期刊
NEUROIMAGE
卷 25, 期 4, 页码 1256-1265出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2004.12.052
关键词
cortical thickness; autism; brain; heat kernel; diffusion smoothing
资金
- NICHD NIH HHS [U19 HD035476] Funding Source: Medline
- NIMH NIH HHS [U54 MH066398] Funding Source: Medline
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 fie 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. (c) 2004 Elsevier Inc. All rights reserved.
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