Prevention of Adolescent Risky Behaviors: Neural Markers of Intervention Effects
Phase: NA
Conditions: Risk-Taking, Adolescent Behavior
Sponsor: University of California, Irvine

Study Protocol and Statistical Analysis Plan 
 
 
 
Study Title: Prevention of Adolescent Risky Behaviors: Neural Markers of Intervention Effects 
 
Clinical Trials ID: NCT03370393 
 
Date Document was Created: March 29, 2016 (for Submission to NIH) 

I. INTRODUCTION TO RESUBMISSION APPLICATION (1 R01 DA040966-01A1) 
 
We thank the reviewers for their valuable comments on the previous application. We combined the concerns in 
resume/summary (“S”) and critiques (“C”) and grouped them in sections. Changes in the proposal are bracketed. 
 
Significance: 1. Practical utility of fMRI (C2). We agree that fMRI is currently fiscally impractical, but believe that 
will change rapidly. In the interim, fMRI has significant contributions to make, as the reviewer acknowledges: 
“mechanistic knowledge may guide interventions… by identifying critical circuits and associated psychological 
functions....” This study will offer insights on how to merge neuroscience methods and prevention science as this 
tool becomes more integrated in the direct assessment of programmatic effects, and offer guidance on how to 
personalize prevention program exposure, addressing continuing concerns about specificity and dosage (III B). 
 
Investigators: 1. Delay in start-up (C3). We do not anticipate undue delay in the start-up of this project because 
of personnel issues. Dr. Rao’s center has staff members on board who are experienced in coordinating various 
types of studies and research activities related to behavioral and mental health. The center has had continuous 
NIH funding since its inception 28 years ago. The research staff transition from one study to the next based on 
the funding cycles of the research projects in the center. In addition, the center has decades of experience in 
successfully recruiting and training personnel for research projects. We have access to a large pool of qualified 
candidates to attract to these positions, given that we have five local universities along with two medical centers. 
 
2. Collaboration history (S, C3). In addition to acquiring pilot data, the team has worked collaboratively on other 
projects and grant proposals (IIIC.2; Rao Biosketch). As cited in the resume/summary (S), Dr. Rao has an 
established record of successfully completing collaborative, multi-disciplinary studies and publishing their results. 
 
Innovation: We very much appreciate all the positive comments. No weaknesses were identified in this section.  
 
Approach: 1. Analysis of multiple measures (S, C1). PAAS is a family intervention. Data from PAAS and SAAF 
indicated that the intervention has both direct and indirect effects. Positive parenting improves self-regulation in 
offspring around pubertal transition which, in turn, protects these youth against behaviors associated with HIV/ 
AIDS and substance abuse risk as they progress through middle and late adolescence (IIIC.3b, Figure 2). Here, 
we will examine the effects of parenting on youth’s neurobiological changes in exploratory analyses (II, IIIC.7d.3). 
 
2. How fMRI tasks reflect PAAS’ effects on regulation processes (S, C1). The primary fMRI task is a decision-
making task in the context of risk/reward opportunities, and this task targets both socio-emotional and cognitive-
control neural substrates (IIIC.3c, Fig 3). Behavioral performance on this task is related to reward-seeking traits 
and risky behaviors in real life. PAAS targets developmental vulnerabilities and exploits sensitivity to rewards by 
incentivizing cognitive-control functioning by enhancing youth’s resistance efficacy skills and raising awareness 
of the association between risk engagement and compromised future goals (IIIC.3b, Fig 2; IIIC.4f.6,Table 2). 
To ensure risky decision-making is not confounded by poor impulse control, we included a control task (IIIC.1). 
 
3. Handling confounds (S, C1). Youth on medications affecting the brain, with behavioral/emotional problems at 
a clinical level, or those using alcohol/drugs three days prior to the fMRI scans will be excluded (IIIC.4c). We 
proposed standardized assessments and urine drug screens to evaluate these factors (IIIC.4f.3, Table 1). 
 
4. Wait-list control (C2). The intervention’s efficacy was proven in randomized, controlled trials and psychological 
“active ingredients” were identified. The focus of this proposal is on their underlying neurobiological substrates. 
The wait-list control does not account for “general” (attention) effects. PAAS-tech does not involve a therapist, 
so this is not an issue. This control provides greater statistical power to detect group differences in addition to 
the benefit of a proven intervention for the control group after completing the experimental phase (Fig6; IIIC.10c). 
 
5. Quality control imaging data (C2). The pilot data were in 9-12 or 12-18 year-olds, which cover the age range 
(i.e. 11-13 years) in the proposed study (IIIC.3). Our neuroimaging studies have included youth as young as 6 
years, with reliable data in 93%-97% of the samples (IIIC.4f.2b.2). We implement many reliable data collection 
methods (IIIC.4f.2b.1, 2b.2). We also institute established methods to reduce dropout rates (IIIC.10a). So, the 
estimated 20% dropout rate is easily attainable. Should this not prove to be the case, we agree with the reviewer: 
“this concern is mitigated by the robust recruitment mechanisms in place which could supply additional subjects.” 
 
6. Motion artifacts (C2). We propose several methods to address motion artifact at individual level. These metrics 
can be summarized to a single number and then compared between groups or used as covariates (IIIC.4f.2b.6). 
 
7. Power analysis (C2). To avoid biased estimates from small sample sizes, we used minimum detectable effect 
size (ES) at a clinically meaningful level. ES from pilot data (.3-.5) are consistent with the power analysis (IIIC.7e). 
 
8. fMRI tasks not novel (C3). We deliberately selected theoretically-based, well-established tasks in this novel 
study on neural basis of a prevention program. Untested tasks could lead to confounds in interpreting the results. 
 
9. Miscellaneous. IQ should be ≥80 (C3). We will provide laptops to those who don’t own them (C3; IIIC.4f.6). 

II. SPECIFIC AIMS 
 
African-American (AA) youth are disproportionately affected by the high morbidity and mortality associated with 
risky behaviors, including human immunodeficiency virus (HIV) infection, acquired immune deficiency syndrome 
(AIDS), and other sexually-transmitted infections (STIs).1-6 Adolescence is characterized as a time of increased 
experimentation and exploration. Although such behaviors aid in the progression towards autonomy, this comes 
at a great cost. For example, motor vehicle accidents, suicide and violence rise dramatically during adolescence, 
7-13 largely due to problems with behavioral and emotional self-regulation, commonly associated with short- and 
long-term consequences of alcohol and addictive drugs.14-16 Recent advances in neuroscience suggest that a 
temporal disassociation in the maturation of the brain’s “socio-emotional” (reward) system, which develops early, 
and the “cognitive-control” (self-regulation) system, which occurs later, creates a period of poor decision-making 
and heightened vulnerability to risk-taking (reward-seeking) behavior with the onset of pubertal maturation.17-24 
However, we know little about how our knowledge of changes in neural circuitry governing adolescent decision-
making can be used to improve intervention programs seeking to reduce the onset and escalation of alcohol and 
drug use in co-occurrence with risky sexual practices among AA youth or those from other ethnic/racial groups. 
 
Reasons for the cross-racial HIV/AIDS disparities are not clear, but extant data suggest that cumulative exposure 
to stress associated with poverty, family disruption, discrimination and neighborhood segregation exacts a toll 
on AA families. These stressors may not only compromise parenting practices and increase youth exposure to 
troubled peers but also influence early brain development with sustaining effects, and as explanatory causes for 
race crossover drug use among AAs as they transition into young adulthood. Our team has developed and tested 
a family-based intervention program designed to deter and avert HIV-risk vulnerability in AA youth, Pathways 
for African Americans’ Success (PAAS). In randomized controlled trials (RCTs), PASS was effective in 
deterring substance use and early sexual activity by influencing both parenting practices and youth psychological 
protective factors (i.e. cognitive as well as emotional self-regulation) (IIIC.3b; Fig 2).25-36 Our long-term goal is 
to develop implementation strategies that maximize the effectiveness of this intervention for high-risk AA youth. 
 
We address a crucial question -- which neurobiological mechanisms are involved in the process of intervention-
induced changes in youth protective factors that evoke behavioral change. This information is necessary to: 1) 
refine the program by focusing on the components that are most effective in changing both behavioral and neural 
circuitry; and 2) guide the development of personalized behavioral interventions by identifying individuals who 
are most likely to benefit from the program. The objective of the study is to identify the effects of PAAS on the 
risk-taking/self-regulation neural circuits and assess whether changes in these circuits correlate with changes in 
youth protective factors (improving self-regulation) in 128, 11-13 year-old AA youth (Fig 1), which we hypothesize 
will dissuade behaviors that place youth at risk for HIV/AIDs, as they transition into middle and late adolescence. 
Our overarching hypothesis is that the balance in the activation of reward and cognitive-control systems 
correlates with youth self-regulation, and that PAAS shifts this balance in the positive direction. 
 
