Faculty Bibliography

Caudate dysfunction is implicated in schizophrenia. However, little is known about the relationship between aggression and caudate volumes. Forty-nine patients received magnetic resonance imaging scanning in a double-blind treatment study in which aggression was measured. Caudate volumes were computed using a semiautomated method. The authors measured aggression with the Overt Aggression Scale and the Positive and Negative Syndrome Scale. Larger caudate volumes were associated with greater levels of aggression. The relationship between aggression and caudate volumes may be related to the iatrogenic effects of long-term treatment with typical antipsychotic agents or to a direct effect of schizophrenic processes on the caudate.

Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology.The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T(2) two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives.

We present a method for automatically finding correspondence in Diffusion Tensor Imaging (DTI) from deformable registration to a common atlas. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with diffeomorphic correspondence between each image. The registration image match metric uses a feature detector for thin fiber structures of white matter, and interpolation and averaging of diffusion tensors use the Riemannian symmetric space framework. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies and for building DTI population atlases.

The emergence of open-source libraries and development tools in the last decade has changed the process of academic software development in many ways. In medical image processing and visualization this change is especially evident, also because open source projects are actively furthered by grant funding institutions. This manuscript presents the use of such development tools and libraries at the UNC Neuro-Image Analysis Laboratory for open source applications and tools. We have also experienced in our research that the development of open source in academics raises the issue of access to unpublished methodology. The strategy at our laboratory is to combine all in-house libraries and applications into a single repository that consists of two parts: a fully open source part that is distributed under a Berkley-style license and a private, closed source part with unpublished tools and methods. Access to the open source part is unrestricted, whereas the private parts can only be downloaded via cvs user login. This setup solved our issues regarding unpublished methodology, as migration from the private to the open source part is very simple. Overall our experience with this development environment within the academic setting is very positive.

Work in progress towards modeling shape statistics of multi-object complexes is presented. Constraints defined by the set of objects such as a compact representation of object shape relationships and correlation of shape changes might have advantages for automatic segmentation and group discrimination. We present a concept for statistical multi-object modeling and discuss the major challenges which are a reduction to a small set of descriptive features, calculation of mean and variability via curved statistics, the choice of aligning sets of multiple objects, and the problem of describing the statistics of object pose and object shape and their interrelationship. Shape modeling and analysis is demonstrated with an application to a longitudinal autism study, with shape modeling of sets of 10 subcortical structures in a population of 20 subjects.

CONTEXT: While the neuroanatomical basis of autism is not yet known, evidence suggests that brain enlargement may be characteristic of this disorder. Inferences about the timing of brain enlargement have recently come from studies of head circumference (HC). OBJECTIVES: To examine brain volume and HC in individuals with autism as compared with control individuals. DESIGN: A cross-sectional study of brain volume was conducted at the first time point in an ongoing longitudinal magnetic resonance imaging study of brain development in autism. Retrospective longitudinal HC measurements were gathered from medical records on a larger sample of individuals with autism and local control individuals. SETTING: Clinical research center. PARTICIPANTS: The magnetic resonance imaging study included 51 children with autism and 25 control children between 18 and 35 months of age (the latter included both developmentally delayed and typically developing children). Retrospective, longitudinal HC data were examined from birth to age 3 years in 113 children with autism and 189 local control children. MAIN OUTCOME MEASURES: Cerebral cortical (including cortical lobes) and cerebellar gray and white matter magnetic resonance imaging brain volumes as well as retrospective HC data from medical records were studied. RESULTS: Significant enlargement was detected in cerebral cortical volumes but not cerebellar volumes in individuals with autism. Enlargement was present in both white and gray matter, and it was generalized throughout the cerebral cortex. Head circumference appears normal at birth, with a significantly increased rate of HC growth appearing to begin around 12 months of age. CONCLUSIONS: Generalized enlargement of gray and white matter cerebral volumes, but not cerebellar volumes, are present at 2 years of age in autism. Indirect evidence suggests that this increased rate of brain growth in autism may have its onset postnatally in the latter part of the first year of life.

The relationship between orbitofrontal cortex (OFC) volumes and functional domains in treatment-resistant patients with schizophrenia or schizoaffective disorder is poorly understood. OFC dysfunction is implicated in several of the behaviors that are abnormal in schizophrenia. However, little is known about the relationship between these behaviors and OFC volumes. Forty-nine patients received magnetic resonance imaging scanning as part of a double-blind treatment study in which psychiatric symptomatology, neuropsychological function, and aggression were measured. OFC volumes were manually traced on anatomical images. Psychiatric symptomatology was measured with the Positive and Negative Syndrome Scale (PANSS). Aggression was measured with the Overt Aggression Scale (OAS) and with the PANSS. Neuropsychological function was assessed using a comprehensive test battery. Larger right OFC volumes were associated with poorer neuropsychological function. Larger left OFC gray matter volumes and larger OFC white matter volumes bilaterally were associated with greater levels of aggression. These findings are discussed in the context of potential iatrogenic effects

RATIONALE AND OBJECTIVES: Malignancy provokes regional changes to vessel shape. Characteristic vessel tortuosity abnormalities appear early during tumor development, affect initially healthy vessels, spread beyond the confines of tumor margins, and do not simply mirror tissue perfusion. The ability to detect and quantify tortuosity abnormalities on high-resolution magnetic resonance angiography (MRA) images offers a new approach to the noninvasive diagnosis of malignancy. This report evaluates a computerized, statistical method of analyzing the shapes of vessels extracted from MRA in diagnosing cancer. MATERIALS AND METHODS: The regional vasculature of 34 healthy subjects was compared with the tumor-associated vasculature of 30 brain tumors before surgical resection. The operator performing the analysis was blinded to the diagnosis. Vessels were segmented from an MRA of each subject, a region of interest was defined in each tumor patient and was mapped to all healthy controls, and a statistical analysis of vessel shape measures was then performed over the region of interest. Many difficult cases were included, such as pinpoint, hemorrhagic, and irradiated tumors, as were hypervascular benign tumors. Tumors were identified as benign or malignant on the basis of histological evaluation. RESULTS: A discriminant analysis performed at the study's conclusion successfully classified all but one of the 30 tumors as benign or malignant on the basis of vessel tortuosity. CONCLUSIONS: Quantitative, statistical measures of vessel shape offer a new approach to the diagnosis and staging of disease. Although the methods developed under the current report must be tested against a new series of cases, initial results are promising.