Chronnectomic patterns and neural flexibility underlie executive function

Abstract

Despite extensive research into executive function (EF), the precise relationship between brain dynamics and flexible cognition remains unknown. Using a large, publicly available dataset (189 participants), we find that functional connections measured throughout 56 minutes of resting state fMRI data comprise five distinct connectivity states. Elevated EF performance as measured outside of the scanner was associated with greater episodes of more frequently occurring connectivity states, and fewer episodes of less frequently occurring connectivity states. Frequently occurring states displayed metastable properties, where cognitive flexibility may be facilitated by attenuated correlations and greater functional connection variability. Less frequently occurring states displayed properties consistent with low arousal and low vigilance. These findings suggest that elevated EF performance may be associated with the propensity to occupy more frequently occurring brain configurations that enable cognitive flexibility, while avoiding less frequently occurring brain configurations related to low arousal/vigilance states. The current findings offer a novel framework for identifying neural processes related to individual differences in executive function.

Dynamic brain network configurations during rest and an attention task with frequent occurrence of mind wandering

“…State 3 is the most frequently occurring state during rest (35%) and is characterized by overall weaker connectivity between and within networks. This finding is in line with prior evidence revealing that the most frequent DFC states during rest are characterized by attenuated between and within network connectivity (Allen et al, ; Nomi, Bolt, et al, ; Nomi, Vij, et al, ). These states have been referred to as “metastable brain states” (Nomi, Vij, et al, ), that are in the middle of a continuum providing the balance between extremely focused (ordered) and extreme unfocused (disordered) states (Hellyer, Scott, Shanahan, Sharp, & Leech, ; Tognoli & Kelso, ).…”

“…Indeed, brain networks dynamically interact over time, as revealed by recent advances in analytical methods for functional neuroimaging data (i.e., time‐varying functional connectivity, also referred to as dynamic functional connectivity, DFC; Allen et al, ; Chang & Glover, ). This analytical method has been applied in both resting‐state and task‐based fMRI investigations and has revealed reoccurring patterns of DFC between networks, referred to as brain states (Allen et al, ; Marusak et al, ; Nomi, Vij, et al, ; Rashid, Damaraju, Pearlson, & Calhoun, ). Emerging evidence has suggested a correspondence between DFC states and mental states (e.g., Gonzalez‐Castillo et al, ).…”

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns

“…Intriguingly, we found that the DFC pattern derived from R‐fMRI showed a notable ability to differentiate between individuals, suggesting that the chronnectome at rest may act as a fingerprint that reflects individual intrinsic characteristics. Previous studies have explored inter‐individual variability in dynamic functional architecture in terms of associations with individual cognitive performance (Bassett, Yang, Wymbs, & Grafton, ; Braun et al, ; Davison et al, ; Gonzalez‐Castillo et al, ; Madhyastha et al, ; Nomi et al, ), individual demographics (Davison et al, ) and clinical characteristics (Damaraju et al, ; Rashid et al, ; Wei et al, ; Zhang et al, ). In a recent study, by performing a hypergraph analysis (a method based on dynamic network theory) on lifespan datasets, Davison et al () showed that one dynamic metric (i.e., hypergraph cardinality) exhibited individual differences and was significantly correlated with age.…”

Multimodal neural correlates of cognitive control in the Human Connectome Project

“…Further, recently developed methods in dynamic rsfcMRI (Calhoun, Miller et al 2014) have identified specific modes of neural resting-state connectivity and that inter-individual differences in the tendencies to use particular modes of connectivity were related to cognitive control. Specifically, modes which showed strong modular networks and anticorrelated relationships from visual and somatosensory areas to cerebellar regions, were significantly correlated with improved performance on several executive tasks including measures of cognitive flexibility, processing speed, and working memory but not with fluid intelligence or inhibition and attention (Nomi, Vij et al 2016). …”