Neural Network Predictors of Treatment Outcome Among Adolescent Assault Victims

描述（由申请人提供）：这是NIMH探索性/发展性资助奖（R21）的提案，旨在确定女性青少年攻击受害者对创伤型认知行为治疗（TF-CBT）的反应相关的神经功能连接模式。青少年遭受攻击是持续性精神病理的一个潜在危险因素，最明显的是PTSD。TF-CBT是青少年创伤后应激障碍受害者唯一具有强大经验支持的治疗方法，但对TF-CBT的反应是可变的，许多受害者在治疗后继续表现出临床显着症状。本提案的总体目标是使用计算神经科学工具来预测和理解这一弱势群体的治疗反应。基于人类神经影像学研究表明，在创伤后应激障碍受害者中，调节情绪反应和情绪调节的神经网络活动和连通性发生了改变，我们假设这些神经网络中的功能连通性模式可以用来预测和理解青少年攻击受害者对TF-CBT的反应。45名11-16岁的青少年性侵受害者将接受为期12周的TF-CBT课程。参与者在治疗前后分别进行情绪反应和情绪调节任务时进行功能磁共振成像扫描。图论分析与支持向量分类和回归的结合将用于识别预测后续对TF-CBT反应的功能连接的治疗前模式（目标1）。同样，图论分析将用于识别与成功（目标2）和不成功（目标3）治疗反应相关的治疗前到治疗后网络组织的变化。这种理解对TF-CBT的可变反应的临床问题的分析方法将促进临床医生使用的具体算法来预测儿童的治疗反应，这是针对这一弱势群体进行个性化治疗的第一步。此外，这种分析方法将确定介导治疗反应的基本神经机制，并为开发新的治疗成分提供目标。这一应用为了解弱势青少年群体的治疗反应提供了一种新的方法，并有望促进更一致的干预措施的发展，以改善与青少年性侵犯暴露相关的高成本。

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.