White matter tractography using diffusion tensor deflection

Department of Physics, University of Utah, Salt Lake City, Utah, USA.
Human Brain Mapping (Impact Factor: 5.97). 04/2003; 18(4):306-21. DOI: 10.1002/hbm.10102
Source: PubMed

ABSTRACT Diffusion tensor MRI provides unique directional diffusion information that can be used to estimate the patterns of white matter connectivity in the human brain. In this study, the behavior of an algorithm for white matter tractography is examined. The algorithm, called TEND, uses the entire diffusion tensor to deflect the estimated fiber trajectory. Simulations and imaging experiments on in vivo human brains were performed to investigate the behavior of the tractography algorithm. The simulations show that the deflection term is less sensitive than the major eigenvector to image noise. In the human brain imaging experiments, estimated tracts were generated in corpus callosum, corticospinal tract, internal capsule, corona radiata, superior longitudinal fasciculus, inferior longitudinal fasciculus, fronto-occipital fasciculus, and uncinate fasciculus. This approach is promising for mapping the organizational patterns of white matter in the human brain as well as mapping the relationship between major fiber trajectories and the location and extent of brain lesions.

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    • "Mori et al. first published the underlying Fiber Assignment by Continuous Tracking (FACT) algorithm (Mori and van Zijl 2002). To get smooth results despite of the low resolution of normal DTI scans, the tensors were interpolated from the surrounding voxels, a method which is called Tensor Deflection (TEND) (Nimsky et al. 2006; Lazar et al. 2003; Weinstein et al. 1999). The major eigenvector of each seed was calculated and bidirectional iteration was initiated until one of the stop criteria FA \0.15 or deviation angle [40° was met. "
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    ABSTRACT: Default in-phase coupling of hand movements needs to be suppressed when temporal coordination is required for out-of-phase bimanual movements. There is lack of knowledge on how the brain overrides these default in-phase movements to enable a required interval of activity between hands. We used a visually cued bimanual temporal coordination (vc-BTC) paradigm with a constant rhythmical time base of 1 s, to test the accuracy of in-phase and out-of-phase (0.1, 0.2,…,0.9) finger tapping. We hypothesized that (1) stronger anatomical and effective interhemispheric connectivity between the hand areas of the primary motor cortex (M1HAND) predict higher temporal offsets between hands in the out-of-phase conditions of the vc-BTC; (2) patients with relapsing-remitting multiple sclerosis (RRMS) and clinically isolated syndrome (CIS) have reduced interhemispheric connectivity and altered between-hand coupling. Anatomical connectivity was determined by fractional anisotropy of callosal hand motor fibers (FA-hCMF). Effective connectivity was probed by short interval interhemispheric inhibition (S-IHI) using paired-coil transcranial magnetic stimulation (TMS). In healthy subjects, higher FA-hCMF and S-IHI correlated with higher temporal offsets between hands in the out-of-phase conditions of the tapping test. FA-hCMF was reduced in patients with RRMS but not in CIS, while S-IHI was reduced in both patient groups. These abnormalities were associated with smaller temporal offsets between hands leading to less deviation from the required phasing in the out-of-phase tapping conditions. Findings provide multiple levels of evidence that callosal anatomical and effective connectivity between the hand areas of the motor cortices play important roles in visually cued bimanual temporal coordination performance.
    Brain Structure and Function 09/2015; DOI:10.1007/s00429-015-1110-z · 5.62 Impact Factor
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    • "Even so, brain networks and their features depend to some extent on the choice of field strength (Zhan et al., 2013c; Dennis et al., 2014), scanners (Zhan et al., 2014a), feature space (Zhan et al., 2014b), imaging acquisition parameters (Zhan et al., 2012), fiber tracking parameters (Dennis et al., 2015a), fiber tracking algorithms used to infer the trajectories of pathways in the brain (Zhan et al., 2013b, 2015a,b). Dozens of tractography algorithms are now available (Conturo et al., 1999; Mori et al., 1999; Basser et al., 2000; Lazar et al., 2003; Parker et al., 2003; Behrens et al., 2007; Aganj et al., 2011) yielding visually very different brain networks. "
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    ABSTRACT: Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.
    Frontiers in Neuroscience 08/2015; 9:257. DOI:10.3389/fnins.2015.00257 · 3.66 Impact Factor
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    • "Tongue muscles have been studied using diffusion tensor imaging (DTI) [4] [5] [6] [7] [8] [9], which provides a noninvasive tool for investigating fiber tracts by imaging the anisotropy of water diffusion [10]. For example, in Gaige et al. [5], based on diffusion tensors, the technique of fiber tracking [10] [11] [12] [13] was used to reconstruct 3D curves representing key muscle fibers and visualize the tongue anatomy. In Felton et al. [6], muscle fibers were studied together with strain rate to demonstrate the relationship between fiber organization and tissue deformation during swallowing . "
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    ABSTRACT: The tongue is a critical organ for a variety of functions, including swallowing, respiration, and speech. It contains intrinsic and extrinsic muscles that play an important role in changing its shape and position. Diffusion tensor imaging (DTI) has been used to reconstruct tongue muscle fiber tracts. However, previous studies have been unable to reconstruct the crossing fibers that occur where the tongue muscles interdigitate, which is a large percentage of the tongue volume. To resolve crossing fibers, multi-tensor models on DTI and more advanced imaging modalities, such as high angular resolution diffusion imaging (HARDI) and diffusion spectrum imaging (DSI), have been proposed. However, because of the involuntary nature of swallowing, there is insufficient time to acquire a sufficient number of diffusion gradient directions to resolve crossing fibers while the in vivo tongue is in a fixed position. In this work, we address the challenge of distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging by using a multi-tensor model with a fixed tensor basis and incorporating prior directional knowledge. The prior directional knowledge provides information on likely fiber directions at each voxel, and is computed with anatomical knowledge of tongue muscles. The fiber directions are estimated within a maximum a posteriori (MAP) framework, and the resulting objective function is solved using a noise-aware weighted ℓ1-norm minimization algorithm. Experiments were performed on a digital crossing phantom and in vivo tongue diffusion data including three control subjects and four patients with glossectomies. On the digital phantom, effects of parameters, noise, and prior direction accuracy were studied, and parameter settings for real data were determined. The results on the in vivo data demonstrate that the proposed method is able to resolve interdigitated tongue muscles with limited gradient directions. The distributions of the computed fiber directions in both the controls and the patients were also compared, suggesting a potential clinical use for this imaging and image analysis methodology. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society 07/2015; 45:63-74. DOI:10.1016/j.compmedimag.2015.07.005 · 1.22 Impact Factor
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