Brain‐based ranking of cognitive domains to predict schizophrenia. Issue 15 (16th July 2019)

• Record Type: Journal Article
• Authors: Karrer, Teresa M.; Bassett, Danielle S.; Derntl, Birgit; Gruber, Oliver; Aleman, André; Jardri, Renaud; Laird, Angela R.; Fox, Peter T.; Eickhoff, Simon B.; Grisel, Olivier; Varoquaux, Gaël; Thirion, Bertrand; Bzdok, Danilo
• Is Part Of: Human brain mapping. Volume 40:Issue 15(2019)
• Page Start: 4487
• Page End: 4507
• Publication Date: 2019-07-16
• Subjects: BrainMap database -- coordinate‐based meta‐analysis -- ontology of the mind -- pattern recognition -- predictive analytics -- statistical learning; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24716
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Schizophrenia is a devastating brain disorder that disturbs sensory perception, motor action, and abstract thought. Its clinical phenotype implies dysfunction of various mental domains, which has motivated a series of theories regarding the underlying pathophysiology. Aiming at a predictive benchmark of a catalog of cognitive functions, we developed a data‐driven machine‐learning strategy and provide a proof of principle in a multisite clinical dataset ( n = 324). Existing neuroscientific knowledge on diverse cognitive domains was first condensed into neurotopographical maps. We then examined how the ensuing meta‐analytic cognitive priors can distinguish patients and controls using brain morphology and intrinsic functional connectivity. Some affected cognitive domains supported well‐studied directions of research on auditory evaluation and social cognition. However, rarely suspected cognitive domains also emerged as disease relevant, including self‐oriented processing of bodily sensations in gustation and pain. Such algorithmic charting of the cognitive landscape can be used to make targeted recommendations for future mental health research.