Neurocomputational substrates of maladaptive uncertainty learning and avoidance in anxiety

这个K23申请将提供申请人，一个临床心理学家在神经成像和

This K23 application will provide the applicant, a clinical psychologist with expertise in neuroimaging and computational modeling, with training and mentored research experience towards an independent research career studying disrupted learning processes in anxiety disorders. Training activities will focus on: 1) clinically informative applications of computational modeling and neuroimaging in anxiety, 2) advanced computational modeling of uncertainty and exploration, and 3) ecological momentary assessment of behavioral avoidance. This training will be facilitated by an interdisciplinary team of experts in computational and neural approaches to understanding psychiatric disorders, neurally-informed computational modeling of uncertainty and avoidance, and ecological assessment of clinically-relevant behaviors. Training will take place at the Department of Psychiatry at the University of Pittsburgh, which has a long and successful track record of supporting junior scientists. To fulfill these training goals, the proposed research adapts approaches from basic neurocomputational studies on uncertainty and exploration to apply to anxiety. Specifically, the proposed research will test the hypotheses that anxiety, particularly anxious arousal, is related to disrupted learning about uncertain, aversive outcomes, as measured by neural and behavioral measures; that disrupted uncertainty learning leads to avoidance of uncertain options in anxiety; and that measures of uncertainty avoidance relate to real-world behavioral avoidance. Participants (n=85), oversampled for high anxiety, will complete a task assessing uncertainty learning while undergoing fMRI scanning. They will then report on real- world avoidance behaviors for two weeks. Participants’ performance on the uncertainty learning task will be fit to a computational model to measure learning from uncertainty as well as the tendency to explore versus avoid options based on uncertainty. Measures of uncertainty estimated from the computational model will be regressed against fMRI BOLD signals and behavioral choices; these effects on neural and behavioral function will be tested for differences with anxious arousal. Individual variation in uncertainty-dependent exploration will be tested for concordance with participants’ current real-world reports of behavioral avoidance and if they predict future real-world behavioral avoidance. The anticipated impact, in line with NIMH’s Strategic Objectives, will be identification of a) neural mechanisms for a complex behavior, maladaptive behavioral avoidance, b) objective assessments of anxiety and avoidance, and c) possible novel treatment targets.