to read it before scheduled phone/video call.  Trained staff will perform consenting process 
including speaking with the individual to discuss the study and highlighting each section of the 
consent form, allowing the participant an opportunity to ask questions before providing consent, and 
giving the participant enough time to consider being in the study.  Study team will inform the individual 
that if they would like to take more time to consider the study, another telephone call can be 
scheduled.  If the individual would like to participate, the participant will sign and date the document 
and return it to study team via mail (phone) or via email to your VA Outlook email address (video).  
Study team will write a “Note to File” that documents everything about the interaction including:  1. 

When and how the consent form was sent 2. When the video/telephone call was made 3. What was 
discussed during the call 4. When the signed consent form was received 5. When the signed consent 
form was signed by the person obtaining consent 6. When a copy of the consent form signed by both 
subject and study team was given to the participant and 7. A description of why signature dates are 
different (if applicable).  Once the study team receives the signed consent form, the person obtaining 
consent should sign the form and date it for the day it was signed. Study procedures will begin once 
the signed copy is received.  A copy of the fully signed consent form will be given to the participant 
via mail or in person at next scheduled visit. 
Figure 9.  Timeline for executing tasks over CDA-2 period
Stratification based on sex will be performed to account for differential pain perception and neurological 
responses to pain. Both men and women are included because despite the preponderance of women with 
fibromyalgia in the general population, there are more male veterans within the V.A. setting (Fig 1). Subjects will 
be stratified based on sex and block-randomized to either sham or active CES device using pre-set settings at 
the manufacturer (blinding both the researchers as well as the participants), then assessed for changes in pain 
and function at 4 and 12 weeks post-treatment initiation. The 4 week time-point is chosen as the acute phase 
following treatment initiation. However, the 12 week time point is chosen to evaluate longer-term neural changes 
as an indicator of neural plasticity. Participants will be instructed in device use by Dr. Kalangara, a board certified 
pain physician who will be blinded to active and sham groups. The PI, Dr. Woodbury, is a pain physician qualified 
to perform the pain and functional assessments and will perform these, similarly blinded to active and sham CES 
groups.  Randomization will be performed at the manufacturer, who will assign each device a study number and 
keep a record of whether they are receiving active or sham CES, thus maintaining blinding of the subject, the 
study physician (Dr. Kalangara), and the P.I. (Dr. Woodbury) until the subject has completed the follow-up period 
and data analyses can be performed.  At the end of the study period, subjects, study physician, and P.I. will be 
asked to evaluate whether they believed the participant received treatment, sham, or “don’t know”.  The research 
coordinator will have access to the list of study numbers from the device manufacturer in order to un-blind for 
study analysis.  The devices will be returned to the manufacturer and assessed for fidelity (to ensure that active 
devices remained active throughout the study period, and sham devices did not deliver active stimulation during 
the intervention period).  

Intervention 
CES will be applied using Alpha-Stim, loaned by the device manufacturer for research purposes. 
Randomization assignment will be established prior to the start of the study by the manufacturer of the Alpha-
Stim device through the assignment of unique numbers (active or sham) to devices. Devices will be assigned to 
subjects in the order listed on the device log. Baseline measures will be taken prior to start of treatment period 
and again at the endpoints of the study (4 weeks, 12 weeks). No change will be made in the medical management 
of the patient during the study. Participants in the active CES and sham groups will be instructed to use the 
Alpha-Stim CES device for 60 continuous minutes each day for 8 weeks. Participants in the CES device group 
will receive devices that are active and preset at the factory to provide maximum of 60 minutes of modified 
square-wave biphasic stimulation at 0.5 Hz and 100 μA, the lowest setting that has been used in previous studies 
with patients with fibromyalgia and below the level of perception. Participants in the sham device group will 
receive sham devices that are identical to the active device, but do not deliver any electrical stimulation. Device 
use will be monitored by asking participants to document at what time and for how long the device was used 
each day. 
