Normative morphometric data for cerebral cortical areas over the lifetime of the adult human brain

Authors: Potvin, OlivierDieumegarde, LouisDuchesne, Simon
Abstract: Proper normative data of anatomical measurements of cortical regions, allowing to quantify brain abnormalities, are lacking. We developed norms for regional cortical surface areas, thicknesses, and volumes based on cross-sectional MRI scans from 2713 healthy individuals aged 18 to 94 years using 23 samples provided by 21 independent research groups. The segmentation was conducted using FreeSurfer, a widely used and freely available automated segmentation software. Models predicting regional cortical estimates of each hemisphere were produced using age, sex, estimated total intracranial volume (eTIV), scanner manufacturer, magnetic field strength, and interactions as predictors. The explained variance for the left/right cortex was 76%/76% for surface area, 43%/42% for thickness, and 80%/80% for volume. The mean explained variance for all regions was 41% for surface areas, 27% for thicknesses, and 46% for volumes. Age, sex and eTIV predicted most of the explained variance for surface areas and volumes while age was the main predictors for thicknesses. Scanner characteristics generally predicted a limited amount of variance, but this effect was stronger for thicknesses than surface areas and volumes. For new individuals, estimates of their expected surface area, thickness and volume based on their characteristics and the scanner characteristics can be obtained using the derived formulas, as well as Z score effect sizes denoting the extent of the deviation from the normative sample. Models predicting normative values were validated in independent samples of healthy adults, showing satisfactory validation R2. Deviations from the normative sample were measured in individuals with mild Alzheimer's disease and schizophrenia and expected patterns of deviations were observed.
Document Type: Article de recherche
Issue Date: 13 May 2017
Open Access Date: 13 May 2018
Document version: AM
This document was published in: NeuroImage, (2017)
Alternative version: 10.1016/j.neuroimage.2017.05.019
Collection:Articles publiés dans des revues avec comité de lecture

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