Predicting individual brain maturity using dynamic functional connectivity

Abstract

Neuroimaging-based functional connectivity (FC) analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited. Here, we examined age-related differences in the temporal variability of FC dynamics with data publicly released by the Nathan Kline Institute (NKI; n = 183, ages 7–30) and showed that dynamic inter-region interactions can be used to accurately predict individual brain maturity across development. Furthermore, we identified a significant age-dependent trend underlying dynamic inter-network FC, including increasing variability of the connections between the visual network, default mode network (DMN) and cerebellum as well as within the cerebellum and DMN and decreasing variability within the cerebellum and between the cerebellum and DMN as well as the cingulo-opercular network. Overall, the results suggested significant developmental changes in dynamic inter-network interaction, which may shed new light on the functional organization of typical developmental brains.

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).…”

Dynamic functional connectivity of neurocognitive networks in children

“…These findings, together with prior research in adult and pediatric samples (Allen et al , 2014; Hutchison et al , 2015; Qin et al , 2015), caution against a labeling scheme in which ICNs are treated as singular and stable entities. Rather, ICNs show significant flexibility in functional coordination with other brain systems.…”

“…Additionally, older individuals showed a higher frequency of state transitions during a cognitive control task, but not during rest. Other work demonstrates that connections among ICNs are more variable with increasing age (Allen et al , 2011; Qin et al , 2015). These findings are in agreement with seminal research by McIntosh and colleagues (2008) in children (ages 8-15) and young adults (ages 20-33) showing that electrophysiological activity increases with age, which corresponds with reduced behavioral variability and more accurate performance.…”

Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

“…This window size was chosen based on previous literature demonstrating that window sizes between 30 and 64s were associated with lower variability of prediction errors, lower permutation p-values and higher stability of bootstrap ratios when using multivariate approaches to examine the association between FC variability and chronological age (Qin et al, 2015). This sliding window size has also been used in previous studies of dynamic FC (Allen et al, 2014; .…”