MRI based composite parameter of multiple tissue types for improved patient-level hemispheric and regional level...

Although voxel-based morphometry (VBM) of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) changes aid in epileptic seizure lateralization, type of T1 pulse sequence, preprocessing steps and tissue segmentation methods lead to variation in tissue classification. Here, we test the prediction accuracy of individual MRI based tissue types and a novel composite ratio parameter [(GM + WM)/CSF], sensitive to parenchymal changes and independent of tissue classification variations. Pediatric patients with partial seizures (both simple and complex), but normal and lesion-free MRI were considered (33 patients; unilateral EEG; 17 female / 16 male; age mean ± SD = 11.5 ± 5 years). MRI based seizure lateralization was performed for each patient and verified with EEG findings alone or in combination with seizure semiology. T1 weighted MRI from patients and normal control subjects was spatially transformed to the Talairach atlas and automatically segmented into GM, WM and CSF tissue types. 41 age matched normal controls (11 female / 30 male; age mean ± SD = 14.6 ± 3 years) served as the null distribution to test tissue type deviations across each epilepsy patient. When verified with the patient EEG prediction, WM, GM and CSF had a hemispheric match of 76%, 70% and 55% respectively, while the composite ratio [(GM + WM)/CSF)] showed the highest accuracy of 85%. When EEG findings and seizure semiology were combined, MRI predictions using the composite ratio improved further to 88%. To further localize the epileptic focus, regional level (frontal, temporal, parietal and occipital) MRI predictions were obtained. The composite ratio performed at 88-91% accuracy, revealing regional MRI changes, not predictable with EEG. The results show inconsistent changes in GM and WM in majority of the pediatric epilepsy patients and demonstrate the applicability of the composite ratio [(GM + WM)/CSF)] as a superior predictor, independent of tissue classification variations. Clinical EEG findings combined with seizure semiology, can overcome scalp EEG's limitations and lean towards the MRI lateralization in specific cases.