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ArXiv Preprint

Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. The massive heterogeneity between neurobiological examinations and clinical assessment is the current biggest challenge in the early diagnosis of Alzheimer's disease, urging for a comprehensive stratification of the aging population that is defined by reliable neurobiological biomarkers and closely associated with clinical outcomes. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification fail to take into account the neuropathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with neurological principles. To fill this knowledge gap, we propose a novel pathology steered stratification network (PSSN) that integrates mainstream AD pathology with multimodal longitudinal neuroimaging data to categorize the aging population. By combining theory-based biological modeling and data-driven deep learning, this cross-disciplinary approach can not only generate long-term biomarker prediction consistent with the end-state of individuals but also stratifies subjects into fine-grained subtypes with distinct neurological underpinnings, where ag-ing brains within the same subtype share com-mon biological behaviors that emerge as similar trajectories of cognitive decline. Our stratification outperforms K-means and SuStaIn in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Importantly, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. A disease evolutionary graph is further provided by quantifying subtype transition probabilities, which may assist pre-symptomatic diagnosis and guide therapeutic treatments.

Enze Xu, Jingwen Zhang, Jiadi Li, Defu Yang, Guorong Wu, Minghan Chen