Specific Aim 1:  Assess the effect of PAAS on reward-drive and cognitive-control neural circuitry. 
Method 1: Youth will be randomized to a 6-week PAAS intervention or a wait-list condition. Functional magnetic 
resonance imaging (fMRI) scans will be acquired at rest and in response to a task involving the balance between 
reward-drive and cognitive-control circuits pre- and post-intervention to assess functional connectivity changes.  
Hypothesis 1: PAAS will produce a greater balance in reward-drive and cognitive-control systems (by increasing 
functional connectivity between these two components of the neural circuitry) compared to the wait-list condition. 
  
Specific Aim 2: Evaluate if the post-intervention neural changes mediate PAAS’ effects on youth self-regulation. 
Method 2: Direct and indirect (i.e. functional connectivity) effects of PAAS on youth cognitive and emotional self-
regulation measured three months post-intervention will be assessed to identify which neural changes mediate 
PAAS’ effects on these self-regulation measures. 
Hypothesis 2: PAAS-induced changes in self-regulation will be mediated by PAAS’ effects on the neural circuitry. 
 
Exploratory Analyses: We will identify subgroups of youth who benefit more from PAAS intervention effects on 
self-regulation defined by demographic, neurobiological and social-contextual factors (e.g. individual differences 
in baseline functional connectivity may predict who benefits most from the intervention [IIIC.3c, Fig 3]). 
 
The expected outcomes of Aims 1 and 2 will contribute to an integration of our understanding of neural circuitry 
governing adolescent decision-making and of the effectiveness of an intervention shown to reduce HIV-related 
risky behaviors among AA youth.37-39 These outcomes will positively impact efforts to improve interventions that 
target high-risk behavior in youth from other ethnic/racial groups by linking biological mechanisms, psychological 
processes, social-contextual factors and behavioral outcomes associated with these interventions.21,37,40-44 

III. RESEARCH STRATEGY 
III A. SIGNIFICANCE  
 
IIIA.1. Individual Differences in Adolescent Risk-taking Behavior: Despite evidence of an overall increase 
in risky behaviors in adolescence compared to childhood, it is important to recognize the substantial individual 
differences. Evidence from longitudinal studies suggests that a significant amount of the problematic behavior 
observed in adolescents is clustered in a small proportion of youth.45-48 It is likely that underlying differences in 
brain function play a role in the dramatic differences in decision-making in these two sub-groups. Thus, we need 
a better understanding of how individual differences in brain function that underlie the more problematic risk-
taking behaviors can be used to address numerous ethnic/racial disparities that continue to plague our society. 
 
IIIA.2. Neural Substrates of Risk-taking Behavior during Adolescence: Some developmental neuroscientists 
postulate that risky behavior during adolescence is the product of the interaction between changes in two distinct 
neurobiological systems: a reward system, localized in the limbic and paralimbic areas of the brain including the 
amygdala, ventral striatum, medial prefrontal cortex (mPFC) and orbitofrontal cortex (OFC); and a cognitive-
control system, consisting mainly of lateral prefrontal and parietal cortices (including dorsolateral prefrontal 
cortex [DLPFC]) and their connections to the anterior cingulate cortex (ACC).17,24 According to this model, there 
is a dramatic increase in dopaminergic activity within the reward system around puberty, which increases reward-
seeking behavior.18,49,50 This increase in reward-seeking precedes the maturation of the cognitive-control system. 
Connections between cognitive-control and reward systems gradually unfold during adolescence and lead to 
better self-regulation and impulse control.19,24,51-57 The temporal disassociation between the maturation of reward 
and cognitive-control systems creates a period of heightened vulnerability to risky behaviors.17-24  
 
IIIA.3. Efficacy of PAAS in Reducing HIV-Risk Behaviors in Youth: PAAS, recently tested in TN, and its 
earlier iterations, the Strong African American Families (SAAF) and SAAF-T (teens) programs, tested in GA, 
were effective in delaying/preventing a cluster of behaviors associated with HIV/AIDS, including substance use 
disorders (SUD), risky sexual behaviors (early onset of sexual activity, unprotected sex) and conduct problems 
through induced positive changes in executive and emotional functioning in AA youth (IIIC.3b; Fig 2).25-36  
 
[IIIA.4. Scientific Premise: Despite significant advances in understanding the developmental changes in neural 
circuitry associated with risky behavior during adolescence, little is known regarding individual differences in the 
functional connectivity between reward-seeking and cognitive-control systems and their maturational course —
factors which may heighten or lower vulnerability for engaging in behaviors that place youth at risk for HIV/AIDS 
and SUD. Similarly, while several prevention programs aimed at curbing high-risk behaviors have been tested, 
they typically did not seek to ameliorate malleable mechanisms, raising concerns about specificity and dosage 
of the interventions that have shown efficacy.41 The development of interventions to avert/delay risky behaviors 
has progressed independently of the neuroscience on neural circuitry governing adolescent decision-making. 
Practicing behavioral/cognitive skills induces structural and functional brain changes,43,58-62 particularly during a 
time of high neuroplasticity.63-65 The proposed study will advance the benefits of PAAS and other evidence-based 
programs by improving our understanding of the ways in which neurobiological mechanisms affect intervention-
induced psychological and behavioral changes, and subsequently influencing the modification of programs to be 
maximally effective and efficient in preventing SUD and HIV-risk behaviors in youth.37,40,43,44] 
 
III B. INNOVATION 
                                                                                          
The combination of fMRI technology and a theoretically-driven, empirically-tested program that is designed to 
foster skills/capacities to avert/delay HIV-risk behaviors, offers promise for identifying the mechanisms through 
which the intervention affects self-regulatory and decision-making skills to yield positive behavioral outcomes. 
The findings have the potential of advancing ways to translate models of personalized medicine to behavioral 
preventive interventions (e.g. a behavioral vaccine).66,67 For example, if the study shows that a subgroup of youth 
who have preferential recruitment of reward-seeking brain regions/circuits relative to cognitive-control structures 
when processing probabilistic rewards don’t show significant improvement in response to PAAS, the intervention 
can be modified/supplemented to take advantage of their reward-seeking tendency by providing incentives, or 
booster sessions, for engaging the cognitive-control functions to shift the functional equilibrium between these 
two circuits, as has been shown previously,44,68-70 and thereby accelerate the maturation of self-regulatory skills. 
 
Although neuroimaging is expensive and may not have practical utility in large-scale prevention and intervention 
programs at the present time, given the extant knowledge on developmental neuroscience of risky behaviors 
17,19,21,24 and the modest effects of current interventions,41 we believe that it is time to incorporate this technology 
into prevention science to better understand ways to target malleable neural mechanisms to explore methods to 
advance the implementation of successful evidence-based interventions,37,40,43,44 and offer insights on ways to 

enhance cultural-tailoring strategies often undertaken to improve acceptability and responsiveness to preventive 
interventions. These methods may include, for example, the development of biopsychosocial profiles which can 
be used efficiently to focus intervention efforts. In addition, recent technological advances such as mobile MRI 
scanners and real-time fMRI (rtfMRI) will likely reduce the cost and make this technology more practical, as has 
been shown for genetic testing. Investigators have begun to use fMRI or rtfMRI to examine the neural basis of 
therapeutic techniques to reduce drug cravings as well as cognitive-behavior therapy (CBT), cognitive training, 
mindfulness therapy and neurofeedback for various psychiatric disorders.43,44,71-77 This work informs current and 
future studies that seek to integrate neuroimaging and prevention science to address health disparities, including 
HIV/AIDS and SUD. Another promising avenue is to use neurobiological information to better understand why 
some youth don’t respond to preventive interventions so that the programs can be modified and/or personalized, 
at least at the subgroup level (e.g. reward/sensation seekers, deficits in inhibitory control). 
 
III C. APPROACH 
 
IIIC.1 Overview: The conceptual model 
guiding our study is presented in Fig 1. 
Aim 1 will assess the effects of PAAS on 
the magnitude of Phase 2 functional 
connectivity between reward-drive and 
cognitive-control neural circuitry at rest 
and during a decision-making task after 
controlling for pre-intervention (Phase 
1) levels. Aim 2 will evaluate if Phase 2 
post-treatment neural circuitry changes 
mediate PAAS’ effects on psychological 
processes (i.e. cognitive and emotional 
self-regulation) at the 3-month follow-up 
(Phase 3) after controlling for Phase 1 
values. Our overarching hypothesis is 
that the functional connectivity between 
reward and cognitive-control networks 
correlates with self-regulation, and that PAAS increases the magnitude of functional connectivity between these 
neural networks. In addition to these hypotheses, we will identify characteristics (demographic, neurobiological, 
psychological, behavioral and social-contextual factors) that moderate PAAS’ effects on the relationship between 
functional brain changes and cognitive-emotional self-regulation in exploratory analyses (IIIC.3b; Fig 2).29,32,34-36 
 
A total of 128 AA volunteers (ages 11-13 years, 50% male and female) and 128 parents (primary caretakers) 
will be recruited. Youth and parents will complete assessments on intervention targets (i.e. parenting processes, 
youth psychological protective factors and cognitions/behaviors associated with HIV and SUD vulnerability). fMRI 
scans will be acquired in the youth to assess functional connectivity between reward-drive and cognitive-control 
neural circuitry during rest and while performing a decision-making task involving probabilistic monetary rewards. 
A second task that assesses general impulsivity/motor response inhibition (in contrast to reward-drive) will be 
included as a supplementary measure. Following assessments, the families will be randomized to either PAAS 
or a wait-list control condition for 6 weeks. fMRI scans will be repeated after 6 weeks to measure functional brain 
changes from pre-to-post intervention. They will repeat the intervention-targeted assessments 3 months later, 
after which the wait-list controls will be offered PAAS as a benefit for participation in the study (IIIC.4f.1; Fig 6). 
 
