Survivors of SARS-CoV-2 Infection Show Neuropsychiatric Sequelae Measured by Surveys, Neurocognitive Testing, and Magnetic Resonance Imaging: Preliminary Results

  1. Laura M. Hack
  2. Jacob Brawer
  3. Megan Chesnut
  4. Xue Zhang
  5. Max Wintermark
  6. Bin Jiang
  7. Philip Grant
  8. Hector Bonilla
  9. Patrick Stetz
  10. Jerome A. Yesavage
  11. Aruna Subramanian
  12. Leanne M. Williams

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Abstract

A significant number of individuals experience physical, cognitive, and mental health symptoms in the months after acute infection with SARS-CoV-2, the virus that causes COVID-19. This study assessed depressive and anxious symptoms, cognition, and brain structure and function in participants with symptomatic COVID-19 confirmed by PCR testing (n=100) approximately three months following infection, leveraging self-report questionnaires, objective neurocognitive testing, and structural and functional neuroimaging data. Preliminary results demonstrated that over 1/5 of our cohort endorsed clinically significant depressive and/or anxious symptoms, and >40% of participants had cognitive impairment on objective testing across multiple domains, consistent with ‘brain-fog’. While depression and one domain of quality of life (physical functioning) were significantly different between hospitalized and non-hospitalized participants, anxiety, cognitive impairment, and most domains of functioning were not, suggesting that the severity of SARS-CoV-2 infection does not necessarily relate to the severity of neuropsychiatric outcomes and impaired functioning in the months after infection. Furthermore, we found that the majority of participants in a subset of our cohort who completed structural and functional neuroimaging (n=15) had smaller olfactory bulbs and sulci in conjunction with anosmia. We also showed that this subset of participants had dysfunction in attention network functional connectivity and ventromedial prefrontal cortex seed-based functional connectivity. These functional imaging dysfunctions have been observed previously in depression and correlated with levels of inflammation. Our results support and extend previous findings in the literature concerning the neuropsychiatric sequelae associated with long COVID. Ongoing data collection and analyses within this cohort will allow for a more comprehensive understanding of the longitudinal relationships between neuropsychiatric symptoms, neurocognitive performance, brain structure and function, and inflammatory and immune profiles.

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  1. ScreenIT

    SciScore for 10.1101/2021.08.23.21256078: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Inclusion criteria for participation in the study were as follows: 1) willing and able to provide written informed consent, or with a legal representative who can provide informed consent; 2) age ≥18 years; and 3) history of symptomatic COVID-19 infection confirmed by PCR test.
    IRB: Institutional approval for the study was obtained through the Stanford Institutional Review Board.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical Analyses for Demographic, Psychiatric, and Neurocognitive Variables: Statistical analyses were run in the python 3 environment and R.
    python
    suggested: (IPython, RRID:SCR_001658)
    Missing values for the SF-36 and WebNeuro measurements in participants who completed at least one item were replaced with the median score of the group for each missing variable in order to achieve a sample size of n = 96 and n= 89, respectively.
    WebNeuro
    suggested: (WebNeuro, RRID:SCR_006049)
    Pre-processing, Selecting Regions of Interest, and Statistical Analyses: Pre-processing and data analyses were performed using Statistical Parametric Mapping (SPM) software (Wellcome Department of Cognitive Neurology) implemented in MATLAB and FSL31 following previously established procedures32, 33.
    Statistical Parametric Mapping
    suggested: (Spatial Statistical Parametric Mapping, RRID:SCR_002592)
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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 of the current study include a small sample size for the neuroimaging subsample and the fact that participants’ prior psychiatric diagnoses and current psychotropics were not considered in the analyses. Future analyses with this cohort will include an expanded neuroimaging sample and consideration of these measures. An additional limitation is the variable timeframe in which the first visit occurred after diagnosis due to the challenges of initiating a study during the pandemic. Additionally, the neuroimaging data was collected after the symptom and cognition data. Finally, while we have compared the sample to normative samples collected prior to the pandemic, we do not have data on these individuals prior to developing COVID-19, so we cannot be certain that the deficits we observe were not present prior to contracting SARS-CoV-2.

    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: We did not find any issues relating to colormaps.

    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.

  2. Version published to 10.1101/2021.08.23.21256078 on medRxiv