Cortical Surface Thickness as a Classifier: Boosting for Autism Classification

Abstract

We study the problem of classifying an autistic group from controls using structural image data alone, a task that requires a clinical interview with a psychologist. Because of the highly convoluted brain surface topology, feature extraction poses the first obstacle. A clinically relevant measure called the cortical thickness has shown promise but yields a rather challenging learning problem – where the dimensionality of the distribution is extremely large and the training set is small. By observing that each point on the brain cortical surface may be treated as a “hypothesis”, we propose a new algorithm for LPBoosting (with truncated neighborhoods) for this problem. In addition to learning a high quality classifier, our model incorporates topological priors into the classification framework directly – that two neighboring points on the cortical surface (hypothesis pairs) must have similar discriminative qualities. As a result, we obtain not just a label { + 1, − 1} for test items, but also an indication of the “discriminative regions” on the cortical surface. We discuss the formulation and present interesting experimental results.

The first author was supported in part by funds from Dept. of Biostatistics and Medical Informatics, UW-Madison and UW Institute for Clinical and Translational Research (ICTR).