Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion

• URL: http://arxiv.org/abs/2409.19130v1
• Date: Fri, 27 Sep 2024 20:25:17 GMT
• Title: Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion
• Authors: Xinxu Wei, Kanhao Zhao, Yong Jiao, Nancy B. Carlisle, Hua Xie, Gregory A. Fonzo, Yu Zhang,
• Abstract summary: We propose a novel approach that leverages self-supervised learning to synergize multi-modal information across domains. We constructed a large-scale pre-training dataset and pretrained MCSP model by leveraging proposed self-supervised paradigms. Our study contributes a significant advancement in the fusion of fMRI and EEG, marking a novel integration of cross-domain features.
• Score: 3.8153469790341084
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting functional abnormalities in various brain disorders. However, existing studies often focus on a single domain or modality, neglecting the valuable complementary information offered by multiple domains from both fMRI and EEG, which is crucial for a comprehensive representation of disorder pathology. This limitation poses a challenge in effectively leveraging the synergistic information derived from these modalities. To address this, we propose a Multi-modal Cross-domain Self-supervised Pre-training Model (MCSP), a novel approach that leverages self-supervised learning to synergize multi-modal information across spatial, temporal, and spectral domains. Our model employs cross-domain self-supervised loss that bridges domain differences by implementing domain-specific data augmentation and contrastive loss, enhancing feature discrimination. Furthermore, MCSP introduces cross-modal self-supervised loss to capitalize on the complementary information of fMRI and EEG, facilitating knowledge distillation within domains and maximizing cross-modal feature convergence. We constructed a large-scale pre-training dataset and pretrained MCSP model by leveraging proposed self-supervised paradigms to fully harness multimodal neuroimaging data. Through comprehensive experiments, we have demonstrated the superior performance and generalizability of our model on multiple classification tasks. Our study contributes a significant advancement in the fusion of fMRI and EEG, marking a novel integration of cross-domain features, which enriches the existing landscape of neuroimaging research, particularly within the context of mental disorder studies.

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