Abstract
Reinforcement learning (RL) is dysregulated in major depressive disorder (MDD). RL is multifaceted and involves sub-processes including valuation, evidence accumulation, salience attribution and learning rate. Computational modeling can be used to quantify these sub-processes and studies show they have different biological underpinnings. The goal of this study is to identify the structural connectome of RL subconstructs using multimodal data-fusion.
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