Can magnetic resonance imaging aid diagnosis of the autism spectrum?

Editor's Note: These short, critical reviews of recent papers in the Journal, written exclusively by graduate students or postdoctoral fellows, are intended to summarize the important findings of the paper and provide additional insight and commentary. For more information on the format and purpose of the Journal Club, please see Review of Ecker et al. Although neurodevelopmental in origin, autism spectrum disorders are not currently diagnosed by neuroanatomical metrics but rather by behavioral observation. Autistic people differ from other people in their social interactions, communication, movement, and the level to which they focus on interests (American Psychiatric Association , 2000). The neurological basis of these behavioral differences has been of great interest. Nearly 200 studies over the past 20 years have proposed neuroanatomical markers of autism spectrum disorders; however, these studies have often been in conflict or unreplicated. Some of the conflicting findings can be explained by variation between and within participant samples (e.g., age and IQ) (Stanfield et al., 2008). Furthermore, most previous research has investigated a single morphometric feature of a single neural region, such as the volume of participants' amygdalae, but because several areas have been implicated (e.g., corpus callosum, caudate, cer-ebellum) (Stanfield et al., 2008) and because identified markers are not limited to a single morphometric feature, future research should consider multiple mor-phometric features across the brain. Ecker et al. (2010b) performed a support vector machine (SVM) classification between autistic and non-autistic participants. Linear SVM is a machine learning method that identifies patterns in a dataset by identifying the hyperplane(s) that maximally distinguish different categories. After a clas-sifier is estimated from a training dataset, the classifier can be used to predict category membership. Ecker and colleagues (2010b) trained their classifier based on five mor-phometric parameters of cortical gray matter: cortical thickness (the distance between white matter and pial surfaces), pial area (surface area of gray matter), metric distortion (Jacobian; the overall degree of cortical folding), average convexity or concavity (sulcal depth and gyral height; primary cor-tical folding), and mean (radial) curvature (secondary and tertiary cortical folding). All participants were right-handed males, 20 – 68 years old. The same MRI scans of 20 autistic adults and 20 non-autistic adults (without any known neuropsychiatric disorders) were used to train and validate the classifier using a leave-two-out cross-validation approach, in which all participants except one from each group were used to train the classifier, which was then used to …