Subcortical volumes 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 capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to examine age‐related trajectories inferred from cross‐sectional measures 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 value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter‐individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age‐related morphometric patterns.


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 capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from crosssectional measures 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 value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age.
The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

| INTRODUCTION
Over the last 20 years, studies using structural magnetic resonance imaging (MRI) have confirmed that brain morphometric measures change with age. In general, whole brain, global and regional gray matter volumes increase during development and decrease with aging (Brain Development Cooperative Group, 2012;Driscoll et al., 2009;Fotenos, Snyder, Girton, Morris, & Buckner, 2005;Good et al., 2001;Pfefferbaum et al., 2013;Pomponio et al., 2019;Raz et al., 2005;Raznahan et al., 2014;Resnick, Pham, Kraut, Zonderman, & Davatzikos, 2003;Walhovd et al., 2011). However, most published studies are constrained by small sample sizes, restricted age coverage and methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain structures. To address these limitations, we formed the Lifespan Working group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium (Thompson et al., 2014(Thompson et al., , 2017 to perform large-scale analyses of brain morphometric data extracted from MRI images using standardized protocols and unified quality control procedures, harmonized and validated across all participating sites. Here we focus on ventricular, striatal (caudate, putamen, nucleus accumbens), pallidal, thalamic, hippocampal and amygdala volumes.
Using data from 18,605 individuals aged 3-90 years from the ENIGMA Lifespan working group we delineated the association between age and subcortical volumes from early to late life in order to (a) identify periods of volume change or stability, (b) provide normative, age-adjusted centile curves of subcortical volumes and (c) quantify inter-individual variability in subcortical volumes which is considered a major source of inter-study differences (Dickie et al., 2013;Raz, Ghisletta, Rodrigue, Kennedy, & Lindenberger, 2010).

| Study samples
The study data derive from 88 samples comprising 18,605 healthy participants, aged 3-90 years, with near equal representation of men and women (48% and 52%) (Table 1, Figure 1). At the time of scanning, participating individuals were screened to exclude the presence of mental disorders, cognitive impairment or significant medical morbidity. Details of the screening process and eligibility criteria for each research group are shown in Table S1).

| Neuroimaging
Detailed information on scanner vendor, magnet strength and acquisition parameters for each sample are presented in Table S1. For each sample, the intracranial volume (ICV) and the volume of the basal ganglia (caudate, putamen, pallidum, nucleus accumbens), thalamus, hippocampus, amygdala and lateral ventricles were extracted using FreeSurfer (http://surfer.nmr.mgh.harvard.edu) from high-resolution T 1 -weighted MRI brain scans (Fischl, 2012;Fischl et al., 2002). Prior to data pooling, images were visually inspected at each site to exclude participants whose scans were improperly segmented. After merging the samples, only individuals with complete data were included outliers were identified and excluded using Mahalanobis distances. All analyses described below were repeated for ICV-unadjusted volumetric measures which yielded identical results and are only presented as a separate supplement.
Approximately 20% of the samples had a multi-scanner design.
During data harmonization the scanner was modeled as a site. In each site, the intracranial volume ( Figure S1) was used to adjust the subcortical volumes via a formula based on the analysis of the covariance approach: slope of regression of a region of interest volume on ICV (Raz et al., 2005).
The values of the subcortical volumes were then harmonized between sites using the ComBat method in R (Fortin et al., 2017(Fortin et al., , 2018Radua et al., 2020). Originally developed to adjust for batch effect in genetic studies, ComBat uses an empirical Bayes to adjust for inter-site variability in the data, while preserving variability related to the variables of interest.

| Fractional polynomial regression analyses
The effect of age on each ICV-and site-adjusted subcortical volume was modeled using high order fractional polynomial regression (Royston & Altman, 1994;Sauerbrei, Meier-Hirmer, Benner, & Royston, 2006) in each hemisphere. Because the effect of site (scanner and Freesurfer version) was adjusted using ComBat, we only included sex as a covariate in the regression models. Fractional polynomial regression is currently considered the most advantageous modeling strategy for continuous variables (Moore, Hanley, Turgeon, & Lavoie, 2011) as it allows testing for a wider range of trajectory shapes than conventional lower-order polynomials (e.g., linear or quadratic) and for multiple turning points (Royston & Altman, 1994;Royston, Ambler, & Sauerbrei, 1999). For each subcortical structure, the best model was obtained by comparing competing models of up to three power combinations. The powers used to identify the best fitting model were −2, −1, −0.5, 0.5, 1, 2, 3 and the natural logarithm (ln) function. The optimal model describing the association between age and each of the volumes was selected as the lowest degree model based on the partial F-test (if linear) or the likelihood-ratio test. To avoid overfitting at ages with more data points, we used the stricter .01 level of significance as the cut-off for each respective likelihood-ratio tests, rather than adding powers, until the .05 level was reached. For ease of interpretation we centered the volume of each structure so that the intercept of a fractional polynomial was represented as the effect at zero for sex. Fractional polynomial regression models were fitted using Stata/ IC software v.13.1 (Stata Corp., College Station, TX). Standard errors were also adjusted for the effect of site in the FP regression.
We conducted two supplemental analyses: (a) we specified additional FP models separately for males and females and, (b) we calculated Pearson's correlation coefficient between subcortical volumes and age in the early (6-29 years), middle (30-59 years), and late-life (60-90 years) age-group. The results of these analyses have been included in the supplemental material. and late-life (60-90 years) age-groups was compared using between-groups omnibus tests for the residual variance around the identified best-fitting nonlinear fractional polynomial model of each structure.

