Linked dimensions of psychopathology and connectivity in functional brain networks

Cedric Huchuan Xia, Zongming Ma, Rastko Ciric, Shi Gu, Richard F. Betzel, Antonia N. Kaczkurkin, Monica E. Calkins, Philip A. Cook, Angel García de la Garza, Simon N. Vandekar, Zaixu Cui, Tyler M. Moore, David R. Roalf, Kosha Ruparel, Daniel H. Wolf, Christos Davatzikos, Ruben C. Gur, Raquel E. Gur, Russell T. Shinohara, Danielle S. Bassett and Theodore D. Satterthwaite ()
Additional contact information
Cedric Huchuan Xia: University of Pennsylvania
Zongming Ma: University of Pennsylvania
Rastko Ciric: University of Pennsylvania
Shi Gu: University of Pennsylvania
Richard F. Betzel: University of Pennsylvania
Antonia N. Kaczkurkin: University of Pennsylvania
Monica E. Calkins: University of Pennsylvania
Philip A. Cook: University of Pennsylvania
Angel García de la Garza: University of Pennsylvania
Simon N. Vandekar: University of Pennsylvania
Zaixu Cui: University of Pennsylvania
Tyler M. Moore: University of Pennsylvania
David R. Roalf: University of Pennsylvania
Kosha Ruparel: University of Pennsylvania
Daniel H. Wolf: University of Pennsylvania
Christos Davatzikos: University of Pennsylvania
Ruben C. Gur: University of Pennsylvania
Raquel E. Gur: University of Pennsylvania
Russell T. Shinohara: University of Pennsylvania
Danielle S. Bassett: University of Pennsylvania
Theodore D. Satterthwaite: University of Pennsylvania

Nature Communications, 2018, vol. 9, issue 1, 1-14

Abstract: Abstract Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology – mood, psychosis, fear, and externalizing behavior – are associated (r = 0.68–0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset (n = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05317-y

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DOI: 10.1038/s41467-018-05317-y

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