Multi-Template Meta-Information Regularized Network for Alzheimer’s Disease Diagnosis Using Structural MRI

Structural magnetic resonance imaging (sMRI) has been widely applied in computer-aided Alzheimer's disease (AD) diagnosis, owing to its capabilities in providing detailed brain morphometric patterns and anatomical features in vivo. Although previous works have validated the effectiveness of incorporating metadata (e.g., age, gender, and educational years) for sMRI-based AD diagnosis, existing methods solely paid attention to metadata-associated correlation to AD (e.g., gender bias in AD prevalence) or confounding effects (e.g., the issue of normal aging and metadata-related heterogeneity). Hence, it is difficult to fully excavate the influence of metadata on AD diagnosis. To address these issues, we constructed a novel Multi-template Meta-information Regularized Network (MMRN) for AD diagnosis. Specifically, considering diagnostic variation resulting from different spatial transformations onto different brain templates, we first regarded different transformations as data augmentation for self-supervised learning after template selection. Since the confounding effects may arise from excessive attention to meta-information owing to its correlation with AD, we then designed the modules of weakly supervised meta-information learning and mutual information minimization to learn and disentangle meta-information from learned class-related representations, which accounts for meta-information regularization for disease diagnosis. We have evaluated our proposed MMRN on two public multi-center cohorts, including the Alzheimer's Disease Neuroimaging Initiative (ADNI) with 1,950 subjects and the National Alzheimer's Coordinating Center (NACC) with 1,163 subjects. The experimental results have shown that our proposed method outperformed the state-of-the-art approaches in both tasks of AD diagnosis, mild cognitive impairment (MCI) conversion prediction, and normal control (NC) vs. MCI vs. AD classification.