Relevant bibliographies by topics / White matter diffusion

Academic literature on the topic 'White matter diffusion'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "White matter diffusion"

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Thomason, Moriah E., and Paul M. Thompson. "Diffusion Imaging, White Matter, and Psychopathology." Annual Review of Clinical Psychology 7, no. 1 (April 27, 2011): 63–85. http://dx.doi.org/10.1146/annurev-clinpsy-032210-104507.

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Nørhøj Jespersen, Sune. "White matter biomarkers from diffusion MRI." Journal of Magnetic Resonance 291 (June 2018): 127–40. http://dx.doi.org/10.1016/j.jmr.2018.03.001.

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Wedeen, V. J., T. L. Davis, B. E. Lautrup, T. G. Reese, and B. R. Rosen. "Diffusion anisotropy and white matter tracts." NeuroImage 3, no. 3 (June 1996): S146. http://dx.doi.org/10.1016/s1053-8119(96)80148-0.

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Chen, H., and Y. Xu. "Diffusion properties of major white matter tracts in individuals white matter hyperintensity." Journal of the Neurological Sciences 405 (October 2019): 75. http://dx.doi.org/10.1016/j.jns.2019.10.358.

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KAMAGATA, KOJI, MASAAKI HORI, KOUHEI KAMIYA, MICHIMASA SUZUKI, AKIRA NISHIKORI, FUMITAKA KUMAGAI, MARIKO YOSHIDA, SHINSUKE KYOGOKU, and SHIGEKI AOKI. "Diffusion MR Imaging of White Matter Pathways." Juntendo Medical Journal 60, no. 2 (2014): 100–106. http://dx.doi.org/10.14789/jmj.60.100.

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Choudhri, Asim F., Eric M. Chin, Ari M. Blitz, and Dheeraj Gandhi. "Diffusion Tensor Imaging of Cerebral White Matter." Radiologic Clinics of North America 52, no. 2 (March 2014): 413–25. http://dx.doi.org/10.1016/j.rcl.2013.11.005.

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van der Lei, H. D. W., M. E. Steenweg, M. Bugiani, P. J. W. Pouwels, W. N. van Wieringen, and M. S. van der Knaap. "P18.3 Restricted diffusion in vanishing white matter." European Journal of Paediatric Neurology 15 (May 2011): S105. http://dx.doi.org/10.1016/s1090-3798(11)70363-4.

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Lazar, Mariana, David M. Weinstein, Jay S. Tsuruda, Khader M. Hasan, Konstantinos Arfanakis, M. Elizabeth Meyerand, Benham Badie, et al. "White matter tractography using diffusion tensor deflection." Human Brain Mapping 18, no. 4 (March 6, 2003): 306–21. http://dx.doi.org/10.1002/hbm.10102.

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Stoeter, Peter, Paulo Roberto Dellani, and Goran Vucurevic. "Diffusion Tensor Imaging of Cerebral White Matter*." Clinical Neuroradiology 18, no. 3 (August 2008): 155–62. http://dx.doi.org/10.1007/s00062-008-8019-3.

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Tench, C. R., P. S. Morgan, M. Wilson, and L. D. Blumhardt. "White matter mapping using diffusion tensor MRI." Magnetic Resonance in Medicine 47, no. 5 (April 22, 2002): 967–72. http://dx.doi.org/10.1002/mrm.10144.

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Dissertations / Theses on the topic "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.

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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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Books on the topic "White matter diffusion"

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L, Ulmer John, ed. White matter in cognitive neuroscience: Advances in diffusion tensor imaging and its applications. New York: New York Academy of Sciences, 2005.

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Passaro, Antony, Foteini Christidi, Vasiliki Tsirka, and Andrew C. Papanicolaou. White Matter Connectivity. Edited by Andrew C. Papanicolaou. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.5.

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The applications of diffusion tensor imaging (DTI) have increased considerably among both normal and diverse neuropsychiatric populations in recent years. In this chapter, the authors examine the contributions of DTI in identifying profiles of trait-specific connectivity in several groups defined in terms of gender, age, handedness, and general intelligence. Additionally, the DTI literature is reviewed across a range of neurodegenerative disorders including Alzheimer’s disease, mild cognitive impairment, frontotemporal dementia, Parkinson disease, multiple sclerosis, and acquired neurological disorders resulting from neuronal injury such as traumatic brain injury, aphasia, agnosia, amnesia, and apraxia. DTI metrics sensitive to psychiatric disorders encompassing obsessive-compulsive disorder, depression, bipolar disorder, schizophrenia, and alcoholism are reviewed. Future uses of DTI as a promising means of confirming diagnoses and identifying in vivo early microstructural changes of patients’ clinical symptoms are discussed.
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Wilde, Elisabeth A., Kareem W. Ayoub, and Asim F. Choudhri. Diffusion Tensor Imaging. Edited by Andrew C. Papanicolaou. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.10.

