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An innovative framework based on GMV-ALFF multi-modal MRI and Gaussian data augmentation for psychiatric disorder
1Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Objective:
Schizophrenia (SZ) and bipolar disorder (BD) exhibit significant overlap in clinical symptoms, posing substantial challenges to accurate diagnosis. This study proposes an innovative multi-modal fusion framework based on convolutional neural networks (CNNs), integrating structural and functional MRI biomarkers-gray matter volume (GMV) and amplitude of low-frequency fluctuation (ALFF).
Methods:
Total 31 patients with schizophrenia, 48 patients with bipolar disorder, and 38 healthy controls (HC) were enrolled and underwent resting-state fMRI scans. The framework comprises three core components: Gaussian data augmentation to mitigate the limitation of small sample sizes; a dedicated CNN architecture for processing GMV and ALFF features to generate class probability outputs for SZ, BD, and HC; and DempsterShafer (D-S) evidence theory for robust multi-modal decision fusion.
Results:
Extensive 5-fold cross-validation confirms the framework's efficacy: it achieves superior performance metrics (Accuracy: 0.9656 ± 0.0324, Precision: 0.9698 ± 0.0319, Recall: 0.9656 ± 0.0324, F1-Score: 0.9661 ± 0.0324) and outperforms traditional machine learning methods (SVM, Random Forest, XGBoost, KNN).
Conclusion:
The findings demonstrate that the proposed multi-modal fusion approach enhances the ability to differentiate among SZ, BD, and HC compared to unimodal classification methods, providing a reliable technical support for the auxiliary diagnosis of psychiatric disorders.
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01:14Imaging Studies I: CT and MRI
Description of the Procedures
Computed Tomography (CT) scan:
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01:30Radiological Investigation II: MRI and Ventilation Perfusion Scan
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
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