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A novel hybrid deep learning method combining CNN, GRU, and a Dynamic Cross-Modality Attention Module to integrate sMRI and fMRI data achieves 96.79% accuracy and outperforms existing models in identifying brain disorders. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

March 21, 2025Frontiers in Human Neuroscience33 citationsOpen Access

Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging

SBSaurabh BhattacharyaGalgotias University SPSashikanta PrustySiksha O Anusandhan University SPSanjay P. PandeYashwantrao Chavan Maharashtra Open University

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Abstract

Introduction: Combining many types of imaging data-especially structural MRI (sMRI) and functional MRI (fMRI)-may greatly assist in the diagnosis and treatment of brain disorders like Alzheimer's. Current approaches are less helpful for forecasting, however, as they do not always blend spatial and temporal patterns from different sources properly. This work presents a novel mixed deep learning (DL) method combining data from many sources using CNN, GRU, and attention techniques. This work introduces a novel hybrid deep learning method combining CNN, GRU, and a Dynamic Cross-Modality Attention Module to help more efficiently blend spatial and temporal brain data. Through working around issues with current multimodal fusion techniques, our approach increases the accuracy and readability of diagnoses. Methods: Utilizing CNNs and models of temporal dynamics from fMRI connection measures utilizing GRUs, the proposed approach extracts spatial characteristics from sMRI. Strong multimodal integration is made possible by including an attention mechanism to give diagnostically important features top priority. Training and evaluation of the model took place using the Human Connectome Project (HCP) dataset including behavioral data, fMRI, and sMRI. Measures include accuracy, recall, precision and F1-score used to evaluate performance. Results: It was correct 96.79% of the time using the combined structure. Regarding the identification of brain disorders, the proposed model was more successful than existing ones. Discussion: These findings indicate that the hybrid strategy makes sense for using complimentary information from several kinds of photos. Attention to detail helped one choose which aspects to concentrate on, thereby enhancing the readability and diagnostic accuracy. Conclusion: The proposed method offers a fresh benchmark for multimodal neuroimaging analysis and has great potential for use in real-world brain assessment and prediction. Researchers will investigate future applications of this technique to new picture kinds and clinical data.

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