Abstract:

There has been growing dissatisfaction with reliance on traditional binary psychiatric diagnoses which are often internally heterogeneous, highly comorbid, and have arguably arbitrary cut-off points as opposed to capturing the way many symptoms vary in a continuous nature across the general population. This has led to new initiatives such as 'HiTOP' and 'RDoC' which have sought to capture the dimensional structure of psychopathology (HiTOP) and to identify neural or cognitive markers of psychopathology (RDoC). In our recent and ongoing work, we have sought to bring these approaches together by characterizing sub-dimensions of anxiety and depression as well as capturing symptom variance common to anxiety and depression and by using computational modeling to link scores on these dimensions to deficits in different aspects of decision-making.