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      Disrupted Brain Functional Status in Patients with Reversible Cerebral Vasoconstriction Syndrome

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          Abstract

          Objectives

          The aim of this study was to investigate the functional networks in subjects with reversible cerebral vasoconstriction syndrome (RCVS) using resting‐state functional magnetic resonance imaging (rs‐fMRI).

          Methods

          We prospectively recruited patients with RCVS and healthy controls (HCs) between February 2017 and April 2021. The rs‐fMRI data were analyzed using graph theory methods. We compared node‐based global and regional topological metrics (Bundle 1) and network‐based intranetwork and internetwork connectivity (Bundle 2) between RCVS patients and HCs. We also explored the associations of clinical and vascular (ie, the Lindegaard index, LI) parameters with significant rs‐fMRI metrics.

          Results

          A total of 104 RCVS patients and 93 HCs were included in the final analysis. We identified significantly decreased local efficiency of the left dorsal anterior insula (dAI; p = 0.0005) in RCVS patients within 30 days after disease onset as compared to HCs, which improved 1 month later. RCVS patients also had increased global efficiency ( p = 0.009) and decreased average degree centrality ( p = 0.045), clustering coefficient ( p = 0.033), and assortativity values ( p = 0.003) in node‐based analysis. In addition, patients with RCVS had increased internetwork connectivity of the default mode network (DMN) with the salience ( p = 0.027) and dorsal attention ( p = 0.016) networks. Significant correlations between LI and regional local efficiency in left dAI ( r s = −0.418, p = 0.042) was demonstrated.

          Interpretation

          The significantly lower local efficiency of the left dAI, suggestive of impaired central autonomic modulation, was negatively correlated with vasoconstriction severity, which is highly plausible for the pathogenesis of RCVS. ANN NEUROL 2023;94:772–784

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          Most cited references61

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          G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
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              A fast diffeomorphic image registration algorithm.

              This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg-Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.
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                Author and article information

                Contributors
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                Journal
                Annals of Neurology
                Annals of Neurology
                Wiley
                0364-5134
                1531-8249
                October 2023
                July 06 2023
                October 2023
                : 94
                : 4
                : 772-784
                Affiliations
                [1 ] Department of Radiology Taipei Veterans General Hospital Taipei Taiwan
                [2 ] School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan
                [3 ] Department of Nuclear Medicine Taipei Veterans General Hospital Taipei Taiwan
                [4 ] Department of Neurology, Neurological Institute Taipei Veterans General Hospital Taipei Taiwan
                [5 ] Brain Research Center National Yang Ming Chiao Tung University Taipei Taiwan
                [6 ] Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan
                [7 ] Division of Translational Research, Department of Medical Research Taipei Veterans General Hospital Taipei Taiwan
                Article
                10.1002/ana.26724
                1ff899bd-c380-40a1-ae66-223d20e4eb76
                © 2023

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