Structural covariance networks across the life span, from 6 to 94 years of age.

Elizabeth Dupre,R Nathan Spreng

doi:10.1162/netn_a_00016

Abstract

Structural covariance examines covariation of gray matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete life span perspective—bridging childhood with early, middle, and late adulthood—on the development of structural covariance networks. Here, we investigate the life span trajectories of structural covariance in six canonical neurocognitive networks: default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual. By combining data from five open-access data sources, we examine the structural covariance trajectories of these networks from 6 to 94 years of age in a sample of 1,580 participants. Using partial least squares, we show that structural covariance patterns across the life span exhibit two significant, age-dependent trends. The first trend is a stable pattern whose integrity declines over the life span. The second trend is an inverted-U that differentiates young adulthood from other age groups. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, our results suggest that structural covariance provides a reliable definition of neurocognitive networks across the life span and reveal both shared and network-specific trajectories.

Highlights

The human cerebral cortex is hierarchically organized into complex brain networks that can be considered at multiple levels of analysis (Mesulam, 1998)
We assessed the structural covariance of six large-scale neurocognitive networks well represented in the literature: the default network (DN), dorsal attention network (DAN), frontoparietal control network (FPCN), somatomotor network (SM), ventral attention network (VAN), and visual systems
We investigated the structural covariance of previously identified large-scale neurocognitive networks including the DN, DAN, FPCN, SM, VAN, and visual networks

Summary

Introduction

The human cerebral cortex is hierarchically organized into complex brain networks that can be considered at multiple levels of analysis (Mesulam, 1998) One such level is structural covariance, or how interindividual differences in regional brain structure covary with other. Structural covariance networks reflect shared variation in gray matter morphology (Mechelli et al, 2005) and are assessed using measures such as cortical thickness and regional volume These networks exhibit reproducible organization at both a population (Alexander-Bloch et al, 2013) and an individual (Tijms, Seris, Willshaw, & Lawrie, 2012) level and have been identified across species (Pagani, Bifone, & Gozzi, 2016), underscoring their role as an intrinsic feature of cortical organization. The source of gray matter shared covariance patterns is unclear and has been hypothesized to reflect both genetic and plastic influences, including maturational timing (Alexander-Bloch, Raznahan, Bullmore, & Geidd, 2013).

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