A cross-sectional case-control study on the structural connectome in recovered hospitalized COVID-19 patients.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
21 09 2023
21 09 2023
Historique:
received:
02
05
2023
accepted:
10
09
2023
medline:
25
9
2023
pubmed:
22
9
2023
entrez:
22
9
2023
Statut:
epublish
Résumé
COVID-19 can induce neurological sequelae, negatively affecting the quality of life. Unravelling this illness's impact on structural brain connectivity, white-matter microstructure (WMM), and cognitive performance may help elucidate its implications. This cross-sectional study aimed to investigate differences in these factors between former hospitalised COVID-19 patients (COV) and healthy controls. Group differences in structural brain connectivity were explored using Welch-two sample t-tests and two-sample Mann-Whitney U tests. Multivariate linear models were constructed (one per region) to examine fixel-based group differences. Differences in cognitive performance between groups were investigated using Wilcoxon Rank Sum tests. Possible effects of bundle-specific FD measures on cognitive performance were explored using a two-group path model. No differences in whole-brain structural organisation were found. Bundle-specific metrics showed reduced fiber density (p = 0.012, Hedges' g = 0.884) and fiber density cross-section (p = 0.007, Hedges' g = 0.945) in the motor segment of the corpus callosum in COV compared to healthy controls. Cognitive performance on the motor praxis and digit symbol substitution tests was worse in COV than healthy controls (p < 0.001, r = 0.688; p = 0.013, r = 422, respectively). Associations between the cognitive performance and bundle-specific FD measures differed significantly between groups. WMM and cognitive performance differences were observed between COV and healthy controls.
Identifiants
pubmed: 37735584
doi: 10.1038/s41598-023-42429-y
pii: 10.1038/s41598-023-42429-y
pmc: PMC10514277
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
15668Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2023. Springer Nature Limited.
Références
European Centre for Disease Prevention and Control. COVID-19 2022. https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases . Accessed 9 June 2022.
Gorbalenya, A. E. et al. The species severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat. Microbiol. 5(4), 536–544 (2020).
doi: 10.1038/s41564-020-0695-z
Hu, B., Guo, H., Zhou, P. & Shi, Z. L. Characteristics of SARS-CoV-2 and COVID-19. Nat. Rev. Microbiol. 19(3), 141–154 (2021).
pubmed: 33024307
doi: 10.1038/s41579-020-00459-7
Najt, P., Richards, H. L. & Fortune, D. G. Brain imaging in patients with COVID-19: A systematic review. Brain Behav. Immun. Health. 16, 100290 (2021).
pubmed: 34230916
pmcid: 8249107
doi: 10.1016/j.bbih.2021.100290
Ermis, U. et al. Neurological symptoms in COVID-19: A cross-sectional monocentric study of hospitalized patients. Neurol. Res. Pract. 3(1), 17 (2021).
pubmed: 33712089
pmcid: 7953515
doi: 10.1186/s42466-021-00116-1
Mandal, S. et al. “Long-COVID”: A cross-sectional study of persisting symptoms, biomarker and imaging abnormalities following hospitalisation for COVID-19. Thorax 76(4), 396–398 (2021).
pubmed: 33172844
doi: 10.1136/thoraxjnl-2020-215818
Aiyegbusi, O. L. et al. Symptoms, complications and management of long COVID: A review. J. R. Soc. Med. 114(9), 428–442 (2021).
pubmed: 34265229
pmcid: 8450986
doi: 10.1177/01410768211032850
Hampshire, A. et al. Cognitive deficits in people who have recovered from COVID-19. eClinicalMedicine. 39, 101044 (2021).
pubmed: 34316551
pmcid: 8298139
doi: 10.1016/j.eclinm.2021.101044
Fernández-Castañeda, A. et al. Mild respiratory COVID can cause multi-lineage neural cell and myelin dysregulation. Cell 185, 2652 (2022).
doi: 10.1016/j.cell.2022.06.008
Meinhardt, J. et al. Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19. Nat. Neurosci. 24(2), 168–175 (2021).
pubmed: 33257876
doi: 10.1038/s41593-020-00758-5
Bourgonje, A. R. et al. Angiotensin-converting enzyme 2 (ACE2), SARS-CoV-2 and the pathophysiology of coronavirus disease 2019 (COVID-19). J. Pathol. 251(3), 228–248 (2020).
pubmed: 32418199
pmcid: 7276767
doi: 10.1002/path.5471
Raman, B. et al. Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge. EClinicalMedicine. 31, 100683 (2021).
pubmed: 33490928
pmcid: 7808914
doi: 10.1016/j.eclinm.2020.100683
Kumar, A. et al. SARS-CoV-2 cell entry receptor ACE2 mediated endothelial dysfunction leads to vascular thrombosis in COVID-19 patients. Med. Hypotheses. 145, 110320 (2020).
pubmed: 33032170
pmcid: 7525265
doi: 10.1016/j.mehy.2020.110320
Mahdizade Ari, M. et al. Neurological manifestations in patients with COVID-19: A systematic review and meta-analysis. J. Clin. Lab Anal. 36(5), e24403-e (2022).
doi: 10.1002/jcla.24403
Douaud, G. et al. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature 604(7907), 697–707 (2022).
pubmed: 35255491
pmcid: 9046077
doi: 10.1038/s41586-022-04569-5
Lu, Y. et al. Cerebral micro-structural changes in COVID-19 patients—An MRI-based 3-month follow-up study. EClinicalMedicine. 25, 100484 (2020).
pubmed: 32838240
pmcid: 7396952
doi: 10.1016/j.eclinm.2020.100484
Qin, Y. et al. Long-term microstructure and cerebral blood flow changes in patients recovered from COVID-19 without neurological manifestations. J Clin Invest. 131(8), e147329 (2021).
pubmed: 33630760
pmcid: 8262559
doi: 10.1172/JCI147329
Tassignon, B. et al. Longitudinal changes in global structural brain connectivity and cognitive performance in former hospitalized COVID-19 survivors: An exploratory study. Exp. Brain Res. 241, 723 (2023).
doi: 10.1007/s00221-023-06545-5
Zhou, H. et al. The landscape of cognitive function in recovered COVID-19 patients. J. Psychiatr. Res. 129, 98–102 (2020).
pubmed: 32912598
pmcid: 7324344
doi: 10.1016/j.jpsychires.2020.06.022
Cothran, T. P. et al. A brewing storm: The neuropsychological sequelae of hyperinflammation due to COVID-19. Brain Behav. Immun. 88, 957–958 (2020).
pubmed: 32590055
pmcid: 7309913
doi: 10.1016/j.bbi.2020.06.008
de Erausquin, G. A. et al. The chronic neuropsychiatric sequelae of COVID-19: The need for a prospective study of viral impact on brain functioning. Alzheimers Dement. 17(6), 1056–1065 (2021).
pubmed: 33399270
pmcid: 10431934
doi: 10.1002/alz.12255
Del Brutto, O. H. et al. Cognitive decline among individuals with history of mild symptomatic SARS-CoV-2 infection: A longitudinal prospective study nested to a population cohort. Eur. J. Neurol. 28, 3245 (2021).
pubmed: 33576150
pmcid: 8014083
doi: 10.1111/ene.14775
Kanberg, N. et al. Neurochemical signs of astrocytic and neuronal injury in acute COVID-19 normalizes during long-term follow-up. EBioMedicine 70, 103512 (2021).
pubmed: 34333238
pmcid: 8320425
doi: 10.1016/j.ebiom.2021.103512
Blazhenets, G. et al. Slow but evident recovery from neocortical dysfunction and cognitive impairment in a series of chronic COVID-19 patients. J. Nucl. Med. 62(7), 910–915 (2021).
pubmed: 33789937
pmcid: 8882885
doi: 10.2967/jnumed.121.262128
Hadad, R. et al. Cognitive dysfunction following COVID-19 infection. J. Neurovirol. 28(3), 430–437 (2022).
