An Explainable Deep Learning Framework for Imaging Genetics: Deriving Brain-Genotype Scores From MRI to Link Genetic Variation, Brain Structure, and Cognition

Imaging genetics aims to understand how genetic variation influences brain structure and cognitive function. Traditional approaches often rely on imaging-derived phenotypes (IDPs), which require high-dimensional brain images to be reduced to predefined summary measures and may therefore miss subtle or spatially distributed genotype-related effects. We developed a two-stage framework that integrates deep learning and statistical modelling to derive and exploit brain-genotype scores--continuous, image-based representations of genetic variation learned directly from structural MRI. In the first stage, we trained a multi-task 3D convolutional neural network (CNN) on T1-weighted MRI scans from th