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NeuroImage 25 (2005) 1256 – 1265

Cortical thickness analysis in autism with heat kernel smoothing
Moo K. Chung,a,b,c,* Steven M. Robbins,f Kim M. Dalton,c Richard J. Davidson,c,d
Andrew L. Alexander,c,e and Alan C. Evansf a Department of Statistics, University of Wisconsin, Madison, 1210 West Dayton Street, WI 53706, USA
Biostatistics and Medical Informatics, University of Wisconsin, Madison, 1210 West Dayton Street, WI 53706, USA c W.M. Keck Laboratory for Functional Brain Imaging and Behavior, University of Wisconsin, Madison, WI 53706, USA d Department of Psychology and Psychiatry, University of Wisconsin, Madison, WI 53706, USA e Department of Medical Physics, University of Wisconsin, Madison, WI 53706, USA f Montreal Neurological Institute, McGill University, Canada b Received 5 October 2004; revised 22 November 2004; accepted 3 December 2004
Available online 10 March 2005
We present a novel data smoothing and analysis framework for cortical thickness data defined on the brain cortical manifold. Gaussian kernel smoothing, which weights neighboring observations according to their
3D Euclidean distance, has been widely used in 3D brain images to increase the signal-to-noise ratio. When the observations lie on a convoluted brain surface, however, it is more natural to assign the weights based on the geodesic distance along the surface. We therefore develop a framework for geodesic distance-based kernel smoothing and statistical analysis on the cortical manifolds. As an illustration, we apply our methods in detecting the regions of abnormal cortical thickness in
16 high functioning autistic children via random field based multiple comparison correction that utilizes the new smoothing technique.
D 2004 Elsevier Inc. All rights reserved.
Keywords: Cortical thickness; Autism; Brain; Heat kernel;