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Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition.

James M Kunert-Graf1, Kristian M Eschenburg2, David J Galas1

  • 1Pacific Northwest Research Institute, Seattle, WA, United States.

Frontiers in Computational Neuroscience
|November 19, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new dynamic mode decomposition (DMD) method to capture brain

Keywords:
RS-fMRIdynamic mode decomposition (DMD)human connectome project (HCP)individualized networksnetwork dynamicsresting state network (RSN)

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Area of Science:

  • Neuroscience
  • Data Science
  • Biomedical Imaging

Background:

  • Resting state networks (RSNs) from fMRI are crucial for understanding brain organization.
  • Traditional RSN analysis assumes static networks, but RSNs are dynamic and altered in neurological disorders.
  • Characterizing RSN dynamics is challenging due to the need for reproducible, time-resolved network extraction.

Purpose of the Study:

  • To develop a novel, robust method for extracting time-resolved resting state networks (RSNs) from fMRI data.
  • To characterize the dynamic properties of RSNs at the group and individual subject levels.
  • To enable reproducible inference of RSN occupancy and transitions.

Main Methods:

  • Developed a novel method based on dynamic mode decomposition (DMD) for analyzing noisy, high-dimensional fMRI data.
  • Applied unsupervised clustering to DMD modes to identify group-level (gDMD) and single-subject-level (sDMD) RSNs.
  • Validated the method on synthetic data and analyzed fMRI data from 120 Human Connectome Project participants.

Main Results:

  • The gDMD modes closely matched canonical RSNs.
  • sDMD modes revealed individualized RSN structures that better represented population RSNs and captured subject-level variations.
  • The DMD-based method achieved high reproducibility in inferring RSN occupancy and transitions.

Conclusions:

  • The automated DMD-based method robustly extracts time-resolved RSNs from fMRI data with second-level temporal resolution.
  • This approach offers a powerful tool for characterizing both spatial and temporal RSN structures in individual subjects.
  • The method advances the study of RSN dynamics and their alterations in neurological conditions.