1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Abnormal brain functional and structural connectivity between the left supplementary motor area and inferior frontal gyrus in moyamoya disease

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Disruption of brain functional connectivity has been detected after stroke, but whether it also occurs in moyamoya disease (MMD) is unknown. Impaired functional connectivity is always correlated with abnormal white matter fibers. Herein, we used multimodal imaging techniques to explore the changes in brain functional and structural connectivity in MMD patients.

          Methods

          We collected structural images, resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging for each subject. Cognitive functions of MMD patients were evaluated using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Trail Making Test parts A and B (TMT-A/-B). We calculated the functional connectivity for every paired region using 90 regions of interest from the Anatomical Automatic Labeling Atlas and then determined the differences between MMD patients and HCs. We extracted the functional connectivity of paired brain regions with significant differences between the two groups. Correlation analyses were then performed between the functional connectivity and variable cognitive functions. To explore whether the impaired functional connectivity and cognitive performances were attributed to the destruction of white matter fibers, we further analyzed fiber integrity using tractography between paired regions that were correlated with cognition.

          Results

          There was lower functional connectivity in MMD patients as compared to HCs between the bilateral inferior frontal gyrus, between the bilateral supramarginal gyrus, between the left supplementary motor area (SMA) and the left orbital part of the inferior frontal gyrus (IFGorb), and between the left SMA and the left middle temporal gyrus ( P < 0.01, FDR corrected). The decreased functional connectivity between the left SMA and the left IFGorb was significantly correlated with the MMSE ( r = 0.52, P = 0.024), MoCA ( r = 0.60, P = 0.006), and TMT-B ( r = -0.54, P = 0.048) in MMD patients. White matter fibers were also injured between the SMA and IFGorb in the left hemisphere and were positively correlated with reduced functional connectivity.

          Conclusions

          Brain functional and structural connectivity between the supplementary motor area and inferior frontal gyrus in the left hemisphere are damaged in MMD. These findings could be useful in the evaluation of disease progression and prognosis of MMD.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12883-022-02705-2.

          Related collections

          Most cited references38

          • Record: found
          • Abstract: found
          • Article: not found

          Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.

          Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The basis of anisotropic water diffusion in the nervous system - a technical review.

            Anisotropic water diffusion in neural fibres such as nerve, white matter in spinal cord, or white matter in brain forms the basis for the utilization of diffusion tensor imaging (DTI) to track fibre pathways. The fact that water diffusion is sensitive to the underlying tissue microstructure provides a unique method of assessing the orientation and integrity of these neural fibres, which may be useful in assessing a number of neurological disorders. The purpose of this review is to characterize the relationship of nuclear magnetic resonance measurements of water diffusion and its anisotropy (i.e. directional dependence) with the underlying microstructure of neural fibres. The emphasis of the review will be on model neurological systems both in vitro and in vivo. A systematic discussion of the possible sources of anisotropy and their evaluation will be presented followed by an overview of various studies of restricted diffusion and compartmentation as they relate to anisotropy. Pertinent pathological models, developmental studies and theoretical analyses provide further insight into the basis of anisotropic diffusion and its potential utility in the nervous system. Copyright 2002 John Wiley & Sons, Ltd.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI.

              Quantitative-diffusion-tensor MRI consists of deriving and displaying parameters that resemble histological or physiological stains, i.e., that characterize intrinsic features of tissue microstructure and microdynamics. Specifically, these parameters are objective, and insensitive to the choice of laboratory coordinate system. Here, these two properties are used to derive intravoxel measures of diffusion isotropy and the degree of diffusion anisotropy, as well as intervoxel measures of structural similarity, and fiber-tract organization from the effective diffusion tensor, D, which is estimated in each voxel. First, D is decomposed into its isotropic and anisotropic parts, [D] I and D - [D] I, respectively (where [D] = Trace(D)/3 is the mean diffusivity, and I is the identity tensor). Then, the tensor (dot) product operator is used to generate a family of new rotationally and translationally invariant quantities. Finally, maps of these quantitative parameters are produced from high-resolution diffusion tensor images (in which D is estimated in each voxel from a series of 2D-FT spin-echo diffusion-weighted images) in living cat brain. Due to the high inherent sensitivity of these parameters to changes in tissue architecture (i.e., macromolecular, cellular, tissue, and organ structure) and in its physiologic state, their potential applications include monitoring structural changes in development, aging, and disease.
                Bookmark

                Author and article information

                Contributors
                hjw94@zju.edu.cn
                ly904@zju.edu.cn
                lizhaoqing@zju.edu.cn
                cjyaway@zju.edu.cn
                caoyang0115@zju.edu.cn
                13757169771@139.com
                zll121@zju.edu.cn
                ruiliangbai@zju.edu.cn
                dr_wang@zju.edu.cn
                Journal
                BMC Neurol
                BMC Neurol
                BMC Neurology
                BioMed Central (London )
                1471-2377
                16 May 2022
                16 May 2022
                2022
                : 22
                : 179
                Affiliations
                [1 ]GRID grid.412465.0, Department of Neurosurgery, , the Second Affiliated Hospital, Zhejiang University School of Medicine, ; Jiefang Road 88th, Hangzhou, 310009 China
                [2 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Key Laboratory of Biomedical Engineering of Education Ministry, College of Biomedical Engineering and Instrument Science, , Zhejiang University, ; 268 Kaixuan Road, South Central Building, Room 708, Hangzhou, 310027 Zhejiang China
                [3 ]GRID grid.412465.0, Department of Radiology, , the Second Affiliated Hospital, Zhejiang University School of Medicine, ; Hangzhou, China
                [4 ]GRID grid.412465.0, Department of Psychiatry, , the Second Affiliated Hospital, Zhejiang University School of Medicine, ; Hangzhou, China
                [5 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Department of Physical Medicine and Rehabilitation of the Affiliated Sir Run Run Shaw Hospital and Interdisciplinary Institute of Neuroscience and Technology, , Zhejiang University School of Medicine, ; Hangzhou, China
                [6 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, MOE Frontier Science Center for Brain Science and Brain-machine Integration, , School of Brain Science and Brain Medicine, Zhejiang University, ; Hangzhou, China
                Article
                2705
                10.1186/s12883-022-02705-2
                9108139
                35578209
                491a9955-d3e5-461b-a1d0-2b634c13100d
                © The Author(s) 2022

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 7 February 2022
                : 4 May 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81870910
                Funded by: FundRef http://dx.doi.org/10.13039/501100004731, Natural Science Foundation of Zhejiang Province;
                Award ID: Y18H090007
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Neurology
                moyamoya disease,functional connectivity,white matter integrity,cognitive function
                Neurology
                moyamoya disease, functional connectivity, white matter integrity, cognitive function

                Comments

                Comment on this article