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Diffusion Magnetic Resonance Imaging in the Central Nervous System
Published in Shoogo Ueno, Bioimaging, 2020
Kouhei Kamiya, Yuichi Suzuki, Osamu Abe
The first generation of tractography algorithms used the first eigenvector of the diffusion tensor to define the local trajectory at each step of tracking. In principle, the tractogram is generated starting from a certain voxel or voxels (seed) and propagates according to the estimated orientation until the tracts are terminated due to a violation of prescribed criteria (e.g., FA value below threshold, bending with angle greater than threshold). Anatomical knowledge is used as a constraint to reconstruct specific white matter bundles. Typical procedures to reconstruct the major white matter bundles can be found, for example, in Wakana et al. (2007) and Catani and Thiebaut de Schotten (2008). A major limitation of DTI tractography is that it can detect only a single direction per voxel. In the human brain, and with the spatial resolution available on current scanners, it has been demonstrated (Jeurissen et al., 2013) that as much as 90% of white matter voxels contain crossing fibers.
Diffusion MR Imaging
Published in Ioannis Tsougos, Advanced MR Neuroimaging, 2018
Diffusion tensor imaging (DTI) evolved from DWI and was developed to remedy the limitations of DWI (see previous section), taking advantage of the preferential water diffusion inside the brain tissue (Le Bihan, 2003; Mukherjee, et al., 2008). The water diffusion in the brain is NOT an isotropic process, due to the natural intracellular (neurofilaments and organelles) and extracellular (glial cells and myelin sheaths) barriers that restrict diffusion towards certain directions. Hence, water molecules diffuse mainly along the direction of white matter axons rather than perpendicular to them (please refer to Figure 1.1). Under these circumstances, diffusion can become highly directional along the length of the tract, and is called anisotropic (Price, 2007) (Figure 1.10). This means that we talk about media that have different diffusion properties in different directions. In other words, in certain regions of the brain, ADC is directionally dependent; it is therefore also clear that a single ADC would be inadequate for characterizing diffusion and a more compound mathematical description is required.
Brain Connectivity Assessed with Functional MRI
Published in Troy Farncombe, Krzysztof Iniewski, Medical Imaging, 2017
Aiping Liu, Junning Li, Martin J. McKeown, Z. Jane Wang
Proper biological interpretation of fMRI connectivity patterns is difficult, not least because the exact origin of all BOLD signal fluctuations is unclear, and it is known that non-neural signals, such as cardiac and respiration pulsations, induce fluctuations in the BOLD signal [2–4], hence preprocessing methods to reduce these influences are crucial for accurate fMRI connectivity analysis [2,3]. However, recent work looking at the relation between resting state BOLD fluctuations and anatomical connectivity assessed by diffusion tensor imaging (DTI) [5] strongly supports the notion that functional and anatomical connectivity are closely linked, implying that neural activity is a key component of spontaneous BOLD signal fluctuations [6]. DTI is a relatively new imaging method, used to measure structural brain connectivity by measuring the diffusion profile of water molecules in the brain, allowing, under fairly strong assumptions, the reconstruction of white matter tracts [7].
Hypertrophic cardiomyopathy or athlete’s heart? A systematic review of novel cardiovascular magnetic resonance imaging parameters
Published in European Journal of Sport Science, 2023
Constantinos Bakogiannis, Dimitrios Mouselimis, Anastasios Tsarouchas, Efstathios Papatheodorou, Vassilios P. Vassilikos, Emmanuel Androulakis
DTI provides a wealth of information about cardiac form and function, but remains hard to implement in clinical practice due to complexity and technical constraints (Mekkaoui, Reese, Jackowski, Bhat, & Sosnovik, 2017). Through multi-dimensional diffusion weighted imaging (DWI), DTI ascertains the direction in which molecules have the lowest impediment to diffusion (the primary eigenvector) for each voxel. In the normal human heart, this usually matches the orientation of local myocytes, whose membranes pose the greatest obstacle to particle diffusion. A visual representation of diffusivity in three dimensions for a particular voxel containing myocytes is usually an ellipsoid, whose major axis is the primary eigenvector. The girth of the ellipsoid represents the ease of diffusion perpendicular to the primary eigenvector and is usually quantified through the parameter of fractional anisotropy (FA) (Lipton, 2008). In HCM, particular attention has been given to the parameter of secondary eigenvector angle (E2A), which is formed between the secondary eigenvector and the plane normal to the primary eigenvector, presumably the cross-myocyte plane. Laminae orientation is thought to be the main determinant of diffusivity in the secondary eigenvector in the heart. The hypercontraction during systole and incomplete relaxation during diastole are characteristics of HCM and increase the angle of laminae in the myocardial tissue, theoretically leading to an increased E2A.
