Dissertations / Theses on the topic 'White matter diffusion'
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1
Campbell, Jennifer S. W. "Diffusion imaging of white matter fibre tracts." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85135.
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This thesis presents the design and validation of a method for digitally reconstructing white matter fibre tracts in vivo using magnetic resonance imaging (MRI). The technique uses diffusion weighted MRI to estimate a likelihood distribution function for the fibre direction(s) in each imaging voxel, and subsequently infers connectivity from any point in the central nervous system to another. The fibre tracking algorithm addresses issues that can confound fibre tract reconstruction, such as imaging noise, subvoxel partial volume averaging of fibre directions, and problems with the estimate of the diffusion probability density function (pdf). It can take as input a diffusion pdf estimated using either the traditional diffusion tensor approach or more recent high angular resolution diffusion approaches. The fibre tracking technique is validated using in vivo human brain diffusion imaging data and using a phantom constructed from excised rat spinal cord, which provides a "gold standard" connectivity map. The results are promising, especially for regions of the brain where tracking using previously described algorithms has been difficult to perform, for example, the regions of complex fibre structure near the cortex. As the cortex is critical for functional activity in the brain, this may have widespread implications for our understanding of the human brain in healthy subjects and in disease.
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O'Donnell, Lauren Jean. "Cerebral white matter analysis using diffusion imaging." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35514.
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Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2006.Includes bibliographical references (p. 183-198).
In this thesis we address the whole-brain tractography segmentation problem. Diffusion magnetic resonance imaging can be used to create a representation of white matter tracts in the brain via a process called tractography. Whole brain tractography outputs thousands of trajectories that each approximate a white matter fiber pathway. Our method performs automatic organization, or segmention, of these trajectories into anatomical regions and gives automatic region correspondence across subjects. Our method enables both the automatic group comparison of white matter anatomy and of its regional diffusion properties, and the creation of consistent white matter visualizations across subjects. We learn a model of common white matter structures by analyzing many registered tractography datasets simultaneously. Each trajectory is represented as a point in a high-dimensional spectral embedding space, and common structures are found by clustering in this space. By annotating the clusters with anatomical labels, we create a model that we call a high-dimensional white matter atlas.
(cont.) Our atlas creation method discovers structures corresponding to expected white matter anatomy, such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, etc. We show how to extend the spectral clustering solution, stored in the atlas, using the Nystrom method to perform automatic segmentation of tractography from novel subjects. This automatic tractography segmentation gives an automatic region correspondence across subjects when all subjects are labeled using the atlas. We show the resulting automatic region correspondences, demonstrate that our clustering method is reproducible, and show that the automatically segmented regions can be used for robust measurement of fractional anisotropy.
by Lauren Jean O'Donnell.
Ph.D.
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Dhital, Bibek. "Characterizing Brain White Matter with Diffusion-Weighted Magnetic Resonance." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-180140.
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It has been known for almost two decades that the water proton NMR signal of diffusing water molecules in brain white matter undergoes a non-monoexponential decay with increasing diffusion gradient factor b. With the help of numerical simulations and analytical expressions, much effort has been directed to describing the signal decay and to extracting relevant biophysical features of the system under investigation. However, the physical basis of such nonmonoexponential behavior is still not properly understood.
The primary difficulty in characterizing this phenomenon is the variation in behavior in the different directions of diffusion measurement. A combined framework that accounts for the diffusion process in all directions requires several parameters. Addition of many such parameters renders a model to be unwieldy and over-complicated, but over-simplifications can be shown to miss crucially relevant information in the data.
In this thesis, I have attempted to handle this problem with simple measurements that span a wide range of parameter space. Compared to often-performed measurements that probe diffusion over a time-scale of 50-100 ms with relatively low diffusion weighting, the measurements here have been done for very short diffusion times of 2 ms and also very long diffusion times up to 2 s. The temperature dependence of the diffusion coefficients has also been extensively probed. To avoid problems related to gross tissue heterogeneity, diffusion-weighted MR imaging in vivo was performed with ultra-high resolution. These simple measurements allowed sequential assessment of many possible arguments that could have led to such non-monoexponential decay curves. Finally, it was concluded that the water in the glial processes was the major contributor to the non-exponential decay, giving rise to a \'slow\' component both along the axonal fibers and transverse to them.
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Maddah, Mahnaz. "Quantitative analysis of cerebral white matter anatomy from diffusion MRI." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45614.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 165-177).
In this thesis we develop algorithms for quantitative analysis of white matter fiber tracts from diffusion MRI. The presented methods enable us to look at the variation of a diffusion measure along a fiber tract in a single subject or a population, which allows important clinical studies toward understanding the relation between the changes in the diffusion measures and brain diseases, development, and aging. The proposed quantitative analysis is performed on a group of fiber trajectories extracted from diffusion MRI by a process called tractography. To enable the quantitative analysis we first need to cluster similar trajectories into groups that correspond to anatomical bundles and to establish the point correspondence between these variable-length trajectories. We propose a computationally-efficient approach to find the point correspondence and the distance between each trajectory to the prototype center of each bundle. Based on the computed distances we also develop a novel model-based clustering of trajectories into anatomically-known fiber bundles. In order to cluster the trajectories, we formulate an expectation maximization algorithm to infer the parameters of the gamma-mixture model that we built on the distances between trajectories and cluster centers. We also extend the proposed clustering algorithm to incorporate spatial anatomical information at different levels through hierarchical Bayesian modeling. We demonstrate the effectiveness of the proposed methods in several clinical applications. In particular, we present our findings in identifying localized group differences in fiber tracts between normal and schizophrenic populations.
by Mahnaz Maddah.
Ph.D.
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Piatkowski, Jakub Przemyslaw. "Probing the brain's white matter with diffusion MRI and a tissue dependent diffusion model." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/8850.
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While diffusion MRI promises an insight into white matter microstructure in vivo, the axonal pathways that connect different brain regions together can only partially be segmented using current methods. Here we present a novel method for estimating the tissue composition of each voxel in the brain from diffusion MRI data, thereby providing a foundation for computing the volume of different pathways in both health and disease. With the tissue dependent diffusion model described in this thesis, white matter is segmented by removing the ambiguity caused by the isotropic partial volumes: both grey matter and cerebrospinal fluid. Apart from the volume fractions of all three tissue types, we also obtain estimates of fibre orientations for tractography as well as diffusivity and anisotropy parameters which serve as proxy indices of pathway coherence. We assume Gaussian diffusion of water molecules for each tissue type. The resulting three-tensor model comprises one anisotropic (white matter) compartment modelled by a cylindrical tensor and two isotropic compartments (grey matter and cerebrospinal fluid). We model the measurement noise using a Rice distribution. Markov chain Monte Carlo sampling techniques are used to estimate posterior distributions over the model’s parameters. In particular, we employ a Metropolis Hastings sampler with a custom burn-in and proposal adaptation to ensure good mixing and efficient exploration of the high-probability region. This way we obtain not only point estimates of quantities of interest, but also a measure of their uncertainty (posterior variance). The model is evaluated on synthetic data and brain images: we observe that the volume maps produced with our method show plausible and well delineated structures for all three tissue types. Estimated white matter fibre orientations also agree with known anatomy and align well with those obtained using current methods. Importantly, we are able to disambiguate the volume and anisotropy information thus alleviating partial volume effects and providing measures superior to the currently ubiquitous fractional anisotropy. These improved measures are then applied to study brain differences in a cohort of healthy volunteers aged 25-65 years. Lastly, we explore the possibility of using prior knowledge of the spatial variability of our parameters in the brain to further improve the estimation by pooling information among neighbouring voxels.
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Hu, Chengliang. "Inferring cerebral white matter fibres from diffusion tensor magnetic resonance images." Thesis, University of York, 2018. http://etheses.whiterose.ac.uk/22002/.
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The dissertation describes the research work on the inference of cerebral white matter fibres from diffusion tensor magnetic resonance images (DT-MRI), derived from the high angular resolution diffusion-weighted imaging (HARDI) data. A novel framework for inferring cerebral white matter fibres from diffusion MR images is presented. It includes feature extraction using graph based methods; feature selection with statistical pattern recognition techniques; and the inference of the white matter fibres applying machine learning methods. Four similarity measures are adopted or proposed for the fibre characterisation. Very good results are produced and a comparison is made. An evaluation of the methodology is conducted on real diffusion MRI data.
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Horne, Nikki Renee. "Microstructural white matter changes in Alzheimer's disease a diffusion tensor imaging study /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3296903.
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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).
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Panagiotaki, E. "Geometric models of brain white matter for microstructure imaging with diffusion MRI." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1310435/.
