Sex Differences, Cognitive Training & Emotion Processing
Phase: NA
Conditions: Alcohol Use Disorder
Sponsor: University of Florida

 
 
 
 
 
 
Cover Page 
 
Official Title: Sex Differences, Cognitive Training & Emotion Processing   
NCT Number: NCT03137654 
Document Date: December 10, 2018 

Protocol 
 
1. Project Title 
Sex Differences, Cognitive Training & Emotion Processing 
2. Investigator(s): 
 
Principal Investigator: Sara Jo Nixon, Ph.D. 
Co-Investigators (Sub-I’s, NIH Key Personnel): Ben Lewis, Ph.D. 
3. Abstract: 
This pilot project leverages the team’s expertise in neurobehavioral assessment to examine the potential 
efficacy of cognitive training in treatment-seeking men and women with alcohol use disorders (AUDs).  
Specifically, we ask whether cognitive training interventions derived from current methods and conceptual 
models has differential benefits for treatment-seeking women vs. men.   Although neurocognitive 
improvement during training is desired, of practical import is whether gains achieved during training transfer 
to other tasks and settings. Therefore, we also examine transfer of training gains to tasks/domains varying 
in their similarity to training demands.  Given noted sex differences in emotional processing and the purported 
role of emotional factors in women’s substance use, we include training engaging emotional processing via 
the use of affective stimuli (faces and words); predicting that women may differentially benefit from such 
training. We recognize that individual characteristics beyond demographics and substance use history (i.e., 
psychosocial variables such as interpersonal problems, mood/affect, and adaptive skills) may influence 
response to training and longer-term outcomes.  In light of these interests and the preliminary nature of the 
work, equal numbers of treatment-seeking men (n=30) and women (n=30) with AUDs will be randomly 
assigned to one of two active training interventions (neutral or affective stimuli).  To control for abstinence-
related recovery, a third group of participants (here Subjects, Ss) (n=30), meeting identical selection criteria, 
will complete pre and post-intervention testing, but will not undergo the training intervention. Ss will complete 
baseline assessment, up to 12 training sessions (for active groups) and post-intervention testing (~2-3wks 
after baseline) and will be contacted monthly for 3 months after discharge .   
4. Background 
 
Neurobehavioral Concomitants of Chronic Substance Misuse.   Although the chronic excessive use of 
numerous licit and illicit substances is known to impact brain and cognition [e.g., 1-3], the prevalence of 
AUDs in the general and treatment-seeking populations has directed a large literature on alcohol’s neurotoxic 
effects. Thus, within this application, we focus on the neurobehavioral consequences associated with 
significant AUDs (herein, alcoholics). While noting considerable heterogeneity between studies and across 
specific tests, research reveals alcohol-related neurobehavioral compromise in a wide range of 
neuropsychological domains [e.g., 2-4], neurophysiological indicators [e.g., 5-7], brain activation patterns and 
measures of structural integrity [e.g., 8-9]. Importantly, alcohol plays a critical role in neurobehavioral integrity 
even among polysubstance abusers [10-11, see also 12].   
 
Recent work has shifted from detailing specific tasks vulnerable to chronic, excessive alcohol use to focusing 
on component processes that may underlie performance across tasks/domains. Among these, processes 
underlying attention and working memory are particularly noteworthy.  Essential to effective attention and 
working memory are the top-down control processes of enhancement and suppression.  Enhancement refers 
to the ability to respond/attend to relevant stimuli whereas suppression refers to the ability to respond 
appropriately to irrelevancy, i.e., the ability to ignore irrelevancy [13-15].  We have previously referred to the 
inability to effectively suppress or inhibit response to irrelevancy as a deficit in efficiency and proposed that 
it was a critical factor underlying alcohol-related compromise [16-18]. On-going work by our team and others 
supports the conclusion that this deficit accounts for substantial, albeit not all, variance in alcohol-related 
compromise [11;19-21]. 
 
Inclusion of Women.  With notable exceptions, research through the early/mid-1980’s was conducted in 
predominantly male facilities with relatively little impetus for the inclusion of women [but see 22-23]. Once 
initiated, mixed sex and female-specific projects generally reported that alcoholic women were compromised 
across similar domains and to a similar, or greater, extent as were alcoholic men, relative to sex-specific 

controls [24-28]. Furthermore, alcoholic women typically reported less intense and/or shorter drinking 
histories than men. Thus, it was commonly concluded that women were more vulnerable than were men to 
alcohol’s neurotoxic effects [29-31].  However, continuing work has not consistently observed this sex-related 
vulnerability in regard to alcohol’s neurobehavioral consequences [32-34].  
   
Relatedly, although women are now recruited for the majority of published studies, numbers are often 
insufficient to allow analysis of sex main or interaction effects [for issues see 34].  Thus, women remain, as 
referenced in the PA and reflected in NIH’s recent notice regarding sex as an essential biological factor, 
grossly underrepresented in addiction science. Without adequately designed, systematic study, the 
neurobehavioral consequences of AUDs and other SUDs among women will not be fully appreciated and 
thus cannot be appropriately addressed.   
   
Summary.  AUDs are associated with significant compromise across a range of neurobehavioral domains.    
Conceptual and methodological advancements have shifted focus from detailing the effects of chronic 
alcohol on specific tests to exploring compromise in processes that may underlie performance across 
tasks/domains.   Through this effort, suppression, a top-down control process essential to constraining 
attention and facilitating working memory, has demonstrated utility as a significant neurobehavioral 
mechanism underlying alcohol-related compromise.  Despite conceptual advances, the implications of these 
findings are significantly constrained by the under-representation of women. 
 
Neurobehavioral Recovery.  Importantly, recovery of function with sustained abstinence can be anticipated 
[35-37].  However, recovery is uneven across domains and specific domains such as those related to 
executive function may require months to years [38-40].  Whether men and women differ in the pattern and 
degree of recovery remains controversial and conclusions are limited by small sample sizes and inconsistent 
analysis of sex differences [27;31;33;37;41;42]. Thus, there remains a need for focused study on sex 
differences in recovery.  
 
The possibility of using cognitive training/rehabilitation to facilitate early recovery among treatment-seekers 
was discussed in earlier decades [43-45].  However, the propositions failed to engage systematic study or 
widespread acceptance.  Factors such as cost and staffing as well as inconsistent findings regarding the 
relevance of neurocognitive abilities assessed in treatment to eventual outcomes may have contributed to 
the reticence [46-49].   
 
Although largely unexplored for decades, there has been a resurgence of interest in the potential benefit of 
cognitive retraining for the addictions [50-52].  Current guiding frameworks focus on the role of bias 
modification and executive functions, such as cognitive control [53-55]. While the training 
procedures/methods differ, the models are derived from the assumption that retraining of attentional/working 
memory processes (which in turn are related to executive function) can lead to not only improved cognitive 
performance but also to more effective control of alcohol or drug use behaviors.  While there is value in 
cognitive bias modification [54-55], given our on-going work and the constraints of the R03, we focus on 
attention/working memory processes wherein preliminary studies report positive outcomes in modulating 
problematic alcohol use [56-57]. Of particular relevance to our work is the fact that success in these studies 
centers on modifying response to irrelevancy, i.e., reducing inefficiency by improving suppression processes.  
Importantly, outcomes in these studies were assessed only through the immediate treatment period [57] or 
to one month beyond the intervention [56]. This limitation reflects the early phase of study. To our knowledge, 
analyses of sex differences have not been reported [but see 58].  
 
