Higher limbic and basal ganglia volumes in surviving COVID-negative patients and the relations to fatigue

  1. Rakibul Hafiz
  2. Tapan Kumar Gandhi
  3. Sapna Mishra
  4. Alok Prasad
  5. Vidur Mahajan
  6. Xin Di
  7. Benjamin H. Natelson
  8. Bharat B. Biswal

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Abstract

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  1. Version published to 10.1016/j.ynirp.2022.100095
  2. Version published to 10.1101/2021.11.23.21266761v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.11.23.21266761: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data Pre-Processing: We performed pre-processing using SPM12 (http://www.fil.ion.ucl.ac.uk/spm/) within the MATLAB environment (Mathworks Inc, Massachusetts, USA).
    http://www.fil.ion.ucl.ac.uk/spm/
    suggested: (IBASPM: Individual Brain Atlases using Statistical Parametric Mapping Software, RRID:SCR_007110)
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    To visualize the sample distributions and group average compartmental and total brain volumes, we customized and adopted a script in RStudio [34] to generate a ‘raincloud’ figure, as depicted in a recent publication [35].
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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:
    Limitations and Future Directions: Despite our emphasis on group level analysis, we understand that in a clinical setting, it may have little transferability, owing to idiosyncrasies associated with each patient. A possible approach to address this issue would be to compare patient specific VBM against a sufficiently sized control sub-group randomly selected from a larger cohort [47]. However, it may not be very practical in a clinical setting unless a well-designed control dataset is available for a clinician. Nevertheless, our group level results from a single site seem to match single patient findings quite well, especially when they were collected from several centers across different countries [48]. Moreover, currently, we only have 46 COVID subjects. While we do observe significant effects, we still need a larger sample size to verify the main effects more conclusively. Another concern we have is the reversibility or transient nature of some effects. These patients were scanned two weeks after hospital discharge. It is possible some critical effects may have already disappeared through recovery or reversible neurological processes. Therefore, a better approach could be to scan the patients at onset, during and after recovery along with behavioral parameters to assess any possible trends unique to the neurological pathology in COVID patients.

    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 20, 21 and 23. 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.

  4. Version published to 10.1101/2021.11.23.21266761v1 on medRxiv