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The research presented in this thesis models the diffusion-weighted MRI signal within brain white matter tissue. We are interested in deriving descriptive microstructure indices such as white matter axon diameter and density from the observed diffusion MRI signal. The motivation is to obtain non-invasive reliable biomarkers for early diagnosis and prognosis of brain development and disease. We use both analytic and numerical models to investigate which properties of the tissue and aspects of the diffusion process affect the diffusion signal we measure. First we develop a numerical method to approximate the tissue structure as closely as possible. We construct three-dimensional meshes, from a stack of confocal microscopy images using the marching cubes algorithm. The experiment demonstrates the technique using a biological phantom (asparagus). We devise an MRI protocol to acquire data from the sample. We use the mesh models as substrates in Monte-Carlo simulations to generate synthetic MRI measurements. To test the feasibility of the method we compare simulated measurements from the three-dimensional mesh with scanner measurements from the same sample and simulated measurements from an extruded mesh and much simpler parametric models. The results show that the three-dimensional mesh model matches the data better than the extruded mesh and the parametric models revealing the sensitivity of the diffusion signal to the microstructure. The second study constructs a taxonomy of analytic multi-compartment models of white matter by combining intra- and extra-axonal compartments from simple models. We devise an imaging protocol that allows diffusion sensitisation parallel and perpendicular to tissue fibres. We use the protocol to acquire data from two fixed rat brains, which allows us to fit, study and evaluate the models. We conclude that models which incorporate non-zero axon radius describe the measurements most accurately. The key observation is a departure of signals in the parallel direction from the two-compartment models, suggesting restriction, most likely from glial cells or binding of water molecules to the membranes. The addition of the third compartment can capture this departure and explain the data. The final study investigates the estimates using in vivo brain diffusion measurements. We adjust the imaging protocol to allow an in vivo MRI acquisition of a rat brain and compare and assess the taxonomy of models. We then select the models that best explain the in vivo data and compare the estimates with those from the ex vivo measurements to identify any discrepancies. The results support the addition of the third compartment model as per the ex vivo findings, however the ranking of the models favours the zero radius intra-axonal compartments.
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Bertò, Giulia. "Supervised Learning for White Matter Bundle Segmentation." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/264971.
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Accurate delineation of anatomical structures in the white matter of the human brain is of paramount importance for multiple applications, such as neurosurgical planning, characterization of neurological disorders, and connectomic studies. Diffusion Magnetic Resonance Imaging (dMRI) techniques can provide, in-vivo, a mathematical representation of thousands of fibers composing such anatomical structures, in the form of 3D
polylines called streamlines. Given this representation, a task of invaluable interest is known as white matter bundle segmentation, whose aim is to virtually group together streamlines sharing a similar pathway into anatomically meaningful structures, called white matter bundles.
Obtaining a good and reliable bundle segmentation is however not trivial, mainly because of the intrinsic complexity of the data. Most of the current methods for bundle segmentation require extensive neuroanatomical knowledge, are time consuming, or are not able to adapt to different data settings. To overcome these limitations, the main goal of this thesis is to develop a new automatic method for accurate white matter bundle segmentation, by exploiting, combining and extending multiple up-to-date supervised learning techniques.
The main contribution of the project is the development of a novel streamline-based bundle segmentation method based on binary linear classification, which simultaneously combines information from atlases, bundle geometries, and connectivity patterns. We prove that the proposed method reaches unprecedented quality of segmentation, and that is robust to a multitude of diverse settings, such as when there are differences in bundle size, tracking algorithm, and/or quality of dMRI data. In addition, we show that some of the state-of-the-art bundle segmentation methods are deeply affected by a geometrical property of the shape of the bundles to be segmented, their fractal dimension.
Important factors involved in the task of streamline classification are: (i) the need for an effective streamline distance function and (ii) the definition of a proper feature space. To this end, we compare some of the most common streamline distance functions available in the literature and we provide some guidelines on their practical use for the task of supervised bundle segmentation. Moreover, we investigate the possibility to include, in a streamline-based segmentation method, additional information to the typically employed streamline distance measure. Specifically, we provide evidence that considering additional anatomical information regarding the cortical terminations of the streamlines and their proximity to specific Regions of Interest (ROIs) helps to improve the results of bundle segmentation.
Lastly, significant attention is paid to reproducibility in neuroscience. Following the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles, we have integrated our pipelines of analysis into an online open platform devoted to promoting reproducibility of scientific results and to facilitating knowledge discovery.
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Sprooten, Emma. "Genetic determinants of white matter integrity in bipolar disorder." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6482.
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Bipolar disorder is a heritable psychiatric disorder, and several of the genes associated with bipolar disorder and related psychotic disorders are involved in the development and maintenance of white matter in the brain. Patients with bipolar disorder have an increased incidence of white matter hyper-intensities, and quantitative brain imaging studies collectively indicate subtle decreases in white matter density and integrity in bipolar patients. This suggests that genetic vulnerability to psychosis may manifest itself as reduced white matter integrity, and that white matter integrity is an endophenotype of bipolar disorder. This thesis comprises a series of studies designed to test the role of white matter in genetic risk to bipolar disorder by analysis of diffusion tensor imaging (DTI) data in the Bipolar Family Study. Various established analysis methods for DTI, including whole-brain voxel-based statistics, tract-based spatial statistics (TBSS) and probabilistic neighbourhood tractography, were applied with fractional anisotropy (FA) as the outcome measure. Widespread but subtle white matter integrity reductions were found in unaffected relatives of patients with bipolar disorder, whilst more localised reductions were associated with cyclothymic temperament. Next, the relation of white matter to four of the most prominent psychosis candidate genes, NRG1, ErbB4, DISC1 and ZNF804A, was investigated. A core haplotype in NRG1, and three of the four key single nucleotide polymorphisms (SNPs) within it, showed an association with FA in the anterior thalamic radiations and the uncinate fasciculi. For the three SNPs considered in ErbB4, results were inconclusive, but this was consistent with the background literature. Most notable however, was a clear association of a non-synonymous DISC1 SNP, Ser704Cys, with FA extending over most of the white matter in the TBSS and voxel-based analyses. Finally, FA was not associated with a genome-wide supported risk SNP in ZNF804A, a finding which could not be attributed to a lack of statistical power, and which contradicts a strong, but previously untested hypothesis. Whilst the above results need corroboration from independent studies, other studies are needed to address the cellular and molecular basis of these findings. Overall, this work provides strong support for the role of white matter integrity in genetic vulnerability to bipolar disorder and the wider psychosis spectrum and encourages its future use as an endophenotype.
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Ratnarajah, Nagulan. "Probabilistic algorithms for white matter fibre tractography and clustering using diffusion MR images." Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.592018.
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The human brain is certainly the most complex biological system as it contains a network of more than l0 to the power of 11 individual nerve cells and interconnections. Fibre tractography using diffusion MR imaging is a promising non-invasive method for reconstructing the 3D fibre architecture of the human brain white matter in vivo. Despite the great potential, white matter tractography is relatively immature. At the current resolution of diffusion MR images, researchers agree that more than one third of imaging voxels in human brain white matter contain crossing fibre bundles. Generally, conventional diffusion tensor imaging (DTI) fibre tracking approaches have difficulties in crossing regions. Also, noise and other artefacts associated with diffusion MR data lead to uncertainty in the estimates of fib re orientation directions. Furthennore. each fib re tracking method has limitations due to the algorithmic approach that they follow and the assumptions they make. This thesis presents novel probabilistic based fibre tracking algorithms aiming to tackle a number of limitations of existing fibre tracking algorithms. Fibre clustering is a key step towards tract-based, quantitative analysis of white matter. Clustering algorithms analyse a collection of fibre curves in 3D and delineate them into anatomically distinct fibre tracts groups. In this thesis, a probabilistic framework is developed and the framework al lows for the clustering of sets of cunres In curve space. This thesis describes a number of original contributions to the field. First, a novel statistical framework is developed for improved fibre tractography and a quantitative analysis tool is introduced for probabilistic tracking methods using the statistical measures. The goal is to elucidate problems with existing detenninistic and probabilistic algorithms used to process diffusion MR images and propose solutions and methods through a new framework. Subsequently, random-walk and modelbased bootstrapping algorithms are developed using a two-tensor field to quantify the uncertainty of fibre orientation and probabilistic fibre tractography. A further problem tackled here is resolving crossing fibre configurations, a major concern in diffusion MR imaging, using data that can be routinely acquired in a clinical setting. Finally, a new probabilistic clustering algorithm is introduced using regression mixtures and the result of clustering is the probabilistic assignment of the fibre trajectories to each cluster. The tract geometry model is estimated using fitted parameters of the probabilistic clustering algorithm. Local reconstruction, tracking results, segmentation and quantitative analysis are shown on simulated datasets, on a hardware phantom and on multiple human brain datasets.
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Liang, Xuwei. "MODELING AND QUANTITATIVE ANALYSIS OF WHITE MATTER FIBER TRACTS IN DIFFUSION TENSOR IMAGING." UKnowledge, 2011. http://uknowledge.uky.edu/gradschool_diss/818.
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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.
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Qiu, Deqiang, and 邱德強. "Diffusion tensor imaging in evaluating normal and abnormal white matter development in childhood." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41508324.
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Wassermann, Demian. "Automated in vivo dissection of white matter structures from diffusion magnetic resonance imaging." Nice, 2010. http://www.theses.fr/2010NICE4066.
