Dataset Information

Long COVID syndrome-associated brain fog.

ABSTRACT: We investigated the frequency of brain fog in a large cohort of patients with documented coronavirus disease-2019 (COVID-19) who have survived the illness. We also scrutinized the potential risk factors associated with the development of brain fog. Adult patients (18-55 years of age), who were referred to the healthcare facilities anywhere in Fars province from February 19, 2020 to November 20, 2020 were included. All patients had a confirmed COVID-19 diagnosis. In a phone call, at least 3 months after their discharge from the hospital, we obtained their current information. A questionnaire was specifically designed for data collection. In total, 2696 patients had the inclusion criteria; 1680 (62.3%) people reported long COVID syndrome (LCS). LCS-associated brain fog was reported by 194 (7.2%) patients. Female sex (odds ratio [OR]: 1.4), respiratory problems at the onset (OR: 1.9), and intensive care unit (ICU) admission (OR: 1.7) were significantly associated with reporting chronic post-COVID "brain fog" by the patients. In this large population-based study, we report that chronic post-COVID "brain fog" has significant associations with sex (female), respiratory symptoms at the onset, and the severity of the illness (ICU admission).

SUBMITTER: Asadi-Pooya AA

PROVIDER: S-EPMC8662118 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Long COVID syndrome-associated brain fog.

Asadi-Pooya Ali A AA Akbari Ali A Emami Amir A Lotfi Mehrzad M Rostamihosseinkhani Mahtab M Nemati Hamid H Barzegar Zohreh Z Kabiri Maryam M Zeraatpisheh Zahra Z Farjoud-Kouhanjani Mohsen M Jafari Anahita A Sasannia Sarvin S Ashrafi Shayan S Nazeri Masoume M Nasiri Sara S Shahisavandi Mina M

Journal of medical virology 20211024 3

We investigated the frequency of brain fog in a large cohort of patients with documented coronavirus disease-2019 (COVID-19) who have survived the illness. We also scrutinized the potential risk factors associated with the development of brain fog. Adult patients (18-55 years of age), who were referred to the healthcare facilities anywhere in Fars province from February 19, 2020 to November 20, 2020 were included. All patients had a confirmed COVID-19 diagnosis. In a phone call, at least 3 month ...[more]

PMID: 34672377

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Project description:ObjectivesLimited research exists on COVID-19 associated brain fog, and on the long-term cognitive and psychiatric sequelae in racially and ethnically diverse patients. We characterize the neuropsychological sequelae of post-acute COVID-19 in a diverse cohort and investigate whether COVID-19 clinical severity remains associated with brain fog and cognitive deficits approximately 2 years post infection.MethodsA cross-sectional study of patients with a history of COVID-19 hospitalization (March-September 2020). COVID-19 clinical severity was indexed using the National Early Warning Score 2 and a comprehensive neuropsychological tele-battery was administered 2 years post discharge. Pearson's r correlations assessed association, while independent sample t-tests examined group differences. Significant outcomes were further analyzed using multiple regression and ANCOVAs, adjusting for key covariates.ResultsIn 41 adult patients (19 female, 30 Hispanic, 13 Black, mean age of 65 (SD = 15), COVID-19 level of severity was associated with greater number of endorsed brain fog symptoms (Pearson's r = .34, 95% CI [.04, .59]), worse overall cognitive functioning (global cognition: r = -.36, 95% CI [-.61, -.05]) and reduced performance on an attention and working memory task (digit span backwards: r = -.41, 95% CI [-.66, -.09]) at 2-year follow-up. Brain fog symptoms most associated with COVID-19 severity included difficulty focusing (r = .46, 95% CI [.18, .67]), detached (r = .41, 95% CI [.12, .64]) and feeling sleepy (r = .40, 95% CI [.11, .63]). Patients' cognitive performance was generally below average (global cognition z-score: M = -.96, SD = .66), with group differences based on sex and ethnicity evidenced on individual cognitive tests.DiscussionThis study emphasizes the importance of continued research on the long-term effects of COVID-19 infection on neuropsychological outcomes, particularly among underrepresented, health-disparate groups. Greater understanding of these associations could improve detection and treatment of those at increased risk of cognitive decline or impairment.

Project description:Brain fog is a kind of mental problem, similar to chronic fatigue syndrome, and appears about 3 months after the infection with COVID-19 and lasts up to 9 months. The maximum magnitude of the third wave of COVID-19 in Poland was in April 2021. The research referred here aimed at carrying out the investigation comprising the electrophysiological analysis of the patients who suffered from COVID-19 and had symptoms of brain fog (sub-cohort A), suffered from COVID-19 and did not have symptoms of brain fog (sub-cohort B), and the control group that had no COVID-19 and no symptoms (sub-cohort C). The aim of this article was to examine whether there are differences in the brain cortical activity of these three sub-cohorts and, if possible differentiate and classify them using the machine-learning tools. he dense array electroencephalographic amplifier with 256 electrodes was used for recordings. The event-related potentials were chosen as we expected to find the differences in the patients' responses to three different mental tasks arranged in the experiments commonly known in experimental psychology: face recognition, digit span, and task switching. These potentials were plotted for all three patients' sub-cohorts and all three experiments. The cross-correlation method was used to find differences, and, in fact, such differences manifested themselves in the shape of event-related potentials on the cognitive electrodes. The discussion of such differences will be presented; however, an explanation of such differences would require the recruitment of a much larger cohort. In the classification problem, the avalanche analysis for feature extractions from the resting state signal and linear discriminant analysis for classification were used. The differences between sub-cohorts in such signals were expected to be found. Machine-learning tools were used, as finding the differences with eyes seemed impossible. Indeed, the A&B vs. C, B&C vs. A, A vs. B, A vs. C, and B vs. C classification tasks were performed, and the efficiency of around 60-70% was achieved. In future, probably there will be pandemics again due to the imbalance in the natural environment, resulting in the decreasing number of species, temperature increase, and climate change-generated migrations. The research can help to predict brain fog after the COVID-19 recovery and prepare the patients for better convalescence. Shortening the time of brain fog recovery will be beneficial not only for the patients but also for social conditions.

Dataset Information

Long COVID syndrome-associated brain fog.

Publications

Long COVID syndrome-associated brain fog.

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