Changes in the interaction of resting-state neural networks from adolescence to adulthood

Abstract

This study examined how the mutual interactions of functionally integrated neural networks during resting-state fMRI differed between adolescence and adulthood. Independent component analysis (ICA) was used to identify functionally connected neural networks in 100 healthy participants aged 12-30 years. Hemodynamic timecourses that represented integrated neural network activity were analyzed with tools that quantified system "causal density" estimates, which indexed the proportion of significant Granger causality relationships among system nodes. Mutual influences among networks decreased with age, likely reflecting stronger within-network connectivity and more efficient between-network influences with greater development. Supplemental tests showed that this normative age-related reduction in causal density was accompanied by fewer significant connections to and from each network, regional increases in the strength of functional integration within networks, and age-related reductions in the strength of numerous specific system interactions. The latter included paths between lateral prefrontal-parietal circuits and "default mode" networks. These results contribute to an emerging understanding that activity in widely distributed networks thought to underlie complex cognition influences activity in other networks.

Figure 1

Cortical renderings [Van Essen et al.,2001] of the spatial structure of 13 ICA components identified during resting‐state fMRI assessment. These renderings depict the spatial structure of neural networks identified during resting‐state fMRI assessment. The data in each rendering was produced using SPM2 one‐sample t test to quantify how the spatial structure of these components is conserved over all n = 100 subjects.

Figure 2

Results of stepwise regression showing significant linear and curvilinear inverse effects of age on systemwide causal density statistics. These results show a general reduction in distributed network interactions with increasing age, with the most significant changes occurring during adolescence.

Figure 3

Age‐differences in effective connections among ICA components. These are results from a supplemental analysis that examined the correlation of age with estimates of the strength of “causal connectivity”, calculated as the log transform of the F statistic testing the significance of Granger Causality between any two network hemodynamic timecourses [Seth,2005]. These exploratory results are evaluated at a liberal statistical threshold of P < 0.05, uncorrected.