IIIC.2. Qualifications of the Research Team: The multi-disciplinary team has the required expertise to achieve 
the stated specific aims and to translate the knowledge gained into new interventions that will benefit vulnerable 
youth. Dr. Rao has expertise in developmental psychopathology of addictive and mood disorders in adolescents, 
neurobiology (including ethnic influences), translational intervention studies and longitudinal study designs.78-88 
She has led several successful research projects on translational neuroscience. Dr. Benningfield’s research is 
focused on the neural substrates of impulsivity/risk-taking as they relate to risk for developing SUD in youth.89-97 
Dr. Murry brings expertise in family systems and socio-cultural processes, and has two decades of experience 
with AA youth and their families employing longitudinal designs.47,98-106 She played a key role in the development, 
design, implementation and testing of PAAS and SAAF (an earlier iteration) programs.25,27-36,103-110 Dr. Rogers’ 
background is in medical physics and neuroimaging. He has expertise in neural functional connectivity, including 
both resting-state and task-based connectivity, in various psychiatric disorders across the life span.111-122 Dr. 
Green has expertise in behavioral/mental health, applied statistics and multivariate statistical models.123-133 
The graph shows measures at different phases to achieve the study aims. 
Rectangles represent observed variables for the primary variables (circles). 

Dr. Brown has expertise in prevention implementation methodology for risky behaviors in youth including SUD 
and HIV-risk.134-147 Dr. Ernst has expertise in the neural substrates of adolescent risk-taking behavior.52,58,148-160 
She has developed and tested many tasks for fMRI study designs, including the task in the proposed study. 
 
[Dr. Rao has well-established connections with the Vanderbilt team. She has advised and collaborated with Dr. 
Benningfield since 2010. Both collaborated with Dr. Rogers, and together with Dr. Murry, conducted a pilot study 
focusing on neural substrates of risky behavior and SAAF. Also, Dr. Rao has a joint appointment at Vanderbilt 
since 2011 and has access to all the resources required by the study. Drs. Ernst and Rao have a long history of 
collaboration (25 years). Drs. Brown and Murry collaborated for many years. Given the history of collaboration, 
we are confident that the team will work cohesively in the execution of the project and the resulting publications.] 
 
IIIC.3. Preliminary Studies 
 
IIIC.3a. Overview: We will describe data on the effects of PAAS on youth self-regulation and HIV-risk behaviors 
followed by data that support the proposed methods for studying the underlying neurobiological mechanisms. 
 
IIIC.3b. PAAS Effects on Parenting Practices, Youth Self-Regulation and HIV-Risk Behavior: In a 6-week 
RCT of PAAS in 412 AA 7th grade students in Middle/West TN, Dr. Murry compared the efficacy of three delivery 
modalities in delaying/deterring risky behaviors among AA youth: PAAS small-group (n = 137), PAAS-technology 
(n = 138) and risk-prevention information home-mailing (n = 137). PAAS involves weekly separate but concurrent 
sessions with parents and youth followed by parent-child sessions. Both PAAS modalities were more effective 
in improving regulated, communicative parenting, which was associated with increased cognitive and emotional 
regulation in youth (Fig 2). These factors impacted youth intentions to engage in HIV-risk behaviors (in opposite 
direction) post-treatment as well as 
in dissuading engagement in risky 
behaviors (alcohol/drug use, sexual 
behaviors) at 18-month follow-up. 
PAAS-tech showed more robust 
effects compared to PASS small-
group (p = .05). PAAS had a direct 
effect in preventing risky behaviors 
at 18 months. The results replicate 
the SAAF findings (earlier iteration 
of the small-group), with sustained 
effects on alcohol and drug use and 
sexual risk behavior at least for up 
to 65 months,25-36 thereby reducing  
HIV risk in highly vulnerable youth. 
 
Parenting practices that include warmth and consistent, inductive discipline protect youth against risky behaviors. 
161,162 Such parenting practices may have an even greater effect in AAs than NHWs.163-167 These data suggest 
that positive parenting improves self-regulation in the offspring around pubertal transition which, in turn, protects 
these youth against behaviors associated with HIV/AIDS and SUD risk as they progress through middle and late 
adolescence. The current proposal plans to explore the neural underpinnings of changes in youth self-regulatory 
processes in response to PAAS with particular interest in youth’s neural responses to risk (reward) opportunities.  
 
IIIC.3c. Neural Responses to Risky Choices and Relation to Treatment Effects: Rao et al. administered the 
Wheel of Fortune (WOF), a two-choice decision-making task involving probabilistic monetary outcomes, to 19 
adolescents who then received CBT for smoking cessation. Fig 3 shows blood-oxygen-level-dependent (BOLD) 
signal changes during high-risk choice compared to the low-
risk choice in responders (red bars) and non-responders 
(blue bars) to the CBT program. Responders had greater 
changes in the PFC (ACC: p = .05, OFC: p = .03, mPFC: p 
= .03, and DLPFC: p = .04) compared to non-responders. 
Utilizing this task, Ernst et al. reported neural changes in 
response to the CBT in adult depressed patients.58 These 
findings suggest that the WOF is useful for probing decision-
making/reward processes (see the next paragraph), and that 
these neural circuits may be sensitive to the acute effects of 
psychosocial interventions, such as CBT and PAAS. 
Fig 3. Baseline Neural Responses in Responders and Non-Responders to Treatment
Regulated, 
communicative 
parenting 
(post-Tx)
involved, vigilant 
parenting
communication 
about sex
articulated norms 
expectations of  
risky behaviors
resistance 
efficacy
future 
orientation
self-control
.36
.38**
Youth 
cognitive 
regulation 
(post-Tx)
Youth 
emotional 
regulation 
(post-Tx)
self-esteem
depression
self-image
Risk behavior 
Intentions
(post-Tx)
impulsivity
-.63**
-.59
PAAS 
Technology
SEM model (N = 249): 436.90; p <.01; CFI = .87; RMSEA = .05 . *p < .05; **p < .01 (one-tailed).
<--- Stability of the construct. All measures were controlled for pre-test scores.
PAAS Small 
Group 
.25*
-.22*
Fig 2. PAAS Effects on Parenting and Youth Processes and Risky Behavior
Risk behavior 
initiation
(18-mth f/u)
-.15*
-.50**
.26**

We administered the WOF to 33 normal adolescent volunteers.160 High-risk choice was associated with greater 
activation in the striatum compared to the low-risk choice (p = .05). A higher frequency of the high-risk choice 
was associated with lower activation in the PFC (ACC: r = -.60, p = .0008; OFC: r = -.58, p = .001; and mPFC: r 
= -.40, p = .04), as well as reward-seeking traits and risky behaviors in real life.88 Ernst et al. used this task with 
a risk-avoidance component (passing the bet; IIC.4f.2b.3, Fig 7) in both adolescents and adults; similar to the 
above findings, neural responses to the task were associated with risky vs. risk-averse behaviors in real life.158,168 
 
IIIC.3d. Neural Responses to Behavioral Inhibition Task in High-Risk Adolescents: Rao et al. administered 
the Go/No-Go Task169,170 to 10 normal controls and 11 healthy adolescents at familial risk for SUD and studied 
their neural responses. Controls had greater activation in ACC (p = .0002), inferior frontal gyrus (IFG; p = .0002) 
and superior frontal gyrus (SFG; p = .0004) than the high-risk group during 
successful No-Go (inhibition) trials (Fig 4A). In contrast, the high-risk youth 
had significantly greater activation in the parahippocampal region (p = 
.0002) (Fig 4B). These data suggest that the task engaged brain regions 
involved in cognitive control more actively in the controls, whereas an area 
involved in emotional processes was engaged more actively in high-risk 
youth, suggesting poorer inhibitory control over negative emotions.171-173 
 
IIIC.3e. Association between Risk-taking Traits and Functional Connectivity at Rest: Benningfield et al. 
examined the relationship of impulsivity, or sensation-seeking, 
with resting-state functional connectivity (RSFC) between ventral 
striatum and other brain regions in 25 children. Impulsivity (Fig 5; 
left panel) was negatively correlated with ventral striatum’s RSFC 
with the ACC, dorsal striatum as well as the occipital cortex (blue). 
Sensation-seeking (right panel) was positively correlated with 
ventral striatum’s RSFC with the precuneus (red; involved in self-
referential processes) but negatively correlated with OFC (blue). 
 
