Combined Transcranial Magnetic Stimulation and Brief Cognitive Therapy to Reduce Suicide Behavior in High-Risk Veterans
Phase: NA
Conditions: Suicide
Sponsor: VA Office of Research and Development

1
Neuroimaging of a Combined Transcranial Magnetic Stimulation 
and Brief Cognitive Behavioral Therapy to Reduce Veteran Suicide
IRB 
Protocol
Version: 
12/19/2022
INTRODUCTION
This study proposes to conduct a fully-powered randomized controlled trial (RCT) 
that will evaluate whether adding repetitive Transcranial Magnetic Stimulation 
(TMS) to Brief Cognitive Behavioral Therapy (BCBT) can reduce suicidality. We 
will also collect magnetic resonance imaging (MRI) and cognitive testing data 
from study participants in order to examine phenotypic biomarkers of suicidality 
and correlates of response to treatment. The proposal will target high-risk suicidal 
Veterans at the Providence VAMC, and deliver these interventions in the highest 
risk period after a suicidal crisis. We will randomly assign enrolled suicidal 
Veterans to either active TMS+BCBT or sham (placebo) TMS+BCBT. Outcomes 
will include assessments at: baseline (i.e., enrollment); weekly intervention 
therapy sessions; endpoint (last intervention therapy session); follow-up 1 (6-
months from start of intervention); follow-up 2 (12-months from start of 
intervention); follow-up 3 (24-months from start of intervention). We will also 
collect MRI, cognitive, and health data from a limited number of non-suicidal 
Veteran control participants.
TABLE OF CONENTS
A. OBJECTIVES
B. BACKGROUND
C. PRELIMINARY STUDIES
D. RESEARCH DESIGN & METHODS
E. DATA ANALYSIS
F. PRIVACY, CONFIDENTIALITY, & INFORMATION SECURITY
G. PROTECTION OF HUMAN SUBJECTS
H. REFERENCES
 
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A. OBJECTIVES
 
 
Our study has two principle objectives: 1) to test the effect of combining 
repetitive TMS with BCBT on reducing Veterans’ rates of suicide ideation and 
related behaviors and 2) to identify brain-based biomarkers of suicidality and 
responsiveness to suicide treatment.
A1. Significance
Suicide prevention is a priority of the VHA with national leadership setting the goal 
of getting Veteran suicide to zero. This study is designed to: decrease suicide rates 
in Veterans during periods of increased risk (i.e., post-hospital discharge, following 
a suicidal crisis); add to our knowledge of how to maximize treatment benefits for 
Veterans with suicidality; discover whether combining TMS to an empirically-
based therapy for suicide increases effectiveness compared to BCBT monotherapy; 
and collect neurocognitive data that will facilitate future optimization of treatment 
delivery and prescription. Despite the promise of both treatment modalities – TMS 
and BCBT – to our knowledge no one has prospectively studied the combination of 
these two interventions. Furthermore, few studies conducted in Veterans have 
examined brain-based biomarkers of suicidality and/or response to interventions 
for suicide. Collectively, our team has expertise in suicide, TMS, and 
neuroimaging, and are well-qualified to evaluate the effectiveness and biological 
mechanisms of a combined TMS+BCBT treatment for suicide.
A2. Primary Hypotheses:

Compared to sham TMS+BCBT, Veterans receiving active TMS+BCBT will 
demonstrate greater reductions in suicidal behaviors.

Suicide symptom reductions after TMS+BCBT will be correlated with 
changes in brain function in executive control regions.
A3. Secondary Hypotheses:
Compared to sham TMS+BCBT, Veterans who receive active TMS+BCBT will have:

Reduced suicide attempts and/or longer time to first attempt.

Superior improvements in psychosocial functioning.

Reduced suicidal ideation severity.

Fewer psychiatric hospitalizations/crisis visits during the follow-up period.
A4. Moderator and Mediator Effects:
In addition to these specific hypotheses, we will conduct:

Exploratory latent growth curve analyses to compare the trajectory 
of change for primary, secondary, and tertiary outcomes (i.e. linear 
ascending,  descending,  or quadratic  between sham TMS+BCBT 
versus active TMS+BCBT.

We will include moderator variables in these models (e.g. diagnosis, 
comorbidity, gender), which may identify specific patient groups who will 
benefit from the addition of active TMS+BCBT versus sham TMS+BCBT.

We will investigate mediating variables to determine the mechanisms 
through which TMS produces its effects on clinical outcomes.
A5. Exploratory Brain-Behavior Hypotheses
 
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
Brain and behavioral correlates of decision-making, impulsivity, and 
executive control at baseline will differ between Veterans with suicidality 
and non-suicidal Veteran controls.
 
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
Improvement in suicide symptoms after treatment will be predicted 
by brain and behavioral correlates of decision-making and executive 
control at baseline.

Improvements in suicidality will be associated with functional changes 
in decision- making circuits.

Machine learning models predicting longitudinal outcomes that merge 
brain, cognitive, and electronic health record (EHR) data will outperform 
models based solely on EHR.
B. BACKGROUND
 
 
The tragedy of Veteran suicide cannot be overstated. Twenty Veterans die each 
day of suicide [1]. Suicide in female Veterans has increased substantially in the 
last 5 years [1]. Suicide attempt rates are also climbing. The Veterans’ Health 
Suicide Prevention Application Network (SPAN) reported that over 900 suicide 
attempts per month occurred in 2014 – an unacceptable increase from 600 per 
month in prior reports [1]. The presence of a single episode of suicidal ideation or 
attempt places an individual at greatly increased future risk [2]. Research in 
civilian populations demonstrated that suicide attempters are 38 times more likely 
to die of suicide than those without history of attempts [3]. In the year following a 
suicide attempt, 0.8 – 2.6% of previous attempters died of suicide; mortality 
estimates 9 years post attempt are as high as 5-11%[4].
One estimate suggests that approximately 15% of all individuals with one or more 
suicide attempts will eventually die of suicide [5].
B1. Despite advances in psychiatric treatment, suicide rates have not 
decreased Psychiatric illness and substance use disorders are strongly associated 
with completed suicides and suicide risk [3]. Given the comorbidity between 
psychiatric disorders and suicidal behavior, one might expect a decline in suicidal 
behavior with recent advances in psychiatric care and the increased availability of 
medications and psychotherapy treatments. Yet, nationwide suicide completion 
rates have increased since the year 2000. Therefore, even though treatment of 
psychiatric disorders has made significant progress, suicide prevention efforts lag 
behind. New treatments, specifically those developed to prevent suicidal behavior 
and designed to work in concert with mental health treatment, are clearly needed. 
The Veterans Health Administration has been at the forefront of suicide research 
making it a top funding priority. Despite this investment, between 2000-2014, 
military suicide rates increased surpassing civilian rates for the first time in 
recorded military history [6].
B2. Time following discharge from hospital or a suicidal crisis is of 
high risk The period following discharge from a psychiatric hospitalization is 
a time of substantially increased risk for suicide [3]. The highest risk period is 
in the first month after hospital
discharge [2, 7]. Risk remains elevated up to one-year post-discharge estimated at 
a 30 to 60- fold increase [2, 7, 8]. Veterans who have recently experienced an 
increase in suicidal ideation or attempted suicide (i.e., a suicidal crisis) even in the 
 
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absence of hospitalization are also at increased risk for suicide. Thus, to be 
maximally effective, prevention efforts should focus on the year following 
hospitalization or suicidal crisis, a time of uniquely high risk for suicide.
B3. What we Know About Psychotherapy Treatments to Prevent Suicide
B3.1 Few trials have been conducted: Despite the public health significance of 
suicide, few controlled trials have evaluated interventions to reduce suicidal 
behavior. In their literature review for the Cochrane Library, Hawton et al. 
(2000) [9] identified 23 controlled trials of
 
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suicidal behavior in adults. Though several additional studies have been published 
since this review [10-13], suicide treatment is clearly under-studied. Moreover, 
only a minority (< 20%) of these trials have been conducted in the United States, 
an important consideration impacting the generalization of findings given the 
differences in health care delivery systems between the US and other countries. 
Only one well-powered US-based published clinical trial evaluating an intervention 
to reduce suicidal behavior in adults has been conducted in the past 10 years [14]. 
Though a handful of trials are currently underway, the historical lack of US based 
clinical trials focusing on suicidal behavior is striking.
B3.2 Treatments are especially under-studied in Veterans: In 2015, a systematic 
review of suicide prevention in Veterans supported by the VA’s Health Services 
Research and Development Service (HSR&D) concluded that there have been 
“insufficient studies of suicide prevention specifically in Veterans” [15]. This 
report reviewed all published suicide prevention intervention efficacy or 
effectiveness studies conducted between 2008 and 2015 that included Veterans, 
military personnel, or demographically comparable non-Veteran/military adults 
from the United States, the United Kingdom, Canada, New Zealand, and Australia. 
Only five randomized controlled trials met these inclusion criteria. In addition to 
these reviewed studies, results from five earlier RCTs also resulted in published 
findings [16], but these studies did not target Veterans and only one recruited 
directly from a military population [54]. In response to the increase in Veteran 
suicide, the VA and Department of Defense have increased funding and resources 
for suicide prevention work with Veterans and military personnel. Most funded 
studies however, are in their infancy and only a modest number of clinical trials 
have been implemented on a large scale compared to other lines of mental health 
intervention research.
B3.4 Results from available psychotherapy studies are mixed: Virtually all reviews 
of psychotherapy for suicide [12, 13, 15] have commented on the paucity of 
studies, small sample sizes, small effects and inconsistent results. Notably, few of 
these studies were conducted in Veteran/military samples and HSRD’s review 
determined that many RCTs in Veteran/military samples were underpowered and 
yielded relatively small effect sizes [15]. The strongest findings come from 
individually based psychotherapy focused specifically on suicidal behavior, such as 
Dialectical Behavior Therapy [16] and Cognitive Therapy for Suicide Attempters 
[18].
Brief Cognitive Behavioral Therapy (BCBT) is an effective treatment for 
both depression and suicide [19] and works in military and VA settings [19]. In a 
recent study of military service members, suicide risk decreased 60% after 
participants received a course of BCBT [17] when compared to those who 
received treatment as usual. CBT is widely used by therapists throughout the VHA 
system and is considered standard of care for treatment of depression.
Although Cognitive Behavioral Therapies decrease psychiatric symptoms and 
suicide, effect sizes vary significantly across studies ranging from (d= .18-.79) 
[20], with most RCTs finding effect sizes in the small to moderate range.
B3.5 Enhancing efficacy is crucial for the reduction of Veteran Suicide
 
 :  In 
summary, suicide rates in Veterans continue to be unacceptably high and existing 
 
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treatments only reduce risk to a moderate degree. Furthermore, not all patients 
respond to standard psychotherapy treatments. Patients with severe symptoms 
tend to show smaller gains in symptom reduction after CBT than those with milder 
symptoms. These individuals are at significantly higher risk of dying by suicide. 
Consequently, finding ways to augment the effect existing treatments is critical.
B4. Transcranial Magnetic Stimulation: An ideal treatment to augment 
BCBT for suicide reduction
 
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B4.1 What is Transcranial Magnetic Stimulation? Our novel approach to augment 
CBT for suicide is with repetitive transcranial magnetic stimulation (rTMS, 
hereafter TMS). This noninvasive treatment has been available since 2008 when it 
was cleared by the U.S. Food and Drug Administration (FDA) for 
pharmacoresistant major depressive disorder (MDD). TMS uses a pulsed magnetic 
field to induce neuronal depolarization in a targeted brain region, typically the left 
dorsolateral prefrontal cortex for MDD. TMS can effectively reduce symptoms of 
MDD as shown in the initial two large efficacy studies [21, 22]. Since that time, 
our group has been involved in evaluating the efficacy of TMS in naturalistic 
samples [23, 24] durability of effect [25], and efficacy in older individuals [26] and 
Veterans with depression and PTSD [26, 27] .
A distinct feature of TMS is that it is not associated with any of the systemic and 
costly side effects associated with standard antidepressant pharmacotherapy (e.g., 
weight gain, diabetes, sexual side effects). The most common side effects from 
TMS are treatment site discomfort, but it is associated with a rare (<1:300,000) 
risk of seizure [28]. It is an outpatient procedure and does not require anesthesia 
– Veterans receive their TMS and then later the same day attend work, school or 
other activities without limitation.
B4.2 TMS
 
  and Suicide:
 
  Despite the large number of studies demonstrating the 
efficacy of TMS to reduce symptoms of MDD [29], fewer studies have evaluated 
whether TMS can reduce suicidal thinking. One early study used a very small 
sample size (n=14) and experimental form of TMS and found no difference 
between active and sham stimulation on suicide risk [30]. Since that time, several 
larger studies have found promising results. The largest study to date (n=178) 
found that active TMS rapidly reduced suicidal thinking, compared to sham, [31]. 
This study used treatment settings identical to those used in our clinic and 
proposed here. Another study, which used an experimental form of TMS in 
Veterans, found initial rapid reductions in suicide only occurred in the patients 
that received active stimulation [32].
To evaluate whether TMS was associated with reductions in suicidality at the 
Providence VA, we reviewed the charts of the last 45 patients who received TMS 
at our Neuromodulation clinic. We found significant and clinically meaningful 
reductions in suicidal ideation and depressive symptoms (see TMS Preliminary 
Data). However, despite the promise of this prior data, no studies have combined 
TMS with evidence-based therapy for suicide to evaluate whether the combination 
could meaningfully reduce suicidality.
B4.3 Mechanisms of action of TMS and why TMS+BCBT makes sense to reduce 
suicidality: TMS is an ideal treatment to add to psychotherapy targeting suicide 
for several reasons. TMS typically targets the dorsolateral prefrontal cortex 
(DLPFC), a brain region involved in “executive function” or “cognitive control” i.e. 
the adaptive, goal-directed regulation of thought and action. Regulation of 
affective states, stress, and emotion responsiveness are key functions of the 
cognitive control system [33]. Notably, cognitive control and emotion 
responsiveness are both disrupted in suicidality [34]. As TMS has been shown to 
improve cognitive control in multiple studies of ill individuals [35], TMS may also 
 
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ameliorate impaired control in suicidality. These neurobiological mechanisms of 
TMS have important implications for TMS. DLPFC is highly involved in CBT [36]. 
One possible reason the modest effect sizes for BCBT is that it is being used by 
individuals who are not able to engage DLPFC. The effects of TMS + BCBT are 
potentially additive –TMS monotherapy reduces suicidality and pairing it with 
BCBT may allow Veterans to better utilize and implement lessons learned in BCBT 
as a result of improved cognitive control. Indeed, several studies indicate that 
stimulation augments cognitive control training [37, 38].
 
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B5. Cognitive neuroscience approaches to suicide prevention.
B5.1 Neuroimaging and cognitive correlates of suicide: Suicide is a complex 
phenomenon and though we still have much to learn about the neurobiology of 
suicide, significant progress has been made over the last 30 years. Findings from 
previous MRI studies indicate that disruptions in the function and anatomy of the 
reward network are present in individuals with suicidality [39-41]. The reward 
network is comprised of ventral frontal cortex, striatum, and midbrain structures. 
Reward neurons compute the subjective pleasantness/unpleasantness of both 
current and expected sensations, objects, and events. Thus, these signals are an 
important component of the construction of emotion. As such, disruptions in the 
reward network may be central to emotional dysregulation in suicidality.
Beyond the reward network, cognitive control function has been implicated 
in how suicidal thoughts and behaviors are manifested. A recent influential model 
has proposed that suicide can be conceptualized as a maladaptive decision 
process, wherein the individual makes an instrumental choice to end their life 
[42]. Studies of decision-making have found a systematic relationship between the 
capacity to forego immediate rewards, and the lethality and planning of previous 
suicide attempts. Notably, those with history of highly planned and lethal attempts 
possess an exaggerated ability to delay gratification – a capacity normally 
associated with high cognitive control function [36,40]. Taken together, these 
theories and observations paradoxically suggest that in suicidal individuals, 
enhanced ability to control behavior may impart greater risk. Thus, neural 
correlates of cognitive control may prove to be a valuable biomarker of a high-risk 
subgroup, though more research in this area is needed.
B5.2 Objective bio-behavioral markers are needed to reduce Veteran suicide 
and optimize treatment:
Improving risk assessment tools is crucial for the early identification of at-risk 
Veterans and the prevention of Veteran suicide. Currently, assessments of risk are 
largely based upon self- reported suicidality. Though some patients accurately 
disclose suicidal thinking, others may be motivated to conceal or minimize their 
suicidality or may lack insight into their symptoms.
The findings discussed in section B5.1 indicate that including cognitive 
neuroscience approaches including neuroimaging and behavioral testing may be 
a promising means of understanding both the underlying pathology of suicide and 
the potential mechanisms of action for suicide interventions. Objective bio-
behavioral markers sensitive to suicide risk severity or specific symptoms 
(planning or lethality) may be helpful for improving the identification of those at 
most imminent risk of making a suicide attempt. Biological markers may also 
improve clinical treatment by informing innovative, targeted treatments such as 
neuromodulation of implicated functional circuits in suicidal Veterans. 
Neuroimaging and cognitive data collected from Veterans taking part in this 
study will be used to identify potential objective markers.
B5.3 New approaches to risk modeling are needed to reduce Veteran suicide: 
Even when risk information is accurate, traditional models based on a limited 
number of individual risk factors predict suicide with chance accuracy [43]. Since 
 
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assessments guide recommendations for proactive suicide interventions, clearly 
improving risk models is critical for prevention. Recent studies of electronic 
health record (EHR)-based machine learning models are promising, indicating 
that theses algorithms are better suited to the task of predicting multi-
dimensional behaviors like suicide. Further attempts to refine and enrich these 
models with novel objective suicide correlates may further improve risk 
surveillance. Machine learning algorithms may also be clinically useful for 
predicting the likelihood that individual patients will respond to
 
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treatments. Incorporating neurocognitive biomarkers identified from the proposed 
research into machine learning-based models of suicide is an avenue for improving 
risk assessment.
B6. Conclusion:
In summary, suicide rates in Veterans continue to be unacceptably high and 
existing treatments only reduce risk to a moderate degree but do not eliminate it. 
Furthermore, not all patients respond to standard psychotherapy treatments. 
Patients with severe depression and those with comorbid personality pathology 
tend to show smaller gains in symptom reduction after CBT than those with 
moderate-mild symptoms and no comorbid personality pathology.
Unfortunately, those with severe symptoms are at significantly higher risk of dying 
by suicide. Consequently, finding ways to augment the effect existing treatments is 
critical. TMS is an ideal treatment to add to brief cognitive behavioral therapy for 
suicide. It targets the same neurobiological mechanisms, it improves they types of 
executive functioning ability required for learning and retaining CBT skills, it is 
being rolled out at VAMCs across the country, and it may be a more acceptable 
treatment than pharmacotherapy. No studies have examined whether adding TMS 
to standard brief cognitive therapy increases the suicide prevention effect of BCBT 
therefore, we propose to examine whether combined TMS + BCBT enhances 
treatment outcomes. Importantly, by including MRI into the design of this study, 
we now have the methodological means to discover etiologically-relevant patterns 
brain function and treatment response.
C. PRELIMINARY
 
