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      The cortico-rubral and cerebello-rubral pathways are topographically organized within the human red nucleus

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          Abstract

          The Red Nucleus (RN) is a large nucleus located in the ventral midbrain: it is subdivided into a small caudal magnocellular part (mRN) and a large rostral parvocellular part (pRN). These distinct structural regions are part of functionally different networks and show distinctive connectivity features: the mRN is connected to the interposed nucleus, whilst the pRN is mainly connected to dentate nucleus, cortex and inferior olivary complex. Despite functional neuroimaging studies suggest RN involvement in complex motor and higher order functions, the pRN and mRN cannot be distinguished using conventional MRI. Herein, we employ high-quality structural and diffusion MRI data of 100 individuals from the Human Connectome Project repository and constrained spherical deconvolution tractography to perform connectivity-based segmentation of the human RN. In particular, we tracked connections of RN with the inferior olivary complex, the interposed nucleus, the dentate nucleus and the cerebral cortex. We found that the RN can be subdivided according to its connectivity into two clusters: a large ventrolateral one, mainly connected with the cerebral cortex and the inferior olivary complex, and a smaller dorsomedial one, mainly connected with the interposed nucleus. This structural topography strongly reflects the connectivity patterns of pRN and mRN respectively. Structural connectivity-based segmentation could represent a useful tool for the identification of distinct subregions of the human red nucleus on 3T MRI thus allowing a better evaluation of this subcortical structure in healthy and pathological conditions.

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          Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.

          Diffusion-weighted (DW) MR images contain information about the orientation of brain white matter fibres that potentially can be used to study human brain connectivity in vivo using tractography techniques. Currently, the diffusion tensor model is widely used to extract fibre directions from DW-MRI data, but fails in regions containing multiple fibre orientations. The spherical deconvolution technique has recently been proposed to address this limitation. It provides an estimate of the fibre orientation distribution (FOD) by assuming the DW signal measured from any fibre bundle is adequately described by a single response function. However, the deconvolution is ill-conditioned and susceptible to noise contamination. This tends to introduce artefactual negative regions in the FOD, which are clearly physically impossible. In this study, the introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution. This approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution. The approach also permits the use of super-resolution, whereby more FOD parameters are estimated than were actually measured, improving the angular resolution of the results. The method provides much better defined fibre orientation estimates, and allows orientations to be resolved that are separated by smaller angles than previously possible. This should allow tractography algorithms to be designed that are able to track reliably through crossing fibre regions.
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            MRtrix: Diffusion tractography in crossing fiber regions

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              Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data.

              Diffusion-weighted imaging can potentially be used to infer the connectivity of the human brain in vivo using fibre-tracking techniques, and is therefore of great interest to neuroscientists and clinicians. A key requirement for fibre tracking is the accurate estimation of white matter fibre orientations within each imaging voxel. The diffusion tensor model, which is widely used for this purpose, has been shown to be inadequate in crossing fibre regions. A number of approaches have recently been proposed to address this issue, based on high angular resolution diffusion-weighted imaging (HARDI) data. In this study, an experimental model of crossing fibres, consisting of water-filled plastic capillaries, is used to thoroughly assess three such techniques: constrained spherical deconvolution (CSD), super-resolved CSD (super-CSD) and Q-ball imaging (QBI). HARDI data were acquired over a range of crossing angles and b-values, from which fibre orientations were computed using each technique. All techniques were capable of resolving the two fibre populations down to a crossing angle of 45 degrees , and down to 30 degrees for super-CSD. A bias was observed in the fibre orientations estimated by QBI for crossing angles other than 90 degrees, consistent with previous simulation results. Finally, for a 45 degrees crossing, the minimum b-value required to resolve the fibre orientations was 4000 s/mm(2) for QBI, 2000 s/mm(2) for CSD, and 1000 s/mm(2) for super-CSD. The quality of estimation of fibre orientations may profoundly affect fibre tracking attempts, and the results presented provide important additional information regarding performance characteristics of well-known methods.
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                Author and article information

                Contributors
                alberto.cacciola0@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 August 2019
                20 August 2019
                2019
                : 9
                : 12117
                Affiliations
                [1 ]ISNI 0000 0001 2178 8421, GRID grid.10438.3e, Department of Biomedical, , Dental Sciences and Morphological and Functional Images, University of Messina, ; Messina, Italy
                [2 ]GRID grid.419419.0, IRCCS Centro Neurolesi “Bonino Pulejo”, ; Messina, Italy
                Author information
                http://orcid.org/0000-0001-9412-4116
                http://orcid.org/0000-0003-3164-6464
                Article
                48164
                10.1038/s41598-019-48164-7
                6702172
                31431648
                b9b4fe19-9462-4a42-b6d0-2ee8002de750
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 March 2019
                : 11 July 2019
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                © The Author(s) 2019

                Uncategorized
                neural circuits,neurological disorders
                Uncategorized
                neural circuits, neurological disorders

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