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Front. Comput. Neurosci. | doi: 10.3389/fncom.2019.00075

Extracting Reproducible Time-Resolved Resting State Networks using Dynamic Mode Decomposition

  • 1Pacific Northwest Research Institute, United States
  • 2University of Washington, United States

Resting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are
believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods
for computing RSNs typically assume these functional networks are static throughout the duration of a scan
lasting 5–15 minutes. However, they are known to vary on timescales ranging from seconds to years; in
addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently,
there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to
extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic
mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data,
allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. After validating
the method on a synthetic dataset, we analyze data from 120 individuals from the Human Connectome
Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD)
and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to
established methods, sDMD modes capture individualized RSN structure that both better resembles the
population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD
analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-
based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual
subjects.

Keywords: Resting State Network (RSN), Dynamic mode decomposition (DMD), RS-fMRI, Human Connectome Project (HCP), Individualized Networks, network dynamics

Received: 04 Apr 2019; Accepted: 11 Oct 2019.

Copyright: © 2019 Kunert-Graf, Eschenburg, Galas, Kutz, Rane and Brunton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. James M. Kunert-Graf, Pacific Northwest Research Institute, Seattle, United States, kunert@uw.edu