Spatial variability of low frequency brain signal differentiates brain states

PLoS One. 2020 Nov 12;15(11):e0242330. doi: 10.1371/journal.pone.0242330. eCollection 2020.

Abstract

Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Brain / diagnostic imaging
  • Brain / physiology*
  • Cognition / physiology
  • Entropy
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging
  • Male
  • Photic Stimulation
  • Reaction Time
  • Spatio-Temporal Analysis
  • Task Performance and Analysis
  • Young Adult

Grants and funding

The study was funded by the Natural Science Foundation of China (31600930(YW), 61533006(HC), U1808204(HC), and 81771919(QC)) and Sichuan Science and Technology Program (2018TJPT0016(HC)).