Surface-based quantification of temporal-pole gray-white matter blurring in hippocampal sclerosis: an exploratory MRI profiling study

Objective Temporal-pole gray-white matter blurring and adjacent superficial white-matter signal abnormality are recognized extrahippocampal Magnetic Resonance Imaging (MRI) features of temporal lobe epilepsy with hippocampal sclerosis (HS), but their spatial organization is rarely quantified. We tested whether automated FreeSurfer-anchored surface-normal MRI profiling can quantify distance-resolved anterior temporal gray-white boundary and superficial white-matter abnormalities on standard T1-weighted MRI. Materials and methods We analyzed a scanner- and sequence-restricted public imaging database for the Epilepsy and Surgery subset comprising 41 patients with HS and 41 matched controls. N4-corrected T1-weighted images were reprocessed locally with FreeSurfer 8.2.0 and underwent focal manual boundary profiling and automated surface-normal sampling from cortex into white matter. Primary endpoints were affected-minus-unaffected relative white-matter contrasts from 1 to 2 mm and 2 to 3 mm at the white-matter ≥1.5-mm gate, standardized to hemisphere-specific controls. Morphology and sampling sensitivity analyses tested whether temporal-pole atrophy or profile geometry explained the primary findings. Results Automated profiling was completed for all subjects. Controls showed right-dominant anterior temporal asymmetry, supporting hemisphere-specific standardization. In patients, affected-side relative white-matter contrast was reduced at 1–2 mm (mean delta z = −0.581, Bonferroni-adjusted p = 0.001) and 2–3 mm (mean delta z = −0.753, Bonferroni-adjusted p < 0.001). The strongest reduction localized to approximately +1 to +3 mm beyond the gray-white boundary. Morphology deltas did not correlate with Surface-LEAP effects, and primary effects remained significant after adjustment. Exploratory machine learning supported subject-level information but showed no significant classification gain over simple white-matter markers. Conclusion Automated surface-based profiling provides an interpretable framework for quantifying a visually recognized anterior temporal MRI phenotype in HS. These exploratory findings should be interpreted as HS-versus-healthy-control abnormalities rather than disease-specific markers; disease-control and external validation are needed.