Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction and fatigue.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
18 May 2024
18 May 2024
Historique:
received:
21
04
2022
accepted:
08
05
2024
medline:
19
5
2024
pubmed:
19
5
2024
entrez:
18
5
2024
Statut:
epublish
Résumé
After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition, marked by neurologic symptoms such as cognitive deficits, olfactory dysfunction, and fatigue. Despite this, biomarkers and pathophysiological understandings of this condition remain limited. Employing magnetic resonance imaging, we conduct a comparative analysis of cerebral microstructure among patients with Post-COVID-Condition, healthy controls, and individuals that contracted COVID-19 without long-term symptoms. We reveal widespread alterations in cerebral microstructure, attributed to a shift in volume from neuronal compartments to free fluid, associated with the severity of the initial infection. Correlating these alterations with cognition, olfaction, and fatigue unveils distinct affected networks, which are in close anatomical-functional relationship with the respective symptoms.
Identifiants
pubmed: 38762609
doi: 10.1038/s41467-024-48651-0
pii: 10.1038/s41467-024-48651-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4256Informations de copyright
© 2024. The Author(s).
Références
Augustin, M. et al. Post-COVID syndrome in non-hospitalised patients with COVID-19: a longitudinal prospective cohort study. Lancet Reg. Health Eur. 6, 100122 (2021).
pubmed: 34027514
pmcid: 8129613
doi: 10.1016/j.lanepe.2021.100122
Peter, R. S. et al. Post-acute sequelae of covid-19 six to 12 months after infection: population based study. BMJ 379, e071050 (2022).
pubmed: 36229057
doi: 10.1136/bmj-2022-071050
Soriano, J. B. et al. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect. Dis. 22, e102–e107 (2022).
pubmed: 34951953
doi: 10.1016/S1473-3099(21)00703-9
Havervall, S. et al. Symptoms and functional impairment assessed 8 months after mild COVID-19 among health care workers. JAMA 325, 2015 (2021).
pubmed: 33825846
pmcid: 8027932
doi: 10.1001/jama.2021.5612
Dressing, A. et al. Neuropsychologic profiles and cerebral glucose metabolism in neurocognitive long COVID syndrome. J. Nucl. Med. 63, 1058–1063 (2022).
pubmed: 34649946
pmcid: 9258569
doi: 10.2967/jnumed.121.262677
Douaud, G. et al. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature 604, 697–707 (2022).
pubmed: 35255491
pmcid: 9046077
doi: 10.1038/s41586-022-04569-5
Díez-Cirarda, M. et al. Multimodal neuroimaging in post-COVID syndrome and correlation with cognition. Brain awac384 https://doi.org/10.1093/brain/awac384 (2022).
Novikov, D. S., Jensen, J. H., Helpern, J. A. & Fieremans, E. Revealing mesoscopic structural universality with diffusion. Proc. Natl Acad. Sci. USA 111, 5088–5093 (2014).
pubmed: 24706873
pmcid: 3986157
doi: 10.1073/pnas.1316944111
Reisert, M., Kellner, E., Dhital, B., Hennig, J. & Kiselev, V. G. Disentangling micro from mesostructure by diffusion MRI: a Bayesian approach. NeuroImage 147, 964–975 (2017).
pubmed: 27746388
doi: 10.1016/j.neuroimage.2016.09.058
Novikov, D. S., Fieremans, E., Jespersen, S. N. & Kiselev, V. G. Quantifying brain microstructure with diffusion MRI: theory and parameter estimation. NMR Biomed. 32, e3998 (2019).
pubmed: 30321478
doi: 10.1002/nbm.3998
Rau, A. et al. Widespread white matter oedema in subacute COVID-19 patients with neurological symptoms. Brain J. Neurol. 145, 3203–3213 (2022).
doi: 10.1093/brain/awac045
Nasreddine, Z. S. et al. The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment: MOCA: a BRIEF SCREENING TOOL FOR MCI. J. Am. Geriatr. Soc. 53, 695–699 (2005).
pubmed: 15817019
doi: 10.1111/j.1532-5415.2005.53221.x
Fazekas, F., Chawluk, J., Alavi, A., Hurtig, H. & Zimmerman, R. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. Am. J. Roentgenol. 149, 351–356 (1987).
doi: 10.2214/ajr.149.2.351
Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).
pubmed: 22248573
doi: 10.1016/j.neuroimage.2012.01.021
Oishi, K. et al. Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer’s disease participants. NeuroImage 46, 486–499 (2009).
pubmed: 19385016
doi: 10.1016/j.neuroimage.2009.01.002
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, 968–980 (2006).
pubmed: 16530430
doi: 10.1016/j.neuroimage.2006.01.021
Ilinsky, I. et al. Human motor thalamus reconstructed in 3D from continuous sagittal sections with identified subcortical afferent territories. eneuro 5, ENEURO.0060-18.2018 (2018).