A core issue for cognitive training strategies is whether improvements seen during training impact 
performance in other settings; i.e., do training gains transfer beyond the training tasks/context [59-61].  
Although it is expected that performance will improve with challenging practice (i.e., gains), the larger 
question is whether these gains transfer to other tasks varying in the degree to which they rely on common 
processes. Many programs have produced disappointing outcomes [61, but see 57]. However, the studies 
cited above [56-57], reporting a reduction in substance use (i.e., enhanced control) following training, 
illustrate meaningful transfer.  Another transfer dimension, the transfer of training between tasks differing in 
stimulus qualities, i.e., neutral or emotional/affective, was examined by Schweizer and colleagues [60;62].  
Recognizing that improved processing of emotionally valent stimuli could benefit real-world interactions, she 

and her colleagues examined this question in young adults. They used a challenging dual n-back working 
memory training task, and varied the task stimuli between groups (affective vs. neutral stimuli). They found 
that gains on an emotional (i.e., faces and affective words) Stroop transfer task was improved only in the 
group trained with affective stimuli.   
 
Importantly, deficits in emotion processing are commonly reported in persons with AUDs [63-68].  Although 
understudied, it is likely that these deficits are relevant to long-term adaptation and recovery [e.g., 64; 69-
73].  Furthermore, even though there are noted sex differences in emotion processing [74-76] and 
emotional/interpersonal dynamics are frequently implicated in women’s drinking [77], both sexes show 
impaired/ dysregulated responding [e.g., 73;78]. Thus, if cognitive benefits achieved during training could be 
applied to the processing of affective/emotionally relevant contexts, long-term outcomes might be further 
enhanced for both sexes.  
 
Summary. There is renewed interest in determining whether cognitive retraining directed to enhancing 
performance on underlying component processes may improve the effectiveness of treatment for SUDs.  
Current studies offer promise and suggest directions for additional research. This project is designed to 
address two areas of clinical and scientific relevance; the systematic inclusion of women and the extension 
of training paradigms to consider transfer of gains to tasks engaging emotion processing. Additionally, given 
the limited data on longer-term effects, we will extend the follow-up to cover the first 90 days following 
discharge, i.e., through a known period of heightened vulnerability [79].  
 
Overall Summary.  Neurobehavioral deficits are common in treatment-seeking persons with AUDs.  Cognitive 
training directed to commonly compromised processes shows promise and merits further research.  As a 
pilot project, the scope of work is necessarily limited. In this application, we seek to extend current work by 
examining sex differences in response to cognitive training in attention/working memory and by investigating 
whether training engaging emotion processes differentially affects outcomes, and if the sexes differ in their 
response to this training.  We will also examine relationships among demographic variables, comorbid 
substance use, performance gains, and post-treatment outcomes (i.e., over a 3 month period), thereby 
providing direction for consideration of individual differences in training outcomes.  Together these data will 
inform questions of feasibility, identify potential subgroups (beyond sex) varying in response, and guide the 
development of a more comprehensive R01 application assessing cognitive training among treatment-
seekers with AUDs. 
 
5. Aims (H=hypothesis, E=empirical question) 
 
Aim 1: Investigate potential sex differences in response to cognitive training.  In the absence of a consistent 
literature, we ask: E1: Will men and women show differential transfer of gains in post-intervention testing? 
E2: Will transfer of gains for men vs. women differ on the basis of transfer task demands? E3: Will they differ 
in the degree to which they demonstrate improvement across training? E4: Will the relationship between 
baseline performance, training gains, relapse and/or post-treatment adaptation differ for women and men?  
Aim 2: Determine whether stimulus characteristics (neutral vs. affective) define transferability of gains and/or 
training trajectories.  Given current literature, we predict that; H1: Active training, regardless of condition, will 
produce greater performance gains than the control condition, H2: Training with affective stimuli as opposed 
to neutral stimuli will differentially benefit performance on transfer tasks engaging affective stimuli, and, H3: 
Training gains will be more readily transferred to tasks engaging highly similar cognitive demands 
(processes) vs. more general processes.  We ask, E5: To what extent do training trajectories vary as a 
function of training stimulus characteristics?   
 
Aim 3:  Explore sex by training condition interactions.  Existing studies suggest that: H4: Training with 
affective stimuli may differentially benefit women as compared to men, although we expect that both sexes 
are likely to show transfer of gains within the affective condition (H5). We ask, E6: Do training trajectories 
vary by sex? E7: Are training trajectories subject to the sex by condition interaction?   

Secondary/Exploratory Aims: The study provides the opportunity to examine relationships between/among 
a variety of individual characteristics beyond those treated as key covariates, e.g., age, education, alcohol 
use history, that may impact the efficacy of training interventions, e.g., pre-treatment levels of functional 
adaptation, mood/affect during treatment, and comorbid substance use.  These associations will be 
examined in exploratory analyses relying on correlational methods and will inform future projects. Similarly, 
Ss acceptability of the intervention will be assessed through preliminary methods and will be considered in 
structuring future interventions.    
6. Research Plan 
The primary design is a 2 (Sex) by 3 (Training Condition: Neutral, Affective, Control) by 2 (Time: Baseline, 
Post-training) mixed design. The control Ss will meet identical selection criteria but complete only baseline 
and retesting, controlling for abstinence-related improvement. R03 limitations prohibit the inclusion of an 
active control condition [56]. Follow-up phone interviews allow preliminary assessment of the impact of 
cognitive training on post-treatment functioning. Cell sizes (Table 1) were informed by power analyses and 
the constraints of the R03 mechanism [see Data Analysis]. 
Assignment to neutral vs. affective training group will be randomized 
within sex. To minimize confounds associated with conducting active 
interventions and control conditions concurrently within the facilities 
(i.e., Ss being unwilling to serve as controls due to differential 
compensation), ~ half of the control Ss will complete participation at 
the initiation of the project and ~half after training groups have been 
recruited. Further, separate ICFs are utilized, with control ICFs 
remaining unlabeled (i.e., not referred to as a “control” group). 
 
The overall structure of the intervention involves baseline cognitive assessment including performance on 
the standard and emotional Stroop, 3 weeks of training on neutral or emotional versions of two tasks with 
strong demands on attention/working memory (specifically the capacity to suppress response to irrelevancy) 
and retesting on the cognitive battery and the emotional and standard Stroop. The primary dependent 
variables are differences between pre- and post-intervention performance on the two Stroop tasks as a 
function of training condition (including neutral, affective and control), sex, and their interaction. Ss complete 
a neurocognitive battery at baseline, affording an additional test of improvement and transfer of gains through 
re-administration after training.  
 
Participant (Ss) Selection Criteria.  Ss are recruited from treatment facilities in north central FL with expected 
length of stays extending across the intervention period. Ss range in age from 25-65 with a minimum of 10 
years of education. Ss may not have medical or psychiatric histories which would confound participation or 
data interpretation, e.g., epilepsy, stroke, untreated hypertension, psychotic disorders, anxiolytic 
medications, etc.  Endorsement of suicidal intent is exclusionary, and is reported to facility clinical staff. 
Comorbid substance use including nicotine use is not exclusionary and is addressed using both categorical 
and continuous measures. Ss are not selected by race/ethnicity (~69% Caucasian, 28% African 
American;7% Hispanic).  
 
