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

ABSTRACT: 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 min. 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

SUBMITTER: Kunert-Graf JM 

PROVIDER: S-EPMC6834549 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature