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      Brain microstructure and connectivity in COVID-19 patients with olfactory or cognitive impairment

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          Graphical abstract

          Highlights

          • COVID-19 patients with olfactory or cognitive deficits showed cerebral alterations.

          • Gray matter atrophy, cortical thinning, and mean diffusivity increase emerged.

          • Structural connectivity revealed a few impaired and several enhanced connections.

          • Impaired connections were mainly located in the left hemisphere.

          • Connectivity impairment primarily involved cingulate and insula.

          Abstract

          Introduction

          The COVID-19 pandemic has affected millions worldwide, causing mortality and multi-organ morbidity. Neurological complications have been recognized. This study aimed to assess brain structural, microstructural, and connectivity alterations in patients with COVID-19-related olfactory or cognitive impairment using post-acute (time from onset: 264[208–313] days) multi-directional diffusion-weighted MRI (DW-MRI).

          Methods

          The study included 16 COVID-19 patients with cognitive impairment (COVID-CM), 35 COVID-19 patients with olfactory disorder (COVID-OD), and 14 controls. A state-of-the-art processing pipeline was developed for DW-MRI pre-processing, mean diffusivity and fractional anisotropy computation, fiber density and cross-section analysis, and tractography of white-matter bundles. Brain parcellation required for probing network connectivity, region-specific microstructure and volume, and cortical thickness was based on T1-weighted scans and anatomical atlases.

          Results

          Compared to controls, COVID-CM patients showed overall gray matter atrophy (age and sex corrected p = 0.004), and both COVID-19 patient groups showed regional atrophy and cortical thinning. Both groups presented an increase in gray matter mean diffusivity (corrected p = 0.001), decrease in white matter fiber density and cross-section (corrected p < 0.05), , and COVID-CM patients also displayed an overall increased diffusivity (p = 0.022) and decreased anisotropy (corrected p = 0.038) in white matter. Graph-based analysis revealed reduced network modularity, with an extensive pattern of connectivity increase, in conjunction with a localized reduction in a few connections, mainly located in the left hemisphere. The left cingulate, anterior cingulate, and insula were primarily involved.

          Conclusion

          Expanding upon previous findings, this study further investigated significant alterations in brain morphology, microstructure, and connectivity in COVID-19 patients with olfactory or cognitive disfunction. These findings suggest underlying neurodegeneration, neuroinflammation, and concomitant compensatory mechanisms. Future longitudinal studies are required to monitor the alterations over time and assess their transient or permanent nature.

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          Most cited references45

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          Complex network measures of brain connectivity: uses and interpretations.

          Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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            SARS-CoV-2 is associated with changes in brain structure in UK Biobank

            There is strong evidence of brain-related abnormalities in COVID-19 1–13 . However, it remains unknown whether the impact of SARS-CoV-2 infection can be detected in milder cases, and whether this can reveal possible mechanisms contributing to brain pathology. Here we investigated brain changes in 785 participants of UK Biobank (aged 51–81 years) who were imaged twice using magnetic resonance imaging, including 401 cases who tested positive for infection with SARS-CoV-2 between their two scans—with 141 days on average separating their diagnosis and the second scan—as well as 384 controls. The availability of pre-infection imaging data reduces the likelihood of pre-existing risk factors being misinterpreted as disease effects. We identified significant longitudinal effects when comparing the two groups, including (1) a greater reduction in grey matter thickness and tissue contrast in the orbitofrontal cortex and parahippocampal gyrus; (2) greater changes in markers of tissue damage in regions that are functionally connected to the primary olfactory cortex; and (3) a greater reduction in global brain size in the SARS-CoV-2 cases. The participants who were infected with SARS-CoV-2 also showed on average a greater cognitive decline between the two time points. Importantly, these imaging and cognitive longitudinal effects were still observed after excluding the 15 patients who had been hospitalised. These mainly limbic brain imaging results may be the in vivo hallmarks of a degenerative spread of the disease through olfactory pathways, of neuroinflammatory events, or of the loss of sensory input due to anosmia. Whether this deleterious effect can be partially reversed, or whether these effects will persist in the long term, remains to be investigated with additional follow-up.
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              Network-based statistic: identifying differences in brain networks.

              Large-scale functional or structural brain connectivity can be modeled as a network, or graph. This paper presents a statistical approach to identify connections in such a graph that may be associated with a diagnostic status in case-control studies, changing psychological contexts in task-based studies, or correlations with various cognitive and behavioral measures. The new approach, called the network-based statistic (NBS), is a method to control the family-wise error rate (in the weak sense) when mass-univariate testing is performed at every connection comprising the graph. To potentially offer a substantial gain in power, the NBS exploits the extent to which the connections comprising the contrast or effect of interest are interconnected. The NBS is based on the principles underpinning traditional cluster-based thresholding of statistical parametric maps. The purpose of this paper is to: (i) introduce the NBS for the first time; (ii) evaluate its power with the use of receiver operating characteristic (ROC) curves; and, (iii) demonstrate its utility with application to a real case-control study involving a group of people with schizophrenia for which resting-state functional MRI data were acquired. The NBS identified a expansive dysconnected subnetwork in the group with schizophrenia, primarily comprising fronto-temporal and occipito-temporal dysconnections, whereas a mass-univariate analysis controlled with the false discovery rate failed to identify a subnetwork. Copyright © 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                12 June 2024
                2024
                12 June 2024
                : 43
                : 103631
                Affiliations
                [a ]Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Ranica, Italy
                [b ]Department of Computer Science, University of Verona, Italy
                [c ]Translational Imaging in Neurology (ThINK), Department of Biomedical Engineering, Faculty of Medicine, Basel, Switzerland
                [d ]Department of Neuroradiology, ASST Papa Giovanni XXIII, Bergamo, Italy
                [e ]Department of Management Information and Production Engineering, University of Bergamo, Dalmine, Italy
                [f ]FROM Research Foundation, ASST Papa Giovanni XXIII, Bergamo, Italy
                [g ]Department of Emergency and Critical Care Area, ASST Papa Giovanni XXIII, Bergamo, Italy
                [h ]Department of Neurology, ASST Papa Giovanni XXIII, Bergamo, Italy
                Author notes
                [* ]Corresponding author at: Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Villa Camozzi via G.B. Camozzi, 3, 24020 Ranica (BG), Italy. anna.caroli@ 123456marionegri.it
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                orcid.org/0009-0006-6057-9685.

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                orcid.org/0000-0002-9402-9925.

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                orcid.org/0000-0002-8450-3806.

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                orcid.org/0000-0001-8423-4485.

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                orcid.org/0000-0002-4301-8927.

                [8]

                orcid.org/0000-0002-4425-7866.

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                orcid.org/0000-0002-2711-3377.

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                orcid.org/0000-0002-9589-0290.

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                orcid.org/0000-0002-4677-6678.

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                Article
                S2213-1582(24)00070-6 103631
                10.1016/j.nicl.2024.103631
                11225694
                38878591
                acb04d9c-8fb4-49f4-88a3-b981f4771454
                © 2024 The Authors. Published by Elsevier Inc.

                This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

                History
                : 5 March 2024
                : 31 May 2024
                : 11 June 2024
                Categories
                Regular Article

                covid-19,diffusion-weighted mri,brain microstructure,brain connectivity,neuroinflammation,neurodegeneration

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