Дисертації з теми "White matter diffusion"
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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.
Повний текст джерелаO'Donnell, Lauren Jean. "Cerebral white matter analysis using diffusion imaging." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35514.
Повний текст джерелаIncludes 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.
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.
Повний текст джерелаMaddah, Mahnaz. "Quantitative analysis of cerebral white matter anatomy from diffusion MRI." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45614.
Повний текст джерелаThis 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.
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.
Повний текст джерелаHu, Chengliang. "Inferring cerebral white matter fibres from diffusion tensor magnetic resonance images." Thesis, University of York, 2018. http://etheses.whiterose.ac.uk/22002/.
Повний текст джерелаHorne, 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.
Повний текст джерелаTitle from first page of PDF file (viewed April 7, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 127-149).
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/.
Повний текст джерелаBertò, Giulia. "Supervised Learning for White Matter Bundle Segmentation." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/264971.
Повний текст джерелаSprooten, Emma. "Genetic determinants of white matter integrity in bipolar disorder." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6482.
Повний текст джерелаRatnarajah, 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.
Повний текст джерелаLiang, Xuwei. "MODELING AND QUANTITATIVE ANALYSIS OF WHITE MATTER FIBER TRACTS IN DIFFUSION TENSOR IMAGING." UKnowledge, 2011. http://uknowledge.uky.edu/gradschool_diss/818.
Повний текст джерелаQiu, 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.
Повний текст джерелаWassermann, Demian. "Automated in vivo dissection of white matter structures from diffusion magnetic resonance imaging." Nice, 2010. http://www.theses.fr/2010NICE4066.
Повний текст джерелаThe 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
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.
Повний текст джерелаLacerda, 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.
Повний текст джерелаUmapathy, 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.
Повний текст джерелаBava, 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.
Повний текст джерелаTitle from first page of PDF file (viewed January 8, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 75-84).
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.
Повний текст джерелаFerizi, 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/.
Повний текст джерелаButler, 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.
Повний текст джерелаSherbondy, 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.
Повний текст джерелаImamura, Hisaji. "Network specific change in white matter integrity in mesial temporal lobe epilepsy." Kyoto University, 2017. http://hdl.handle.net/2433/226747.
Повний текст джерелаStamile, Claudio. "Unsupervised Models for White Matter Fiber-Bundles Analysis in Multiple Sclerosis." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1147/document.
Повний текст джерелаDiffusion 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
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.
Повний текст джерелаTelford, 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.
Повний текст джерелаNicolas, 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.
Повний текст джерелаBajaj, 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.
Повний текст джерелаVallee, 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.
Повний текст джерелаHo, 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.
Повний текст джерелаHo, 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.
Повний текст джерелаMani, Meenakshi. "Quantitative analysis of open curves in brain imaging : applications to white matter fibres and sulci." Rennes 1, 2011. http://www.theses.fr/2011REN1S026.
Повний текст джерелаCette 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
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.
Повний текст джерелаCheung, 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.
Повний текст джерелаLewis, 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.
Повний текст джерелаTitle from first page of PDF file (viewed November 10, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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.
Повний текст джерелаKhong, 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.
Повний текст джерелаBoespflug, 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.
Повний текст джерелаDhital, 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.
Повний текст джерелаGongvatana, 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.
Повний текст джерелаTitle from first page of PDF file (viewed September 4, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 81-94).
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.
Повний текст джерелаHu, 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.
Повний текст джерелаTitle 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).
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.
Повний текст джерелаGuevara, 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.
Повний текст джерелаThe 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
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.
Повний текст джерелаFenoll, 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.
Повний текст джерелаLa 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.
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.
Повний текст джерелаErrangi, Bhargav Kumar. "A diffusion tensor imaging study of." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28156.
Повний текст джерелаCommittee Chair: James K. Rilling; Committee Chair: Xiaoping Hu; Committee Member: Shella Keilholz; Committee Member: Todd M. Preuss.
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.
Повний текст джерелаPopov, 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.
Повний текст джерелаMapping 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