Individualized brain systems and depression

点击翻译按钮获取中文摘要

PROJECT SUMMARY / ABSTRACT The goal of this proposal is to advance neural models of major depressive disorder (MDD). Prior studies of MDD and related conditions have relied on group-level information when making inferences about individual brains, and have yielded limited translation and clinical impact. Such group-level approaches are limited given robust evidence that the brain exhibits substantial individual variability in its organization. This proposal describes a computational psychiatry approach rooted in new computational neuroimaging methods that will provide improved detail in mapping the brains of individuals with MDD, including in relation to diagnostic status, symptom and behavioral profiles, and predicting treatment response. More specifically, the team proposes an advanced fMRI-based brain mapping approach that will be used to deeply characterize the rich organizational structure of functional brain systems at the level of individuals (yielding “individualized brain systems”). The proposed research will be completed by leveraging over 700 existing datasets acquired through data sharing. This proposal is feasible, in part due, to data sharing and the strong theoretical and methodological foundations provided by the PI and the team’s prior research. MDD is a particularly promising focus for this proposal given that it is (1) highly heterogeneous and thus an ideal target for mapping individual variability; (2) highly prevalent and the leading contributor to global disease burden; and that (3) fewer than one in three MDD patients remit after treatment. The Specific Aims of this proposal are to: (1) Map individualized brain systems in MDD; (2) Characterize relations between individualized brain systems and core MDD symptoms and behavioral deficits; and, finally, to (3) Explicate predictive relations between individualized brain systems and MDD clinical trial outcomes to three mechanistically distinct treatments. In addition to theory-driven studies, this proposal includes the development of a complementary data-driven machine learning approach that will use only individualized brain system features to make clinically meaningful predictions about specific patients. This will include predicting diagnostic status, symptom and behavioral profiles, and treatment outcomes. Precision medicine has considerably impacted several medical fields, including cardiology and oncology. We have yet to see similar developments in psychiatry, given, in part, due to the challenge of mapping relations among clinical features of mental illness and the brain. The development of computational neuroimaging approaches, including those in the current proposal, now provide new opportunities to address this challenge and translational gap.