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

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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.