Screening. After providing informed consent, Ss complete questionnaires related to general health, 
substance use history, and mood/affect. Recent alcohol use is quantified using quantity/frequency measures 
adapted from Cahalan et al., [82]. Nicotine dependence is ascertained using the Fägerstrom Tobacco and 
Nicotine Dependence [FTND; 83] measure. Other substance use is queried in regard to chronicity, quantity, 
and frequency. Mood/affect are assessed with the Beck Depression Inventory-II [BDI-II; 84] and Spielberger 
State Anxiety Inventory [AI; 85]. Family history for alcohol or other drug use is assessed using a modified 
family tree [86]. Ss then complete selected modules from the Structured Clinical Interview for DSM-5 (SCID-
5) [81].  Ss receive $20 for completing the screening questionnaires and $10 for completing the structured 
interview. Reimbursement is pro-rated if Ss discontinue or are disqualified prior to completing. Data will be 
used in descriptive and post-hoc analysis to examine potential subgroups and/or covariates, as appropriate.  
 
Baseline Assessment. This assessment will include administration of the cognitive efficiency battery, re-
administration of the BDI-II and AI (applied during screening), and administration of the emotional Stroop 
task. Ss will receive $25 for completion of baseline assessment.  
Condition: 
Sex: 
 
Men 
Women 
Total 
Neutral 
15 
15 
30 
Affective 
15 
15 
30 
Control 
15 
15 
30 
 
90 
Table 1: Overall Study Design  

 
   Cognitive Efficiency Battery. The battery utilizes paper/pencil and computerized tasks including Trail-
Making Task A/B [89], Digit Symbol Substitution Task [90], Little Man Task [91], Visual-Perceptual Analysis 
Task [91], Wisconsin Card Sorting Task [92], Sternberg Short-Term Memory Task [93-94], and the standard 
Color-Word (standard) Stroop Task [95-96]. A simple reaction time task quantifies gross motor function. By 
including tasks with varying dependence on attentional processing/working memory, the battery enables 
separation of improvement/transfer of gains into process-specific and generalized effects. Accuracy (ACC), 
reaction times (RT), and the efficiency ratio (ACC/RT), for individual tests and the overall battery, are derived, 
as appropriate. Time: ~25 min 
 
  Emotional Stroop. We utilized the emotional Stroop task reported by Schweizer et al [60]; developed by 
Preston & Stansfield, 2008 [97] as a primary measure of training effects.  Emotional stimuli are words overlaid 
on faces from the Pictures of Facial Affect stimuli set [PFA; 98]. Words are 
centered horizontally within the frame and vertically at vertical midpoint of the 
nose. Words are presented at 25% opacity (Fig. 2). Ss are instructed to respond 
to the word presented as quickly and accurately as possible. Ss respond to the 
word by pressing a corresponding response key. Three responses are possible: 
HAPPY, SAD, and ANGRY. Ss respond with their dominant hand on labeled 
keyboard keys. The overlaid words were taken from a list of eight emotion 
adjectives prototypical of the three emotional response categories [99; see 
Appendix]. Trials will consist of congruent (word and face have matching 
emotions), incongruent (word and face expressions do not match), and neutral 
(neutral face) conditions. Four emotions (HAPPY, SAD, ANGRY, NEUTRAL) 
from four actors in the PFA set will be used (2 men; 2 women). The combination 
of these 16 facial stimuli and 24 adjectives creates a pool of 384 unique stimuli. From this pool, Ss will view 
192 congruent stimuli, 96 neutral stimuli, and 96 incongruent stimuli (384 total trials). The higher proportion 
of congruent trials are used because Stroop effects (>RT on incongruent trials) are known to be sensitive to 
habituation. No stimuli will be repeated more than once. Stimuli will remain on the screen until Ss respond or 
5 secs have elapsed (resulting in an omission). A colored (red/green for correct/incorrect) border provides 
accuracy feedback. Schwiezer’s timing of 500 msec for feedback and ITI will be tested and adjusted, as 
needed, during task development. Dependent variables will include RT, ACC in each emotion/congruency 
condition, and efficiency (ACC/RT), with particular interest in the incongruent conditions. Time: ~15 min. 
 
Cognitive Training.  Training will be conducted over up to three weeks at the treatment facility. Training will 
involve performance-based progression, an important characteristic of effective training [56; 60]. We will use 
the dual n-back as reported by Schwiezer et al [60] and a modification of a directed attend/ignore tasks used 
in our prior work [e.g., 100]. Ss will be instructed to complete twelve training sessions over three weeks 
(4/week), each lasting 45-50 min. Ss completing 2 or fewer sessions in the first week or withdrawing during 
this time will be discontinued from study and replaced. Drop-outs after the 1st week of training will not be 
replaced.  The number of sessions completed/week will inform future study development and will be used as 
a descriptive variable and in covariate analyses, as appropriate. Given study objectives, compensation will 
be provided on a daily basis ($15/session), with a bonus for completing at least nine sessions ($20 for 
sessions 9-12). Study staff will be present for all training to ensure S identification, assist with software issues, 
provide compensation, and ensure data integrity. Throughout training, one group will train with versions of 
the tasks using emotionally-laden stimuli (Affective Training Group); one group will train using emotionally-
neutral versions of the tasks (Neutral Training Group). To parallel Schwiezer’s work and given alcoholics’ 
deficits in the processing of negative emotion [65; 71; 101], affective stimuli will reflect negative states. All 
face models will be presented an equal number of times across affect categories (i.e., no individual face actor 
will appear more often as “angry” than “fearful”). 
 
   Training sessions. Although treatment facilities are smoke-free, several provide areas for smoke breaks. 
Therefore, Ss will be informed during consenting and reminded prior to the training session that smoke 
breaks will not be allowed during training and advised to have their last smoke within an hour before each 
training session. At the initiation of each session, Ss will provide information regarding time/quantity of last 
nicotine use. These data will be used in descriptive and covariate analyses, as appropriate. To provide pilot 
data regarding training acceptability and presuming that acceptability may vary with progress, after each 
Fig 2. Example of a 
congruent trial w/congruent 
word & facial emotion  

session, Ss will indicate  on a visual analogue scale the degree to which they found the tasks a) tiring, b) 
boring, c) engaging, d) effortful, e) enjoyable, and f) stimulating. Ss are asked to rate degree of improvement 
and indicate if they would recommend similar training to others. These data will be used as descriptors and 
may aid in directing future interventions. Finally, training task order will be counterbalanced to mitigate 
sequencing effects. [See Appendix for additional training task detail] 
 
   Dual n-Back Task  The dual n-back task [60] involves both auditory and visual 
cues. Ss are presented with a 4 x 4 grid. A face is presented in one of the grid 
tiles, while an auditory stimulus (a single word) is delivered simultaneously via 
headset (Fig. 3). Ss indicate whether the position of the visual stimulus OR 
content of the auditory stimulus matches that of the trial “n-back”; e.g., if n=1, 
Ss indicate if current stimuli match those from immediately preceding 
presentation. For visual stimuli, Ss are instructed to ignore the content of the 
image and attend to the location in which it is presented. One training session 
includes 20 blocks of 20+n trials. Twelve target trials (6 per modality) will be 
included in each block. Training begins at n=1. The n-back designation 
changes dynamically with Ss performance. After each block, if Ss make < 3 
errors, n increases by 1; if > 5 errors are made, n decreases by 1. Otherwise, 
n remains the same. Due to its manipulation of bimodal stimuli, the task places 
a high demand on executive control processes, limiting the development of 
task-specific strategies. Schweizer et al. [60] found that college-aged controls 
progressed from a mean of ~4.5 n-back at initiation to ~6.5 n-back over 9 training days. Baseline performance 
among our Ss is unlikely to be this high. However, observable improvement should occur across the 12 
sessions. Should pilot data indicate that the dual modality task is subject to floor effects, we will shift to a 
visual modality with combined cues.  Time: ~20-25 min/day. 
 
