Predicting Postoperative Outcomes in MRI-Negative Refractory Temporal Lobe Epilepsy Patients Using Dynamic Regional Homogeneity

Motivation: This study is motivated by the need for better predictive indexs for postoperative outcomes in MRI-negative refractory temporal lobe epilepsy (TLE) patients. Goal(s): To ascertain whether machine learning models using dynamic regional homogeneity (dReHo) can predict surgical success in these patients. Approach: The approach involved analyzing resting-state fMRI data from TLE patients and healthy controls, calculating ReHo and dReHo values, and applying these as features in a support vector machine classifier. Results: The classifier using dReHo achieved 73.3% accuracy in predicting postoperative outcomes, significantly outperforming the ReHo-based model. Impact: The ability to predict postoperative outcomes using dReHo could guide clinical decision-making and patient counseling, potentially leading to improved management of TLE.