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Le cerveau est organisé tel un réseau reliant différentes régions. Ce réseau est important pour le développement de fonctions comme le langage. Certains troubles cognitifs peuvent être expliqués par des problèmes de connexion entre régions plus qu’à des dommages de ces dernières. Malgré plusieurs décennies de travail sur ces réseaux, nos connaissances sur le sujet n’ont pas beaucoup évoluées depuis le début du siècle dernier. Récemment, un développement spectaculaire des techniques de l’imagerie par résonance magnétique (IRM) a permis l’étude vivant du cerveau humain. Une technique permettant l’exploration des faisceaux de la matière blanche (MB) in vivo est l’IRM de diffusion (IRMd). En particulier, la trajectographie à partir de l’IRMd facilite le traçage des faisceaux de la MB. C’est donc une technique prometteuse afin d’explorer l’aspect cognitif de l’anatomie humaine ainsi que de ses troubles. La motivation de cette thèse est la dissection in vivo de la MB. Cette procédure permet d’isoler les faisceaux de la MB, qui jouent un rôle particulier dans le fonctionnement du cerveau, de façon à pouvoir les analyser. L’exécution manuelle de cette tache requiert une grande connaissance du cerveau et demande plusieurs heurs de travail. Le développement d’une technique automatique est donc de la plus grande importance. Cette thèse contient plusieurs contributions : nous développons des moyens d’automatiser la dissection de la MB, c’est-à-dire le cadre mathématique nécessaire à sa compréhension. Ces outils nous permettent ensuite de développer des techniques d’analyse de la moelle épinière et de recherche de différences dans la MB entre des individus sains et schizophrènesThe brain is organized in networks that are made up of tracks connecting different regions. These networks are important for the development of brain functions such as language. Lesions and cognitive disorders are sometimes better explained by disconnection mechanisms between cerebral regions than by damage of those regions. Despite several decades of tracing these networks in the brain, our knowledge of cerebral connections has progressed very little since the beginning of the last century. Recently, we have seen a spectacular development of magnetic resonance imaging (MRI) techniques for the study of the living human brain. One technique for exploring white matter (WM) tissue characteristics and pathway in vivo is diffusion MRI (dMRI). Particulary, dMRI tractography facilitates the tracing the WM tracts in vivo. DMRI is a promising technique to explore the anatomical basis of human cognition and its disorders. The motivation of this thesis is the in vivo dissection of the WM. This procedure isolates the WM tracts that play a role in a particular function or disorder of the brain so they can be analysed. Manually performing this task requires a great knowledge of brain anatomy and several hours of work. Hence, the development of a technique to automatically perform the identification of WM structures is of utmost importance. This thesis has several contributions : we develop means for the automatic dissection of WM tracts from dMRI, this is based on a mathematical framework for the WM and its tracts ; using these tools, we develop techniques to analyse the spinal chord and to find group differences in the WM particulary between healthy and schizophrenic subjects
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Qiu, Deqiang. "Diffusion tensor imaging in evaluating normal and abnormal white matter development in childhood." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B41508324.
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Lacerda, Luis Miguel Rosa Sousa Prado De. "Quantitative white matter metrics : diffusion imaging and advanced processing for detailed investigation of brain microstructure." Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/quantitative-white-matter-metrics(9058c64a-93a0-4db0-9799-c0bba7bd55fe).html.
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Diffusion imaging is a non-invasive imaging method which has been successfully applied to study white matter. Most clinical approaches, based on Diffusion Tensor Imaging (DTI), are limited by the simple model of the underlying tissue imposed, failing to reconstruct the diffusion propagator, which fully encodes the displacement of water molecules. To do so, more comprehensive sampling schemes such as Diffusion Spectrum Imaging (DSI) have been developed. In this thesis, I have investigated the effect of different tissue configurations, sampling and processing steps in the performance of DSI. I identified specific configurations where DSI is unable to characterise diffusion without artefacts, namely aliasing caused by fast diffusion components. Furthermore, processing of the diffusion orientation distribution function (ODF) in these environments can lead to generation of spurious fibres in tractography reconstructions. To overcome this, I have applied a novel step in the processing pipeline of DSI, namely a different way of computing the ODF, which consists of restricting the range of integration to probabilities based on the physical displacement of “axonlike” diffusivities. Alternatively, it is possible to use a mathematical representation of the acquired signal, of which the Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) and Mean Apparent Propagator Magnetic Resonance Imaging (MAP-MRI) are examples. I have here used these methods and further provided optimised acquisitions based on standard propagator metrics. Finally, I have introduced new metrics that use microstructural information available at the different displacement scales, and can facilitate exploration of brain organisation even when no a-priori biophysical model is available.
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Umapathy, Lavanya, and Lavanya Umapathy. "Assessment of White Matter Integrity in Bonnet Macaque Monkeys using Diffusion-weighted Magnetic Resonance Imaging." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/622837.
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Diffusion-weighted magnetic resonance imaging (dMRI) has been used to non-invasively investigate the integrity of white matter and the connectivity of the brain. In this work, high angular resolution diffusion imaging (HARDI), an advanced dMRI methodology was developed and employed in bonnet macaque monkeys to study the connectivity of the orbitofrontal cortex (OFC) and amygdala, two gray matter regions involved in making reward-guided decisions. With age, it is believed that there is a decline in the white matter connectivity between these two regions, also known as uncinate fasciculus (UF), and that this affects reward-value assignment and feedback learning in older adults. The analysis pipeline involved correction for distortions due to eddy currents and field inhomogeneity, noise reduction using a local principal component analysis based technique and subsequent registration to the high-resolution T1-weighted images. Gray matter regions corresponding to OFC and amygdala were identified on the T1-weighted images and probabilistic tractography was carried out to delineate the tracts belonging to UF. The output connectivity map from tractography was used to extract imaging parameters of interest such as fractional anisotropy, axial and radial diffusivity along the UF. A significant reduction in the fractional anisotropy index and the axial diffusivity index along the UF tract was observed with increased age of monkeys. Compared to the left hemisphere, stronger trends were observed in the right hemisphere of the monkeys, indicating possible laterality.
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Bava, Sunita. "Reduced microstructural white matter integrity in a genetic metabolic disorder a diffusion tensor MRI study /." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3274808.
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Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2007.Title from first page of PDF file (viewed January 8, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 75-84).
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Peled, Sharon. "Two approaches to white matter nuclear magnetic resonance : water diffusion and inhaled laser-polarized xenon." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10350.
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Ferizi, U. "Compartment models and model selection for in-vivo diffusion-MRI of human brain white matter." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1455976/.
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Diffusion MRI microstructure imaging provides a unique noninvasive probe into tissue microstructure. The technique relies on mathematical models, relating microscopic tissue features to the MR signal. The assumption of Gaussian diffusion oversimplifies the behaviour of water in complex media. Multi-compartment models fit the signal better and enable the estimation of more specific indices, such as axon diameter and density. A previous model comparison framework used data from fixed rat brains to show that three compartment models, designed for intra/extra-axonal diffusion, best explain multi-b-value datasets. The purpose of this PhD work is to translate this analysis to in vivo human brain white matter. It updates the framework methodology by enriching the acquisition protocol, extending the model base and improving the model fitting. In the first part of this thesis, the original fixed rat study is taken in-vivo by using a live human subject on a clinical scanner. A preliminary analysis cannot differentiate the models well. The acquisition protocol is then extended to include a richer angular resolution of diffusion- sampling gradient directions. Compared with ex-vivo data, simpler three-compartment models emerge. Changes in diffusion behaviour and acquisition protocol are likely to have influenced the results. The second part considers models that explicitly seek to explain fibre dispersion, another potentially specific biomarker of neurological diseases. This study finds that models that capture fibre dispersion are preferred, showing the importance of modelling dispersion even in apparently coherent fibres. In the third part, we improve the methodology. First, during the data pre-processing we narrow the region of interest. Second, the model fitting takes into account the varying echo time and compartmental tissue relaxation; we also test the benefit to model performance of different compartmental diffusivities. Next, we evaluate the inter- and intra-subject reproducibility of ranking. In the fourth part, high-gradient Connectom-Skyra data are used to assess the generalisability of earlier results derived from a standard Achieva scanner. Results showed a reproducibility of major trends in the model ranking. In particular, dispersion models explain low gradient strength data best, but cannot capture Connectom signal that remains at very high b-values. The fifth part uses cross-validation and bootstrapping as complementary means to model ranking. Both methods support the previous ranking; however, the leave-one-shell-out cross- validation supports less difference between the models than bootstrapping.
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Butler, Rebecca. "Using diffusion weighted imaging to map changes in white matter connectivity in chronic stroke aphasia." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/using-diffusion-weighted-imaging-to-map-changes-in-white-matter-connectivity-in-chronic-stroke-aphasia(287f2b2a-3bdd-492a-ab90-ca9cb7c9ad90).html.
