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      Early functional connectivity alterations in contralesional motor networks influence outcome after severe stroke: a preliminary analysis

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          Abstract

          Connectivity studies have significantly extended the knowledge on motor network alterations after stroke. Compared to interhemispheric or ipsilesional networks, changes in the contralesional hemisphere are poorly understood. Data obtained in the acute stage after stroke and in severely impaired patients are remarkably limited. This exploratory, preliminary study aimed to investigate early functional connectivity changes of the contralesional parieto-frontal motor network and their relevance for the functional outcome after severe motor stroke. Resting-state functional imaging data were acquired in 19 patients within the first 2 weeks after severe stroke. Nineteen healthy participants served as a control group. Functional connectivity was calculated from five key motor areas of the parieto-frontal network on the contralesional hemisphere as seed regions and compared between the groups. Connections exhibiting stroke-related alterations were correlated with clinical follow-up data obtained after 3–6 months. The main finding was an increase in coupling strength between the contralesional supplementary motor area and the sensorimotor cortex. This increase was linked to persistent clinical deficits at follow-up. Thus, an upregulation in contralesional motor network connectivity might be an early pattern in severely impaired stroke patients. It might carry relevant information regarding the outcome which adds to the current concepts of brain network alterations and recovery processes after severe stroke.

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          Unified segmentation.

          A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A component based noise correction method (CompCor) for BOLD and perfusion based fMRI.

            A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A unified statistical approach for determining significant signals in images of cerebral activation.

              We present a unified statistical theory for assessing the significance of apparent signal observed in noisy difference images. The results are usable in a wide range of applications, including fMRI, but are discussed with particular reference to PET images which represent changes in cerebral blood flow elicited by a specific cognitive or sensorimotor task. Our main result is an estimate of the P-value for local maxima of Gaussian, t, chi(2) and F fields over search regions of any shape or size in any number of dimensions. This unifies the P-values for large search areas in 2-D (Friston et al. [1991]: J Cereb Blood Flow Metab 11:690-699) large search regions in 3-D (Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900-918) and the usual uncorrected P-value at a single pixel or voxel. Copyright (c) 1996 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                h.braass@uke.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                7 July 2023
                7 July 2023
                2023
                : 13
                : 11010
                Affiliations
                GRID grid.13648.38, ISNI 0000 0001 2180 3484, Department of Neurology, , University Medical Center Hamburg-Eppendorf, ; Martinistraße 52, 20246 Hamburg, Germany
                Article
                38066
                10.1038/s41598-023-38066-0
                10328915
                37419966
                4fb64e8a-9cc0-4076-a886-905b6e3151a9
                © The Author(s) 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 March 2023
                : 2 July 2023
                Funding
                Funded by: Gemeinnützige Hertie-Stiftung (Hertie Network of Excellence in Clinical Neuroscience)
                Funded by: Else Kröner Exzellenzstipendium from the Else Kröner-Fresenius-Stiftung
                Award ID: 2018_EKES.04
                Award ID: 2020_EKES.16
                Award Recipient :
                Funded by: DFG, German Research Foundation) SFB 936 - Project C1
                Award ID: 178316478
                Award Recipient :
                Funded by: DFG in cooperation with the National Science Foundation of China (NSFC) (SFB TRR-169/A3
                Funded by: FundRef http://dx.doi.org/10.13039/501100003042, Else Kröner-Fresenius-Stiftung;
                Award ID: 2016_A214
                Award Recipient :
                Funded by: Universitätsklinikum Hamburg-Eppendorf (UKE) (5411)
                Categories
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                © Springer Nature Limited 2023

                Uncategorized
                stroke
                Uncategorized
                stroke

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