The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students
Brain Struct Funct. 2021-07-20; 226(7): 2057-2085
DOI: 10.1007/s00429-021-02334-4
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Tsuchida A(1)(2)(3), Laurent A(1)(2)(3), Crivello F(1)(2)(3), Petit L(1)(2)(3), Joliot M(1)(2)(3)(4), Pepe A(1)(2)(3), Beguedou N(1)(2)(3), Gueye MF(1)(2)(3)(4), Verrecchia V(1)(2)(3)(4), Nozais V(1)(2)(3)(4), Zago L(1)(2)(3), Mellet E(1)(2)(3), Debette S(5)(6), Tzourio C(5)(6), Mazoyer B(7)(8)(9)(10)(11).
Author information:
(1)Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.
(2)Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.
(3)Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France.
(4)Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France.
(5)Université de Bordeaux, INSERM, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France.
(6)Centre Hospitalier Universitaire Pellegrin, Bordeaux, France.
(7)Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France. .
(8)Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.
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(9)Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France.
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(10)Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France. .
(11)Centre Hospitalier Universitaire Pellegrin, Bordeaux, France. .
We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1870 young healthy adults, aged 18-35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility-weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early ageing.