Neural Network Predictors of Treatment Outcome Among Adolescent Assault Victims

青少年袭击受害者治疗结果的神经网络预测因素

• 批准号: 8352499
• 负责人: Joshua M Cisler
• 金额: $ 22.12万
• 依托单位: UNIV OF ARKANSAS FOR MED SCIS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-15 至 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8352499
• 关键词: Adolescent; Aftercare; Algorithms; Area; Arkansas; Biological Neural Networks; Brain imaging; Characteristics; Child; Classification; Clinical; Clinical Research; Clinical Services; Cognitive Therapy; Collaborations; Development; Diagnostic; Emotions; Exhibits; Exploratory/Developmental Grant; Exploratory/Developmental Grant for Diagnostic Cancer Imaging; Female Adolescents; Fostering; Functional Magnetic Resonance Imaging; Goals; Graph; Human; Intervention; Lead; Machine Learning; Mediating; Medical; Mental Depression; Mental disorders; Modality; Modeling; National Institute of Mental Health; Participant; Patients; Pattern; Population; Post-Traumatic Stress Disorders; Prevalence; Psychopathology; Recovery; Research; Research Project Grants; Residual state; Risk; Risk Factors; Scanning; Science; Services; Substance abuse problem; Symptoms; Translational Research; Trauma; Treatment Efficacy; Treatment outcome; Universities; Violence; Vulnerable Populations; Work; aged; alternative treatment; assault; base; clinically significant; computational neuroscience; cost; emotion regulation; improved; insight; meetings; neural patterning; neuroimaging; neuromechanism; novel; novel strategies; programs; relating to nervous system; response; theories; tool; treatment response; vector

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.

描述（由申请人提供）：NIMH探索性/发展赠款奖（R21）的提案旨在确定与女性青少年攻击受害者对以创伤为中心的认知行为疗法（TF-CBT）的反应相关的神经功能连通性模式。青春期的突击暴露是持续性心理病理学的有效风险因素，最著名的是PTSD。 TF-CBT是唯一对具有强烈经验支持的青少年PTSD受害者的治疗方法，但对TF-CBT的反应是可变的，许多受害者在治疗后仍会表现出临床上显着的症状。该提案的总体目标是使用计算神经科学工具来预测和理解该脆弱人群之间的治疗反应。基于人类神经影像学研究，表明神经网络中的活动和连通性改变了PTSD受害者之间的情绪反应性和情绪调节，我们假设这些神经网络中功能连通性的模式可用于预测和理解青少年攻击受害者对TF-CBT的反应。 45名11-16岁的青少年攻击受害者将为TF-CBT提供12周的课程。参与者将在治疗前后从事情绪反应性和情绪调节任务时进行fMRI扫描。图理论分析和支持向量分类和回归的组合将用于确定功能连通性的预处理模式，以预测随后对TF-CBT的响应（AIM 1）。图理论分析将类似地用于识别网络组织的变化，从与成功（AIM 2）和失败（AIM 3）治疗响应相关的前到关键验证。理解对TF-CBT的可变反应的临床问题的这种分析方法将促进临床医生使用的混凝土算法来预测儿童的治疗反应，这是对这种脆弱人群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。该应用提出了一种新的方法，以理解脆弱的青少年人群之间的治疗反应，并希望有助于发展更一致的干预措施，以减轻与青少年攻击相关的高成本。 公共卫生相关性：该提案调查了被袭击的青少年女孩中治疗结果的神经网络预测指标。这项研究将使人们更好地了解治疗的工作原理以及为什么有些孩子对治疗没有反应。