Optimizing Cognitive Remediation in VA Mental Health Rehabilitation Settings
Conditions: Schizophrenia, Stress Disorders, Post-Traumatic, Generalized Anxiety Disorder, Mood Disorders
Sponsor: VA Office of Research and Development

D3395-W, Optimizing Cognitive Remediation in VA Mental Health Rehabilitation Settings 
NCT04395157 
Study Protocol and Statistical Analysis Plan 
10/30/2025 

Over one hundred thousand Veterans annually utilize mental health residential rehabilitation programs 
(RRTPs) and psychosocial rehabilitation and recovery centers (PRRCs). Through support and comprehensive 
programming, these mental health rehabilitation milieus help Veterans with mental illness overcome barriers to 
community reintegration. Despite the success of these milieus, cognitive impairment is commonly observed in 
Veterans with mental illness, reduces gains from programming, and limits successful recovery. Cognitive 
impairment is a common transdiagnostic illness dimension conferred both directly and indirectly by mental 
illnesses. Cognitive rehabilitation through remediation strategies can potentially attenuate cognitive impairment 
for these Veterans and improve outcomes, but there are two inter-related problems that limit the effectiveness 
of such interventions. First, we are not able to identify which Veterans will benefit from mental health 
rehabilitation programming at program entry. Second, we are not able to predict which Veterans are going to 
benefit from any specific cognitive remediation intervention.  
This CDA-2 application seeks to test whether an electroencephalographic (EEG) biomarker, mismatch 
negativity (MMN), can be used to predict Veteran recovery in mental health rehabilitation treatment settings, 
and identify Veterans who will respond to cognitive remediation interventions. MMN is an event-related 
potential which is considered to be a biomarker of information processing, linked to cognition in healthy 
subjects and cognitive impairment in a variety of neuropsychiatric illnesses. MMN also mediates psychosocial 
and functional outcomes in individuals without any psychiatric comorbidities and individuals with mental illness. 
Recent work also suggests that MMN can identify individuals who will experience gains from a full course of 
cognitive remediation when measured over the first hour of cognitive remediation. These biomarker 
relationships (cognition, cognitive remediation sensitivity, functioning) have not yet been definitively established 
in a heterogenous Veteran population receiving care in real-world settings like RRTPs and PRRCs. 
Veterans with mental illness will be recruited from the VA San Diego Healthcare system RRTP and PRRC at 
program entry. Baseline measures of functioning, psychosocial disability, cognition and treatment engagement 
will be collected. Following these assessments, Veterans will undergo testing to collect MMN data, and then 
will be challenged with a one-hour cognitive remediation exercise, which is a typical component of full multi-
hour cognitive remediation programs. Veterans will be followed with monthly assessments of psychosocial 
disability and treatment engagement. At the end of study, functioning will also be re-assessed.  
If successful, results from the studies proposed will create an objective, precision-medicine platform which 
could fundamentally change how RRTP and PRRC approach rehabilitative programming for Veterans with 
mental illness. In carrying out the studies proposed, the PI will gain critical training in advanced EEG biomarker 
analyses, computational/statistical methodology and clinical trial design and implementation which will expand 
his scientific skill set and lead to scientific independence. This CDA-2 will allow the PI to work towards his 
career goal of using EEG biomarkers to personalize cognitive rehabilitation interventions for Veterans with 
mental illness.  
Contact PD/PI: Joshi, Yash
Project Summary/Abstract                                                                                      
 Page 6

Veterans with mental illness face challenges with community reintegration, including achieving vocational 
success, attaining their educational goals and going back to school, and maintaining a high quality of life. VA 
mental health Residential Rehabilitation Treatment Programs and Psychosocial Rehabilitation and Recovery 
Centers are designed to help Veterans overcome these barriers, but cognitive impairment often seen in 
Veterans with mental illness limits gains from these settings. Cognitive remediation interventions can be 
helpful, but they are either “one-size fits all,” and thus may not be useful for all Veterans with mental illness, or 
are too narrow in scope, focusing on specific mental illnesses, limiting generalizability. This project will test 
whether an objective biomarker can better match the “right” Veteran to the “right” cognitive remediation 
treatment regardless of their specific mental health diagnosis.  Information gained from this study will help 
establish a precision-medicine approach towards cognitive rehabilitation for Veterans with mental illness.  
Contact PD/PI: Joshi, Yash
Project Narrative                                                                                             
 Page 7

SPECIFIC AIMS 
Hundreds of thousands of Veterans annually utilize mental health residential rehabilitation treatment 
programs (RRTP) and psychosocial rehabilitation and recovery centers  (PRRCs) across >200 medical 
centers. RRTPs and PRRCs help functionally disabled Veterans with mental illness acquire new skills, make 
use of vocational services, and manage symptoms. Despite the menu of rehabilitation programming offered in 
these settings, the cognitive impairment commonly observed in this population, reported in up to 80% of 
Veterans in these settings, limits engagement in recovery-oriented activities, reduces gains from rehabilitative 
activities and jeopardizes functional outcomes. Developing interventions to address cognitive impairment 
in order to improve outcomes would be beneficial to Veterans who make use of RRTP and PRRC 
services. However, at present there are key challenges limiting such needed innovation: first, there are no 
objective measures to help identify at program entry which Veterans will have a positive recovery trajectory; 
second, there are no objective measures to predict which Veterans will favorably respond to interventions 
aimed at improving cognitive functioning.  
To address these issues, our group has studied an EEG biomarker - mismatch negativity (MMN) - to 
better select the “right” individual for the “right” pro-cognitive intervention to improve outcomes in real-world 
treatment settings. Pre-treatment measures of MMN predict 1) performance on cognitive remediation exercises 
during the first training session, and 2) cognitive benefit after 30 hours of training (3 months later) for 
individuals with mental illness in community treatment settings. Gains from these cognitive remediation 
training programs in community-based mental health residential settings are associated with 
increased milieu therapeutic, social, and vocational activities, and linked with improved quality of life.  
MMN demonstrates high test-retest reliability, is validated for multi-site studies, and can be collected by 
non-specialists. MMN mediates psychosocial outcomes, including measures of overall functioning in patients 
with mental illness. MMN is also associated with cognitive functioning not only in healthy subjects, but also in 
those suffering from specific neuropsychiatric illnesses. These lines of evidence suggest that MMN can be 
used to objectively identify which individuals with mental illness will benefit from community-based mental 
health services and resources, and can distinguish who may benefit from pro-cognitive interventions. 
However, before biomarker-guided precision medicine approaches are ready to be deployed in VA 
settings, two key questions must be answered: 1) Is MMN associated with functioning and recovery 
trajectory in VA mental health rehabilitation programs? and, 2) Can MMN predict sensitivity to cognitive 
remediation in a heterogenous group of Veterans with mental illness?  
In order to answer these questions we will recruit Veterans with mental illness engaged in VASDHS 
RRTP/PRRCs (n = 104) within the first month of entry into these programs. MMN, cognition, psychosocial 
disability, treatment engagement and functional performance will be assessed. Veterans will also undergo one 
hour of cognitive remediation exercises which are typical components of therapeutic 30-hour courses of 
cognitive remediation. Veterans will be followed monthly for a total of 4 months.  
Data obtained from this CDA-2 will form the core of a precision-medicine approach for Veterans with 
mental illness engaged in rehabilitation programming in RRTPs and PRRCs across the VA Healthcare System, 
while simultaneously providing a rich training opportunity to launch my career as a VA clinical scientist. The 
Specific Aims of this proposal are: 
Specific Aim 1: Determine whether MMN is related to functioning, psychosocial recovery and treatment 
engagement in VA rehabilitation milieus. We hypothesize that greater MMN amplitude will predict 
improvement on functional performance, reduced psychosocial disability and greater treatment engagement 
over 4 months.  
Specific Aim 2: Determine whether MMN is linked to cognition and predicts cognitive remediation 
exercise performance in a heterogenous group of Veterans with mental illness. We hypothesize that 
MMN will correlate with global cognition and that higher performance on a one hour cognitive remediation 
exercise will be predicted by greater MMN amplitude. Recent work has identified demographic factors that 
affect performance on cognitive remediation, including age, cognition, and anticholinergic medication burden. 
We hypothesize that these factors affect cognitive remediation performance in this Veteran population. 
Exploratory Aim: Assess feasibility and acceptability of using biomarker-guided cognitive 
rehabilitation interventions in VA rehabilitation settings. We will assess Veteran motivation and interest in 
undergoing EEG biomarker assessment prior to a cognitive remediation program. We will also interview staff 
and clinicians at each site to identify ways to optimally deploy future biomarker-informed cognitive remediation 
trials.  
Contact PD/PI: Joshi, Yash
VA Specific Aims                                                                                              
 Page 15

