4.8 Article

High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns

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NATURE COMMUNICATIONS
卷 12, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41467-021-25876-x

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资金

  1. NSF EPSCoR Award [1632738]
  2. DARPA RAM [N66001-14-2-4-032]

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The coordinated patterns of brain activity reflect cognitive processes, showing a fractal-like hierarchy during story listening. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain's functional connectome, with complex thoughts reflected in higher-order patterns of dynamic network interactions throughout the brain.
Coordinated patterns of brain activity reflect cognitive processes. Here the authors use a mathematical framework for describing dynamic patterns in brain networks to show they organize in a fractal-like hierarchy during story listening. Our thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain's functional connectome that display homologous lower-level dynamic correlations. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We develop an approach to estimating high-order dynamic correlations in timeseries data, and we apply the approach to neuroimaging data collected as human participants either listen to a ten-minute story or listen to a temporally scrambled version of the story. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We find that classifiers trained to decode from high-order dynamic correlations yield the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.

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