   
STUDIES C1. Pilot BCBT 
Data:
Co-Investigator Ivan Miller, PhD has trained, supervised and conducted research 
utilizing Cognitive Behavioral Therapy for 30+ years, including multiple clinical 
trials involving cognitive therapy for severely depressed and suicidal inpatients 
[44-47]. MPI Jennifer Primack, PhD is a clinical psychologist, national expert in 
suicide prevention, and has worked closely with Dr.
Miller on several clinical trials. Dr. Bryan co-developed the BCBT protocol that 
will be used in this study [19] and may provide consultation on therapist training 
and assessment of treatment fidelity.
C2. Pilot TMS Data:
MPI Noah Philip, MD directs the Neuromodulation clinic at Providence VA 
Medical Center. He has published evaluations of TMS outcomes [24], multisite 
efficacy studies of TMS [23,25], and has been the PI of studies involving TMS 
(RR&D I21 RX002032 and NIH R21 DA042989). To evaluate the feasibility of TMS 
for suicide and inform power analyses and study design, we conducted a chart 
review of the last 45 Veterans who received TMS at our Neuromodulation clinic at 
the Providence VA Medical Center. We compared baseline and endpoint scores of 
MDD using two standardized rating scales, the Inventory of Depressive 
 
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Symptomatology, Self-Report (IDSSR) [48] and 9-item Patient Health 
Questionnaire (PHQ9) [49]. We then evaluated whether treatment was associated 
with reductions in depressive symptoms and whether TMS treatment was 
associated with reduction in the suicide items (i.e., items 18 and 9 on the IDSSR 
and PHQ9, respectively). Depressive symptoms were reduced significantly post-
TMS (p<.001 for both the IDSSR and PHQ9). We also observed a significant 
reduction in suicide items on the IDSSR (p = .025) and PHQ9 (p=.016). 
Reductions in depressive and suicide symptoms were
 
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highly correlated (all r>.55, all p <.005). This preliminary data shows that not 
only can TMS reduce symptoms of depression, but it can also reduce suicidality.
C3. Pilot Neuroimaging Data:
MPI Jennifer Barredo, PhD is a cognitive neuroscientist that has conducted basic 
and clinical neuroimaging research, including research in Veterans and in 
suicidality. In a recent publication co-authored with MPI Philip, she conducted a 
secondary analysis that used structural and functional neuroimaging to 
characterize features of brain organization associated with suicidality in Veterans 
and civilians with PTSD. This study found evidence of a systematic relationship 
between self-reported suicidality (item 18 on the IDSSR) and the structural and 
functional organization of executive control regions involved in inhibition and 
reappraisal.
Another manuscript under review summarizes a secondary analysis that identified 
brain circuits where functional changes are correlated with suicide symptom 
reductions (change in IDSSR item 18) after TMS. These preliminary studies 
demonstrate the feasibility of using neuroimaging to identify correlates of suicide 
symptom reductions after TMS.
D. RESEARCH DESIGN
 
  &
    
METHODS D1. Participants
The study population will include Veterans 18-75 years old. They will be men or 
women from the greater Providence area receiving care at the VA who have been 
discharged from active duty, or who served in the National Guard or reserve units.
Two groups will be recruited. One group will consist of Veterans admitted to the 
PVAMC psychiatric inpatient unit or medical unit or identified in outpatient 
settings for suicide attempt or suicidal ideation with any degree of suicide plan, 
and/or intent to attempt suicide. This group of Veterans may be enrolled into 
either active or sham TMS+BCBT, if eligible. The second group will consist of 
DSM-V diagnosis-matched Veterans without current suicidality to act as controls. 
Controls will also be matched for age ± 5 years, and sex. The control Veterans, if 
eligible, may be enrolled in the neuroimaging and cognitive battery, and not be 
eligible to receive the TMS+BCBT intervention. The matching procedure is used to 
limit the influence disorder- specific characteristics, sex, and age-related 
brain/cognitive differences on group comparisons. If we are unable to meet annual 
recruitment goals, sex differences will be handled through statistical covariation 
and strata for age-matching will be broadened (±10 years).
D2. Participant Selection:
Participants will consist of Veteran inpatients or outpatients of the Providence 
VAMC with active suicide ideation or related behaviors, and non-suicidal 
Veteran controls. The inclusion and exclusion criteria are as follows:
D2.1.Inclusion
 
  criteria:
 
 
Inclusion
 
  criteria
 
  for
 
  all
   participants:
 
 
1)
Participants must be Veterans aged 18-75 discharged from active duty, 
National Guard, or reserves units, receiving care at the VA. They must 
be able to comply with all study related procedures and visits and be 
 
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capable of independently reading and understanding study materials 
and providing informed consent.
Inclusion
 
  criteria
 
  for
 
  Veterans
 
  with
 
  suicidal
 
  thoughts
 
  and
 
  behaviors:
 
 
1) Admission to a hospital or identified in outpatient settings within 2 weeks 
of suicidal ideation with any methods, intent and/or plans, or within 2 
weeks after an attempt as
 
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indicated on hospital chart and confirmed by administration of the 
Columbia Suicidal Severity Rating Scale (C-SSRS).
Inclusion
 
  criteria
 
  for
 
  non-suicidal
 
  Veterans
 
  (controls)
 
 
1) No evidence of current suicidality in the medical record or 
endorsement of current suicidal thoughts and behaviors on the C-
SSRS.
2) Matched to a participant with suicidal thoughts and/or behaviors 
on DSM-V diagnosis, age ± 5 years, and sex.
Exclusion
 
  criteria:
 
 
Exclusion
 
  criteria
 
  for
 
  all
   participants:
 
 
1) Primary psychotic disorder (e.g. schizophrenia, schizoaffective disorder)
2) Cognitive impairment (as indicated by neuropsychological 
evaluationsand/or diagnoses related to cognitive impairment [e.g., 
dementia] in the chart)
3) Pregnancy/lactation, planning to become pregnant during the study, 
childbearing potential and does not agree to consistent use of a 
measure of birth control during the scanning and TMS portions of the 
study. Confirmation of non-pregnancy among hospitalized patients will 
be obtained from the medical record review as pregnancy tests are 
required for psychiatric hospitalization. Pregnancy tests will be 
administered prior to MRI scanning and/or TMS for control participants 
or suicidal participants identified outpatient.
4) Lifetime history of moderate-to-severe traumatic brain injury.
5) Current unstable medical condition.
6) Current (or past, if appropriate) significant neurological disorder.
7) Lifetime history of: seizures, CNS tumors, stroke, cerebral aneurysm.
8) Active moderate-to-severe substance use disorder (within 
last month, excluding nicotine/caffeine dependencies).
Exclusion
 
  criteria
 
  specific
 
  to
   TMS+BCBT:
 
 
1) For safety, participants must meet established screening criteria 
following MRI safety. MRI involves magnetic fields at similar 
intensity to those emitted from the TMS stimulation coil. Unless 
devices are MRI-safe patients may not have:
a. Cardiac pacemaker, implanted device (deep brain stimulation) or 
metal in the brain, cervical spinal cord, or upper thoracic spinal 
cord
2) Other exclusions include conditions that could be worsened by TMS, such:
b. Bipolar I disorder or an increased risk of seizures in the 
case of severe and uncontrolled substance use disorder
3)   Patients who have previously received Theta Burst 
Stimulation cannot be enrolled in this study 
a) The risk of the patient recognizing the difference 
between active versus the sham would jeopardize the 
double-blinding framework proposed in this protocol as 
well as the credibility of reported data. 
Exclusion
 
  criteria
 
  specific
 
  to
   Neuroimaging:
 
 
 
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1) MRI contraindications (e.g., cardiac pacemaker, non-MRI safe implants, 
shrapnel, permanent makeup).
D2.2 Feasibility of enrolling targets: The enrollment, recruitment, and 
inclusion/exclusion criteria used in this study were modeled after procedures used 
by Dr. Jennifer Primack in her PVAMC IRB-approved study “Veterans Coping Long 
Term with Active Suicide.” Dr. Primack’s staff screened 168 potential patients 
from the PVAMC inpatient unit ultimately enrolling 100 participants in 3.5 years 
of active enrollment. In her study, thirty-five (21%) of screened individuals were 
excluded for with borderline personality disorder which is not exclusionary for this 
study. We expect that ~20% of potential participants will have an MRI 
contraindication based on exclusion rates from PVAMC IRB-approved studies (PI: 
Noah S. Philip, M.D.). Enrollment targets are feasible, especially given the longer 
5-year timeline of this study.
D3. Pre-screening, Recruitment, & Informed Consent
D3.1 Identification and Pre-Screening of Potential Participants: Pre-screening 
allows for the identification of a rare, low base rate study population. Pre-
screening will also allow us to identify potential diagnosis, sex, and age matched 
Veteran controls while minimizing the number of screens reducing burden for 
potential participants. We will administer MRI and TMS safety questions verbally 
to interested potential participants during either in-person or telephone pre-
screening to limit burden of completing informed consent for individuals that 
cannot be scanned or stimulated safely (e.g. shrapnel not reported in medical 
record, history of metal-work without proper protective gear, permanent makeup). 
Please see questionnaires for additional information.
D3.1.1
 
  Pre-Screening
 
  of
   Potential
 
  Participants
 
  with
 
  STBs.
 
 
A HIPAA waiver will be obtained for screening purposes. Veterans will be 
recruited from the psychiatric inpatient units or outpatient care at the 
Providence VAMC through three primary strategies:
1)Chart reviews: Research staff will screen charts daily for patients newly 
admitted  to the  inpatient  unit  for  suicidality.  Identified  charts  will  be 
screened for MRI and/or TMS contraindications (e.g. pacemakers) and other 
medical exclusionary criteria.
2) Suicide Prevention Coordinator (SPC) referrals: Research study staff 
will also check -in with the local SPC or SPC staff bi-weekly to identify 
potential study participants.
3) Clinician referrals: A member of the research staff will visit the 
inpatient unit weekly to consult with staff about potential study 
participants. Research staff will also introduce the study to mental health 
professionals at psychiatric outpatient clinics at the Providence VAMC.
For prospective patients in inpatient settings: If clinical staff deem that 
participation is appropriate for the Veteran under consideration, study staff 
or treatment staff familiar with the study will provide a description of the 
study and study aims (see attached Study Overview and Combined Study 
Overview). They will inform the patient that research staff will visit over the 
 
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next few days to provide further detail about the study and discuss if 
interested and eligible to participate.
For prospective patients in outpatient settings: We will give providers and 
clinicians IRB-approved study brochures (Study Overview and Combined 
Study Overview) to distribute to potential participants. If a potential 
participant expresses interest in the study, clinicians will send study staff 
an encrypted email with participant’s last name and last four SSN. We will 
then send the Veteran an official recruitment letter including information 
about when we plan to contact the Veteran. If the Veteran prefers not to 
wait for an official letter, they will be notified that they can to call study 
staff using the contact information on the recruitment brochure. IRB-
approved study brochures and flyers (Combined Study Flyer) will be posted 
in PVHCS outpatient waiting areas.
Interested participants may contact our study team using the contact 
information on the brochure and flyer at their own discretion.
Any pre-screening assessments collected from any Veteran who does not 
meet pre- screening eligibility criteria or who is uninterested in 
participating in the study will be destroyed in accordance with VA policy. 
Any assessments collected for these individuals will be destroyed in 
appropriate locked shredding bins available throughout the hospital and 
research building as soon as possible upon completion of the meeting. If a 
potential participant does not participate in the study, they will continue 
with their usual mental health care plans. If a potential participant declines 
to participate when approached or contacted by study staff, staff will ask for 
permission to take a one-question verbal survey as to why they are 
declining participation. This response will be recorded on a form by study 
staff. This response will not be linked to the patient in any way. This data 
will only be used by Investigators as a systematic way of understanding our 
recruitment population. 
Veterans deemed initially eligible at completion of pre-screen, endorsing 
suicidality in the prior 2 weeks(as indexed by the C-SSRS) and meeting 
MRI and/or TMS safety
requirements, will be asked to complete an informed consent form followed 
by baseline assessments as part of the study and to further confirm 
continued eligibility. This process can take 2 to 3 hours to complete.
D3.1.2
 
  Pre-Screening
 
  of
   Potential
 
  Participants
 
  without
 
  STBs
 
  (controls)
 
 
1)Chart reviews: Research staff will use CPRS to identify potential 
matched Veteran controls. Matching limits the influence disorder-specific 
characteristics, sex, and age- related brain/cognitive differences on 
comparisons between Veterans with and without STBs. If we are unable to 
meet recruitment goals, age-matching strata will be broadened (±10 years). 
We will review charts for MRI contraindications (e.g. pacemakers) and 
other medical exclusions such as primary psychotic disorder, history of 
moderate-to-severe TBI and by the age, sex, and DSM-V diagnosis of 
Veterans with STBs enrolled in the study. Study staff will send potentially 
eligible Veterans an official letter explaining the study and indicating that a 
 
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specific research staff member will contact the patient on a specific day 
about the study. Staff will call the patient on the designated date.
2) Clinician referrals: Study personnel may contact providers and staff in 
the Primary Care Behavioral Health, Psychiatric Neuromodulation, 
Polytrauma, Trauma Recovery Service clinics for referrals for potential 
control participants. Clinicians will ask if potential participants are 
interested in learning more about the study and if a patient expresses 
interest, the clinician will inform study staff. Study staff will then send the 
potentially eligible Veterans an official letter (see above). Or, if the Veteran 
prefers, the clinician will call study staff directly. If available, a member of 
the research staff will meet the potential participant at the clinician’s office 
and will walk them to PVAMC bldg. 32 where they will talk with the patient 
about the study and answer any questions.
D3.2 Pre-screening Log: The information reviewed during the initial medical 
record pre-screen process (e.g. Patient name, date of birth, telephone number, 
provider, and contact history) will be recorded in a recruitment log once the 
patient has been identified as potentially eligible. This information allows us to 
track recruitment efforts and contact potential eligible participants.
Additionally, this information is necessary for tracking recruitment, contacting 
patients, and conducting follow-up procedures. The recruitment log will be stored 
in a restricted folder on the secure Providence VA research server, which will be 
accessible only by study staff (see F. Privacy, Information, and Confidentiality 
Security for full details).
Due to the confined nature of our recruitment to one inpatient unit and outpatient 
referrals at the Providence VAMC, it will be necessary to keep this log until the 
end of the study. This log will be updated if any patient becomes ineligible or 
declines participation at the in-person pre- screening. It will also be updated to 
track and confirm we are not contacting/burdening the same patients over the 
course of the study who may have been ineligible or uninterested previously. It is 
necessary to keep recruitment log entries for ineligible or uninterested patients 
until the end of the study for this purpose.
At the end of the study, all recruitment log data from participants who were pre-
screened but not enrolled will be permanently deleted/destroyed. At all times, the 
recruitment log will be kept separately from all other data from this study (see F. 
Privacy, Information, and Confidentiality Security for full details).
D3.3 Enrollment and Informed Consent of Potential Participants: Group specific 
procedures are detailed below. Written informed consent will be obtained from all 
participants. Participants that consent to participate but are excluded during later 
assessments and interviews will receive
$25 compensation for their time.
D3.3.1
 
  Potential
 
  Participants
 
  with
 
  STBs
 
 
Informed consent and enrollment screening of Veterans with STBs will 
occur in-person while the participant is on the inpatient unit or during a 
 
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research appointment at PVAMC Building 32 for patients identified through 
outpatient settings. Research staff will coordinate with inpatient and 
referring outpatient staff to ensure that a) the patient is able to understand 
the study and research requirements and b) the informed consent, 
screenings, assessments, and intervention sessions are minimally 
disruptive. Patients that potentially meet study criteria after pre-screening 
will be provided with a description of the study and study aims, and asked if 
they would like to participate in a screening procedure to determine 
eligibility. If they are interested, they will complete informed consent prior 
to enrollment screening.
D3.3.2
 
  Potential
 
  Participants
 
  without
 
  STBs
 
  (controls)
 
 
All informed consent and enrollment screening will occur in-person in a 
private consult room in PVAMC bldg. 32. During this interview, potentially 
eligible participants will be provided with a description of the study and 
study aims and asked if they would like to participate in a screening 
procedure to determine eligibility. If they decide that they are interested in 
participating, they will complete informed consent prior to any assessment 
or study participation.
D3.3.3
 
  Contacting
 
  Participants
 
 :
Study staff may contact participants (potential or consented) via 
telephone or USPS mail. Materials sent via USPS may include COVID-19 
Participant Packet, approved recruitment materials, therapy treatment 
materials (i.e., handouts or worksheets, consistent with good clinical 
practice), or questionnaires discussed during follow-up telephone visits.
In the instances in which a participant does not have a permanent domicile 
and is unable to receive mail, or an in-person appointment needs to become 
a telephone visit due to inclement weather or pandemic concerns, study 
staff may send pertinent materials via encrypted electronic mail at the 
request of the participant. These emails will be sent using Azure RMS 
encryption. Participant email addresses will be obtained and confirmed 
during the consent process and associated risks will be explained. If the 
instance does occur where mailing will not suffice, study staff will again 
receive verbal confirmation from the participant before sending study 
materials via encrypted email.
The subject lines of the encrypted email will read “Requested Information 
Attached.” Encrypted emails sent to participants will not contain any 
PHI/PII and will contain the following text in the body:
“Please find attached the requested information. Email is not secure. 
Please do not reply back to this message with any personal information or 
health information. Please call x#### with any questions.”
In the event a participant replies to an encrypted email sent by study staff, 
study staff will NOT reply to the participant's email, but will instead call the 
participant to answer any questions.
 
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D4. Study Procedures
Participants will be assessed at baseline, fMRI visits (if eligible), during 
weekly therapy intervention sessions (if eligible), follow-up 1, follow-up 2, 
and follow-up 3.
Study 
Session
s
Baselin
e 
Visit
MRI 
1
As 
soon 
as 
sched
uling 
allows 
after 
baseli
ne 
visit 
Intervention
 
  Visits
 
 
Begin as soon as scheduling 
allows after  baseline visit
MRI
 
  2  
As soon as 
schedulin
g allows 
interventi
on 
completio
n
Follow
- Up 
Visits 
(3 
total) 
6-, 
12-,
and 24- 
months 
from 
first 
treatme
nt visit
Session 
Duratio
n
TMS
15 
mins.
BCBT
1 hour
Endpoin
t 2 
hours
2-3 hours
2
hour
s
(up to 5 
times/wee
k for 15-
36 visits)
(1
2
weekly 
session
s)
(at the 
last 
BCBT
session)
2 hours
2 hours
Compensatio
n
$75
($25
screening,
$50
$75
N/A
$50
$75
$50,
each 
follow
- up 
visit
completio
n)
D4.1
 
  Baseline
 
  Visit
 
 
Research staff will complete baseline assessment measures with eligible 
participants once screened, consented and confirmed eligible. The baseline 
assessments will be completed on the same day as the initial screening, consent 
and eligibility determination. If needed, baseline activities can be extended into a 
second day to lessen patient burden. Baseline visit activities for Veterans 
hospitalized for suicidality will take place in a private conference room within  
inpatient units (psychiatric and medical units being used to treat psychiatric 
patients) at the PVAMC under the supervision of the PI or trained study staff, or in 
a private room at Building 32  for patients identified through outpatient care. In 
the rare case that hospitalized patients cannot be screened on the unit (e.g., 
discharging too quickly to screen), baseline visit activities can occur in PVAMC 
building 32 under the supervision of the PI or trained study staff following 
discharge from the hospital. Activities for control participants will take place in a 
private consult room in PVAMC bldg. 32. Participants not on the in-patient unit 
will complete urine/breathalyzer screens and will be removed from the study for a 
failed drug screen.
 
