Moo K. Chung

A Unified Statistical Approach to Deformation-Based Morphometry

We present a unified statistical framework for analyzing temporally varying brain morphology using the 3D displacement vector field from a nonlinear deformation required to register a subjects brain to an atlas brain. The unification comes from a single model for structural change, rather than two separate models, one for displacement and one for volume changes. The displacement velocity field rather than the displacement itself is used to set up a linear model to account for temporal variations. By introducing the rate of the Jacobian change of the deformation, the local volume change at each voxel can be computed and used to measure possible brain tissue growth or loss. We have applied this method to detecting regions of a morphological change in a group of children and adolescents. Using structural magnetic resonance images for 28 children and adolescents taken at different time intervals, we demonstrate how this method works.

Functional but not structural subgenual prefrontal cortex abnormalities in melancholia

Major depression is a heterogeneous condition, and the search for neural correlates specific to clinically defined subtypes has been inconclusive. Theoretical considerations implicate frontostriatal, particularly subgenual prefrontal cortex (PFC), dysfunction in the pathophysiology of melancholia—a subtype of depression characterized by anhedonia—but no empirical evidence has been found yet for such a link. To test the hypothesis that melancholic, but not nonmelancholic depression, is associated with the subgenual PFC impairment, concurrent measurement of brain electrical (electroencephalogram, EEG) and metabolic (positron emission tomography, PET) activity were obtained in 38 unmedicated subjects with DSM-IV major depressive disorder (20 melancholic, 18 nonmelancholic subjects), and 18 comparison subjects. EEG data were analyzed with a tomographic source localization method that computed the cortical three-dimensional distribution of current density for standard frequency bands, allowing voxelwise correlations between the EEG and PET data. Voxel-based morphometry analyses of structural magnetic resonance imaging (MRI) data were performed to assess potential structural abnormalities in melancholia. Melancholia was associated with reduced activity in the subgenual PFC (Brodmann area 25), manifested by increased inhibitory delta activity (1.5–6.0 Hz) and decreased glucose metabolism, which themselves were inversely correlated. Following antidepressant treatment, depressed subjects with the largest reductions in depression severity showed the lowest post-treatment subgenual PFC delta activity. Analyses of structural MRI revealed no group differences in the subgenual PFC, but in melancholic subjects, a negative correlation between gray matter density and age emerged. Based on preclinical evidence, we suggest that subgenual PFC dysfunction in melancholia may be associated with blunted hedonic response and exaggerated stress responsiveness.

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.

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.

Diffusion tensor imaging of white matter in the superior temporal gyrus and temporal stem in autism.

Recent MRI studies have indicated that regions of the temporal lobe including the superior temporal gyrus (STG) and the temporal stem (TS) appear to be abnormal in autism. In this study, diffusion tensor imaging (DTI) measurements of white matter in the STG and the TS were compared in 43 autism and 34 control subjects. DTI measures of mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were compared between groups. In all regions, fractional anisotropy was significantly decreased and both mean diffusivity and radial diffusivity were significantly increased in the autism group. These results suggest that white matter microstructure in autism is abnormal in these temporal lobe regions, which is consistent with theories of aberrant brain connectivity in autism.

Early Stress Is Associated with Alterations in the Orbitofrontal Cortex: A Tensor-Based Morphometry Investigation of Brain Structure and Behavioral Risk

Individuals who experience early adversity, such as child maltreatment, are at heightened risk for a broad array of social and health difficulties. However, little is known about how this behavioral risk is instantiated in the brain. Here we examine a neurobiological contribution to individual differences in human behavior using methodology appropriate for use with pediatric populations paired with an in-depth measure of social behavior. We show that alterations in the orbitofrontal cortex among individuals who experienced physical abuse are related to social difficulties. These data suggest a biological mechanism linking early social learning to later behavioral outcomes.

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.

Weighted Fourier Series Representation and Its Application to Quantifying the Amount of Gray Matter

We present a novel weighted Fourier series (WFS) representation for cortical surfaces. The WFS representation is a data smoothing technique that provides the explicit smooth functional estimation of unknown cortical boundary as a linear combination of basis functions. The basic properties of the representation are investigated in connection with a self-adjoint partial differential equation and the traditional spherical harmonic (SPHARM) representation. To reduce steep computational requirements, a new iterative residual fitting (IRF) algorithm is developed. Its computational and numerical implementation issues are discussed in detail. The computer codes are also available at http://www.stat.wisc.edu/ ~mchung/softwares/weighted-SPHARM/weighted-SPHARM.html . As an illustration, the WFS is applied in quantifying the amount of gray matter in a group of high functioning autistic subjects. Within the WFS framework, cortical thickness and gray matter density are computed and compared

Anatomic development of the oral and pharyngeal portions of the vocal tract: An imaging study

The growth of the vocal tract (VT) is known to be non-uniform insofar as there are regional differences in anatomic maturation. This study presents quantitative anatomic data on the growth of the oral and pharyngeal portions of the VT from 605 imaging studies for individuals between birth and 19 years. The oral (horizontal) portion of the VT was segmented into lip-thickness, anterior-cavity-length, oropharyngeal-width, and VT-oral, and the pharyngeal (vertical) portion of the VT into posterior-cavity-length, and nasopharyngeal-length. The data were analyzed to determine growth trend, growth rate, and growth type (neural or somatic). Findings indicate differences in the growth trend of segments/variables analyzed, with significant sex differences for all variables except anterior-cavity-length. While the growth trend of some variables displays prepubertal sex differences at specific age ranges, the importance of such localized differences appears to be masked by overall growth rate differences between males and females. Finally, assessment of growth curve type indicates that most VT structures follow a combined/hybrid (somatic and neural) growth curve with structures in the vertical plane having a predominantly somatic growth pattern. These data on the non-uniform growth of the vocal tract reveal anatomic differences that contribute to documented acoustic differences in prepubertal speech production.

General Multivariate Linear Modeling of Surface Shapes Using SurfStat

Although there are many imaging studies on traditional ROI-based amygdala volumetry, there are very few studies on modeling amygdala shape variations. This paper presents a unified computational and statistical framework for modeling amygdala shape variations in a clinical population. The weighted spherical harmonic representation is used to parameterize, smooth out, and normalize amygdala surfaces. The representation is subsequently used as an input for multivariate linear models accounting for nuisance covariates such as age and brain size difference using the SurfStat package that completely avoids the complexity of specifying design matrices. The methodology has been applied for quantifying abnormal local amygdala shape variations in 22 high functioning autistic subjects.