IIIC.3f. Integration of Pilot Data: Together, these pilot data demonstrate two aspects relevant to the proposed 
study: (1) feasibility: we can recruit and conduct prevention trials and functional neuroimaging studies in youth; 
and (2) support for the proposed hypotheses: PAAS is effective in improving cognitive-emotional self-regulation 
around puberty which serves as a deterring agent for risky behaviors as AA youth progress through adolescence. 
The reward-processing (decision-making) task is ecologically valid for assessing risky behaviors in youth, and 
the neural circuits tapped by this task have a predictive power in determining differential clinical response and 
sensitivity to the effects of psychosocial interventions. The proposed study will pair these validated paradigms to 
examine neural changes in response to PAAS and will examine whether the neural changes (a balance between 
reward-drive and cognitive-control/self-regulation circuitry) mediate the intervention’s effects on cognitive and 
emotional self-regulatory processes. Risky behaviors in AA youth are a source of considerable morbidity and 
mortality. A better understanding of the moderating and mediating factors associated with the intervention effects 
to reduce HIV-risk behaviors will facilitate the development of personalized programs for high-risk youth. 
 
IIIC.4. Research Design and Methods 
 
IIIC.4a. Program Sites and Participants: Participants will be recruited from pediatric primary care programs in 
Vanderbilt University Medical Center (VUMC), behavioral and mental health programs at Centerstone and VUMC 
School-based Mental Health Program of Metropolitan Nashville Public Schools. Over 1,000 adolescent patients 
(41% AA) were seen in the General Pediatrics Division at VUMC in the previous 12 months (see letter from Dr. 
Patterson). In the Adolescent and Young Adult Clinic at VUMC, more than 2,000 adolescent patients (40% AA) 
visited the clinic in the previous 12 months (letter from Dr. Walker). Most of the patients in these primary care 
clinics are seen for routine physical examinations or minor acute illnesses, such as the flu, and are free from 
chronic medical and psychiatric conditions. Centerstone offered behavioral and mental health services to 10,000 
youth (37% AA) in the past year (letter from Dr. Rhea). The clientele at VUMC and Centerstone is representative 
of the local census, and the clinics offer care at subsidized rates to low-income families. The VUMC School-
based Mental Health Program operates in nine middle schools; 88% of students are AA (letter from Mr. Majors). 
Dr. Benningfield is the Medical Director of this program. Drs. Benningfield and Rao have secondary appointments 
in the Department of Pediatrics at VUMC. Dr. Rao works closely with Drs. Patterson, Rhea and Walker, and she 
has previously utilized these resources for her research (R01 MH068391, U54 MD007593 and G12 MD007586). 
 
Based on these substantial resources and our previous successes in recruiting for such studies, recruiting 128 
youth is very feasible. We will recruit participants from the full range of socioeconomic status (SES). As the state 
capital with employment opportunities in government and home to three Historically-Black Colleges/Universities 
Fig 5. RSFC and Impulsivity (L) or Sensation-seeking (R)
OFC
Precuneus
Occipital Cortex
ACC
Dorsal Striatum
Figure 4. Activation During Inhibition
SFG
IFG
ACC
A. Controls
Parahippocampal
Region
B. High-Risk Youth

(HBCUs; namely, Meharry Medical College, Fisk University and Tennessee State University), Nashville offers 
the opportunity to recruit racial-minority groups representing the full range of SES. In the 2010 Census, some 
racial-minority neighborhoods comprising of AAs reported an average income of $75,000 or higher, while other 
neighborhoods having a large number of the same racial-minority residents consisted of low-income families.174 
 
IIIC.4b. Inclusion Criteria: Participants will be between 11 years 0 months and 13 years 11 months of age and 
of AA racial status (self-reported). They will speak and read English. The youth and a parent/legal guardian must 
agree to participate in the 6-week PAAS program and complete the assessments. 
 
IIIC.4c. Exclusion Criteria: Youth with major medical problems (e.g. neurological disorders) or on medications 
that affect the central nervous system will be excluded. Persons with behavioral/emotional problems at a clinical 
level (by parent and/or youth report) will be excluded because psychiatric disorders can affect neural responses. 
Pregnant females, or those of suspected of being pregnant, will be excluded. Individuals with color-blindness, 
claustrophobia or metallic implants will be excluded. Persons using alcohol and/or drugs in the three days prior 
to neuroimaging studies (based on history and/or urine drug screen) will be excluded. 
 
IIIC.4d. Rationale for the Restricted Age Range: We selected 11-13 year-olds because most youth at this 
developmental stage do not abuse alcohol or drugs and are not sexually active, but they are typically beginning 
to experience social pressures and pubertal changes that increase their vulnerability to these behaviors; in our 
previous samples of AAs, about 20% had ever used alcohol/drugs and 10% had sexual debut. Second, we have 
found the PAAS intervention to be more salient for youth at this age (IIIC.3b).25,28,32,34 Third, sensation-seeking 
traits and the brain regions that respond to rewards (specifically the striatum) are more active around this period, 
with heightened vulnerability to risky behaviors in the context of an immature cognitive-control system.23,57,175,176  
 
IIIC.4e. Rationale for Excluding Other Ethnic/Racial Groups: AA youth are selected because of the high rates 
of risky behaviors and associated negative consequences (e.g. HIV/AIDS, violence) compared to other groups 
(see II). PAAS was designed specifically for AAs. Although it contains elements that are generalizable, some 
components of the program are specific to AA (e.g. adaptive racial socialization, and managing stress associated 
with racial discrimination). If neurobiological mediators of this program are identified, future investigations will be 
able to test these effects in other ethnic/racial groups, by modifying ethnic-specific elements of the intervention. 
 
IIIC.4f. Methods 
IIIC.4f.1. Overview: A description of the 
protocol/procedures is provided (IIIC.4f.2 -
IIIC.4f.6; Fig 6,Table 1). Eleven-to-thirteen 
year-old AA youth and their parents will 
complete information on demographic and 
health status as part of their clinic visits. 
Families selected randomly from the 
eligible pool will be invited to participate, 
while at the clinic or later via mail (IVA.2a, 
Human Subjects). Research is an integral 
part of these participating clinical programs 
and processes are in place for a smooth 
transition from clinical care to research that 
follows the Institutional Review Board and 
Health Insurance Portability and Accountability Act (HIPAA) guidelines. When contact is made, they will be 
provided information about the study, with an overview of the time commitment and assessments. Interested 
families will be screened further to determine eligibility by obtaining medical, psychiatric and treatment history.  
 
Phase 1 (Pre-intervention Assessments): Information on psychiatric symptoms, psychosocial factors and risky 
behaviors will be completed. Those who meet eligibility criteria will have a blood draw for gonadal hormones and 
receive training on the fMRI tasks and have a mock scan. A urine sample will be obtained to test for alcohol/drug 
use and pregnancy. They then will complete baseline fMRI studies. Phase 2 (Intervention Phase): The families 
will be randomized using procedures described in IIIC.4f.5 to PAAS or wait-list condition (1:1 ratio) for the 6-
week intervention period. PAAS consists of 6 web- or DVD-based weekly sessions, 90 minutes per session; the 
first half includes individual, concurrent curricula for caregivers and youth, and the second half is a family-joint 
session designed for practicing the skills learned in the individual sessions. At the end of the 6-week intervention 
period, the urine drug test and fMRI studies will be repeated in both groups. Phase 3 (Follow-up Phase): Three 
months after the intervention phase, both psychosocial and risky-behavior assessments will be repeated. The 
wait-list group then will be offered the PAAS intervention as a benefit for participation in the study. 
Phase 1
Baseline 
Measures
PAAS
Wait-list
6-week
Post-test
6-week
Post-test
3-month 
Follow-up
3-month
Follow-up
PAAS
Informed consent
Med/psych screen
Risky behaviors
Psychosocial scales
Urine drug test
fMRI scans
6 weekly sessions
Parent session (45 min)
Youth session (45 min)
Family session (45 min)
Risky behaviors
Psychosocial scales
Urine drug test
fMRI scans
Urine drug test
fMRI scans
Risky behaviors
Psychosocial scales
Fig  6. Protocol Outline
Phase 2
Phase 3
Informed consent
Med/psych screen
Risky behaviors
Psychosocial scales
Urine drug test
fMRI scans
6 weekly sessions
Parent session (45 min)
Youth session (45 min)
Family session (45 min)
Treatment provided for 
participants’ benefit 

IIIC.4f.2. Measures for Primary Hypothesis Testing 
 
IIIC.4f.2a. Youth Psychological Protective Processes: This index consists of seven measures (Appendix 1). 
 
