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Brain signal complexity and variability link to functional brain network features. Higher centrality regions showed less BOLD signal variability but more complexity, depending on temporal scale.

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Area of Science:

  • Neuroscience
  • Complex Systems

Background:

  • Understanding brain function relies on analyzing brain signal dynamics and complexity.
  • Functional brain networks (FBN) show links between BOLD signal variability/complexity and network features.
  • The relationship between signal variability/complexity and regional centrality in FBN is underexplored.

Purpose of the Study:

  • Investigate the association between BOLD signal variability/complexity and static/dynamic nodal features of FBN.
  • Utilize graph theory analysis on fMRI BOLD data during naturalistic movie watching.

Main Methods:

  • Graph theory analysis applied to fMRI BOLD data.
  • Examined static and dynamic nodal features of Functional Brain Networks (FBN).
  • Correlated BOLD signal variability and complexity with network centrality and clustering coefficients.

Main Results:

  • BOLD signal variability positively correlated with fine-scale complexity and negatively with coarse-scale complexity.
  • Regions with high centrality and clustering coefficient exhibited less variable but more complex signals.
  • These relationships generally held for dynamic FBN, though some centrality dynamics associations became insignificant.

Conclusions:

  • The interplay between BOLD signal variability, complexity, and FBN features is dependent on the temporal scale of complexity.
  • Time-varying FBN characteristics reflect the complex coevolution of BOLD signal variability and complexity.