Bayesian Generalized Sparse Symmetric Tensor-on-Vector Regression
Published in Technometrics, 2021
Sharmistha Guha, Rajarshi Guhaniyogi
This section illustrates the inferential ability of SGTM for symmetric tensor responses with continuous-valued cell entries in the context of a diffusion tensor magnetic resonance imaging (DTI) dataset. DTI is an imaging procedure that allows the measurement of restricted diffusion of water in brain tissues to construct neural tract images. In the context of DTI, the human brain is divided into 68 cortical ROIs, with 34 regions each in the left and the right hemispheres, respectively, using the Desikan brain atlas (Desikan et al. 2006). Using DTI, a brain network for each subject is constructed as a symmetric matrix with row and column indices corresponding to different ROIs, and entries corresponding to the estimated number of “fibers” connecting pairs of brain regions. We standardize each entry of the network response matrix by centering and scaling over all the subjects. The centered and scaled network response matrices have cell entries in which allows us to assume normality of the error distributions. For each subject, the dataset also has information on a measure of creativity, known as the CCI. The CCI measure, proposed by Jung et al. (2010), is formulated by linking measures of divergent thinking and creative achievement to cortical thickness of young (23.7 ± 4.2 years), healthy subjects. Three independent judges grade the creative products of a subject from which the CCI is derived. The DTI dataset we consider consists of brain network information along with CCI for n = 79 individuals. As mentioned before, both the symmetric brain network tensor and CCI values are standardized over individuals. Age (standardized) and sex (binary) are also available as additional covariates.
The effect of Tai Chi practice on brain white matter structure: a diffusion tensor magnetic resonance imaging study
Published in Research in Sports Medicine, 2019
Jian Yao, Qipeng Song, Kai Zhang, Youlian Hong, Weiping Li, Dewei Mao, Yan Cong, Jing Xian Li
Diffusion tensor imaging (DTI) allows the in vivo examination of the micro- and macro-structures of brain white matter to form an image through the dispersion characteristics of water molecules (Bonney et al., 2017). Diffusion tensor and Fractional anisotropy (FA) obtained from DTI are the quantitative measures of the diffusion direction and displacement rate of water molecules (Svard et al., 2017). The dispersion characteristics of water molecules in human cerebral white matter exhibit strong anisotropy. The value of FA is ranged from 0 to 1. When FA = 0, the direction of water molecule diffusion is completely random and could be used to calibrate the microstructure of the cerebrospinal fluid. When FA = 1, the direction of water molecule diffusion is strictly limited, thus providing information regarding the microstructure of cerebral white matter. This inherent contrast mechanism enables DTI to identify the changes and the integrity of the microstructure of brain white matter and its specific influence on the central nervous system that cannot be detected through volume decrease (Svard et al., 2017). DTI provides valuable information on the study of human brain development and diseases (Svard et al., 2017) and aging (Bennett, Madden, Vaidya, Howard, & Howard, 2010) and has become an important in vivo technique for non-invasive study to examine the microstructure and morphology of cerebral white matter. In the present study, we used DTI to investigate the brain microstructure of two populations, namely, long-term Tai Chi practitioners and their counterparts, and examine the effects of long-term Tai Chi practice on the microstructural changes in brain white matter and the relation of these changes with exercise duration and skill level. The findings from this study might add the understanding of exercise, such as Tai Chi, on brain white matter structure, and contribute to scientific recommendation for preventing age related brain structural changes.