Characterizing Multivariate Regional Hubs for Schizophrenia Classification, Sex Differences, and Brain Age Estimation Using Explainable AI

Introduction: Schizophrenia (SZ) affects 1% of the population and can cause behavioral dysfunctions. Personalized treatment options are lacking. Researchers developed AI models to classify and predict SZ using MRI and demographic data. The explainable AI approach identifies multivariate regional contributors, and patients with alterations in brain hubs should consider the risk of SZ and undergo further testing. This study aimed to investigate multivariate regional patterns for schizophrenia (SZ) classification, sex differences, and brain age by utilizing structural MRI, demographics, and explainable artificial intelligence (AI). Methods: Various AI models were employed, and the outperforming model was identified for SZ classification, sex differences, and brain age predictions. For the SZ and sex classification tasks, support vector classifier (SVC), k-nearest neighbor (KNN), and deep learning neural network (DL) models were compared. In the case of regression-based brain age prediction, Lasso regression (LR), Ridge regression (RR), support vector regression (SVR), and DL models were compared. For each regression or classification task, the optimal model was further integrated with the Shapley additive explanations (SHAP), and significant multivariate brain regional patterns were identified. Results: Our results demonstrated that the DL model outperformed other models in SZ classification, sex differences, and brain age predictions. We then integrated outperforming DL model with SHAP, and this integrated DL-SHAP model was used to identify the individualized multivariate regional patterns associated with each prediction. Using the DL-SHAP approach, we found that individuals with SZ had anatomical changes, particularly in the left pallidum, left posterior insula, left hippocampus, and left putamen regions, and such changes associated with SZ were different between female and male patients. Finally, we further applied the DL-SHAP method to brain age prediction and suggested important brain regions related to aging in health controls (HC) and SZ processes. Discussion: The study analyzed the brain regions linked to Schizophrenia (SZ), revealing that the DL model outperformed other machine learning models in classification and regression-based predictions. The DL model was found to be more complex, capturing complex brain volumetric changes. The study also identified brain regions for sex classification, showing varying patterns in male and female patients. The study suggests that sex differences in SZ could be due to biological and environmental factors. However, the study has limitations, including modest sample size. Conclusion: This study systematically utilized predictive modeling and novel explainable AI approaches and identified the complex multivariate brain regions involved with SZ classification, sex differences, and brain aging, thereby building a deeper understanding of neurobiological mechanisms involved in the disease, offering new insights into future SZ diagnosis and treatments, and laying the foundation for the development of precision medicine.