Edlow, B. L. et al. Neuroanatomic connectivity of the human ascending arousal system critical to consciousness and its disorders. J. Neuropathol. Exp. Neurol. 71, 531–546 (2012).
pubmed: 22592840
doi: 10.1097/NEN.0b013e3182588293
Williamson, N. H. et al. Magnetic resonance measurements of cellular and sub-cellular membrane structures in live and fixed neural tissue. eLife 8, e51101 (2019).
pubmed: 31829935
pmcid: 6977971
doi: 10.7554/eLife.51101
Jelescu, I. O., de Skowronski, A., Geffroy, F., Palombo, M. & Novikov, D. S. Neurite Exchange Imaging (NEXI): a minimal model of diffusion in gray matter with inter-compartment water exchange. NeuroImage 256, 119277 (2022).
pubmed: 35523369
doi: 10.1016/j.neuroimage.2022.119277
Olesen, J. L., Østergaard, L., Shemesh, N. & Jespersen, S. N. Beyond the diffusion standard model in fixed rat spinal cord with combined linear and planar encoding. NeuroImage 231, 117849 (2021).
pubmed: 33582270
doi: 10.1016/j.neuroimage.2021.117849
Kamiya, K., Hori, M. & Aoki, S. NODDI in clinical research. J. Neurosci. Methods 346, 108908 (2020).
pubmed: 32814118
doi: 10.1016/j.jneumeth.2020.108908
Billiet, T. et al. Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiol. Aging 36, 2107–2121 (2015).
pubmed: 25840837
doi: 10.1016/j.neurobiolaging.2015.02.029
Merluzzi, A. P. et al. Age-dependent differences in brain tissue microstructure assessed with neurite orientation dispersion and density imaging. Neurobiol. Aging 43, 79–88 (2016).
pubmed: 27255817
pmcid: 4893194
doi: 10.1016/j.neurobiolaging.2016.03.026
Kamagata, K. et al. Gray matter abnormalities in idiopathic Parkinson’s disease: evaluation by diffusional kurtosis imaging and neurite orientation dispersion and density imaging: gray matter abnormalities in Parkinson’s Disease. Hum. Brain Mapp. https://doi.org/10.1002/hbm.23628 (2017).
Gatto, R. G. et al. Unveiling early cortical and subcortical neuronal degeneration in ALS mice by ultra-high field diffusion MRI. Amyotroph. Lateral Scler. Front. Degener. 20, 549–561 (2019).
doi: 10.1080/21678421.2019.1620285
Jack, C. R. et al. NIA‐AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14, 535–562 (2018).
pubmed: 29653606
doi: 10.1016/j.jalz.2018.02.018
Bohnen, N. I., Djang, D. S. W., Herholz, K., Anzai, Y. & Minoshima, S. Effectiveness and safety of
pubmed: 22173840
doi: 10.2967/jnumed.111.096578
Mavrikaki, M., Lee, J. D., Solomon, I. H. & Slack, F. J. Severe COVID-19 Induces Molecular Signatures of Aging in the Human Brain. https://doi.org/10.1101/2021.11.24.21266779 (2021).
Fick, R. H. J. et al. Comparison of biomarkers in transgenic alzheimer rats using multi-shell diffusion MRI. in Computational Diffusion MRI (eds. Fuster, A., Ghosh, A., Kaden, E., Rathi, Y. & Reisert, M.) 187–199 (Springer International Publishing, Cham, 2017) https://doi.org/10.1007/978-3-319-54130-3_16 .
Dowell, N. G. et al. Interferon-alpha-Induced changes in NODDI predispose to the development of fatigue. Neuroscience 403, 111–117 (2019).
pubmed: 29292074
doi: 10.1016/j.neuroscience.2017.12.040
Thakur, K. T. et al. COVID-19 neuropathology at Columbia University Irving medical center/new york presbyterian hospital. Brain 144, 2696–2708 (2021).
pubmed: 33856027
pmcid: 8083258
doi: 10.1093/brain/awab148
Matschke, J. et al. Neuropathology of patients with COVID-19 in Germany: a post-mortem case series. Lancet Neurol. 19, 919–929 (2020).
