Tuesday, October 15, 2024
10/15/2024
PROJECT SUMMARY/ABSTRACT Major depressive disorder (MDD) is a highly prevalent and costly mental health disorder. A potentially important contributor to MDD is impaired reward-based reinforcement learning (RBRL). RBRL are a set of processes reflecting difficulty learning which actions are likely to lead to rewarding outcomes, and are clinically significant features of MDD that predicts future depressive symptoms and worse treatment response. MDD, however, is heterogeneous and RBRL may relate to particular MDD symptoms - specifically psychomotor retardation (PmR), a vastly understudied but impairing aspect of MDD that may be especially detrimental for RBRL. Despite overlapping neural circuitry between PmR and RBRL, no study has assessed the impact of PmR on RBRL- related behavior and neural activity in MDD. This is a critical question, as the degree to which RBRL is impaired and which RBRL processes are altered in MDD may depend on PmR. Furthermore, this is highly clinically relevant as ameliorating RBRL deficits related to PmR in MDD may depend on targeting specific RBRL processes. Thus, the overall goal of the proposed K23 is to clarify the role of PmR on RBRL deficits in MDD using innovative computational modeling to parse specific RBRL processes and fMRI. Building on an ongoing R01 (PIs: Dr. Shankman, Mittal, Walther [mentors on this K23]), Aim 1 will test whether experimentally manipulating the demand for psychomotor speed impacts behavioral and neural indicators of RBRL in individuals (ages 18-60) with MDD (N=66) vs. healthy controls (N=44). Aim 2 will test whether a fine-grained laboratory assessment of PmR in MDD predicts behavioral and neural indicators of RBRL, and whether associations between PmR and RBRL are stronger when faster psychomotor speed is required. Aim 3 will examine whether PmR-related RBRL impairment in MDD predicts the course of depression over 1-year. The proposed career development award also aims to provide Dr. Letkiewicz with experiences and training activities that will support her goals of increasing her knowledge of (1) the neural and behavioral basis of psychomotor functioning in MDD, (2) advanced computational modeling to assess reinforcement learning in the context of psychomotor symptoms, (3) multimethod RDoC approaches to studying mechanisms of psychopathology, and (4) grantsmanship and mentorship skills. Her training goals will primarily be accomplished through formal didactics, training with her mentorship team, and the interdisciplinary centers and institutes at Northwestern University. Her mentorship team consists of leading experts in MDD and transdiagnostic research (Dr. Shankman), reinforcement learning and computational modeling (Dr. Kahnt), neuroimaging and motor systems (Dr. Mittal), and psychomotor assessment (Dr. Walther), all of whom have all have active NIH grants and an excellent track record of mentoring early career scientists. This award will prepare Dr. Letkiewicz to submit an R01 grant application and provide her with the skills needed to achieve her long-term career goal of examining the unique and combined contribution of cognitive and motor systems to psychopathology as an independent clinical scientist.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
