Subcortical Volume Trajectories across the Lifespan: Data from 18,605 healthy individuals aged 3-90 years

Abstract

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalised on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine the age-related morphometric trajectories of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum early in life; the volume of the basal ganglia showed a gradual monotonic decline thereafter while the volumes of the thalamus, amygdala and the hippocampus remained largely stable (with some degree of decline in thalamus) until the sixth decade of life followed by a steep decline thereafter. The lateral ventricles showed a trajectory of continuous enlargement throughout the lifespan.Significant age-related increase in inter-individual variability was found for the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to derive risk predictions for the early identification of diverse clinical phenotypes.

Atlas55+: Brain Functional Atlas of Resting-state Networks for Late Adulthood

“…This finding was expected as the age range across all participants was 40 years. This was also consistent with prior neuroimaging studies that described an effect of aging on both brain structure and function in populations over age 50 (Betzel et al, 2014;Chan et al, 2014;Damoiseaux, 2017;Dima et al, 2020;Frangou et al, 2020;Luis et al, 2015;Luo et al, 2020b;Varangis et al, 2019a). The overall negative impact of age on each network largely confirms a reduction of functional cohesiveness of the major brain networks, particularly those supporting higher-order cognitive functions (Betzel et al, 2014;Damoiseaux et al, 2008;He et al, 2013;Mowinckel et al, 2012;Yaple et al, 2019).…”

Age and Sex Effects on Advanced White Matter Microstructure Measures in 15,628 Older Adults: A UK Biobank Study

“…To assess the robustness of results, two additional statistical approaches were used: fractional polynomials and binarizing age into a discrete variable. Fractional polynomials allow continuous independent variables, such as age, to be flexibly modeled in a non-linear manner without specifying a priori the relationship between the independent and dependent variable [24][25][26] . We used fractional polynomials to find the best-fitting model for age for each diffusivity metric (DTI-FA, TDF-FA, NODDI-ICVF, NODDI-ODI, NODDI-ISOVF) by testing one-and two-term curvilinear models for age using the following possible powers: -2, -1, -0.5, 0, 0.5, 1, 2 and 3, where x 0 corresponds to ln(x); these analyses also included our covariates of non-interest (educational attainment, socioeconomic status, waist-hip ratio, population structure) and participant sex.…”

“…To further assess the robustness of results to the chosen statistical model, we also followed previously published statistical recommendations 27 to repeat analyses after discretizing the continuous variable of interest. Specifically, we binarized age by splitting participants into two groups: > 60 years old and < 60 years old; this binarization threshold was selected based on previous large-scale neuroimaging studies examining aging 25,26 . We then tested the interaction between age group and sex for each diffusivity metric using a linear regression model that also included the main effects of age group and sex, as well as our covariates of non-interest.…”

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

“…Establishing biological benchmarks for sex-specific brain changes across the lifespan can break this pattern [Pomponio et al, 2020]. The ENIGMA consortium has received federal funding to develop normative charts of brain structure in males and females across the lifespan using multimodal neuroimaging [Dima et al, 2020;Wierenga et al, 2020]. These charts can serve as an important guidepost for evaluating individual brain health and have potential to improve disease detection and monitoring, inform disease mechanisms, and provide a standardized data bank that can be leveraged for clinical trials and interventions.…”