Brain-based ranking of cognitive domains to predict schizophrenia

Teresa M. Karrer, Danielle S. Bassett, Birgit Derntl, Oliver Gruber, André Aleman, Renaud Jardri, Angela R. Laird, Peter T. Fox, Simon B. Eickhoff, Olivier Grisel, Gaël Varoquaux, Bertrand Thirion, Danilo Bzdok

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)4487-4507
Number of pages21
JournalHuman Brain Mapping
Issue number15
StatePublished - Oct 15 2019


  • BrainMap database
  • coordinate-based meta-analysis
  • ontology of the mind
  • pattern recognition
  • predictive analytics
  • statistical learning

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology


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