Computational Psychiatric Approach to Depression

The goal for this study will be to test the following overall hypotheses: (1) that adaptive decision-making processes are disrupted in patients with treatment-resistant depression, and (2) the neural and behavioral changes associated with treatment-resistant depression can be assessed using functional magnetic resonance imaging paired with computationally constrained adaptive decision-making games. The investigators will use computational modeling of incentivized decision-making tasks, brain imaging (functional magnetic resonance imaging, fMRI), and standard clinical assessments, to characterize patients referred to Wake Forest Baptist Medical Center for ECT. Characterization will occur over multiple visits to be aligned with the normal course of patient care. Data collected may be used in hypothesis driven analyses to: 1. classify treatment resistant depression versus healthy controls and non-treatment resistant depression 2. predictors of ECT treatment success versus failure 3. characterize brain and behavioral changes consistent with treatment success versus failure 4. predict who will need maintenance ECT therapy 5. determine what changes in decision-making behavior are apparent in patients who require ECT maintenance therapy versus those who do not.