Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance

• Published in: Frontiers in neuroscience Vol. 13; p. 1448
• Main Authors: Deshpande, Gopikrishna, Jia, Hao
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 06.02.2020; Frontiers Media S.A
• Subjects: Algorithms; behavioral relevance; Brain; Brain mapping; Clustering; Cognition; Cortex (frontal); Cortex (motor); Cortex (parietal); dynamic brain connectivity; effective connectivity; Functional magnetic resonance imaging; human connectome; Investigations; Neural networks; Neuroimaging; Neuroscience; Occipital lobe; Principal components analysis; resting state fMRI; Synchronization; Temporal cortex; Time series; dynamic brain connectivity; behavioral relevance; clustering; resting state fMRI; human connectome; effective connectivity
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2019.01448

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 ( = 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) ( = 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.