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Multi-Granularity Whole-Brain Segmentation Based Functional Network Analysis Using Resting-State fMRI.

Yujing Gong1, Huijun Wu1,2, Jingyuan Li1,3

  • 1Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.

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|January 9, 2019
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Summary

This study reveals that brain network topology is robust to changes in node definition and granularity. Small-worldness is an intrinsic property of resting-state functional networks, regardless of analysis choices.

Keywords:
brain networkfMRImulti-atlas segmentationmulti-granularityontology relationshipresting-statesmall-worldness

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Understanding the human brain's functional network architecture is crucial for neuroscience.
  • Resting-state functional magnetic resonance imaging (rs-fMRI) provides insights into brain connectivity.
  • The impact of nodal definitions on brain network topology requires systematic investigation.

Purpose of the Study:

  • To systematically analyze the effects of multi-granularity nodal definitions on human brain functional network topology.
  • To compare topological properties across different granularity levels and anatomical definition types.
  • To determine the intrinsic properties of brain networks independent of analytical choices.

Main Methods:

  • Utilized rs-fMRI data from 69 healthy young subjects (19 in the primary dataset, 49 for validation).
  • Applied a multi-granularity whole-brain segmentation scheme with two types of anatomical definitions (Type I and Type II).
  • Computed and compared topological properties, including nodal degree and betweenness, across various network granularities and definitions.

Main Results:

  • Nodal degree and betweenness values changed with granularity, but relative network values remained stable.
  • Network sparsity influenced average nodal degree, while nodal definitions specifically impacted other topological properties.
  • Decreasing granularity within the same ontology type enhanced information propagation efficiency.
  • Small-worldness was identified as an intrinsic property of resting-state functional networks, independent of definition or granularity.

Conclusions:

  • Brain functional network topology exhibits robustness to variations in nodal definitions and granularity.
  • Information propagation efficiency is influenced by network granularity.
  • Small-world properties are fundamental to the intrinsic organization of the human brain's resting-state functional network.