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Diffusion tensor imaging (DTI) is a method of specifying and visualizing the functional integrity of white matter tracts that contribute to the functional and structural connectivity among different brain regions through the examination of water diffusion through tissue. It has gained rapid popularity in the past two decades, particularly for elucidating the process of normal white matter development and the effects of aging on it, as well as providing some insights into the possible neuroanatomical correlates of numerous psychiatric and neurologic disorders. This chapter outlines the instrumentation and the procedures employed in deriving estimates of the functional integrity of anatomical connections in the brain, and issues regarding the reliability and validity of the different DTI procedures are systematically addressed.
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Cooley's Anemia Symposium 2005 Lake Buen and Elliott P. Vichinsky. White Matter in Cognitive Neurosciences (Annals of the New York Academy of Sciences). New York Academy of Sciences, 2006.

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Moseley, Michael, John Gabrieli, and Lawrence Parsons. White Matter in Cognitive Neuroscience: Advances in Diffusion Tensor Imaging and Its Applications (Annals of the New York Academy of Sciences). Blackwell Publishing Limited, 2005.

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Boedhoe, Premika S. W., and Odile A. van den Heuvel. The Structure of the OCD Brain. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0023.

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This chapter summarizes the most consistent findings of structural neuroimaging studies of obsessive-compulsive disorder (OCD), and discusses their relationship within the implicated brain networks. The techniques used in these studies are diverse, and include manual tracing of specific regions of interest, whole-brain voxel-based morphometry (VBM) for both gray matter and white matter volume comparisons, FreeSurfer to investigate differences in cortical thickness and subcortical volumes, and other methods such as covariance analyses. Findings on white matter integrity with tract-based spatial statistics (TBSS) and in diffusion tensor imaging (DTI) studies are discussed as well.The literature shows that the pathophysiology of OCD cannot be explained by alterations in function and structure of the classical cortico-striato-thalamo-cortical (CSTC) regions exclusively, but that fronto-limbic and fronto-parietal connections are important as well, and the role of the cerebellum needs more attention in future research.
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Papanicolaou, Andrew C., ed. The Oxford Handbook of Functional Brain Imaging in Neuropsychology and Cognitive Neurosciences. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764228.001.0001.

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A large part of the contemporary literature involves functional neuroimaging. Yet few readers are sufficiently familiar with the various imaging methods, their capabilities and limitations, to appraise it correctly. To fulfill that need is the purpose of this Handbook, which consists of an accessible description of the methods and their clinical and research applications. The Handbook begins with an overview of basic concepts of functional brain imaging, magnetoencephalography and the use of magnetic source imaging (MSI), positron emission tomography (PET), diffusion tensor imaging (DTI), and transcranial magnetic stimulation (TMS). The authors then discuss the various research applications of imaging, such as white matter connectivity; the function of the default mode network; the possibility and the utility of imaging of consciousness; the search for mnemonic traces of concepts the mechanisms of the encoding, consolidation, and retrieval of memories; executive functions and their neuroanatomical mechanisms; voluntary actions, human will and decision-making; motor cognition; language and the mechanisms of affective states and pain. The final chapter discusses the uses of functional neuroimaging in the presurgical mapping of the brain.
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Ussishkin, Daniel. New Wars. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190469078.003.0005.

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Chapter 4 examines the ways in which the totalization of warfare during the twentieth century signaled the diffusion of the concept of morale from the military context to civil society, while the transformation of the terrain of conduct it referred to expanded from matters related to individual conduct and character to collective attitude. It takes stock of “total war” as describing both a process and a set of political and cultural aspirations. Further, the chapter explores the relations between morale, mobilization, and the experience of war to debates about social change and modernization.
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Arthur, Richard T. W. Passive Force and Corporeal Substance. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198812869.003.0007.