Resting State Functional Connectivity Magnetic Resonance Imaging (rs-fcMRI): 
The design for our MRI acquisition protocol and data analysis will be performed in direct collaboration 
with Dr. Venkatagiri Krishnamurthy, a VA-funded investigator and CVNR Neuroimaging Core member with 
expertise in MR-physics and neurophysiology.  Baseline scans will be collected within 1 week prior to 
commencement of treatment and within 1 week of the final treatment to evaluate changes in resting state 
connectivity, using metrics our group has previously associated with chronic pain severity. BOLD rs-fcMRI 
images will be acquired on a 3T Siemens Prisma scanner with a 32-channel phased array head coil using a 
gradient echoplanar imaging (EPI) sequence with following MR parameters: FOV = 220 mm; TR/TE = 2000/25 
msec,  multiband-acceleration factor =3 ; flip angle = 60˚; 110 x 110 matrix size; slice thickness = 2mm;  GRAPPA 
factor = 2; Partial fourier of 6/8; 34 phase-encode reference lines, seventy-two interleaved axial slices covering 
the whole brain, roughly 320 scan volumes to yield eight minutes of resting state fMRI data for stable estimation 
of connectivity networks. T2/FLAIR scans also will be performed to evaluate any white matter disease that may 
occur in older veterans using validated evaluations for leukoaraiosis. Those with significant leukoaraiosis based 
on methodology established by Junque et al. will be excluded due to the potential effects of white matter disease 
on resting state connectivity metrics.55,56  
Prior to scanning, subjects will be asked to rate the intensity of their fibromyalgia pain as part of the 
DVPRS, a validated measure of pain for military and veteran populations.57 In the scanner, foam cushions will 
be used to reduce head movement and headphones to dampen scanner noise. Patients will be asked to keep 
their eyes open during the resting state scan. Physiological data will be collected simultaneously to the fMRI data 
(time-locked), as cardio-respiratory fluctuations are known to artifactually influence fMRI intrinsic connectivity 
estimation within several brain networks.58,59 Cardiac data will be acquired using an MR-compatible infrared 
pulse oximeter attached to the right middle finger and supplemented at times by an MRI compatible 4-lead ecg. 
Respiratory rate data will be acquired using an MR-compatible respiratory rate monitor and plethysmograph. De-
noising will be performed using validated tools (described below).  The basis for analyses of rs-fcMRI findings 
used in the present study is adapted from prior studies by Napadow and Hemington et al. using rs-fcMRI to 
examine intrinsic brain connectivity in fibromyalgia patients before and after treatment with pregabalin and 
acupuncture, as previously noted.31,37,45,60 Data will be preprocessed and analyzed using the validated FSL 
(FMRIB’s Software Library) package and cardiorespiratory physiologic artifacts will be mitigated using validated 
software packages. Further, artifacts related to subject motion will be minimized in rs-fcMRI time series using 
the validated ICA-FIX tool. This algorithm is a data-driven method to identify and reduce motion-related artifacts 
(ICA components) from fMRI data for structured noise removal.61 Though we have previously used ICA-FIX 
successfully, we will continue to collect cardiac and respiratory data in case ICA-FIX is insufficient for denoising 
in our sample population and RETROICOR or another denoising tool becomes necessary.
Co-registration of the functional images to a common template space will be accomplished in two steps. 
First, a boundary-based registration between the functional image and high resolution T1-MPRAGE 
(magnetization prepared rapid acquisition gradient echo) will bring the functional and structural data into co-
registration. Second, the high-resolution T1-MPRAGE will be co-registered to standard Montreal Neurological 
Institute (MNI) space using non-linear co-registration algorithms (FNIRT, FSL). The spatially normalized 
functional data will be temporally filtered using high-pass temporal filtering (f = 0.008 Hz) and smoothed using a 
Gaussian kernel (FWHM of 6 mm) to enhance the functional signal-to-noise ratio. In order to quantify brain 

connectivity, rs-fcMRI data will be analyzed with both a seed-voxel and ICA approach. These approaches are 
complementary, as they quantify intrinsic brain connectivity on a network level (dual regression pICA) and a 
more specific regional level (seed-voxel). 
