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Related Concept Videos

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Related Experiment Video

Updated: May 25, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Multivariate synchrony modules identified through multiple subject community detection in functional brain networks.

Marcos E Bolaños1, Edward M Bernat, Selin Aviyente

  • 1Department of Electrical Engineering, Michigan State University, 2120 Engineering Building, East Lansing, MI 48824, USA. bolanosm@msu.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to identify common community structures in functional brain networks across multiple subjects. This approach helps understand brain organization during cognitive control tasks like error monitoring.

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Published on: July 1, 2014

Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Human brain functional connectivity can be modeled as complex networks.
  • These scale-free networks exhibit heterogeneous structures with dense clusters representing multivariate relationships.
  • Existing network clustering algorithms classify nodes without prior information.

Purpose of the Study:

  • To propose a novel method for partitioning functional brain networks across subjects.
  • To reveal common community structures within these networks.
  • To evaluate brain organization during cognitive control, specifically error-monitoring.

Main Methods:

  • Modeling brain interactions as graphs of vertices and edges.
  • Utilizing network clustering algorithms based on similarity measures.
  • Applying a community identifying algorithm to functional brain networks during an error-related negativity (ERN) task.

Main Results:

  • The proposed method successfully partitions networks and reveals common community structures.
  • The algorithm identified how the brain organizes itself during error-monitoring.
  • Demonstrated the utility of graph theory in understanding brain functional organization.

Conclusions:

  • The study presents a new computational approach for analyzing multi-subject brain networks.
  • This method enhances understanding of brain network topology and function during cognitive tasks.
  • Findings contribute to the field of cognitive neuroscience and network analysis.