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Participants will be compensated $75 for completion of the Baseline visit ($25 for 
completion of screening procedures, $50 for remainder of visit).
D4.1.1
 
  Assessments
 
 
Participants will be assessed in-person at baseline. In addition to demographic 
variables, we will conduct assessments in seven areas: a) psychiatric diagnosis, b) 
suicidality, c) trauma, d) depression and anxiety, e) sleep, f) and impulsivity. 
Measures are listed in Table 2. A Description of measures and their psychometric 
properties is limited to the references.
D4.1.1a
 
  Assessment
 
  Personnel
 
  Training
 
 
All assessments will be conducted by trained interviewers and will be supervised 
by study Co- PIs. Our training procedures consist of: a) review of relevant written 
materials, b) didactic instruction from senior staff and experienced interviewers, 
c) practice interviews with review, feedback and reliability ratings from senior 
staff and experienced interviewers and d) continued practice, training and 
feedback until high agreement with established consensus ratings are met (e.g. 
three consecutive C-SSRS ratings are within 2 points of our consensus ratings).
Additionally, all interviewers will be certified on the C-SSRS using established 
training procedures. Following initial training, all interviewers and senior staff will 
meet to review any questions/issues as needed. 
 
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Assessment
Collected At:
Demographics, Safety, and History
Demographics
BL
TMS Safety Form
BL
MRI Safety Form
BL
Treatment History
BL
Treatment Utilization (chart review)
BL, EP, F/U
Treatment History Interview (THI)
EP, F/U
TBS Blinding Assessment
EP
Diagnosis and Cognitive Impairment
Montreal Cognitive Assessment (MoCA)
BL**
Structured Clinical Interview for DSM-V (SCID-V)
BL
McLean Screen for Borderline Personality Disorder (MSBPD)
BL
Alcohol Use Disorders Identification Test (AUDIT)
BL, EP, F/U
Drug Use Disorders Identification Test (DUDIT)
BL, EP, F/U
Suicidal Thoughts and Behaviors
Columbia Suicidal Severity Rating Scale (C-SSRS), including 
Military- Specific Risk Assessment Questions
BL, EP, F/U
PhenX Beck Scale for Suicide Ideation (SSI)
BL, , I^, F/U*
PhenX Self-Injurious Thoughts and Behaviors 
Interview (SITBI, Items 51-72)
BL**, EP, F/U
Longitudinal Interval Follow-Up Evaluation (L.I.F.E.)
- sections: suicidal ideation and behavior
EP, F/U
Beck Hopelessness Scale (BHS)
BL, EP, F/U
Brief Symptom Inventory (BSI)
BL**, EP, F/U
Trauma
PTSD Checklist (PCL-5)
BL, I, EP, F/U
Childhood Trauma Questionnaire (CTQ)
BL**
Deployment Risk and Resilience Inventory (DRRI, Sexual 
Harassment, Combat Subscales)
BL**
Depression and Anxiety
Inventory of Depressive Symptomatology (IDS-SR)
BL, I, EP, F/U
Depression, Anxiety, and Stress Scale (DASS)
BL**, F/U
World Health Organization Disability Assessment Scale 2.0 
(WHODAS 2.0)
BL**, EP, F/U
Sleep
Pittsburgh Sleep Quality Index (PQSI)
BL**, F/U
Impulsivity
PhenX UPPS Impulsive Behavior Scale
BL**
The Barratt Impulsiveness Scale (BIS-11)
BL**
Table 2. Assessment battery.
BL = Baseline visit
BL** = If assessments are not completed at baseline visit, they may be completed at next 
research appointment (i.e., first MRI appointment)
I = Intervention – weekly (every 5 session) rating scales are assessed following standard 
PVAMC clinical neuromodulation treatment protocols
I^ = Assessment is given during the 12 weekly BCBT therapy visit
EP = 12th intervention therapy visit, 12 weeks (3 months) from start of intervention
F/U = 6-, 12-, and 24- month follow-up visits. Veterans will attend the follow-up visits per study 
involvement as described in Section D4.1 Study Visits.
F/U* = Measure is not administered in the absence of history of STBs
 
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D4.2
 
  MRI
 
  Visits
 
  and
 
  Cognitive
 
  Battery
 
 
All MRI visit activities (pre-intervention/post-intervention and for control 
participants) will involve the same procedures. Specifically, all will include self-
reports, a brief battery of PhenX and NIH Toolbox cognitive test delivered via iPad, 
and an MRI scan. All visits will take place in a private consult room in PVAMC 
building 32 and the PVAMC MRI Suite.
MRI Visit 1 activities will occur as soon as scheduling allows after baseline visit, 
prior to the start of TMS+BCBT intervention, if applicable, and MRI Visit 2 will 
occur post-intervention, if applicable, as soon as scheduling allows following the 
final intervention appointment. Control participants will have the MRI visit as 
soon as scheduling allows following the baseline visit.
Cognitive testing data is collected with a laptop during some MRI scans. Images 
will be acquired on a Siemens (Erlangen, Germany) PRISMA 3T scanner and 64-
channel head coil at the PVAMC MRI facility. All equipment used in the scanning 
room are MRI compatible. Participants will receive $75 for completion of the MRI 
visit. Compensation will be prorated for participants who do not complete the MRI 
scan if an attempt to complete the MRI scan is made.
D4.3.1 Urine and breathalyzer screens
 
 :  All participants will complete 
urine/breathalyzer screens prior to the neuroimaging and cognitive battery. 
Participants will only undergo the urine/breathalyzer screens if they are 
participating in the neuroimaging portion of the study, prior to each neuroimaging 
and cognitive battery visit. The urine and drug screen will be evaluating active use 
of alcohol and the following substances prior to a participant entering an MRI 
scanner: amphetamines, barbiturates, buprenorphine, cocaine, marijuana (THC), 
methamphetamine, MDMA, opiates, oxycodone, phencyclidine, and propoxyphene. 
Since THC can be detected on drug tests for up to 30 days after use, we will 
confirm with participants that they are not actively under the influence of THC 
before proceeding with other study procedures. During consent and throughout 
the study, research staff will clearly explain the purpose of the breathalyzer and 
urine drug screens, informing participants that that they will be removed from the 
neuroimaging portion of the study for failure of a drug screen.
If a participant is involved in both the neuroimaging and treatment aspects of the 
study and fails the drug screen, they will still be eligible to participate in the 
treatment arm of the study. Prior to each administration of TMS, participants will 
answer questions regarding recent (i.e., current intoxication) drug and alcohol use 
per existing VA TMS protocols to ensure safety during the administration.
Study staff will use protective clothing (e.g. gloves) during collection of urine and 
breath samples for alcohol and drug screens prior to cognitive testing. Staff are 
required to wash their hands after disposal of urine and breathalyzer guards.
D4.3.1a
 
  Breathalyzer
 
 
 
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1) A new mouthpiece will be used for each participant. Study staff will assist 
the participant with the breathalyzer placing it in the participant’s mouth and 
holding it during testing.
2) The mouth piece will be disposed of into regular trash after the test.
 
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D4.3.1b
 
  Urine
 
  drug
 
  screens
 
 
1) The participant will be instructed to go to the bathroom and give a sample.
2) Participant will be instructed to make sure the sample lid is closed and to 
place it on the designated tray for the research staff to review.
3) Participant is to wash his/her hands after using the bathroom for sample collection.
4) Study staff will examine the cup for positive readings on any indicator 
strip after the allotted time to results has passed per manufacturer 
instructions.
5) Study staff will empty the cup into the toilet and will flush the urine down the toilet
6) Empty cups will be disposed of in receptacles appropriate for urine per by VA 
regulations
i.e. red bags.
7) Urine kit tray will be de-sanitized with appropriate wipes.
D4.3.2
 
  Decision-making
 
  tasks
 
 : These tasks will be used to measure differences in 
decision- making behavior between non-suicidal Veterans and Veterans with STBs.
D4.3.2a
 
  PhenX
 
  Toolbox
 
  Monetary-Choice
 
  Questionnaire
 
 [50]
A  27-trial  questionnaire  measuring  the  participant’s  willingness  to  delay 
immediate rewards in favor of a later, bigger reward. Two reward options will 
be presented to the participant on every trial of this verbally administered task.
D4.3.2b
 
  PhenX
 
  Toolbox
 
  The
 
  Iowa
 
  Gambling
 
  Test™
 
  [51]
This measure of risk-tolerance will be administered via iPad. Participants are 
given a “loan” at the start of the task which they use to place “gambles” on 
cards selected from four different decks, one card per trial. Cards either add 
or subtract from virtual winnings. The goal is to maximize profit over the 
length of the task. Two of these decks yield small rewards/punishments and a 
modest small profit over time, the other decks yield large 
rewards/punishments and large losses over trials. Dependent measures are 
total winnings, and number of high/low risk selections.
D4.3.3
 
  Cognitive
 
  Control
 
  tasks:
 
  These are used to characterize differences in 
cognitive control function between Veterans with and without STBs, and to 
examine the influence of cognitive control function, on decision-making and STBs.
D4.3.3a
 
  NIH
 
  Toolbox
 
  Dimensional
 
  Change
 
  Card
 
  Sort
 
  Task
 
  [52]
A measure of cognitive flexibility. On each trial, picture cards that vary along 
two dimensions (e.g., shape and color) are presented via iPad with the NIH 
Toolbox. Participants sort cards according to one dimension, but criteria 
change over the course of the experiment.
D4.3.3b
 
  NIH
 
  Toolbox
 
  Flanker
 
  Task
 
  [53]
 
 
The Flanker tests inhibitory cognitive control and attention. On each trial, the 
participant reports the direction of an arrow stimulus presented in the center 
of an iPad screen via key press. The central arrow is flanked by additional 
 
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arrows on either side that either point in the same (congruently) or opposite 
(incongruent) direction from the central arrow.
Suppressing attention to the potentially interfering peripheral arrows 
requires cognitive control.
 
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D4.3.4
 
  MRI
 
  Procedures
 
 : 
D4.3.4a Patient Preparation for MRI. PI or trained study staff will discuss MRI 
Visit procedures, including safety measures, in detail with the participant prior 
to MRI Visit. Participants will complete urine and breathalyzer screens 
following the protocol described above. Participants will be asked to change 
into hospital gowns/scrubs prior to scanning. Subjects will be screened for the 
presence of a metal implants or accidental lodging of metal fragments three 
times: 1) by the PI or study staff conducting the enrollment screening, 2) by 
the PI or study staff at the MRI facility outside of the scanner room during Visit 
2, and 3) by the MRI technician just prior to entering the scanner room.
Sequence
Localize
r
MEMPRAG
E
Resting 
State 
(MB=2)
Reward 
Task 
(MB=2)
SSRT 
Task 
(MB=2)
DSI 
(MB=2
)
Slice (mm)
10
1
2.4
2
2
2
TE (ms)
5
1.69
32.6
28
28
71.6
TR (ms)
20
2530
1000
1000
100
0
3600
FOV (mm)
2802
2562
2112
1922
1922
2082
Orientatio
n
Sagitt
al
Axia
l
Table 3. MRI acquisition sequences. Abbreviations as follows: TE = time to echo; TR 
= repetition time; FOV = field of view; MB = multiband; MEMPRAGE = multi-echo 
MPRAGE; SSRT = Stop-signal reaction time; DSI = diffusion spectrum images.
D4.3.4b MRI Acquisition Procedures. Sequences are all FDA-approved Siemens 
product sequences (detailed in Table 3). Scans include a low-resolution 
localizer (to plot slices), a high-resolution multi-echo MPRAGE structural scan 
for morphometry, a field-mapping scan used to identify inhomogeneities in the 
magnetic field, resting and task functional MRI scans which measure the blood 
oxygen level-dependent signal, and a high-resolution diffusion scan to visualize 
white matter anatomy. Multi-band imaging is used to enable rapid, high- 
resolution scanning. Scan order is detailed in Table 4.
D4.3.4c Patient monitoring during MRI scans
 
 .  MRI operators and study staff 
will monitor the participants using 2-way voice intercom and infrared camera. 
Participants will be trained by study staff to use a hand-held squeeze bulb to 
signal that they need to communicate with scanner operators and study staff 
prior to scanning. Study staff will ask participants to confirm that they are 
comfortable and able to continue with the session periodically throughout the 
scanning session.
 
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D4.3.4d Procedure for incidental findings. This protocol is not optimized or 
intended to diagnose neurological conditions. However, if a MRI image appears 
abnormal, study staff or MRI operators will inform the PI of the abnormality, 
but are not permitted to communicate the suspected incidental finding to the 
participant. The PI will consult with a radiologist at
 
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the Providence VA Medical Center who will examine the images to determine if 
an abnormality is present and requires medical attention.
Run
Duration 
(sec)
Sequence
Task
Volumes 
(TRs)
--
9.2
Localizer
N/A
N/A
1
362
MEMPRAGE
N/A
N/A
2
69
Field Map
N/A
N/A
3
360
Resting-state 
fMRI
Passive 
fixation
150
4
360
Resting-state 
fMRI
Passive 
fixation
150
5
480
Task fMRI
Reward Task
480
6
480
Task fMRI
Reward Task
480
7
312
Task fMRI
SSRT Task
340
8
312
Task fMRI
SSRT Task
340
9
479
3-shell DSI
Video Option
N/A
Table 4. MRI acquisition order. Abbreviations as follows: TE = time to echo; TR = 
repetition time; FOV
= field of view; MB = multiband; MEMPRAGE = multi-echo MPRAGE; SSRT = Stop-signal 
reaction time; DSI = diffusion spectrum images.
D4.3.4e fMRI tasks
 
 .  Custom-coded Matlab-based experimental control scripts 
are installed on a research-owned Apple laptop computer that is synced to the 
MRI scanner. These programs deliver visual task stimuli to a high-resolution 
MRI-safe display positioned at the back of the scanner bore. Behavioral 
responses collected via a MRI-safe button box are also relayed to the laptop 
during scanning.
I.  Resting-state
 
  fMRI
 
 
Functional MRI data are collected while participants are quietly resting in 
the scanner. Participants will be instructed to visually fixate on a white 
crosshair presented in the center of a black background while remaining as 
still as possible during the scan.
II.
  Reward
 
  expectancy
 
  task
 
 
 
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This task identifies brain areas 
involved in processing rewards 
and expected values. [54,55]. 
The task is divided into two, 48 
trial, 7-minute scanner runs 
(Figure 2). On each trial, 
participants virtually “bet” via 
button press whether the 
number on the next card 
displayed (range=1-9) will be 
higher or lower than 5 (guess 
(?) phase). Next, an expectancy 
card is shown communicating 
the likelihood that the bet will 
result in a win, a loss, no 
change, or win/loss equally 
likely (cue phase). The number 
is then shown (# phase), 
followed by the win/loss 
amount (feedback phase). 
Expectancy cues are accurate 
on 50% of trials (except for 
neutral trials) producing neural 
“prediction errors” on 
misleading cue trials. Trials are 
separated by a
0.5–1.5s jittered inter-trial interval. Trial order is randomized with 12 trials 
per category (win, loss, neutral, ambiguous).
III.
 
 Stop-signal
 
  reaction
 
  time
 
  task
 
  (SSRT)
 
 
The SSRT measures inhibitory cognitive control [57,58] The task is divided 
into two, 128 trial scanner runs. On each trial, participants indicate whether 
a visually-presented ‘go’ arrow stimulus points to the left or right (50/50% 
probability) using a MRI-safe button box. During 25% of trials (random 
assignment), a stop-signal (up arrow) indicating that responses are to be 
withheld is delivered at a delay after the go arrow. On stop trials, the delay 
between the go cue and the stop signal is the ‘stop-signal delay’ (SSD). The 
SSD is varied based on performance[58]. Null events ranging from 0.5 and 4 
s (mean, 1 s) will be interspersed between trials.
D4.4
 
  Intervention
 
  [(active
 
  or
   sham)
 
  TMS
 
  +BCBT]
 
 
If eligible and willing to participate in the intervention, participants will be 
scheduled for the start of TMS+BCBT as soon as scheduling allows following 
the baseline visit and following their first MRI visit (if applicable). However, for 
Fig. 2 The reward expectancy card guessing task54 On each trial, 
participants guess (?) whether the number on a later card (#) is 
higher or lower than 5. An intervening cue card (Cue) sets an 
outcome expectancy. Feedback ($) after the card reveal 
corresponds to wins, losses, or a push. The difference between the 
actual outcome – win, loss, or push (“$”) – and the expected value 
is the prediction error.
 