DATA SECURITY AND MONITORING PLAN
Only personnel who are qualified and IRB approved and trained for maintaining the privacy of each 
participant will be permitted to view records. Each participant will be assigned a unique study ID that will be used 
on all study documents. All identified patient data will be maintained on a secure VA research server. All 
participant study documents which are identified by the participant’s unique study ID will be kept in locked filing 
cabinets in a locked research office accessible by study personnel only. Data collected on computers during 
assessment will be de-identified. These data will be exported as a text file and transferred to the secure VA 
network using a VA-issued USB drive.
Data will be monitored by Dr.Woodbury for quality control and any data questions will be discussed during 
study staff meetings.  Patients from both groups will be followed-up weekly during the 4-week treatment 
intervention period. Patients will also be given the clinic phone number and the P.I.’s cell phone number to 
contact if potential issues arise during treatment. If complications occur, the procedure will be aborted and the 
patient would not be included in the study. The practitioners are well-trained in resuscitation techniques since 
both Dr. Kalangara and Dr. Woodbury are anesthesiologists with ACLS training. Phone reminders and telephone 
contact will be made on a regular basis in order to encourage follow-up and compliance. Compliance can be 
easily assessed as described in the research protocol, since device removal is apparent and the device cannot 
be reinserted except by a skilled practitioner. Follow-up by phone will also be performed following the final fMRI 
to assess for any potential delayed reactions related to the device or to the MRI exposure. Data will be monitored 
by collaborator Dr. Jerry Kalangara during the treatment period. Dr. Anna Woodbury, P.I., will be blinded to 
treatment and control groups for fMRI acquisition and data analysis. All analysis will be done on VA approved 
servers using a data usage agreement with approved entities. All data security will follow local policy and 
procedures as outlined through Emory IRB and Atlanta VAHCS R&D.   
Adverse event reporting
In the case of a reportable event (Adverse Event, Serious Adverse Event, Unexpected Problem), the 
Principal Investigator will follow Atlanta VAHCS and Emory IRB reporting requirements.
POTENTIAL PITFALLS, LIMITATIONS, AND ALTERNATIVE STRATEGIES
Pitfalls
Potential pitfalls include 1) the possibility of adverse events 2) inadequate patient recruitment and 
possible non-compliance with the treatment modalities 3) potential for no correlation/connectivity in ROIs 4) 
potential effects of white matter disease on connectivity, and 5) possibility of bias and inadequate blinding should 
subjects be able to determine sham from active CES treatment.
Alternative Strategies
These potential pitfalls can be addressed as follows: 
1) Adverse events are unlikely, but may include dizziness or drowsiness on initial application. If syncope 
occurs, the practitioners are well-trained in resuscitation techniques since both Dr. Kalangara and Dr. Woodbury 
are anesthesiologists with ACLS training. In this case, the procedure would be aborted and the patient would not 
be included in the study.  Participants will be asked to use the device at home, preferably before bed time, in 
case of drowsiness. 
2)  Though our experiences has shown our participants are highly motivated, given the duration of the 
study, recruitment and drop-out may be an issue. Recruitment is feasible based on the preliminary data 
presented above, the number of cases seen at the Atlanta VAMC, and our demonstration of ability to fully recruit 
20 subjects within a 1.5 year period even with no compensation offered.  We anticipate that loss to follow-up will 
decrease with a monetary incentive to complete the study.  Dr. Rauch, Dr. Woodbury’s co-mentor, directs 
the Emory Veterans Program, which can serve as an additional base for recruitment, if needed.  Since the 
scanner at Emory University is a 5-minute drive from the VAMC pain clinic, it is reasonable for the patients to 

acquire their scans before or after appointments at the Atlanta VA. Most of our subjects are highly motivated to 
progress research in their condition and to explore modalities that may improve their pain and function. However, 
it is possible that some patients would remove the device if they find it uncomfortable and would be dropped 
from the study due to inadequate treatment duration. Analysis will be based on intent-to-treat. Phone reminders 
and telephone contact will be made on a regular basis to encourage continued participation. Patients will be 
interviewed for compliance and checked at weekly follow-ups. Subjects will also be offered compensation only 
after completion of the follow-up period.  Should the patient drop-out rate be too high, this may necessitate the 
recruitment of greater numbers of patients. Though our preference is to report and analyze only the data that is 
actually collected, a multiple imputation technique to handle missing data using a Mixed Effects models approach 
may be considered if missingness is a significant problem. 