2.1 BACKGROUND AND SIGNIFICANCE___________________________________ 
2.1.1 SIGNIFICANCE 
VA mental health rehabilitation settings are necessary to help Veterans with mental illness overcome 
psychosocial disability. More than one out of three Veterans are thought to need mental health services and 
more than one out of every four U.S. Veterans is diagnosed with a mental illness, including: anxiety and post-
traumatic stress disorders (PTSD), mood disorders (e.g., major depressive disorder, MDD; bipolar affective 
disorder, BAD), and chronic psychotic disorders (e.g., schizophrenia, SZ and related disorders). [1, 2] Veterans  
with mental illness account for >30% of those with a service connected disability rating between 50-90, and 
>50% of those with a rating of 100 [3, 4]. Veterans with mental illness also account for up to 80% of unemployed
Veterans, >50% of Veterans who are disabled or unable to work and are at high risk to be homeless[4-6]. In
order to address these challenges, the Veterans Health Administration (VHA) has established mental health
Residential Rehabilitation Treatment Programs (RRTPs) and Psychosocial Rehabilitation and Recovery
Centers (PRRCs) to help Veterans with mental illness acquire skills and receive support to improve successful
community reintegration. In total, hundreds of thousands of Veterans with mental illness utilize RRTPs and
PRRCs annually, which currently operate at >200 sites associated with VA medical centers.
Cognitive impairment is a core transdiagnostic illness dimension in Veterans with mental illness and 
severely limits community reintegration. Although the pathophysiology of mental illnesses commonly 
observed in Veterans are distinct, myriad and complex, cognitive impairments have been widely reported in 
each condition. Even though the specific constellation of cognitive impairment differ by illness (see Table 1), 
they converge to result in moderate-to-severe impairments in global cognition[7, 8]. These deficits are magnified 
with co-morbidities found at relatively high rates in Veterans with mental illness: medical/neurological illness 
including traumatic brain injury (TBI), physical disabilities, substance use, and stressors including 
homelessness [9-14]. Clinically significant cognitive impairment has been reported in up to 80% of Veterans with 
mental illness who use mental health rehabilitation services [15]. 
Table 1. Overview of 
cognitive impairment 
in PTSD, chronic 
psychotic disorders 
(e.g., SZ), anxiety 
disorders (including 
generalized anxiety 
disorder and panic 
disorder), and mood 
disorders (BAD, 
MDD). Adapted from 
Millan et al. 2012[6] 
+++ = core, severe, virtually universal deficit  
+
= present, but may not be pronounced
 ++  = common, marked characteristic 
0/+  = mild, poorly documented or absent.
Yet, regardless of whether cognitive impairment is central to the disease processes underlying mental illness 
(for example, with chronic psychotic disorders), or driven by symptoms of mental illness (as with cognitive 
impairment in MDD), it predicts poor outcomes. Cognitive impairment is associated with compromised ability to 
achieve independent living, limited vocational success, and reduced quality of life[16-18]. Furthermore, cognitive 
impairment limits engagement in medical and mental health treatment as well as reduces successful utilization 
of rehabilitative programming such as those found in RRTPs and PRRCs[19, 20].    
Addressing cognitive impairment can improve community reintegration but it is not clear which 
cognitive remediation strategies are ideal for use in VA mental health rehabilitation settings. Cognitive 
remediation strategies have demonstrated efficacy in reducing cognitive impairment in patients with mental 
illness, and individuals with the most severe cognitive impairment tend to experience the greatest gains[20-28]. 
Strategies range from compensatory cognitive training, where skills and habits are taught in an effort to 
Attention/Vigilance
Working Memory
Executive Functioning
Episodic Memory
Semantic Memory
Visual Memory
Verbal Memory
Fear Extinction
Processing Speed
Procedural Memory
Social Cognition
Language
PTSD
+++
+
+
++
+
+
++
+++
+
0/+
+
+
Psychosis
+++
+++
+++
+++
++
+
+++
++
++
+
+++
+++
Anxiety
++
+
0/+
0/+
0/+
0/+
+
+
0/+
0/+
0/+
0/+
BAD
++
++
++
++
+
+
++
+
++
0/+
++
++
MDD
+
++
++
++
+
+
+
0/+
++
+
+
+
Contact PD/PI: Joshi, Yash
VA Research Plan                                                                                              
 Page 16

overcome functional deficits resulting from cognitive impairment; “drill-and-train” programs, which focus on 
improving specific cognitive domains; and “bottom-up” cognitive training approaches, where basic sensory 
processes are enhanced to yield improvements in higher-order cognitive function. In ideal settings, these 
cognitive remediation strategies improve functional outcomes, including enhancing quality of life and reducing 
disability for those with mental illness [25, 28-30]. However, these studies typically have significant rates of 
treatment non-response, have limited success when translated from academic labs to real-world mental health 
treatment settings, or fail to replicate when expanding from small single-site trials to larger multi-site trials[29, 31, 
32]. Evaluating the usefulness of various cognitive remediation strategies is also made difficult due to the sheer 
volume of interventions, which have expanded considerably in the last decade [33]. These strategies now span 
the gamut from smart phone apps, to websites, handheld and console-based games, and laptop/computer-
based platforms[34, 35]. Thus, while cognitive remediation strategies have the potential to be helpful to Veterans 
with mental illness, it is difficult to evaluate 1) which Veterans will respond to specific cognitive remediation 
interventions, 2) whether a positive response to a cognitive remediation intervention augments gains from VA 
mental health rehabilitation programming, and 3) whether gains from VA mental health rehabilitation 
programming imparted by cognitive training enhances psychosocial success for Veterans. 
 
The central problem: there are currently no objective measures which can help determine which 
cognitive remediation interventions should be used in VA mental health rehabilitation settings in order 
to improve community reintegration for Veterans with mental illness. Objective measures would help 
better match the “right” cognitive remediation intervention to the “right” Veteran and inform a precision-
medicine approach to rehabilitation in RRTPs and PRRCs. Ideally, objective measures would 1) identify which 
Veterans with mental illness are at risk for poor gains from rehabilitation at program entry, and, once these 
Veterans are identified, 2) predict which of these Veterans would benefit from a cognitive remediation 
intervention to improve gains from rehabilitative programming. If such objective measures are identified, they 
can be used to better evaluate cognitive remediation strategies for use in VA mental health rehabilitation 
settings.  
 
2.1.2 INNOVATION 
 
The core innovation of this application is that it will establish whether an EEG biomarker – mismatch 
negativity (MMN) – can identify Veterans with mental illness at risk for limited gains from rehabilitation 
programming, and predict which Veterans will respond to cognitive remediation.  
 
This innovation will advance the field of cognitive rehabilitation by creating a platform on which current 
cognitive remediation strategies can be evaluated and future novel pro-cognitive strategies can be developed. 
The studies proposed will help establish a precision-medicine approach to cognitive rehabilitation whereby 
Veterans will receive personalized cognitive interventions as opposed to inefficient “one-size-fits-all” 
approaches. Possessing an objective biomarker that can identify Veterans with mental illness at risk for limited 
gains from rehabilitation programming will also improve treatment response in future cognitive remediation 
trials, improve translation of results from efficacy trials conducted in laboratory environments to effectiveness 
trials carried out in usual treatment settings, helping scale-up promising interventions. 
 
2.1.3 RELEVANCE TO VETERAN HEALTH, IMPACT ON VA REHABILITATION PROGRAMMING, AND 
IMPORTANCE TO VA HEALTHCARE SYSTEM 
 
This application directly addresses the VA RR&D priority area of enhancing community reintegration through 
cognitive/psychological rehabilitation. If successful, this application will allow the VA research community to 
better evaluate cognitive remediation interventions and their impact on outcomes for Veterans with mental 
illness by using an objective EEG biomarker. RRTPs and PRRCs will be able to create more impactful 
rehabilitative programming by more effectively implementing high-yield interventions, and de-implementing 
lower-yield interventions. The scientific products from this application will help maximize the returns on 
resources, staff and funds the VHA has invested into RRTPs and PRRCs, and capitalize on Veteran time and 
energy spent in rehabilitative programming. The knowledge gained from the studies proposed will not only be 
relevant to the VA San Diego Healthcare System where it will be conducted, but also will be able to translate to 
RRTPs and PRRCs VA-wide.  
 