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The role of white matter pathways in language networks has received much attention inrecent years. This is largely due to advances in diffusion imaging techniques, which haveenabled exploration of white matter properties in vivo. The emergent model from suchwork proposes that language processing is underpinned by a dorsal and a ventral pathwayconnecting anterior and posterior regions involved in language. This thesis aimed toexplore whether consideration of white matter measures could aid understanding ofperformance profiles in chronic stroke aphasia. To this end, a group of participants withchronic stroke aphasia were recruited and their performance on a large battery oflanguage assessments was related to their neuroimaging data. The neuroimaging datacomprised high resolution T1-weighted structural scans, fractional anisotropy (FA) maps,and data generated using a tractography-based technique called Anatomical ConnectivityMapping (ACM) which provides an index of long-range connectivity that has not yetbeen applied to chronic stroke aphasia.Chapter 3 established, in a small series of case examples, that connectivityinformation from ACM can help explain variations in performance in chronic strokeaphasia. Chapter 4 extended this work to a larger group of participants. Differencesbetween aphasic participants and controls, and between groups with different aphasicsubtypes and controls, were calculated and compared across imaging methods. ACMoffered insights into connectivity differences that were complementary to informationfrom T1-weighted and FA data. In addition to revealing areas where connectivity wasreduced relative to controls, ACM revealed an increase in connectivity in the righthemisphere dorsal route homologue of aphasic participants.Chapter 5 aimed to improve our ability to capture aphasic performance and torelate it to neuroimaging data. Principal components analysis (PCA) was used to derivefactors underlying performance on the language battery. Phonological, semantic, andcognitive factors emerged from the PCA and participants’ factor scores were used ascontinuous regressors in a voxel-level analysis of their T1-weighted images. Regions thatemerged as significantly related to language abilities aligned with those found usingother methodologies. Chapter 6 brought together work from the previous chapters byrelating PCA-derived factor scores to FA maps and ACM, in order to assess therelationship between behavioural performance and the status of key white matterpathways. In line with recent characterisations of the dual route system, phonologicalperformance related to dorsal route measures and semantic performance related to ventralroute measures. Better cognitive performance was found to relate to increasedconnectivity relative to controls in the right frontal lobe. Overall these results suggest thatconsideration of white matter abnormalities, both reductions and increases, can helpexplain patterns of performance in chronic stroke aphasia and that ACM can be a usefulsource of such information given its sensitivity to connectivity remote from the lesion.These findings both provide hypotheses for future research and could be used to informtherapeutic interventions.
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Sherbondy, Anthony. "Measurement and visualization of white matter fascicles using magnetic resonace diffusion-weighted imaging fiber tractography /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Imamura, Hisaji. "Network specific change in white matter integrity in mesial temporal lobe epilepsy." Kyoto University, 2017. http://hdl.handle.net/2433/226747.
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Stamile, Claudio. "Unsupervised Models for White Matter Fiber-Bundles Analysis in Multiple Sclerosis." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1147/document.
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L’imagerie de résonance magnétique de diffusion (dMRI) est une technique très sensible pour la tractographie des fibres de substance blanche et la caractérisation de l’intégrité et de la connectivité axonale. A travers la mesure des mouvements des molécules d’eau dans les trois dimensions de l’espace, il est possible de reconstruire des cartes paramétriques reflétant l’organisation tissulaire. Parmi ces cartes, la fraction d’anisotropie (FA) et les diffusivités axiale (λa), radiale (λr) et moyenne (MD) ont été largement utilisés pour caractériser les pathologies du système nerveux central. L’emploi de ces cartes paramétriques a permis de mettre en évidence la survenue d’altérations micro structurelles de la substance blanche (SB) et de la substance grise (SG) chez les patients atteints d’une sclérose en plaques (SEP). Cependant, il reste à déterminer l’origine de ces altérations qui peuvent résulter de processus globaux comme la cascade inflammatoire et les mécanismes neurodégénératifs ou de processus plus localisés comme la démyélinisation et l’inflammation. De plus, ces processus pathologiques peuvent survenir le long de faisceaux de SB afférents ou efférents, conduisant à une dégénérescence antero- ou rétrograde. Ainsi, pour une meilleure compréhension des processus pathologiques et de leur progression dans l’espace et dans le temps, une caractérisation fine et précise des faisceaux de SB est nécessaire. En couplant l’information spatiale de la tractographie des fibres aux cartes paramétriques de diffusion, obtenues grâce à un protocole d’acquisitions longitudinal, les profils des faisceaux de SB peuvent être modélisés et analysés. Une telle analyse des faisceaux de SB peut être effectuée grâce à différentes méthodes, partiellement ou totalement non-supervisées. Dans la première partie de ce travail, nous dressons l’état de l’art des études déjà présentes dans la littérature. Cet état de l’art se focalisera sur les études montrant les effets de la SEP sur les faisceaux de SB grâce à l’emploi de l’imagerie de tenseur de diffusion. Dans la seconde partie de ce travail, nous introduisons deux nouvelles méthodes,“string-based”, l’une semi-supervisée et l’autre non-supervisée, pour extraire les faisceaux de SB. Nous montrons comment ces algorithmes permettent d’améliorer l’extraction de faisceaux spécifiques comparé aux approches déjà présentes dans la littérature. De plus, dans un second chapitre, nous montrons une extension de la méthode proposée par le couplage du formalisme “string-based” aux informations spatiales des faisceaux de SB. Dans la troisième et dernière partie de ce travail, nous décrivons trois algorithmes automatiques permettant l’analyse des changements longitudinaux le long des faisceaux de SB chez des patients atteints d’une SEP. Ces méthodes sont basées respectivement sur un modèle de mélange Gaussien, la factorisation de matrices non-négatives et la factorisation de tenseurs non-négatifs. De plus, pour valider nos méthodes, nous introduisons un nouveau modèle pour simuler des changements longitudinaux réels, base sur une fonction de probabilité Gaussienne généralisée. Des hautes performances ont été obtenues avec ces algorithmes dans la détection de changements longitudinaux d’amplitude faible le long des faisceaux de SB chez des patients atteints de SEP. En conclusion, nous avons proposé dans ce travail des nouveaux algorithmes non supervisés pour une analyse précise des faisceaux de SB, permettant une meilleure caractérisation des altérations pathologiques survenant chez les patients atteints de SEPDiffusion Magnetic Resonance Imaging (dMRI) is a meaningful technique for white matter (WM) fiber-tracking and microstructural characterization of axonal/neuronal integrity and connectivity. By measuring water molecules motion in the three directions of space, numerous parametric maps can be reconstructed. Among these, fractional anisotropy (FA), mean diffusivity (MD), and axial (λa) and radial (λr) diffusivities have extensively been used to investigate brain diseases. Overall, these findings demonstrated that WM and grey matter (GM) tissues are subjected to numerous microstructural alterations in multiple sclerosis (MS). However, it remains unclear whether these tissue alterations result from global processes, such as inflammatory cascades and/or neurodegenerative mechanisms, or local inflammatory and/or demyelinating lesions. Furthermore, these pathological events may occur along afferent or efferent WM fiber pathways, leading to antero- or retrograde degeneration. Thus, for a better understanding of MS pathological processes like its spatial and temporal progression, an accurate and sensitive characterization of WM fibers along their pathways is needed. By merging the spatial information of fiber tracking with the diffusion metrics derived obtained from longitudinal acquisitions, WM fiber-bundles could be modeled and analyzed along their profile. Such signal analysis of WM fibers can be performed by several methods providing either semi- or fully unsupervised solutions. In the first part of this work, we will give an overview of the studies already present in literature and we will focus our analysis on studies showing the interest of dMRI for WM characterization in MS. In the second part, we will introduce two new string-based methods, one semi-supervised and one unsupervised, to extract specific WM fiber-bundles. We will show how these algorithms allow to improve extraction of specific fiber-bundles compared to the approaches already present in literature. Moreover, in the second chapter, we will show an extension of the proposed method by coupling the string-based formalism with the spatial information of the fiber-tracks. In the third, and last part, we will describe, in order of complexity, three different fully automated algorithms to perform analysis of longitudinal changes visible along WM fiber-bundles in MS patients. These methods are based on Gaussian mixture model, nonnegative matrix and tensor factorisation respectively. Moreover, in order to validate our methods, we introduce a new model to simulate real longitudinal changes based on a generalised Gaussian probability density function. For those algorithms high levels of performances were obtained for the detection of small longitudinal changes along the WM fiber-bundles in MS patients. In conclusion, we propose, in this work, a new set of unsupervised algorithms to perform a sensitivity analysis of WM fiber bundle that would be useful for the characterisation of pathological alterations occurring in MS patients
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Nicolas, Renaud, Florent Aubry, Jérémie Pariente, Xavier Franceries, Nicolas Chauveau, Laure Saint-Aubert, François Chollet, Stephane Breil, and Pierre Celsis. "Water diffusion in q-space imaging as a probe of cell local viscosity and anomalous diffusion in grey and white matter." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-186332.
