PhD, University of Cincinnati, 2009, Medicine : Neuroscience/Medical Science Scholars Interdisiplinary

INTRODUCTION: Lead is a potent environmental toxicant. Childhood lead exposure is associated with persistent cognitive and behavioral deficiencies, suggesting underlying neuroanatomic changes. This dissertation is an investigation of the effects of childhood lead exposure on young adult gray matter volume and white matter structure. METHODS: We investigated a subset of the long-running Cincinnati Lead Study, a prospective birth cohort study investigating the effects of environmental lead exposure on a primarily black, urban inner-city cohort. Participants received 23 serial assessments of blood lead concentration during childhood, high-resolution volumetric magnetic resonance imaging at approximately 21 years of age, and diffusion tensor imaging at approximately 24 years of age. Associations between gray matter volume and mean childhood blood lead, and yearly mean blood lead levels from years 1 to 6, were investigated by adjusted voxel-wise multiple regression analysis using voxel-based morphometry (VBM) techniques. Associations between mean childhood lead levels and white matter diffusivity changes were investigated using adjusted multiple regression analyses. RESULTS: Mean childhood lead levels were associated with significant adult gray matter volume loss, particularly in the frontal lobes, and particularly in men. Analysis of yearly mean blood lead levels revealed that blood levels later in childhood were more strongly associated with adult gray matter volume loss than earlier blood lead levels. The most extensive and significant regions of lead-associated gray matter volume loss were found in the frontal lobes of men associated with lead levels measured later at 5 and 6 years of age. Investigation of white matter diffusivity changes by diffusion tensor imaging analysis revealed widespread changes in fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. The observed patterns of diffusivity changes was consistent with signific (open full item for complete abstract)

Committee: Kim Cecil PhD (Advisor); Caleb Adler MD (Committee Member); James Herman PhD (Committee Member); Bruce Lanphear MD MPH (Committee Member); M. Douglas Ris PhD (Committee Member); Stephen Woods PhD (Committee Member) Subjects: Health; Neurology; Radiology

PhD, University of Cincinnati, 2006, Engineering : Electrical Engineering

In this dissertation, we address two kinds of the biomedical images/volumes segmentation problems: 1). Segmentation of white matter, gray matter, and cerebral spinal fluid from MR brain images/volumes; 2). Extraction of the vertebrae from MR spinal images. We propose a statistical decision model under maximum a posterior probability (MAP) estimation and Markov random field (MRF) framework to segment MR brain images, where the spatial-varying Gaussian mixture (SVGM) is used to represent the intensity probability distribution of each of the three brain tissues, and MRF is used to estimate the prior probability. Three methods, the supervised method, the automatic (unsupervised) method, and the 3D method, are proposed to achieve the final segmentation. The supervised method is a 2D method proposed to effectively learn the expert's segmentation and pursue the tissue labeling on 3D MR brain images with severe intensity non-uniformity. The fully automatic or unsupervised 2D method is presented to accurately segment WM, GM, and CSF. The parameters of SVGM are estimated from the reference images using either the expectation maximization (EM) algorithm or the MKM algorithm, and the final tissue labeling are obtained by using the ICM algorithm. Furthermore we impose a criterion that minimizes the intensity means difference within the same segmentation tissue to improve the accuracy of the parameters of SVGM. Due to the nature three dimensional characteristics of MR brain volumes, we extend the 2D SVGM-MRF method to 3D and employ the 3D spatial and intensity information for a more accurate conditional probability representation. To reduce the large computation time and memory requirements for 3D implementation, the algorithms using a local window instead of the whole volume are proposed to perform the necessary parameter estimations and achieve the tissue labeling. For MR spine images segmentation problem, we propose a two-step algorithm that can detect and segment the vertebra (open full item for complete abstract)

Committee: Dr. William Wee (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2007, Neuroscience

Spinal cord injury (SCI) is a long lasting, debilitating condition with no cure. Cervical SCI is the most common form of human SCI, often leaving patients paralyzed with a 15-20 year decrease in life expectancy. The majority of animal SCI contusion models are focused on thoracic injury. SCI at this level results in deficits almost entirely due to white matter damage that disconnects the rostral nervous system from the caudal spinal cord. Damage at the cervical level is different; in addition to the disconnection, gray matter damage affects the neurons controlling the upper extremities and diaphragm. To investigate injury at the cervical level, we characterized a unilateral C5 cervical contusion model in rats. By examining six-week behavioral recovery after SCI, we demonstrated that functional deficits are dependent upon the severity of injury. Analysis of the histopathology revealed that behavioral consequences are a result of damage to both the gray and white matter. Unilateral injury provides within-subject controls and preserves bladder and respiratory function. Many treatments for experimental rat SCI improve behavioral and histological outcomes but have yet to be implemented after human SCI. Treatments must be safe and tested in clinically relevant models to move from animals to humans. We examined the effects of three different clinically acceptable drugs. Methlyprednisolone and minocycline have anti-inflammatory effects if given after injury. Topiramate blocks glutamate receptors and hence excitotoxicity, an important component of secondary injury. Minocycline and methylprednisolone treatment yielded no significant behavioral or histological improvements when tested after moderate-severe unilateral cervical contusion injury. Topiramate was first tested in a model of excitotoxicity and then after cervical SCI and was compared to NBQX, a standard AMPA-receptor antagonists used in animal models of disease. Both drugs preserved neurons after excitotoxic injury, b (open full item for complete abstract)

Committee: Jacqueline Bresnahan (Advisor) Subjects: Biology, Neuroscience

Doctor of Philosophy, Case Western Reserve University, 2011, Biomedical Engineering

Multiple sclerosis (MS) has been traditionally characterized by primary demyelination and inflammation in white matter (WM). However, recent histopathologic studies have shown that gray matter (GM) of MS patients is also abnormal. My aim was to develop methods for quantifying GM damage in terms of GM atrophy and cortical atrophy, to investigate the evolution in various MS disease stages, and to assess relevance to clinical status. First, I developed an automated algorithm that segmented GM and WM in magnetic resonance images (MRI) and measured the normalized GM volume. The algorithm was designed to be applicable to MRI of MS patients, which had focal lesions and significant atrophy. The algorithm was validated and applied in a longitudinal study that included patients with clinically isolated syndrome (CIS), relapsing-remitting (RRMS) and secondary progressive (SPMS) MS as well as healthy normal controls. Other conventional MRI markers of focal damage and clinical measures were available to explore the correlations and predictors of GM atrophy. We found that (1) the rate of GM atrophy increased in a stage-dependent manner, which was similar to that of whole brain atrophy, (2) GM atrophy had moderately strong correlations with the disability measures, and (3) predictors of GM atrophy changed from RRMS to SPMS. Next, I developed a registration and deformable model-based longitudinal method (CLADA, Cortical Longitudinal Atrophy Detection Algorithm) that had high reproducibility and could measure global and regional cortical atrophy in terms of cortical thickness and its change. CLADA was validated and applied to the full longitudinal MRI dataset to explore the evolution of cortical thinning, its clinical correlations, and predictors. The rate of cortical thinning increased with advancing disease, correlated with clinical disability, and distinguished stable and worsening patients with more significance than GM atrophy. In summary, my research showed that GM and cortica (open full item for complete abstract)

Committee: Andrew Rollins PhD (Committee Chair); Elizabeth Fisher PhD (Advisor); Bruce Trapp PhD (Committee Member); David Wilson PhD (Committee Member) Subjects: Biomedical Engineering