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Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox.

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We introduce a novel framework modeling brain functional connectivity dynamics as a random walk. This approach quantifies brain network reconfiguration speed and identifies modules, offering potential biomarkers for neural activity.

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Area of Science:

  • Neuroscience
  • Complex Systems
  • Data Analysis

Background:

  • Understanding brain functional connectivity dynamics is crucial for neuroscience.
  • Previous methods often rely on discrete states, limiting nuanced analysis.
  • Characterizing the continuous reconfiguration of brain networks remains a challenge.

Purpose of the Study:

  • To develop a novel framework for analyzing brain functional connectivity dynamics (dFC).
  • To model dFC as a complex random walk in the space of functional networks.
  • To introduce quantitative metrics for characterizing this dFC random walk.

Main Methods:

  • Developed a random walk framework for dFC, treating it as a smooth reconfiguration process.
  • Introduced dFC speed analysis to quantify reconfiguration rates and identify biomarkers.
  • Utilized meta-connectivity (MC) analysis to detect dFC modules and controllers.
  • Implemented these analyses in an open-source MATLAB toolbox (dFCwalk).

Main Results:

  • The dFC random walk framework captures continuous network changes without discrete states.
  • dFC speed distributions reveal typical reconfiguration rates and deviations from Gaussianity.
  • MC analysis identifies novel dFC modules and meta-hubs, enabling module-specific speed analysis.
  • The dFCwalk toolbox provides accessible tools for these advanced analyses.

Conclusions:

  • The random walk framework offers a powerful new perspective on brain dFC.
  • dFC speed and MC analyses provide valuable biomarkers and insights into network organization.
  • The dFCwalk toolbox facilitates the application of these methods to various neural and non-neural time-series data.