  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$_ 1$-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.