IIIC.4f.2a.1. Resistance Efficacy: Participants respond to three versions of a scenario to report the actions they 
believe they would take if presented with the chance to drink alcohol, smoke cigarettes or use marijuana.30,177 
 
IIIC.4f.2a.2. Self-control: The 25-item Self-Control Inventory includes items of good self-control (e.g. delay of 
gratification, planning, problem-solving, dependability, and soothability) and poor self-control (e.g. impulsivity, 
immediate gratification, distractibility, and low frustration tolerance).178 
 
IIIC.4f.2a.3. Future Orientation: The Time Perspective Inventory consists of the future, present/hedonistic, and 
present/fatalistic subscales.179 The future subscale reflects formulation of goals and plans to achieve them. 
 
IIIC.4f.2a.4. Emotion Regulation: On 
the Coping Processes Inventory,180 
respondents are provided definitions 
of various coping behaviors and asked 
how often they use each approach for 
dealing with problems concerning 
school, parents, health and sadness. 
 
IIIC.4f.2a.5. Self-image: Body Image 
subscale from the Sexual Self-concept 
Inventory consists of eight items that 
index the youth’s evaluation of his/her 
own physical appearance.181 
 
IIIC.4f.2a.6. Self-esteem: Rosenberg 
Self-esteem Scale includes a 5-item 
general positive self-esteem subscale 
and a 5-item general negative self-
esteem subscale.182 
 
IIIC.4f.2a.7. 
Impulsivity: 
Eysenck 
Impulsiveness Scale includes 13 items 
that assess personality traits related to 
impulsivity and venturesomeness.183 
 
IIIC.4f.2b. Neuroimaging Protocol 
 
IIIC.4f.2b.1. Mock MRI Scan: At the baseline assessment, participants will be provided take-home information 
that will help to prepare them for MRI scanning. They will complete a mock scan to practice staying still in the 
scanner, get used to the noise and practice the fMRI tasks. This also will allow us to assess for claustrophobia. 
 
IIIC.4f.2b.2. Data Acquisition: Neuroimaging data will be acquired on a 3 Tesla Philips Achieva MRI scanner 
with a 32-channel head coil located at Vanderbilt University Institute of Imaging Science (VUIIS; letter from Dr. 
Smith). Padding behind the head and neck will be used to increase comfort, and the forehead will be strapped 
to prevent head motion. A 20-25 minute break will be provided between resting and task-based scans to reduce 
motion artifacts and impaired performance on the task. To correct for the confounding effect of overlapping 
physiological signals, pulse and respiration will be measured with MRI-compatible non-invasive equipment. In 
our multi-modal imaging protocols lasting 60-90 minutes, we had reliable data in 93%-97% of the samples. 
 
A high-resolution T1-weighted scan (1 mm isotropic resolution; 6.5 min) will provide anatomical information to 
serve as a basis for cortical parcellation and a template for registering the functional imaging data. This will be 
followed by a B0 mapping sequence (1 min) to correct image distortions due to magnetic field errors. Next, 
resting-state (rs-fMRI) scans will be obtained to measure intrinsic functional connectivity. T2* weighted BOLD 
images with a voxel size of 3mm isotropic and a volume acquisition time of 2 sec will be acquired using a 2D 
gradient echoplanar imaging (EPI) sequence over 12 min (360 image volumes) to obtain more reliable data. 
Participants will be instructed to relax with their eyes open and to look at a fixation cross. They will be monitored 
with eye-tracking equipment to ensure that they do not fall asleep. For task-based fMRI scans (45 min), stimulus 
presentation will occur with E-Prime, with the images projected onto an overhead LCD panel. A 5-button box will 
be used for recording behavioral data. Functional images will be acquired with a gradient-echo, EPI sequence: 
34 oblique axial slices (3 mm thick, 0 mm gap), oriented to the AC-PC line and encompassing the entire cerebrum 
Duration
Screen
Phase 3
Phase Window
Parent
Youth
Minutes
3 Mth
Visit
1
2
3
4
5
6
7
8
9
Intervention Week
Wk 1 Wk 2 Wk 3 Wk 4 Wk 5 Wk 6
Clinic/Telephone Screen
X
X
15-30
X
Informed Consent/Assent
X
X
45-60
X
Demographic Information
X
10-20
X
Psychiatric Diagnstic Screen
X
X
30-60
X
Youth Beh/Emotional Problems
X
X
20-30
X
Reward-drive Traits
X
15-20
X
Risky Behaviors
X
15-20
X
X
Resistance Efficacy
X
10-15
X
X
Self-control
X
15-20
X
X
Future Orientation
X
10-15
X
X
Emotion Regulation
X
5-10
X
X
Self-image
X
5-10
X
X
Self-esteem
X
5-10
X
X
Impulsivity
X
5-10
X
X
Involved-vigilant Parenting
X
X
10-15
X
X
Risk-behavior Communication
X
X
10-15
X
X
Adaptive Racial Socialization
X
X
10-15
X
MRI Safety Screen
X
X
5-10
X
X
Handedness Scale
X
5-10
X
Tanner Stages
X
5-10
X
Serum Gonadal Steriods
X
10-15
X
Urine Alcohol/Drug Test
X
10-15
X
X
Urine Pregnancy Test
X
0
X
X
Task Practice/Mock Scanner
X
60-90
X
Resting and Task-based fMRI
X
90-120
X
X
PAAS Intervention
X
X
90-100
X
X
X
X
X
X
Starts within  14 Days of Screen
Phase 2 - Intervention 
Table 1. Assessment Schedule
Phase 1
 5-10 Days
Informant

and most of the cerebellum (TR/TE = 2000/25 msec, FOV = 24 cm, matrix = 64 × 64). This sequence was 
designed to preserve the signal in the striatum and ventral PFC, areas which are susceptible to signal drop-out. 
An automated higher-order shim procedure will be applied to minimize potential magnetic field inhomogeneities. 
 
IIIC.4f.2b.3. The WOF Task: This task allows for the analysis of both risk-seeking and risk-avoidance (Fig 7).158 
During each trial, the participants will be shown a wheel on which the probabilities of winning and losing points 
are shown as pink and yellow sections, respectively. The number of points that can be won or lost is printed on 
the wheel sections. The words “Bet” and “Pass” are displayed each time, prompting participants to select one or 
the other. For the present study, this “decision phase” is the period of interest. After 3 seconds, the wheel spins 
for a variable, randomly jittered period (1, 3, 5, or 7 sec), and the final result is displayed for 1 sec. If participants 
choose to bet, and the wheel lands on the pink section, 
they win the points indicated. Conversely, if the wheel 
lands on the yellow section they lose the number of points 
shown. These amounts will be added (or subtracted) to a 
running total that is not revealed until the end of the task. 
If participants choose to pass, they still see the outcome 
but no points will be added or deducted. 
 
Win amounts are kept constant while probability and loss amounts are manipulated orthogonally resulting in 6 
different wheels that represent 4 win/loss ratios (100/0, 60/40, 40/60, 0/100) and 2 levels of expected value (EV; 
+12 and −12). Participants are not explicitly told about the uniformity of expected values. The 0/100 wheel (100% 
chance of losing 12 points) and the 100/0 wheel (100% chance of winning 12 points) serve as no-risk control 
conditions and each is presented 8 times. Each 40/60 and 60/40 wheel (EV+ and EV−) is presented 22 times 
across 4 blocks. Behavioral outcomes include risk-seeking (percent of trials betted), risk-avoidance (percent of 
trials passed), and response times across conditions of varying probabilities and expected values. We found no 
practice effects with repeated tasks in at-risk adolescents during longitudinal follow-up (Rao, unpublished data).  
 
IIIC.4f.2b.4. Go/No-Go Task: The task assesses behavioral responses, with the subjects selectively responding 
to target stimuli (“X” ~.80 probability) and inhibiting responses to infrequent non-target stimuli (“K”~.20 probability; 
IIIC.3d).169,170 The task consists of two runs, with 245 trials per run (7 min) and a random inter-stimulus interval. 
 
IIIC.4f.2b.5 Quality Assurance (QA): Weekly QA data will be collected using the American College of Radiology 
Phantom Test Guidance Manual. The Functional Biomedical Informatics Research Network QA protocol will be 
used so that changes in image characteristics over the study period can be monitored and corrected. 
 
IIIC.4f.2b.6. fMRI Data Processing and Analysis: Each participant's functional images (rest and task) will be 
corrected for head motion and co-registered to a common atlas space, then smoothed with a 6 mm kernel. After 
initial pre-processing, the time series at each voxel will have white matter, cerebrospinal fluid and motion-related 
signals removed via regression,184,185 and will be low-pass filtered at 0.1 Hz to retain low frequencies relevant for 
connectivity estimation. Head motion can still be a significant confound in fMRI connectivity studies, particularly 
in children.186 Hence, we will identify volumes that show large displacement (>0.5mm) or large change in global 
signal (>0.5%) relative to the preceding volume, and remove them prior to connectivity analysis.187 [The resulting 
3 metrics can be summarized to a single number and then compared between groups or used as covariates.] 
 
