A common brain network links development, aging, and vulnerability to disease

Abstract

Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8-85 y) reveals a largely--but not only--transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer's disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer's disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer's disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.

Keywords: Alzheimer's disease; aging; brain structure; development; schizophrenia.

Fig. 1.

Of all eight age-related components, only two achieved clear practical significance. We assessed post hoc the relationship of each of the 70 components with age (using polynomial fit). Of eight statistically significant components (all P < 0.05 corrected for multiple comparisons), only two achieved clear practical significance (IC1 and IC4), as measured by the percentage of age-related variance explained with a quadratic fit (indicated by the R² values): the “global” dominant mode showing monotonic decrease of the whole gray matter with age (IC1), with 90% of the variance of IC1 across subjects explained by age (R² = 0.9), and the inverted-U component (IC4), with 50% of IC4 variance explained by age (R² = 0.5). R² values for all other components were below 0.1. The inverted-U component IC4 showed a symmetric, strong nonmonotonic relationship with age and presented the strongest quadratic fit as measured by its quadratic coefficient (q = −1.8 × 10⁻³). a.u., arbitrary unit.

Fig. 2.

Network of gray matter regions showing the inverted-U relationship with age. (A) Spatial network corresponding to the second age-related independent component IC4 (orange) overlaid on the gray matter average across all 484 healthy participants (thresholded for better visualization at Z > 4). Left is right. (B) Second age-related independent component IC4 load for each of the 484 participants plotted against age (quadratic fit is in turquoise; P = 6 × 10⁻⁷³) (SI Materials and Methods). a.u., arbitrary unit.

Fig. 3.

Regions of the inverted-U network develop relatively slowly during adolescence but present accelerated age-related degeneration at an old age. In the ICA approach, the gray matter volume relationship with age at each voxel is explained by a weighted combination of all ICA components contributing to that voxel. (A) A widespread component including most of the gray matter explains 50% of the structural variation in the images (IC1). (B) The component of interest (IC4) explains 3% of the structural variation across images. (C) The relationship with age in the “core” of the inverted-U network of IC4 (as defined here for visual interpretation by using a threshold of Z > 4; B) is therefore explained by a combination of A and B, meaning that there is an additional effect on top of the dominant pattern of monotonic decrease in whole gray matter volume with increasing age as seen in IC1. As a result, compared with the whole of the gray matter (gray line in C), regions of the inverted-U network of IC4 (turquoise line in C) develop relatively slowly during adolescence and young adulthood (the turquoise line shows a less steep slope than the gray line) but also show accelerated age-related degeneration at old age (the turquoise line shows a steeper slope than the gray line). a.u., arbitrary unit.

Fig. 4.

The inverted-U component spatially corresponds to the structural pattern of abnormalities in Alzheimer’s disease and correlates with episodic memory in healthy subjects. (A) The spatial network corresponding to the inverted-U component IC4 (orange) closely matches the gray matter found to be atrophic in Alzheimer’s disease compared with healthy elderly (blue; thresholded for better visualization at P < 0.001; n = 120; voxel-by-voxel spatial cross-correlation: r = 0.55; P < 10⁻³). (B) The inverted-U component load for each of the healthy participants plotted against episodic memory score (CVLT long-delay recall; n = 370; linear fit is in turquoise; r = 0.31; P = 1.2 × 10⁻⁹) (SI Materials and Methods). Results presented here have not been age-corrected, as the relationship between episodic memory scores and age was highly nonlinear. In fact, their lifespan trajectory matched that of the inverted-U component (Fig. S6), explaining the linear relationship between the two presented in B. a.u., arbitrary unit.

Fig. 5.

The inverted-U component spatially corresponds to the structural pattern of abnormalities in adolescent-onset schizophrenia and correlates with intelligence scale in healthy subjects. (A) The spatial network corresponding to the inverted-U component IC4 (orange) closely matches the gray matter showing altered trajectory in adolescent-onset schizophrenia compared with healthy adolescents (green; thresholded for better visualization at P < 0.05; n = 24; voxel-by-voxel spatial cross-correlation: r = 0.48; P < 10⁻³). (B) The inverted-U component load for each of the healthy participants plotted against intellectual ability [e.g., block design score (fluid intelligence) from the Wechsler Abbreviated Scale of Intelligence; n = 439; linear fit is in turquoise; r = 0.40; P = 1.8 × 10⁻¹⁸] (SI Materials and Methods and Fig. S5 shows the plot for crystallized intelligence). Results presented here have not been age-corrected, as the relationship between block design scores and age was highly nonlinear. As for episodic memory scores, lifespan trajectory of fluid intelligence matched that of the inverted-U component (Fig. S6), explaining the linear relationship between the two presented in B. a.u., arbitrary unit.