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Diffusion Tensor Imaging (DTI) is a medical imaging method that measures tissue structure. This is valuable when applied to the central nervous system (CNS) because it can provide structural information about white matter tracts. DTI of the spinal cord has been suggested as the next great leap in clinical diagnostics for spinal cord injury and disease because it may provide a measurable correlate of the physical structure of the cord with the associated functional deficit. Collecting precise structural information from the site of injury could be used to improve diagnostics and guide treatments. While these are the long term goals of DTI research, there are currently fundamental questions with regards to image resolution and motion-related artifacts in spinal cord which have not been thoroughly addressed. DTI is a sensitive imaging method which requires multiple mathematical calculations and approximations to complete. The limitations of the method compound with the limitations of imaging the spinal cord leading to the query: How reliable is DTI in the spinal cord? It is the goal of this study to begin to address these concerns. First, the effect of spinal cord motion on tissue discrimination was examined by comparing DTI results obtained in the presence and absence of a correctional measure for cardiac-induced motion called 'cardiac gating'. Tissue discriminability was found to be greatest in the cervical cord. Second, DTI results were subjected to two classification algorithms and compared with known anatomy to assess tissue discrimination accuracy as well as the types of associated errors. The proportion of errors in tissue classification was very high, presenting itself in all subjects. This result indicated that the DTI values associated with particular tissues were not unique to only those tissues. Finally, a theoretical model was implemented to assess the degree to which image resolution specifically affected the tissue classification accuracy obtained in the above experiments, as opposed to other errors such as MRI ghosting, blurring or distortions. It was found that DTI provides a systematically biased representation of spinal cord tissues. To overcome this limitation, future studies should concentrate efforts on increasing image resolution. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2010-09-24 02:29:32.619

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Diffusion tensor imaging (DTI) is a structural magnetic resonance imaging (MRI) technique to record incoherent motion of water molecules and has been used to detect micro structural white matter alterations in clinical studies to explore certain brain disorders. A variety of DTI based techniques for detecting brain disorders and facilitating clinical group analysis have been developed in the past few years. However, there are two crucial issues that have great impacts on the performance of those algorithms. One is that brain neural pathways appear in complicated 3D structures which are inappropriate and inaccurate to be approximated by simple 2D structures, while the other involves the computational efficiency in classifying white matter tracts. The first key area that this dissertation focuses on is to implement a novel computing scheme for estimating regional white matter alterations along neural pathways in 3D space. The mechanism of the proposed method relies on white matter tractography and geodesic distance mapping. We propose a mask scheme to overcome the difficulty to reconstruct thin tract bundles. Real DTI data are employed to demonstrate the performance of the pro- posed technique. Experimental results show that the proposed method bears great potential to provide a sensitive approach for determining the white matter integrity in human brain. Another core objective of this work is to develop a class of new modeling and clustering techniques with improved performance and noise resistance for separating reconstructed white matter tracts to facilitate clinical group analysis. Different strategies are presented to handle different scenarios. For whole brain tractography reconstructed white matter tracts, a Fourier descriptor model and a clustering algorithm based on multivariate Gaussian mixture model and expectation maximization are proposed. Outliers are easily handled in this framework. Real DTI data experimental results show that the proposed algorithm is relatively effective and may offer an alternative for existing white matter fiber clustering methods. For a small amount of white matter fibers, a modeling and clustering algorithm with the capability of handling white matter fibers with unequal length and sharing no common starting region is also proposed and evaluated with real DTI data.

Magnetic resonance imaging (MRI) is an imaging technique to visualize internal structures of the body. Diffusion MRI is an MRI modality that measures overall diffusion effect of molecules in vivo and non-invasively. Diffusion tensor imaging (DTI) is an extended technique of diffusion MRI. The major application of DTI is to measure the location, orientation and anisotropy of fiber tracts in white matter. It enables non-invasive investigation of major neural pathways of human brain, namely tractography. As spatial resolution of MRI is limited, it is possible that there are multiple fiber bundles within the same voxel. However, diffusion tensor model is only capable of resolving a single direction. The goal of this dissertation is to investigate complex anatomical structures using high angular resolution diffusion imaging (HARDI) data without any assumption on the parameters. The dissertation starts with a study of the noise distribution of truncated MRI data. The noise is often not an issue in diffusion tensor model. However, in HARDI studies, with many more gradient directions being scanned, the number of repetitions of each gradient direction is often small to restrict total acquisition time, making signal-to-noise ratio (SNR) lower. Fitting complex diffusion models to data with reduced SNR is a major interest of this study. We focus on fitting diffusion models to data using maximum likelihood estimation (MLE) method, in which the noise distribution is used to maximize the likelihood. In addition to the parameters being estimated, we use likelihood values for model selection when multiple models are fit to the same data. The advantage of carrying out model selection after fitting the models is that both the quality of data and the quality of fitting results are taken into account. When it comes to tractography, we extend streamline method by using covariance of the estimated parameters to generate probabilistic tracts according to the uncertainty of local tract orientations.