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Extraction of accurate quantitative parameters to characterize
water diffusion in complex porous media like brain tissue in neuroimaging is a challenging inverse problem, that depends on medium\'s structural and geometrical factors [1,3]. If the role of membranes is generally invoked, probe collisions with the insoluble cytoskeleton network and water hydrodynamic interactions with dissolved macromolecules and cytoskeleton occur as well [2]. The latter two interactions have been shown to determine the phenomenological “anomalous diffusion” of probes in the cytoplasm [4,5,6,7,8]. Additionally, the diffusion of small solutes in cytoplasm could be slowed by the local micro-viscosity of the aqueous phase, a phenomenon generally not taken into account in simulations. We suggest that the Grey and White Matter contrast in Diffusion Decay Imaging (DDI) could be caused by differences in cytoskeleton structures, composed respectively of actin and tubulin that could act by the elimination of one possible water diffusion pathlength by the volume occupied by the network phase. This could explain why anomalous DDI signal has been shown to be independent of membrane integrity [9]. Cytoplasm is able to rapidly shift from a sol (aqueous solutions embedded with insolubles particles) to a gel state (a colloidal solutions with a structured semi-solid and an aqueous fluid phase) or to a viscous solution when the insoluble particles become soluble. Does water have the ability of being a sensor of its local “self-viscosity” ? What is the length of the water diffusion\'s path compared to cells size ? Compared to this path length, how many cellular structures should be probed by water\'s translational diffusion ? We try to respond to these questions by investigating Diffusion Decay Imaging models and their effects on the hypothese-free q-space diffusion propagator shape [3], containing all informations concerning viscosity-slowed gaussian diffusion, structural informations [3] and restricted diffusion [1].
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Telford, Emma Jane. "The effect of preterm birth on white matter tracts and infant cognition." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/29557.
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Preterm birth (defined as birth before 37 weeks) is a leading cause of neurocognitive impairment in childhood, including difficulties in social cognition and executive function. Microstructural divergence from typical brain development in the preterm brain can be quantified using diffusion magnetic resonance imaging (dMRI) tractography during the neonatal period. The relationship between dMRI tractography metrics and later cognitive difficulties remains inconclusive. A general measure of white matter microstructure (gWM) offers a neural basis for cognitive processes in adults, however it remains unclear when gWM is first detectable in the developmental trajectory. Eye-tracking is a technique which assesses eye-gaze behaviour in response to visual stimuli, which permits inference about underlying cognitive processes, such as social cognition and executive function in infancy. The primary aims of this thesis were to test the hypotheses: dMRI tractography reveals significant differences in tract-average fractional anisotropy (FA) and mean diffusivity (MD) between preterm and term infants, and variance in tract-average FA and MD is shared across major tracts. Secondly, infants born preterm have altered social cognition and executive function compared to term born peers, assessed by eye-tracking and finally, neonatal MRI gWM is associated with cognitive function in infancy. Preterm (birth weight ≤ 1500g) and term infants (born ≥ 37 weeks’ post-menstrual age [PMA]) were recruited and underwent a MRI scan at term equivalent age (between 38 - 42 weeks’ PMA) and an eye-tracking assessment six to nine months later. Preterm infants were assessed at two years using the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III). dMRI tractography metrics were generated using probabilistic neighbourhood tractography (PNT) in eight pre-defined tracts-of-interest. Principal component analyses (PCA) were used to determine the correlations between the eight tracts-of-interest for four tract-averaged water diffusion parameters. dMRI metrics were compared to the eye-tracking performance and two year outcome data. Quantitative microstructural changes were identifiable within the preterm brain when compared to infants born at term. PCA revealed a single variable that accounts for nearly 50% of shared variance between tracts-of-interest, and all tracts showed positive loadings. Eye-tracking revealed group-wise differences in infant social cognition, attributable to preterm birth, but executive functions inferred from eye-tracking did not differ between groups. dMRI tractography metrics within the neonatal period did not relate to later outcome measures. This thesis shows that variance in dMRI parameters is substantially shared across white matter tracts of the developing brain and suggests that anatomical foundations of later intelligence are present by term equivalent age. Social cognition is altered by preterm birth, however social cognitive ability in infancy is independent of gWM.
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Nicolas, Renaud, Florent Aubry, Jérémie Pariente, Xavier Franceries, Nicolas Chauveau, Laure Saint-Aubert, François Chollet, Stephane Breil, and Pierre Celsis. "Water diffusion in q-space imaging as a probe of cell local viscosity and anomalous diffusion in grey and white matter." Diffusion fundamentals 14 (2010) 3, S. 1-4, 2010. https://ul.qucosa.de/id/qucosa%3A12798.
Full textAbstract:
Extraction of accurate quantitative parameters to characterize
water diffusion in complex porous media like brain tissue in neuroimaging is a challenging inverse problem, that depends on medium\''s structural and geometrical factors [1,3]. If the role of membranes is generally invoked, probe collisions with the insoluble cytoskeleton network and water hydrodynamic interactions with dissolved macromolecules and cytoskeleton occur as well [2]. The latter two interactions have been shown to determine the phenomenological “anomalous diffusion” of probes in the cytoplasm [4,5,6,7,8]. Additionally, the diffusion of small solutes in cytoplasm could be slowed by the local micro-viscosity of the aqueous phase, a phenomenon generally not taken into account in simulations. We suggest that the Grey and White Matter contrast in Diffusion Decay Imaging (DDI) could be caused by differences in cytoskeleton structures, composed respectively of actin and tubulin that could act by the elimination of one possible water diffusion pathlength by the volume occupied by the network phase. This could explain why anomalous DDI signal has been shown to be independent of membrane integrity [9]. Cytoplasm is able to rapidly shift from a sol (aqueous solutions embedded with insolubles particles) to a gel state (a colloidal solutions with a structured semi-solid and an aqueous fluid phase) or to a viscous solution when the insoluble particles become soluble. Does water have the ability of being a sensor of its local “self-viscosity” ? What is the length of the water diffusion\''s path compared to cells size ? Compared to this path length, how many cellular structures should be probed by water\''s translational diffusion ? We try to respond to these questions by investigating Diffusion Decay Imaging models and their effects on the hypothese-free q-space diffusion propagator shape [3], containing all informations concerning viscosity-slowed gaussian diffusion, structural informations [3] and restricted diffusion [1].
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Bajaj, Sahil, John R. Vanuk, Ryan Smith, Natalie S. Dailey, and William D. S. Killgore. "Blue-Light Therapy following Mild Traumatic Brain Injury: Effects on White Matter Water Diffusion in the Brain." FRONTIERS MEDIA SA, 2017. http://hdl.handle.net/10150/626295.
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Mild traumatic brain injury (mTBI) is a common and often inconspicuous wound that is frequently associated with chronic low-grade symptoms and cognitive dysfunction. Previous evidence suggests that daily blue wavelength light therapy may be effective at reducing fatigue and improving sleep in patients recovering from mTBI. However, the effects of light therapy on recovering brain structure remain unexplored. In this study, we analyzed white matter diffusion properties, including generalized fractional anisotropy, and the quantity of water diffusion in isotropic (i.e., isotropic diffusion) and anisotropic fashion (i.e., quantitative anisotropy, QA) for fibers crossing 11 brain areas known to be significantly affected following mTBI. Specifically, we investigated how 6 weeks of daily morning blue light exposure therapy (compared to an amber-light placebo condition) impacted changes in white matter diffusion in individuals with mTBI. We observed a significant impact of the blue light treatment (relative to the placebo) on the amount of water diffusion (QA) for multiple brain areas, including the corpus callosum, anterior corona radiata, and thalamus. Moreover, many of these changes were associated with improvements in sleep latency and delayed memory. These findings suggest that blue wavelength light exposure may serve as one of the potential non-pharmacological treatments for facilitating structural and functional recovery following mTBI; they also support the use of QA as a reliable neuro-biomarker for mTBI therapies.
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Vallee, Emmanuel. "Improving sensitivity and specificity in diffusion MRI group studies." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:11b235ef-c05f-4db3-a8fb-291ab07d4f84.
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Diffusion MRI provides a unique opportunity to study the brain tissue architecture at a microscopic level. More specifically, it allows to infer biophysical properties of the axons in the white matter in-vivo. Microstructural parameters are widely used in multi-subject studies to track pathological processes, follow normal development and aging, or investigate behaviour. This thesis aims to identify and potentially address the limitations and pitfalls in voxelwise comparison of diffusion MRI parameters across subjects. To allow for accurate matching of brain structures across subjects, non-linear transformation that spatially aligns the data is required. We demonstrate that using advanced registration methods, we can outperform the standard registration-projection approach both in terms of sensitivity and specificity. The coarse resolution of the data typically causes partial volume effects that bias the diffusion parameters and potentially mislead the interpretation of a group study outcome. We provide evidence that these effects can be addressed by constraining the diffusion model parameter space, which leads to marginally lower sensitivity, but allows an accurate interpretation of the results. Additionally, we suggest that additional information inferred with a data driven approach might mitigate the loss in sensitivity. Finally, we design an original tract-specific modelling framework that enables to estimate microstructural parameters unbiased by the presence of foreign fibre populations or tissues. We demonstrate the sensitivity of our method in a study relating microstructure and behaviour.
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Ho, Nga-yee. "Longitudinal study of white matter fractional anisotropy in childhood medulloblastoma survivors by diffusion tensor MR imaging." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B39849041.
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Ho, Nga-yee, and 何雅儀. "Longitudinal study of white matter fractional anisotropy in childhood medulloblastoma survivors by diffusion tensor MR imaging." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B39849041.