Contact PD/PI: Joshi, Yash
VA Research Plan                                                                                              
 Page 17

2.1.4 BACKGROUND 
Mismatch negativity (MMN): an EEG biomarker of cognitive functioning 
Mismatch negativity (MMN) is an event-related potential (ERP) evoked on electroencephalography (EEG) 
when a train of standard stimuli is interrupted by an oddball, or “deviant”, stimulus. MMN is pre-attentive, 
reflecting an automatic response to sensory stimuli, and is able to be elicited without any effort, behavioral 
response, or conscious awareness [36, 37]. MMN can be evoked via multiple sensory modalities, but MMN 
evoked by auditory paradigms represents the vast majority of MMN literature [38] (see Figure 1). Deviant stimuli 
only need to differ from stimuli in one dimension (i.e., pitch, volume, timbre, or duration) to elicit a reliable MMN 
response. MMN is reported as a negative value. 
Figure 1. Mismatch negativity. Compared to event-related potentials evoked from “standard” sound stimuli (“s”; 
in red), rare “deviant” stimuli (blue) evoke event-related potentials that are characteristically different between 
~100 to 200 ms after stimulus onset. The difference waveform resulting from subtracting standards from 
deviants yields mismatch negativity (MMN) in the ~100 to 200 ms time window, a negative amplitude value. In 
this example, standard stimuli only differ from deviant stimuli in duration ( 50 vs 100 ms in length). 
MMN represents the first objective index of sensory discrimination that occurs when input from the external 
environment enters the central nervous system. The ability to identify and differentiate small changes in 
sensory information is a key aspect of all neural systems, and as such, MMN (or analogous 
electrophysiological correlates) has been described not only in humans, but also in monkeys, rodents, birds 
and amphibians [39-41]. Since appropriate sensory discrimination underlies all higher order cognitive processes, 
including attention, memory, emotion/social cognition, and executive functioning, MMN has been described as 
“primitive sensory intelligence,” and is thought to reflect general brain plasticity [42]. In humans, the MMN 
response is thought to be generated from a number of different brain regions distributed across cortical and 
subcortical regions (see Figure 2), and involve modality-specific primary and secondary association cortices. 
Previous work has consistently localized auditory MMN to be distributed across fronto-temporal regions[43]. 
 
Figure 2. Brain regions involved in the MMN response. Yellow regions indicate areas of high current density 
associated with MMN including temporal, frontal and parietal regions. Adapted from Takahashi et al. 2013[44] 
 
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VA Research Plan                                                                                              
 Page 18

MMN is abnormal in multiple psychiatric diseases and is linked to psychosocial outcomes. 
MMN abnormalities have been repeatedly linked to multiple impairments in processing speed, attention, verbal 
and working memory, fluency and social cognition[45-48].MMN amplitude abnormalities have been reported in 
psychotic disorders for nearly three decades in hundreds of publications: they are observed in patients with 
chronic illness, those who have been medicated with antipsychotics, unmedicated patients, those at high risk 
for conversion to psychosis, and in first degree relatives of patients with chronic psychotic disorders [49, 50].    
Several studies have reported abnormalities in MMN in mood disorders, and to mood-associated 
neurocognitive deficits. Reduced MMN amplitude has been described in patients in the first episode of 
depression, as well in those with recurrent MDD; attenuation of MMN amplitudes have been linked to impaired 
processing of emotional stimuli in patients with MDD, even in patients with MDD who are in remission[51-54]. 
Patients with BAD have MMN abnormalities compared to healthy non-psychiatric subjects, but are less 
impaired than patients with psychotic disorders (consistent with genetic studies linking psychotic disorders with 
bipolar disorder) [54-56].  Limited studies have been carried out investigating MMN in PTSD and anxiety 
disorders: while varying MMN paradigms have been used in these studies, they seem to generally support the 
association between abnormal MMN and trauma-associated or anxiogenic- stimuli, with either mild or no 
deficits to neutral stimuli [57-60]. Both acute and chronic alcohol use is associated with diminished MMN, which is 
associated with memory impairments and processing speed [61-63]. Hallucinogenic or psychotogenic substances 
(e.g., ketamine, PCP) also produce MMN abnormalities similar to those with psychotic disorders[64, 65]. Chronic 
cannabis users have impaired MMN, but not casual users, and MMN returns to normal values with continued 
abstinence[66-68].  
MMN correlates with global assessment of functioning in adults without any psychiatric diagnoses [69]. 
Additionally, abnormal MMN is associated with social, occupational and overall disability; reduced quality of 
life; impairments in functioning in patients with psychotic and mood disorders[70-72]. As exemplars, Figures 3 
and 4 show such a relationship between MMN and global assessment of functioning in healthy non-psychiatric 
subjects and patients with schizophrenia[69, 72]. 
MMN has been tested in large multi-site trials, has high test-retest reliability, is a candidate biomarker 
for pro-cognitive therapeutics. 
MMN has been tested in large multi-site trials in both patients with mental illness as well in subjects without 
psychiatric history. One of the largest studies to utilize MMN as a routine biomarker has been the Consortium 
on Schizophrenia Genetics (COGS) study [73]. In this study, 1800 subjects were recruited across 5 sites 
(UCSD, UCLA, University of Washington, University of Pennsylvania, and Mount Sinai School of Medicine). In 
this study, where EEG was collected by non-specialists who had received ~2.5 hours of training, site only 
accounted for ~1% variance in aggregate data. MMN has also demonstrated high test-retest reliability with a 
one year intraclass correlation coefficient of > 0.8. Based on the ability of MMN to index cognitive functioning, 
the link between MMN and functional outcomes, and the ability of MMN (or MMN analogues) to be elicited in 
lower-order model system, MMN is thought to be an ideal biomarker for screening and testing pro-cognitive 
therapeutics [74, 75].To that end, our group has detailed how memantine, a cognition-enhancing medication 
commonly used in the treatment of Alzheimer’s disease, can improve MMN deficits in patients with 
schizophrenia[76]. 
Figure 3.  
Relationship 
between MMN 
and global 
assessment of 
functioning in 
healthy non-
psychiatric 
subjects 
Figure 4.  
Relationship 
between MMN 
and global 
assessment of 
functioning in 
patients with 
schizophrenia 
Contact PD/PI: Joshi, Yash
VA Research Plan                                                                                              
 Page 19

2.2 PRELIMINARY STUDIES_____________________________________________ 
Adding to the literature, we have investigated whether MMN can be useful in predicting response to and gains 
from a cognitive remediation intervention in patients with schizophrenia. Below we describe the principal 
findings, which are all published. 
MMN predicts performance on a one hour cognitive remediation exercise 
We assessed MMN in patients with schizophrenia (n = 28) 
prior to completing one hour of cognitive training delivered 
via the Posit Science/BrainHQ computerized cognitive 
remediation platform as reported in Perez et al., 2017[77]. In 
this task, participants listened to a series of two successive 
tone sweeps (varying in frequency range and interstimulus 
interval (ISI)), and then indicated with two corresponding 
button presses whether the frequency increased or 
decreased within each tone, respectively. The exercise 
contained multiple stimulus sets composed of 
combinations of base frequency and duration. Subjects 
completed stimulus sets with longer-duration stimuli before 
stimulus sets with shorter-duration stimuli are made 
available. Auditory exercises were psychophysically 
adaptive, and task difficulty increased and decreased 
systematically and parametrically with performance 
changes. These exercises were comfortably tolerated by all subjects. Auditory perceptual learning gains for 
each subject were defined as the mean percentage of improvement in auditory perceptual learning speed on 
psychophysically smaller threshold differences in tone duration and frequency. As seen in Figure 5, we found 
that larger auditory perceptual gains on a one hour cognitive remediation exercise were associated with larger 
pre-training MMN amplitudes. These findings suggest that MMN can predict performance on cognitive 
remediation exercises used in cognitive remediation programs. 
MMN predict cognitive gains after a full course of cognitive remediation 
We were encouraged to find that MMN predicts response to a one hour 
cognitive remediation exercise. We then wanted to investigate whether 
early changes in MMN predicted gains after a full course of cognitive 
remediation (using the same cognitive remediation program as above). 
Since previous studies had reported that cognitive remediation exercises 
improved cognition in participants in academic labs, we wanted to see if this 
was also true in usual care mental health rehabilitation settings.  
As reported in Thomas et al., 2018[29], we carried out a full 30 hour cognitive 
remediation training program at a locked inpatient residential treatment 
facility, recruiting participants with chronic psychosis. These patients were 
severely ill, and so gravely disabled by their symptoms that they were under 
permanent conservatorship . Participants were randomized to treatment as 
usual (TAU, n = 22) or TAU with 30 hours of cognitive remediation 
(CogRem, n = 24). CogRem participants underwent cognitive training in 1 
hour blocks, up to three times a week, and on average required  3 months 
to complete the full course.  
At follow up, as shown in Figure 6, we found that participants in the 
CogRem group had made significant gains in verbal learning, with a large 
effect size, compared to TAU. 
Figure 6 
Figure 5 
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VA Research Plan                                                                                              
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In a subset of participants in this study who 
underwent cognitive remediation, we 
measured MMN changes before and after 
one hour of cognitive training. We then 
asked whether these initial MMN changes 
from the first hour of training predicted 
benefits after a full 30 hours of training. As 
reported in Hochberger et al., 2019[31], and 
represented in Figure 7, we found changes 
in MMN peak latency measured after the 
first hour of training significantly correlated 
with changes in verbal learning, 3 months 
later (after 30 hours of training, p<0.05). 
Additionally, our recently submitted, 
unpublished, analyses have also revealed 
that MMN and related EEG metrics 
measured over the first hour of training 
predict clinical improvement from cognitive 
remediation with a sensitivity of >90% and a 
specificity of >80% (data not shown). 
These findings suggest that MMN measured at study entry can predict gains from a full course of cognitive 
remediation in usual care mental health rehabilitation settings. 
Cognitive remediation is associated with increased treatment engagement 
Based on our knowledge of MMN, its relationship 
to cognitive functioning, and its association with 
outcomes, we next asked if gains imparted by 
cognitive remediation generalized to more distal 
endpoints in the study described above. We were 
interested in seeing if gains in cognition translated 
to greater engagement in rehabilitation.  
We were able to track cumulative rehabilitative 
activities in this residential treatment center 
including: groups attended, therapeutic activities 
engaged, and self-care activities recorded by staff. 
As shown in Figure 8 (adapted from data published 
in Thomas et al., 2018[30]), the CogRem group 
cumulatively accrued more rehabilitative activities 
than the TAU group. Specifically, the 30 hours of 
cognitive remediation in the CogRem group yielded 
an average of one extra week of total rehabilitation 
per participant over the course of the study 
compared to  the TAU group. 
These findings suggest that gains from cognitive remediation can generalize and enhance recovery in usual 
care mental health rehabilitation settings. 
In total, taking into account previously published literature, and these preliminary studies, MMN is a 
robust general biomarker of sensitivity to cognitive remediation. 
Figure 7 
Figure 8
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VA Research Plan                                                                                              
 Page 21