    Directed Attend/Ignore Task Ss will also 
train on a task developed by Gazzaley et al. 
[13] to examine top-down control processes 
directing selective attention and modified from 
our previous work [100]. Ss are directed to 
either “Remember faces but ignore scenes” or 
“Remember scenes but ignore faces”.  [See 
Fig. 4]. Following a delay, a probe stimulus is 
provided (either face or scene, depending on 
instructional set) and Ss indicate via button 
press whether the probe stimulus matched the presentation stimuli. Each training session will include 4 
blocks (2 per instructional set) of 20 trials. The duration of stimuli presentation and delay intervals used at 
training initiation are depicted in Fig 4. Task difficulty will change dynamically with Ss’ performance with 
improved accuracy resulting in extended delay time and shortened stimulus presentation.. Training Time: 
~25 min/day 
 
Post-Training Assessment/Retesting.  Within 1-5 days following training completion (dictated by discharge 
dates), Ss will be re-administered the BDI-II, AI, the emotional Stroop, and the cognitive efficiency battery. 
Time: ~ 1 hr. Reimbursement: $25. 
 
Follow-up.  Follow-up interviews are conducted approximately 30, 60, and 90 days following treatment discharge 
(or post-training assessment, for outpatient participants). Interviews use the Project MATCH’s modification of the 
timeline follow-back [TLFB; 102, 103] for assessing quantity and frequency of daily substance use (alcohol, 
nicotine, other drugs) over the previous month. Ss also complete the “Your friends and family” items from Moos’ 
Health and Daily Living Scales [104]. The Profile of Mood States (POMS) 2-A Short [105] and Mini Alcohol 
Craving Experience questionnaire [107] will also be administered. Ss receive $20 for each completed follow-up 
interview. Estimates suggest that ~ 60% of the sample will complete all interviews.  Time: ~30 min. [See Appendix 
for listing] 
 
Fig 4. Modified from Gazzaley et al., 2008. Lines below the cue stimuli 
reflect task relevance for illustration and are not present during task 
administration. 
Fig 3. Modified from Schweizer 
et al., 2011. Each trial (Stimuli 
+ ITI) = 3000 msec 

Data Analysis Strategy. General considerations: Statistical analyses will apply SAS Version 9.4 (SAS Institute). 
Data will be examined for completeness, outliers, and distributional fit with transformations employed, as 
appropriate. Descriptive statistics, graphical methods and correlational matrices will be used to characterize 
the data. To examine outcomes over time generalized linear mixed models (GLMM) and linear contrasts will 
be used. Dependent variables are noted in task descriptions. Aims 1-3 are addressed in both transfer and 
training analyses. Feasibility: The design should minimize dropout, however we will evaluate dropout rates 
and the number of trainings completed prior to dropout as a function of sex, training condition, and the 
interaction. For selected analyses, missing data techniques will be employed; pattern-mixture models based 
on point of loss and sensitivity analysis will aid in interpretation. Transfer effects: GLMM analyses will be 
employed to examine effects of training condition, sex, and their interaction on transfer task performance. 
Dependent variables include RT, ACC, and efficiency (ACC/RT) for each Stroop condition (congruent, 
incongruent, neutral) and the cognitive battery. Training Effects: Linear contrasts will be utilized to compare 
performance on the training tasks across training session, group, and sex. We will explore whether the two 
training tasks are differentially related to transfer of gains. Secondary aim: Exploratory methods will be 
applied to a) examine associations between/among measures of functional adaptation, mood/affect, transfer 
gains and post-treatment substance use, and b) explore potential subgroups/subtypes that vary in response 
to training and/or post-treatment outcomes. Training session ratings of acceptability and perceived 
improvement will be compiled to provide proxy indicators of engagement to inform training session 
development. Power: Schweizer et al. [60], report large transfer effects (Cohen’s f’s, .82-1.10) but do not 
address sex differences. Given the developmental nature of the investigation and the intent to identify 
preliminary data regarding sex differences meriting further study, we will consider sex main effects at α =.10. 
With an estimated moderate effect size (Cohen’s f=.25) and a correlation between measures of rho=.5, our 
power to detect sex effects is .78. Our power to detect transfer effects relative to controls is 1.0 with power 
=.98 to detect more modest between-group training effects (Cohen’s f ~.40; α =.05).  For the critical 
interaction of sex and training condition, and estimating a moderate effect, our power is .96 (α =.05, rho =.5). 
This estimate fails to account for Ss dropout. Absent guiding estimates, we conducted an analysis with a 
dropout of 10 Ss/ condition and maintained power =.80.  
 
 
 
 
 

