A deep learning model for brain age prediction using minimally preprocessed T1w images as input

IntroductionIn the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (C...

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Auteurs principaux:	Caroline Dartora, Anna Marseglia, Gustav Mårtensson, Gull Rukh, Junhua Dang, J-Sebastian Muehlboeck, Lars-Olof Wahlund, Rodrigo Moreno, José Barroso, Daniel Ferreira, Helgi B. Schiöth, Eric Westman, for the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing, the Japanese Alzheimer’s Disease Neuroimaging Initiative, the AddNeuroMed Consortium
Format:	Artigo
Langue:	Inglês
Publié:	Frontiers Media S.A. 2024-01-01
Collection:	Frontiers in Aging Neuroscience
Sujets:	brain age neurodegeneration normal ageing CNN UK Biobank ageing prediction
Accès en ligne:	https://www.frontiersin.org/articles/10.3389/fnagi.2023.1303036/full
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