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This chapter treats various issues concerning Leibniz’s accounts of force and of corporeal substance: the status of passive force, given that substance is a thing that acts, how extension is founded in force, and the issue of substantial bonds. It is argued that Leibniz’s position is that an entelechy can never act in isolation: its action must always take into consideration the actions of the other created substances with which it coexists. Extension arises from the resistance to penetration resulting from the diffusion of entelechies and passive forces throughout matter, while the unity and continuity of body is relative to the observer. Leibniz does not propose substantial bonds out of dissatisfaction with this standard account, but in an attempt to satisfy the Jesuits’ demand for the real union of body and soul, which ultimately conflicts with the actuality of subordinate forms implicit in his own position.
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Harding, Dennis. Rewriting History. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198817734.001.0001.

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‘Every generation re-writes history in its own way’. Re-writing History applies Collingwood’s dictum to a series of topics and themes, some of which have been central to prehistoric and protohistoric archaeology for the past century or more, while some have been triggered by more recent changes in technology or social attitudes. Some issues are highly controversial, like the proposals for the Stonehenge World Heritage sites. Others challenge long-held popular myths, like the deconstruction of the Celts and by extension the Picts. Yet some traditional tenets of scholarship have gone unchallenged for too long, like the classical definition of civilization itself. But why should it matter? Surely it is in the order of things that each generation rejects received wisdom and adopts ideas that are radical or might offend previous generations? Is this not simply symptomatic of healthy and vibrant debate? Or are there grounds for believing that current changes are of a more disquieting character, denying the basic assumptions of rational argument and freedom of enquiry and expression that have been the foundation of western scholarship since the eighteenth century Enlightenment? Re-writing History addresses contemporary concerns about information and its interpretation, including issues of misinformation and airbrushing of politically-incorrect history. Its subject matter is the archaeology of prehistoric and early historic Britain, and the changes witnessed over two centuries and more in the interpretation of the archaeological heritage by changes in the prevailing political and social as well as intellectual climate. Far from being topics of concern only to academics in ivory towers, the way in which seemingly innocuous issues such as cultural diffusion or social reconstruction in the remote past are studied and presented reflects important shifts in contemporary thinking that challenge long-accepted conventions of free speech and debate.
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Book chapters on the topic "White matter diffusion"

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Stieltjes, Bram. "Normal Diffusion Tensor Imaging-Based White Matter Anatomy." In Diffusion Tensor Imaging, 231–71. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3118-7_12.

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Chamberland, Maxime, Samuel St-Jean, Chantal M. W. Tax, and Derek K. Jones. "Obtaining Representative Core Streamlines for White Matter Tractometry of the Human Brain." In Computational Diffusion MRI, 359–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05831-9_28.

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Kuijf, Hugo J., Chantal M. W. Tax, L. Karlijn Zaanen, Willem H. Bouvy, Jeroen de Bresser, Alexander Leemans, Max A. Viergever, Geert Jan Biessels, and Koen L. Vincken. "The Added Value of Diffusion Tensor Imaging for Automated White Matter Hyperintensity Segmentation." In Computational Diffusion MRI, 45–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11182-7_5.

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Moreno, Gali Zimmerman, Guy Alexandroni, Nir Sochen, and Hayit Greenspan. "Sparse Representation for White Matter Fiber Compression and Calculation of Inter-Fiber Similarity." In Computational Diffusion MRI, 133–43. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54130-3_11.

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Moreno, Gali Zimmerman, Guy Alexandroni, and Hayit Greenspan. "White Matter Fiber Set Simplification by Redundancy Reduction with Minimum Anatomical Information Loss." In Computational Diffusion MRI, 171–82. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28588-7_15.

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Chamberland, Maxime, Mia Winter, Thomas A. W. Brice, Derek K. Jones, and Emma C. Tallantyre. "Beyond Lesion-Load: Tractometry-Based Metrics for Characterizing White Matter Lesions within Fibre Pathways." In Computational Diffusion MRI, 227–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73018-5_18.

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Tax, Chantal M. W., Elena Kleban, Muhamed Baraković, Maxime Chamberland, and Derek K. Jones. "Magnetic Resonance Imaging of $$T_2$$- and Diffusion Anisotropy Using a Tiltable Receive Coil." In Mathematics and Visualization, 247–62. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_12.