2.3 STUDIES PROPOSED_______________________________________________
2.3.1 Rationale for studies proposed 
The preliminary studies we have already 
conducted have supported a link between MMN 
and cognitive remediation exercise performance, 
and between cognitive remediation gains and 
enhanced mental health rehabilitative milieu 
engagement. Previously published literature has 
also described the link between MMN and 
psychosocial disability. Together these studies 
would suggest that MMN may be useful in better 
identifying and characterizing Veterans at entry 
into mental health rehabilitation programming 
that have high psychosocial disability, who may 
not be able to maximally benefit from 
rehabilitative programming due to cognitive 
impairment, and who may be responsive to 
cognitive remediation.  
Using MMN in mental health rehabilitation 
milieus in such a way would enhance efforts to 
maximize gains from programming and better 
aid these Veterans in successfully achieving 
community reintegration. An idealized schema 
depicting a future MMN-guided approach toward 
rehabilitation is shown in  Figure 9. 
However, before MMN can be routinely used as part of a precision medicine approach for cognitive 
remediation in VA mental health rehabilitation settings, two critical questions must be answered: 
1) Is MMN associated with overall functioning and recovery trajectory in VA mental health rehabilitation
settings?
2) Can MMN predict response to cognitive remediation in a heterogenous group of Veterans with
mental illness?
Indeed, Veterans have unique constellations of medical and mental healthcare needs that put them at greater 
risk for psychosocial disability than their civilian counterparts. Consequently, programming in VA mental health 
rehabilitation settings (PRRC and RRTPs), is focused on the unique needs of Veterans. How these factors 
affect the relationship between MMN, psychosocial disability and outcomes has not been characterized in 
Veterans to the best of our knowledge. Additionally, Veterans have unique co-morbidities (discussed above) 
which would contribute to cognitive impairment in ways beyond those imparted by mental illnesses in civilian 
populations. Thus, without having clear answers to these two questions, a full scale MMN-guided 
cognitive remediation trial is not yet warranted in VA mental health rehabilitation setting for Veterans 
with mental illness.  
2.3.2 Synopsis of research proposed and considerations taken into account in designing the study 
We will assess MMN, cognition and functioning in Veterans with mental illness recruited at entry into 
VASDHS’s PRRC and RRTP. We will then assess performance on a one hour cognitive remediation exercise. 
We will follow these Veterans for three additional monthly assessments of psychosocial disability and 
treatment engagement for a total of 4 months of study. The design of this study and Specific Aims will address 
knowledge gaps described above. 
The main goal of recruiting from two rehabilitation milieus, with different constellations of mental illness 
diagnoses (see below), is to test and extend the generalizability of MMN as a useful biomarker. This is also 
central to my training and future career goals of becoming an expert in developing and using biomarker-guided 
cognitive rehabilitation interventions across VA treatment milieus. Additionally, there are secondary reasons for 
using two milieus: 1) to increase the range of functioning at baseline in order to avoid potential floor/ceiling 
Figure 9 
Contact PD/PI: Joshi, Yash
VA Research Plan                                                                                              
 Page 22