For the task-based data, event-related neural responses will be modeled with separate canonical hemodynamic 
response functions for each of the conditions (e.g. wheel types). For instance, BOLD responses to Risk vs. No-
Risk trials will be compared, within/across probabilities and expected values. Group-level analysis will compare 
the trials during which the participants passed the trials (risk-avoidance) to trials where they bet. For the Go/No-
Go task, successful Go trials will be compared to both successful and unsuccessful No-Go (inhibition) trials. 
 
Resting-state functional connectivity will be calculated as the Z-transformed correlation coefficient between pre-
processed time series, using the CONN toolbox.188 Connectivity between seed region time series and each gray 
matter voxel will be calculated to create connectivity maps for each seed region. Task-based connectivity will be 
calculated using the gPPI toolbox.189-191 For both types of connectivity, we will use seed-region approaches with 
the following seeds: inferior and superior parts of the ventral striatum,192,193 ventral and dorsal parts of the ACC,194 
mPFC, OFC and DLPFC. Seed regions will be defined using probabilistic atlases derived from structural tracings. 
Connectivity maps for each seed region will be compared at each voxel among the groups, using a restricted 
maximum likelihood approach that accounts for heteroskedasticity (SPM8 software implementation).195 The 
resulting T maps will be thresholded at p<0.05, correcting for the multiple voxel comparisons based on spatial 
smoothness using random field theory,196-198 to identify brain regions where the groups differ in connectivity. 
Appraisal
Execution
Anticipation 
Feedback
Fig 7. WOF Task

In addition to hypothesis-driven seed-based functional connectivity of fronto-striatal circuits, the Wager/Lindquist 
Multilevel Mediation and Moderation (M3) Toolbox199 will be used in conjunction with SPM195 and MATLAB200 to 
conduct whole-brain mediation analysis.201 This analysis allows voxels to be identified that make the strongest 
case for mediation or search for maps of each effect in the mediation. 
 
IIIC.4f.3. Variables to Determine Eligibility or Supplementary/Exploratory Variables in Hypothesis Testing 
 
IIIC.4f.3a. Demographic Information: Caregivers will report their education level, yearly income, per capita 
income, employment status, duration of unemployment in the past 2 years, hours worked per week, numbers 
and ages of children and adults in the household, and relations of all household members to one another. 
 
IIIC.4f.3b. Psychiatric Diagnostic Screen: The Kiddie Schedule for Affective Disorders and Schizophrenia – 
Present and Lifetime Version (K-SADS-PL), a semi-structured interview, will be administered separately to the 
parent and youth to screen for present and lifetime history of psychiatric illness.202,203 
 
IIIC.4f.3b. Behavioral/Emotional Problems: The Child Behavior Check-List (CBCL) is an instrument designed 
for parents to rate the child’s problem behaviors (120 items) as well as competencies (20 items) in the past 6 
months.204 Youth rate the same items using the Youth Self-Report (YSR).204 
 
IIIC.4f.3c. Reward-drive Traits: The Behavioral Inhibition System and the Behavioral Activation System (BIS/ 
BAS) is a 24-item self-report scale that assesses motivational systems.205 The BIS is sensitive to the signals of 
punishment and non-reward, whereas the BAS taps into appetitive motivation and is sensitive to the signals of 
reward. The BAS correlated positively with the high-risk choice on the WOF task.88 We will assess whether BAS 
score modulates neural and cognitive-emotional self-regulation changes in response to the PAAS intervention. 
 
IIIC.4f.3d. Risky Behaviors: This construct consists of alcohol/drug use and sexual behavior, each with three 
separate constructs (willingness, intention, and actual behavior). Intention and willingness will be measured in 
addition to behaviors as the participants are young and will have limited opportunity to engage in risky behaviors 
within the short follow-up period despite manifesting some risky behaviors. Prior longitudinal research has shown 
that intention and willingness are good predictors of alcohol/drug use and sexual risk behavior (IIIC.3b).34,206-209 
 
Behavior Willingness is assessed by asking participants to imagine themselves in different situations and then 
to think about how they might respond if they were in that situation (Appendix 1). Opportunities for access to 
risky situations and prototype peers also are assessed. It includes 32 items for alcohol/drug use and 23 items 
for sexual behavior. Behavior Intention is measured with four items on alcohol/drug use, and four items on sexual 
behavior. Behavior Engagement consists of three items for alcohol/drug use (two concerning lifetime behavior 
and one concerning recent use) and three items to assess age of onset of sexual activity and unprotected sex. 
 
IIIC.4f.3e. Regulated, Communicative Parenting: This index consists of three measures (Appendix 1). 
 
IIIC.4f.3e.1. Involved-vigilant Parenting: The scale is composed of 19 items, rated on a 5-point Likert scale, 
that assesses parental involvement, inductive discipline, consistent discipline and monitoring.30,210,211 
 
IIIC.4f.3e.2. Communication about Risky Behaviors: Articulated norms and expectations regarding risky 
behaviors consists of two scales. The first is a 6-item scale that assesses parents’ communication of expectations 
regarding the use of alcohol and other substances on a 5-point Likert scale.212 Another scale modified for 
communication patterns about sexual issues assesses whether parents ever talked to the child about topics such 
as reproduction/having babies, menstruation, sexually transmitted diseases and HIV/AIDS.213 
 
IIIC.4f.3e.3. Adaptive Racial Socialization: The Racial Socialization Scale includes 15 items rated on a 3-point 
Likert scale214 to assess whether parents transmit explicit messages to youth about strong work orientation, race 
relations, and coping with racism and discrimination in the workplace, educational institutions and community.215 
 
IIIC.4f.3f. MRI Safety Screen: Screening for metallic objects/devices and claustrophobic symptoms will be done 
with a standardized instrument utilized by VUIIS. 
 
IIIC.4f.3g. Handedness: Handedness will be verified using the modified version of the Edinburgh Handedness 
Test, with right-handedness defined by a score >20 out of possible 28.216 
 
IIIC.4f.3h. Physical/Laboratory Measures: Participants will rate their pubertal status based on the illustrations 
of stages of pubertal development.217,218 Blood will be drawn for serum gonadal steroids (estradiol, progesterone 
and testosterone).219 In exploratory analyses, we will examine the effect of pubertal stage and gonadal hormones 
on neural responses, as there likely will be a greater variation in pubertal maturation at this age range (11-13 
years). A urine test will provide qualitative and quantitative information on substance use (amphetamines, benzo-
diazepines, cannabinoids, cocaine, ethanol, opiates, oxycodone, and phencyclidine). Sexually active females 
will have a urine test to rule out pregnancy for the MRI studies. 

IIIC.4f.4. Data Collection Protocol: Trained staff members will conduct the assessments. They will have a 
minimum of 40 hours of training and supervised practice on how to obtain informed assent/consent, build rapport, 
interview, and assist participants on the computer-based data collection system and PAAS-technology program. 
Data collection will occur at clinical sites, the PI’s lab, participants’ homes or community settings, based on the 
participants’ choices. Families will use the computer-based data system to complete assessments using audio 
computer-assisted self-interview (ACASI) procedures, which are integrated into the remote data management 
system (IIIC.7a). Each computer will have headphones, the computer screen will display one question at a time, 
and all information that appears on-screen also will be read to participants by the computer. Compared with face-
to-face interviews and self-administered surveys, ACASI offers privacy and confidentiality that, in turn, elicits less 
social desirability bias,220 and more accurate reports on questions about sensitive issues or negative behaviors. 
The use of ACASI in our previous studies reduced missing data, increased consistency and eliminated literacy 
requirements.221 Psychiatric diagnostic interviews will be conducted by trained research assistants. Dr. Rao was 
involved in the development and testing of the K-SADS-PL. Prior to initiating the study, reliability will be assessed 
(requiring ≥85% agreement). In order to avoid reliability drift over time, reliability checks will be made annually. 
All interviews will be audio-taped, and a random selection of 15% of the interviews will be co-rated by Dr. Rao. 
 
IIIC.4f.5. Randomization: A 2 (gender) x 2 (Tanner stage) x 2 (intervention) randomized, matched-pairs, block-
design will be used to stratify participants based on gender and Tanner stage (I-II, III-V) comprising 4 blocks for 
128 participants. Within each block, 16 pre-randomized (PAAS or wait-list) pairs based on order of intake will be 
prepared in advance. Randomization will proceed as follows: the first youth to enter the study in each block will 
be assigned to either PAAS or wait-list (based on pre-randomization for that pair) and the second will be assigned 
to the opposite condition. The third youth will be assigned to either PAAS or wait-list (based on pre-randomization 
for that pair) and the fourth will be assigned to the opposite condition. The randomization based on the order of 
entry will continue in this manner until all matched pairs within each of the 4 blocks are filled. 
 
