35 research outputs found

    Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

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    Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described

    White Matter Development in Early Puberty: A Longitudinal Volumetric and Diffusion Tensor Imaging Twin Study

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    White matter microstructure and volume show synchronous developmental patterns in children. White matter volume increases considerably during development. Fractional anisotropy, a measure for white matter microstructural directionality, also increases with age. Development of white matter volume and development of white matter microstructure seem to go hand in hand. The extent to which the same or different genetic and/or environmental factors drive these two aspects of white matter maturation is currently unknown. We mapped changes in white matter volume, surface area and diffusion parameters in mono- and dizygotic twins who were scanned at age 9 (203 individuals) and again at age 12 (126 individuals). Over the three-year interval, white matter volume (+6.0%) and surface area (+1.7%) increased, fiber bundles expanded (most pronounced in the left arcuate fasciculus and splenium), and fractional anisotropy increased (+3.0%). Genes influenced white matter volume (heritability ∼85%), surface area (∼85%), and fractional anisotropy (locally 7% to 50%) at both ages. Finally, volumetric white matter growth was negatively correlated with fractional anisotropy increase (r = –0.62) and this relationship was driven by environmental factors. In children who showed the most pronounced white matter growth, fractional anisotropy increased the least and vice-versa. Thus, white matter development in childhood may reflect a process of both expansion and fiber optimization

    Differential Development of Human Brain White Matter Tracts

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    Neuroscience is increasingly focusing on developmental factors related to human structural and functional connectivity. Unfortunately, to date, diffusion-based imaging approaches have only contributed modestly to these broad objectives, despite the promise of diffusion-based tractography. Here, we report a novel data-driven approach to detect similarities and differences among white matter tracts with respect to their developmental trajectories, using 64-direction diffusion tensor imaging. Specifically, using a cross-sectional sample comprising 144 healthy individuals (7 to 48 years old), we applied k-means cluster analysis to separate white matter voxels based on their age-related trajectories of fractional anisotropy. Optimal solutions included 5-, 9- and 14-clusters. Our results recapitulate well-established tracts (e.g., internal and external capsule, optic radiations, corpus callosum, cingulum bundle, cerebral peduncles) and subdivisions within tracts (e.g., corpus callosum, internal capsule). For all but one tract identified, age-related trajectories were curvilinear (i.e., inverted ‘U-shape’), with age-related increases during childhood and adolescence followed by decreases in middle adulthood. Identification of peaks in the trajectories suggests that age-related losses in fractional anisotropy occur as early as 23 years of age, with mean onset at 30 years of age. Our findings demonstrate that data-driven analytic techniques may be fruitfully applied to extant diffusion tensor imaging datasets in normative and neuropsychiatric samples

    CNS involvement in OFD1 syndrome: A clinical, molecular, and neuroimaging study

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    Comparison of white matter tract reconstruction from 2 D and 3 D diffusion tensor images

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    Thalamocingulate Interactions In Performance Monitoring

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    Contains fulltext : 99756.pdf (publisher's version ) (Open Access)Performance monitoring is an essential prerequisite of successful goal-directed behavior. Research of the last two decades implicates the anterior midcingulate cortex (aMCC) in the human medial frontal cortex and frontostriatal basal ganglia circuits in this function. Here, we addressed the function of the thalamus in detecting errors and adjusting behavior accordingly. Using diffusion-based tractography, we found that, among the thalamic nuclei, the ventral anterior (VA) and ventral lateral anterior (VLa) nuclei have the relatively strongest connectivity with the aMCC. Patients with focal thalamic lesions showed diminished error-related negativity, behavioral error detection, and posterror adjustments. When the lesions specifically affected the thalamic VA/VLa nuclei, these effects were significantly pronounced, which was reflected by the complete absence of the error-related negativity. These results reveal that the thalamus, particularly its VA/VLa region, is a necessary constituent of the performance-monitoring network, anatomically well connected and functionally closely interacting with the aMCC.9 p

    The Speed-Accuracy Tradeoff in the Elderly Brain: A Structural Model-Based Approach

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    Contains fulltext : 99785.pdf (publisher's version ) (Open Access)Even in the simplest laboratory tasks older adults generally take more time to respond than young adults. One of the reasons for this age-related slowing is that older adults are reluctant to commit errors, a cautious attitude that prompts them to accumulate more information before making a decision (Rabbitt, 1979). This suggests that age-related slowing may be partly due to unwillingness on behalf of elderly participants to adopt a fast-but-careless setting when asked. We investigate the neuroanatomical and neurocognitive basis of age-related slowing in a perceptual decision-making task where cues instructed young and old participants to respond either quickly or accurately. Mathematical modeling of the behavioral data confirmed that cueing for speed encouraged participants to set low response thresholds, but this was more evident in younger than older participants. Diffusion weighted structural images suggest that the more cautious threshold settings of older participants may be due to a reduction of white matter integrity in corticostriatal tracts that connect the pre-SMA to the striatum. These results are consistent with the striatal account of the speed-accuracy tradeoff according to which an increased emphasis on response speed increases the cortical input to the striatum, resulting in global disinhibition of the cortex. Our findings suggest that the unwillingness of older adults to adopt fast speed-accuracy tradeoff settings may not just reflect a strategic choice that is entirely under voluntary control, but that it may also reflect structural limitations: age-related decrements in brain connectivity

    Restoration of EPI Susceptibility Distortion at 3 Tesla: Comparison of Fluid Registration with Phase-Encoding Gradient Reversion

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    From mere visual inspection (Fig. 2) results obtained by fluid-dynamic image registration schemes appear to be better than those obtained using reversedgradient methods, because they account for large displacements and are able to correct for distortions in slice select gradient direction, too. A closer inspection of the (computed) distortion field revealed implausible reconstruction, primarily in read-out direction. This effect is not in accordance with susceptibility distortion on SE-EPI images, and requires to be taken under control. Since susceptibility distortions increase with field strength, we suppose that fluid-dynamic registration may be the better solution to estimate the distortion field at 3 T or above, especially for high-resolution imaging. It may be interesting to modify fluid-dynamic image registration to implement a fluid-dynamic regularization for reversed-gradient methods to unify the advantages of a reconstruction based on the information from both images and the feature of the fluid registration to account for large displacements
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