Deformation-based surface morphometry applied to gray matter deformation

被引:212
作者
Chung, MK
Worsley, KJ
Robbins, S
Paus, T
Taylor, J
Giedd, JN
Rapoport, JL
Evans, AC
机构
[1] Univ Wisconsin, Dept Stat, Madison, WI 53706 USA
[2] Univ Wisconsin, WM Keck Lab Funct Brain Imaging & Behav, Madison, WI 53706 USA
[3] McGill Univ, Dept Math & Stat, Montreal, PQ H3A 2T5, Canada
[4] McGill Univ, Montreal Neurol Inst, Montreal, PQ H3A 2T5, Canada
[5] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
[6] NIMH, Child Psychiat Branch, NIH, Bethesda, MD 20892 USA
关键词
cerebral cortex; cortical surface; brain development; cortical thickness; brain growth; brain atrophy; deformation; morphometry;
D O I
10.1016/S1053-8119(02)00017-4
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
We present a unified statistical approach to deformation-based morphometry applied to the cortical surface. The cerebral cortex has the topology of a 2D highly convoluted sheet. As the brain develops over time, the cortical surface area, thickness, curvature, and total gray matter volume change. It is highly likely that such age-related surface changes are not uniform. By measuring how such surface metrics change over time, the regions of the most rapid structural changes can be localized. We avoided using surface flattening, which distorts the inherent geometry of the cortex in our analysis and it is only used in visualization. To increase the signal to noise ratio, diffusion smoothing, which generalizes Gaussian kernel smoothing to an arbitrary curved cortical surface, has been developed and applied to surface data. Afterward, statistical inference on the cortical surface will be performed via random fields theory. As an illustration, we demonstrate how this new surface-based morphometry can be applied in localizing the cortical regions of the gray matter tissue growth and loss in the brain images longitudinally collected in the group of children and adolescents. (C) 2003 Elsevier Science (USA). All rights reserved.
引用
收藏
页码:198 / 213
页数:16
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