Bibliography & References Cited 
 
1. Cadet JL, Bisagno V. Neuropsychological consequences of chronic drug use: relevance to treatment 
approaches. Front Psychiatry. 2015;6:189. PMID: 26834649. 
2. Nixon SJ, Boissoneault, J., Sklar, A., Prather, R.A. Neuropsychological precursors and consequences of 
addiction. In: Miller P, editor. Biological Research on Addiction. San Diego, CA: Academic Press; 2013. 
3. Fernandez-Serrano MJ, Perez-Garcia M, Verdejo-Garcia A. What are the specific vs. generalized effects 
of drugs of abuse on neuropsychological performance? Neurosci Biobehav Rev. 2011;35(3):377-406. 
PMID: 20451551. 
4. Oscar-Berman M, Marinkovic K. Alcohol: effects on neurobehavioral functions and the brain. 
Neuropsychol Rev. 2007;17(3):239-57. PMID: 17874302. 
5. Pandey AK, Kamarajan C, Rangaswamy M, Porjesz B. Event-related oscillations in alcoholism research: 
a review. J Addict Res Ther. 2012;Suppl 7(1). PMID: 24273686. 
6. Kamarajan C, Porjesz B. Advances in electrophysiological research. Alcohol Res. 2015;37(1):53-87. 
PMID: 26259089. 
7. Rangaswamy M, Porjesz B. Understanding alcohol use disorders with neuroelectrophysiology. 
Handbook of clinical neurology. 2014;125:383-414. PMID: 25307587. 
8. Fortier CB, Leritz EC, Salat DH, Lindemer E, Maksimovskiy AL, Shepel J, Williams V, Venne JR, Milberg 
WP, McGlinchey RE. Widespread effects of alcohol on white matter microstructure. Alcohol Clin Exp Res. 
2014;38(12):2925-33. PMID: 25406797. 
9. Schulte T, Oberlin BG, Kareken DA, Marinkovic K, Muller-Oehring EM, Meyerhoff DJ, Tapert S. How 
acute and chronic alcohol consumption affects brain networks: insights from multimodal neuroimaging. 
Alcoholism, clinical and experimental research. 2012;36(12):2017-27. PMID: 22577873. 
10. Nixon SJ, Paul R, Phillips M. Cognitive efficiency in alcoholics and polysubstance abusers. Alcohol Clin 
Exp Res. 1998;22(7):1414-20. PMID: 9802522. 
11. Nixon SJ, Lawton-Craddock A, Tivis RD, Ceballos NA. Nicotine’s effects on attentional efficiency in 
alcoholics. Alcohol Clin Exp Res. 2007;31(12):2083-91. 
12. Mon A, Durazzo TC, Abe C, Gazdzinski S, Pennington D, Schmidt T, Meyerhoff DJ. Structural brain 
differences in alcohol-dependent individuals with and without comorbid substance dependence. Drug 
Alcohol Depend. 2014;144:170-7. PMID: 25263262. 
13. Gazzaley A, Clapp W, Kelley J, McEvoy K, Knight RT, D'Esposito M. Age-related top-down suppression 
deficit in the early stages of cortical visual memory processing. Proc Natl Acad Sci U S A. 
2008;105(35):13122-6. PMID: 18765818. 
14. Gazzaley A. Influence of early attentional modulation on working memory. Neuropsychologia. 
2011;49(6):1410-24. PMID: 21184764. 
15. Zanto TP, Gazzaley A. Neural suppression of irrelevant information underlies optimal working memory 
performance. J Neurosci. 2009;29(10):3059-66. PMID: 19279242. 
16. Nixon SJ. Application of theoretical models to the study of alcohol-induced brain damage. In: Hunt WA, 
Nixon SJ, editors. Alcohol-induced brain damage. Rockville, MD: National Institutes of Health; 1993. p. 
213-30. 
17. Nixon SJ, Parsons OA. Alcohol-related efficiency deficits using an ecologically valid test. Alcohol Clin 
Exp Res. 1991;15(4):601-6. PMID: 1928633. 
18. Nixon SJ, Tivis R, Ceballos N, Varner JL, Rohrbaugh J. Neurophysiological efficiency in male and female 
alcoholics. Prog Neuropsychopharmacol Biol Psychiatry. 2002;26(5):919-27. PMID: 12369267. 
19. Marinkovic K, Rickenbacher E, Azma S, Artsy E. Acute alcohol intoxication impairs top-down regulation 
of Stroop incongruity as revealed by blood oxygen level-dependent functional magnetic resonance 
imaging. Hum Brain Mapp. 2012;33(2):319-33. PMID: 21391268. 
20. Tedstone D, Coyle K. Cognitive impairments in sober alcoholics: performance on selective and divided 
attention tasks. Drug Alcohol Depend. 2004;75(3):277-86. PMID: 15283949. 
21. Sullivan EV, Desmond JE, Lim KO, Pfefferbaum A. Speed and efficiency but not accuracy or timing 
deficits of limb movements in alcoholic men and women. Alcohol Clin Exp Res. 2002;26(5):705-13. PMID: 
12045480. 
22. Lisansky ES. Alcoholism in women: social and psychological concomitants. I. Social history data. Q J 
Stud Alcohol. 1957;18(4):588-623. PMID: 13506019. 
23. Zelen SL, Fox J, Gould E, Olson RW. Sex-contingent differences between male and female alcoholics. 
J Clin Psychol. 1966;22(2):160-5. PMID: 4379977. 

24. Flannery B, Fishbein D, Krupitsky E, Langevin D, Verbitskaya E, Bland C, Bolla K, Egorova V, Bushara 
N, Tsoy M, Zvartau E. Gender differences in neurocognitive functioning among alcohol-dependent 
Russian patients. Alcohol Clin Exp Res. 2007;31(5):745-54. PMID: 17386068. 
25. Glenn SW, Parsons OA. Neuropsychological efficiency measures in male and female alcoholics. J Stud 
Alcohol. 1992;53(6):546-52. PMID: 1434630. 
26. Silberstein JA, Parsons OA. Neuropsychological impairment in female alcoholics. Curr Alcohol. 
1979;7:481-95. PMID: 552343. 
27. Silberstein JA, Parsons OA. Neuropsychological impairment in female alcoholics: replication and 
extension. J Abnorm Psychol. 1981;90(2):179-82. PMID: 7288011. 
28. Nixon SJ. Cognitive deficits in alcoholic women. Alcohol Health & Research World. 1994;18(3):228-32. 
29. Diehl A, Croissant B, Batra A, Mundle G, Nakovics H, Mann K. Alcoholism in women: is it different in 
onset and outcome compared to men? Eur Arch Psychiatry Clin Neurosci. 2007;257(6):344-51. PMID: 
17629733. 
30. Mann K, Batra A, Gunthner A, Schroth G. Do women develop alcoholic brain damage more readily than 
men? Alcohol Clin Exp Res. 1992;16(6):1052-6. PMID: 1471759. 
31. Nixon SJ, Glenn SW. Cognitive psychosocial performance and recovery in female alcoholics. Recent 
Dev Alcohol. 1995;12:287-307. PMID: 7624548. 
32. Pfefferbaum A, Rosenbloom MJ, Fama R, Sassoon SA, Sullivan EV. Transcallosal white matter 
degradation detected with quantitative fiber tracking in alcoholic men and women: selective relations to 
dissociable functions. Alcohol Clin Exp Res. 2010;34(7):1201-11. PMID: 20477772. 
33. Demirakca T, Ende G, Kammerer N, Welzel-Marquez H, Hermann D, Heinz A, Mann K. Effects of 
alcoholism and continued abstinence on brain volumes in both genders. Alcohol Clin Exp Res. 
2011;35(9):1678-85. PMID: 21599718. 
34. Nixon SJ, Prather R, Lewis B. Sex differences in alcohol-related neurobehavioral consequences. 
Handbook of clinical neurology. 2014;125:253-72. PMID: 25307580. 
35. Stavro K, Pelletier J, Potvin S. Widespread and sustained cognitive deficits in alcoholism: a meta-
analysis. Addict Biol. 2013;18(2):203-13. PMID: 22264351. 
36. Rosenbloom MJ, Rohlfing T, O'Reilly AW, Sassoon SA, Pfefferbaum A, Sullivan EV. Improvement in 
memory and static balance with abstinence in alcoholic men and women: selective relations with change 
in brain structure. Psychiatry Res. 2007;155(2):91-102. PMID: 17407808. 
37. Fein G, Torres J, Price LJ, Di Sclafani V. Cognitive performance in long-term abstinent individuals. 
Alcohol Clin Exp Res. 2006;30(9):1538-44. 
38. Fein G, Greenstein D. Gait and balance deficits in chronic alcoholics: no improvement from 10 weeks 
through 1 year abstinence. Alcohol Clin Exp Res. 2013;37(1):86-95. PMID: 22691134. 
39. Fein G, Cardenas VA. Neuroplasticity in human alcoholism: studies of extended abstinence with potential 
treatment implications. Alcohol Res. 2015;37(1):125-41. PMID: 26259093. 
40. Pitel AL, Rivier J, Beaunieux H, Vabret F, Desgranges B, Eustache F. Changes in the episodic memory 
and executive functions of abstinent and relapsed alcoholics over a 6-month period. Alcohol Clin Exp 
Res. 2009;33(3):490-8. PMID: 19120052. 
41. Smith S, Fein G. Persistent but less severe ataxia in long-term versus short-term abstinent alcoholic men 
and women: a cross-sectional analysis. Alcohol Clin Exp Res. 2011;35(12):2184-92. PMID: 21919921. 
42. Ruiz SM, Oscar-Berman M, Sawyer KS, Valmas MM, Urban T, Harris GJ. Drinking history associations 
with regional white matter volumes in alcoholic men and women. Alcohol Clin Exp Res. 2013;37(1):110-
22. PMID: 22725728. 
43. Goldman MS. Cognitive impairment in chronic alcoholics. Some cause for optimism. Am Psychol. 
1983;38(10):1045-54. PMID: 6357006. 
44. Goldman MS. Recovery of cognitive functioning in alcoholics: the relationship to treatment. Alcohol 
Health & Research World. 1995;19(2):148-54. 
45. McCrady BS, Smith DE. Implications of cognitive impairment for the treatment of alcoholism. Alcohol Clin 
Exp Res. 1986;10(2):145-9. PMID: 3521371. 
46. Parsons OA, Sinha R, Williams HL. Relationships between neuropsychological test performance and 
event-related potentials in alcoholic and nonalcoholic samples. Alcohol Clin Exp Res. 1990;14(5):746-
55. PMID: 2264605. 
47. Bates ME, Pawlak AP, Tonigan JS, Buckman JF. Cognitive impairment influences drinking outcome by 
altering therapeutic mechanisms of change. Psychol Addict Behav. 2006;20(3):241-53. PMID: 16938062. 

