Spatio‐temporal dynamics of resting‐state brain networks improve single‐subject prediction of schizophrenia diagnosis

Abstract

Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding-window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting-state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting-state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting-state dynamics are not trivially attributable to sampling variability and head motion.

Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network

“…In this study, we provided a novel characterization of these disturbances by explicitly modeling the dynamics of neural activity as a function of time, whereas most previous functional neuroimaging studies of schizophrenia have focused on static characterizations of activation and connectivity that represent time averages over the entire data acquisition interval. Furthermore, aberrant connectivity dynamics in the disorder are heritable (Su et al, 2016) and enable accurate, single-subject prediction of diagnostic status (Kottaram et al, 2018). Furthermore, aberrant connectivity dynamics in the disorder are heritable (Su et al, 2016) and enable accurate, single-subject prediction of diagnostic status (Kottaram et al, 2018).…”

“…Time-resolved analyses of functional connectivity in schizophrenia suggest that the disorder is characterized by reduced dynamism in connectivity (Miller et al, 2016), shorter dwell times in highly integrated states and altered thalamo-sensory dynamics . Furthermore, aberrant connectivity dynamics in the disorder are heritable (Su et al, 2016) and enable accurate, single-subject prediction of diagnostic status (Kottaram et al, 2018).…”

“…Neural dynamics give rise to spatially distributed networks of functionally interconnected regions that emerge and dissolve over multiple timescales (Cabral, Kringelbach, & Deco, 2014Calhoun, Miller, Pearlson, & Adalı, 2014;Deco, Jirsa, & McIntosh, 2011;Hutchison et al 2013a;Kottaram et al, 2018;Preti, Bolton, & Van De Ville, 2016). An emerging idea is that of a functional repertoire of putative brain states that is continually revisited and rehearsed against a background of noise-driven endogenous neural activity (Deco & Jirsa, 2012;Sporns, 2013).…”

“…Previous studies suggest that large-scale neural dynamics in schizophrenia are aberrant and characterized by reduced dynamism Miller et al, 2016), heritable (Su et al, 2016), distinguishable from bipolar disorder (Rashid et al, 2014) and healthy comparison individuals (Kottaram et al, 2018), and associated with working memory performance (Fu et al, 2017). These previous studies have primarily focused on characterizing functional connectivity dynamics (i.e., interregional coupling), rather than the dynamics of brain activity per se (i.e., blood-oxygenation level-dependent [BOLD] signal dynamics).…”

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

“…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].…”