pubmed: 35618983
pmcid: 9134977
doi: 10.1007/s13365-022-01079-y
Jeurissen, B., Leemans, A., Tournier, J.-D., Jones, D. K. & Sijbers, J. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum. Brain Mapp. 34(11), 2747–2766 (2013).
pubmed: 22611035
doi: 10.1002/hbm.22099
Dhollander, T. et al. Fixel-based analysis of diffusion MRI: Methods, applications, challenges and opportunities. Neuroimage 241, 118417 (2021).
pubmed: 34298083
doi: 10.1016/j.neuroimage.2021.118417
Farquharson, S. et al. White matter fiber tractography: Why we need to move beyond DTI. J. Neurosurg. 118(6), 1367–1377 (2013).
pubmed: 23540269
doi: 10.3171/2013.2.JNS121294
Jones, D. K., Christiansen, K. F., Chapman, R. J. & Aggleton, J. P. Distinct subdivisions of the cingulum bundle revealed by diffusion MRI fibre tracking: Implications for neuropsychological investigations. Neuropsychologia 51(1), 67–78 (2013).
pubmed: 23178227
pmcid: 3611599
doi: 10.1016/j.neuropsychologia.2012.11.018
Raffelt, D. A. et al. Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres. Neuroimage 117, 40–55 (2015).
pubmed: 26004503
doi: 10.1016/j.neuroimage.2015.05.039
Raffelt, D. A. et al. Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage 144, 58–73 (2017).
pubmed: 27639350
doi: 10.1016/j.neuroimage.2016.09.029
Dhollander, T. & Connelly, A. (eds.) A novel iterative approach to reap the benefits of multi-tissue CSD from just single-shell (+ b= 0) diffusion MRI data. In Proceedings of the ISMRM (2016).
Bispo, D. D. C. et al. Brain microstructural changes and fatigue after COVID-19. Front. Neurol. 13, 1029302 (2022).
pubmed: 36438956
pmcid: 9685991
doi: 10.3389/fneur.2022.1029302
WMA. Declaration of Helsinki: Ethical principles for medical research involving human subjects. J. Am. Coll. Dent. 81(3), 14–18 (2014).
Smith, R. E., Tournier, J.-D., Calamante, F. & Connelly, A. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 62(3), 1924–1938 (2012).
pubmed: 22705374
doi: 10.1016/j.neuroimage.2012.06.005
Smith, R. E., Tournier, J. D., Calamante, F. & Connelly, A. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage 119, 338–351 (2015).
pubmed: 26163802
doi: 10.1016/j.neuroimage.2015.06.092
Tournier, J. D., Calamante, F. & Connelly, A. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35(4), 1459–1472 (2007).
pubmed: 17379540
doi: 10.1016/j.neuroimage.2007.02.016
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006).
pubmed: 16530430
doi: 10.1016/j.neuroimage.2006.01.021
MRtrix. Fibre Density and Cross-Section—Single-Tissue CSD—MRtrix 3.0 Documentation. https://mrtrix.readthedocs.io/en/dev/fixel_based_analysis/st_fibre_density_cross-section.html .
Radwan, A. M. et al. An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI. Neuroimage 254, 119029 (2022).
pubmed: 35231632
doi: 10.1016/j.neuroimage.2022.119029
Basner, M. et al. Development and validation of the cognition test battery for spaceflight. Aerosp. Med. Hum. Perform. 86(11), 942–952 (2015).
pubmed: 26564759
pmcid: 4691281
doi: 10.3357/AMHP.4343.2015
Ihaka, R. Gentleman R. R (version 4.1. 3). [Software]. (R Development Core Team, 2022).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. arXiv preprint arXiv:14065823 (2014).
Kuznetsova, A., Brockhoff, P. & Christensen, R.H. Tests in Linear Mixed Effects Models [R Package lmerTest Version 2.0-36]. (Comprehensive R Archive Network (CRAN), 2017).
Rosseel, Y. lavaan: An R package for structural equation modeling. J. Stat. Softw. 48(2), 1–36 (2012).
doi: 10.18637/jss.v048.i02
Van Laar, S. & Braeken, J. Understanding the Comparative Fit Index: It’s all about the base!. Pract. Assess. Res. Eval. 26(1), 133 (2021).
