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Dynamic functional connectivity using state-based dynamic community structure: method and application to opioid

Lucy F Robinson1, Lauren Y Atlas2, Tor D Wager3

  • 1Department Epidemiology and Biostatistics, Drexel University, 3215 Market St, Philadelphia, PA 19104, USA.

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This summary is machine-generated.

We developed a new method to detect dynamic community structure in brain networks, revealing time-varying connectivity changes related to neurological states and pain perception.

Keywords:
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Area of Science:

  • Neuroscience
  • Network Science
  • Data Science

Background:

  • Functional connectivity analyses often assume static brain network behavior.
  • Changes in brain network organization may indicate shifts in neurological state.
  • Identifying unknown time points of network change is crucial for understanding brain dynamics.

Purpose of the Study:

  • To develop a method for detecting time-dependent community structure in brain networks.
  • To investigate whether brain network topology changes over time at unknown points.
  • To explore the relationship between network organization shifts and neurological states.

Main Methods:

  • Introduced State-based Dynamic Community Structure (SDCS).
  • Utilized a hidden Markov stochastic blockmodel to define time-dependent community structure.
  • Applied the method to functional magnetic resonance imaging (fMRI) data.

Main Results:

  • The SDCS method successfully detected time-dependent community structure in brain networks.
  • Identified distinct profiles of time-varying connectivity in networks related to pain, working memory, and emotion.
  • Demonstrated that network organization changes occur at unknown time points.

Conclusions:

  • Brain network topology is not always static and exhibits dynamic changes.
  • The SDCS method provides a novel approach to analyze time-varying functional connectivity.
  • Understanding dynamic network changes is important for studying neurological states and their impact on functions like pain perception.