Tracking the Brain's Functional Coupling Dynamics over Development

Abstract

The transition from childhood to adulthood is marked by pronounced functional and structural brain transformations that impact cognition and behavior. Here, we use a functional imaging approach to reveal dynamic changes in coupling strength between networks and the expression of discrete brain configurations over human development during rest and a cognitive control task. Although the brain's repertoire of functional states was generally preserved across ages, state-specific temporal features, such as the frequency of expression and the amount of time spent in select states, varied by age in ways that were dependent on condition. Increasing age was associated with greater variability of connection strengths across time at rest, while there was a selective inversion of this effect in higher-order networks during implementation of cognitive control. The results suggest that development is characterized by the modification of dynamic coupling to both maximize and constrain functional variability in response to ongoing cognitive and behavioral requirements.

Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature

“…There is evidence that this property is important for executive functioning, learning, and switching between challenging task demands (Bassett et al, 2011; Braun et al, 2015; Chen et al, 2016). Over development, rs-fcMRI networks show increased within-subject variability (Hutchison and Morton, 2015; Marusak et al, 2016; Qin et al, 2015), consistent with EEG studies showing that signal complexity increases over development (McIntosh et al, 2008; Vakorin et al, 2011). Recent simultaneous EEG-fMRI work (Fransson et al, 2013) links developmental differences (infants versus adults) in rs-fcmri network dynamics with differences in EEG power spectra, consistent with the notion that temporal variability in very low frequency correlations is an emergent, hidden property of higher frequency power spectra (Chang et al, 2013; Tagliazucchi et al, 2012), which is known to change continuously throughout development (Miskovic et al, 2015; Rodriguez-Martinez et al, 2015; Smit et al, 2012; Whitford et al, 2007).…”

Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling

“…Flexible and dynamic cross-network functional interactions are essential for mature brain function [5,34], yet little is known about the nature of dynamic organization and time-varying connectivity in children relative to adults. Studies using static connectivity analyses suggest that functional brain networks undergo significant reconfiguration from childhood to adulthood, with analysis of time-averaged whole-brain connectivity patterns suggesting prominent increases as well as decreases in connectivity between childhood and adulthood.…”

“…In a previous study we showed that time-averaged connectivity within key nodes of the SN and DMN as well as their inter-network interactions is weaker in children relative to adults [28]. Recent reports suggest that time-varying connectivity between distributed brain areas changes significantly with age, with greater temporal variability of connection strengths in children compared to adults[34]. Based on these observations, we hypothesized that compared to adults, children would show immature and less flexible patterns of dynamic connectivity between the SN, CEN and DMN.…”

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series

“…To date, sFC has been applied to many areas of the brain research, from a general understanding of the network topology of the brain (De Luca et al, 2006; Fransson, 2005; Greicius et al, 2003), task modulation (Fransson, 2006), neurodevelopment (Power et al, 2010), and clinical applications (Fox and Greicius, 2010). In the case of dFC, quantitative studies of the fluctuations of signal covariance over time offers a possibility to explore the dynamics of the brain and it has already found applications; from understanding basic brain processes such as levels of consciousness (Barttfeld et al, 2015), mind wandering (Schaefer et al, 2014), and development (Hutchison and Morton, 2015), to clinical applications such as depression (Kaiser et al, 2016) and schizophrenia (Damaraju et al, 2014; Ma et al, 2014). …”

“…In the neuroimaging dFC literature, the variance is often stabilized by applying the Fisher transformation to the connectivity time series (a nonexhaustive list includes: Allen et al, 2014; Barttfeld et al, 2015; Damaraju et al, 2014; Elton and Gao, 2015; Hutchison and Morton, 2015; Kaiser et al, 2016; Kucyi and Davis, 2014; Leonardi et al, 2014; Schaefer et al, 2014). Generally, there are good reasons for doing so, since a reasonably stable signal variance is required to be able to accurately quantify changes in dynamic brain functional connectivity, which often is the primary goal of the analysis.…”