Sub-cortical brain morphometry and its relationship with cognition in rolandic epilepsy.

PURPOSE: Rolandic epilepsy (RE), also called benign epilepsy with centrotemporal spikes (BECTS) is the most common childhood epilepsy syndrome. RE is associated with cognitive difficulties, which can affect children's quality of life. The underlying causes of these cognitive impairments are unclear. The objective of this prospective study is to investigate sub-cortical morphological alterations in RE children with left, right, or bilateral hemispheric focus and its association with cognition. METHODS: Participants include 41 children with rolandic epilepsy and 38 healthy controls (age 8-14 years), recruited from CHU Sainte-Justine Montreal Children Hospital (N=40) and Basel's Children Hospital (N=39). Quantitative volumetric assessment of putamen and caudate structures was performed on T1-weighted MR scans along with the morphological analysis to test for differences between patients and controls. These analyses were performed considering the side of epilepsy focus in all participants. Correlations were investigated between the sub-cortical morphometry and cognitive indices such as intelligence quotient (IQ), verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), and processing speed index (PSI). RESULTS: Children with bilateral BECTS showed statistically significant volume reduction in right caudate (p<.05), while no statistically significant putamen volumetric changes were detected in BECTS participants compared to normal controls. According to a spectral-based groupwise shape analysis, regional alterations were found in both putamen and caudate structures of children with BECTS. In particular, children with left BECTS showed significant outward local deformity in left putamen and individuals with bilateral BECTS showed inward local group differences in both right putamen and right caudate. The correlation assessment showed positive association between the volume of the left caudate and cognitive indices in the group containing all BECTS participants. Negative correlation was found between putamen sub-regional shape alterations and cognition in individuals with right BECTS and in all BECTS participants. Negative associations between caudate sub-regional morphologies and cognitive indices were detected in left cohort. SIGNIFICANCE: We have confirmed putamen and caudate shape alterations in children with BECTS. However, our results further suggest that variations in sub-cortical shape affect cognitive functions. Importantly, we have demonstrated that shape alterations and their relation with cognition depend on the side of epilepsy focus. Our results point to different syndromic entities in the BECTS population.

Statistical shape analysis of subcortical structures using spectral matching.

Studying morphological changes of subcortical structures often predicate neurodevelopmental and neurodegenerative diseases, such as Alzheimer's disease and schizophrenia. Hence, methods for quantifying morphological variations in the brain anatomy, including groupwise shape analyses, are becoming increasingly important for studying neurological disorders. In this paper, a novel groupwise shape analysis approach is proposed to detect regional morphological alterations in subcortical structures between two study groups, e.g., healthy and pathological subjects. The proposed scheme extracts smoothed triangulated surface meshes from segmented binary maps, and establishes reliable point-to-point correspondences among the population of surfaces using a spectral matching method. Mean curvature features are incorporated in the matching process, in order to increase the accuracy of the established surface correspondence. The mean shapes are created as the geometric mean of all surfaces in each group, and a distance map between these shapes is used to characterize the morphological changes between the two study groups. The resulting distance map is further analyzed to check for statistically significant differences between two populations. The performance of the proposed framework is evaluated on two separate subcortical structures (hippocampus and putamen). Furthermore, the proposed methodology is validated in a clinical application for detecting abnormal subcortical shape variations in Alzheimer's disease. Experimental results show that the proposed method is comparable to state-of-the-art algorithms, has less computational cost, and is more sensitive to small morphological variations in patients with neuropathologies.