Using Computational Modeling to Test Reinforcement Learning as a Predictor of Response in Family-Based Treatment for Adolescent Anorexia Nervosa

使用计算模型来测试强化学习作为青少年神经性厌食症家庭治疗反应的预测因子

• 批准号: 10693328
• 负责人: Erin E. Reilly
• 金额: $ 19.63万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693328
• 关键词: Adolescence; Adolescent; Adolescent Development; Adult; Anorexia Nervosa; Anxiety; Behavior Therapy; Behavioral; Biometry; Body Weight; Child; Chronic; Clinical; Clinical Research; Cognitive Science; Computer Models; Computing Methodologies; Data; Development; Disease; Disease remission; Eating; Eating Behavior; Eating Disorders; Effectiveness; Evidence based treatment; Exposure to; Family; Feedback; Food; Formulation; Fright; Goals; Hyperphagia; Impairment; Individual; Intervention; Intervention Studies; Interview; Investigation; K-Series Research Career Programs; Learning; Link; Medical; Mental Depression; Mental disorders; Mentorship; Methods; Neurobehavioral Manifestations; Neurobiology; Neurocognition; Neurocognitive; Obsessive compulsive behavior; Outcome; Parents; Participant; Performance; Process; Psychiatry; Psychological reinforcement; Psychopathology; Punishment; Research; Research Personnel; Rewards; Risk; Sampling; Selection for Treatments; Shapes; Stimulus; Symptoms; Testing; Theoretical model; Training; Treatment Failure; Treatment outcome; United States; Weight; Weight Gain; Work; Youth; behavior change; behavioral response; career; cognitive neuroscience; cost; economic cost; evidence base; experience; feeding; follow-up; improved; improved outcome; indexing; insight; learning outcome; medical complication; neural; patient oriented; predicting response; prevent; psychologic; psychosocial; research based treatment; response; skill acquisition; skills; success; therapy development; treatment response; treatment risk

PROJECT SUMMARY/ABSTRACT Anorexia nervosa (AN) is associated with significant risk for deadly medical complications and an annual cost to the US of around $11.2 billion. Although Family-Based Treatment (FBT) for adolescent AN has demonstrated effectiveness in targeting symptoms of AN, up to 60% of individuals who receive FBT do not remit fully. Notably, no prior work has explored neurocognitive predictors of FBT response, which may help to facilitate the identification of treatment mechanisms and formulation of targeted treatments for non-responders. When considering what neurocognitive processes may be implicated in FBT response, increasing work suggests that adult AN may be characterized by alterations in reinforcement learning. Further, work in other forms of psychopathology suggests that reinforcement learning may predict response to behavioral treatments. However, few studies to date have tested alterations between reinforcement learning and treatment outcome, and none have explored associations between reinforcement learning and FBT outcome. The current investigation will leverage methods from cognitive neuroscience and computational modeling to explore reinforcement learning in adolescents with AN (n = 58) and healthy control subjects (n = 58), as well as its associations with treatment outcome in FBT. I will test the following hypotheses: Aim 1: Consistent with existing data in adults, the AN group will demonstrate poorer performance in the learning task compared to HC, decreased loss learning, and poorer exploitation of prior learned information. Aim 2: Within the AN group, lower rates of learning from loss, as well as lower explore/exploit parameter values will relate to poorer outcomes at 1- and 6-month follow-ups, operationalized as lower body weight and greater eating disorder cognitive symptoms. With the mentorship of five experts across biostatistics, adolescent clinical research, computational modeling, and cognitive neuroscience, the current patient-oriented career development award will allow me access to training that will facilitate unique expertise at the intersection of these fields. Short-term, data from the current investigation will yield insights that can be used to understand the persistence of AN symptoms and identify potential methods to improve treatment outcomes. Long-term, the current project will allow me to launch my career and take the next steps in a programmatic line of research merging complementary expertise in neurocognition, computational methods, and adolescent intervention development.

项目摘要/摘要 神经性厌食症(AN)与致命的医疗并发症和每年的医疗费用有关。 美国约112亿美元。尽管针对青少年AN的家庭治疗(FBT)已经证明 在针对AN症状的有效性方面，高达60%接受FBT的个人没有完全缓解。值得注意的是， 以前没有工作探索FBT反应的神经认知预测因素，这可能有助于促进 确定治疗机制并为无应答者制定有针对性的治疗。什么时候 考虑到神经认知过程可能与FBT反应有关，越来越多的研究表明 成人AN的特征可能是强化学习的改变。此外，以其他形式的工作 精神病理学表明，强化学习可以预测对行为治疗的反应。然而， 到目前为止，很少有研究测试强化学习和治疗结果之间的变化，也没有研究 探索了强化学习和FBT结果之间的联系。目前的调查将 利用认知神经科学和计算建模的方法来探索强化学习 患有AN的青少年(n=58)和健康对照组(n=58)及其与治疗的关系 自由贸易协定的结果。我将检验以下假设：目标1：与成人现有数据一致，AN组 在学习任务中表现出比HC更差的表现，减少了学习损失，并且更差 利用先前学到的信息。目标2：在AN组内，从失败中学习的比率也较低 由于较低的探索/利用参数值将与1个月和6个月随访的较差结果相关， 表现为体重减轻和进食障碍认知症状加重。在他的指导下 生物统计学、青少年临床研究、计算建模和认知领域的五位专家 神经科学，目前的以患者为导向的职业发展奖将使我能够获得培训，将 在这些领域的交叉点上提供独特的专业知识。短期内，当前调查的数据将 提供可用于了解症状的持久性并确定潜在方法的见解 改善治疗结果。从长远来看，目前的项目将允许我开始我的职业生涯，并进行下一步 在融合神经认知、计算领域互补专业知识的研究方案线中的几个步骤 方法和青少年干预发展。