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Mani, Meenakshi. "Quantitative analysis of open curves in brain imaging : applications to white matter fibres and sulci." Rennes 1, 2011. http://www.theses.fr/2011REN1S026.
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This thesis is a study of how the physical attributes of open curves can be used to advantage in the many varied quantitative applications of white matter fibers and sulci. Shape, scale, orientation and position, the four physical features associated with open curves, have different properties so the usual approach has been to design different metrics and spaces to treat them individually. We use a comprehensive Riemannian framework where joint feature spaces allow for analysis of combinations of features. This is an alternative approach where we can compare curves using geodesic distances. In this thesis, we validate the metrics we use, demonstrate practical uses and apply the tools to important clinical problems. To begin, specific tract configurations in the corpus callosum are used to showcase clustering results that vary with the different Riemannian distance metrics. This nicely argues for the judicious selection of metrics in various applications, a central premise in our work. The framework also provides tools for computing statistical summaries of curves, a first step in statistical analysis. We represent fiber bundles with a mean and variance which describes their essential characteristics. This is a convenient way to work with the large volume in white matter fiber analysis. Next, we design and implement methods to detect morphological changes in the corpus callosum and to track progressive white matter disease. With sulci, we address the specific problem of labeling. An evaluation of physical features and methods such as clustering leads us to a pattern matching solution in which the sulcal configuration itself is the best featureCette thèse se propose d'étudier comment les caractéristiques des courbes ouvertes peuvent être exploitées afin d'analyser quantitativement les sillons corticaux et les faisceaux de matière blanche. Les quatre caractéristiques d'une courbe ouverte--forme, taille, orientation et position--ont des propriétés différentes, si bien que l'approche usuelle est de traiter chacune séparément à l'aide d'une métrique ad hoc. Nous introduisons un cadre riemannien adapté dans lequel il est possible de fusionner les espaces de caractéristiques afin d'analyser conjointement plusieurs caractéristiques. Cette approche permet d'apparier et de comparer des courbes suivant des distances géodésiques. Les correspondances entre courbes sont établies automatiquement en utilisant une métrique élastique. Dans cette thèse, nous validerons les métriques introduites et nous montrerons leurs applications pratiques, entre autres dans le cadre de plusieurs problèmes cliniques importants. Dans un premier temps, nous étudierons spécifiquement les fibres du corps calleux, afin de montrer comment le choix de la métrique influe sur le résultat du clustering. Nous proposons ensuite des outils permettant de calculer des statistiques sommaires sur les courbes, ce qui est un premier pas vers leur analyse statistique. Nous représentons les groupes de faisceaux par la moyenne et la variance de leurs principales caractéristiques, ce qui permet de réduire le volume des données dans l'analyse des faisceaux de matière blanche. Ensuite, nous présentons des méthodes permettant de détecter les changements morphologiques et les atteintes de la matière blanche. Quant aux sillons corticaux, nous nous intéressons au problème de leur labellisation
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Cheung, Vinci, and 張穎思. "Structural white matter abnormalities in never-medicated patients withfirst-episode schizophrenia: a diffusiontensor imaging study." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B39793734.
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Cheung, Vinci. "Structural white matter abnormalities in never-medicated patients with first-episode schizophrenia : a diffusion tensor imaging study /." View the Table of Contents & Abstract, 2008. http://sunzi.lib.hku.hk/hkuto/record/B39716375.
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Lewis, John D. "Size always matters an investigation of the influence of connection length on the organization of white-matter in typical development and in autism /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3320224.
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Thesis (Ph. D.)--University of California, San Diego, 2008.Title from first page of PDF file (viewed November 10, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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Khong, Pek-Lan. "Diffusion tensor MR imaging in the evaluation of treatment-induced white matter injury in childhood cancer survivors." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B38320666.
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Khong, Pek-Lan, and 孔碧蘭. "Diffusion tensor MR imaging in the evaluation of treatment-induced white matter injury in childhood cancer survivors." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B38320666.
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Boespflug, Erin L. "Component diffusion tensor analysis suggests disparate temporal stem and fornix white matter pathology in Mild Cognitive Impairment." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1336137888.
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Dhital, Bibek [Verfasser], Robert [Akademischer Betreuer] Turner, and Daniel [Gutachter] Alexander. "Characterizing Brain White Matter with Diffusion-Weighted Magnetic Resonance / Bibek Dhital ; Gutachter: Daniel Alexander ; Betreuer: Robert Turner." Leipzig : Universitätsbibliothek Leipzig, 2015. http://d-nb.info/1239658192/34.
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Gongvatana, Assawin. "Microstructural white matter integrity in HIV-infected individuals in the HAART era a diffusion tensor imaging study /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3316192.
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Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2008.Title from first page of PDF file (viewed September 4, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 81-94).
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Wang, Silun. "Diffusion tensor MR imaging as a biomarker for the evaluation of white matter injury in rodent models." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43085416.
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Hu, Christi Perkins. "The status of white matter in patients with hemiparesis given CI therapy : a diffusion tensor imaging study /." Birmingham, Ala. : University of Alabama at Birmingham, 2009. https://www.mhsl.uab.edu/dt/2009p/hu.pdf.
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Thesis (Ph. D.)--University of Alabama at Birmingham, 2009.Title from PDF title page (viewed Mar. 31, 2010). Additional advisors: N. Shastry Akella, James E. Cox, Gitendra Uswatte, Victor W. Mark. Includes bibliographical references (p. 50-60).
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Rowe, Kelly Cathryn. "Beyond the cortex: implications of white matter connectivity for depression, cognition, and vascular disease." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/2765.
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The current study investigates the effects of vascular disease on white matter health by comparing participants with atherosclerotic vascular disease (AVD) to healthy control participants (HC). The comparison between groups will help elucidate the differences between early-stage mild vascular disease and normal aging processes in terms of their effects on white matter health as measured by diffusion tensor imaging (DTI). Relationships between white matter health and depression, attention, and processing speed are studied by the application of a variety of DTI neuroimaging techniques, which will allow investigation of these relationships at the levels of global, lobe-wise, and subregional analysis. The specific subregion of interest in the depression study is Brodmann Area 25, which has shown significant relationships with depressive symptomatology in patients with treatment refractory depression, but has not been studied in the context of aging, vascular disease, or subthreshold depressive symptoms. Results indicate that there are significant differences between AVD and HC participants in global and regional FA measures. Within the AVD group, significant relationships of FA with depressive symptoms and attentional function have been observed in the current study. Several unexpected findings emerged, most important of which was the observation that there is a significant relationship between FA in Brodmann Area 25 and depressive symptoms in AVD participants which is specific to the right hemisphere. These findings have implications for the treatment of depressive symptoms in older adults and participants with vascular disease.
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Guevara, Olivares Miguel. "Disentangling the short white matter connections using a fiber's geometry based dimensional reduction approach." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST053.