48. Bates ME, Voelbel GT, Buckman JF, Labouvie EW, Barry D. Short-term neuropsychological recovery in 
clients with substance use disorders. Alcohol Clin Exp Res. 2005;29(3):367-77. PMID: 15770112. 
49. Buckman JF, Bates ME, Cisler RA. Social networks and their influence on drinking behaviors: differences 
related to cognitive impairment in clients receiving alcoholism treatment. J Stud Alcohol Drugs. 
2007;68(5):738-47. PMID: 17690808. 
50. Tapert SF. Special Issue: Cognitive enhancement and rehabilitation. Neuropsychology Review. 
2013;23(1). 
51. Bates ME, Buckman JF, Nguyen TT. A role for cognitive rehabilitation in increasing the effectiveness of 
treatment for alcohol use disorders. Neuropsychol Rev. 2013;23(1):27-47. PMID: 23412885. 
52. Sofuoglu M, DeVito EE, Waters AJ, Carroll KM. Cognitive enhancement as a treatment for drug 
addictions. Neuropharmacology. 2013;64:452-63. PMID: 22735770. 
53. Wilcox CE, Dekonenko CJ, Mayer AR, Bogenschutz MP, Turner JA. Cognitive control in alcohol use 
disorder: deficits and clinical relevance. Reviews in the neurosciences. 2014;25(1):1-24. PMID: 
24361772. 
54. Bickel WK, Quisenberry AJ, Moody L, Wilson AG. Therapeutic opportunities for self-control repair in 
addiction and related disorders: change and the limits of change in trans-disease processes. Clin Psychol 
Sci. 2015;3(1):140-53. PMID: 25664226. 
55. Wiers RW, Gladwin, T.E., Hofmann, W., Salemink. E., Differinkhof, K.R. Cognitive bias modification and 
cognitive control training in addiction and related psychopathology: mechanisms, clinical perspectives, 
and ways forward. Clinical Psychological Science. 2013;1(2):192-212. 
56. Houben K, Wiers RW, Jansen A. Getting a grip on drinking behavior: training working memory to reduce 
alcohol abuse. Psychol Sci. 2011;22(7):968-75. PMID: 21685380. 
57. Rupp CI, Kemmler G, Kurz M, Hinterhuber H, Fleischhacker WW. Cognitive remediation therapy during 
treatment for alcohol dependence. J Stud Alcohol Drugs. 2012;73(4):625-34. PMID: 22630801. 
58. Boffo M, Pronk T, Wiers RW, Mannarini S. Combining cognitive bias modification training with 
motivational support in alcohol dependent outpatients: study protocol for a randomised controlled trial. 
Trials. 2015;16:63. PMID: 25888158. 
59. Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ. Improving fluid intelligence with training on working 
memory. Proceedings of the National Academy of Sciences. 2008;105(19):6829-33. 
60. Schweizer S, Hampshire A, Dalgleish T. Extending brain-training to the affective domain: increasing 
cognitive and affective executive control through emotional working memory training. PLoS One. 
2011;6(9):e24372. PMID: 21949712. 
61. Owen AM, Hampshire A, Grahn JA, Stenton R, Dajani S, Burns AS, Howard RJ, Ballard CG. Putting 
brain training to the test. Nature. 2010;465(7299):775-8. PMID: 20407435. 
62. Schweizer S, Grahn J, Hampshire A, Mobbs D, Dalgleish T. Training the emotional brain: improving 
affective control through emotional working memory training. J Neurosci. 2013;33(12):5301-11. PMID: 
23516294. 
63. Foisy ML, Kornreich C, Fobe A, D'Hondt L, Pelc I, Hanak C, Verbanck P, Philippot P. Impaired emotional 
facial expression recognition in alcohol dependence: do these deficits persist with midterm abstinence? 
Alcohol Clin Exp Res. 2007;31(3):404-10. PMID: 17295724. 
64. Maurage P, Grynberg D, Noel X, Joassin F, Philippot P, Hanak C, Verbanck P, Luminet O, De Timary P, 
Campanella S. Dissociation between affective and cognitive empathy in alcoholism: a specific deficit for 
the emotional dimension. Alcohol Clin Exp Res. 2011;35(9):1662-8. 
65. Salloum JB, Ramchandani VA, Bodurka J, Rawlings R, Momenan R, George D, Hommer DW. Blunted 
rostral anterior cingulate response during a simplified decoding task of negative emotional facial 
expressions in alcoholic patients. Alcohol Clin Exp Res. 2007;31(9):1490-504. PMID: 17624997. 
66. Marinkovic K, Oscar-Berman M, Urban T, O'Reilly CE, Howard JA, Sawyer K, Harris GJ. Alcoholism and 
dampened temporal limbic activation to emotional faces. Alcohol Clin Exp Res. 2009;33(11):1880-92. 
PMID: 19673745. 
67. Townshend JM, Duka T. Mixed emotions: alcoholics' impairments in the recognition of specific emotional 
facial expressions. Neuropsychologia. 2003;41(7):773-82. PMID: 12631528. 
68. Monnot M, Lovallo WR, Nixon SJ, Ross E. Neurological basis of deficits in affective prosody 
comprehension among alcoholics and fetal alcohol-exposed adults. J Neuropsychiatry Clin Neurosci. 
2002;14(3):321-8. PMID: 12154157. 
69. Maurage P, Campanella S, Philippot P, Martin S, de Timary P. Face processing in chronic alcoholism: a 
specific deficit for emotional features. Alcohol Clin Exp Res. 2008;32(4):600-6. PMID: 18241315. 

