Abstract
Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.
Highlights
The view that human brain functions as a coordinated system with functional segregation and integration between different regions has been corroborated and widely accepted (Friston et al, 1993; Greicius et al, 2009; Guye et al, 2010; Rogers et al, 2010)
Dimensionality reduction is often employed and an exemplary work employing wholebrain regions/voxels for static Effective connectivity (EC) can be found in Wu et al (2013). These challenges become even more acute while computing whole-brain dynamic EC. We address these challenges by adopting a dynamic multivariate autoregressive (MVAR) for characterizing dynamic EC in combination with a dimensionality reduction strategy based on multi-level clustering of dynamic EC patterns across spatial location, time, and subjects
As we have shown in the case of static connectivity (Deshpande et al, 2011b), absence of significant synchronous connectivity does not imply the absence of brain connectivity, rather, such regions could be communicating via non-synchronous relationships that may be captured via EC
Summary
The view that human brain functions as a coordinated system with functional segregation and integration between different regions has been corroborated and widely accepted (Friston et al, 1993; Greicius et al, 2009; Guye et al, 2010; Rogers et al, 2010). Recent evidence points to the fact that resting state FC is not stationary in time and an array of methods have been proposed to capture dynamic variations in FC (Deshpande et al, 2006; Sato et al, 2006; Britz et al, 2010; Chang and Glover, 2010; Sakoðlu et al, 2010; Chang et al, 2013a,b; Majeed et al, 2011; Cribben et al, 2012; Dimitriadis et al, 2012; Fornito et al, 2012; Handwerker et al, 2012; Hutchison et al, 2012, 2013; Rack-Gomer and Liu, 2012; Tagliazucchi et al, 2012; Keilholz et al, 2013; Lee et al, 2013; Leonardi et al, 2013) This raises the possibility that dynamic alterations in resting state EC cannot be ignored and needs to be investigated. To the best of our knowledge, there has been scant literature on dynamic EC of resting state fMRI (but see Jin et al, 2017; Zhao et al, 2017; Rangaprakash et al, 2018), and most investigations of dynamic EC have focused on taskbased fMRI (Sato et al, 2006; Havlicek et al, 2010; Grant et al, 2014, 2015; Lacey et al, 2014; Wheelock et al, 2014; Hutcheson et al, 2015; Feng et al, 2016, 2018; Hampstead et al, 2016; Wang et al, 2017; Ramaihgari et al, 2018; Rao et al, 2018)
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