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L'étude de la substance blanche superficielle (SBS) a souvent été laissée de côté car elle est difficile à accéder et très variable. Des méthodes d'acquisition de meilleure qualité et le développement de nouveaux outils d'analyse ont facilité son étude à partir de l'IRM de diffusion et la tractographie. La connectivité du cortex et son plissement sont fortement liés, en particulier pour les fibres en U qui contournent les sillons. Comme la morphologie des motifs du plissement est spécifique à chaque être humain, la configuration des fibres sous-jacentes devrait l'être aussi. Un pipeline pour démêler les différentes configurations des connexions de la SBS et pour caractériser leur relation avec d'autres structures a été conçu. Une méthode pour définir les faisceaux courts à partir de tractographies a d’abord été élaborée selon une approche hybride, en extrayant les fibres reliant deux régions d'intérêt puis en les regroupant en faisceaux reproductibles d'un sujet à l'autre. Une transformation affine fondée sur l’IRM T1 et une base de données de tractographie déterministe ont été utilisées. Cela a permis de générer un atlas des faisceaux du cerveau entier, utilisé pour segmenter les faisceaux de nouveaux sujets, afin de réaliser des études cliniques sur des connexions spécifiques. Cet atlas a été comparé à deux autres atlas publics afin d'évaluer la reproductibilité des faisceaux. Un grand nombre de faisceaux ont été trouvé similaires entre les trois atlas. La définition des faisceaux de la SBS n’étant pas encore consensuelle, une sur-segmentation peut être néanmoins observée d’un atlas à l’autre. Cependant, une plus grande granularité que celle de ces atlas est souhaitable pour étudier la variabilité de leurs configurations entre les individus. Le niveau de démêlage escompté a été obtenu en utilisant une ISOMAP, un algorithme de réduction de dimension, pour stratifier la population en fonction de la géométrie des fibres locales avant la définition des faisceaux. Pour chaque région étudiée, les fibres contournant un sulcus spécifique ont été ciblées et des ROI ont été sélectionnées en conséquence. Ces régions correspondent aux sillons central, temporal supérieur, cingulaire et au gyrus précentral. La méthode a été appliquée sur les données de tractographie probabiliste de 816/897 sujets de la base HCP. Pour chaque région, les fibres ont été extraites puis utilisées dans le calcul de l’ISOMAP, qui à son tour a été utilisé pour diviser la population en dix groupes. Dans chaque groupe, la méthode d'identification des faisceaux courts a été appliquée, afin d'obtenir des faisceaux reproductibles. Ceux-ci ont ensuite été automatiquement mis en correspondance avec ceux des autres groupes, sur la base d'une distance entre centroïdes. Un principe d'hystérésis a été utilisé pour récupérer certains faisceaux précédemment rejetés. Afin d'identifier les faisceaux à l'origine des différences reflétées par les dimensions de l'ISOMAP, une distance «faisceau à tractogramme» pour chaque paire de sujets a été corrélée à leur position dans l’ISOMAP. Une corrélation élevée a été observée entre les premières dimensions de l’ISOMAP fondée sur les fibres et de celle fondée sur la morphologie des sillons. Les faisceaux contribuant à cette dimension de l’ISOMAP montrent des transitions morphologiques cohérentes, et sont situés dans des zones où le sillon présente également des transitions de forme. De plus, les changements des faisceaux sont également spatialement corrélés aux changements des activations fonctionnelles. Ces résultats prouvent le lien entre le câblage cérébral et le plissement cortical. De plus, ils montrent qu'une délimitation plus fine des faisceaux permet de voir des différences qui, la plupart du temps, sont brouillées en raison du mélange des configurationsThe study of superficial white matter (SWM) has often been left aside, mainly because of its high variability. Higher quality acquisition methods and the development of new analysis tools have facilitated the study of SWM from diffusion MRI and tractography. Brain connectivity and cortical folding pattern must be strongly related, especially for short U-fibers, which circumvent the folds. As the folding patterns morphology is specific to each human being, so should be the underlying fibers configuration. In this work we created a pipeline to disentangle the short white matter connections into their different configurations and to characterize their relation with other structures.First a method to delineate short bundles from a tractography set was built using a hybrid approach, by extracting fibers connecting two cortical regions of interest (ROIs) (incorporating anatomical information) and then clustering them into bundles (considering their shape), reproducible across subjects. Subjects were aligned by a T1-based affine transformation and a deterministic tractography database (79 subjects) was used. This generated a whole brain streamline bundle atlas, which allows distance-based segmentation of the bundles in new subjects, in order to perform clinical studies over specific connections. The bundles obtained were compared against other two publicly available atlases (using alternative non-linear alignment across subjects), to evaluate their reproducibility given different methods and databases. A non-negligible number of bundles were found similar among the three atlases. As SWM bundle definition is still a subjective matter, over-segmentation can nevertheless occur. However, even greater granularity is required when aiming to classify the different bundle configurations. This level of disentanglement was achieved by an ISOMAP dimensionality reduction algorithm. It aimed to stratify the population based on their fibers using geometrical changes across subjects. For each region under study, the fibers surrounding a specific sulcus were targeted and therefore the ROIs were selected accordingly. These regions are: central sulcus, superior temporal sulcus, cingulate sulcus and precentral gyrus. The method was applied over 816/897 subjects of the S900 release of the HCP database and a preprocessed probabilistic tractography database. For each region the fibers were extracted, sampled and then used in the ISOMAP computation, which in turn was employed to split the population in ten groups. In each group a refined version of a short bundle identification method was applied, in order to obtain reproducible bundles. These were then automatically matched with their corresponding ones in the other groups, based on a centroid fiber distance. A Hysteresis principle was used to recover missing bundles (previously discarded) in each group. In order to identify the bundles driving the differences reflected on each ISOMAP dimension, the correlation of the fibers geometry with the subjects ISOMAP values was performed, by using a “bundle to tractogram” distance for each pair of subjects. The fiber-based ISOMAP values were also compared to a sulcus-based ones, obtaining a high correlation for the first dimension. The bundles showing correlation with the ISOMAP values show coherent morphological transitions along the groups, and are located in areas where the sulcus also exhibits differences in shape. Moreover, the bundles are also spatially correlated to changes in functional activations. These results prove the link between the brain wiring and the cortical folding pattern. Moreover, they evidence that a finer delineation of the bundles allow the detection of differences that most of the time are blurred out due to the mixing of configurations
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Clemente, Adam. "Fibre-specific white matter in chronic traumatic brain injury patients : Towards single-subject profiles." Phd thesis, Australian Catholic University, 2021. https://acuresearchbank.acu.edu.au/download/9b395d078ab2723066c3643d308093662cf0bd64fdddd41b646f39d7a0114753/26511759/Adam_Clemente_2021_Fibre_specific_white_matter_in_chronic_%5BREDACTED%5D.pdf.
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Aims and Background: Moderate-to-severe traumatic brain injury (m-sTBI) leads to poor functional outcomes due to chronic deficits in cognitive and motor functions. These long-term functional outcomes are often difficult to treat and predict. The overarching aim of this thesis was to develop a science-led and principled paradigm to help better understand and potentially improve functional outcomes in chronic m-sTBI patients. m-sTBI patients are highly heterogeneous due to the nature and location of injuries, which is an important predictor of functional outcomes. Structural magnetic resonance imaging (MRI) provides quantitative measurements of macroscopic (i.e., anatomical MRI) and microscopic (i.e., diffusion MRI; dMRI) characteristics of these injuries. Imaging-based characterisation when combined with training can help identify behaviourally relevant biomarkers (i.e., neurological metrics that are associated with aspects of behaviour), which are notable indicators of one’s response to training or therapy. Therefore, the current thesis attempted to address the overarching aim by incorporating relevant structural neuroimaging and behaviourally relevant biomarkers in response to training.
Method and Results: This overarching aim of this thesis was addressed across three studies. Study 1 was designed to summarise the recent findings on training-induced structural neuroplasticity research in acquired brain injury (ABI) populations following cognitive and/or motor training (i.e., targeting the most prevalent chronic symptoms) due to scarce research in m-sTBI. The critical review revealed that most studies have used (1) dMRI as the primary structural MRI modality, which may be more sensitive to training-related changes; (2) non-biologically specific dMRI tensor-based metrics; and (3) non-intensive single-modality training protocols (i.e., cognitive/motor training alone, not combined). It was also argued that developing robust longitudinal single-subject profiling designs (i.e., 1 patient vs X controls), shifting away from group-comparisons, may be necessary in m-sTBI patients to control for excessive heterogeneity in their injuries. Given these findings, the overarching aim of the thesis was addressed in the empirical studies through a two-part paradigm. This was to first develop behaviourally relevant biomarkers in a healthy cohort (i.e., as those developed in m-sTBI patients are limited by excessive heterogeneity); then develop and present a preliminary, longitudinal single-subject profiling framework utilising fibre-specific white matter which may potentially be interpreted with the developed behaviourally relevant biomarkers. This was conducted in order to understand our knowledge of neurocognitive function in m-sTBI and how functional outcomes change due to the effects of different training regimes, at the individual-subject level.
Two empirical studies were conducted to present (1) how to develop a behaviourally relevant biomarker using fibre-specific dMRI metrics in a healthy cohort (Study 2); and (2) a proof-of-concept longitudinal single-subject profiling approach for individual m-sTBI subjects (Study 3). To demonstrate the paradigm, Study 2 aimed to develop a robust behaviourally relevant biomarker for attentional lapses in a healthy cohort; a common yet under-studied symptom of m-sTBI which may precipitate other cognitive deficits and certain motor deficits. The novel findings were that decreased white matter fibre-density of the superior longitudinal fasciculus-I is associated with greater susceptibility to attentional lapses in healthy controls, and may be a behaviourally relevant biomarker that can be targeted in m-sTBI patients. The aim of Study 3 was to develop and present a clinically specific longitudinal single-subject profiling framework for chronic m-sTBI patients which incorporates (1) subject and tract specific characterisations of white matter microstructure; and (2) attention (e.g., attentional lapses) and motor (e.g., fine motor skills) behaviours following a combined cognitive and motor training. These novel longitudinal single-subject profiles may be compared to putative behaviourally relevant biomarkers for further interpretations, such as those discovered in Study 2. These novel profiles await further validation and extensions, but ultimately may assist with diagnostic and/or treatment decisions made to individual m-sTBI patients.
Conclusion: The current thesis presents a principled, case-based neuroimaging paradigm that may help better understand and potentially improve functional outcomes in chronic m-sTBI patients. This novel paradigm provides an important stepping stone for future research to further expand upon single-subject profiling and by developing comprehensive behaviourally relevant biomarkers targeting m-sTBI deficits for in-depth interpretations. With further validations and extensions of this approach, the case-based paradigm developed in this thesis may assist with conventional care options and continued work may lead to neuroimaging-guided training to help better understand, predict and assist recovery with outcomes in chronic m-sTBI patients.
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Fenoll, Sanguino Raquel. "The influence of selected genetic and environmental factors on white matter pathway structure measured with diffusion tensor imaging." Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/565943.
