Biomedical subjects

Moo K Chung

Publications and source records attributed to Moo K Chung.

5 recordsLinked to original sources

PubMed2006

Integrating VBM into the General Linear Model with voxelwise anatomical covariates.

A current limitation for imaging of brain function is the potential confound of anatomical differences or registration error, which may manifest via apparent functional "activation" for between-subject analyses. With respect to functional activations, underlying tissue mismatches can be regarded as a nuisance variable. We propose adding the probability of gray matter at a given voxel as a covariate (nuisance variable) in the analysis of voxelwise multisubject functional data using standard statistical techniques. A method is presented to assess the extent to which a functional activation can reliably be explained by underlying anatomical differences, and simultaneously, to assess the component of the functional activation which cannot be attributed to anatomical difference and thus is likely due to functional difference alone. Extension of the method to other intermodal imaging applications is discussed. Two exemplary data sets, one PET and one fMRI, are used to demonstrate the implementation and utility of this method, which apportions the relative contributions of anatomy and function for an apparent functional activation. The examples show two distinct types of results. First, a so-called functional activation may actually be caused by a systematic anatomical difference which, when modeled, diminishes the functional effect. In the second result type, including the anatomical differences in the model can account for a large component of otherwise unmodeled variance, yielding an increase in the functional effect cluster size and/or magnitude. In either case, ignoring the readily available structural information can lead to misinterpretation of functional results.

Brain↗

PubMed2005

Cortical thickness analysis in autism with heat kernel smoothing.

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.

Adolescent↗

PubMed2004

Quantitative analysis of diffusion tensor orientation: theoretical framework.

Diffusion-tensor MRI (DT-MRI) yields information about the magnitude, anisotropy, and orientation of water diffusion of brain tissues. Although white matter tractography and eigenvector color maps provide visually appealing displays of white matter tract organization, they do not easily lend themselves to quantitative and statistical analysis. In this study, a set of visual and quantitative tools for the investigation of tensor orientations in the human brain was developed. Visual tools included rose diagrams, which are spherical coordinate histograms of the major eigenvector directions, and 3D scatterplots of the major eigenvector angles. A scatter matrix of major eigenvector directions was used to describe the distribution of major eigenvectors in a defined anatomic region. A measure of eigenvector dispersion was developed to describe the degree of eigenvector coherence in the selected region. These tools were used to evaluate directional organization and the interhemispheric symmetry of DT-MRI data in five healthy human brains and two patients with infiltrative diseases of the white matter tracts. In normal anatomical white matter tracts, a high degree of directional coherence and interhemispheric symmetry was observed. The infiltrative diseases appeared to alter the eigenvector properties of affected white matter tracts, showing decreased eigenvector coherence and interhemispheric symmetry. This novel approach distills the rich, 3D information available from the diffusion tensor into a form that lends itself to quantitative analysis and statistical hypothesis testing.

Adolescent↗

PubMed2004

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

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.

Adolescent↗

PubMed2003

Deformation-based surface morphometry applied to gray matter deformation.

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.

Adolescent↗