Detecting Gray Matter Maturation via Tensor-based Surface Morphometry Human Brain Mapping Conference 2003 # 807 Detecting Gray Matter Maturation via Tensor-based Surface Morphometry M.K. Chung123, K.J. Worsley4, S. Robbins4, T. Paus4, J. N. Giedd5 , J. L. Rapoport5 , A. C. Evans4 1Department of Statistics, 2Department of Biostatistics and Medical Informatics 3W.M. Keck Laboratory for functional Brain Imaging and Behavior University of Wisconsin, Madison, USA 4Montreal Neurological Institute, McGill University, Montreal, Canada 5Child Psychiatry Branch, National Institute of Mental Health, MD, USA Correspondence: mchung@stat.wisc.edu 1. Motivation We present a unified tensor-based surface morphometry in characterizing the gray matter anatomy change in the brain development longitudinally collected in the group of children and adolescents. 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. This poster is based on a recently published paper Chung et al. (2003). 2. Image preprocessing Two T1-weighted magnetic resonance scans were acquired for each of 28 normal subjects at different times on the GE Sigma 1.5-T superconducting magnet system. The first scan was obtained at the age 11.5 years and the second scan was obtained at the age 16.1 years in average. MR images were spatially normalized and tissue types were classified. Afterwards, a triangular mesh for each cortical surface was generated by deforming a mesh to fit the proper boundary in a segmented volume using a deformable surface algorithm (MacDonald et al., 2000). This algorithm is further used in surface registration and surface template construction. Left: The Gyri are extracted by thresholding the thin-plate energy functional on the inner surface. Middle & Right: Individual gyral patterns mapped onto the template surface. The gyri of a subject match the gyri of the template surface illustrating a close homology between the surface of the individual subject and the template. Left: Individual cortical surfaces (blue: interface between the gray and white matter, yellow: outer cortical surface). Right: The surface template is constructed by averaging the coordinates of homologous vertices. 4. Surface Data Smoothing To increase the signal-to-noise ratio and to satisfy the random fields assumptions, surface-based smoothing is essential. Isotropic diffusion smoothing (Beltrami-flow) is developed. The figure below shows a simulation of the diffusion smoothing. 3. Tensor Geometry Based on the local quadratic surface parameterization, Riemannian metric tensors are computed and used to characterize the cortical shape variations. The cortical thickness, local surface area, local gray matter volume, curvatures are computed. In particular, we report the cortical shape change based on the curvature change for the first time. 6. Morphometric Findings (ages 12-16) Gray matter volume: total gray matter volume shrinks. Local growth in the parts of temporal, occipital, somatosensory, and motor regions. Cortical Surface area: total area shrinks. highly localized area growth along the left inferior frontal gyrus and shrinkage in the left superior frontal sulcus. Cortical thickness: no statistically significant local cortical thinning on the whole cortex. Predominant thickness increase in the left superior frontal sulcus. Cortical curvature: no statistically significant curvature decrease. Most curvature increase occurs on gyri. No curvature change on most sulci. Curvature increase in the superior frontal and middle frontal gyri. Dynamic pattern: it seems that the cortical thickness increase and local surface area shrinking in the left superior frontal sulcus cause increased folding in the neighboring middle and superior frontal gyri. While the gray matter is shrinking in both total surface area and volume, the cortex itself seems to get more folded to give increasing curvature. Top: Thin-plate spline energy functional computed on the inner cortical surface of a 14-year-old subject. It measures the amount of folding of the cortical surface. Bottom: t-statistic map showing statistically significant region of curvature increase. Most of the curvature increase occurs on gyri while there is no significant change of curvature on most of sulci. Also there is no statistically significant curvature decrease detected, indicating that the complexity of the surface convolution increase between ages 12 and 16. 5. Random Fields Theory Statistical analysis was based on the random field theory (Worsley et al., 1996). The gaussianess of the surface metrics was checked with Lilliefors test. The isotropic diffusion smoothing was found to increase both the smoothness as well as the isotropicity of the surface data. The validity of our method was checked by generating null data. The null data were created by reversing time for the half of subjects chosen randomly. In the null data, most t values were well below the threshold indicating that our image processing and statistical analysis do not produce false positives. References Chung, M.K. et al., Deformation-based Surface Morphometry applied to Gray Matter Deformation, NeuroImage. 18:198–213, 2003. MacDonald, J.D. et al., Automated 3D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI, NeuroImage. 12:340-356, 2000. Worsley, K.J., et al., A unified statistical approach for determining significant signals in images of cerebral activation, Human Brain Mapping. 4:58-73, 1996.