70. Kornreich C, Philippot P, Foisy ML, Blairy S, Raynaud E, Dan B, Hess U, Noel X, Pelc I, Verbanck P. 
Impaired emotional facial expression recognition is associated with interpersonal problems in alcoholism. 
Alcohol Alcohol. 2002;37(4):394-400. PMID: 12107044. 
71. Trick L, Kempton MJ, Williams SC, Duka T. Impaired fear recognition and attentional set-shifting is 
associated with brain structural changes in alcoholic patients. Addict Biol. 2014;19(6):1041-54. PMID: 
25123156. 
72. Uekermann J, Daum I. Social cognition in alcoholism: a link to prefrontal cortex dysfunction? Addiction. 
2008;103(5):726-35. PMID: 18412750. 
73. Valmas MM, Mosher Ruiz S, Gansler DA, Sawyer KS, Oscar-Berman M. Social cognition deficits and 
associations with drinking history in alcoholic men and women. Alcohol Clin Exp Res. 2014;38(12):2998-
3007. PMID: 25581654. 
74. Garcia-Garcia M, Dominguez-Borras J, SanMiguel I, Escera C. Electrophysiological and behavioral 
evidence of gender differences in the modulation of distraction by the emotional context. Biol Psychol. 
2008;79(3):307-16. PMID: 18722500. 
75. Proverbio AM, Adorni R, Zani A, Trestianu L. Sex differences in the brain response to affective scenes 
with or without humans. Neuropsychologia. 2009;47(12):2374-88. PMID: 19061906. 
76. Lighthall NR, Sakaki M, Vasunilashorn S, Nga L, Somayajula S, Chen EY, Samii N, Mather M. Gender 
differences in reward-related decision processing under stress. Social cognitive and affective 
neuroscience. 2012;7(4):476-84. PMID: 21609968. 
77. Administration SAaMH. Substance abuse treatment: addressing the specific needs of women 2015. 
78. Breese GR, Sinha R, Heilig M. Chronic alcohol neuroadaptation and stress contribute to susceptibility for 
alcohol craving and relapse. Pharmacol Ther. 2011;129(2):149-71. PMID: 20951730. 
79. Hunt WA, W. Barnett, L.G. Branch. Relapse rates in addiction programs. Journal of Clinical Psychology. 
1971;27(4):455-6. 
80. ASHA. Guidelines for audiologic screening 1997. Available from: www.asha.org/policy. 
81. First MB, Williams JBW, Karg RS, Spitzer RL: Structured Clinical Interview for DSM-5 Disorders, Clinician 
Version (SCID-5-CV). Arlington, VA, American Psychiatric Association, 2015 
82. Cahalan D, Cissin L, Crossley H. American drinking practices: a national study of drinking behaviors and 
attitudes (Monograph No. 6). New Brunswick, NJ: Rutgers Center of Alcohol Studies; 1969. 
83. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerstrom test for nicotine 
dependence: a revision of the Fagerstrom tolerance questionnaire. Br J Addict. 1991;86(9):1119-27. 
PMID: 1932883. 
84. Beck AT, Steer RA, Brown GK. Beck Depression Inventory, Second Edition. San Antonio: The 
Psychological Corporation; 1996. 
85. Spielberger CD. Manual for State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press; 
1983. 36 p. 
86. Mann RE, Sobell LC, Sobell MB, Pavan D. Reliability of a family tree questionnaire for assessing family 
history of alcohol problems. Drug Alcohol Depend. 1985;15(1-2):61-7. PMID: 4017879. 
87. Horowitz LM, Alden LE, Wiggins JS, Pincus AL. Manual for the inventory of interpersonal problems. San 
Antonio, TX: Psychological Corporation; 2000. 
88. Jones SL, Lanyon RI. Relationship between adaptive skills and outcome of alcoholism treatment. J Stud 
Alcohol. 1981;42(5):521-5. PMID: 7278294. 
89. Reitan RM, Wolfson D. The Halstead-Reitan neuropsychological test battery : theory and clinical 
interpretation. 2nd ed. Tucson, Ariz.: Neuropsychology Press; 1993. xv, 912 p. p. 
90. Wechsler D. Wechsler Adult Intelligence Scale--Revised: Manual. New York: The Psychological 
Corporation; 1981. 
91. Acker W, Acker W. Bexley Maudsley Automated Processing Screening and Bexley Maudsley Category 
Sorting Test Manual. Great Britain: NFER-Nelson Publishing; 1982. 
92. Heaton RK. Wisconsin Card Sorting Test Manual. Odessa, FL: Psychological Assessment Resources; 
1981. 
93. Sternberg S. High-speed scanning in human memory. Science. 1966;153(736):652-4. 
94. Sternberg S. Memory scanning: new findings and current controversies. Quarterly Journal of 
Experimental Psychology. 1975;27:1-32. 
95. Trenerry MR. Stroop Neuropsychological Screening Test manual. Odessa, FL: Psychological 
Assessment Resources; 1989. ii, 11 p. p. 
96. Stroop JR. Studies of interference in serial verbal reactions. Experimental Psychology. 1935;18:643-62. 

97. Preston SD, Stansfield RB. I know how you feel: task-irrelevant facial expressions are spontaneously 
processed at a semantic level. Cogn Affect Behav Neurosci. 2008;8(1):54-64. PMID: 18405046. 
98. Ekman, P. Pictures of Facial Affect (POFA). http://www.paulekman.com/product/pictures-of-facial-affect-
pofa/. 
99. Shaver P, Schwartz J, Kirson D, O'Connor C. Emotion knowledge: further exploration of a prototype 
approach. J Pers Soc Psychol. 1987;52(6):1061-86. PMID: 3598857. 
100. Boissoneault J, Sklar A, Prather R, Nixon SJ. Acute effects of moderate alcohol on psychomotor, set 
shifting, and working memory function in older and younger social drinkers. J Stud Alcohol Drugs. 
2014;75(5):870-9. PMID: 25208205. 
101. Donadon MF, Osorio Fde L. Recognition of facial expressions by alcoholic patients: a systematic 
literature review. Neuropsychiatr Dis Treat. 2014;10:1655-63. PMID: 25228806. 
102. Sobell LC, Sobell, M.B. Timeline Followback user's guide: a Calendar method for assessing alcohol and 
drug use. Toronto, Ontario, Canada: Addiction Research foundation; 1996. 
103. Miller WR. What is a relapse? Fifty ways to leave the wagon. Addiction. 1996;91 Suppl:S15-27. PMID: 
8997778. 
104. Moos RH, Cronkite RC, Finney JW. Health and Daily Living Form Manual. 2nd ed. Menlo Park, CA: 
Mind Garden, Inc; 1990. 
105. Heuchert JP, McNair DM. Profile of mood states, POMS-2. Multi-Health Systems Inc, North Tonawanda, 
NY. 2012. 
106. Singleton EG, Tiffany ST, Henningfield JE, editors. Development and validation of a new questionnaire 
to assess craving for alcohol. Problems of Drug Dependence; 1994. Rockville, MD: The College on 
Problems of Drug Dependence, Inc.; 1995. 
107. Coates JM, Gullo MJ, Feeney GF, Kavanagh DJ, Young RM, Dingle GA, May J, Andrade J, Statham 
DJ, Connor JP. The mini alcohol craving experience questionnaire: development and clinical application. 
Alcohol Clin Exp Res. 2017;41(1):156-164. PMID: 28019645. 
 
7. Possible Discomforts and Risks 
 
Selection criteriaDetoxified alcohol dependent Ss (N=90; 45 women) will be recruited from inpatient, 
residential and intensive outpatient alcohol/drug treatment facilities across north central Florida. Ss will be 
between 25-65 with a minimum of 10 years of education. Ss will have completed medical detoxification (if it 
was required) and be abstinent from psychoactive drugs used to manage withdrawal symptoms. Only Ss 
reporting chronicity of alcohol problems ≥ 5 yrs will be recruited. This threshold has not unduly compromised 
recruitment in previous work with this population. Given the reported rates of cigarette smoking in our 
treatment populations (~88%), current and past smoking history will be queried and data will be used in 
descriptive and correlational analyses.  
Due to the nature of this work as a pilot/feasibility study, exclusionary criteria primarily reflect conditions 
which would confound data interpretation or limit subjects’ ability to participate Ss will be excluded if reporting 
histories of a) significant neurologic insult (e.g., epilepsy, stroke), or b) medical disorders which would 
confound data interpretation or c) the on-going use of medications which could compromise testing (e.g., 
specific antihistamines, benzodiazepines, narcotic pain medication, etc.).Ss will also be excluded from study 
if reporting current diagnosis for any psychotic disorder (e.g., any of the schizophrenic disorders or psychotic 
depression), current/unremitted panic disorder, or bipolar disorder. Consistent with IRB guidance and facility 
preference, reasons for the S’s exclusion are not revealed to clinical staff.  
Ss provide written, informed consent prior to completing any screening, assessment, or training procedure. 
They are paid for their participation in each project phase (individually for screening, assessment, training, 
and follow-up). 
Should Ss indicate suicidal intent on the Beck Depression Inventory-II, during administration of the SCID-5, 
or through incidental communication, clinical staff are immediately notified and Ss are discontinued from 
study. For other psychiatric issues, Ss are asked if they have discussed these issues with clinical staff. If 
they have not, they are encouraged to do so.  However, only in the case of intent to commit suicide or injure 
another (including child or elder abuse) is the confidentiality of the test session breached (i.e., immediate 
contact with clinical staff). No direct inquiry regarding intended or historic injury to others is made. Thus, this 