IIIC.4f.6. PAAS Protocol: PAAS is a 6-week, technology-delivered, family-based youth risk intervention program 
(Table 2). PAAS uses a set of six (one per week) DVDs or the web to deliver separate sessions for parents and 
youth, and joint sessions in which they both engage on the same computer to integrate and practice the skills 
they have just learned in their separate sessions. Each weekly session takes about 45 minutes to complete, for 
an average of 90 minutes per participant (45 minutes separate plus 45 minutes joint), and a total of 9 hours for 
the 6-week intervention. Each session includes a review, a virtual discussion, and observing and interacting with 
four parent and four youth Avatars that reflect phenotypes of AAs, with voice-overs by AA parents and youth. 
Videos portraying family interactions and intrapersonal processes are integrated into each session to convey 
key points of the intervention along with interactive activities to promote skill-building and to reinforce learning. 
PAAS also includes a technology tutorial and an introductory session. After receiving detailed instructions face-
to-face, the program will be accessible through a website to participants who prefer using the Internet, or it will 
be downloaded onto laptop computers and delivered to them at their homes or other locations convenient to the 
participants, such as participating clinics or community settings (e.g. churches, community centers). 
 
The PAAS curriculum will likely influence the neural pathways by: 1) providing opportunities for youth to negotiate 
novel situations in a safe environment until they gain better adaptive skills in self-control;222,223 2) fostering 
resistance-efficacy in high-risk situations, and reinforcing positive self-image and encouraging future orientation 
through goal-setting;224-227 and 3) encouraging youth to think about the potential consequences of risky behaviors 
from their parents’ perspective (IIIC.3b; Table 2). Each of these pathways has evidence to suggest that reward-
drive and cognitive-control/self-regulation neural circuits are involved.43,228-236 Parents play an important role in 
protecting youth against risky behaviors by providing warmth as well as consistent, inductive discipline.161,162 
 
Data from the computer/web, which tracks time spent on the computer and the completion of activities, will be 
downloaded to a secure server. Participants also will complete questions pertinent to each intervention session 
to ensure that they grasped the main concepts. The computer-based program was selected for the following 
reasons. Families can experience a computer intervention when it is most convenient for them and at their own 
pace. It can be accessed at home or another convenient site where a computer is available. Computer literacy 
and familiarity, even among those from economically-disadvantaged households, is nearly universal now.237 Our 
studies (IIIC.3b), as well as those of others, that included AA youth and adults have shown that computer-based 
prevention programs can be very engaging and motivating.238,239 The privacy that computer-based interventions 
afford is particularly important in close-knit, ethnic-minority communities, in which participants typically know one 
another outside the intervention context and privacy is a key concern.240,241 
  

Table 2. PAAS Curriculum: Program-Targeted Behaviors 
  
Session 
Parent Component  
Parent-targeted Behavior 
Youth Component 
Youth-targeted Behavior 
Family Component 
Family-targeted Behavior 
1 
Supportive 
parenting 
Importance of supportive 
parenting; normative dev. 
patterns of adolescence; 
parental expectations and 
goals; effective ways to 
support youth goals, dreams 
Future orientation  
Identify and visualize 
goals and dreams 
Positive parent-
child relationship 
Build nurturing, supportive 
relationships; enhance 
parental involvement 
2 
Family rules and 
routines; 
nurturing, involved 
parenting 
 
Understand the values of 
having specific house rules; 
appropriate and effective 
punishment for misbehavior 
Self-discovery 
and autonomy 
Identify positive self-
qualities and capacities; 
clarification of values and 
social norms; association 
between responsibility and 
autonomy and privilege-
granting by parents  
Family values 
Share family rules and 
chores; discuss family values; 
create a family shield of 
values  
3 
Adaptive racial 
socialization; 
encouraging racial 
pride 
Identifying and managing 
racial discrimination; 
preparation for racial bias 
and promoting racial pride 
Dealing with unfair 
situations 
Identifying and clarifying 
reasons for differential 
treatment; active coping 
strategies to manage 
unfair & difficult situations 
Encouraging 
racial pride 
Learn strategies of handling 
difficult situations; identify 
special strengths of African-
American families  
4 
Linking school 
and academic 
performance to 
goals and dreams 
to youths’ future 
orientation 
Understand the importance 
of success in school; learn 
ways to help youth succeed 
in school; learn effective 
ways to be an advocate for 
your child in school settings 
Being cool and 
smart 
Understanding the 
differences between 
passive, aggressive and 
assertive behaviors; 
adaptive responses that 
are smart and cool 
Positive, 
affectionate 
family relations 
Identify each other’s 
stressors; reinforce ways to 
help each other to reach 
family goals and relieve stress 
5 
Protecting against 
dangerous behavior 
Understand risk prevalence, 
overall and for your 
community;  
Importance of being an 
approachable parent 
Peer pressure; 
parents’ concerns 
about peer 
affiliation 
Identify peer pressure; 
compare risk engagers 
from non-risk engagers; 
dealing with temptation; 
prosocial peer affiliation 
Caregivers and 
youth working 
together to protect 
youth from risk 
behaviors 
Develop family plan for 
handling peer pressure and 
temptation; share 
expectations and values 
about risk and friendship  
6 
Parental 
protections that 
reduce high risk 
behaviors 
Learn how to effectively 
monitor youth; understand 
the prevalence of sexual 
activity in teens; establish 
expectations about sex 
Dealing with 
sexual and 
substance use 
temptations 
identifying and avoiding 
dangerous situations; 
connecting temptations, 
dreams, goals, school and 
future orientation 
Our family plan and 
pledge for positive 
youth development 
Share expectations, dreams, 
and hopes; discus/identify 
family strengths; establish a 
family creed of strength, 
growth and competence 
 
IIIC.5. Primary Outcome Criteria: Primary outcome variables include fronto-striatal functional connectivity at 
rest and while performing the WOF task and youth psychological protective factors (cognitive and emotional self-
regulation). Other neural measures/circuits, psychological/social-contextual and behavioral variables (Appendix 
1) will be used either as supplementary measures or as potential covariates. By sharpening our focus to a priori 
specified neural circuits and psychological measures, we hope to increase statistical power and, in turn, our 
ability to identify important differences in the responses of these circuits according to youth protective factors. 
 
IIIC.6. Sample Attrition: The team has experience with intensive longitudinal studies involving difficult-to-reach 
samples with retention rates exceeding 90% for short-term follow-up (IIIC.10a). Even with a liberal estimate of 
20% attrition, we will have 102 participants, which will give us sufficient power to detect medium effects (IIIC.7e). 
 
IIIC.7. Statistical Methods 
 
IIIC.7a. Data Management: Data management will be done via remote electronic data capture (REDCap).242 
REDCAP was developed at VUMC to insure data integrity and completeness, including compliance with HIPAA 
(over 300,000 users). Data will be entered immediately after testing on data entry screens designed to duplicate 
the data collection forms (ACASI; IIIC.4f.4). Reports can be generated to track all contacts with the participants. 
 
IIIC.7b. Procedures for Handling Missing Data: Missing data will be accounted for by including "missingness" 
as a covariate if the proportion of missing data is small,243-246 or alternatively using multiple imputation methods 
if the proportion of missing data is large, e.g. >5%.247 
 
IIIC.7c. Data Reduction: Each hypothesis will be examined using a primary variable, with supplemental 
information gained from analysis of other related measures (see IIIC.5). 
 
IIIC.7d. Data Analysis: Descriptive statistics (e.g. means and standard deviations, frequencies and histograms) 
will be computed for all variables, along with 95% bootstrap confidence intervals. Significance tests will be two-
tailed, with p < 0.05, controlling for multiple comparisons.248-250 Pubertal stage/gonadal hormones, gender and 
SES will be included as concomitant variables in analyses where they are known to correlate with the outcome. 
These covariates, while not of primary interest, will reduce residual variance and increase the precision of effect 
estimates for improved statistical power.251 Secondary analyses will determine if these covariates significantly 
affect the outcomes. Behavioral/emotional problem score (CBCL/YSR), lifetime history of substance use and 
dose of the intervention (number of completed sessions) will serve as additional potential covariates. 
 
IIIC.7d.1. Hypothesis 1: PAAS will induce greater functional connectivity changes in the reward-drive and 
cognitive-control (fronto-striatal) circuitry compared to the wait-list condition. Post-treatment neural connectivity 

scores will be regressed on group assignment (PAAS vs. wait-list) as the independent variable and baseline 
(pre-treatment) neural connectivity score as a covariate. Two separate models will be run for resting-state and 
task-based fMRI connectivity measures. Other covariates (IIIC.7d) will be included if there are group differences. 
 
