Brain-based ranking of cognitive domains to predict schizophrenia

Hum Brain Mapp. 2019 Oct 15;40(15):4487-4507. doi: 10.1002/hbm.24716. Epub 2019 Jul 16.


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.

Keywords: BrainMap database; coordinate-based meta-analysis; ontology of the mind; pattern recognition; predictive analytics; statistical learning.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Brain Mapping*
  • Cognition / physiology*
  • Connectome
  • Emotions / physiology
  • Female
  • Humans
  • Likelihood Functions
  • Machine Learning
  • Magnetic Resonance Imaging
  • Male
  • Mental Processes / physiology
  • Models, Neurological
  • Models, Psychological
  • Psychomotor Performance / physiology
  • Schizophrenia / diagnosis*
  • Schizophrenia / physiopathology
  • Schizophrenic Psychology*
  • Young Adult