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# Alzheimer's Disease Progression Modeling

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  1. arXiv

    A single brain scan can still place a patient on the disease timeline: ELPAC anchors on clinical endpoints and integrates backward

    Most members of an Alzheimer's cohort contribute only one partially observed cross-sectional snapshot, leaving their stage, subtype and stage-varying multimodal coupling unresolved. ELPAC anchors subtype trajectories at clinically reliable endpoint groups and integrates backward along biologically constrained velocity fields, then factorizes residual multimodal variation around that stage axis into sparse coordination axes that strengthen or fade with stage. On synthetic data and a harmonized ADNI+NACC/SCAN cohort it recovers the latent structure, returns calibrated prediction intervals and yields multimodal coordination programs that differ by subtype.

    Shengxian Ding（First author）
    ElpacVariational InferenceAlzheimer's Disease Progression Modeling
  2. medRxiv

    Alzheimer's cohort stops sorting patients by diagnosis alone: TRACE lifts 24-month phenotype purity 3.1 points over the strongest baseline

    TRACE maps longitudinal visit records to interpretable progression phenotypes and lets the number of phenotypes exceed the number of diagnostic classes. In the label-matched setting on the ADNI cohort it achieves the highest purity, NMI, ARI, silhouette index, accuracy and macro-F1 at the current, 12-month and 24-month horizons; at 24 months it exceeds the strongest baseline by 3.1 and 5.8 percentage points in purity and silhouette index. With the phenotype count freed, it splits a new phenotype out of the cognitively-normal- and MCI-dominant groups, one whose cognitive-scale trajectories sit in between while its hippocampal-volume trajectory coincides with the MCI group.

    Georgia Institute of Technology
    Asal Roudbari（First author）Mohammadreza Zandehshahvar（Corresponding author）
    TRACETemporal PhenotypingContrastive LearningAttention MechanismAlzheimer's Disease Progression Modeling

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