The Prediction of Brain Activity from Connectivity: Advances and Applications

Michal Bernstein-Eliav, Ido Tavor*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

Abstract

The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain–behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research.

Original languageEnglish
Pages (from-to)367-377
Number of pages11
JournalNeuroscientist
Volume30
Issue number3
DOIs
StatePublished - Jun 2024

Funding

FundersFunder number
Israel Science Foundation1603/18
Israel Science Foundation

    Keywords

    • brain activity
    • brain–behavior associations
    • clinical populations
    • functional connectivity
    • individual traits
    • prediction models
    • resting-state fMRI
    • structural connectivity
    • task fMRI

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