Deformation-based surface morphometry applied to gray matter deformation

Chung, Moo K.; Worsley, Keith J.; Robbins, Steven; Paus, Tomáš; Taylor, Jonathan; Giedd, Jay N.; Rapoport, Judith L.; Evans, Alan C.

doi:10.1016/s1053-8119(02)00017-4

Cited by 255 publications

(193 citation statements)

References 40 publications

Supporting

Mentioning

191

Contrasting

Order By: Relevance

“…Note that we are not trying to compute corrected P value via random permutation test but rather to check if our image processing and data analysis procedures might give false positives. A similar approach of generating null data and checking possible image processing artifacts has been proposed in Chung et al (2003) for a child brain development study.…”

Section: Resultsmentioning

confidence: 99%

Less white matter concentration in autism: 2D voxel-based morphometry

Chung¹,

Dalton²,

Alexander³

et al. 2004

NeuroImage

Self Cite

143

View full text Add to dashboard Cite

Autism is a neurodevelopmental disorder affecting behavioral and social cognition, but there is little understanding about the link between the functional deficit and its underlying neuroanatomy. We applied a 2D version of voxel-based morphometry (VBM) in differentiating the white matter concentration of the corpus callosum for the group of 16 high functioning autistic and 12 normal subjects. Using the white matter density as an index for neural connectivity, autism is shown to exhibit less white matter concentration in the region of the genu, rostrum, and splenium removing the effect of age based on the general linear model (GLM) framework. Further, it is shown that the less white matter concentration in the corpus callosum in autism is due to hypoplasia rather than atrophy. D

show abstract

Section: Resultsmentioning

confidence: 99%

Less white matter concentration in autism: 2D voxel-based morphometry

Chung¹,

Dalton²,

Alexander³

et al. 2004

NeuroImage

Self Cite

143

View full text Add to dashboard Cite

show abstract

“…This algorithm has also been validated by comparison to manual measurements (Kabani et al, 2001). In order to improve sensitivity, each subject's cortical thickness map was blurred using a 20 mm, surface-based blurring kernel (Chung et al, 2003). This diffusion smoothing method follows the curvature of the cortical surface respecting anatomical boundaries, unlike the volumetric blurring typically used in voxel-based morphometry.…”

Section: Cortical Thickness Measurementmentioning

confidence: 99%

Focal cortical atrophy in multiple sclerosis: Relation to lesion load and disability

Charil¹,

Dagher²,

Lerch³

et al. 2007

NeuroImage

Self Cite

174

151

View full text Add to dashboard Cite

Multiple sclerosis (MS) is thought to predominantly affect white matter (WM). Recently, however, loss of cortical gray matter has also been described. Little is known about the cause of cortical atrophy in MS, whether it occurs early in the disease course, and whether it affects all cortical regions equally or if there is a preferential pattern of focal cortical atrophy. An automated method was used to compute the thickness at every vertex of the cortical surface of the brains of 425 early relapsing-remitting MS patients. We correlated cortical thickness with the WM lesion load and the Expanded Disability Status Scale score. Mean cortical thickness correlated with WM lesion load and disability. The correlations of cortical thickness with total lesion load and disability were most significant in cingulate gyrus, insula, and associative cortical regions. Conversely, primary sensory, visual, and motor areas showed a less significant relationship. The highest amount of atrophy per lesion volume or disability scale unit was in the anterior cingulate cortex. This study confirms the relation between cortical atrophy, WM lesion load, and disability in MS, and suggests that cortical atrophy occurs even in MS patients with only mild disability. Most interestingly, we show a specific regional pattern of focal atrophy in MS that is distinctively different from the one in normal aging. The predilection of the atrophic process for areas that are heavily interconnected with other brain regions suggests that interruption of WM tracts by MS plaques contributes, at least in part, to the development of cortical atrophy.

show abstract

“…The vertices of a cortical surface mesh are not, however, arranged on a grid with regular spacing; instead distances and angles between neighboring vertices are slightly variable (Fischl et al, 1999a;Chung et al, 2003). Because of this, direct application of a Gaussian blurring kernel --as is done with 3D datasets --is computationally intensive for surface-based data.…”

Section: Introductionmentioning

confidence: 99%

“…A computationally efficient method is to iteratively perform nearest-neighbor averaging; where each vertex's value is averaged with those of its neighbors. Slightly more complicated iterative smoothing algorithms have also been developed, which rely on a heat diffusion model and involve unequally weighting the contribution from each neighbor in the post-iteration value of a given vertex (Andrade et al, 2001;Chung et al, 2003;Chung et al, 2005). For example, in the latest and simplest version of this method, called heat kernel smoothing, the weights are calculated based on the distances between a vertex and each of its neighbors (Chung et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…For heat kernel smoothing, the full-width-half-max (FWHM) size of the Gaussian blurring kernel equivalent to a particular number of iterations can be predicted from heat diffusion equations (Chung et al, 2003;Chung et al, 2005). The accuracy of those predictions, however, is limited by the extent to which the assumptions of the heat diffusion model are met.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data

2006

View full text Add to dashboard Cite

Cortical surface-based analysis of fMRI data has proven to be a useful method with several advantages over 3-dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with surface-based analyses. Operating within the framework of the FreeSurfer software package, we have implemented a surface-based version of the cluster-size exclusion method used for multiple comparisons correction. Furthermore, we have a developed a new method for generating regions of interest on the cortical surface using a sliding threshold of cluster exclusion followed by cluster growth. Cluster size limits for multiple probability thresholds were estimated using random field theory and validated with Monte Carlo simulation. A prerequisite of RFT or cluster-size simulation is an estimate of the smoothness of the data. In order to estimate the intrinsic smoothness of group analysis statistics, independent of true activations, we conducted a group analysis of simulated noise datasets. Because smoothing on a cortical surface mesh is typically implemented using an iterative method, rather than directly applying a Gaussian blurring kernel, it is also necessary to determine the width of the equivalent Gaussian blurring kernel as a function of smoothing steps. Iterative smoothing has previously been modeled as continuous heat diffusion, providing a theoretical basis for predicting the equivalent kernel width, but the predictions of the model were not empirically tested. We generated an empirical heat diffusion kernel width function by performing surface-based smoothing simulations and found a large disparity between the expected and actual kernel widths.

show abstract

Deformation-based surface morphometry applied to gray matter deformation

Cited by 255 publications

References 40 publications

Less white matter concentration in autism: 2D voxel-based morphometry

Less white matter concentration in autism: 2D voxel-based morphometry

Focal cortical atrophy in multiple sclerosis: Relation to lesion load and disability

Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data

Contact Info

Product

Resources

About