The pandemic brain: Neuroinflammation in non-infected individuals during the COVID-19 pandemic

  1. Ludovica Brusaferri
  2. Zeynab Alshelh
  3. Daniel Martins
  4. Minhae Kim
  5. Akila Weerasekera
  6. Hope Housman
  7. Erin J. Morrissey
  8. Paulina C. Knight
  9. Kelly A. Castro-Blanco
  10. Daniel S. Albrecht
  11. Chieh-En Tseng
  12. Nicole R. Zürcher
  13. Eva-Maria Ratai
  14. Oluwaseun Akeju
  15. Meena M. Makary
  16. Ciprian Catana
  17. Nathaniel D. Mercaldo
  18. Nouchine Hadjikhani
  19. Mattia Veronese
  20. Federico Turkheimer
  21. Bruce R. Rosen
  22. Jacob M. Hooker
  23. Marco L. Loggia

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1016/j.bbi.2022.02.018
  2. ScreenIT

    SciScore for 10.1101/2021.09.21.21263740: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: Data were pooled for several protocols, which were approved by the Partners Healthcare / Mass General Brigham Institutional Review Board.
    Consent: All participants gave written informed consent at the time of their screening.
    Sex as a biological variablenot detected.
    RandomizationGroup differences were assessed by using a permutation test (FSL randomise; 5000 permutations; cluster-forming threshold of p=0.01; cluster size threshold of p=0.05), again accounting for age, binding affinity, and scanner.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    All subjects in the HCPOST cohort had a negative COVID-19 antibody test (Elecsys® Anti-SARS-CoV-2, Roche Diagnostics; 99.81% accuracy; 95% confidence interval (CI): 99.65-99.1) at the time of the scan.
    Anti-SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    Dynamic PET/MR scans were performed with two different Siemens scanners: a 3T Tim Trio whole-body MRI with a dedicated brain PET insert (BrainPET; Scanner 1) and a 3T Verio whole-body, MRI whole-body PET tomograph (Biograph mMR; Scanner 2).
    Biograph
    suggested: (Spiral Genetics Incorporated, RRID:SCR_011048)
    Demographics, ROI PET and behavioral analyses were performed using Statistica (
    Statistica
    suggested: (STATISTICA , RRID:SCR_014213)
    Transcriptomic analyses were conducted using the Web-based gene set analysis toolkit (WebGestalt).
    WebGestalt
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    When interpreting the results of our study, the reader should be mindful of several limitations. First, HCPOST had a relatively small sample size, particularly compared to the HCPRE cohort (a limitation largely imposed by the COVID-19 pandemic-related disruption on clinical research)29. However, multiple factors provide high confidence in the solidity of our observations, including the consistency of our observations across individuals, scanners, age groups, genotypes, and sexes (see Appendix, Suppl. Figure 3), the relative stability of the pre-lockdown markers, the observed elevation of two separate imaging markers measured using independent imaging modalities and the finding of significant enrichment in genes highly expressed in glial cells. Second, our analysis was largely based on unpaired comparisons of pre- and post-lockdown groups. While our single subject scanned both pre- and post-lockdown demonstrated PET signal elevations in the same regions observed in the group comparison, longitudinal assessments of more individuals scanned in the same manner will be needed to better understand the impact of the COVID-19 pandemic on brain health. Third, because pandemic-specific questionnaires were not collected at the time of the scan visits, behavioral analyses were conducted using retrospectively collected questionnaire data, and only from a subset of subjects. As such the link between neuroinflammation and clinical symptoms presented in this report should be regarded as prel...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 24 and 27. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.09.21.21263740v1 on medRxiv