Neural Network Predictors of Treatment Outcome Among Adolescent Assault Victims

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DESCRIPTION (provided by applicant): This proposal for a NIMH Exploratory/Developmental Grant Award (R21) seeks to identify neural functional connectivity patterns associated with response to Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) among female adolescent assault victims. Adolescent assault exposure is a potent risk factor for persistent psychopathology, most notably PTSD. TF-CBT is the only treatment for adolescent PTSD victims with strong empirical support, yet response to TF-CBT is variable and many victims continue to exhibit clinically significant symptoms following treatment. The overall goal of this proposal is to use computational neuroscience tools to predict and understand treatment response among this vulnerable population. Based on human neuroimaging studies demonstrating altered activity and connectivity within neural networks mediating emotion reactivity and emotion regulation among PTSD victims, we hypothesize that patterns of functional connectivity within these neural networks can be used to predict and understand response to TF-CBT among adolescent assault victims. 45 adolescent assault victims aged 11-16 will be provided with a 12-week course of TF-CBT. Participants will undergo fMRI scanning while engaged in emotion reactivity and emotion regulation tasks before and after treatment. A combination of graph theory analyses and support vector classification and regression will be used to identify pre-treatment patterns of functional connectivity that predict subsequent response to TF-CBT (Aim 1). Graph theory analyses will similarly be used to identify changes in network organization from pre-to-post-treatment associated with successful (Aim 2) and unsuccessful (Aim 3) treatment response. This analytic approach to the clinical problem of understanding the variable response to TF-CBT will foster concrete algorithms to be used by a clinician to predict a child's treatment response, which is the first step towards personalizing treatments for this vulnerable population. Further, this analytic approach will identify the essential neural mechanism mediating treatment response and provide targets for the development of novel treatment components. This application proposes a novel approach towards understanding treatment response among a vulnerable adolescent population and will hopefully facilitate the development of more consistent interventions to ameliorate the high cost associated with adolescent assault exposure. PUBLIC HEALTH RELEVANCE: This proposal investigates neural network predictors of treatment outcome among assaulted adolescent girls. This research will lead to a better understanding of how treatment works and why some children do not respond to treatment.