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Cortical Thickness Trajectories across the Lifespan: Data from 17,075 healthy individuals aged 3-90 years
- Publication Year :
- 2020
- Publisher :
- Cold Spring Harbor Laboratory, 2020.
-
Abstract
- Delineating age-related cortical trajectories in healthy individuals is critical given the association of cortical thickness with cognition and behaviour. Previous research has shown that deriving robust estimates of age-related brain morphometric changes requires large-scale studies. In response, we conducted a large-scale analysis of cortical thickness in 17,075 individuals aged 3-90 years by pooling data through the Lifespan Working group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium. We used fractional polynomial (FP) regression to characterize age-related trajectories in cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma (LMS) method. Inter-individual variability was estimated using meta-analysis and one-way analysis of variance. Overall, cortical thickness peaked in childhood and had a steep decrease during the first 2-3 decades of life; thereafter, it showed a gradual monotonic decrease which was steeper in men than in women particularly in middle-life. Notable exceptions to this general pattern were entorhinal, temporopolar and anterior cingulate cortices. Inter-individual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results reconcile uncertainties about age-related trajectories of cortical thickness; the centile values provide estimates of normative variance in cortical thickness, and may assist in detecting abnormal deviations in cortical thickness, and associated behavioural, cognitive and clinical outcomes.
- Subjects :
- 0303 health sciences
medicine.medical_specialty
Fractional polynomial
Cognition
Audiology
Biology
Regression
Pooling data
03 medical and health sciences
0302 clinical medicine
Neuroimaging genetics
Healthy individuals
medicine
General pattern
Analysis of variance
030217 neurology & neurosurgery
030304 developmental biology
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....948524ee9850777d3bcbd047efc8a582
- Full Text :
- https://doi.org/10.1101/2020.05.05.077834