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participants who are scheduled to start the intervention phase more than 2 
weeks  after  the  baseline  visit,  the  C-SSRS,  IDSSR,  and  PCL-5  will  be 
readministered within 1 week prior to start of intervention.
The intervention period will consist of 15 - 36 visits occurring up to 5 days per 
week on business days (Monday – Friday) of TMS (either active or sham as 
assigned). All participants will receive BCBT. BCBT will be provided one time per 
week following a TMS session. TMS sessions will be modeled after those delivered 
in the PVAMC Psychiatric Neuromodulation Clinic, using 
 
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implemented safety and administration procedures. Individual stimulation 
sessions can last up to 15 minutes. BCBT sessions will last approximately 60 
minutes. Days with TMS+BCBT sessions will require the participant to be 
available for approximately 1 hour and 15 minutes. The intervention period will 
last for 3 months (12 weeks) with TMS ending prior to the end of the 12 weeks.
D4.4.1 TMS Procedures: Participants will receive a form of TMS, using theta burst 
stimulation (TBS), a novel form of TMS with the benefit of much shorter 
administration time (i.e., standard TMS = 45 minutes per session, versus TBS = 3-
10 minutes). The PVAMC Psychiatric Neuromodulation clinic is currently 
switching over TBS for standard clinical care following the publication of a large 
(N>400) non-inferiority study showing this form of TMS is equivalent to standard 
TMS. TMS in this study will start as soon as scheduling allows postbaseline visit.
All TMS will utilize a MagStim Super Rapid 2+1 system; two systems are available 
at the Providence VA. Prior to each TMS session, an un-blinded study member will 
assure the setup of either active or sham coil according to the participants’ 
randomization code.
TMS procedures will start with a motor threshold determination, defined as the 
amount of energy required to induce movement in the contralateral hand in at 
least 50% of stimulations. Because using the treatment coil for motor threshold 
determination could lead to un-blinding, an unblinded 70mm coil will be used for 
motor threshold determination. Clinic staff will then position the TMS coil over 
the dorsolateral prefrontal cortex using the Beam/F3 method [59,60]. TMS 
sessions will occur business weekdays (typically 5 per week) up to 30 treatments, 
followed by six taper treatments over the subsequent three weeks [61,62]. We 
will use standard left sided, high frequency stimulation parameters following 
those utilized in our clinic to maximize safety and efficacy [25] in Veterans [22].
TBS parameters include intermittent TBS of 600-1800 pulses, delivered at up to 
120% of motor threshold (based on [63]; and Philip et al., American Journal of 
Psychiatry under review). We will follow all standard TMS operating procedures, 
including those regarding rating scales, safety and administration.
Following our standard operating procedures, during TMS sessions Veterans will 
sit quietly wearing hearing protection and will not interact with study staff unless 
there are problems related to device positioning, etc. Motor threshold 
determination may be repeated if clinically warranted. The minimum number of 
TMS sessions in the acute series will be 15. The treatment series may conclude 
before #36 if a patient demonstrates two consecutive weeks of remission. 
Remission and number of sessions required will be determined by checking 
rating scales following standard clinical procedures (e.g., using the inventory of 
depressive symptomatology (IDS-SR <15)).
Safety will be assessed at every TMS session by recording spontaneously reported
 
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adverse events. Participants will be queried daily about potential side effects 
associated with TMS such as headache and dizziness as well as any changes in 
medications, usage of drugs or alcohol, and mood. We will use a standardized self-
report questionnaire (the TMS Daily Screener) to ask these questions. The TMS 
Daily Screener is administered by TMS staff on a dry- erase sheet without any 
identifiable information or participant ID. Responses on this screener are used to 
determine participant safety for daily TMS procedures, are not recorded/saved as 
 
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part of study data collection, and are erased at the end of the TMS session. If any 
reported
changes indicate that TMS administration may be unsafe, the session may be 
rescheduled. If reported changes are significant e.g., previously unreported 
consumption of illicit drugs that contraindicate TMS administration (i.e., render 
administration unsafe), the participant may be removed from the treatment part of 
the study. Participants discontinued from treatment may still be eligible for follow-
ups associated with the MRI component of the study and will be asked if they wish 
to continue with that portion of the study. Following standard TMS operating 
procedures, a TMS technician will monitor participants for treatment-emergent 
safety issues such as seizures. Side effects will be coded using the current version 
of the Medical Dictionary for Regulatory Activities, following procedures used in 
prior TMS studies [64,65]. As described above, participants will be required to 
wear hearing protection during TMS. Reflecting the at- risk nature of the Veteran 
participants, a licensed physician with experience with neuromodulation and 
suicidality will always be available during study procedures.
D4.4.1a
 
  Considerations
 
  regarding
 
  pregnancy
 
 
TMS has been safely used on pregnant women, (e.g., [6]), but it is not FDA 
cleared for use in this population. We will utilize standard practices for 
investigational device and investigational TMS research (e.g., [25]). As noted 
above, pregnancy/lactation, planning to become pregnant during the study, or 
those who do not agree to consistently use a measure birth control during the 
study are excluded. Pregnancy tests are administered as part of standard 
clinical procedures during hospitalization. We will administer pregnancy tests 
to women identified in outpatient settings to verify safety for TMS/MRI. 
Participants will be asked to inform study personnel if they become pregnant; 
participants may also be asked to receive a urine pregnancy test if situations 
emerge where there is a possibility of pregnancy. Prior to the administration of 
each TMS session participants will be asked about any changes in medications, 
this includes birth control use. Participants will re-affirm continued use of their 
designated choice of birth control. Because pregnancy is an exclusion criterion, 
if they become pregnant during the study they will be removed from 
participation and managed following best clinical practices.
D4.4.1b
 
  TMS
 
  Assignments [active
 
  VS
   sham]:
 
 
We will use the urn randomization strategy and ensure balance among 
treatment groups [67,68]. We have successfully used urn randomization in 
previous treatment studies [44, 69] and developed a computer program that 
conducts the randomization. Urn randomization is a biased coin randomization 
technique, which randomly assigns Veterans of a given subgroup to treatment 
conditions (i.e., active TMS+BCBT or sham TMS+BCBT) while systematically 
biasing randomization in favor of balance among the treatment conditions on 
select variables. This program enables ongoing monitoring of the effectiveness 
of our stratification and randomization procedures. Since the number of 
variables which can be successfully balanced is limited, for the current study 
we will include the following variables in our randomization: a) current 
 
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admission due to suicide attempt vs. suicide risk, b) number of previous suicide 
attempts (none, single vs. multiple), and c) gender (male vs. female).
D4.4.2 BCBT Procedures: Participants will receive a standard course of BCBT (12 
weekly individual therapy sessions). These sessions will be completed in-person at 
the Providence VAMC. In rare circumstances (e.g., pandemic, extreme weather), 
these sessions may be provided via phone or video (VA approved VA Video 
Connect). When therapy sessions are conducted remotely, de-identified data may 
be collected by study staff at home due to remote
 
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connection issues (due to COVID-19). Therapy is divided into three phases: 
orientation, skill focus, and relapse prevention. Phase one (orientation) includes 
developing basic self- management skills, creating a model of how suicide 
functions for the patient, crisis response and safety plan, treatment motivation, and 
developing basic emotion regulation skills. Phase two (skills focus) centers on the 
consolidation of emotional regulation skills, problem solving, mindfulness, and 
cognitive appraisal. Veterans complete worksheets and practice learned skills in 
session and at home. Phase three (relapse prevention) continues with skills learned 
in phase two but with a focus on skill generalization and maintenance. Each patient 
will work through all skills with a, BCBT-trained therapist. Therapy will consist of 
once a week, 60-minute individual therapy sessions. Participants will also be asked 
to complete weekly homework tasks to practice skills and strategies learned in 
each therapy session. Sessions will be recorded to rate therapist fidelity. To limit 
heterogeneity of therapy administration, if a participant is engaged in suicide-
focused psychotherapy or treatment that significantly overlaps with BCBT for 
suicide, participants may be asked to temporarily refrain from other individual 
psychotherapy while receiving BCBT. However, they will be instructed to continue 
with all other usual mental health care (i.e. continue prescribed mental health 
medications and working with their mental health providers).
D4.4.2a
 
  BCBT
 
  Therapy
 
  Compliance:
 
 
The therapist will be a VA mental health counselor. The therapist will have 
experience and training in CBT principles and be trained and supervised by 
qualified research personnel to ensure training meets standards set forth in 
the BCBT protocol. In the event that therapy must be provided to patients via 
telehealth (e.g., due to pandemic, inclement weather), the therapist may 
provide therapy via VA-approved VVC or phone. Prior to providing therapy via 
these alternative means, the therapist will have completed telehealth training 
in TMS (4279741). Additionally, to establish the reliability and validity of the 
adherence and competence ratings of BCBT, therapy sessions are required to 
be audio recorded and will be conducted according to approved standard VA 
practice for audio recording research subjects (see E. Information Security 
and Confidentiality Protections section for full details). The therapy sessions 
will be rated by the PI (an expert in BCBT) for fidelity using a checklist from 
the BCBT manual.
D4.4.3 Discontinuation or Lost to Follow-Up: Participants are required to attend a 
minimum number of TMS sessions to assess a response. The project PI will assess 
each and determine how many TMS sessions each participant will require. 
Participants are expected to attend sessions as close to daily (during business 
days) as possible, completing the PI determined number of TMS visits necessary. 
The PI will also evaluate participants for study compliance (i.e. consistency of 
TMS visits) and determine if the participant may need to be discontinued from the 
study prior to the completion of the intervention period as appropriate.
If a participant is discontinued or lost to follow-up, the study investigator will 
notify the participants mental health provider about their discontinuation in the 
study following good clinical practice procedures.
 
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D4.5
 
  Intervention
 
  Endpoint
 
  Visit
 
 
The endpoint visit, if applicable, will occur at the 12th intervention therapy visit, 12 
weeks (3 months) from the start of intervention. This visit will be conducted in-
person, or if necessary, by telephone. When assessments are conducted remotely, 
de-identified data may be collected by study staff at home due to remote 
connection issues (due to COVID-19). Please see Table 2 for a list of scales and 
assessments administered at endpoint. Participants will be paid $50 in electric 
funds transfer (EFT) or gift cards for the completion of this visit.
 
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D4.5
 
  Follow-Up
 
  Visits
 
 
Follow-up assessments will be conducted in-person or if necessary, by telephone 
6, 12, and 24 months from the start of the intervention period. When assessments 
are conducted remotely, de-identified data may be collected by study staff at 
home due to remote connection issues (due
to COVID-19). To reduce attrition, subjects will be paid $50 in electronic funds 
transfer (EFT) or gift cards for completion of each follow-up appointment. Veterans 
participating in TMS+BCBT and neuroimaging will attend follow-up visits at 6, 12, 
and 24-months. Veterans only participating in TMS+BCBT will attend follow-up 
visits at 6- and 12-months. Veterans and matched controls participating only in the 
neuroimaging and cognitive battery will attend
follow-ups at 6-, 12-, and 24-months.
D4.5.1 Blinded Assessment Administration
 
 :   At the intervention endpoint visit, and 
6-, 12-, and 24-month follow-up visits, assessments will be administered by study 
personnel who are blind to TMS condition (active vs. sham). For veterans who 
participate in the intervention component of the study, these assessments will not 
be administered by the study therapist or TMS provider even though they are also 
blind to the TMS condition. All study personnel will be blind to the study condition 
at baseline.
 
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D5. Electronic Health Record (EHR) Data 
Extraction
Study staff will manually extract EHR data from 
participants’ VA CPRS files. Extracted values will be 
entered into VA REDCap in anonymized format (by 
patients’ study ID only). The initial data extraction will 
occur at baseline no later than one week of the 
participant’s MRI Visit or beginning of intervention. 
Subsequent data extractions will take place six months, 
one year, and two years after starting intervention. At 
each time point (baseline,
+6months, +1 year, +2 years), data for the 
current month, and for each of the 12 prior 
months will be extracted.
D5.1 EHR predictor extraction: 
Participants’ CPRS files will be manually 
reviewed by the PI or by trained study staff. 
The following data types will be manually 
extracted from CPRS files: 1) Health 
services utilization; 2) pharmaceutical 
utilization;
3) changes in pharmaceutical utilization; 4) 
mental health diagnoses. Factors under 
consideration were selected based on prior 
demonstrations of machine learning applied 
to EHR 39-41. Related information such as 
names of medications and medical 
conditions will also be extracted.
D5.2 EHR variable construction: EHR 
measures are listed in Table 5. Monthly 
EHR sums will be averaged by quarter and 
by year. If categorical variables change over 
an aggregated time point, the most 
consistent designation over the analyzed 
epoch will be used.
Health Services Utilization
Outpatient Visits (any service)
Outpatient Visits (MH service)
Emergency or Urgent Care Visits
Inpatient MH
Inpatient (any service)
Pharmaceutical Utilization
Antidepressant
Antipsychotic
Mood Stabilizer
Anxiolytics
Other medications (non-psychiatric)
Change in Pharmaceutical
Antidepressant
Antipsychotic
Mood Stabilizer
Anxiolytics
Other medications (non-psychiatric)
Mental Health (Current and Lifetime)
Depression
Bipolar disorder
Schizophrenia or schizoaffective disorder
Generalized Anxiety Disorder
Posttraumatic Stress Disorder
Specific Phobia
Panic Disorder
Social Phobia
Personality Disorder
ADHD
SUD
Gambling Disorder
Eating Disorders
Suicidality
 
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Table 5. EHR data extracted at baseline and follow-
 
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E.
DATA
 
  ANALYSIS
 
 
E1. Design and Sample Size
This is a single-site, two-arm RCT with individual participant urn randomization. 
We propose to randomize 130 Veterans. Approximately 65 will be randomized to 
active TMS+BCBT and 65 participants will be randomized to sham TMS+BCBT. 
Recruitment for the intervention will cease once randomization goals are met (i.e. 
n= ~65 Veterans per condition). Additionally, we will be enrolling 60 Veterans, 
who will be matched on age (within 5 years), primary psychiatric diagnosis, and 
sex, for a total of 190 Veterans to be enrolled. A subsample of these veterans (120 
in total; one group (n=30) with suicide ideation and another group (n=30) with 
suicide attempts; and controls (n=60) will be used in the analyses of brain-based 
biomarkers for suicidality. Veterans will be assessed at the study timepoints 
outlined in C3. Assessments.
E1.1
 
  Overall
 
  Analytic
 
  Approach
 
  across
 
  Aims
 
 
Preliminary analyses will include descriptive statistics to examine the 
distributional and psychometric properties of the variables (e.g., normality, 
internal consistency). Variables will be transformed to achieve normality if 
necessary. We will also examine post-inclusion attrition by comparing study 
completers to dropouts on sociodemographic, baseline, and length of admission 
data to determine if they differ systematically. Preliminary analyses may also 
include analyses of adverse events, progress of recruitment and retention, and 
quality markers as the study progresses. In keeping with the intention to treat 
principle, missing data will be handled with multiple imputations (m=20) with 
assumptions checked with sensitivity analyses [43,44] [32]. Our main analysis will 
not adjust for covariates other than baseline value of the outcome and design 
factors (balancing factors used in the urn randomization procedure) under the 
assumption that randomization produced groups balanced on measured and 
unmeasured confounders. Additionally, the data from this study across all Aims 
will be combined with data from subjects who completed neuroimaging 
procedures in PVAMC IRB protocol #2018-051.
Data from this study, across all Aims, will also be combined with data from 
subjects who completed procedures for PVAMC IRB protocol #IRB-2020-008. 
Data from this study related to recorded interviews, de-identified with the 
exception of assessment dates, will also be shared with the PVAMC research 
study “Longitudinal Assessment of the Sleep-Suicide Link in Veterans Discharged 
from Inpatient Psychiatric Care” (IRB-2020-008), for training of staff and inter-
rater reliability analysis (i.e., to calculate agreement across interviewers and to 
make final decisions on ratings).
E2. Aim 1 Analytical Plan (Analyses Related to Treatment)
The data from this study for Aim 1 may be combined with data from subjects 
who completed neuroimaging procedures in PVAMC IRB protocol #2018-051. 
Data from this study, for Aim 1 may also be combined with data from subjects 
who completed procedures for PVAMC IRB protocol #IRB-2020-008.
 
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E2.1
 
  Interim
 
  analysis
 
  and
 
  early
 
  stopping
 
  of
   treatment
 
 
We will conduct 1 interim analysis and assess our primary aim when we have 
enrolled 32 persons per treatment group (~50% enrollment). The analyst and 
investigators will be blinded to treatment assignment in the interim analysis. We 
will test our a priori hypotheses and suggest consideration of early stopping to the 
DSMB if either group shows an effect on the primary outcome using O’Brien-
Fleming stopping bounds [45] (i.e., a significance level of 0.0054). We will retain a 
significance level of 0.049 for our primary hypothesis at project completion.
 
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E2.2
 
  Primary
 
  hypothesis
 
 
We hypothesize that compared to sham TMS+BCBT, Veterans receiving active 
TMS+BCBT will demonstrate superior improvements in suicidal ideation and 
fewer suicide attempts. Our primary outcome for this aim is a composite measure 
of suicide behavior based on occurrence of any of five types of suicidal behavior: 
death by suicide, suicide attempt, interrupted or aborted attempts, and suicide 
preparatory acts. This composite score is derived from the Columbia Suicide 
Severity Rating Scale (C-SSRS). The C-SSRS captures data on number of attempts, 
aborted attempts, and interrupted attempts. Number of suicide behaviors will be 
added across these categories to yield a total suicide events score. Death by 
suicide will be captured by review of medical records and death records. The main 
exposure variable of interest is the random treatment assignment. Our analytic 
approach will be an ANCOVA-type mixed effect regression model [72]. This means 
the outcome composite measure at follow-up time points (e.g., 3, 6, 12 months) will 
be regressed on baseline value of the outcome, design factors, and treatment 
assignment, dummy variables for time (baseline as reference) and interactions of 
treatment assignment and time dummies. Random intercepts and exchangeable 
error covariance structure will accommodate non-independence of observations 
owing to the repeated measures design.
The main effect of TMS as adjunctive to BCBT will be tested with the main effect 
of group assignment to the joint intervention condition. The time effect 
interactions of TMS assignment test hypotheses about the maintenance of gains at 
3, 6 and 12 months. Our main hypothesis is that there is an immediate beneficial 
effect of TMS exposure. For our secondary outcome, (time to first suicide attempt 
over 12 months) we will use a Cox proportional hazards model analysis 
framework.
E2.2.1 Statistical power and sample size for our primary outcome: With 65 
persons per group, Lehr’s equation [73] instructs that minimum detectable 
standardized mean difference effect size difference (d) between treatment 
groups at the baseline assessment is sqrt(16/n), where n is the per-group 
sample size, or 0.50 standard deviation units, a medium effect size in 
Cohen’s effect size taxonomy [74]. Given the ANCOVA design, we will be 
able to detect a smaller effect depending upon the magnitude of the pre-post 
correlation (r) of the suicidal behavior composite outcome {d = sqrt([16(1-
r^2)]/n}, which under the assumption of a moderate correlation (r = .2) the 
minimum detectable effect size is d = .47. These are medium or typical 
effect size magnitudes [75] and are likely to describe effects of minimal 
clinical significance or practical importance. Of note, these are also 
comparable with prior TMS studies [64,65] who described effect sizes of
.5-.52, and more recent studies indicating larger (d=.9) effect sizes of TMS 
used with medications [76]. While we recognize these are effect sizes for 
depressive symptoms, in our pilot work we found reduction in depressive 
and suicide-related symptoms to be highly correlated. For our secondary 
outcome, we used simulations to determine the minimum detectable 
difference. If we assume that 21% of the sample will experience a suicide 
attempt over 12 months follow-up, if the cumulative risk of suicide attempt 
is 4.5% over 12 months in the TMS+BCBT group (hazard ratio = 0.19) we 
 
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will have 81.1% power to detect such an effect using a type-I error rate of 
5% in a Cox proportional hazards model framework.
E2.3
 
  Secondary
 
  hypotheses
 
 
For this aim we hypothesize, compared to sham TMS+BCBT, Veterans who 
receive active TMS+BCBT will have superior improvements in psychosocial 
functioning and fewer psychiatric hospitalizations/crisis visits during the follow-up 
period. The outcomes for this aim are a)
 
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WHODAS score b) number of psychiatric hospitalizations/crisis visits c) reduced 
suicide ideation as measured by the C-SSRS. The analysis for the psychosocial 
functioning outcome, (a), and the suicidal ideation severity score from the C-SSRS, 
(c), will be a repeated measures ANCOVA, as with the primary hypothesis, using a 
generalized linear mixed effect model. For part (b), the outcome will be a count 
variable, for which we will use negative binomial regression.
E2.3.1
 
  Statistical
 
  power
 
  and
 
  sample
 
  size
 
  considerations:
 
