فهرست نشریات علمی دسترسی آزاد ایران

Early detection of schizophrenia remains a significant challenge in the field of neuroscience, primarily due to the complex and subtle alterations in brain activity associated with the disorder. These changes are often difficult to detect using conventional MRI analysis techniques. Schizophrenia is characterized by disrupted patterns of neural connectivity and functional dynamics, which require advanced computational methods for accurate identification. In this study, we propose a novel hybrid framework that integrates Vision Transformers (ViTs) and Graph Neural Networks (GNNs) to analyze functional MRI (fMRI) data. This combined approach aims to enhance the detection and classification of schizophrenia by capturing both local visual patterns in brain scans and the global functional connectivity structure of the brain.