effects for longitudinal analyses; 2) increase the range of cognition measured for more robust correlation 
analyses with MMN; 3) include Veterans with chronic psychotic disorders so that exploratory analyses can 
better contextualize similarities and differences between civilian and Veteran studies linking MMN and 
cognitive remediation performance in chronic psychosis; 4) compare Veterans with and without chronic 
psychotic disorders in exploratory analyses; 5) see if the utility of measuring MMN at study entry in one type of 
treatment milieu, if found, translates to other milieus; and 6) ensuring the study achieves its recruitment goals.  
We have selected a PositScience/BrainHQ-based cognitive remediation exercise because we have experience 
with it, have published methodology operationalizing performance on it, and because it is representative of 
remediation platforms currently available. The PI, mentors, consultants have no real or apparent financial 
interests in PositScience/BrainHQ, and remain agnostic about which specific remediation programs ought to be 
most helpful in VA settings.  
While we would anticipate a full course of cognitive remediation will improve cognition and potentially improve 
outcomes, we do not expect the one hour of cognitive training proposed to change cognition or functional 
outcomes. We have opted to use a one hour of cognitive remediation exercise as opposed to a full cognitive 
remediation program for several reasons. First, our preliminary data has established that one hour of cognitive 
remediation is sufficient for MMN to predict treatment response in patients with chronic psychotic disorders. 
Based on previous studies establishing MMN as a biomarker of general plasticity, we have every expectation 
that this relationship will generalize to cognitive impairment regardless of mental health diagnosis. However, 
this has yet to be empirically confirmed. This CDA-2 project will help empirically establish the utility of MMN in 
predicting gains in cognitive remediation for a heterogenous population of Veterans with mental illness. 
Second, a full course of cognitive remediation would require 30 hours to complete, placing a strain on the 
resources and staff at RRTPs/PRRCs. Given the time required for a full course of cognitive remediation (3 
months per participant), and the resources afforded by a CDA-2, such a trial would not achieve the sample 
required to accomplish the proposed Aims. This is particularly salient given that cognitive remediation 
interventions have been reported to have treatment non-response rates of  20-40%, which we have also found 
in our published study. Having biomarkers which can better identifying Veterans who will respond to cognitive 
remediation will justify the resources necessary for a full-scale cognitive remediation trial at RRTP/PRRCs. 
Relatedly, in our full cognitive remediation study conducted in a community mental health rehabilitation center, 
we generated substantial interest from potential participants, and had a fair degree of success in recruitment, 
but found higher than expected drop out between the start of cognitive remediation and finishing 30 hours 
(~25% attrition). This first-hand experience has inspired the exploratory aim in order to assess feasibility and 
acceptability of carrying out a 30-hour cognitive remediation trial in VA mental health rehabilitation settings. I 
am fortunate to have co-mentors with expertise in implementing and evaluating evidence-based practices for 
the population with mental illnesses in VA settings (Cohen, Twamley, Marder). Successful completion of this 
CDA-2 project will help justify a larger rehabilitation trial with more resources so that a full course of cognitive 
remediation can be effectively implemented and matched to the appropriate Veterans.  
2.3.3 RESEARCH PLAN 
Participants, recruitment, and screening. Veterans who are in their first 4 weeks of receiving services 
through the VASDHS’s RRTP and PRRC will be recruited to participate.  
The VASDHS PRRC serves Veterans with psychotic disorders (e.g., schizophrenia, schizoaffective disorder, 
etc.) with significantly impaired global assessment of functioning (GAF <50). The PRRC Veterans are typically 
seen at least monthly by a mental health clinician, and engage in recovery coaching, groups and 
psychotherapy, peer services and supported employment at least twice a month. The VASDHS PRRC has a 
census of 100 Veterans, with a planned increase in census to ~120 by end of 2019. In FY18 it served 238 
unique Veterans. Most Veterans who complete intake stay for >4 months (>90%). The VASDHS RRTP serves 
Veterans who are currently homeless or at risk of being homeless and who have OEF/OIF service with combat 
related PTSD. Frequently co-morbid in this cohort are significant mood and anxiety symptoms, and related 
mood and anxiety disorders. The VASDHS RRTP served a total of 84 Veterans in FY18, with 61 Veterans 
staying for 4 months or more (>72%). Mental health rehabilitative programming occurs for typically 4 hours a 
day (groups, psychotherapy, vocational rehabilitation). 
Contact PD/PI: Joshi, Yash
VA Research Plan                                                                                              
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In both PRRC and RRTP, an active substance use disorder, active suicidal or homicidal ideation, are 
exclusionary. Veterans will primarily be recruited to this study via: 1) referral from mental health providers in 
PRRC and RRTP and 2) advertisements placed in the PRRC and RRTP milieu. Veterans may also self-refer 
based on word-of-mouth. When Veterans contact study staff to enroll in the study, verbal consent will be 
obtained to review CPRS for confirmation that the Veteran is enrolled in PRRC/RRTP programming. If the 
Veteran is eligible and willing to engage, they will be scheduled for an initial session to complete informed 
consent, undergo screening, and baseline assessments.  
Study design, and participant compensation.  
As shown in Figure 10, after Veterans are referred to 
the study CPRS is reviewed, we will schedule the 
Veteran for Visit 1, which includes: informed 
consent, demographic information, screening (see 
below), diagnostic interview/symptom scales, 
cognitive assessment (Penn Computerized 
Neurocognitive Battery; PCNB), World Health 
Organization Disability Assessment Schedule 2.0 
(WHODAS 2.0) and UCSD Performance-Based 
Skills Assessment, brief version (UPSA). At study 
entry a baseline assessment of treatment 
engagement will also be collected (Service 
Engagement Scale, SES; completed by clinician). 
Visit 1 will be carried out on RRTP/PRRC premises.  
In order to provide flexibility in scheduling, Visit 2 
may be scheduled up to one week after Visit 1, and 
include: preparing the Veteran for EEG assessment, 
EEG assessment, a one hour cognitive remediation 
exercise delivered on laptop, and an assessment of 
Veteran attitudes towards EEG assessment and 
cognitive remediation for the exploratory aim. Visit 2 
will be carried out in Dr. Light’s laboratory. 
Following Visit 2, we will perform assessments of overall functioning (WHODAS 2.0), symptoms, and SES 
monthly, three times. These visits will be scheduled at RRTP/PRRC premises or Dr. Light’s laboratory (based 
on Veteran preference) in a way that does not disrupt rehabilitation programming. At the last follow up, we will 
also reassess UPSA. 
At the end of study, we will have collected two measurements of UPSA (baseline, month 4), and 4 
measurements of WHODAS 2.0 and SES (baseline, months 2-4). We chose 4 months for follow up because 
based on discussions with PRRC/RRTP leadership, there is significant variability at both milieus about how 
long Veterans stay in programming beyond 4 months. A shorter time frame will likely not adequately capture 
usual early drop out, and thus may create spurious relationships between MMN and longitudinal data. A longer 
time frame for follow up would be preferred, and would be ideal for a larger study/longer award, but not feasible 
within the bounds of a CDA-2. Characterizing the relationship between MMN and this 4 month longitudinal data 
will justify larger studies where more distal time points can be assessed. 
Visit 1 and 2 are both estimated to take ~180 min (including breaks) each. Follow ups will be ~30 min each, 
except for the last follow up which will include an additional 15 minute UPSA assessment. Participants will 
receive $80 for completing Visit 1 and $175 for Visit 2 to limit potential attrition and encourage participation 
between Visit 1 and Visit 2. They will receive $30 for each follow up meeting, for a final compensation of up to 
$375 for ~9.5 hours of participation. 
Screening. Veterans will be screened at study entry on the following criteria. Inclusion criteria: 1) Veterans 
must receive services in VASDHS PRRC/RRTP; 2) are within 4 weeks of VASDHS PRRC/RRTP entry; 3) 
have a DSM-5 mental illness, including schizophrenia, schizoaffective disorder, delusional disorder, MDD, BAD 
Figure 10 
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VA Research Plan                                                                                              
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I or II, generalized anxiety disorder, PTSD verified by Structured Clinical Interview for DSM-5 (SCID-5); 4) Age 
> 18 years; 5) fluent and literate in English; 6) able to hear 500, 1000 and 6000 Hz tones binaurally at a 45-dB
sound pressure level, 7) able to see with an acuity of 20/40 with both eyes tested together (corrected if
applicable) by a standard printed Snellen eye chart reading card. Exclusion criteria: 1) active substance use
within the last 30 days as determined by CPRS review, self-report, staff report, or positive urine drug screen
conducted as part of the screening process; 2) determined by PRRC/RRTP and/or study staff to be at
significant risk of exacerbation of symptoms, have acute/ongoing suicidal or homicidal ideation, or other risk
due to study participation; 3) Veterans with a history of violence per CPRS review, staff report and study staff
judgement; 4) intellectual disability or a major neurocognitive disorder (i.e. dementia).Exclusion criteria are
intentionally minimized so that the study sample accurately represents Veterans who utilize PRRC/RRTP
services. Up to ~10% of Veterans with mental illness have suicide flags attached to their record (including at
the VASDHS PRRC/RRTP). We will not exclude based on presence of a suicide flag as Veterans in both of
these milieus have successfully participated in other VA research studies without adverse outcomes.
Sample size and power considerations.  
We will recruit 104 Veterans (n=52 in each milieu) to complete Visits 1 and 2 who we will follow for up to 4 
months post Visit 1, anticipating a final n = 42/milieu (total final n = 84 across both milieus) accounting for an 
anticipated attrition rate of 20%. 
Power was determined broadly for the two categories of analysis planed. First, analyses that rely on linear 
mixed-effects models and second for analyses that rely on bivariate regression analysis. For the linear-mixed 
effects model, power was determined using the methods described by Heddeker et al 1999 [78]. With 2-time 
points, a medium effect (Cohen’s d = .5), reliability of outcome measures equal to 0.7, and alpha of 0.05 (2-
tail), n = 43 per group is sufficient for power = 0.8. For the bivariate regression analysis, G*Power (v3.1.9.4) 
was used to determine that n = 41 per group gives the ability to detect a medium effect size (point-biserial 
correlation of 0.3) using an alpha of 0.05 (2-tail) with power of 0.8. Based on these power calculations, a final 
n=42 completers/site was determined to be sufficient for analyses below.  
Demographic information to be collected. Age, sex, ethnicity, education history, current medication list, 
medications taken with in the last 24 hours, nicotine product use/smoking status (and amount if applicable), 
service connection (yes/no and percent), medical history/diagnoses, housing status, current employment 
(yes/no; number of hours; place of employment) or most recent employment, weeks worked within the last 12 
months will be collected.  
Anticholinergic medication burden will be calculated by using the Anticholinergic Cognitive Burden (ACB) scale 
as previously reported in Joshi et al., 2019[79]  for use in Specific Aim 2.3. Briefly, the ACB scale is an 
empirically derived instrument which assigns risk to medications with anticholinergic properties with potential 
for cognitive impairment. Medications on this scale are rated from 0 (no known risk for cognitive impairment) to 
3 (high risk for cognitive impairment). An individual’s total ACB score is the summed from ACB ratings for all 
individual medications a patient is taking. Measures of anticholinergic burden have been described to affect 
cognitive functioning and reduce gains from cognitive remediation.  
Symptom scales. The following scales will be used to assess current symptoms: mood (PHQ-9; Patient 
Health Questionnaire-9[80]), anxiety (GAD-7; General Anxiety Disorder-7[81]), trauma (PCL-5; PTSD Checklist 
