Predicting individual variability in task-evoked brain activity in schizophrenia

Niv Tik, Abigail Livny, Shachar Gal, Karny Gigi, Galia Tsarfaty, Mark Weiser, Ido Tavor*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

What goes wrong in a schizophrenia patient's brain that makes it so different from a healthy brain? In this study, we tested the hypothesis that the abnormal brain activity in schizophrenia is tightly related to alterations in brain connectivity. Using functional magnetic resonance imaging (fMRI), we demonstrated that both resting-state functional connectivity and brain activity during the well-validated N-back task differed significantly between schizophrenia patients and healthy controls. Nevertheless, using a machine-learning approach we were able to use resting-state functional connectivity measures extracted from healthy controls to accurately predict individual variability in the task-evoked brain activation in the schizophrenia patients. The predictions were highly accurate, sensitive, and specific, offering novel insights regarding the strong coupling between brain connectivity and activity in schizophrenia. On a practical perspective, these findings may allow to generate task activity maps for clinical populations without the need to actually perform any tasks, thereby reducing patients inconvenience while saving time and money.

Original languageEnglish
Pages (from-to)3983-3992
Number of pages10
JournalHuman Brain Mapping
Volume42
Issue number12
DOIs
StatePublished - 15 Aug 2021

Keywords

  • Connectome
  • cognitive function
  • fMRI
  • machine learning
  • resting-state
  • schizophrenia

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