Early Diagnostic Classification of Alzheimer's Disease Using Structural and Resting-State Functional MRI.

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Abstract

<b>Background</b>: Alzheimer's disease (AD) is an irreversible neurodegenerative disorder where early diagnosis at the mild cognitive impairment (MCI) stage is critical for timely intervention. While structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) provide complementary pathological information, clinical translation is limited by high feature heterogeneity and the heavy computational burden of 3D deep learning networks. This study aims to propose a lightweight, hybrid multimodal fusion model to overcome these limitations and improve diagnostic efficiency. <b>Methods</b>: A lightweight MobileViT-XS network was utilized to extract global and local pathological features from gray-matter sMRI images to reduce computational costs. Concurrently, graph theory was applied to rs-fMRI data to construct brain functional networks and calculate global topological properties. Following pretraining, feature-level fusion was performed to integrate sMRI features, rs-fMRI properties, demographic information, and two clinical neuropsychological examinations. <b>Results</b>: The validation results show that the proposed hybrid model achieved high diagnostic performance in the three-class classification task, yielding an accuracy of 0.928 ± 0.010, sensitivity of 0.901 ± 0.014, precision of 0.933 ± 0.014, and an F1 score of 0.911 ± 0.012. On the held-out testing subset of the independently collected dataset after dataset-specific fine-tuning, the proposed model achieved an accuracy of 0.857, with a sensitivity of 0.867, a precision of 0.867, and an F1 score of 0.859. <b>Conclusions</b>: The proposed framework successfully minimizes computational overhead while maintaining high classification accuracy. These findings suggest that the hybrid multimodal fusion model provides a promising computer-aided approach for three-class classification of AD, MCI, and NC.

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