Classification of schizophrenia based on graph product depth neural network fusion of fMRI and dMRI multidimensional information

Lu Li; Jinnan Gong; Wei Yan; Xiaorong Feng; Zhihuan Yang; Fei Wen; Cheng Luo

doi:10.1117/12.2687406

28 August 2023 Classification of schizophrenia based on graph product depth neural network fusion of fMRI and dMRI multidimensional information

Lu Li, Jinnan Gong, Wei Yan, Xiaorong Feng, Zhihuan Yang, Fei Wen, Cheng Luo

Author Affiliations +

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272421 (2023) https://doi.org/10.1117/12.2687406
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

At present, the diagnosis of schizophrenia mainly depends on scale evaluation and expert interview, which is subjective. It may lead to misdiagnosis. Therefore, an objective classification algorithm of schizophrenia based on multimodal information fusion is proposed. First, the brain magnetic resonance images (MRI) of schizophrenic patients and healthy controls were acquired. Then, information from multi-modal MRI was extracted and fused to build a graph. At last, a deep graph neural network was used to classify the individual graph of two groups GradCAM method was used to visualize the feature weights of the model. It’s found that two groups of participants can be effectively classified based on graph features. It also revealed that neuroimaging features from the occipital lobe, frontal lobe and limbic system played an important role in classification process. The present work indicated that the integration of neural science and information science has good application potential in disease diagnosis and pathological imaging.

Citation Download Citation

Lu Li, Jinnan Gong, Wei Yan, Xiaorong Feng, Zhihuan Yang, Fei Wen, and Cheng Luo "Classification of schizophrenia based on graph product depth neural network fusion of fMRI and dMRI multidimensional information", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272421 (28 August 2023); https://doi.org/10.1117/12.2687406

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