pubmed: 33031735
pmcid: 7535629
doi: 10.1016/S1474-4422(20)30308-2
Tran, V.-T., Porcher, R., Pane, I. & Ravaud, P. Course of post COVID-19 disease symptoms over time in the ComPaRe long COVID prospective e-cohort. Nat. Commun. 13, 1812 (2022).
pubmed: 35383197
pmcid: 8983754
doi: 10.1038/s41467-022-29513-z
Squire, L. R. & Zola-Morgan, S. The medial temporal lobe memory system. Science 253, 1380–1386 (1991).
pubmed: 1896849
doi: 10.1126/science.1896849
Ritter, A., Hawley, N., Banks, S. J. & Miller, J. B. The association between montreal cognitive assessment memory scores and hippocampal volume in a neurodegenerative disease sample. J. Alzheimers Dis. 58, 695–699 (2017).
pubmed: 28453481
pmcid: 5467712
doi: 10.3233/JAD-161241
Munsch, F. et al. Stroke location is an independent predictor of cognitive outcome. Stroke 47, 66–73 (2016).
pubmed: 26585396
doi: 10.1161/STROKEAHA.115.011242
Mitchell, A. S. & Chakraborty, S. What does the mediodorsal thalamus do? Front. Syst. Neurosci. 7, 37 (2013).
pubmed: 23950738
pmcid: 3738868
doi: 10.3389/fnsys.2013.00037
Rolls, E. T. The functions of the orbitofrontal cortex. Brain Cogn. 55, 11–29 (2004).
pubmed: 15134840
doi: 10.1016/S0278-2626(03)00277-X
Soudry, Y., Lemogne, C., Malinvaud, D., Consoli, S.-M. & Bonfils, P. Olfactory system and emotion: common substrates. Eur. Ann. Otorhinolaryngol. Head. Neck Dis. 128, 18–23 (2011).
pubmed: 21227767
doi: 10.1016/j.anorl.2010.09.007
Courtiol, E. & Wilson, D. A. The olfactory thalamus: unanswered questions about the role of the mediodorsal thalamic nucleus in olfaction. Front. Neural. Circuits 9, 49 (2015).
pubmed: 26441548
pmcid: 4585119
doi: 10.3389/fncir.2015.00049
Zhang, Z. et al. Cerebellar involvement in olfaction: an fMRI Study. J. Neuroimag. 31, 517–523 (2021).
doi: 10.1111/jon.12843
Schwabenland, M. et al. Deep spatial profiling of human COVID-19 brains reveals neuroinflammation with distinct microanatomical microglia-T-cell interactions. Immunity 54, 1594–1610.e11 (2021).
pubmed: 34174183
pmcid: 8188302
doi: 10.1016/j.immuni.2021.06.002
Yildirim, D., Kandemirli, S. G., Tekcan Sanli, D. E., Akinci, O. & Altundag, A. A comparative olfactory MRI, DTI and fMRI Study of COVID-19 related anosmia and post viral olfactory dysfunction. Acad. Radiol. 29, 31–41 (2022).
pubmed: 34810059
doi: 10.1016/j.acra.2021.10.019
Harrington, M. E. Neurobiological studies of fatigue. Prog. Neurobiol. 99, 93–105 (2012).
pubmed: 22841649
pmcid: 3479364
doi: 10.1016/j.pneurobio.2012.07.004
Chaudhuri, A. & Behan, P. O. Fatigue and basal ganglia. J. Neurol. Sci. 179, 34–42 (2000).
pubmed: 11054483
doi: 10.1016/S0022-510X(00)00411-1
Cotter, G. et al. Post-stroke fatigue is associated with resting state posterior hypoactivity and prefrontal hyperactivity. Int. J. Stroke 17, 906–913 (2022).
doi: 10.1177/17474930211048323
Boissoneault, J., Sevel, L., Robinson, M. E. & Staud, R. Functional brain connectivity of remembered fatigue or happiness in healthy adults: Use of arterial spin labeling. J. Clin. Exp. Neuropsychol. 40, 224–233 (2018).
pubmed: 28553882
doi: 10.1080/13803395.2017.1329407
Pedraz, B. & Sammer, G. The importance of glutamate in the neuro-endocrinological functions in multiple sclerosis, related to fatigue. Rev. Neurol. 67, 387–393 (2018).
pubmed: 30403282
Baraniuk, J. N., Amar, A., Pepermitwala, H. & Washington, S. D. Differential effects of exercise on fMRI of the midbrain ascending arousal network nuclei in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Gulf War Illness (GWI) in a Model of Postexertional Malaise (PEM). Brain Sci. 12, 78 (2022).
