Structural covariance across the lifespan: Brain development and aging through the lens of inter-network relationships.

Recent studies have revealed that brain development is marked by morphological synchronization across brain regions. Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within-network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big-data framework and a novel application of covariate-adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7-90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter-network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter-network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. This study is the first to characterize brain development and aging in terms of inter-network structural covariance across the lifespan.

Retrospective Correction of Physiological Noise: Impact on Sensitivity, Specificity, and Reproducibility of Resting-State Functional Connectivity in a Reading Network Model.

It is well accepted that physiological noise (PN) obscures the detection of neural fluctuations in resting-state functional connectivity (rsFC) magnetic resonance imaging. However, a clear consensus for an optimal PN correction (PNC) methodology and how it can impact the rsFC signal characteristics is still lacking. In this study, we probe the impact of three PNC methods: RETROICOR: (Glover et al., 2000 ), ANATICOR: (Jo et al., 2010 ), and RVTMBPM: (Bianciardi et al., 2009 ). Using a reading network model, we systematically explore the effects of PNC optimization on sensitivity, specificity, and reproducibility of rsFC signals. In terms of specificity, ANATICOR was found to be effective in removing local white matter (WM) fluctuations and also resulted in aggressive removal of expected cortical-to-subcortical functional connections. The ability of RETROICOR to remove PN was equivalent to removal of simulated random PN such that it artificially inflated the connection strength, thereby decreasing sensitivity. RVTMBPM maintained specificity and sensitivity by balanced removal of vasodilatory PN and local WM nuisance edges. Another aspect of this work was exploring the effects of PNC on identifying reading group differences. Most PNC methods accounted for between-subject PN variability resulting in reduced intersession reproducibility. This effect facilitated the detection of the most consistent group differences. RVTMBPM was most effective in detecting significant group differences due to its inherent sensitivity to removing spatially structured and temporally repeating PN arising from dense vasculature. Finally, results suggest that combining all three PNC resulted in "overcorrection" by removing signal along with noise.