for DSM-5[82]), psychosis (BPRS; Brief Psychiatric Rating Scale[83]). These ratings scales are selected for this 
study due to the fact that these measure are already widely utilized VA-wide (PHQ-9, GAD-7, PCL-5), or are 
already utilized for tracking PRRC milieu outcomes (BPRS).  
Assessments.  
Functional assessment. All subjects will undergo the UCSD Performance-Based Skills Assessment, brief 
version (UPSA) at study entry and 4 months[84]. The UPSA takes ~15 min to complete. The UPSA incorporates 
role play to assess functional capacity in four broad domains: planning recreational activities, finances, 
communication and transportation. Raw scores on each subtest are transformed into a standardized subscale 
scores (0-25), which are summated to an overall score (0-100). The UPSA is widely used for various 
neuropsychiatric illnesses and in multiple contexts. Using the UPSA will provide an object measure of 
functional disability. 
Contact PD/PI: Joshi, Yash
VA Research Plan                                                                                              
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Psychosocial recovery. All subjects will complete the World Health Organization Disability Schedule 2.0 
(WHODAS 2.0) at study entry, and on monthly follow up[85]. The WHODAS 2.0 is an assessment instrument 
that is designed to be used across both healthy subjects and those with neuropsychiatric illnesses, produces 
standardized disability levels and profiles, applicable across cultures, in all adult populations and is directly 
linked at the level of the concepts to the International Classification of Functioning, Disability and Health (ICF). 
WHODAS 2.0 is composed of 12-item patient-rated measure using a 5-point Likert scale focusing on six 
domains: cognition, mobility, self-care, getting along with people, life activities, participating in society. The 
WHODAS 2.0 has been recommended by the DSM-5 Task Force committee to take the place of the global 
assessment of functioning (GAF) found in previous iterations of DSMs due to the WHODAS conceptually 
unlinking functional impairment from etiology and weakness of GAF as a construct, especially questionable 
psychometrics in routine practice[86]. The WHODAS 2.0 12-item takes <15 min to complete.  
Treatment engagement. Treatment engagement will be measured using the Service Engagement Scale (SES), 
a 14-item clinical-rated measure using a 4-point Likert scale that focusing on patient availability, collaboration, 
help-seeking, and treatment adherence[87]. SES will be completed on a monthly basis by staff at RRTP and 
PRRC (including at baseline). The SES has been used in usual practice settings and whose psychometric 
properties have been studied with strong results. At both milieus, four clinicians (psychiatrists, psychologists, 
nurses, LCSW etc.,) who regularly evaluate and treat Veteran participants, will be asked to complete SES (n=4 
raters at each site). The same staff will be used longitudinally throughout the study to the extent possible. The 
SES takes less <10 min to complete. We will train clinicians/staff on anchors on SES during the 1st quarter of 
the study to standardize assessment. If staff members choose not to participate in SES data collection mid-
study we will work with PRRC/RRTP medical/center directors to identify another staff member to participate in 
this study. We will provide training to new staff who join the study. 
Cognition. Cognitive functioning will be assessed via the Penn Computerized Neurocognitive Battery (PCNB) 
at study entry[88]. The PCNB is a laptop-based assessment which provides measures of accuracy and speed 
on 9 neurocognitive domains that have been experimentally linked to specific brain systems with functional 
neuroimaging studies. This battery has been validated for ages 8-84, has been used in healthy subjects as well 
as those with various neuropsychiatric illnesses, and has been employed in large-scale genomic and imaging 
studies. The PCNB is designed to take ~ 1 hour to complete and includes tests to measure the following 
domains: abstraction and mental flexibility, attention, working memory, episodic memory (word, face, spatial 
recognition), language reasoning, spatial processing, sensorimotor, motor speed, and emotion identification. 
Results are reported as individual domain age- and gender- corrected z-scores. These individual scores can 
be combined to yield global age- and gender- corrected z-scores to allow easy comparison to normative data. 
The PCNB has high reliability, with item consistency having high Cronbach alpha coefficients, with moderate-
to-high within domain inter-item correlation, and low-to-nil correlation between domains. 
EEG data acquisition. Veterans will undergo EEG testing, similar to our previously published studies using a 
64 channel BioSemi ActiveTwo system[31, 43, 69, 72, 73, 77]. Electrodes will also be placed on mastoids and nose for 
referencing, and electrodes above and below the right eye at the outer canthi of both eyes will be used to 
record vertical and horizontal electrooculogram data and to correct EEG for eye movement and blink artifacts. 
Participants will be viewing a silent movie and instructed to ignore auditory stimuli and that they may be asked 
questions afterwards. Auditory stimuli will be presented to participants at 85 dB via Etymotic ER3-A insert 
earphones. Participants will be presented with 1 kHz 85-dB stimuli with a 1-ms rise/fall, with a 500 msec 
stimulus onset asynchrony, of which 90% are standards (50 msec) and 10% are duration deviants (100 msec), 
presented in pseudorandom order with a minimum of 6 standard stimuli presented between each deviant. EEG 
data will be continuously digitized at a rate of ~2.0 kHz and down sampled and referenced off-line (see below). 
Recorded voltages at analog-to-digital conversion for each electrode site were made relative to a common 
mode voltage based on the ActiveTwo’s CMS/DRL feedback loop. In order to get sufficient data for exploratory 
analyses, the total recording time will be ~45 min. The total time allotted to EEG acquisition will be ~60 min, 
accounting time takes to remove EEG electrodes, EEG cap, and for Veterans to clean hair etc., if necessary. 
Cognitive remediation exercise. We will use a one hour laptop-based cognitive remediation exercise as we 
have previously published[77, 89]. Participants will complete the exercise “Sound Sweeps” from the suite of 
cognitive remediation exercises included in the PositScience/BrainHQ platform. This exercise will be used 
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VA Research Plan                                                                                              
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because our group has found MMN robustly correlates with cognitive performance on this exercise (see above, 
preliminary data). Participants listen to a series of two successive tone sweeps (varying in frequency and range 
and interstimulus interval), and then indicate with two corresponding button presses wither the frequency 
increased or decreased within each tone. This exercise contains 15 stimuli sets composed of combinations of 
base frequency (0.5, 1.0, and 2.0 kHz) and duration. Participants must first complete stimulus sets with longer-
duration stimuli by demonstrating sustained successful performance at short ISI (i.e., 20 msec) before stimuli 
sets within shorter-duration stimuli are made available. Auditory exercises are psychophysiologically adaptive: 
parameters within each stimulus set require a participant to maintain 80% correct performance when 
established, and task difficulty increases or decreases systematically and parametrically with performance 
changes. After completing a brief practice block to ensure that participants understand the task, they will 
engage in three 20-min blocks separated by 5 min breaks. These exercises have been tolerated by all subjects 
we have tested so far, which include subjects with and without neuropsychiatric illnesses. 
Cognitive remediation exercise performance. Each subject’s performance will be monitored by recording 
the number of stimuli sets (measured as a percentage of all available stimuli sets) that are completed over 
training. Higher values indicate the subject reliably advanced through more of the stimuli content of the 
exercise (i.e., became proficient on trials with shorter frequency-modulated sweep durations), while lower 
values indicate remaining at easier training levels. Performance will be operationalized via auditory perceptual 
learning gains for each subject, defined as the mean percentage of improvement in auditory perceptual 
learning speed on psychophysically smaller threshold differences in tone duration and frequency. We have 
previously published this methodology as a way to operationalize performance on cognitive remediation 
exercises (also, described above in preliminary data)[77, 89]. 
EEG data processing. EEG data processing will be carried out as previously described[31, 43, 50, 69, 73, 76, 77]. For 
primary MMN analyses, continuous EEG data will be parsed into 600-ms epochs (−100 to 500 ms) for each 
stimulus type. After independent component (IC) analysis artifact reduction and baseline correction (−100 to 
0 ms prestimulus baseline), EEG epochs containing amplitudes exceeding ±75 μV in any frontocentral 
electrode (F3, Fz, F4, C3, Cz and C4) will be rejected. Event-related potential averages for standards and 
deviants will be generated separately, and the resultant difference waves will be low-pass filtered at 20 Hz 
(zerophase shift, 24 dB/octave rolloff) to remove any residual high-frequency artifact, consistent with 
established methods. Confirmation of polarity inversion for MMN difference waves will be performed at mastoid 
electrodes sites. MMN amplitude will be defined as the total mean area between 120 and 200 msec in the 
deviant-minus-standard difference wave. For exploratory source analysis, in brief, we will run IC analysis over 
each subject dataset and find the best-fitting single equivalent dipole model for each IC. To enable group-level 
analysis, we will use k-means to find clusters of equivalent ICs across subjects based on IC equivalent dipole 
locations, time course, mean log power spectra, and scalp map, obtaining 20 IC clusters allowing identification 
of IC source-resolved EEG processes occurring in response to processing of auditory deviance. For extended 
description of methods related to source localization, please see Rissling et al., 2014[43]. 
General aspects of data management. Drs. Light and Thomas will provide supervision in creating a secure 
database in the first quarter of the CDA-2 award period that will be maintained on a secure VA research server 
using the Velos/REDCap software prior to recruitment. Participant names will be kept separate from data. 
Veterans will be assigned an anonymous ID number, and a file linking each ID number to Veteran participant 
will be kept in an encrypted electronic file.  
General analysis approach: All analyses will be conducted in the general linear model and mixed model 
frameworks as relevant for Aims 1 and 2. Consistent with standard recommendations, violations of statistical 
assumptions will be identified using statistical tests and diagnostic plots. When appropriate, robust standard 
errors will be used to derive test statistics. Outliers will be detected using Cook’s distance. Missing data over 
time will be handled by analyzing all available data (assuming data are missing at random) and by using full 
information maximum likelihood estimation, or through multiple imputation of data. Analyses will be conducted 
using R with the assistance of Dr. Thomas. Linear mixed-effects models will rely on the R ‘lme4’ package. As 
needed, latent change score analyses will be conducted using the R ‘lavaan’ package.  
Contact PD/PI: Joshi, Yash
VA Research Plan                                                                                              
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SPECIFIC AIM 1: Determine whether MMN is related to functioning, psychosocial recovery and 
treatment engagement in VA rehabilitation milieus.  
Rationale: Veterans with mental illness that utilize VA RRTPs and PRRCs are vulnerable to cognitive 
impairment, which predisposes them to poor functioning, limits gains in psychosocial recovery, and reduces 
ability to make use of programming. Objective biomarkers that can be assessed on intake which can identify 
which Veterans will eventually benefit or fail to benefit from rehabilitation programming will better help identify a 
sub-group of Veterans that are at risk for poor outcomes and who could potentially benefit from cognitive 
remediation. In Specific Aim 1, we seek to understand whether MMN is related to: 1) Changes in performance-
based measures of functioning from entry VA rehabilitation programming, 2) Trajectory of self-reported 
psychosocial recovery in Veterans with mental illness, and 3) Clinician-measured engagement of Veterans with 
mental illness in rehabilitation programming.  
 
