XXXX with surface-based MRI morphometry.

@entity1 @entity410 @entity1625 @entity75 @entity600 @entity2526 @entity854 @entity302
Magnetic resonance imaging has revolutionized the detection of @entity2526 in @entity1 with @entity302 . However, many @entity854 remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to @entity600 to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single @entity1 with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected @entity2526 to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous @entity1 sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of @entity75 (sensitivity) with few false positives (96% specificity), successfully discriminating @entity1 from controls 94% of the time. The detected @entity2526 were related to the @entity410 onset zones, abnormal histology and positive outcome in all surgical @entity1 . However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of @entity1 with intractable @entity1625 who may benefit from surgery.
