ArticleImaging neuroscience (Cambridge, Mass.)2025
The utility of explainable AI for MRI analysis: Relating model predictions to neuroimaging features of the aging brain.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Improving racial fairness in brain age models using style-transfer synthesis.bioRxiv : the preprint server for biology · 2026Article
- Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.Brain informatics · 2026Article
- Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification.Insights into imaging · 2025Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
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9 authors.
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Abstract
Deep learning models highly accurately predict brain age from MRI but their explanatory capacity is limited. Explainable artificial intelligence (XAI) methods can identify relevant voxels contributing to model estimates, yet they do not reveal which biological features these voxels represent. In this study, we closed this gap by relating voxel-based contributions to brain-age estimates, extracted with XAI, to human-interpretable structural features of the aging brain. To this end, we associated participant-level XAI-based relevance maps extracted from two ensembles of 3D-convolutional neural networks (3D-CNNs) that were trained on T1-weighted and fluid-attenuated inversion recovery images of 1855 participants (age range 18-82 years), with regional cortical and subcortical gray matter volume and thickness, perivascular spaces (PVS), and water diffusion-based fractional anisotropy of major white matter tracts. We found that all neuroimaging markers of brain aging, except for PVS, were highly correlated with the XAI-based relevance maps. Overall, the strongest correlation was found between ventricular volume and relevance (
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