Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks
Abstract
Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current approaches rely on preparing the data with multiple preprocessing steps, such as registering voxels to a standardized brain atlas, which yields a significant computational overhead, hampers widespread usage and results in the predicted brain-age to be sensitive to preprocessing parameters. Here we describe a 3D Convolutional Neural Network (CNN) based on the ResNet architecture being trained on raw, non-registered T-weighted MRI data of N=10,691 samples from the German National Cohort and additionally applied and validated in N=2,173 samples from three independent studies using transfer learning. For comparison, state-of-the-art models using preprocessed neuroimaging data are trained and validated on the same samples. The 3D CNN using raw neuroimaging data predicts age with a mean average deviation of 2.84 years, outperforming the state-of-the-art brain-age models using preprocessed data. Since our approach is invariant to preprocessing software and parameter choices, it enables faster, more robust and more accurate brain-age modeling.
Cite
@article{arxiv.2103.11695,
title = {Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks},
author = {Lukas Fisch and Jan Ernsting and Nils R. Winter and Vincent Holstein and Ramona Leenings and Marie Beisemann and Kelvin Sarink and Daniel Emden and Nils Opel and Ronny Redlich and Jonathan Repple and Dominik Grotegerd and Susanne Meinert and Niklas Wulms and Heike Minnerup and Jochen G. Hirsch and Thoralf Niendorf and Beate Endemann and Fabian Bamberg and Thomas Kröncke and Annette Peters and Robin Bülow and Henry Völzke and Oyunbileg von Stackelberg and Ramona Felizitas Sowade and Lale Umutlu and Börge Schmidt and Svenja Caspers and German National Cohort Study Center Consortium and Harald Kugel and Bernhard T. Baune and Tilo Kircher and Benjamin Risse and Udo Dannlowski and Klaus Berger and Tim Hahn},
journal= {arXiv preprint arXiv:2103.11695},
year = {2021}
}