Exploration of identifying subtypes of schizophrenia from structural MRI using deep learning

Background At present, the subtype classification based on clinical symptoms has the limitations of strong subjectivity and unclear biological basis. However, the structural Magnetic Resonance Imaging (sMRI) technology has been proven to capture the morphological changes of the brain. It provides potential biological markers for the objective identification of subtypes of schizophrenia. The research proposes to mine the brain morphological features in sMRI data based on deep learning models to achieve automatic identification of schizophrenia subtypes and provide imaging support for the formulation of subtype-specific treatment plans. Methods A total of 426 patients with schizophrenia aged 18 to 55 (218 cases of paranoid type and 208 cases of undifferentiated type) and 210 healthy controls were selected for the study. All subjects underwent 3.0 T MRI scans to obtain high-resolution T1-weighted sMRI images. The research constructs a 3D Convolutional Neural Network (3D-CNN) model integrating the attention mechanism, taking the preprocessed whole-brain sMRI images as the input. The multi-dimensional morphological features such as volume, thickness and density of gray matter, white matter and cerebrospinal fluid in the brain are automatically extracted through convolutional layers and pooling layers. The attention module focuses on strengthening the feature weights of key brain regions related to schizophrenia, such as the prefrontal lobe and hippocampus. Support Vector Machine (SVM) and Random Forest (RF) were introduced as controls. Results The experimental results show that the constructed 3D-CNN model performs the best in the task of identifying subtypes of schizophrenia, with an overall accuracy rate of 86.4% (95% confidence interval: Among them, the recognition accuracy rate of the paranoid type was 88.2% and the recall rate was 85.3%, the recognition accuracy rate of the undifferentiated type was 84.6% and the recall rate was 87.5%, and the F1 score was 86.9%. In the control models, the accuracy rate of the SVM model was 72.3%, that of the RF model was 68.5%, and the accuracy rate of the 3D-CNN model was 14.1 percentage points higher than that of the optimal traditional model (p<.001). The key differentiating features extracted by the model are mainly concentrated in the prefrontal cortex, hippocampus, thalamus and cingulate gyrus. Among them, the volume reduction of the prefrontal cortex is more significant in patients with paranoid type, while patients with undifferentiated type show combined morphological abnormalities of the hippocampus and thalamus. Discussion The research successfully achieved efficient identification of schizophrenia subtypes from sMRI data through a deep learning model, verifying the specific association between brain morphological features and schizophrenia subtypes. The constructed 3D-CNN model significantly outperformed traditional machine learning methods due to its advantages of automatic feature extraction and attention enhancement in key brain regions. This result provides a non-invasive and objective subtype identification tool based on imaging for clinical practice, which helps to reduce the subjectivity of clinical diagnosis. Future research can expand the sample size of multiple centers, incorporate more subtype categories and combine functional MRI data to further enhance the generalization ability and recognition accuracy of the model.