Detection of Epileptogenic Cortical Malformations with Surface-Based MRI Morphometry

Abstract

Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations 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 patients 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 structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. 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 patients with intractable focal epilepsy who may benefit from surgery.

Individual Differences in Verbal Abilities Associated with Regional Blurring of the Left Gray and White Matter Boundary

“…Given the sensitivity of localized gray-white matter blurring in the detection of developmental cortical malformations (Chan et al, 1998;Barkovich et al, 2001;Thesen et al, 2011), it may prove a useful metric to investigate cognitive deficits associated with neuro-migrational abnormalities. Although the participation of dysplastic cortex in functional networks is debated (Janszky et al, 2003;Kirschstein et al, 2003;Danckert et al, 2007;Duchowny, 2009), domain-specific deficits have been demonstrated in FCD, with lesions in the left hemisphere associated with verbal deficits in children (Klein et al, 2000) and in the left parietal region with left-right disorientation, finger agnosia, and dyscalculia (Oki et al, 1999).…”

“…Furthermore, postmortem dissection has revealed lowgrade dysplastic changes in the dominant hemisphere planum temporale of individuals with developmental dyslexia (Galaburda et al, 1985). Regardless of whether dysplastic regions are functionally networked, their presence impacts cognitive efficiency (Duchowny, 2009), the degree to which might be further investigated in vivo with GWC or a combination of GWC and cortical thickness metrics (Thesen et al, 2011).…”

“…Regional blurring is an established marker of focal cortical neuropathology, particularly in the radiological diagnosis of focal cortical dysplasia (FCD) (Barkovich et al, 2001;Colombo et al, 2003;Huppertz et al, 2005;Thesen et al, 2011). In addition to histological abnormalities in gray matter, such as large dysmorphic neurons and balloon cells, which can lead to cortical thickening in FCD (Blümcke et al, 2011), there is a greater density of white matter neurons just adjacent to the gray-white boundary (Andres et al, 2005;Sisodiya et al, 2009).…”

“…Together with hypomyelination and gliosis, this can result in blurring of the graywhite boundary in the dysplastic region (Bernasconi et al, 2011). Using cortical thickness and GWC metrics together detects the presence of epileptogenic cortical malformations (of which FCD is one subtype) with high sensitivity and specificity (Thesen et al, 2011).…”

Automated detection of cortical dysplasia type II in MRI-negative epilepsy

“…Limited generalizability also stems from the fact that these approaches have been validated mainly with lesions recognized on routine radiologic evaluation. [7][8][9][10][11] We address the pressing issue of lesion detection in MRI-negative FCD. We combined machine learning with surface-based analysis in patients with FCD type II initially diagnosed as MRI-negative on routine radiologic inspection.…”