Kenny, D. A., Kaniskan, B. & McCoach, D. B. The performance of RMSEA in models with small degrees of freedom. Sociol. Methods Res. 44(3), 486–507 (2015).
doi: 10.1177/0049124114543236
Kline, R. B. Principles and Practice of Structural Equation Modeling (Guilford Publications, 2010).
Bentler, P. M. EQS Structural Equations Program Manual (Multivariate software Encino, 1995).
Epskamp, S. semPlot: Unified visualizations of structural equation models. Struct. Equ. Model. 22(3), 474–483 (2015).
doi: 10.1080/10705511.2014.937847
R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).
Kenny, D.A. Measuring Model Fit 2014. http://davidakenny.net/cm/fit.htm .
Novo, A.A. & Schafer, J.L. norm: Analysis of Multivariate Normal Datasets with Missing Values (2022).
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn. (Springer, 2002).
doi: 10.1007/978-0-387-21706-2
Raffelt, D. et al. Apparent fibre density: A novel measure for the analysis of diffusion-weighted magnetic resonance images. Neuroimage 59(4), 3976–3994 (2012).
pubmed: 22036682
doi: 10.1016/j.neuroimage.2011.10.045
Brown, C. H. T. et al. Association of hospitalization with long-term cognitive and brain MRI changes in the ARIC cohort. Neurology 84(14), 1443–1453 (2015).
pubmed: 25762715
pmcid: 4395884
doi: 10.1212/WNL.0000000000001439
Aldén, M. et al. Intracellular reverse transcription of Pfizer BioNTech COVID-19 mRNA vaccine BNT162b2 in vitro in human liver cell line. Curr. Issues Mol. Biol. 44(3), 1115–1126 (2022).
pubmed: 35723296
pmcid: 8946961
doi: 10.3390/cimb44030073
Irrgang, P. et al. Class switch towards non-inflammatory, spike-specific IgG4 antibodies after repeated SARS-CoV-2 mRNA vaccination. Sci. Immunol. 8, 2798 (2023).
doi: 10.1126/sciimmunol.ade2798
Seneff, S., Nigh, G., Kyriakopoulos, A. M. & McCullough, P. A. Innate immune suppression by SARS-CoV-2 mRNA vaccinations: The role of G-quadruplexes, exosomes, and MicroRNAs. Food Chem. Toxicol. 164, 113008 (2022).
pubmed: 35436552
pmcid: 9012513
doi: 10.1016/j.fct.2022.113008
Mannan, V. et al. COVID-19 vaccination-associated myocarditis: A literature review. Cureus 14(11), e32022 (2022).
pubmed: 36589180
pmcid: 9797869
Oster, M. E. et al. Myocarditis cases reported after mRNA-based COVID-19 vaccination in the US from December 2020 to August 2021. JAMA 327(4), 331–340 (2022).
pubmed: 35076665
pmcid: 8790664
doi: 10.1001/jama.2021.24110
Fiolet, T., Kherabi, Y., MacDonald, C. J., Ghosn, J. & Peiffer-Smadja, N. Comparing COVID-19 vaccines for their characteristics, efficacy and effectiveness against SARS-CoV-2 and variants of concern: A narrative review. Clin. Microbiol. Infect. 28(2), 202–221 (2022).
pubmed: 34715347
doi: 10.1016/j.cmi.2021.10.005
Meurisse, M. et al. Homologous and heterologous prime-boost vaccination: Impact on clinical severity of SARS-CoV-2 Omicron infection among hospitalized COVID-19 patients in Belgium. Vaccines. 11, 378 (2023).
pubmed: 36851257
pmcid: 9961733
doi: 10.3390/vaccines11020378
Huang, S. et al. Persistent white matter changes in recovered COVID-19 patients at the 1-year follow-up. Brain 145(5), 1830–1838 (2022).
pubmed: 34918020
doi: 10.1093/brain/awab435