  Sample size considerations for 
part
(a)are similar to the primary hypothesis, and the minimum detectable effect size 
difference is a standardized mean difference of 0.5SD units. Without pilot data on 
the number of hospitalizations/crisis visits, it is difficult to forecast the 
distribution and detectable difference in distribution on the count outcome (part 
b). Rules of thumb provided by Van Belle [77] suggest we will be able to detect a 
relative rate of exp(4/sqrt(n)), where n is the per-group sample size, or a 
reduction in the rate of hospitalizations of about 61% or more among those 
receiving active TMS+BCBT plus versus sham.
E2.4
 
  Moderator
 
  and
 
  mediator
 
  effects
 
 
In addition to the above hypotheses, we will conduct exploratory latent growth 
curve analyses to compare the trajectory of change for primary, secondary, and 
tertiary outcomes (i.e. linear ascending, descending, or quadratic) between sham 
TMS+BCBT versus active TMS+BCBT. We will include moderator variables in 
these models (e.g. diagnosis, comorbidity, gender), which may identify patients 
who will benefit more or less to the addition of active TMS+BCBT versus sham 
TMS+BCBT. We also will investigate mediating variables to determine the 
mechanisms through which TMS produces its effects on clinical outcomes. The set 
of analyses in this aim are motivated by [78], who suggest exploratory, hypothesis 
generating analyses be conducted after primary study aims are addressed to 
attempt to decide for which sample sub-groups the intervention might be more 
efficacious or have more lasting effects. The analytic approach will be like that 
described for the primary and secondary aims, although in addition to the 
timepoint effect indicators, we will examine models with more parsimonious time 
structure (e.g., linear, piecewise linear, negative exponential) and then attempt to 
describe factors that predict variations in important components of the implied 
change trajectory. We will make use or generalized linear mixed effect models and 
compare alternative time bases using information criteria before adding 
covariates, mediators, and moderators. Treatment effect mediation will be 
evaluated by comparing treatment effects estimated before and after adjusting for 
the main effect of putative mediators. Moderation effects will be examined as 
three-way interactions of the putative moderator, treatment assignment, and time.
E2.4.1 Statistical power and sample size considerations: As we view tertiary 
analyses as exploratory and hypothesis generating, we do not present a 
priori specified sample size or minimum detectable effect size statistics. We 
have no doubt that we may be underpowered to detect all but large 
moderation effects. Our inferences will be made cautiously and be guided 
 
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by confidence intervals obtained by bootstrap resampling of the observed 
data.
E3. Aim 2 Analytical Plan (Analyses Related to Cognition and Neuroimaging)
All de-identified data from this study for Aim 2 will be combined with data 
from subjects who completed neuroimaging procedures in PVAMC IRB 
protocol #2018-051.
E3.1 Evaluate the relationship between STBs and reward circuits involved in 
decision-making in Veterans.
Hypothesis: Neural and behavioral correlates of decision-making will 
differentiate Veterans with STBs from those without STBs.
Rationale: Suicidality is associated with maladaptive decision-making. 
Neuroimaging in non-Veterans has shown that STBs are accompanied by 
functional and anatomical differences in the striatal-to-VMPFC reward 
circuit implicated in decision-making. It is unknown if these potential 
neurocognitive markers of suicide generalize to Veterans.
Between-group contrasts of decision-making, structural MRI, and functional 
MRI will be used to test our hypothesis.
E5.1 Participant groups: The STB group will be comprised of Veterans 
hospitalized for suicide attempt (up to n=30) or suicidal ideation (up to n=30). 
The diagnosis matched control group is comprised of Veterans (up to n=60) 
without current STBs. Per power estimations computed with G Power software42, 
85% power to detect a medium-sized effect of group (0.4) in an ANCOVA model 
with three groups and two covariates is afforded by a total sample size of 72, or 
n=24 participants per group. Our enrollment targets exceed this estimate to 
account for loss of participants that are unable to complete all aspects of the 
study.
Data sources: Decision-making tasks include the scanned reward expectancy task 
(card guessing), and the un-scanned Iowa Gambling and Monetary Choice (Delay 
Discounting) tasks. Structural MRI and diffusion MRI data. FMRI data used in Aim 
1 analyses will come from either the card guessing reward task or resting state 
functional MRI runs.
General statistical details: Age and sex will be treated as covariates of no interest 
in all statistical analyses. Intracranial volume (computed by Freesurfer) will also 
be used as a nuisance covariate in morphometry analyses to control for size-
related variance. Residual effects of motion on functional connectivity estimates 
will be tested post hoc by regressing individuals’ frame-to- frame motion against 
 
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functional connectivity beta values 55. Potential effects of impulsivity, depression, 
anxiety, and sleep on outcomes will be explored in post hoc sensitivity analyses.
Behavioral
 
  Data
 
  Analysis:
 
 
PhenX Toolbox Monetary-Choice Questionnaire43: The protocol is scored by 
calculating where the respondent’s answers place him or her amid reference 
discounting curves; placement amid steeper curves indicates higher levels of 
impulsivity. Subject-specific discounting rates will be entered into MATLAB 
ANCOVA models testing group differences.
PhenX Toolbox The Iowa Gambling Test™ 4: Total winnings and the ratio of 
high vs. low risk gambles will be entered into MATLAB ANCOVA models 
testing group differences.
MRI Analyses: Analyses for primary hypothesis testing will employ a region of 
interest (ROI) approach. ROIs in VMPFC, orbitofrontal cortex, and the striatum will 
be used in structural and functional neuroimaging analyses. Cortical ROI 
definitions will be based on the Human Connectome Project multi-modal 
parcellation atlas [81]; subcortical ROIs will be based on Freesurfer automated 
parcellations. ROI analyses will be considered significant at the two-tailed alpha of 
p < .05 after Bonferroni-correction for the number of ROI comparisons. We may 
also conduct exploratory voxel-level fMRI analyses. These analyses will be 
corrected for multiple
comparisons by applying a voxel-height threshold of p<.001, and a cluster-size 
threshold of p- FDR<.05 to all second-level models.
Grey
 
  matter
 
  comparisons
 
  between
 
  Veterans
 
  exhibiting
 
  STBs
 
  and
 
  matched
 
  controls.
 
 
Cortical thickness and grey matter volume (as computed by Freesurfer) will be 
extracted from the VMPFC and striatum. Measures will be compared between 
Veterans with STBs and matched controls to test the hypothesis that these 
metrics are reduced in Veterans with STBs.
Differences
 
  in
   white
 
  matter
 
  integrity
 
  between
 
  Veterans
 
  with
 
  STBs
 
  and
 
  controls.
 
 
Deterministic tractography of diffusion data will be conducted with DSI Studio 
(http://dsi- studio.labsolver.org/). The generalized q-sampling algorithm will be 
used for fiber reconstruction. This algorithm permits estimation of multiple 
fibers per voxel improving reconstruction accuracy in areas rich in crossing 
fibers [82]. Fiber streamlines will be estimated from “seed” ROIs and then 
filtered to exclude streamlines passing through, but not ending in, target ROIs. 
If the seed is in striatum, targets will be located in VMPFC/orbitofrontal 
cortex, or vice versa. To test the hypothesis that white matter pathways 
connecting VMPFC-to-striatum are compromised in Veterans with STBs, 
individual-level track statistics (anisotropy, density) will be entered into 
MATLAB ANCOVA models.
Differences
 
  in
   fMRI
 
  activation
 
  during
 
  decision-making
 
  between
 
  Veterans
 
  with/out
 
  
STBs.
 
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These analyses test the hypothesis that neural signals in the striatal-to-VMPFC 
circuit related to rewards and decision-making are attenuated in Veterans with 
STBs. Preprocessed fMRI data from the reward expectancy task will be used 
for these analyses. Whole-brain first-level fixed-effect GLMs will be constructed 
for each subject with SPM12. A parametric regressor will be used to model 
univariate fMRI activation related to reward expectancy.
Parametric values are set to +0.5 for probable wins, –0.375 for probable 
losses, +0.125 for ambiguous cues, and zero for the neutral cues (see D5.5.2) 
[83]. Separate regressors for positive (better than expected) and negative 
(worse than expected) prediction errors will also be constructed. GLMs will be 
created by convolving the canonical hemodynamic response function with 
regressors of interest and nuisance regressors (motion parameters and 
derivatives, mean signal in cerebrospinal fluid). Subjects will be treated as 
random effects in second-level univariate fMRI models. To evaluate whether 
reward-related signals are blunted in Veteran with STBs, the averaged blood 
oxygen level-dependent (BOLD) signal for our conditions of interest will be 
extracted from VMPFC and striatal ROIs and contrasted between groups. 
Whole-brain statistical maps for reward expectancy and prediction errors will 
also be contrasted between Veterans with STBs and controls.
Statistically significant clusters from this univariate whole-brain analysis 
may be used as ROIs in functional connectivity models below.
Differences
 
  in
   functional
 
  connectivity
 
  between
 
  Veterans
 
  with
 
  STBs
 
  and
 
  controls.
 
 
These analyses test the hypothesis that the VMPFC-to-striatal decision-making circuit 
is less functionally cohesive in Veterans with STBs. Functional connectivity analyses of 
both task (reward expectancy) and resting-state will be conducted using the CONN 
Toolbox.50
Unsmoothed residual BOLD time courses will be extracted from each subjects’ 
preprocessed data and then cross-correlated. The resulting correlations will be 
converted to z-scores via Fisher’s R-to-Z transformation in order to improve 
conformation to the assumptions of the GLMs used for seed-based hypothesis 
testing. Z-scores will be entered into GLMs comparing VMPFC-to-striatal 
connectivity between groups. For task-based functional connectivity, a 
weighted GLM approach will be used to compare connectivity during the 
expected reward and prediction error task conditions of between Veterans 
with and without STBs. Additional analyses using ROIs functionally-defined in 
the univariate analysis described above as seeds may also be run.
Design Considerations & Alternative Outcomes: This hypothesis testing sequence 
focuses on the VMPFC-to-striatum decision-making circuit. However, effects may 
be mediated by regions outside this a priori network e.g. in the amygdala-to-
VMPFC circuit implicated in bipolar disorder with suicidality [85]. The role of this 
circuit could be explored should we fail to reject the null hypothesis. Alternatively, 
a data-driven method such as multi-voxel pattern analysis could be used to 
functionally identify neural circuits differing between Veterans with and without 
STBs.
 
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E3.2
 
  Evaluate
 
  whether
 
  correlates
 
  of
   decision-making
 
  and
 
  cognitive
 
  control
 
  indicate
 
  risk
 
   
severity.
Hypotheses: Decision-making will be less risky and/or impulsive in higher- 
vs. lower- risk Veterans with STBs. Structural and functional integrity of 
cognitive control regions will be superior in higher-risk Veterans with STBs.
Rationale: Studies comparing decision-making between individuals who 
have made high-risk (well-organized, highly lethal) attempts vs. those 
making less organized or less medically serious attempts have found that 
decision-making is less impulsive in higher- risk attempters [86]. In fact, 
decision-making in high-risk attempters is even less
impulsive than it is in healthy controls. Some propose that the capacity to 
make a serious suicide attempt develops alongside the ability to inhibit 
prepotent pain and fear responses associated with death [87]. Thus, 
paradoxically, greater inhibitory cognitive control may facilitate 
development of suicidal behavior. As a test of this hypothesis, we will 
compare behavioral and functional neuroimaging data collected during the 
SSRT, an inhibitory cognitive control task, between high- and lower-risk 
Veterans with STBs. We will also examine associations between suicide and 
other cognitive control measures to assess the specificity of the link between 
attempts and inhibitory control. to assess the specificity of the link between 
attempts and inhibitory control.
Participant groups: The high-risk group will be comprised of Veterans with 
history of suicide attempt (up to n=30), while the low-risk group will include 
Veterans with ideation only (up to n=30). See alternative methods for risk 
grouping in the Design Considerations section below. Diagnosis matched 
controls (up to n=60) are also used in some analyses.
Data sources: Structural data (cortical thickness and volume) and diffusion MRI 
data. FMRI data used in Aim 2 analyses will come from either the SSRT, the 
scanned decision-making task, or resting state runs. Behavioral measures of 
decision-making include data from the scanned decision-making task. Behavioral 
measures of cognitive control include data from the scanned SSRT, and the un-
scanned Intra-Dimensional Card Sort and Flanker tasks.
General statistical details: Age and sex will be treated as covariates of no interest 
in all statistical analyses. Intracranial volume (computed by Freesurfer) will also 
be used as a nuisance covariate in morphometry analyses to control for size-
related variance. Residual effects of motion on functional connectivity estimates 
will be tested post hoc by regressing individuals’ frame-to- frame motion against 
functional connectivity beta values 55. Potential effects of impulsivity, depression, 
anxiety, and sleep on outcomes will be explored in post hoc sensitivity analyses.
Behavioral
 
  Data
 
  Analysis:
 
 
 
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NIH Toolbox Dimensional Change Card Sort Task: Accuracy and reaction time 
scores will be computed for each subject (both raw and age-adjusted); higher 
accuracy and lower reaction times are associated with greater cognitive 
flexibility. Subject-specific scores will be entered into MATLAB ANCOVA 
models testing group differences.
NIH Toolbox Flanker Inhibitory Control and Attention Test: Total winnings 
and the ratio of high vs. low risk gambles will be entered into MATLAB 
ANCOVA models testing group differences.
MRI Analyses: Analyses for primary hypothesis testing will employ a region of 
interest (ROI) approach. ROIs in the cognitive control areas of ventrolateral PFC 
(VLPFC- pars orbitalis, pars triangularis, pars opercularis), dorsolateral PFC 
(DLPFC-approx. Brodmann’s Areas 46/9), and anterior cingulate (ACC-rostral and 
dorsal) will be used in structural and functional neuroimaging analyses. Striatal 
ROIs will also be tested as they are part of thalamo-cortical loops that include 
control regions [89]. Cortical ROI definitions will be based on the Human 
Connectome Project multi-modal parcellation atlas [90]; subcortical ROIs will be 
based on Freesurfer automated parcellations. ROI analyses will be considered 
significant at the two-tailed alpha of p < .05 after Bonferroni-correction for the 
number of ROI comparisons. We may also conduct exploratory voxel-level fMRI 
analyses. These analyses will be corrected for multiple comparisons by applying a 
voxel-height threshold of p<.001, and a cluster-size threshold of p- FDR<.05 to all 
second-level models.
Grey matter comparisons between groups. Anatomical differences in cognitive 
control regions may contribute to poor cognitive control and the decision-
making impairments observed in non-attempters relative to attempters and 
controls. As a test of this hypothesis, cortical thickness and grey matter 
volume in cognitive control regions (extracted with Freesurfer) will be 
compared between high-risk Veterans with STBs, low-risk Veterans with STBs, 
and controls. Morphometry statistics will be entered into GLM group contrasts 
for each ROI.
Differences in white matter integrity between groups. PFC regions involved in 
cognitive control are anatomically connected to striatum by white matter 
pathways, e.g. the cortico- striato-thalamic loops2. Damage to these pathways 
may undermine efficient neurotransmission of top-down cognitive control 
signals which are fundamental for goal- directed behaviors including decision-
making5. In individuals with STBs, efficiency of the cognitive control system 
may permit the development of more deliberate suicidal behaviors rather than 
impulsive attempts. Deterministic tractography statistics will be contrasted 
between groups to test this hypothesis. Tractography procedures match those 
used above (Differences in white matter integrity between Veterans with STBs 
and controls). Subjects’ track statistics (anisotropy, track density) will be 
entered into GLMs contrasting high-risk and low-risk Veterans with STBs.
 
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Univariate fMRI differences in inhibitory control. This analysis tests the 
hypothesis that response inhibition, a component of cognitive control, will be 
superior in high-risk Veterans relative to their lower-risk Veterans and 
controls. We anticipate that the fMRI signal in VLPFC during successful 
inhibition will be strongest in the high-risk group relative to
lower-risk Veterans with STBs and controls.
Preprocessed SSRT will be entered into subject-level fixed-effect GLMs 
constructed with SPM12. Regressors for the following trial types will be 
constructed: Go trials, Stop Inhibit trials, Stop Respond trials, along with two 
nuisance regressors (go errors, non- response trials). Stop-Inhibit and Stop 
Respond trials represent successful, and unsuccessful attempts to halt 
responses in progress. Null events constitute an implicit baseline. GLMs will 
be created by convolution of the canonical hemodynamic response function 
with model regressors. Motion estimates and mean signal in cerebrospinal 
fluid will also be included as nuisance regressors. Contrasts for each trial type 
and Stop Inhibit– Stop Respond trials will be included in first-level models. 
Second-level models where subjects are treated as random effects will be 
estimated by regressing first-level contrast betas against group. Averaged 
betas across voxels from ROIs will also be extracted from each subject and 
contrasted between groups to conduct ROI-level hypothesis tests.
Adaptive decision-making and inhibitory control. Here, we will test the 
hypothesis that high-risk Veterans’ superior capacity for inhibitory cognitive 
control enables less impulsive decision-making. Separate GLMs will be 
constructed predicting either delay discounting (Monetary-Choice 
Questionnaire), or the proportion of risky choices (Iowa Gambling Task). 
Predictor variables include Group (high-risk, low-risk, controls) and Inhibitory 
Control (stop signal reaction time), Impulsivity (BIS-11 or alternatively, UPPS 
scores), or fMRI activation during correct inhibit SSRT trials. Follow-up control 
analyses in which the performance metrics from the Dimensional Card Sort 
and Flanker Tasks (see Research Plan
– Methods and measures) are substituted for the Inhibitory Control measure 
will be conducted to determine if risk is specifically related to inhibitory 
control or is a by-product of generally better cognitive control.
Resting-state functional connectivity in the cognitive control network and its 
relationship to decision-making. Greater functional cohesion amongst ROIs in 
cognitive control networks is associated with better cognitive control 
performance 62. Greater activation in cognitive control regions is also 
associated with decisions that promote long-term goals [92]. Thus, correlations 
between cognitive control ROIs may be stronger in high-risk suicidal Veterans 
given that their better decision-making performance may be due in part, to 
superior cognitive control. Functional connectivity analyses will be conducted 
using the CONN Toolbox and according to methods outlined in D9. The GLM 
testing logic follows the sequence above in Analysis D10.5.5, however, beta 
values describing functional connectivity amongst ROIs in the control network 
 
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for each subject will be used to predict Monetary Choice and Gambling 
performance.
 