IIIC.7d.2. Hypothesis 2: PAAS-induced changes in youth self-regulation (cognitive and emotional regulation) at 
3 months will be mediated by PAAS’ effects on changes in neural circuitry. A mediation process will be modeled 
using a sequential process of the associations between specified brain responses and self-regulation.252 The 
Wager/Lindquist Mediation Toolbox (IIIC.4f.2b.6) will be deployed.199,201 Mediation effects are estimated from 
the product of the regression coefficient (a) when a mediator of interest (e.g. neural response) is regressed on 
the intervention and regression coefficient (b) when the outcome criterion (self-regulation at 3 mon.) is regressed 
on the mediator, controlling for the intervention effect. The standard error of the mediated effect can be calculated 
in various ways, including the bootstrap method.252,253 This method is referred to as the product-of-coefficients 
approach for testing mediation, and provides an efficient overall test of mediation processes.252-254  
 
IIIC.7d.3. Exploratory Analyses: We will examine whether baseline characteristics moderate PAAS’ effect (e.g. 
if reward-drive [BAS] score/ neural connectivity in the fronto-striatal circuit interacts with PAAS) on improvement 
in self-regulation. We will assess the association of neural changes (local/whole brain) with behavioral and social-
contextual variables targeted by PAAS (i.e. risky behaviors and parenting behavior). Other functional connectivity 
changes (e.g. default-mode, cognitive and salience networks, and the fronto-limbic circuit) will be identified and 
the association between functional connectivity patterns at rest and during task performance will be evaluated. 
 
IIIC.7e. Power Analysis: Statistical power was estimated with G power 3.1.9.2 software.255 We used traditional 
criteria (alpha = .05 two-tailed, power = 80%) to estimate statistical power.256 [Rather than base power estimates 
on modest pilot samples, we assessed power by minimum detectable effect size (MDES).257] For fMRI studies, 
Thirion et al. recommend sample sizes of 40 per group for reliable estimates of group differences.258 Given the 
multiple variables of interest, we selected larger sample sizes. Randomized subjects will be compared in two 
conditions using a multiple regression model. With N = 102 participants (after accounting for attrition), a multiple 
regression would have adequate power to detect an effect size as low as r = .31 (R2 = .10). Cohen259 considers 
r ~ .10/.30/.50 to be small/medium/large effect sizes. Power will be sufficient to detect medium effects. Because 
we will use a repeated measures model with covariates, actual power should be somewhat better. A sample size 
of n = 102 is also sufficient to detect mediation in an analysis with medium-sized α and β paths.260  
 
IIIC.8. Timeline: Table 3 shows the expected timeline of activities for the proposed research. 
 
Table 3. Timeline of Research Activities 
 
 
Year 01 
Year 02 
Year 03 
Year 04 
Year 05 
Research Activities Per Quarter 
1 
2 
3 
4 
1 
2 
3 
4 
1 
2 
3 
4 
1 
2 
3 
4 
1 
2 
3 
4 
Train staff, set up database 
x 
x 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Recruit subjects and conduct baseline assessments 
 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
 
 
 
Implement PAAS intervention for the active group 
 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
 
 
Post-intervention measures 
 
 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
 
 
Follow-up assessments (3 months post-treatment) 
 
 
 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
 
Implement PAAS for the wait-list group 
 
 
 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
Data entry and management 
 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
x 
Data analysis 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
x 
x 
x 
Preparation of manuscripts 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
x 
x 
x 
 
IIIC.9. Expected Outcomes and Future Directions: The study is expected to contribute new knowledge in the 
following areas: (1) whether functional connectivity changes in decision-making (a balance between reward-drive 
and cognitive-control) neural circuitry occur in response to a preventive intervention (PAAS); (2) whether these 
functional connectivity changes mediate the improvement in youth self-regulation processes that are targeted by 
the intervention; (3) whether socio-demographic features, reward-drive traits and baseline functional connectivity 
can identify subgroups of youth who respond better to the intervention; and (4) if intervention-induced changes 
in functional connectivity and self-regulation are associated with a reduction in risky behaviors (willingness and 
intentions). The study will advance the benefits of PAAS and other evidence-based programs by improving our 
understanding of the ways in which neurobiological mechanisms affect intervention-induced behavioral changes. 
 
If, indeed, brain changes in the decision-making circuitry mediate the intervention effects on self-regulation and 
risky behaviors, the intervention program can be tailored to individual youth to improve outcomes.40,42,43 Novel 
pharmacological, psychosocial and neurofeedback treatments can be developed to target specific neural circuits 
as adjunctive interventions for reducing risky behaviors in this population.71,73,76,261-266 Additionally, “personalized” 

interventions can be developed for specific subgroups of at-risk youth to reduce risky behaviors and their long-
term consequences. These methods may include, for example, the development of biopsychosocial profiles that 
can be used efficiently to focus intervention efforts. Recent studies in animals and humans have suggested that 
behavioral and psychological manipulations, including diet, exercise and CBT can induce neural changes.267-270 
The neural markers can be used to predict treatment outcome more effectively79,263,271,272 and to index treatment 
response71,73,76,261-263,266,273-276 in RCTs, which then can change clinical practice and public policy. 
 
IIIC.10. Potential Problems and Alternative Strategies 
 
IIIC.10a. Recruitment and Retention Issues: Recruitment and retention will be challenging. However, we are 
confident that we can manage these challenges because: (1) as previously described in IIIC.4a, we will have as 
our recruitment partners primary care and nonprofit service providers that see large numbers of youth and that 
Dr. Rao has successfully worked with on prior studies; and (2) we will employ retention procedures that we have 
used successfully with similar populations. 
 
In previous studies, we have successfully recruited high-risk samples (low SES, mood and addictive disorders) 
and implemented intensive protocols (multi-dimensional clinical and biological measures with admissions for 
sleep/neuroendocrine studies lasting up to 3 consecutive days) that included much more lengthy longitudinal 
designs (follow-up assessments at 6-month intervals for up to 5 years). Also, Dr. Murry has more than 20 years 
of experience conducting community-based research, recruiting and retaining over 2,000 AA parents and youth 
in developmental and prevention studies. We had low attrition rates (10-20%) in these studies, and we will follow 
the same procedures, including evening/weekend appointments to accommodate participants, reminder calls 
the day before and on the appointment day, providing meals/snacks during visits, engaging local community 
partners and offering clinical and social service referrals to families when needed. We will provide reimbursement 
for travel and use a reimbursement protocol that is incentive-based (i.e. based on the number of visits attended). 
The PAAS intervention (tech version) also is feasible to implement at home at the participants’ convenience. 
 
IIIC.10b. Inherent Confounds in the Study Design: Even though invitations will be sent to randomly selected 
families, the participation will be based on their willingness and meeting subsequent stringent eligibility criteria. 
Hence, the findings might not generalize to the larger AA population. The participating clinics maintain electronic 
databases with demographic and clinical characteristics, and we will compare the sample with the remaining 
clinic population (and those who declined to participate or were excluded after the invitation) on these features. 
The other confounds related to restricted age range and race/ethnicity have been addressed (IIIC.4d, IIIC.4e). 
 
IIIC.10c. Trial Design: We considered several alternative designs, including classic RCT with an active or non-
treatment control group. [“Active ingredients” of the intervention were identified in previous RCTs (IIIC.3b).25-36] 
We opted for this design for following reasons: (1) a wait-list control group will provide greater power in detecting 
group differences than an active control group; and (2) from an ethical standpoint and to engage participants in 
the study, we are able to offer the control group an intervention that has been shown to be effective in prior RCTs 
after the wait-list control period. Since the control group also will be at high risk for HIV/AIDS and other negative 
consequences related to risky behaviors, this approach provides an opportunity to prevent such problems. 
 
IIIC.10d. Accounting for Treatment Effects: The waitlist does not account for general treatment effects in the 
active condition. PAAS-tech program does not provide direct personal contact with “trained therapists”, thereby 
minimizing therapeutic effects accounted for by therapist-participant interactions during intervention sessions. In 
the event we do not find the hypothesized changes in the fronto-striatal circuits in response to the intervention, 
we will perform whole-brain analysis to explore changes in other brain regions/circuits (IIIC.4f.2b.6, IIIC.7d.3). 
 
IIIC.10e. Intervention Effects in Rural Versus Urban Areas: The intervention was originally designed for rural 
AA families to address the problem of limited access to mental health services in the context of rising prevalence 
of HIV-risk behaviors in this population. However, the socio-cultural and risk factors for urban families are similar 
to rural families. The program was tested in small towns near urban areas (near Athens, GA, and Memphis, TN), 
where the population resembles patients from outlying areas of Nashville that are seen in the participating clinics. 
We examined site effects between rural and non-rural areas and found no differences in intervention effects. 
 
IIIC.11. Scientific Rigor: As can be gleaned from the Research Design and Methods Section (IIIC.4), we have 
given careful consideration to the study design with respect to sample selection including biological and social 
factors, procedures to reduce attrition, alternative designs, randomization process, and pre-post-intervention 
assessments. We proposed quality control methods for data collection and processing to eliminate or minimize 
bias, a priori selected primary outcome criteria (IIIC.5) and controls for potential confounding variables in the 
data analysis (IIIC.7d). We considered anticipated problems and proposed ways to minimize them (IIIC.10).   

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