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This paper introduces Disease Continuum Positioning (DCP), a longitudinal Bayesian learning framework that continuously estimates Alzheimer's disease severity from neuroimaging data, providing an uncertainty-aware score for disease progression prediction.
A study published in Nature Communications identifies imidazole propionate (ImP), a compound produced by gut bacteria, as a potential link between gut microbiome and Alzheimer's disease by weakening the blood-brain barrier and exacerbating amyloid beta and tau pathology.
The article reports on a 2024 study finding that taxi and ambulance drivers have lower Alzheimer's mortality rates, and explores the hypothesis that continuous spatial navigation strengthens the hippocampus and protects against the disease.
This paper proposes MPP-GNN, a Meta Probabilistic Pooling GNN that adaptively discovers subject-specific brain modules for fMRI-based Alzheimer's disease classification, achieving state-of-the-art AUC on two public datasets and aligning with canonical brain network organization.
This paper introduces iLENS, an interpretable LLM-guided mixture-of-experts framework for survival prediction and patient subtyping in Alzheimer's disease using neuroimaging data. The approach provides transparent, biologically grounded rationales for its routing decisions, bridging high-performance survival analysis with interpretable clinical decision support.
This paper proposes a Multi-View Gated Graph Attention Network for Alzheimer's Disease detection from spontaneous speech, using semantic, dependency, and co-occurrence graphs with an adaptive gated fusion mechanism. The model achieves 90.00% accuracy on the ADReSSo dataset, and the source code is publicly available.
LongMoE proposes a unified framework that jointly addresses modality missingness and longitudinal dynamics in multimodal clinical learning, using context-aware imputation, attentional tokenization, trajectory-aware encoding, and sparse mixture-of-experts routing. Experiments on ADNI, OASIS-3, and MIMIC-IV demonstrate improved robustness under missing modalities while remaining competitive in full-modality settings.
This paper introduces GNOVA, a GRU-Neural ODE Variational Autoencoder framework for reconstructing and forecasting Alzheimer's disease cognitive trajectories from routine clinical data without expensive neuroimaging or biomarkers, achieving low error and uncertainty estimation on the ADNI dataset.
This paper proposes a cross-linguistic transfer learning approach for detecting Alzheimer's Disease from speech across multiple languages, achieving F1 scores of 82% and supporting real-time screening applications.
This study develops an XGBoost classifier using SHAP explainability on eight clinical biomarkers from the ADNI dataset to achieve three-class Alzheimer's disease detection (normal cognition, MCI, AD), reaching a macro AUC of 0.982 and Cohen's kappa of 0.909 on the held-out test set. SHAP analysis identifies CDR Global as the dominant predictor for NC and MCI, while CDR-SB and MMSE together drive AD classification.
This paper proposes a residual gap-aware transformer that combines a mixed-effects statistical reference with transformer-based residual learning to forecast 24-month CDR-SB change from ADNI clinical and biomarker histories, achieving reduced MSE and improved correlation over baselines.