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Design considerations & alternative strategies for Aim 2. This design uses a 
categorical factor to define groups that approximates risk by history of attempt 
versus ideation, based on the strength of previous attempt as a risk factor for 
subsequent attempts. While straight-forward, we acknowledge that within-group 
heterogeneity may invalidate this grouping strategy. For instance, the ideation 
group could contain a significant number of Veterans with highly detailed suicide 
plans that have begun obtaining means that would be better classified as high-
risk. In recognition of this issue, we will also compute empirically-determined risk 
categorization thresholds based on Beck Lethality Scale, Suicide Intent Scale, and 
Scale for Suicide Ideation scores. Alternatively, we may use continuous variables 
operationalizing suicidal planning, intent, or lethality for these analyses.
E3.3
 
  (Exploratory)
 
  Integrate
 
  EHR
 
  data
 
  and
 
  neurocognitive
 
  markers
 
  into
 
  predictive
 
  
models
 
  of
   STBs.
We will combine EHR data and objective cognitive measures forming 
a high- dimensional, multi-modal dataset. Machine learning algorithms 
will be applied to examine the predictive utility of this approach.
Rationale: Machine learning algorithms can accommodate large predictor 
sets and can model complex interactions among factors. Models built on 
EHR outperform traditional models with predictive accuracy in excess of 
AUC +0.70. We will subject an EHR dataset enriched with additional, 
objective, cognitive measures of suicidality (defined in Aims 1 & 2) to 
machine learning classification. Three types of supervised machine learning 
models (described below) will be used for this exploratory analysis. Models 
will be constructed both with, and without, Aim 1 & 2 measures to test if 
their inclusion improves suicide prediction.
Data sources: Aggregated EHR data (Section D6.2) and subject-level performance 
outcomes on the reward expectancy scanned task, Delay Discounting task, Iowa 
Gambling Task, Flanker Task, Dimensional Card Sort Task, and SSRT task.
General approach: The dataset comprised of subject-level aggregated EHR 
variables and behavioral outcomes from cognitive control and reward tasks will be 
treated as a “discovery data set,” meaning that it will be used to train machine-
learning algorithms and make initial identifications of subsets of the dataset 
associated with distinct suicide outcomes. Classifier training, cross-validation, and 
optimization will be performed using this discovery data set. The goal is to predict 
participants with: 1) history of attempt, 2) suicidal ideation without history of 
attempt, 3) no history of STBs. Future studies will attempt to replicate findings in 
an independent sample.
Software: Part of this exploratory aim involves testing implementations against 
one another. Modeling will be carried out using Julia64, an open-source 
computing language, MATLAB, and R.
Machine
 
  learning
 
  algorithms:
 
 
 
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Elastic
 
  Net
 
  Regularized
 
  Logistic
 
  Regression
 
  65 Unlike other regularized 
regression methods such as ridge or lasso, elastic net models are stable when 
multicollinearity amongst
 
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predictors is high. Elastic net regularization penalizes model complexity by 
specifically modeling overfitting simultaneously combining the shrinkage and 
recovery approaches of lasso and ridge regularization. This means that 
coefficients are all retained but undergo shrinkage toward zero. We will use 
a 10-fold cross-validation to assess the performance of the classifier.
Random forest machine clustering Random forest clustering is a tree-based 
supervised learning algorithm that can handle high multi-collinearity and is 
robust to overfitting [95]. Decision trees are built by recursively subsampling 
predictors from the training dataset [95,96]. At each “split” of a tree, the 
algorithm searches for a given variable and the binary decision that splits the 
predictor subset. As trees are built, progressively smaller predictor 
subsamples are drawn and used to predict earlier data splits forming “parent” 
and “child” nodes. The number of predictors at each node will be determined 
by the square root of the total number of predictors to limit over-fitting [96] 
and an error minimization criterion will be applied to select the predictor and 
cut point at each node of the decision tree that optimizes binary splits. This 
iterative process will continue down the tree until node size is equal to 10% of 
the total study sample. A 10-fold cross-validation will also be run as a 
preliminary validation of estimates.
Support Vector Machines (SVM) A supervised non-linear kernel function is 
used to transform the input data to a high-dimensional space, wherein 
individual observations are plotted as points in this n-dimensional space 
(n=size of predictor set). Classification is performed by identifying the 
mapping function that maximally segregates data classes. The choice of an 
underlying kernel function and its parameters can be used to balance the 
trade- off between classifier performance and complexity of dimensional space. 
Probability estimates, tuning of kernel, and regularization parameters will be 
computed by means of cross-validation.
Model comparisons Receiver Operating Characteristics (ROC) will be used to 
compare accuracy of models’ predictions. The area under the ROC curve 
statistic (AUC) will be used as the metric of algorithm classification 
performance, the model with the highest AUC value will be considered the 
best-fitting model. Models will undergo 10-fold cross-validation and bootstrap 
optimization analyses to measure model robustness.
Model interpretation: Although building models to predict STBs is the primary 
focus of this Aim, these models can yield additional valuable information that will 
advance our understanding of suicidal phenotypes. As post-hoc analyses, we will 
analyze the approach and strategy used by each of the classifiers listed above to 
predict STB. Different machine learning algorithms have various degrees of 
predictability. In Logistic Regression for example, studying the regression 
coefficients can provide insights about the information content of the features 
selected by the model for classification. Specific algorithms have been developed 
for other classifiers that estimate feature contribution [97]. This would lead to a 
 
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data-driven and unbiased identification of risk factors, many of which could be 
modifiable by simple interventions and as a result could reduce the risk of STBs.
Design considerations and alternative approaches: This study sample size is small 
because of budgetary restrictions. We recognize that this decreases the likelihood 
that models will generalize to an independent sample. A transdiagnostic 
participant sample is planned because
 
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this naturalistic sample will be more representative of Veterans with STBs. 
Heterogeneity however, may beget misclassification errors i.e. classification by 
disorder versus suicidal outcome. The AUC statistic is our planned metric for 
model comparisons because it easily interpreted. However, AUC metrics can 
overestimate true performance when applied to a larger dataset where one group 
is comparatively over-represented i.e. a high AUC is achieved by algorithms 
optimizing the identification of controls over cases. Alternative performance 
measures that simultaneously evaluate positive predictive value and sensitivity 
like precision- recall statistics may be a more robust alternative to AUC.
E3.4.2 Volume-space structural preprocessing
 
 .  T1-weighted images will undergo 
intensity normalization, tissue segmentation, and volume-space registration to the 
Montreal Neurological Institute (MNI)-152 Atlas.
E3.4.3 Surface-space structural preprocessing. T1-weighted images will be 
resampled to Freesurfer-conformed space, then submitted to intensity 
normalization, tissue segmentation, spherical registration, volume-space 
registration to the MNI-152 Atlas. Freesurfer preprocessing also includes surface-
space cortical parcellation and volume-space subcortical parcellation.
E3.4.4 General fMRI preprocessing. The following preprocessing steps apply to all 
fMRI data. Steps include slice-time correction, head motion estimation, 
realignment, inhomogeneity correction via field map, functional segmentation, 
registration to MNI-152 space, and an artifact analysis in which high motion 
volumes (>0.5mm translational or 0.02 rotational motion), or volumes where global 
signal variance exceeds 3 standard deviations are flagged for nuisance regression 
in subsequent models. Spatial smoothing with a 4mm full-width, half max Gaussian 
kernel will be applied to fMRI data subjected to univariate analyses to improve 
conformation to the assumptions of Random Field Theory.
E3.4.5 Functional connectivity preprocessing. The following additional subject-
level steps carried out with CONN reduce the impact of motion-related artifacts 
and non-neuronal signal on functional connectivity estimates [100-102]. The 
anatomical CompCor (aCompCor) routine (implemented in CONN) is an effective 
method for removing motion-related artefact that does not involve global signal 
regression [103], a practice that complicates interpretation of correlations 
[104,105]. Five principal components are extracted from the averaged fMRI 
signal in each aCompCor compartment (white matter, CSF). Principle components 
are regressed from subject-level data, along with regressors for linear trend, six 
estimated motion parameters and their 1st derivatives, and flagged high-motion 
or high global signal variance time points to limit potential sources of spurious 
variance. Residuals will be band-pass filtered (high-pass = 0.008, low-pass = 
0.15) after confound regression [106].
E3.4.6 Diffusion MRI preprocessing
 
 :  Preprocessing steps include: eddy-current 
compensation, head  motion  estimation  and  correction,  and  intra-subject 
registration.  Diffusion  preprocessing will be carried out using either FSL or 
FreeSurfer.
 
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F. PRIVACY,
 
  CONFIDENTIALITY,
 
  &
   INFORMATION
 
  SECURITY
 
 
F1. Privacy interests and confidentiality
Minimizing risk of loss of Protected Health Information (PHI) is a priority of the 
proposed study. In accordance with VA guidelines and laws of the State of Rhode 
Island and Providence Plantations, confidentiality of the participants and their 
information will be upheld as outlined in this document. In agreement with the 
Department of Veterans Affairs Record Control Schedule 10-1, records will be 
maintained as outlined in this document. All documentation and data obtained 
over the course of this study will be stored as outlined in this document.
To protect participants’ confidentiality, study participants will be assigned a 
numerical code that contains no personal identifiers or PHI. Participants will be 
listed only by this unique participant identification code on data files and data 
capture forms to maintain their anonymity. Codes will be sequential in numbering, 
e.g. 7001, 7002, 7003 etc. Identification codes will be applied to all cognitive 
testing, assessments, health service utilization, and MRI data collected over the 
course of the study.
The only documents or files containing PHI are: 1) informed consent, HIPAA 
documents, and demographic forms, 2) a password-protected Excel file containing 
contact information used for recruitment, and 3) a password-protected Excel log 
file linking participants to study numbers. Paper files will be stored in a locked file 
cabinet in a designated, lockable office in building 32. Electronic files containing 
PHI will be stored on a secured VA server in a restricted access folder (\\
R04pronas21\RESEARCH_PROTOCOLS\Philip\MRI_TMS+BCBT-R).
If a situation arises that is mandated by law (e.g. suspected child or elder 
abuse) or to ensure adherence to good clinical practice (e.g. participant in 
imminent medical danger and EMS is called; participant discloses intention to 
make an imminent suicide attempt and is taken to in-patient mental health), 
participants’ confidentiality may be breached.
F2. Data use
Data collected from this study will be used for research purposes only. 
Participant-identifiable information will not be shared outside the VA. 
Participant-identifiable information will not be released or published without 
written permission unless required to do so by law. Access to participant-
identifiable information is limited to researchers included in this protocol.
Information that could potentially permit the identification of individual 
participants will not be included in publications or reports, nor will they be shared 
with researchers who are not members of study staff.
De-identified data (e.g. questionnaires, self-report measures, imaging data, 
cognitive task data, scores from interviews) with the exception of assessment 
dates, from this project may be shared with PVAMC protocol #2018-051 entitled 
“Neuroimaging of a Suicidal Thoughts and Behaviors,” led by investigator Dr. 
Jennifer Barredo and PVAMC protocol #IRB-2020-008 entitled “Longitudinal 
 
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Assessment of the Sleep- Suicide Link in Veterans Discharged from Inpatient 
Psychiatric Care,” led by investigators Dr. Jennifer Primack and Dr. John 
McGeary.
Interview data, de-identified with the exception of assessment dates, from this 
study may also be shared with the PVAMC protocol “Longitudinal Assessment of 
the Sleep-Suicide Link in Veterans Discharged from Inpatient Psychiatric Care” 
(IRB-2020-008) led by investigators Dr. Jennifer Primack and Dr. John McGeary. 
These interview recordings would be shared for training purposes. Only approved 
members of the study will have access to recordings containing identifiable data.
 
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62
F4. De-identification of data
Data will be de-identified with a unique subject identification code being used 
instead of PHI being associated with the data. Identification codes will not be 
based on participant PHI. They will be sequential in numbering, e.g. 7001, 7002, 
7003 etc.
F5. Information security training
All employees that handle data will be trained in confidentiality policies and 
procedures. To remain active on this study, research staff must remain up to date 
with VA Privacy and Information Security and Rules of Behavior training. All staff 
training is tracked through Talent Management System (TMS). Appropriate 
supervisors are selected, and expiration notices are sent to both to ensure that 
users are up to date. Additionally, computer access is denied if training lapses.
F6. Software & computing equipment
F6.1 VA furnished software: VA-licensed MS Office and Excel will be used for log 
records and staff communication. The participant ID link log will be maintained 
using Excel with key access. VA-licensed SPSS software may be used to organize 
and analyze data kept on the VA research server. Speech Exec Pro Dictate 
(Phillips, v11) will be used for the transfer of audio recordings from approved 
recording devices to the research study folder on the VA server. The software has 
been installed onto select study staff’s computers and sanctuary requests have 
been granted to allow staff to connect audio devices to their VA networked 
computers. These resources will be accessed via standard-issue VA-networked PC 
workstations.
VA TRM has approved the use of Speech Exec Pro with constraints [7,8]:
[7]: Users must ensure that Microsoft .NET Framework is implemented with VA-
approved baselines. (refer to the ‘Category’ tab under ‘Runtime Dependencies’)
Speech Exec software has only been installed on VA-baselined computers with a 
Windows operating system.
[8]: Veterans Affairs (VA) users must ensure VA sensitive data is properly 
protected in compliance with all VA regulations. All instances of deployment using 
this technology should be reviewed by the local ISO (Information Security Officer) 
to ensure compliance with VA Handbook 6500.
The data uploaded using the Speech Exec software is stored on the protected 
research server, restricted to only study staff members.
F6.2
 
  Specialized
 
  computing
 
  workstations
 
 : These are required to carry out the 
neuroimaging and computational modeling analyses in this proposal approved by 
CSR&D (1 IK2 CX001824-01A1). Multi-core Apple MacintoshTM Super Computers 
(Unix operating system) will be used because most functional and structural brain 
imaging software packages run more efficiently on Unix.
The two Apple Super Computer used for neuroimaging analyses and 
computational modeling are housed in double-secured PVAMC Building #32 (rm. 
149). The Super Computer is a non-VA networked, non-encrypted, multi-core 
Macintosh computer and high-capacity external drive. These Super Computer will 
be connected to internet (currently supplied by PVAMC bldg. 32 Cox small 
business account), but not the VA intranet. Due to size and amount of 
neuroimaging data produced for each participant, data analysis fails when 
 
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63
encryption is used due to errors produced by encumbered input output (I/O) 
function. Only de-identified data will be analyzed or stored on these non-
networked, non-encrypted VA workstations.  The super computer will have unique 
administrator and user accounts. Administrator accounts will not have access to 
Web Email.  External data storage drives (not encrypted) will be partitioned for 
backup of Apple Super Computer. Super Computer and administrator accounts 
will be maintained and managed by the Neuroimaging and Scientific Computing 
Core (see attached SOPs). See Figure 3 for device EE numbers and locations.
F6.3 Software for super computer workstations: Super computers will be outfitted 
with general purpose and specialized scientific software necessary for the 
neuroimaging analyses and computational modeling aims of CSR&D (1 IK2 
CX001824-01A1). 
Details of package origins and  usage provided below. Open-source software is 
available for download from the NeuroImaging Tools and Resources Collaboratory 
(NITRC), or from academic neuroimaging repositories.
*Denotes Brown licensed software. **Software to be installed with Homebrew.
MATLAB* (R2020b) (https://www.mathworks.com/products/matlab.html)
MATLAB is a commercial mathematical software package used for 
analysis, modeling, and algorithm development. MATLAB is a dependency 
of several neuroimaging software packages (Freesurfer, SPM, FSL). 
Although VA does have an institutional license for MATLAB, the available 
VA version of MATLAB is several versions behind the current stable 
version causing compatibility problems with software dependent on 
MATLAB. MATLAB will be downloaded through Brown University license.
MRIcron (https://www.nitrc.org/projects/mricron)
MRIcron is a suite of neuroimaging programs developed at the 
McCausland Center for Brain Imaging used for image viewing and 
conversion of dicom format images (the ‘raw’ format on data DVDs) to 
compressed files or NifTI images (also referred to as .nii files). Download 
available through NITRC from above link.
OsiriX (http://www.osirix-viewer.com/)
Alternative neuroimaging viewing software. OsiriX has a commercial and 
a free version, the freeware version is used here. OsiriX can be used to 
view images that have not been converted into .nii files which can be 
useful if errors occurred while data was being written to DVD. Not 
available at VA, download without charge from above link.
Statistical Parametric Mapping (SPM12)
(http://www.fil.ion.ucl.ac.uk/spm/software/spm12/)
The SPM software package is used for the analysis of brain imaging data 
sequences. The sequences can be a series of images from different 
cohorts, or time-series from the same subject. The current release is 
designed for the analysis of fMRI, PET, SPECT, EEG and MEG. Developed 
by researchers at University College London. Not available at VA, 
download without charge from above link.
CONN: Functional Connectivity Toolbox
(https://www.nitrc.org/frs/?group_id=279)
 
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64
CONN is a Matlab-based cross-platform software for the computation, 
display, and analysis of functional connectivity in fMRI (fcMRI). CONN 
includes a rich set of connectivity analyses (seed-based correlations, ROI-
to-ROI graph analyses, group ICA, masked ICA, generalized PPI, ALFF, 
ICC, GCOR, LCOR, etc.) in a simple-to-use and powerful software package. 
CONN is available for resting state data (rsfMRI) as well as task-related 
designs. It covers the entire pipeline from raw fMRI data to hypothesis 
testing. Developed by researchers at Harvard/MGH. Not available at VA, 
download without charge from above link.
XQuartz (https://www.xquartz.org/)
XQuartz is an open-sourced graphic system for Unix(Mac OS) machines 
and is a Mac OS compatible version of X11. XQuartz is a dependency 
program for several neuroimaging programs including, but not limited to, 
AFNI and FSL. Not available at VA, download without charge from above 
link.
FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation)
FSL is a comprehensive library of image analysis and statistical tools for 
FMRI, MRI and DTI brain imaging data. FSL is written mainly by members 
of the Analysis Group, FMRIB, Oxford, UK. Not available at VA, download 
without charge from above link.
Analysis of Functional Neuroimaging (AFNI) (https://afni.nimh.nih.gov/
pub/dist/doc/htmldoc/background_install/download_link s.html#b-
downloading-a-set-of-the-newest-precompiled-afni-binaries)
AFNI is a set of C programs for processing, analyzing, and displaying FMRI data. It 
runs on Unix+X11+Motif systems, including SGI, Solaris, Linux, and Mac OS X. 
Funded and maintained by researchers at NIH. Not available at VA, download without 
charge from above link.
FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall)
FreeSurfer is a software package for the analysis and visualization of 
structural and functional neuroimaging data from cross-sectional or 
longitudinal studies. It is developed by the Laboratory for Computational 
Neuroimaging at the Athinoula A. Martinos Center for Biomedical 
Imaging. It is the gold-standard software package for conducting 2D 
surface-based analyses. FreeSurfer is the structural MRI analysis 
software of choice for the Human Connectome Project. Not available at 
VA, download without charge from above link.
Artifact Detection Tools (https://www.nitrc.org/projects/artifact_detect/)
Toolbox for post-processing fMRI data. Includes software for 
comprehensive analysis of sources of artifacts in timeseries data including 
spiking and motion. Most compatible with SPM processing, but adaptable 
for FSL as well. Not available at VA, download without charge from above 
link.
Connectome Workbench (https://www.humanconnectome.org/software/
get- connectome-workbench). Connectome Workbench is an open source, 
freely available visualization and discovery tool used to map neuroimaging 
data, especially data generated by the Human Connectome Project. The 
distribution includes wb_view, a GUI-based visualiation platform, and 
 