| Inter-individual variability
We conducted 16 tests (one for each structure) and accordingly the critical alpha value was set at 0.003 following Bonferroni correction for multiple comparisons.
The second approach entailed the quantification of the mean individual variability of each subcortical structure through a meta-analysis F I G U R E 1 ENIGMA lifespan samples. Details of each sample are provided Table 1 and in the supplemental material. Abbreviations are provided in Table 1 of the SD of the adjusted volumes according to the method proposed by Senior, Gosby, Lu, Simpson, and Raubenheimer (2016).

| Centile curves
Reference curves for each structure by sex and hemisphere were produced from ICV-and site-adjusted volumes as normalized growth centiles using the parametric Lambda (λ), Mu (μ), Sigma (σ) (LMS) method (Cole & Green, 1992) implemented using the Generalized Additive Models for Location, Scale and Shape (GAMLSS) in R (http://cran.r-project.org/web/ packages/gamlss/index.html) (Rigby & Stasinopoulos, 2005;Stasinopoulos & Rigby, 2007). LMS allows for the estimation of the distribution at each covariate value after a suitable transformation and is summarized using three smoothing parameters, the Box-Cox power λ, the mean μ and the coefficient of variation σ. GAMLSS uses an iterative maximum (penalized) likelihood estimation method to estimate λ, μ and σ as well as distribution dependent smoothing parameters and provides optimal values for effective degrees of freedom (edf) for every parameter (Indrayan, 2014). This

| Fractional polynomial regression analyses
The volume of the caudate, putamen, globus pallidus and nucleus accumbens peaked early during the first decade of life and showed a linear decline immediately thereafter (Figure 2, Figures S2-S4). The association between age and the volumes of the thalamus, hippocampus and amygdala formed a flattened, inverted U-curve (Figure 3, Figures S5 and S6). Specifically, the volumes of these structures were largest during the first 2-3 decades of life, remained largely stable until the sixth decade and declined gradually thereafter (Table S2).
The volume of the lateral ventricles increased steadily with age bilaterally ( Figure S7). The smallest proportion of variance explained by age and its FP derivatives was noted in the right amygdala (7%) and the largest in the lateral ventricles bilaterally (38%) ( Inter-individual variability: For each structure, the mean interindividual variability in volume in each age-group is shown in Table S5. Inter-individual variance was significantly higher for the hippocampus, thalamus amygdala and lateral ventricles bilaterally in the late-life age-group compared to both the early-and middle-life group. F I G U R E 2 Fractional polynomial plots for the volume of the basal ganglia. Fractional Polynomial plots of adjusted volumes (mm 3 ) against age (years) with a fitted regression line (solid line) and 95% confidence intervals (shaded area) These findings were recapitulated when data were analyzed using a meta-analytic approach ( Figure S8).

Normative Centile Curves:
Centile normative values for each subcortical structure stratified by sex and hemisphere are shown in Figure 4 and Tables S6-S8.

| DISCUSSION
We analyzed subcortical volumes from 18,605 healthy individuals from multiple cross-sectional cohorts to infer age-related trajectories between the ages of 3 and 90 years. Our lifespan perspective and our large sample size complement and enrich previous age-related findings in subcortical volumes.
We found three distinct patterns of association between age and Although multisite studies have to account for differences in scanner type and acquisition, lengthy longitudinal designs encounter similar issues due to inevitable changes in scanner type and strength and acquisition parameters over time. In this study, the use of ageoverlapping samples from multiple different countries has the theoretical advantage of diminishing systematic biases reflecting cohort and period effects (Glenn, 2003;Keyes, Utz, Robinson, & Li, 2010) that are likely to operate in single site studies.
In medicine, biological measures from each individual are typically categorized as normal or otherwise in reference to a population F I G U R E 3 Fractional polynomial plots for the volume of the thalamus, hippocampus and amygdala. Fractional polynomial plots of adjusted volumes (mm 3 ) against age (years) with a fitted regression line (solid line) and 95% confidence intervals (shaded area) derived normative range. This approach is yet to be applied to neuroimaging data, despite the widespread use of structural MRI for clinical purposes and the obvious benefit of a reference range from the early identification of deviance (Dickie et al., 2013;Pomponio et al., 2019).
Alzheimer's disease provides an informative example as the degree of baseline reduction in medial temporal regions, and particularly the hippocampus, is one of the most significant predictors of conversion from mild cognitive impairment to Alzheimer's disease (Risacher et al., 2009). The data presented here demonstrate the power of international collaborations within ENIGMA for analyzing large-scale datasets that could eventually lead to normative range for brain volumes for well-defined reference populations. The centile curves presented here are a first-step in developing normative reference values for neuroimaging phenotypes and further work is required in establishing measurement error and functional significance (see Supplement). These curves are not meant to be used clinically or to provide valid percentile measures for a single individual.
In conclusion, we used existing cross-sectional data to infer agerelated trajectories of regional subcortical volumes. The size and agecoverage of the analysis sample has the potential to disambiguate