4.7 Article

The Functional Relevance of Task-State Functional Connectivity

Journal

JOURNAL OF NEUROSCIENCE
Volume 41, Issue 12, Pages 2684-2702

Publisher

SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.1713-20.2021

Keywords

computational model; human connectome project; machine learning; network coding models; network neuroscience; task connectivity

Categories

Funding

  1. US National Institutes of Health (NIH) [R01-AG-055556, R01MH109520]
  2. McDonnell Center for Systems Neuroscience at Washington University
  3. [1U54-MH-091657]

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Resting-state functional connectivity provides insights into brain network organization, but the functional importance of task-related changes remains unclear. Task-state functional connectivity can predict cognitive task activations better, driven by individual-specific functional connectivity patterns. These findings suggest task-related changes play a role in reshaping brain network organization and altering neural activity flow during task performance.
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 task-evoked 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.

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