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wb_command, a command-line program for performing a variety of 
algorithmic tasks using volume, surface, and grayordinate data. 
wb_command is necessary for running HCP data processing pipelines. Not 
available at VA, download without charge from above link.
R and RStudio (https://www.r-project.org/) R is a free software environment 
for statistical computing and graphics. Available through VA or at the above 
link used to conduct nightly updates to R on the VA VINCI server.
Julia (https://julialang.org/) A specialized programming language 
appropriate for scientific computing and intensive computational 
modeling applications. Developed and maintained by the Massachusetts 
Institute of Technology. Not available through VA, but can be 
downloaded without charge at the above link.
SPSS  v26*  IBM  SPSS  platform  for  advanced  statistical  analysis. 
Available via the VA, albeit for an out-of-date version (v21). Up-to-date 
version has been downloaded at no cost to VA through the academic 
licenses associated with Brown University.
Python. (https://www.python.org/). Versions 2.7 and 3.8. A programming 
language that integrates several neuroimaging processing and analysis 
packages. Both versions are required for different software. Python2.7 is 
already  installed  on  MacOS  operating  systems.  To  install  Python  3.8 
using the bash shell and Homebrew, use the following:
< brew install python@3.8 >
PIP3 (https://pip.pypa.io/en/stable/). Python package installer / manager. 
Not  available  at  VA.  To  install  pip,  use  <python3  get-pip.py>  or 
<python3 get-pip.py –user>. In order to upgrade pip <pip3 install -U 
pip>.
Apsera
 
Connect
 
Browser
 
Plugin 
(https://www.ibm.com/products/aspera/downloads?list).  Browser  plugin 
used for downloading freely available Human Connectome Project data. 
Not available at VA, download without charge from above link.
Heudiconv
 
(0.9.0) 
(https://heudiconv.readthedocs.io/en/latest/usage.html).  Heudiconv  is  a 
python-based dicom to BIDS converter. Not available at VA. To install 
heudiconv fom the bash shell using python, use the following: 
< pip3 install heudiconv[all] >
Docker
 
(https://www.docker.com/products/docker-desktop)
 is
 an 
application  used to  build containerized software  that can  be  used to 
update MRIQC and FMRIPrep image processing pipelines. Install from a 
disk image downloaded from Docker’s website.
MRIQC
 
(0.16.0) 
(https://mriqc.readthedocs.io/en/prepare-release/about.html). MRIQC is a 
neuroimaging program that extracts quality assurance information from 
structural and functional MRI data. It is open-source software developed 
by the Stanford Center for Reproducible Neuroscience. Not available at 
VA.  MRIQC  can  be  used  by  pulling  the  latest  docker  image  < 
poldracklab/mriqc:latest >.
fMRI  Prep  (20.2.1)  (https://fmriprep.org/en/stable/).  fMRI  Prep  is  a 
preprocessing pipeline developed by the Poldrack lab at the Stanford 
 
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Center for Reproducible Neuroscience. fMRIPrep can be used by pulling 
the latest docker image < poldracklab/fmriprep:latest >.
Bids-validator  (https://github.com/bids-standard/bids-validator).  BIDS-
validator will validate any BIDS datasets. To install via python use:
< pip3 install bids_validator >
DSIStudio (http://dsi-studio.labsolver.org/). DSI Studio is a tractography 
software tool used ot map brain connections and correlations. It is freely 
available from the link above and was developed by Frang-Cheng Yeh. 
Not available at VA, download without charge from above link.
Java  (java.com).  Java  is  a  programming  language  and  computing 
platform. It is a dependency software for the NIH NDA GUID tool. Not 
available at VA, download without charge from above link.
Adobe Creative Cloud
A software suite for non-medical image processing. Includes Photoshop 
and Illustrator which are used for creation of publication quality graphical 
images used for dissemination of VA research in peer-reviewed 
publications, national meetings, and when approved, press releases.
F7. Web applications
F7.1
 
   VA
 
   REDCap
 
 : REDCap is a web-based application and electronic data 
capture tool enabling VA users to securely collect and store research data. VA 
REDCap will be used to store de-personalized information from assessments, 
interviews, and computerized behavioral testing data. De-personalized data will 
be uploaded from text files imported to VA workstations via a VA-encrypted 
USB or manually entered by study staff.
F8. Data flow
See Figure 3 for illustration of devices involved in data collection processing and 
storage. The following data will be collected at each stage of the study:
F8.1 Pre-Screening and Recruitment: Contact information for potential study 
participants will be collected during CPRS chart reviews, clinician referrals, or 
during in-person or telephone contact prior to initial screening. These data will be 
used for recruitment of potential participants via an initial letter and subsequent 
phone call. These data will be kept in password- protected Excel spreadsheets 
stored in a restricted study database located on a secure, VA- networked server 
behind the VA firewall.
F8. 2 Study Enrollment: Data on paper and digitally recorded audio data will be 
generated during initial in-person evaluations for study enrollment which will 
occur at the PVAMC. Study staff will administer informed consent and HIPAA 
paperwork per PVAMC IRB-approved procedures to all potential participants. 
These documents, which contain PHI, will be physically transported by study staff 
in an opaque folder with a fastener to the designated lockable office in double-
secured bldg. 32. Study staff will also scan consent and HIPAA forms into CPRS 
per standard PVAMC research procedures.
Eligibility evaluations will take place on the in-patient psychiatric units, in-
patient medical units being used to treat psychiatric patients, or in office space 
dedicated for clinical interviews in Bldg. 32, room 143. These evaluations are 
used to confirm suicidality and diagnostic information that may impact eligibility. 
 
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Paper copies of screening forms used during inclusion/exclusion evaluation will 
be labeled with de- identified subject numbers only. Forms will be stored in a 
locked cabinet accessible only by study staff in PVAMC Bldg. 32, rm. 143. Digital 
audio recordings of interviews during screens will be uploaded to a restricted 
folder on the secure VA research server immediately after the session. Digital 
audio recordings are used to enable scoring by a second rater (IRB approved 
study staff) in ambiguous cases e.g. level of suicidal intent is unclear. Recordings 
will be uploaded to a restricted folder on the secure VA research server 
immediately after the session and will be erased from the digital voice recorder 
after upload.
Data about VA health care utilization (e.g. number of primary care visits in 
the year prior to enrollment) will be extracted from participants’ CPRS files if they 
meet enrollment criteria and give informed consent. CPRS data will be de-
personalized during data extraction and entered into VA REDCap. De-identified 
data may be exported from REDCap and transferred to Apple Super Computers via 
VA-encrypted hard drive for further analysis. These computers are outfitted with 
software packages that are unavailable through the VA (see above), but are 
necessary for computational modeling.
F8.5 Baseline, Endpoint, and Follow-ups: Digital voice recordings collected during 
interviews will be recorded. Recordings will be uploaded to a restricted folder on 
the secure VA research server immediately after the session and will be erased 
from the digital voice recorder after upload. Data on paper generated during 
follow-up will be labeled with de-identified subject numbers only. Forms will be 
stored in a locked cabinet accessible only by study staff in PVAMC Bldg. 32, rm. 
143. Data about VA health care utilization extracted from participants’ CPRS files 
at follow-up, de-personalized during extraction, and entered into VA REDCap. De-
identified data may be exported from REDCap and shared with study staff via VA-
encrypted USB for analysis on research-owned, non-encrypted, password-
protected Apple Super Computer and external drive/file serves in PVAMC bldg. 
32, rms. 150 and 143.
F8. 3 MRI and Cognitive Battery Visit: Cognitive tasks will be administered at 
PVAMC on non- networked, non-encrypted research-owned computing devices 
(non-VA networked Apple iPad). The NIH Toolbox application will be downloaded 
to iPad and used for cognitive testing. Use of NIH common data elements in the 
NIH Toolbox requires the use of iPads for test administration. For additional NIH 
Toolbox information see: http://www.healthmeasures.net/index.php?
option=com_content&view=category&layout=blog &id=150&Itemid=844. 
Cognitive testing data will be collected using subject ID numbers only.
De-identified raw task data stored by subjects’ unique participant numbers will be 
transferred to Apple Super Computers via VA-encrypted hard drive. Cognitive 
measures may be incorporated into imaging analyses conducted on Apple Super 
Computers. These computers are outfitted with software packages that are 
unavailable through the VA, but necessary for analyses. Final summaries of these 
analyses in Excel spreadsheets or Word documents will be transferred to the VA 
network via encrypted hard drive and saved on the secure VA research server.
Magnetic resonance imaging (MRI) data will be gathered at the PVAMC imaging 
facility in Building 1. Raw/original imaging data is collected in de-identified format 
using only participant ID numbers. MRI data will be transferred from the PVAMC 
MRI acquisition computer to the PI’s AEGIS Fortress L3 encrypted hard drive. 
(Please see attached SOP for all details related to the hard drive). Following the 
secure transfer of data from the MRI acquisition computer to the AEGIS hard 
 
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drive, data will be transferred to two separate computers. First, the MRI data will 
be transferred from the hard drive to the PVAMC Research server (the PI’s secure 
folder: \\r04pronas21.v01.med.va.gov\RESEARCH_PROTOCOLS\Barredo\NSTB-R). 
Secondly, the MRI data will be transferred from the hard drive to the designated 
Apple Super Computer for analysis.  As raw imaging data is neither saved nor 
stored on the PVAMC MRI acquisition computer, a copy of the MRI data will be 
made to back-up raw imaging data. This copy will be kept in a locked file cabinet 
in Building 32, Room 149. Once it has been verified that the MRI data is securely 
stored on the PVAMC research server and a back-up CD/DVD copy of the data has 
been made, the Aegis hard drive will be reset which erases all stored data.
MRI data are recorded in Digital Imaging and Communications in Medicine 
(DICOM) standard format. DICOM image files are large.  DICOMS will be 
converted into the more compact NIH-standard Neuroimaging Informatics 
Technology Initiative (NiFti) format during download onto Apple Super 
Computers. Any PII in the DICOM metadata headers (e.g. dates) are removed 
during the conversion to NiFti files. NiFti files will not contain any identifiable 
information. Conversion is executed with custom scripts built on software written 
by the PI. Secondary files created during neuroimaging analyses on Apple Super 
Computers will be stored on Apple Super Computer partitioned external drives. 
These partitioned external drives allow for redundant storage of imaging data. 
Analytical results and secondary files will remain de-identified.
Cognitive tasks will be administered during MRI scanning at PVAMC on a 
non- networked, non-encrypted research-owned Apple laptop. Use of the non-
networked laptop facilitates the use of custom code for cognitive tasks built with 
software unavailable at the VA (outlined above). Cognitive task data will be 
collected using subject ID numbers only. Cognitive data will be transferred via 
encrypted hard drive: 1) to the VA network stored in a restricted folder on the 
research server, and 2) to Apple Super Computers. Study staff will also import 
transferred cognitive testing data on the VA research server into VA REDCap 
providing a second back-up.
F8.6 Analyses on Brown University’s CCV OSCAR: De-identified data (including DICOMS, 
cognitive testing data, and exported REDCap data) will be transferred to Brown 
University’s CCV OSCAR (Ocean State Center for Advanced Resources) for advanced 
analyses.  Please view Appendix A for more details.
F9. Data security plan
All study information will only be accessible by study staff included in the IRB-approved 
study protocol.
F9.1
 
  Paperwork
 
 : Paper documents containing de-personalized data will be stored 
in building 32 (doubled locked building) in a locked file cabinet in the PI’s office, 
or in a locked cabinet accessible by only study staff in room 143. Phone screening 
questionnaires, HIPAA authorizations, informed consent forms, and demographic 
forms, will be stored in a locked cabinet separate from study data files in the PI’s 
lockable office.
F9.2 Electronic data and audio-recordings: All VA sensitive information containing 
PHI (contact information, subject identification logs, audio-recordings) will be 
stored on a secure VA server location in a restricted folder e.g.
\\R04pronas21\RESEARCH_PROTOCOLS\Philip\MRI_TMS+BCBT-R, unless 
otherwise indicated (e.g. HIPAA, informed consent paperwork). These computer 
files are protected from unauthorized access by IRM-assigned permissions 
 
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granted to study staff. Identifiable contact information collected during the 
prescreening process will be recorded in an electronic password-protected 
recruitment log. Links to subject identification codes will be stored in a 
password-protected Excel log file. Logs with identification codes will not be 
stored in the same subfolder as the main research database. Audio-recordings 
collected during Visit 1 will be uploaded immediately at the end of the session 
and will be deleted from hand-held recorders after upload.
F9.3 MRI: CD/DVDs (raw, de-identified MRI data) will be stored in building 32. 
Following the transfer of data from the PVAMC scanner to the encrypted hard 
drive, the data will be stored for back-up on the secure research server. A back-up 
copy will be made onto a CD/DVD and stored in a locked cabinet the PI’s office. 
Another copy will also be transferred to the Apple Super Computer for analysis. 
F10. Data on hard drives
F10.1 Cognitive tasks: Data obtained from cognitive tasks on either the password 
protected Apple laptop or iPad will be temporarily stored on the device hard drive 
prior to transfer to the Aegis Fortress L3 encrypted hard drive. Data will be 
deleted from the laptop after successful transfer  to the secured research server (\\
r04pronas21.v01.med.va.gov\RESEARCH_PROTOCOLS\Philip\MRI_TMS+BCBT-
R).
Study staff will assure that this information is deleted by emptying the trash bin 
after deleting the information.
F10.2 MRI data: An Aegis Fortress L3 encrypted hard drive will be used to transfer 
MRI data from the MRI acquisition computer to the VA research server and to 
Apple Super Computers. This encrypted drive is VA FIPS-140-2 validated and 
requires and pins to unlock. See attached SOP for more details. De-identified MRI 
data currently under analysis will be stored on VA Research- owned, password-
protected, non-encrypted Apple Super Computers with removable large- capacity 
external solid state hard drives/file servers. The workstation is in the double- 
locked research Building 32, room 149. The Apple Super Computer and drive are 
not connected to the VA network. The hard drives are not encrypted due to data 
access/read-write speed requirements. 
F11. Mobile devices
Mobile devices used in this study are a VA-approved audio recorder (Phillips 
Digital Voice Recorder DPM-8000), Apple laptop, iPad, and Aegis Fortress L3 
encrypted drive. These devices are not on the VA network and will have wireless or 
802.11 connectivity disabled (laptop and iPad). Identifiable information will not be 
entered into iPads or laptops. Laptop and iPads are un-encrypted, because 
encryption prevents these devices from interacting properly with the MRI scanner 
during data collection. The hard drive is encrypted – see attached SOP for more 
information.
F12. Removal of sensitive VA data from the protected VA environment
No identifiable information will be shared outside the Providence VAMC.
F13. Protection of media stored at an alternate site
The principle investigator may share de-identified final datasets, statistics, and 
results by depositing these data at the National Library of Medicine (NLM) 
PubMed Central website repository. This is a VA-supported data repository. 
 
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Additional documentation including metadata such as information about data 
collection, analysis code, and definitions of variables may also be shared. 
Scientists and the public benefit from data sharing because it incentivizes 
scientists to uphold best research and data management practices. Data sharing 
also promotes independent replication of results by external research groups. 
Sharing also promotes collaboration amongst researchers which is especially 
important when conducting research on low base rate behaviors such as suicide.
F14. Data transmission
No sensitive electronic data are transferred in this study. Anonymous information 
may be shared with the research staff electronically for the purpose of study 
discussion and analysis.
F15. Data backup
Original VA research data saved on the VA network will be backed up regularly 
and stored securely within the VA’s protected environment. . The MRI data is 
duplicated on DVD/CDs and backed-up on the VA research server. Secondary 
imaging files created during analyses conducted on non-networked workstations 
will be backed up on partitioned large-capacity external hard drive/file serves 
stored in Bldg. 32, room 149. Partitioning enables redundant backup of imaging 
data. The size of the completed final analysis may be ~8 terabytes. This is 
estimated based on 1 TB size of a recently completed imaging analysis that used a 
lesser amount of imaging data. Weekly scheduled backups of non-networked 
computers will be managed by the PI.
F16. Shipping data
No data are shipped in this study.
F17. Data destruction
Records will be maintained in accordance with the Department of Veterans 
Affairs Record Control Schedule 10-1: it will be destroyed 6 years after the 
end of the fiscal year of the IRB closure of this study.
F18. Termination of data access
The PI will be responsible for assuring that VA access accounts are terminated 
when a user no longer needs access.
F19. Incident reporting
If theft, loss, unauthorized access of sensitive data or storage devices, or non-compliance 
with security  controls  occurs,  the  Information  Security  Officer  (ISO)  will  be  notified 
immediately. Research staff will carry out any necessary procedures given by the ISO to 
resolve the situation.
 
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Figure 3. Devices and technology flow
 
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G. HUMAN SUBJECTS PROCEDURES
G1. Sources of Material
Data collection will be conducted in accordance with HIPAA guidelines and 
PVAMC IRB- approved protocol. Potential Veteran subjects with STBs will 
be recruited from either the inpatient psychiatric unit at PVAMC or 
through clinician or Suicide Prevention Coordinator referrals. If the 
patient appears appropriate, the study will be explained to the patient's 
treating physician. If the attending physician agrees to the patient's 
participation, a member of the research team will then approach the 
patient. The nature, purpose, and risks and benefits of the study will be 
explained to the patient and informed consent will be obtained.
Data collected for this project will be used for research purposes only. 
Separate locked, secure files will be used to store study materials for each 
participant. An identity masking subject number will be assigned to each 
participant. Collected information will be identified by subject number only. 
A list linking the names of the participants with their subject numbers will 
be kept in a secure, password-protected computer account accessible only 
to the Principal Investigator(s).
De-identified data (e.g. questionnaires, self-report measures, imaging data, 
cognitive task data, scores from interviews),with the exception of 
assessment dates, from this project may be shared with PVAMC protocol 
#2018-051 entitled “Neuroimaging of a Suicidal Thoughts and Behaviors,” 
led by investigator Dr. Jennifer Barredo and PVAMC protocol #IRB-2020-
008 entitled “Longitudinal Assessment of the Sleep- Suicide Link in 
Veterans Discharged from Inpatient Psychiatric Care,” led by investigators 
Dr.
Jennifer Primack and Dr. John McGeary. De-identified interview data, with the 
exception of assessment dates, from this study may also be shared with the PVAMC 
protocol “Longitudinal Assessment of the Sleep-Suicide Link in Veterans Discharged 
from Inpatient Psychiatric Care” (IRB-2020-008) led by investigators Dr. Jennifer 
Primack and Dr. John McGeary. These interview recordings would be shared for 
training purposes. Only approved members of the study will have access to 
recordings containing identifiable data.
Five sources of data will be included in this project. These include: 1) the initial 
screens,
2) questionnaires, 3) structured and semi-structured interviews, 4) MRI data, 
and 5) electronic health record data obtained from participants CPRS files.
G1.1. Screens: Each potential participant will be interviewed by research 
staff to determine initial study eligibility. All information from this screen 
will be identified with subject numbers only.
G1.2. Questionnaires: Self-report measures will be administered at during 
the initial testing session. This information will be identified with subject 
numbers only. Paper copies of data collected will be stored in a locked 
filing cabinet in PVAMC bldg. 32, rm.
 
