Integrating Multimodal Neuroimaging Features to Predict Working Memory and Psychiatric Disability

COEX 

Individual differences in working memory (WM) capacity have been linked to variation in higher order cognition and psychiatric disability. One goal of the NIH Research Domain Criteria (RDoC) paradigm is to formally characterize this relationship and relate neurocognitive markers of WM to psychopathology across broad diagnostic categories [1].
Although a variety of structural and functional brain measures have been shown to account for variance in WM capacity, recent work integrating distinct modalities of brain structure and function explain more variance than can any single modality on its own [2,3]. The present investigation sought to leverage machine learning to characterize the relative importance of different functional and structural neuroimaging measures for predicting WM task performance, trait-level WM capacity, and overall psychiatric disability.