MMN is reported a negative value. A more negative MMN value denotes better information processing. Thus, 
greater MMN amplitude refers to a more negative MMN value (which is beneficial, indicating better information 
processing). 
 
Hypothesis 1.1: Greater MMN amplitude measured at study entry will predict improvements in UPSA scores 
over 4 months in Veterans receiving care in RRTPs and PRCCs. 
 
Hypothesis 1.2: Greater MMN amplitude measured at study entry will predict improvements in WHODAS 2.0 
total score at 4 months in Veterans who utilize receiving care in RRTPs and PRCCs. 
 
Hypothesis 1.3: Greater MMN amplitude measured at study entry will predict greater average monthly SES 
scores in Veterans who utilize receiving care in RRTPs and PRCCs. 
 
Primary outcome/Analysis 1.1: A linear mixed-effects model will be used to predict change in UPSA scores 
over 4 months from MMN values. The model will include the fixed main effect of MMN, the fixed main effect of 
time (orthogonal linear contrast coded), and the fixed interaction effect MMN by time. The interaction of MMN 
and time will uniquely predicted changes in UPSA scores over time. The model will also include random 
intercepts for subjects. This analysis is similar to predicting change scores from typical, non-mixed regression 
models, but has the advantage of relaxing certain missing data assumptions as above. A positive interaction 
term will support the associated hypothesis.   
 
Primary outcome/Analysis 1.2: A linear mixed-effects model will be used to predict change in WHODAS 2.0 
total score over 4 months from baseline MMN values. The model will include the fixed main effect of MMN, the 
fixed main effect of time (orthogonal linear contrast coded), and the fixed interaction effect MMN by time. The 
model will also include random intercepts for subjects and random slopes for time. A positive interaction term 
will support the associated hypothesis 
 
Primary outcome/Analysis 1.3: A linear mixed-effects model will be used to predict average monthly SES 
scores over 4 months from MMN values. The model will include just the fixed main effect of MMN. The model 
will also include random intercepts for subjects.  A positive interaction term will support the associated 
hypothesis.   
 
Potential challenges and alternative analyses for Specific Aim 1 
If no direct relationships between MMN and Aim 1 outcomes are found we will analyze individual subdomains 
of assessment instruments in Aim 1.  For UPSA, WHODAS 2.0 and SES scales we will determine whether 
changes in individual subdomains scores correlate with MMN. Similarly we will explore whether high vs low 
MMN values (determined via median split) are associated with change in UPSA, WHODAS 2.0 or SES scores 
over the course of the study. There is a possibility that relationships between baseline MMN, on the one hand, 
and UPSA, WHODAS 2.0 and SES scores, on the other, will be biased by regression to the mean artifacts. In 
addition to the above main analyses, we will fit latent change models to the data using the methods described 
by McArdle and Nesselroade[90]. Results will be compared to those obtained from the primary analyses 
described above; this will allow us to determine whether regression artifacts significantly impacted the results 
obtained. These analyses will be supervised by Dr. Thomas and will be a key aspect of the training (Career 
plan, Objective 3). 
Contact PD/PI: Joshi, Yash
VA Research Plan                                                                                              
 Page 28

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Psychophysiol, 2015. 95(2): p. 145-55.
56.
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biomarker of intermediate effect? Schizophr Res, 2018. 191: p. 132-139.
57.
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EEG Neurosci, 2019. 50(3): p. 147-153.
58.
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disorder as measured by a multi-feature paradigm. Biol Psychol, 2015. 105: p. 77-82.
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potential N270. Neuropsychiatr Dis Treat, 2015. 11: p. 1405-11.
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traumatic stress disorder. Biol Psychol, 2006. 71(2): p. 123-47.
61.
Ahveninen, J., et al., Acute and chronic effects of alcohol on preattentive auditory processing as
reflected by mismatch negativity. Audiol Neurootol, 2000. 5(6): p. 303-11.
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Jaaskelainen, I.P., et al., Low dose of ethanol suppresses mismatch negativity of auditory event-related
potentials. Alcohol Clin Exp Res, 1995. 19(3): p. 607-10.
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Ramlakhan, J.U., et al., Using Mismatch Negativity to Investigate the Pathophysiology of Substance
Use Disorders and Comorbid Psychosis. Clin EEG Neurosci, 2018. 49(4): p. 226-237.
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antagonist in healthy volunteers. Biol Psychiatry, 2002. 51(5): p. 400-6.
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PCP/NMDA model of schizophrenia. Schizophr Bull, 2012. 38(5): p. 958-66.
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Broyd, S.J., et al., Mismatch Negativity and P50 Sensory Gating in Abstinent Former Cannabis Users.
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schizophrenia patients. Arch Gen Psychiatry, 2005. 62(2): p. 127-36.
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analyzing stages of change data. Ann Behav Med, 1999. 21(1): p. 61-70.
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Konecky, B., et al., Using the WHODAS 2.0 to assess functional disability associated with DSM-5
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Washington, D.C.: American Psychological Association. xi, 426 pages.
Contact PD/PI: Joshi, Yash
References Cited                                                                                              
 Page 11

IRB Protocol Amendment (Version 1.0)
1.0
 
VASDHS IRB Protocol Amendment
(For staff changes other than the PI use the IRB Staff Change 
Form instead)
v20140917
Principal Investigator:
Yash B. Joshi, MD, PhD
IRB Protocol Number:
H190131
Title:
Optimizing Cognitive Remediation in VA Mental Health Rehabilitation Settings
Protocol Nickname:
Optimizing Cognitive Remediation
Amendment Preparation Date:
10/29/2020
Press Save and Continue            Use the ? to the right for an 
Amendment Guide
2.0
 Amendment Description
2.1  Enter a brief title or reference phrase for this modification 
Limit 20 characters, to allow you to easily identify this modification from the list of modifications for this 
protocol.
Amendment 10-29-20
2.2  Describe all features of the amendment request and the rationale for the change:
There are no changes to study aims. 
In the initially submitted grant, inclusion criteria included being within 4 weeks of admission to 
programming at VASDHS’s psychosocial rehabilitation and recovery center (PRRC) via the CORE Program 
in Mission Valley or its mental health residential treatment program (RRTP) at the ASPIRE center. Due to 
COVID-19, the ASPIRE Center is currently operating at 50% capacity due to the COVID-19 pandemic, and 
CORE program has moved to essentially all virtual care. There is significant and appropriate concern 
about Veterans in the mental health residential treatment program/ASPIRE Center leaving their residential 
VA premises mid-treatment to participate in VA research and the move to virtual care is currently felt to 
reduce the likelihood of Veterans from CORE/ASPIRE Center engaging in the study within the first 4 weeks 
of treatment exclusively (i.e., 4 weeks may be too restrictive and with substantially lower throughput in 
screening and enrollment). We do not therefore believe that we would be able to meet study targets by 
recruiting from CORE and ASPIRE only at the start of their inclusion into their programs within 4 weeks.
In order to meet study targets we will modify and expand our inclusion criteria to Veterans currently 
engaged in or within 6 weeks after discharge from any VASDHS RRTP, PRRC, VASDHS inpatient mental 
health treatment, or VASDHS outpatient mental health treatment. Specifically, this modification
/expansion would now allow Veterans engaged in or recently discharged from VASDHS’s Substance Abuse 
Residential Rehabilitation Treatment Program (SARRTP), Veterans who are discharged from 2 South 

