Dynamic functional connectivity of neurocognitive networks in children

Abstract

The human brain is highly dynamic, supporting a remarkable range of cognitive abilities that emerge over the course of development. While flexible and dynamic coordination between different neural systems is firmly established for children, our understanding of brain functional organization in early life has been built largely on the implicit assumption that functional connectivity (FC) is static. Understanding the nature of dynamic neural interactions during development is a critical issue for cognitive neuroscience, with implications for neurodevelopmental pathologies that involve anomalies in brain connectivity. In this work, we delineate FC dynamics of neurocognitive networks in a sample of 146 youth from varied sociodemographic backgrounds. We apply independent component analysis, sliding time window correlation, and k-means clustering to resting-state fMRI data. Our analyses revealed six FC states that re-occur over time and that complement, but significantly extend, measures of static connectivity averaged across the experiment. Moreover, the occurrence and amount of time spent in specific FC states are related to the content of self-generated thought during the scan. We also found age-related increases in temporal variability of FC among neurocognitive networks. In addition, some connections are more variable over time than are others, including those between inferior parietal lobe and precuneus. These regions contribute to multiple large-scale systems and likely play a role in adaptive processes in early life. Taken together, these findings lay the groundwork for understanding how variation in the developing chronnectome is related to risk for neurodevelopmental disorders. Understanding how brain systems reconfigure with development should provide insight into the ontogeny of complex, flexible cognitive processes in adulthood.

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).…”

Refined measure of functional connectomes for improved identifiability and prediction

“…However, many works indicated that during the resting state, significant variations could be observed in a short time, and dynamic FC analysis has drawn more and more attention (Cai, Zhang, et al, 2018;Cai, Zille, et al, 2018;Damaraju et al, 2014;Hutchison et al, 2013;Marusak et al, 2017;Rashid et al, 2014). This suggests that both individual distinctiveness and cognitive prediction using the resting state fMRI are proven to be the most challenging for identifying individual differences.…”

Comparing test-retest reliability of dynamic functional connectivity methods

“…Brain states can then be summarized as the patterns of connectivity at each centroid, and additional summary metrics such as the amount of time each subject spends in a given state can be computed. Using this definition of brain state, it has been shown that the patterns of connectivity describing each state are reliably observed across groups and individuals (Yang et al, 2014), while other characteristics such as the amount of time spent in specific states and the number of transitions between states vary with meaningful individual differences such as age (Hutchison and Morton, 2015; Marusak et al, 2017) or disease status (Damaraju et al, 2014; Rashid et al, 2014). However, this approach towards understanding what has recently been termed the “chronnectome” is still in its infancy (Calhoun et al, 2014).…”