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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Brain Imaging Methods
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1480871
This article is part of the Research Topic Advancing Early Alzheimer's Detection Through Multimodal Neuroimaging Techniques View all articles

Enhancing Early Alzheimer's Disease Classification Accuracy through the Fusion of sMRI and rsMEG Data: A Deep Learning Approach

Provisionally accepted
Yuchen Liu Yuchen Liu *Ling Wang Ling Wang Xiaolin Ning Xiaolin Ning Defeng Wang Defeng Wang Yang Gao Yang Gao
  • Beihang University, Beijing, China

The final, formatted version of the article will be published soon.

    Objective: Early detection and prediction of Alzheimer's Disease are paramount for elucidating neurodegenerative processes and enhancing cognitive resilience. Structural Magnetic Resonance Imaging (sMRI) provides insights into brain morphology, while resting-state Magnetoencephalography (rsMEG) elucidates functional aspects. However, inherent disparities between these multimodal neuroimaging modalities pose challenges to the effective integration of multimodal features. Approach: To address these challenges, we propose a deep learning-based multimodal classification framework for Alzheimer's disease, which harnesses the fusion of pivotal features from sMRI and rsMEG to augment classification precision. Utilizing the BioFIND dataset, classification trials were conducted on 163 Mild Cognitive Impairment cases and 144 cognitively Healthy Controls. Results: The study findings demonstrate that the InterFusion method, combining sMRI and rsMEG data, achieved a classification accuracy of 0.827. This accuracy significantly surpassed the accuracies obtained by rsMEG only at 0.710 and sMRI only at 0.749. Moreover, the evaluation of different fusion techniques revealed that InterFusion outperformed both EarlyFusion with an accuracy of 0.756 and LateFusion with an accuracy of 0.801. Additionally, the study delved deeper into the role of different frequency band features of rsMEG in fusion by analyzing six frequency bands, thus expanding the diagnostic scope. Discussion: These results highlight the value of integrating resting-state rsMEG and sMRI data in the early diagnosis of Alzheimer's disease, demonstrating significant potential in the field of neuroscience diagnostics.

    Keywords: Alzheimer's disease, structural MRI, Magnetoencephalography, deep learning, Multimodal fusion sMRI and rsMEG Fusion for Alzheimer's Disease Frontiers

    Received: 14 Aug 2024; Accepted: 24 Oct 2024.

    Copyright: © 2024 Liu, Wang, Ning, Wang and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yuchen Liu, Beihang University, Beijing, China

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