information would be revealed only through incidental communication. This possibility is explained verbally 
and in the informed consent  
Sources of material. All data are coded by alphanumeric code independent from any personal identifiers. 
Data are reported in such a way that the identity of Ss cannot be discerned from the database. No 
biospecimens are collected.. Only research staff completing HIPAA, local IRB and NIH training regarding 
human research will have access to individually identifiable private information from Ss.   
Potential Risks. The primary risk is a violation of confidentiality. To reduce this risk, all data are coded by 
alphanumeric code and are scored, filed, and retained in a manner designed to facilitate confidentiality. Data 
and consent forms are kept in separate locked files and subject numbers do not appear on consent forms.  
The code linking the name and number is retained on a UF encrypted and password protected file. This link 
is not stored with study data. The link is destroyed at the end of study. Ss participating in the follow-up 
component may complete the interviews in the lab or by phone.  Regardless, follow-up information is coded 
only by subject number and will not include personal identifiers. A second risk is that Ss may also experience 
some discomfort in responding to some of the questions or fatigue during testing. The assistants are carefully 
trained regarding the sensitivity of these questions, frequently reminding Ss of the confidential nature of this 
information and the opportunity to skip questions if they are too uncomfortable. In the hundreds of interviews 
conducted in our previous and on-going NIH grants, these interviews have been conducted without incident. 
To reduce fatigue, R03 training sessions are completed in ~45 min. 
ADEQUACY OF PROTECTION AGAINST RISKS 
Recruitment and Informed Consent: Ss will be recruited from substance abuse treatment facilities through 
in-person announcements by trained research assistants from our laboratory. Potential Ss express their 
interest directly to our research assistants, who describe the study, and then, if Ss are interested, initiates 
the informed consent process. Ss are reassured that their decision regarding participation will not impact 
their clinical care.  
Ss will be provided a full description of the assessment and training components of the study prior to 
participating, enabling them to review it with friends/family/peers prior to their initial assessment. As noted 
elsewhere, research assistants are trained in seeking informed consent (through both IRB, NIH and PI 
directed training).  
Protection Against Risk: To protect privacy, individuals complete the screening packet in an area separate 
from clinical activities with the aid of trained research assistants. The SCID-5, baseline assessment, 
computerized training, and post-intervention assessment are administered in designated rooms at the 
treatment facilities. To protect against potential breaches of confidentiality, data are coded by a number 
unrelated to the subject and cannot be directly connected to individual Ss. Data are reported in aggregate 
form wherein no individuals can be identified, and stored on the Department of Psychiatry’s encrypted and 
password-protected server. Additionally, Ss are protected by a Certificate of Confidentiality.  
Ss may withdraw at any point in the screening, testing, or training sessions without impact on clinical care. If 
Ss discontinue during the conduct of the active study, they receive prorated compensation. Follow-up 
interviews are reimbursed after each completed interview.  
 
8. Possible Benefits 
 
Individuals in the training groups may experience some improvements in cognitive performance.  
 
 
 
 
9. Conflict of Interest 
 
There is no conflict of interest beyond the professional benefit from academic publication or presentation of the 
results. 

 
 
 

DATA AND SAFETY MONITORING PLAN (approved by NIAAA) 
The demonstration/pilot project involves the recruitment of otherwise healthy men and women seeking treatment 
for a substance use disorder. Individuals with medical or comorbid psychiatric disorders that would confound 
interpretation of neurocognitive function are excluded from study. Depending on the phase of the project, 
qualifying and consenting participants are assigned to the control group, or randomly assigned to one of the two 
active computerized training groups (affective vs. neutral), with the constraint that every attempt is made to 
recruit equal numbers of men and women across the three groups. Cognitive capacity is assessed prior to the 
intervention and tracked across the intervention (multiple sessions across multiple weeks). After the intervention, 
all participants are contacted by phone and provide feedback regarding post-treatment drinking and psychosocial 
adaptation at three time-points (30, 60, and 90 days post-treatment). The intervention does not involve the 
administration of alcohol or any other drug or pharmacological agent.  
 
1. Reporting and Monitoring of Adverse Events.  AEs, regardless of severity will be defined by FDA and local 
IRB standards. Every serious adverse event (SAE), as determined by the PI, Sara Jo Nixon, Ph.D., will be 
reported to both the University of Florida Health Science Center Institutional Review Board (IRB-01) and the 
NIAAA project officer within 48 hours. In addition, a summary of all adverse events, regardless of severity, will 
be submitted to both the NIAAA project officer and IRB-01 on an annual basis. Given selection criteria and the 
study protocol that involves only participation in “brain-training” or cognitive practice tasks, no SAEs are 
anticipated.  The most common events are likely to be fatigue and/or headache. The protocol is designed to 
mitigate the probability of each with breaks, etc.  Headache will be reported in real-time to the participant’s 
counselor on-site, or his/her designee, to ensure appropriate intervention.  All events will be reviewed during 
weekly staff meetings including research staff, graduate research assistants, and the PI. Should a pattern of 
adverse events be detected, the protocol will be re-evaluated and amended, as appropriate.  
 
2. Follow-up:  The intervention is complete at the time that the cognitive training concludes (within three weeks 
of initiation). One of the objectives of the study is to examine to what degree cognitive abilities and/or their 
improvement during training and early recovery may support post-treatment abstinence and recovery. In phone 
interviews, using established instruments focusing on substance use, current mood, and psychosocial 
adaptation, participants provide information at 30, 60, and 90 days after discharge/intervention. This study is not 
a direct intervention study and thus will not provide direct referral to treatment.  In the event that persons indicate 
that they are a threat to self or others, we will provide referral mental health information.  Additional action may 
involve a follow-up phone interview, direct referral to a mental health facility and/or Emergency Department, or 
the involvement of other authorities, if a direct threat is issued. In our history, we have not had to use these latter 
options, but they are available if needed.  
 
4. Data Quality Assurance and Confidentiality. Participant data will be given an alphanumeric code 
independent of personal identifiers. The “key” to this code will be stored securely in an encrypted, password-
protected database on the restricted-access servers of the Department of Psychiatry. Trained research 
assistants will hand-enter all data from pencil/paper tasks and questionnaires into REDCap (Research Electronic 
Data Capture, Vanderbilt University).  The REDCap system will have protected health information tagged for de-
identification of data. REDCap provides a means of data verification using second entry that is will be applied to 
ensure accuracy. Discrepancies not linked to direct error (i.e., interpretation) are resolved through review of data, 
and consensus derived through staff review.   To provide greater confidentiality, we have obtained a Certificate 
of Confidentiality from the NIH to cover data obtained throughout the project.