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
2022-10-12
