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      White Matter Connectivity of the Visual–Vestibular Cortex Examined by Diffusion-Weighted Imaging

      1 , 2 , 1 , 3 , 4 , 1 , 1 , 5
      Brain Connectivity
      Mary Ann Liebert Inc

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          Abstract

          <p class="first" id="d4705550e115">The parieto-insular vestibular cortex (PIVC) and the posterior insular cortex (PIC) are key regions of the cortical vestibular network, both located in the midposterior section of the lateral sulcus. Little is known about the structural connectivity pattern of these areas. We used probabilistic fiber tracking based on diffusion-weighted magnetic resonance imaging (MRI) and compared the ipsilateral connectivity of PIVC and PIC. Seed areas for the tracking algorithm were identified in each brain by functional MRI activity during caloric and visual motion stimulation, respectively. Cortical track terminations were investigated by a surface-based approach. Both PIVC and PIC shared ipsilateral connections to the insular/lateral sulcus, superior temporal cortex, and inferior frontal gyrus. However, PIVC showed significantly more connections than PIC with the anterior insula and Heschl's gyrus in both hemispheres and with the precuneus, intraparietal sulcus, and posterior callosum of the right hemisphere. In contrast, PIC connectivity was more pronounced with the supramarginal gyrus and superior temporal sulcus. Subcortical tracks were examined by a region-of-interest-based approach, which was validated on cortico-thalamic motor tracts. Both PIVC and PIC were connected with lateral nuclei of the thalamus and the basal ganglia (primarily putamen). PIVC tracks but not PIC tracks showed a right-hemispheric lateralization in cortical and subcortical connectivity. Overall, these results suggest that human PIVC and PIC share cortical and even subcortical connections. Nevertheless, they also differ in their primary connectivity pattern: PIVC is linked with posterior parietal and inferior frontal cortex, whereas PIC is linked with superior temporal and inferior parietal cortex. </p>

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          Most cited references51

          • Record: found
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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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            Advances in functional and structural MR image analysis and implementation as FSL.

            The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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              • Record: found
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              • Article: not found

              A multi-modal parcellation of human cerebral cortex

              Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.
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                Author and article information

                Journal
                Brain Connectivity
                Brain Connectivity
                Mary Ann Liebert Inc
                2158-0014
                2158-0022
                May 2018
                May 2018
                : 8
                : 4
                : 235-244
                Affiliations
                [1 ]Institute of Psychology, University of Regensburg, Regensburg, Germany.
                [2 ]Department of Neurology, University Hospital of Regensburg, Regensburg, Germany.
                [3 ]Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire.
                [4 ]Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island.
                [5 ]Institute of Psychology, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
                Article
                10.1089/brain.2017.0544
                29571264
                caeb6eee-fdb9-4a28-831f-e9d45be66ef6
                © 2018
                History

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