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Related Experiment Video

Updated: Sep 24, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional

Robyn L Miller1, Victor M Vergara1, Godfrey D Pearlson2

  • 1The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.

Frontiers in Neuroscience
|May 6, 2022
PubMed
Summary

This study introduces a novel framework for analyzing dynamic functional network connectivity (dFNC) in the brain. The new method, EVOdFNCs, better captures brain connectivity changes associated with schizophrenia symptoms than traditional approaches.

Keywords:
dynamic functional network connectivity (dFNC)functional magnetic resonance imaging (fMRI)functional network connectivity (FNC)resting state fMRIschizophreniauniform manifold approximation and embedding (UMAP)

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Network Science

Background:

  • Brain connectivity is increasingly studied as a dynamic, time-varying property.
  • Existing methods often capture only discrete, static connectivity states from resting-state functional magnetic resonance imaging (rs-fMRI).
  • Analyzing group-level, temporally evolving connectivity patterns is crucial for fully utilizing rs-fMRI data.

Purpose of the Study:

  • To introduce a flexible, data-driven framework for identifying group-level dynamic functional network connectivity (dFNC) states.
  • To develop a method for representing and analyzing high-dimensional, time-varying brain connectivity patterns.

Main Methods:

  • Utilized uniform manifold approximation and embedding (UMAP) for dimensionality reduction and visualization of dFNC.
  • Developed a method to derive multiframe, high-dimensional dFNC trajectories (EVOdFNCs) from 2D embeddings.
  • Employed EVOdFNCs as dynamic basis objects to characterize observed dFNC trajectories.

Main Results:

  • The framework successfully identified group-level multiframe dFNC states.
  • EVOdFNCs revealed anomalous patterns of dynamic brain connectivity associated with schizophrenia.
  • This dynamic characterization was more sensitive to positive schizophrenia symptoms (hallucinations, delusions) than conventional methods.

Conclusions:

  • The proposed EVOdFNC framework offers a more sensitive approach to characterizing dynamic brain connectivity.
  • This method provides new insights into the neural underpinnings of schizophrenia.
  • Dynamic characterization of brain connectivity is essential for understanding complex brain disorders.