Benign epilepsy with centrotemporal spikes (BECTS) is one of the most common childhood epilepsy syndromes, which occurs in children aged from three to 13 years old. Initially, BECTS was considered as benign, but recently some studies have found cognitive and behavioral deficits, which may persist even after remission. Recent neuroimaging studies have found a link between these cognitive deficits and dysfunctions in specific brain structures, which shows the possibility of neuroanatomical alterations in these brain regions. In this research project, we aim to propose an automatic morphological analysis framework in BECTS to detect the subtle neuroanatomical alterations in children with BECTS, compared to normal controls. To this end, we develop a group-wise coregistration and cosegmentation process, which enables automatic segmentation of sub-cortical structures in multiple images. Then, we design a framework for matching 3D sub-cortical surface meshes and investigating the group-wise structural differences between two populations of surfaces, i.e., healthy and pathological subjects. Finally, we propose a methodology to assess the association between morphological alterations and cognition.
