The functional relevance of task-state functional connectivity

Michael W. Cole, Takuya Ito, Carrisa Cocuzza, Ruben Sanchez-Romero

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the ability of the human brain to adaptively alter its functionality via rapid changes in inter-regional relationships. We used activity flow mapping - an approach for building empirically derived network models - to quantify the functional importance of task-state functional connectivity (above and beyond resting- state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how taskevoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance.

Original languageEnglish (US)
Pages (from-to)2684-2702
Number of pages19
JournalJournal of Neuroscience
Volume41
Issue number12
DOIs
StatePublished - Mar 24 2021

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

Keywords

  • Computational model
  • Human connectome project
  • Machine learning
  • Network coding models
  • Network neuroscience
  • Task connectivity

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