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143. Responses from assessments will be entered into VA REDCap and 
identified only by subject number.
G1.3. Structured and semi-structured interviews: These interviews will be 
audio- recorded. Recordings will be immediately uploaded to a restricted 
VA server and identified by participant number only. Recordings will be 
deleted from tape recorders
 
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immediately upon uploading to the server. Tape recorders will be kept in a 
locked filing cabinet in the offices of the principal investigator(s) or research 
assistants. All information on paper obtained from these interviews will be 
identified with subject numbers only and will be kept in a locked filing 
cabinet behind keycard-secured doors in PVAMC building 32. Electronic 
records of responses made during interviews will be entered into VA 
REDCap. Data in REDCap is only identified by subject number.
G1.4.
 
  MRI
 
  data
 
 : All MRI data will be collected using subject numbers only and will be 
recorded
G1.5. Electronic health record data: All information obtained from 
patients CPRS files will be identified with subject numbers only and 
will be recorded in VA REDCap.
G2. Potential Risks and Protections for Subjects
G2.1 Informed Consent: Administration of informed consent procedures 
will be performed under conditions approved by the PVAMC IRB. 
Informed consent will be obtained in-person by trained study staff 
familiar with both the study and with MRI methodology. Prior to consent, 
subjects will be informed of their rights as a research subject, warned 
about any known risks or benefits of the methodology, and will be 
informed of study procedures. Research personnel will review all parts of 
the consent form with participants, including the assessments and 
protocol, limits to confidentiality, alternatives to participation, as well as 
the right to withdraw from the study without penalty.
All potential participants will be asked to read and complete an 
informed consent after going through the form in detail with a member of 
the research staff. Research staff will be available to answer questions and 
ensure that the participant understands the form. Research personnel will 
review all parts of the consent form with participants, including the 
description of study assessments and protocol, limits to confidentiality, 
alternatives to participation, and potential risks and benefits, as well as the 
right to withdraw from the study without penalty.
If a participant withdraws (officially or unofficially) at any time from 
the study, the study investigator will follow-up with the participants mental 
health provider notifying them of discontinuation in the study following 
good clinical practice procedures.
Participants will be informed that their involvement in this study, or 
refusal to participate, will not affect their treatment with the VA. 
Participants will be given contact numbers for PIs and for the IRB 
(including afterhours contact information). The PIs will be available to 
answer any questions or provide additional information.
Participants may be asked to sign releases of information to non-VA 
providers to gather additional information for this study. Consent will be 
documented on an IRB approved form.
G2.2 Coercion: Though it will be emphasized during recruitment and 
the informed consent procedure that participation is voluntary and 
 
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may be terminated by the participant at any time, there is a possibility 
that Veterans may feel coerced into participating.
The risk of potential coercion will be minimized by following 
standard procedures for obtaining informed consent. We will fully explain 
the study procedures, risks, benefits and alternatives to all patients and 
significant others. Also, patients who do not consent or who withdraw at 
any time will receive usual clinical treatment with no
 
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prejudice. Although compensation for participation is provided, we do not 
believe that the total compensation amount (as noted in in the Study 
Procedures) is excessive or coercive.
G2.3 Confidentiality: There is some risk to patient confidentiality 
associated with participation in research clinical trials, as more data are 
collected than would happen in usual medical practice. Steps will be taken 
to protect privacy of patient health information. Patients will be told that 
information about their TMS treatment may be shared with their 
prescribing mental health provider and/or primary care provider, per best-
practice standards. Every effort to maintain participant confidentiality will 
be made.
All interviews will be conducted at the Providence VAMC research 
building and all hard copy data will be maintained on hospital grounds. 
Audio files of the sessions will be immediately uploaded to a VA secure, 
restricted server. Participants’ records/assessments will not become a part 
of their permanent medical record. Study forms and data will be identified 
by code numbers only, and will be stored in locked file cabinets or on VA-
owned computing resources. Identifying information (contact information, 
name, consent documents) will be stored separately from research data in a 
dedicated locked file cabinet or in a password-protected Excel file in a 
dedicated restricted folder on the secured VA-research server. Personal 
participant information will not be presented in publications or 
presentations resulting from this research. Only the PIs and study staff will 
have access to study materials. The Principal Investigators are responsible 
for ensuring that study personnel are trained in the responsible conduct of 
research.
In exceptional circumstances, confidentiality may be compromised. 
These circumstances include mandatory reporting requirements for the 
State of Rhode Island for child and elder abuse and in situations in which 
the risk of suicide or homicide is imminent. Patients will be informed of 
these potential risks to confidentiality during the informed consent process.
G2.4 Inconvenience and Burden of Time/Travel: Subjects may engage in 
screening procedures and learn they are not eligible for participation in 
the research treatment trial. Emotional discomfort may be associated with 
completing the assessments and questionnaires. Frequent visits to the 
research clinic for the TMS treatments may represent an inconvenience, 
especially if a subject must travel a great distance or has other constraints 
on their time or transportation. A small payment will be offered (see Study 
Procedures for details) to cover part of the subject’s expenses related to 
participation in this research study, but subjects will not be offered 
reimbursement for all the expenses they may incur.
G2.5 Risks of Assessments: There is a risk of discomfort when being 
asked to rate clinical symptoms or disclose information about suicidal 
thoughts and behaviors. Subjects will be informed that if they experience 
discomfort during symptom assessment, study personnel will take 
appropriate measures, including debriefing and referral to the principal 
investigators for further evaluation.
 
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G2.6 Procedures for Handling Suicidality: The potential for adverse events 
is possible during the study, including suicidal ideation or behavior or other 
adverse events and will be minimized by the following procedures:
1. If the patient’s psychotherapy is focused on suicide or overlaps 
significant with BCBT for suicide, patients may be asked to refrain 
from this suicide-for individual psychotherapy for the active 
treatment phase of the project (3 months). We will discuss this 
temporary change before enrollment with the treating therapist to 
ensure that it is clinically appropriate with the understanding that 
the patient may return to his/her usual psychotherapy after receiving 
study treatment. However, all subjects will continue to receive their 
pharmacotherapy as usual during the study. Thus, adverse events 
will be minimized by continued medication treatment and increased 
clinical monitoring as part of study procedures/assessments in both 
intervention conditions.
2. All subjects in the study will receive enhanced monitoring of their 
clinical condition. BCBT therapists will routinely monitor levels of 
suicidal  ideation and risk, as well as overall psychiatric 
symptoms, and take appropriate clinical action when necessary to 
reduce risk. In addition, all participants will be monitored closely 
and assessed at posttreatment follow-up sessions (see C3. Data 
Collection).
3. We will have procedures in place to respond to clinical 
deterioration. If a safety risk or deterioration is identified, we will 
inform the treatment team and provide emergency or referral 
services as needed.
4. If any patient reports suicidal ideation or behavior, this will be 
assessed and appropriate measures will be taken as noted in 
previous sections.
Regarding imminent self- or other-harm, there is a specific protocol within 
the Providence VA Medical Center System for handling behavioral crises, 
which is contingent upon whether the person(s) is on-site or off-site when 
such emergencies arise. On site, we have “Team 3” buttons for each 
clinician or research assistant that are directly linked to the Providence VA 
Medical Center police department and additionally request a team of 
mental health workers trained in crisis management. If there is imminent 
risk to self or another, we will collaborate with Providence VA Medical 
Center’s staff in Interim Care to triage the patient to receive the 
appropriate emergency services. If a participant is at imminent risk, the PI 
or trained study staff will escort the participant to Interim Care or 
Emergency Services at the Providence VA Medical Center. The clinical staff 
there will evaluate the need for emergency services, and triage the patient 
to receive the appropriate services. We will also provide a list of crisis 
hotline numbers and community resources that participants may access, for 
example, the Department of Veterans Affairs suicide prevention hotline 
number (800-273-TALK).
Relevant to risk for harm to self or others and suspected abuse or 
neglect of vulnerable persons (see below), participants are informed in the 
Informed Consent Procedures that these are circumstances in which 
confidentiality is not protected. In addition, if a participant must be 
terminated from the protocol due to safety concerns, if there is no referring 
 
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clinician and there is the need for referral, we will provide referral 
resources for the participant.
G2.7
 
  Risks
 
  Related
 
  to
   Intervention:
 
 
G2.7.1
 
  Side
 
  effects
 
  of
   TMS
 
  treatments:
 
 
During treatments, patients may experience a sensation of tapping or 
painful sensations around the place where the treatment coil is 
positioned on the head. Most patients who have had TMS therapy usually 
report these sensations to be mild and find it diminishes over time as 
their body adjusts to the daily treatment procedure.
Other possible side effects associated with TMS delivered to this 
standard target on the head are scalp, jaw, face, or neck discomfort or 
muscle twitching in those area, toothache, and headache. When TMS has 
been delivered to areas of the brain outside of the target location where 
the coil is placed in this study, there have been reports of transient 
dizziness, fainting, and brief changes in attention and thinking. These are 
considered extremely rare and highly unlikely to occur in subjects as 
planned in this study. The device also emits a loud clicking noise that can 
potentially lead to hearing problems.
 
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Some treatment coil adjustments may be possible to make the 
experience more comfortable. Over-the-counter pain medications such 
as acetaminophen or ibuprofen are helpful for reducing discomfort 
from TMS treatments. Assessment of the subjects’ well- being and 
functioning before and after each treatment, special positioning and 
targeting procedures to ensure the coil is placed over the specific brain 
region in this study, and constant monitoring during each treatment 
session, will all be done to minimize the risk of experiencing side 
effects. Because the device can emit a loud clicking noise during 
treatments, all subjects will wear protective earplugs or occlusive ear 
buds during TMS.
G2.7.2
 
  Seizure
 
  from
 
  TMS:
 
 
It is estimated that in ordinary clinical use, standard clinical TMS 
treatments have caused a seizure in approximately 0.1% of patients, 
representing a risk of seizure in approximately 1 in 30,000 treatments. 
The TMS treatments in this study will be administered by staff 
personnel who have extensive experience with safe delivery of brain 
stimulation and who are trained in steps to prevent and manage 
seizures.
Medical personnel and equipment are available at PVAMC to monitor 
subjects and apply appropriate medical procedures if a seizure occurs. 
Most seizures last only a few minutes and spontaneously end, with the 
patient in a somewhat confused state that resolves over several hours. 
Management of a seizure during TMS may involve transferring patients 
to the emergency room for further evaluation, if that is determined to 
be necessary by the study doctor. Should a subject experience a seizure 
that is related to TMS in this research, a doctor will provide them with a 
letter stating that the seizure was produced under specific brain 
stimulation conditions in this study, and that there is no reason to 
expect another seizure would occur when not receiving TMS.
Furthermore, a licensed study physician with experience and TMS 
training will determine the motor threshold and deliver, or supervise 
delivery, of the TMS pulses. This physician will be available throughout 
all TMS sessions, and a qualified technician will be present during all 
TMS sessions. Side effects of TMS will be used to identify and address 
any TMS-emergent adverse effects following best clinical practices. Risk 
of seizure and other serious adverse events related to stimulation will be 
mitigated by careful screening of past health history, identification of 
underlying risk factors, application of the study inclusion/exclusion 
criteria and use of chosen stimulation parameters. Risk of discomfort 
from stimulation will be addressed through pre-TMS assessment of 
tolerability and potential reduction of the intensity of delivered pulses.
G2.7.3
 
  Worsening
 
  Symptoms
 
  or
   Lack
 
  of
   Improvement:
 
 
There is a potential lack of positive response to BCBT or TMS, worsening 
of psychiatric symptoms, and no guarantee that the treatment will lead 
to improvement of symptoms. Suicidality may increase if symptoms do 
not improve. All participants will be receiving VA treatment as usual. If 
our staff identify an individual with significant clinical deterioration or 
who report any suicidal ideation with plan or intent they will contact one 
of the designated VA research study Psychiatrists who will evaluate the 
patient over the phone or in person. A list of mental health services will 
 
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be available in case of emergency. If a study participant reports 
significant deterioration but is not in immediate danger of hurting 
himself, we will take the following actions. First, we will inform patients 
about the procedures for contacting emergency services should they find 
themselves at risk for self-harm. Second, we will contact their outpatient 
psychiatrist or other primary clinician to inform them of their 
deterioration. Third, if there is an increase in suicidality during any TMS 
sessions, the TMS provider will immediately alert the designated study 
clinical
 
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coverage personnel. In any event that a participant is acutely suicidal, 
the research staff member will walk the Veteran over to Interim Care or 
the ER for immediate safety assessment. All serious adverse events will 
be immediately recorded and reported to the Providence VAMC IRB and 
DSMB according to policy.
G2.8 Risks Related to Neuroimaging: There are no known physical, 
psychological, social, legal or other risks associated with participation 
in MRI at the level of magnetic field proposed here beyond those posed 
by the existence of an implanted non-MR safe metal device or metal 
fragment, or of claustrophobia. Multiple screens for contraindications 
are administered to eliminate significant risk. On rare occasions, 
subjects have reported heating of tattoos in the MRI. Participants with 
tattoos will be monitored for heating while in the MRI scanner.
The PI or trained study staff will discuss MRI visit procedures, 
including safety measures, in detail with the participant at the end of the 
baseline visit. Safety information is also included in the Informed Consent 
materials. They will be informed that there is a risk of claustrophobia 
during the scan; investigators and research technicians trained in 
screening procedures will evaluate for claustrophobia and take 
appropriate measures, including referrals to treatment. Subjects will be 
informed of the risk of heating from radiofrequency coils and instructed 
to inform the research technicians if this occurs. Subjects will also be 
informed that the scan session provided does not constitute a clinical 
scan and that researchers are not board- certified radiologists. If there is 
concern for a physical abnormality on structural scan, the PI will provide 
an appropriate referral. All serious treatment- emergent adverse events 
will be brought to the VA IRB as required in IRB protocols.
G2.8.1 MRI safety training of study staff Study staff involved 
in Visit 2 sessions will have completed at a minimum Level 1 
MRI safety training through the VA Talent Management 
System (TMS). Training involves viewing of safety videos, 
reviews of safety white papers, and one-on-one training with 
MRI facility operators or managers. Before scanning staff 
must demonstrate knowledge of:

Subject preparation procedures (clothing, securing 
valuables, execution of screening form, explanation 
of MRI exam).

MRI scan room and equipment (door overrides, table 
controls, intercom,  emergency  squeeze  ball,  linen, 
storage and use of RF coils)

Emergency procedures for medical emergencies e.g. cardiac arrest.

Emergency procedures for situations presenting an 
immediate threat to human life or to the facility 
infrastructure i.e. procedures for Siemens quench 
and emergency run-down and subject evacuation 
during an adverse event.
G2.8.2 MRI-contraindication screening. Potential participants 
CPRS files will be reviewed for potential MRI contraindications 
by the PI or study staff during pre- screening. Subjects will 
 
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undergo screening for MRI contraindications, metal implants, or 
the possibility of accidental lodging of metal fragments three 
times: 1) by the PI or study staff conducting the enrollment 
screening, 2) by the PI or study staff at the MRI facility outside 
of the scanner room during Visit 2, and 3) by the MRI technician 
just prior to entering the scanner room.
 
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G2.8.3 Participant safety and comfort monitoring. MRI operators 
and study staff will monitor participants using 2-way voice 
intercom, infrared camera, and psychophysiologic data (i.e., pulse 
and respiration rate). Prior to scanning, participants will be 
trained to use a squeeze bulb alarm system that will immediately 
signal distress to the scanner operator. One co-Principle 
Investigators is a Board-
Certified Psychiatrist (Noah S. Philip, MD) and can be 
available to assess clinical needs and make referrals if acute 
management is needed.
G3. Potential Benefits of the Research to Subjects and Others
The potential benefits of identifying effective treatments for suicidal 
patients appear to outweigh the potential risks of this study. Both BCBT and 
TMS alone have been found to reduce suicidal ideation and we believe that 
participants in both conditions will experience significant improvement in 
symptoms. Improvements in assessment and treatment of suicidal patients 
are urgently needed. The major risk, that of adverse events, should not be 
increased by study participation and in fact should be reduced by the 
enhanced monitoring and risk reduction procedures noted above. If 
neuroimaging biomarkers are clinically useful, participants may gain access 
to related technology that may help them self-monitor their suicidal risk at a 
later date.
Any incidental neurological information gained during scanning of the brain may 
be of potential benefit to subject and the subject’s physician.
G4. Importance of the Knowledge to be Gained
Suicidal behavior is a significant public health issue and of critical 
importance to Veterans. The evaluation of interventions to reduce suicidal 
behavior may have important benefits for multiple aspects of society. 
Additionally, this study will advance our understanding of the neurobiology 
of suicidality. These findings can be used to develop neurobiology-based 
clinical criteria for identifying Veterans at elevated risk for suicide. 
Importantly, these objective measures are less susceptible to the reliability 
issues that affect the accuracy of self-report measures. Knowledge gained 
will be fundamental for the development of targeted therapeutic 
interventions that can modulate neural function in regions implicated in 
suicide. Thus, our results will have important implications for a variety of 
stakeholders, including patients, family members, healthcare providers, 
managed care organizations, health insurers, administrators, and policy 
makers.
G5. Data and Safety Monitoring Plan
Weekly meetings with study staff and study investigators will be held to 
review progress with regard to enrollment, any adverse events, and 
attrition/noncompliance. Circumstances surrounding any identified 
adverse events, incidents of subject dissatisfaction, or subject 
noncompliance/withdrawal of consent will be tracked regularly and 
discussed. Adverse events tracking files will be routinely updated. If data 
patterns consistent with any safety issues are suggested, the principal 
investigator will seek consultation with, and peer review by, other 
 
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experienced research colleagues who have executed similar studies with 
the methods and procedures of concern.
Serious adverse events will be identified and promptly 
reported to the Providence VA IRB as required. A member of the 
research team will be on-site during all
sessions, and in the event of any subject becoming unstable or demonstrating 
clinical symptoms, the principal investigator and/or study team will assess the 
subject and facilitate subsequent treatment or referral. All research and staff 
members are trained in
 
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basic first aid, CPR, and appropriate MRI, and brain stimulation 
safety/evacuation protocols. All members of the research team have 24-
hour access to investigators or covering psychiatrists on site for 
management of any clinical emergency that may arise. To ensure the 
integrity of the data the PI and study team will review all the data for 
errors or inaccuracy within one week after it is obtained. All data will be 
entered into a research database as it is collected (i.e. RedCAP), and the 
research assistant will meet with PI weekly or as appropriate to review 
ongoing subject data.
G6. Inclusion of Women, Minorities and Children
This proposal acknowledges, and will adhere to, VA policy to include 
women, minorities and children in research. The gender and minority 
balance of the Providence VAMC reflects the broader demographic of 
Veterans and local demographic distribution of Providence, Rhode Island. 
Pursuant to VA policy, people below the age of 18 will not be included in 
this research. Veterans below the age of 21, but above the age of 18 will 
not be excluded when they meet eligibility for the study and sign informed 
consent. The number of participants that are between 18 and 21 years of 
age is anticipated to be limited since this age range is not broadly 
represented in patients receiving care at the Providence VA.
 
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86
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