3) We have made certain assumptions about our rs-fcMRI data and purposely used well-tested analyses 
in order to yield the highest power for a limited sample size. Although we have cited evidence to support 
investigating the specific regions of the SN, DMN, and SMN identified, it is possible that these areas will not be 
correlated in our study. Therefore, ancillary analysis of more traditional structures will also be performed, 
including placement of seeds in the ventral posterior medial nucleus of the thalamus. We will also plan to perform 
unbiased data-driven rs-fcMRI analyses using independent component analysis (hc-ICA approach, discussed 
above) for new hypothesis generation from this pilot study in case the stated aims of the hypotheses are not 
supported. It is possible that results will be discordant with previously published data regarding fibromyalgia and 
pain.  In previously published literature regarding transcranial magnetic stimulation, for example, both concordant 
and discordant results are present.  Given the novelty of the investigation using rs-fcMRI to evaluate auricular 
CES, it is entirely possible that the mechanism of altered cross-network connectivity differs from previously 
published studies.62  However, the results should still help to elucidate the neural substrates of fibromyalgia and 
CES-induced neural plasticity.  
4) White matter disease has the potential to effect connectivity. In order to minimize these structural 
changes from aging, we have limited our age range to veterans <60 years old. Further, any veterans who are 
found to have significant leukoaraiosis on T2/FLAIR will be excluded from the study and their primary care 
provider will be contacted for further evaluation of white matter disease.  
5) The active CES device and sham device are indistinguishable in terms of placement and exterior 
appearance.  However, the sham device will not deliver electrical stimulation.  To ensure adequate blinding, 
subjects will be surveyed at end of study to determine whether they believe they received active treatment.  
Potential Limitations
1) Possibility of Active Placebo: Pressure of the ear is similar to auricular acupressure and may result in 
some effects on pain and the limbic system, thus serving as an active placebo.  However, should no difference 
be found between sham and active CES, it would support the hypothesis that auricular acupuncture (or another 
modality similar to sham CES) could provide similar results to CES delivered via alpha-stim.  
2) Possibility of Non-CES Therapies Confounding: In the interest of study participant comfort and 
compliance, these subjects suffering from chronic pain will be allowed to continue their previously initiated pain 
medication regimen, unless an increase or decrease in analgesic consumption is indicated by the patient’s pain 
and functional measures.  In prior studies, pregabalin decreased insula-DMN connectivity, correspondent with 
decreasing pain scores.37  Thus, these pharmaceutical agents act as confounders in their ability to alter cross-
network connectivity, though they may do so in a different manner than our neuromodulatory technique.  Because 
of the possibility of confounding, analgesic consumption will be carefully monitored and documented throughout 
the study.  Documentation of cognitive behavioral therapy, acupuncture, and other techniques will also be 
performed, as these non-pharmacological techniques may exhibit synergistic effects with CES to induce neural 
plasticity and offer another avenue for further research and exploration.