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The present doctoral thesis is focused on describing the effects that different environmental and genetic modulators have on white matter pathways and its consequences measured with diffusion tensor imaging. We chose to focus on two examples of each type of modulators. Firstly, we selected as environment modulating factors: pollutants and video games. On one side, pollution as an external factor that enters passively the brain and may influence developmental trajectories. And on the other hand, we used video games as a good example of active behavior that can modify white matter tracts through practice. Secondly, Down syndrome and Prader-Willi syndrome were selected as representative genetic syndromes that may interfere on white matter growth because, although Down syndrome has higher incidence rate than Prader- Willi syndrome, both show behavioral and cognitive alterations, indicating an abnormal brain development. The results of this doctoral thesis lead to the conclusion that white matter pathways development is not an immutable process and it can be modified by diverse modulators. In the same way, diffusion tensor imaging is a good-quality technique to capture and identify those white matter changes through life.La presente tesis doctoral se centra en describir los efectos que diferentes moduladores ambientales y genéticos tienen sobre las vías de la sustancia blanca y sus consecuencias a través de imágenes de tensor de difusión. Decidimos centrarnos dos ejemplos de cada tipo de moduladores. En primer lugar, se seleccionó como factores de modulación ambiental: contaminantes y videojuegos. Por un lado, la contaminación es un factor externo que penetra pasivamente el cerebro y puede influir en las trayectorias del desarrollo. Y por otro lado, los videojuegos son un buen ejemplo de comportamiento activo que puede modificar los tractos de la materia blanca a través de la práctica. En segundo lugar, se seleccionaron el síndrome de Down y síndrome de Prader-Willi como síndromes genéticos representativos que pueden interferir en el crecimiento de la materia blanca ya que, aunque el síndrome de Down tiene una tasa de incidencia superior al síndrome de Prader-Willi, ambos muestran alteraciones cognitivas y conductuales fruto de un subdesarrollo de las vías de sustancia blanca. Los resultados de esta tesis doctoral nos llevan a la conclusión de que el desarrollo de vías de sustancia blanca no es un proceso inmutable y puede ser modificado por diversos moduladores. De la misma manera, el tensor de difusión es una técnica adecuada para capturar e identificar los cambios en la sustancia blanca que acontecen a lo largo de la vida.
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Gauthier, Yvan. "Measurement of the apparent diffusion coefficient of water in white matter using magnetic resonance imaging, a phantom study." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0016/MQ48500.pdf.
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Errangi, Bhargav Kumar. "A diffusion tensor imaging study of." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28156.
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Thesis (M. S.)--Biomedical Engineering, Georgia Institute of Technology, 2009.Committee Chair: James K. Rilling; Committee Chair: Xiaoping Hu; Committee Member: Shella Keilholz; Committee Member: Todd M. Preuss.
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Schmit, Matthew Bolesaw. "Diffusion and Structural Magnetic Resonance Imaging of White Matter Pathology Can Predict Cognitive Performance in a Tract-Specific Manner." Thesis, The University of Arizona, 2014. http://hdl.handle.net/10150/321948.
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Popov, Alexandros. "Global inference of the structural connectivity of white matter fiber bundles using deep learning approaches and microstructural prior knowledge." Thesis, université Paris-Saclay, 2022. https://tel.archives-ouvertes.fr/tel-03789629.
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La cartographie de la connectivité anatomique du cerveau humain est un défi scientifique majeur. Décrire la trajectoire et les connexions réalisées par les cent milliards de neurones qui composent le cerveau est une tâche titanesque et multi-échelle.Les grands faisceaux ont été décrits par des approches anatomiques classiques dès le 20ème siècle. Ces travaux ont également révélé l'existence de faisceaux plus courts, appelés superficiels, qui définissent la connectivité entre les régions anatomiques voisines. La taille réduite et la forme complexe de ces faisceaux posent un sérieux défi à leur visualisation, si bien que leur description demeure à ce jour débattue.Le premier axe de recherche de cette thèse vise à repousser les limites de l'IRM de diffusion et proposer un nouveau jeu de données ex-vivo du cerveau humain entier, intitulé Chenonceau, dédié à la caractérisation de la connectivité fine du cerveau.Le jeu de données est composé de deux acquisitions anatomiques pondérées en T2 à une résolution de 100 et 150 microns, ainsi que 175 jeux de données d'IRMd à une résolution de 200 microns et une pondération s'élevant jusqu'à 8000 s/mm2. Plus de 4500 heures d'acquisitions, réparties sur deux ans et demie ont été nécessaires pour acquérir ces données.Chenonceau met à profit la puissance de l'IRM pré-clinique Bruker 11.7T, doté à la fois d'un champ magnétique élevé et d'un tunnel de gradients puissants (780mT/m) permettant d'atteindre la résolution mésoscopique et une très forte pondération en diffusion.Pour concilier la taille imposante du cerveau humain avec l'imageur pré-clinique, un nouveau protocole d'acquisition est proposé. Celui-ci repose sur la séparation du cerveau en échantillons de taille réduite, qui sont sont imagés individuellement, puis réassemblés en post-traitement pour reconstituer le volume intégral.L'ensemble de la démarche est présenté, incluant le protocole de coupe et de préservation des pièces anatomiques, le détail des séquences IRM utilisées ainsi que la description du pipeline de traitement des images. Une attention particulière est portée à la définition de l'étape de recalage qui recompose le volume entier à partir des acquisitions individuelles.Les premières inférences de la connectivité anatomique issues de ce nouveau jeu de données sont également présentées. Les techniques de tractographie et de clustering permettent d'extraire non seulement les faisceaux longs de Chenonceau, mais également les faisceaux superficiels.La seconde partie de la thèse a porté sur le développement d'une nouvelle méthode de suivi de fibres, fondée sur l'utilisation d'un modèle de verres de spins.Ce dernier exprime le problème de tractographie sous la forme d'un ensemble de fragments de fibres, appelés spin, distribués dans l'échantillon et dont la position et l'orientation, ainsi que les connexions qu'ils établissent sont associés à une quantité d'énergie. La construction des tracts résulte du déplacement et de la connexion des spins, dans le but d'atteindre le minimum global d'énergie.Cette thèse propose de remplacer la méthode de Metropolis-Hastings utilisée pour l'optimisation par un agent entraîné dans un cadre d'apprentissage par renforcement.Cette nouvelle formulation vise à améliorer le choix des actions, qui ne seraient plus tirées aléatoirement, mais dictées par une stratégie apprise par l'agent, fruit de ses interactions passées avec des environnement semblables.Les capacités d'anticipation et de projection d'un tel agent apparaissent particulièrement adéquates pour proposer la trajectoire la plus pertinente dans des régions ou l'information de diffusion est ambiguë. De même, la possibilité pour l'algorithme d'apprendre au travers d'interactions permet de contourner la difficulté d'établir des ensembles de faisceaux considérées véritablesMapping the structural connectivity of the human brain is a major scientific challenge. Describing the trajectory and connections made by the hundred billion neurons that make up the brain is a titanic and multi-scale task.The major fiber bundles have been described by classical anatomical approaches since the 20th century. These studies also revealed the existence of shorter bundles, called superficial bundles, that ensure the connectivity between neighboring anatomical regions. The small size and complex shape of these bundles set a serious challenge to their visualization, so that their description remains under discussion to this day.The first research axis of this thesis aims at pushing the limits of diffusion MRI and proposing a new ex-vivo dataset of the whole human brain, called Chenonceau, dedicated to the characterization of the fine connectivity of the brain.The dataset consists of two T2-weighted anatomical acquisitions at 100 and 150 micron resolution, as well as 175 dMRI datasets at 200 micron resolution with diffusion weighting reaching 8000 s/mm2. More than 4500 hours of acquisition, distributed across two and a half years were necessary to acquire this data.Chenonceau takes advantage of the Bruker 11.7T preclinical MRI system, equipped with both a high magnetic field and a powerful gradient tunnel (780mT/m) allowing to reach the mesoscopic resolution and a very high diffusion weighting.To reconcile the large size of the human brain with the preclinical system, a new acquisition protocol is proposed. It is based on the separation of the brain into smaller samples, which are imaged individually, then reassembled in post-processing to reconstitute the full volume.The whole process is presented, including the protocol for the cutting and the storage of the anatomical samples, the details of the MRI sequences and the description of the image processing pipeline. Special attention is dedicated to the definition of the registration step which recomposes the whole volume from the individual acquisitions.The first inferences of anatomical connectivity from this new dataset are also presented. Tractography associated with clustering techniques allow the extraction of the long and superficial bundles of Chenonceau.The second part of the thesis focused on the development of a new method for fiber tracking, based on the use of the spin glass model.The latter expresses the tractography problem as a set of fiber fragments, called spins, distributed in the sample and whose position and orientation, as well as the connections they establish, are associated with an amount of energy. The construction of the tracts results from the displacement and connection of the spins, with the aim of reaching the global minimum of energy.This thesis proposes to replace the Metropolis-Hastings method used for optimization by an agent trained in a reinforcement learning framework.This new formulation aims at improving the choice of actions, which would no longer be randomly drawn, but dictated by a strategy learned by the agent, fruit of its past interactions with similar environments.The anticipation and projection capacities of such an agent appear particularly adequate to propose the most relevant trajectory in regions where the diffusion information is ambiguous. Moreover, the possibility for the algorithm to learn through interactions allows to circumvent the difficulty of establishing datasets of ground-truth bundles