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      Cognitive Fatigue Is Associated with Altered Functional Connectivity in Interoceptive and Reward Pathways in Multiple Sclerosis

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          Abstract

          Cognitive fatigue is common and debilitating among persons with multiple sclerosis (pwMS). Neural mechanisms underlying fatigue are not well understood, which results in lack of adequate treatment. The current study examined cognitive fatigue-related functional connectivity among 26 pwMS and 14 demographically matched healthy controls (HCs). Participants underwent functional magnetic resonance imaging (fMRI) scanning while performing a working memory task (n-back), with two conditions: one with higher cognitive load (2-back) to induce fatigue and one with lower cognitive load (0-back) as a control condition. Task-independent residual functional connectivity was assessed, with seeds in brain regions previously implicated in cognitive fatigue (dorsolateral prefrontal cortex (DLPFC), ventromedial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC), insula, and striatum). Cognitive fatigue was measured using the Visual Analogue Scale of Fatigue (VAS-F). Results indicated that as VAS-F scores increased, HCs showed increased residual functional connectivity between the striatum and the vmPFC (crucial in reward processing) during the 2-back condition compared to the 0-back condition. In contrast, pwMS displayed increased residual functional connectivity from interoceptive hubs—the insula and the dACC—to the striatum. In conclusion, pwMS showed a hyperconnectivity within the interoceptive network and disconnection within the reward circuitry when experiencing cognitive fatigue.

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          N4ITK: improved N3 bias correction.

          A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.
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            Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

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              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.
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                Author and article information

                Journal
                Diagnostics (Basel)
                Diagnostics (Basel)
                diagnostics
                Diagnostics
                MDPI
                2075-4418
                10 November 2020
                November 2020
                : 10
                : 11
                : 930
                Affiliations
                [1 ]Kessler Foundation, East Hanover, NJ 07936, USA; mchen@ 123456kesslerfoundation.org (M.H.C.); jdeluca@ 123456kesslerfoundation.org (J.D.); hgenova@ 123456kesslerfoundation.org (H.M.G.); byao@ 123456kesslerfoundation.org (B.Y.)
                [2 ]Department of Physical Medicine and Rehabilitation, New Jersey Medical School, Rutgers University, Newark, NJ 07103, USA
                Author notes
                Author information
                https://orcid.org/0000-0003-3745-4156
                Article
                diagnostics-10-00930
                10.3390/diagnostics10110930
                7696273
                33182742
                4189963c-576a-47b7-8926-8278d28bae48
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 24 September 2020
                : 06 November 2020
                Categories
                Article

                multiple sclerosis,functional connectivity,fatigue,neuroimaging,fmri

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