Detecting corpus callosum abnormalities in autism subtype using planar conformal mapping

A number of studies have documented that autism has a neurobiological basis, but the anatomical extent of these neurobiological abnormalities is largely unknown. In this paper, we apply advanced computational techniques to extract 3D models of the corpus callosum (CC) and subsequently analyze local shape variations in a homogeneous group of autistic children. Besides the traditional volumetric analysis, we explore additional phenotypic traits based on the oriented bounding rectangle of the CC. In shape analysis, a new conformal parameterization is applied in our shape analysis work, which maps the surface onto a planar domain. Surface matching among different individual meshes is achieved by aligning the planar domains of individual meshes. Shape differences of the CC between autistic patients and the controls are computed using Hotelling T² two-sample metric followed by a permutation test. The raw and corrected p-values are shown in the results. Additional visualization of the group difference is provided via mean difference map.