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Abstract:
AbstractThe anisotropic microstructure of white matter is reflected in various MRI contrasts. Transverse relaxation rates can be probed as a function of fibre-orientation with respect to the main magnetic field, while diffusion properties are probed as a function of fibre-orientation with respect to an encoding gradient. While the latter is easy to obtain by varying the orientation of the gradient, as the magnetic field is fixed, obtaining the former requires re-orienting the head. In this work we deployed a tiltable RF-coil to study $$T_2$$ T 2 - and diffusional anisotropy of the brain white matter simultaneously in diffusion-$$T_2$$ T 2 correlation experiments.
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Aarabi, Mohammad Hadi, and Hamid Saligheh Rad. "Diffusion-Map: A Novel Visualizing Biomarker for Diffusion Tensor Imaging of Human Brain White Matter." In Computational Diffusion MRI, 65–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11182-7_7.

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Dolatshahi, Mahsa, Farzaneh Rahmani, Mohammad Hadi Shadmehr, Timm Peoppl, Ahmad Shojaie, Farsad Noorizadeh, Mohammad Hadi Aarabi, and Somayeh Mohammadi Jooyandeh. "Working Memory Function in Recent-Onset Schizophrenia Patients Associated with White Matter Microstructure: Connectometry Approach." In Computational Diffusion MRI, 201–9. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54130-3_17.

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Yin, Haoran, Pengbo Xu, Hui Cui, Geng Chen, and Jiquan Ma. "DC$$^2$$U-Net: Tract Segmentation in Brain White Matter Using Dense Criss-Cross U-Net." In Computational Diffusion MRI, 115–24. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21206-2_10.

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Conference papers on the topic "White matter diffusion"

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Descoteaux, Maxime, Christophe Lenglet, and Rachid Deriche. "Diffusion tensor sharpening improves white matter tractography." In Medical Imaging, edited by Josien P. W. Pluim and Joseph M. Reinhardt. SPIE, 2007. http://dx.doi.org/10.1117/12.708988.

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Vidotto, Marco, Daniele Dini, and Elena De Momi. "Effective Diffusion and Tortuosity in Brain White Matter." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8513443.

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Behjat, Hamid, Iman Aganj, David Abramian, Anders Eklund, and Carl-Fredrik Westin. "Characterization Of Spatial Dynamics Of Fmri Data In White Matter Using Diffusion-Informed White Matter Harmonics." In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433958.

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Di, Qian, Tingting Wang, Li Yao, and XiaoJie Zhao. "White Matter Fiber Tracts Based On Diffusion Tensor Imaging." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.819.

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Wu, Zhanxiong, and Xun Li. "Tacking of white matter fiber bindles based on diffusion QBI." In 2014 IEEE Workshop on Electronics, Computer and Applications (IWECA). IEEE, 2014. http://dx.doi.org/10.1109/iweca.2014.6845756.

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Lv, Yu-ting, Xu-feng Yao, Xi-xi Bu, Song Xu, and Gang Huang. "Evaluation of Diffusion Tensor White Matter Network of Alzheimer’s Disease." In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021. http://dx.doi.org/10.1109/iaeac50856.2021.9390844.

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Aranda, Ramon, Mariano Rivera, and Alonso Ramirez-Manzanares. "Self-oriented Diffusion Basis Functions for white matter structure estimation." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556680.

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Wang, Xizhen, Shanshan Gao, Shuai Wang, Hongwei Sun, Yanyu Wang, Nengzhi Jiang, and Guohua Xie. "First-episode depression: Diffusion tensor imaging of deep white matter." In 2014 2nd International Conference on Systems and Informatics (ICSAI). IEEE, 2014. http://dx.doi.org/10.1109/icsai.2014.7009413.

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Ingalhalikar, Madhura A., Nancy C. Andreasen, Jinsuh Kim, Andrew L. Alexander, and Vincent A. Magnotta. "White matter degeneration in schizophrenia: a comparative diffusion tensor analysis." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.844283.

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Tsao, Sinchai, Niharika Gajawelli, Darryl H. Hwang, Stephen Kriger, Meng Law, Helena Chui, Michael Weiner, and Natasha Lepore. "Mapping of ApoE4 related white matter damage using diffusion MRI." In SPIE Medical Imaging, edited by Maria Y. Law and Tessa S. Cook. SPIE, 2014. http://dx.doi.org/10.1117/12.2043925.

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Reports on the topic "White matter diffusion"

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Little, Deborah M. High Resolution Diffusion Tensor Imaging of Cortical-Subcortical White Matter Tracts in TBI. Fort Belvoir, VA: Defense Technical Information Center, October 2009. http://dx.doi.org/10.21236/ada513063.

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Little, Deborah M. High Resolution Diffusion Tensor Imaging of Cortical-Subcortical White Matter Tracts in TBI. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada549548.

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