Cortical surface-based descriptors for childhood autism classification using segmentation-guided cortical reconstruction from sMRI

OBJECTIVES: Autism spectrum disorder (ASD) involves subtle and heterogeneous brain-structural differences that are not usually visible on routine structural magnetic resonance imaging (sMRI). This study investigated a segmentation-guided sMRI feature-classification framework for childhood ASD analysis. METHODS: Pediatric T1-weighted sMRI scans were obtained from the publicly available Autism Brain Imaging Data Exchange II (ABIDE-II) dataset. Preprocessing used the FMRIB Software Library (FSL) Brain Extraction Tool (BET), N4 bias-field correction, intensity normalization, and Advanced Normalization Tools (ANTs) registration. Gray matter, white matter, and cerebrospinal fluid (CSF) were segmented using FSL FAST, and cortical reconstruction/parcellation was performed using FreeSurfer recon-all. Extracted features included global structural summaries, regional cortical gray-matter volume, cortical surface area, cortical thickness, cortical-thickness variability, mean curvature, and folding index. Each participant was represented by a 414-dimensional numerical feature vector. Features were normalized, filtered, selected using minimum-redundancy maximum-relevance (mRMR) analysis, and classified using support vector machine, random forest, k-nearest neighbors, and multilayer perceptron models. RESULTS: The multilayer perceptron achieved 69.4 % accuracy, 67.9 % precision, 70.0 % sensitivity, 68.8 % specificity, 68.9 % F1-score, and 72.6 % area under the receiver operating characteristic curve (AUC). CONCLUSIONS: Extracted quantitative sMRI features supported ASD-versus-typical-control classification.