{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T10:07:42Z","timestamp":1784110062856,"version":"3.55.0"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Structural MRI-based Alzheimer's disease classification remains challenging due to subtle anatomical variations and the intermediate nature of mild cognitive impairment (MCI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose APLG-Net, an anatomy-guided local-global hybrid network with progression-aware supervision for NC\/MCI\/AD classification. The model integrates a global whole-brain encoder and a local ROI-based encoder, followed by cross-attention fusion and vector-gated integration. An ordinal supervision strategy is introduced to model disease progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On the ADNI dataset, APLG-Net achieves 87.1% accuracy, 86.4% balanced accuracy, 86.8% Macro-F1, and 85.6% MCI F1, outperforming CNN-based, Transformer-based, and hybrid baselines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The results demonstrate that incorporating anatomical priors, local-global feature interaction, and ordinal supervision significantly improves MCI discrimination and overall classification robustness.<\/jats:p>\n                  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