Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

Abstract

Large-scale brain dynamics measures repeating spatiotemporal connectivity patterns that reflect a range of putative di↵erent brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood and whether switching between these states is important for behavior has been little studied. Our aim here is to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved functional MRI (fMRI) connectivity from one-hour long data recordings in 1003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data was used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching which we define as the rate at which each brain region transits between di↵erent fMRI networks. We found i) an inverse relationship between network switching and connectivity dynamics -defined as the di↵erence in variance between time-resolved fMRI connectivity signals and phase randomized surrogates-; ii) brain connectivity was lower during intervals of network switching; iii) brain areas with frequent network switching had greater temporal complexity; iv) brain areas with high network switching were located in association cortices; and v) using cross-validated Elastic Net regression, network switching predicted intersubject variation in working memory performance, planning/reasoning and amount of sleep. Our findings shed new light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

Characterization of network switching in disorder of consciousness at multiple time scales

“…With a relatively long epoch length, patients are more likely to show lower flexibility, implying that the functional interaction ability may be impaired in patients with DOC. This observation is similar to the findings showing a positive correlation with the behavioral performance using fMRI data, which has a temporal resolution of at least 2 s in most cases [29,45,46]. In fact, previous studies have linked flexible reconfiguration of functional networks with learning, cognitive flexibility and working memory [30,47].…”

“…Functional brain networks were constructed using the EEG source connectivity method. The reconstructed regional time series were filtered in different frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Then the functional connectivity between the regional time series was computed using the phase locking value (PLV) for each frequency band [38].…”

“…This leads to a measure known as network flexibility that assesses how often the brain regions change their community assignment between adjacent time points [28]. Intriguingly, network flexibility has been found to correlate with learning, attention and fatigue [27,[29][30][31]. However, the relationship between consciousness and the time-varying dynamics of community structure is poorly explored.…”

A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction

“…For example, the time series of dFC has long-range sequential correlations that vary across the human adult lifespan ( Battaglia et al, 2020 ) and specific temporal structures of several FC microstates have been reported to be subject-specific and heritable, and significantly linked to individual cognitive traits ( Vidaurre et al, 2017 ). Moreover, network switching in dFC is related to task performance and sleep ( Pedersen et al, 2018 ), attention ( Madhyastha et al, 2015 ), schizophrenia ( Damaraju et al, 2014 ), and depression ( Zheng et al, 2017 ). In particular, a growing body of researches links observed patterns of non-stationary switching between FC states with aspects of the underlying neural dynamics ( Hansen et al, 2015 ; Thompson, 2018 ), indicating short-term alteration in FC time series along with shifting in cognitive states.…”