Future Directions 
My primary goal is to obtain proficiency in using rs-fcMRI techniques for the study of chronic pain 
supported by the CVNR Neuroimaging Core so that I will have an additional, advanced tool for evaluating chronic 
pain patients and pain treatments in future studies. This tool is becoming accepted within the field of pain 
research as the most objective measure currently available for assessing the experience of pain. Recently, 
several research funding announcements from the NIH have called for an evaluation of biomarkers for pain, 
including neuroimaging techniques.  Thus, there is a well-known knowledge gap when it comes to adequate 
biomarkers for the assessment of chronic pain.  This especially holds true in the case of fibromyalgia.  Current 
tools are primarily based on self-report and are subject to possible malingering and secondary-gain. Analysis 

and interpretation of rs-fcMRI findings is a new skill that I have developed over the course of my CDA-1, and 
though I have gained an immense amount of knowledge regarding pre-processing steps for rs-fcMRI data, I 
believe that experience with in-depth data analytic and post-processing techniques are necessary to further my 
training and increase my ability to communicate and collaborate with other neuroimagers and imaging 
biostatisticians. When I have obtained more experience with neuroimaging, I intend to further investigate the 
pathophysiology of fibromyalgia, other pain syndromes and their treatments to better understand the neural 
correlates of pain and the mechanisms underlying pain and analgesia. If CES provides increased benefit and 
long-term changes to rs-fcfMRI connectivity, this would support the hypothesis that CES directly modulates a 
centrally-mediated pain mechanism in fibromyalgia.  
When this project is complete, we expect to have better insight into the neural substrates of pain related 
to fibromyalgia and CES-induced treatment effects.  This project has the potential to not only establish CES as 
a non-pharmacologic treatment for fibromyalgia, but also to further develop rs-fcMRI as a clinical tool to assess 
pain.  Based on the results of the proposed research project, I plan to submit Merit Review proposals:
A) rs-fcMRI as a Biomarker for Treatment Response in Fibromyalgia.  In our analysis of rs-fcMRI for 
CES-induced changes in veterans suffering from fibromyalgia, we may identify differences in rs-fcMRI among 
treatment responders and non-responders through our Aim 3 exploratory analysis.  The ability to identify and 
separate responders from non-responders using rs-fcMRI will allow us to use pre-treatment rs-fcMRI to gauge 
whether CES or another neuromodulatory technique (such as transcranial magnetic stimulation) will have a 
positive effect.  Furthermore, rs-fcMRI may help to distinguish or categorize certain sub-types of fibromyalgia, in 
line with the ideals of personalized, precision medicine.
B) CES-induced Neural Plasticity as a Primer for Biobehavioral Therapies.  Cognitive behavioral therapy 
(CBT) has been shown to induce brain changes related to neural plasticity for a variety of disorders, including 
social anxiety disorder, chronic fatigue syndrome, and chronic pain.  Gray matter volumes in the prefrontal cortex 
have been specifically implicated in chronic fatigue syndrome and chronic pain, which both share overlap with 
fibromyalgia syndrome, and CBT has been shown to slow gray matter loss in patients with fibromyalgia and 
insomnia.63  Should our results reveal that CES does indeed induce neural plasticity, it would be worthwhile to 
explore whether CES increases treatment response and neural plasticity related to CBT for chronic pain. 
C) CES-induced Gray Matter Changes.  Because gray matter (GM) abnormalities have also been 
identified in subjects with fibromyalgia, we plan to evaluate whether CES rectifies these GM changes in an 
exploratory analysis using data from the proposed investigation for further hypothesis generation.  Using 
previously reported discrete volumes of interest (VOIs) identified in fibromyalgia subjects,45,64,65 we will create 
masks of the VOI’s and apply them to our segmented data.  Areas previously identified to exhibit significant gray 
matter reductions in fibromyalgia patients include medial frontal and insular cortices, cingulate and 
parahippocampal gyri, among others.  This has been identified as a high priority area of research by the American 
Fibromyalgia Society.
D) Evaluation of DMN Stimulation on SMN/SN.  Our current proposal evaluates altered connectivity 
between the DMN and SMN/SN nodes.  To evaluate the proposed causal pathway that CES-induced pain relief 
is accomplished through changes in the DMN resulting in an effect on the SMN or SN, we will propose direct 
stimulation using rTMS or tDCS on specific nodes of the DMN and evaluate changes in the SMN and SN, as 
well as in clinical outcomes.
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