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 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. We demonstrate this method on 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 subjectlevel 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.

Metastable Resting State Brain Dynamics

“…The present work takes a step further and considers a direct approach by employing a data-driven computational method to investigate the stable switching attractors hypotheses of RSNs, leveraging a recently developed method, called recurrence structure analysis (RSA) (beim Graben and Hutt, 2013, 2015; beim Graben et al, 2016). We note however that the proposed framework is not the only method for extracting reproducible time-resolved networks from data as very recently a competing framework based on dynamic mode decomposition was proposed (Kunert-Graf et al, 2018). Briefly, our method utilizes advanced theories of dynamical systems and time series analysis, attempting to extract optimal symbolic dynamics from time series observations that display transient and recurrent states (i.e., metastable states).…”

Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia

“…However, studies have shown that changes in the FNC patterns imply changes in the spatial networks (Calhoun et al, 2008). Hence, spatio-temporal dFNC analysis relaxes the assumption of stationarity in both the spatial and temporal domain, and provides a more general framework for capturing time-varying FNC patterns (Ma et al, 2014; Kottaram et al, 2018; Kunert-Graf et al, 2018). The availability of higher number of samples in the spatial domain also guarantees reliable estimation of functional correlations (Hero and Rajaratnam, 2016), thus providing a promising direction for the use of spatial domain for dFNC analysis.…”

Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA

“…We introduce pt-cIVA method such that it controls the amount of correspondence between the estimated source and the reference signal. In this case, the reference signals are the group components estimated using GICA and exhibit variability across time windows [17], [18], [19]. Using a fixed constraint parameter controls the amount of correspondance between the reference signal and estimated source, and constrains the variability of the reference signals across time windows.…”

“…Most dFC analysis techniques examine time-varying associations among the activation patterns of spatial networks while assuming that the spatial evolution of the networks is stationary. However, studies have shown that changes in functional connectivity patterns imply changes in the spatial networks [17], [18], [19]. Region of interest (ROI)-based analyses on resting-state networks (RSNs) have shown better classification of subjects when variability in both spatial and temporal domains is considered compared with variability assumed in either spatial or temporal domain [17], [18].…”

“…Region of interest (ROI)-based analyses on resting-state networks (RSNs) have shown better classification of subjects when variability in both spatial and temporal domains is considered compared with variability assumed in either spatial or temporal domain [17], [18]. Dynamic mode decomposition (DMD), a spatio-temporal modal decomposition technique, has demonstrated changes in the temporal activation of RSNs [19]. Although these techniques provide interesting results, the use of pre-defined RSNs causes the estimated functional connectivity to be sensitive to network selection whereas DMD requires significant dimension reduction that may restrict the method to estimation of few spatial components.…”