ArticlemedRxiv : the preprint server for health sciences2025
Multivariate whole brain neurodegenerative-cognitive-clinical severity mapping in the Alzheimer's disease continuum using explainable AI.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Neurodegeneration and cognitive impairment are commonly reported in Alzheimer's disease (AD); however, their multivariate links are not well understood. To map the multivariate relationships between whole brain neurodegenerative (WBN) markers, global cognition, and clinical severity in the AD continuum, we developed the explainable artificial intelligence (AI) methods, validated on semi-simulated data, and applied the outperforming method systematically to large-scale experimental data (N=1,756). The outperforming explainable AI method showed robust performance in predicting cognition from regional WBN markers and identified the ground-truth simulated dominant brain regions contributing to cognition. This method also showed excellent performance on experimental data and identified several prominent WBN regions hierarchically and simultaneously associated with cognitive declines across the AD continuum. These multivariate regional features also correlated with clinical severity, suggesting their clinical relevance. Overall, this study innovatively mapped the multivariate regional WBN-cognitive-clinical severity relationships in the AD continuum, thereby significantly advancing AD-relevant neurobiological pathways.
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