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Fluctuations between high- and low-modularity topology in time-resolved functional connectivity.

Makoto Fukushima1, Richard F Betzel2, Ye He3

  • 1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA.

Neuroimage
|August 22, 2017
PubMed
Summary

Brain network modularity fluctuates over time, with high modularity periods showing distinct default mode network interactions. These temporal variations explain individual differences in long-term brain network organization.

Keywords:
ConnectomicsModularityNetworksResting stateTime-resolved functional connectivity

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Area of Science:

  • Neuroscience
  • Network Science
  • Cognitive Neuroscience

Background:

  • Modularity is a key feature of functional brain networks, varying across individuals and within individuals over short time scales.
  • Previous research has linked network modularity to demographics and cognitive performance.
  • The specific characteristics of time-resolved functional networks during high and low modularity states remain largely unknown.

Purpose of the Study:

  • To investigate the spatiotemporal properties of time-resolved functional brain networks during rest.
  • To focus on spatial connectivity patterns, temporal homogeneity, and test-retest reliability during high and low modularity periods.
  • To understand how short-term network configurations relate to long-term modularity differences.

Main Methods:

  • Analysis of time-resolved functional connectivity from human fMRI data during rest.
  • Characterization of spatial connectivity patterns, specifically the dissociation between default mode network and task-positive network modules.
  • Assessment of temporal homogeneity within high and low modularity periods and inter-session test-retest reliability.

Main Results:

  • High modularity periods exhibit increased dissociation between default mode network and task-positive network modules.
  • Low modularity periods show decreased module dissociation, with the default mode network interacting variably with other networks.
  • The occurrence of high and low modularity periods varies across individuals, demonstrating moderate test-retest reliability and correlation with long-timescale modularity.

Conclusions:

  • Time-resolved functional networks exhibit distinct spatiotemporal organization during high and low modularity states.
  • Individual differences in long-timescale network modularity can be attributed to the variable occurrence of specific network configurations at shorter timescales.
  • This study provides insights into the dynamic nature of brain network organization and its link to individual variability.