PTSD Classification Based on Surface-Based Morphometry: Integrating SHAP Analysis for Key Feature Identification

Background Post-Traumatic Stress Disorder (PTSD) is associated with neurobiological alterations, which can be examined using surface-based morphology (SBM). While machine learning (ML) approaches have shown potential in classifying PTSD based on SBM features, further exploration is needed to improve interpretability and clinical relevance. Objectives This study seeks to integrate ML-based classification of PTSD with SHAP analysis to identify important SBM features and their potential associations with PTSD symptomatology, providing insights into the structural changes underlying PTSD. Methods High-resolution T1-weighted MRI data from 101 participants (62 PTSD, 39 healthy controls) were analyzed using FreeSurfer’s SBM pipeline, extracting cortical thickness, surface area, and curvature features from the aparc.a2009s atlas. Several ML models, including Random Forest, SVM, and XGBoost, were trained and evaluated using ten-fold cross-validation. SHAP analysis was applied to determine feature importance, and correlation analyses were conducted to examine relationships between key features and PTSD symptom severity. Results Sixteen cortical regions were identified with significant structural differences in PTSD, including reduced cortical thickness in the left lingual gyrus and increased thickness in the bilateral central sulcus. The Random Forest model achieved the highest accuracy (91%) in PTSD classification. SHAP analysis highlighted the left lingual gyrus and parahippocampal gyrus as key features. Correlation analysis suggested potential links between these features and specific PTSD symptom clusters. Conclusion The integration of SBM and interpretable ML methods provides a promising approach for investigating structural brain changes in PTSD. While further validation is needed, these findings contribute to a better understanding of PTSD neurobiology and may support future research on diagnostic and therapeutic strategies.