Discovering Linked Dimensions of Psychopathology and Dysconnectivity in High-Dimensional Brain Networks

• Activity Number: 150 - Recent Advances for Modeling Neuroimaging Data
• Type: Topic Contributed
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Theodore Satterthwaite* and Cedric H Xia and Rastko Ciric and Zongming Ma and Russell Taki Shionhara and Richard Betzel and Monica E Calkins and Phillip A Cook and Angel Garcia de la Garza and Tyler M Moore and David Roalf and Kosha Ruparel and Daniel H Wolf and Raquel E Gur and Ruben C Gur and Danielle S Bassett
• Companies: Univ of Pennsylvania and UPenn and University of Pennsylvania and University of Pennsylvania and UPenn and UPenn and UPenn and UPenn and UPenn and UPenn and University of Pennsylvania and University of Pennsylvania and UPenn and University of Pennsylvania and University of Pennsylvania and UPenn
• Keywords: neuroimaging ; high-dimensional ; neuropsychiatry ; CCA

Abstract

It is increasingly realized that neurobiological abnormalities associated with mental illnesses do not map cleanly to diagnostic categories used in clinical practice. This suggests common mechanisms of circuit-level abnormalities. Here we sought to identify brain-based dimensions of psychopathology using sparse Canonical Correlation Analysis (sCCA) in a sample of nearly 1000 youth imaged as part of the Philadelphia Neurodevelopmental Cohort. To find relationships between functional network connectivity and psychopathology data, we used sCCA, which aims to simultaneously find linear combinations of variables in each dataset that are maximally correlated with each other, with L1 regularization to achieve sparsity. This analysis revealed three dimensions of psychopathology that were highly associated with specific brain connectivity patterns including psychosis, externalizing behavioral, and obsessive-compulsive symptoms. All modes of covariation were replicated in a completely independent dataset. Taken together, these results suggest that common patterns of dysconnectivity are associated with dimensions of psychopathology across categorical clinical diagnostic categories.