Hemispheric Asymmetry Features and Interpretable Machine Learning for Focal Cortical Dysplasia Classification in Drug-Resistant Epilepsy

Focal cortical dysplasia (FCD) is a principal cause of pharmacoresistant focal epilepsy, yet its structural MRI signature, subtle cortical thickening, blurring of the gray-white matter junction, is frequently undetected even by experienced neuroradiologists, delaying or precluding referral for curative surgical resection. Here we develop a machine learning pipeline for FCD detection that prioritizes mechanistic interpretability over model complexity. In a subsample of 50 subjects (25 FCD, 25 age-matched controls) drawn from a public structural MRI cohort, we register all scans to a common stereotactic template and derive hemispheric asymmetry features across 48 cortical regions, exploiting the characteristic unilaterality of FCD pathology. Among four classifiers evaluated under leave-one-out cross-validation, an L1-regularized logistic regression achieves the highest accuracy (78\%, permutation p=0.02), substantially outperforming tree-based ensembles, which perform at or below chance in this feature-to-sample regime. The fitted model selects a sparse subset of 21 of 96 features, with the largest-magnitude contributions localized to inferior and middle frontal gyri and temporal pole and superior temporal gyrus, regions consistent with the known anatomical distribution of FCD. These findings indicate that hemispheric asymmetry, combined with a sufficiently regularized, interpretable classifier, captures a modest but statistically robust and anatomically grounded signal for FCD detection, offering a transparent complement to existing deep learning approaches for presurgical evaluation.