In der vorliegenden Arbeit wird der Hypothalamus, eine kleine, aber bedeutsame Struktur des Zwischenhirns untersucht. Er spielt unter anderem eine Rolle bei der Regulation des Schlaf-Wach-Rhythmus, des Sexualverhaltens, der Stimmungslage, autonomer und Stoffwechsel-Funktionen. Veränderungen einzelner oder mehrerer spezifischer Kerngruppen sind bei neuropsychiatrischen bzw. -endokrinologischen Erkrankungen, wie Narkolepsie, Schizophrenie, affektiver Störung, Demenz, Borderline-Persönlichkeitsstörung, Pädophilie oder Adipositas zu beobachten. Die Substrukturierung und Darstellung der einzelnen Kerngruppen gelang bisher nur in Postmortem-Studien. Im Rahmen dieser Studie konnte mit Hilfe der Diffusions-Tensor-Bildgebung erstmals eine in-vivo Substrukturierung des Hypothalamus konsistent bei zehn gesunden Probanden vorgenommen werden. Dabei wurden nach einem Algorithmus zunächst die Segmentierung und anschließend die Parzellierung durchgeführt, woraus sich drei konsistente Cluster ergaben. Der topografische Vergleich der erhaltenen Cluster mit Postmortem-Studien der Literatur ergab vergleichbare und anatomisch plausible Korrelate. Mit der von uns entwickelten Methode könnten anhand einer größeren Patientengruppe pathophysiologische Zusammenhänge neuropsychiatrischer und –endokrinologischer Störungen genauer eruiert werden und zu einem besseren Verständnis des Krankheitsverlaufs und der Therapie beitragen.

<p>Understanding the structure of the brain has been a major goal of neuroscience research over the past century, driven in part by the understanding that brain structure closely follows function. Normative brain maps, or atlases, can be used to understand normal brain structure, and to identify structural differences resulting from disease. Recently, diffusion tensor magnetic resonance imaging has emerged as a powerful tool for brain atlasing; however, its utility is hindered by image resolution and signal limitations. These limitations can be overcome by imaging fixed ex-vivo specimens stained with MRI contrast agents, a technique known as diffusion tensor magnetic resonance histology (DT-MRH). DT-MRH represents a unique, quantitative tool for mapping the brain with unprecedented structural detail. This technique has engendered a new generation of 3D, digital brain atlases, capable of representing complex dynamic processes such as neurodevelopment. This dissertation explores the use of DT-MRH for quantitative brain atlasing in an animal model and initial work in the human brain. </p><p>Chapter 1 describes the advantages of the DT-MRH technique, and the motivations for generating a quantitative atlas of rat postnatal neurodevelopment. The second chapter covers optimization of the DT-MRH hardware and pulse sequence design for imaging the developing rat brain. Chapter 3 details the acquisition and curation of rat neurodevelopmental atlas data. Chapter 4 describes the creation and implementation of an ontology-based segmentation scheme for tracking changes in the developing brain. Chapters 5 and 6 pertain to analyses of volumetric changes and diffusion tensor parameter changes throughout rat postnatal neurodevelopment, respectively. Together, the first six chapters demonstrate many of the unique and scientifically valuable features of DT-MRH brain atlases in a popular animal model.</p><p>The final two chapters are concerned with translating the DT-MRH technique for use in human and non-human primate brain atlasing. Chapter 7 explores the validity of assumptions imposed by DT-MRH in the primate brain. Specifically, it analyzes computer models and experimental data to determine the extent to which intravoxel diffusion complexity exists in the rhesus macaque brain, a close model for the human brain. Finally, Chapter 8 presents conclusions and future directions for DT-MRH brain atlasing, and includes initial work in creating DT-MRH atlases of the human brain. In conclusion, this work demonstrates the utility of a DT-MRH brain atlasing with an atlas of rat postnatal neurodevelopment, and lays the foundation for creating a DT-MRH atlas of the human brain.</p> / Dissertation

The purpose of this study was to examine the relationships among mentalizing abilities, self-reported autism traits, and two white matter tracts, uncinate fasciculus (UF) and inferior longitudinal fasciculus (ILF), in neurotypical adults. UF and ILF were hypothesized to connect brain regions implicated in a neuroanatomical model of mentalizing. Data were available for 24 neurotypical adults (mean age = 21.92 (4.72) years; 15 women). Tract-based spatial statistics (TBSS) was used to conduct voxelwise cross-participant comparisons of fractional anisotropy (FA) values in UF and ILF as predicted by mentalizing abilities and self-reported autism traits. Self-reported autism traits were positively related to FA values in left ILF. Results suggest that microstructural differences in left ILF are specifically involved in the expression of subclinical autism traits in neurotypical individuals.

Diffusion Tensor Magnetic Resonance Imaging was used to examine the age-related changes in white matter structural integrity in the common chimpanzee. Fractional Anisotropy(FA), a measure derived from the diffusion tensor data is sensitive to developmental and pathological changes in axonal density, myelination, size and coherence of organization of fibers within a voxel and thus reflects the white matter structural integrity. There is substantial evidence that white matter structural integrity decreases with age in humans. The long-term goal of this study is to compare the age-related changes in the white matter structural integrity among humans and chimpanzess to provide potential insights into the unique features of human aging. Different methods, including Region Of Interest (ROI) analysis, Tract Based Spatial Statistics (TBSS) are used to describe age-related changes in FA in a group of 21 chimpanzees. Strengths and limitations of these methods were discussed.

Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2008. / Title from first page of PDF file (viewed April 7, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 127-149).