pubmed: 35053821
pmcid: 8774249
doi: 10.3390/brainsci12010078
Bispo, D. D. 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
Coelho, S. et al. Reproducibility of the standard Model of diffusion in white matter on clinical MRI systems. NeuroImage 257, 119290 (2022).
pubmed: 35545197
doi: 10.1016/j.neuroimage.2022.119290
Wartolowska, K. A. & Webb, A. J. S. Blood pressure determinants of cerebral white matter hyperintensities and microstructural injury: UK Biobank Cohort Study. Hypertens. Dallas Tex. 78, 532–539 (2021).
doi: 10.1161/HYPERTENSIONAHA.121.17403
Ashina, S., Bentivegna, E., Martelletti, P. & Eikermann-Haerter, K. Structural and functional brain changes in migraine. Pain. Ther. 10, 211–223 (2021).
pubmed: 33594593
pmcid: 8119592
doi: 10.1007/s40122-021-00240-5
Bashir, A., Lipton, R. B., Ashina, S. & Ashina, M. Migraine and structural changes in the brain: a systematic review and meta-analysis. Neurology 81, 1260–1268 (2013).
pubmed: 23986301
pmcid: 3795609
doi: 10.1212/WNL.0b013e3182a6cb32
Ständiger Arbeitskreis Der Kompetenz- Und Behandlungszentren Für Krankheiten Durch Hochpathogene Erreger. Hinweise zu Erkennung, Diagnostik und Therapie von Patienten mit COVID-19 https://doi.org/10.25646/6539.24 (2020).
Flachenecker, P. et al. [“Fatigue” in multiple sclerosis. Development and and validation of the ‘Würzburger Fatigue Inventory for MS’]. Nervenarzt 77, 165–166 (2006).
pubmed: 16160812
doi: 10.1007/s00115-005-1990-x
Yesavage, J. A. et al. Development and validation of a geriatric depression screening scale: a preliminary report. J. Psychiatr. Res. 17, 37–49 (1982).
pubmed: 7183759
doi: 10.1016/0022-3956(82)90033-4
Kobal, G. et al. Sniffin’ sticks’: screening of olfactory performance. Rhinology 34, 222–226 (1996).
pubmed: 9050101
Veraart, J. et al. Denoising of diffusion MRI using random matrix theory. NeuroImage 142, 394–406 (2016).
pubmed: 27523449
doi: 10.1016/j.neuroimage.2016.08.016
Kellner, E., Dhital, B., Kiselev, V. G. & Reisert, M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn. Reson. Med. 76, 1574–1581 (2016).
pubmed: 26745823
doi: 10.1002/mrm.26054
Jespersen, S. N., Kroenke, C. D., Østergaard, L., Ackerman, J. J. H. & Yablonskiy, D. A. Modeling dendrite density from magnetic resonance diffusion measurements. NeuroImage 34, 1473–1486 (2007).
pubmed: 17188901
doi: 10.1016/j.neuroimage.2006.10.037
Novikov, D. S., Veraart, J., Jelescu, I. O. & Fieremans, E. Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI. NeuroImage 174, 518–538 (2018).
pubmed: 29544816
doi: 10.1016/j.neuroimage.2018.03.006
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61, 1000–1016 (2012).
pubmed: 22484410
doi: 10.1016/j.neuroimage.2012.03.072
Genç, E. et al. Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nat. Commun. 9, 1905 (2018).
pubmed: 29765024
pmcid: 5954098
doi: 10.1038/s41467-018-04268-8
Schröter, N. et al. Disentangling nigral and putaminal contribution to motor impairment and levodopa response in Parkinson’s disease. NPJ Park. Dis. 8, 132 (2022).
doi: 10.1038/s41531-022-00401-z
Radhakrishnan, H., Bennett, I. J. & Stark, C. E. Higher-order multi-shell diffusion measures complement tensor metrics and volume in gray matter when predicting age and cognition. NeuroImage 253, 119063 (2022).
pubmed: 35272021
doi: 10.1016/j.neuroimage.2022.119063
Ashburner, J. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95–113 (2007).
pubmed: 17761438
doi: 10.1016/j.neuroimage.2007.07.007
Smith, S. M. & Nichols, T. E. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage 44, 83–98 (2009).
pubmed: 18501637
doi: 10.1016/j.neuroimage.2008.03.061
Hosp, J. A. et al. Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction, and fatigue. Dryad Database. https://doi.org/10.5061/DRYAD.KKWH70S9G (2024).
Hosp, J. A. et al. Cerebral microstructural alterations in Post-COVID-Condition are related to cognitive impairment, olfactory dysfunction, and fatigue. ZENODO Database. https://doi.org/10.5281/ZENODO.8288991 (2024).