inpatient treatment, and Veterans who are currently enrolled in outpatient mental health treatment to 
participate in this study.
This modification will impact only one consideration in terms of inclusion criteria. As initially submitted we 
had written in our protocol that inclusions criteria required having a DSM-5 mental illness of 
schizophrenia, schizoaffective disorder, delusional disorder, major depressive disorder, bipolar affective 
disorder I or II, generalized anxiety disorder or PTSD verified by the structured clinical interview. We 
would modify this diagnostic inclusion criterion to also include the presence of a substance use disorder (i.
e, alcohol use disorder, cannabis use disorder, stimulant use disorder, opioid use disorder, etc.) given that 
we will be also recruiting from the SARRTP milieu. We will not change our requirement that Veterans 
would be excluded from the study should that have active substance use within the past 30 days, given 
that successful completion of SARRTP treatment involves 28 days of sobriety. We will still utilize CPRS 
review, self-report, staff report or positive urine drug screen conducted as part of the screen process to 
exclude for active use. 
We will not alter our target sample size inclusion, and still plan to recruit 104 Veterans in total, with a 
final total n = 84, accounting for planned attrition. Since we are including more milieus/treatment settings 
in the study with this modification, we will also include/recruit clinicians/staff from these settings in our 
focus groups for our exploratory aim. 
In the initially submitted grant, we had utilized the following structure: ~ 3 hour Visit 1 (getting informed 
consent, demographics, structured interview, symptom scales, cognitive assessment, functional 
assessments), followed by ~ 3 hour Visit 2 within 1 week of Visit 1 (EEG biomarker collection, 1 hr of 
cognitive training, assessing Veteran attitudes towards EEG/cognitive training), followed by three monthly 
assessments of functioning (either in person or on the telephone). This was designed to minimally 
interfere with Veteran rehabilitation/recovery program. 
Given our modification to study recruitment above, this modification to the study structure will be as 
follows: Visit 1 will be ~7 hours with several breaks and breaks for lunch and include getting informed 
consent, demographics, structured interview, symptom scales, cognitive assessment, functional 
assessments, EEG biomarker collection, 1 hr of cognitive training, assessing Veteran attitudes towards EEG
/cognitive training. This will be followed by three ~monthly assessments of functioning via telephone. This 
will reduce burden on Veterans (they will be making 1 in-person trip, instead of 2 in-person trips), reduce 
attrition between our initially proposed Visit 1 and Visit 2 and also reduce potential risk of COVID-19 
associated with participation in the study (to the degree that more trips and contacts between Veterans 
and study staff would nominally increase already present COVID-19 risk in the community). 
Our initial study called for assessment of the UCSD Performance-Based Skills assessment, brief version 
(UPSA) to be given at study enrollment, and at the last monthly follow up. Given our project modification, 
we will not collect UPSA as it would not be scientifically relevant/appropriate to all Veteran participants.
Given the changes to study recruitment and structure, we will make the following scientifically trivial 
changes to account for the new study structure above:
1) Instead of the Penn Computerized Neurocognitive Battery (MCCB), we will use the MATRICS Cognitive 
Consensus Battery (MCCB) to assess cognition. These cognitive assessments take similar times to 
complete. This will result in no additional time burden 
2) Given that not all Veteran participants will be participating in a mental health rehabilitation setting 
when enrolling, we will not carry out the Symptom Engagement Scale. In its place, we will assess 
recovery via the World Health Organization Quality of Life (WHOQOL-BREF). This will result in no 
additional time burden.
3) We will include the Positive and Negative Symptom Scale (PANSS) for Veteran participants with 
schizophrenia/schizoaffective disorder. The PANSS is widely regarded to allow for better cross-study 
comparisons. Administering PANSS will result in a minimal additional time burden.
4) We had initially proposed utilizing the Structured Clinical Interview for DSM-IV (SCID-5) to assess 
diagnoses. We now propose to use the Mini International Neuropsychiatric Interview (MINI). These are 
similar assessments, both equally valid. We are making this change due to ease of use. This will add no 
additional time burden.
5) We had initially proposed using audiology thresholds to determine inclusion and exclusion into the 
study. We will add two additional measures of auditory fidelity, the Words-in-Noise test, and the Quick 
Speech in Noise (QSIN) test to help better characterize contributions from hearing problems. This will add 
~10 min to the test day.
In addition, we are removing Amy Cohen, Ph.D, who was listed in the protocol as one of the Other VA 
Collaborator(s). Dr. Cohen is no longer with the VA.
We are attaching revised consents (patient and staff) to reflect these changes. We have also revised the 
recruitment flyers (Veterans at the ASPIRE Center, Veterans at the CORE Clinic, and staff) and included 3 
new flyers (Veterans recently discharged from inpatient facilities, Veterans in outpatient programs, and 
Veterans discharged from SARRTP). Other attachments, such as the Intrinsic Motivation Inventory (IMI) 
and Veteran Interview Script, which were part of the approved protocol, will remain unchanged.
2.3  What is the anticipated impact of this amendment on the risk-benefit ratio?  (If none, indicate "none")
.

2 
Version 1.0 
1. VA PI (complete for all types of requests)
Last Name, First Name, Middle Initial, Degree(s) 
Telephone 
VA email 
eRA Grant Number 
Project Start Date 
Project End Date 
VA Project ID 
Project Title 
 VAMC Name and Location (City, State) 
Station No. 
Electronic signature of the PI 
 Date 
2. Proposed PI (if changing PI or adding study site)
Last Name, First Name, Middle Initial, Degree(s) 
Telephone 
VA email 
Number of VA eighths to be held by PI during the award period  
VAMC Name and Location (City, State) 
Station No 
 Electronic signature of proposed PI/Site-PI 
 Date 
3. New VAMC (if transfer of station or adding new study site)
VAMC Name and Location (City, State) 
Station No. 
Location of research space for this study at the new VAMC 
ACOS/R Last name, First Name, Middle Initial, Degree(s) 
Electronic Signature of the new VAMC ACOS/R (By signing this form, the ACOS is affirming that all VA requirements 
regarding the conduct of VA research for this study will be met (e.g. appropriate committee approvals).) 
Date 
JOSHI, YASH, B, MD, PhD MBE
yash.joshi@va.gov
1IK2RX003395­1
D3395­W
05/01/2020
04/30/2025
OPTIMIZING COGNITIVE REMEDIATION IN VA MENTAL HEALTH REHABILITATION SETTINGS
VA SAN DIEGO HEALTHCARE SYSTEM, LA JOLLA, CA
664
03/24/2025

3 
Version 1.0 
4. Project Extension
New end date requested 
Total amount, if additional funds are requested 
5. Change in Eighths of PI
Current Eighths 
Requested Eighths 
6. Explanation or Justification (see page 1 for required information)
ACOS/R Last name, First Name, Middle Initial, Degree(s) 
Electronic Signature of the current VAMC ACOS/R (By signing this form, the ACOS is affirming that all VA 
requirements regarding the conduct of VA research for this study will be met (e.g. appropriate committee approvals).) 
Date 
04/30/2026
$ 0.00
We are submitting this project modification to request: 1) a no cost extension until 4/30/2026 to ensure 
adequate time to analyze results to complete the project; 2) a minor change related to the Exploratory Aim to 
remove VA staff interviews which are redundant; and 3) an update to a sub-hypothesis related to Aim 1 based 
on a previously approved recruitment modification. There are no changes to study personnel or study budget. 
No redistribution of funds are requested. The modifications do not change the scientific focus or merit of the 
study, or impact expected deliverables, or alter the expected benefit to VHA. 
  
Below we provide additional detail on each request: 
  
1.  No Cost Extension to 4/30/2026. 
Despite the COVID pandemic, multiple staffing delays, the PI's parental leave, the project was successfully able 
to reach end points in a meaningful way to support valuable results and publications. This study included 
Veterans coming in for an initial visit in-person (Aim 2 outcomes collected at this time point), with three 
telephone visits assessing symptom ratings monthly (Aim 1 endpoints). Our goal was to consent 104 Veterans, 
and we expected 20% attrition to generate data for n=84 Veteran for both Scientific Aims 1 and 2. We 
consented 100 Veterans (96% of target), and for Aim 1 have 80/84 Veteran data (95% of target as of 
3/19/2025). For Aim 2, we have exceeded our targets, currently at 86/84 (102% as of 3/19/2025). We have 
already completed the Exploratory Aim, publishing one paper and are currently in the process of submitting 
another manuscript. We request this no cost extension so that we can complete the analyses for Aims 1 and 2. 
The VA San Diego Mental Healthcare Line has committed to maintaining the PI as a full-time VA physician with 
2/8ths (25%) protected time dedicated to research to continue this research activity beyond 4/30/2025. At this 
level of effort we expect analyses to be finalized and the study fully completed by 4/30/2026.  
  
2. Modification to Exploratory Aim. 
In the original proposal, the Exploratory Aim sought to interview Veterans and VA staff to better prepare for the 
next step in implementation the project idea and to secure a future Merit grant to support it. A further project 
modification in 2021 extended this to test a full course of the intervention in a short pilot in support of a future 
Merit. As noted above, we have already reported on Veteran attitudes in implementation. Furthermore, since 
the CDA2 was originally proposed, 1) our clinical services have been reorganized at VA San Diego, and the PI 
and his clinic has already become better integrated with recovery/rehabilitation staff which would be 
i
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i
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t
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f th
t d
d 2) th
PI CDA2 d t
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li
i
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Afari, Niloofar, PhD
03/25/2025

4 
Version 1.0 
7. ORD Decision (for Central Office use only)
  Approved  
  Disapproved 3DUWLDO$SSURYDO
Name 
Title 
Electronic Signature 
Date 
Comments 
Electronic signatures are preferred, but a scanned copy will be accepted. 
To attach the required documents, compile them into a single pdf and attach as follows:  
(1) from the Main Menu, select View > Comment > Annotations to open the Annotations sidebar;  
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Email a signed version of this form with the required attachments either using the appropriate submit 
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but the attachment will be valid once sent.
 
Attach required 
documents (per the 
instruction 
document) in a single 
pdf here  
Submit this form via email to 
BLR&D or CSR&D 
vhablrd-csrd@va.gov 
Submit this form via email to 
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rrdreviews@va.gov 
Submit this form via email to 
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