Subcortical Volume Trajectories across the Lifespan: Data from 18,605 healthy individuals aged 3-90 years
Dima, D.; Papachristou, E.; Modabbernia, A.; Doucet, G. E.; Agartz, I.; Aghajani, M.; Akudjedu, T. N.; Albajes-Eizagirre, A.; Alnaes, D.; Alpert, K. I.; Andersson, M.; Andreasen, N.; Andreassen, O. A.; Asherson, P.; Banaschewski, T.; Bargallo, N.; Baumeister, S.; Baur-Streubel, R.; Bertolino, A.; Bonvino, A.; Boomsma, D. I.; Borgwardt, S.; Bourque, J.; Brandeis, D.; Breier, A.; Brodaty, H.; Brouwer, R. M.; Buitelaar, J. K.; Busatto, G. F.; Buckner, R. L.; Calhoun, V.; Canales-Rodriguez, E. J.; Cannon, D. M.; Caseras, X.; Castellanos, F. X.; Cervenka, S.; Chaim-Avancini, T. M.; Ching, C. R.; Cl
Show 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.
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