Learning Explainable Imaging-Genetics Associations Related to a Neurological Disorder
This work addresses the need for interpretable models in imaging-genetics for researchers studying neurological disorders, though it is incremental as it builds on existing deep learning and attention mechanisms.
The paper tackles the problem of interpreting imaging-genetics associations in neurological disorders by proposing NeuroPathX, an explainable deep learning framework that outperforms baseline methods and reveals biologically plausible links in autism and Alzheimer's disease.
While imaging-genetics holds great promise for unraveling the complex interplay between brain structure and genetic variation in neurological disorders, traditional methods are limited to simplistic linear models or to black-box techniques that lack interpretability. In this paper, we present NeuroPathX, an explainable deep learning framework that uses an early fusion strategy powered by cross-attention mechanisms to capture meaningful interactions between structural variations in the brain derived from MRI and established biological pathways derived from genetics data. To enhance interpretability and robustness, we introduce two loss functions over the attention matrix - a sparsity loss that focuses on the most salient interactions and a pathway similarity loss that enforces consistent representations across the cohort. We validate NeuroPathX on both autism spectrum disorder and Alzheimer's disease. Our results demonstrate that NeuroPathX outperforms competing baseline approaches and reveals biologically plausible associations linked to the disorder. These findings underscore the potential of NeuroPathX to advance our understanding of complex brain disorders. Code is available at https://github.com/jueqiw/NeuroPathX .