Most long-COVID sufferers battle neurological symptoms, including some cognitive issues never seen before

Authors: Jocelyn Solis-Moreira JUNE 20, 2022

People continue to experience neurological problems six months after recovering from a COVID-19 infection, finds a recent study from the University of California San Diego. In fact, scientists say most coronavirus long-haulers battle brain-related issues.

The findings are part of a long-term study tracking the progression of neurological symptoms in people with long COVID. Not only do neurological symptoms persist, the researchers also found never-before-seen motor coordination and cognitive issues in long-haulers.

“It’s encouraging that most people were showing some improvement at six months, but that wasn’t the case for everyone,” says Dr. Jennifer S. Graves, associate professor at UC San Diego School of Medicine and neurologist at UC San Diego Health in a media release. “Some of these participants are high-level professionals who we’d expect to score above average on cognitive assessments, but months after having COVID-19, they’re still scoring abnormally.” 

Between October 2020 to October 2021, the research team tracked the health of 56 people who developed neurological issues after a mild to moderate COVID-19 infection. None of the people had a history of neurological conditions before becoming sick from the virus. People first received a neurological exam, cognitive test, survey questions on symptoms, and the option for a brain scan.

In the first visit, 89% of people reported fatigue, and 80% said they felt constant headaches. Other neurological symptoms ranged from memory troubles, insomnia, and loss of concentration. About 80% of people said the neurological symptoms affected their quality of life.

After a 6-month follow-up, only one-third of people fully recovered from their neurological symptoms. The other two-thirds continued to show neurological symptoms, though the symptoms decreased in severity. For those that continued to have symptoms, the most common was memory impairment and lack of focus.

One surprising finding for the team was that 7% of people had a set of symptoms that to their knowledge have never observed in people with long COVID. The symptoms included cognitive deficits, tremors, and trouble keeping their balance. The authors labeled the symptoms as Tremor, Ataxia, and Cognitive deficit (PASC-TAC).

“These are folks who had no neurological problems before COVID-19, and now they have an incoordination of their body and possible incoordination of their thoughts,” comments Dr. Graves. “We didn’t expect to find this, so we want to get the word out in case other physicians see this too.” 

There is still much work to be done to study how the virus penetrates the brain. Dr. Graves hypothesizes that inflammatory autoimmune responses in the brain caused by the infection is likely the reason behind these delayed neurological symptoms.

The study is projected to last for 10 years, with researchers following up with people every year. Other parts of the research will focus on how different COVID-19 variants and vaccines impact persistent neurological symptoms.

“To have people’s cognition and quality of life still impacted so long after infection is something we as a society need to be taking a serious look at,” says Dr. Graves. “We still need to know how common this is, what biological processes are causing this, and what ongoing health care these people will need. This work is an important first step to getting there.”

The study is published in the journal Annals of Clinical and Translational Neurology.

Lifestyle risk behaviors among adolescents: a two-year longitudinal study of the impact of the COVID-19 pandemic

Authors: Anne Gardner1, Jennifer Debenham1, Nicola Clare Newton1, Cath Chapman Fiona Elizabeth Wylie2, Bridie Osman1, Maree Teesson, Katrina Elizabeth Champion The British Medical Journal


Objective To examine changes in the prevalence of six key chronic disease risk factors (the “Big 6”), from before (2019) to during (2021) the COVID-19 pandemic, among a large and geographically diverse sample of adolescents, and whether differences over time are associated with lockdown status and gender.

Design Prospective cohort study.

Setting Three Australian states (New South Wales, Queensland and Western Australia) spanning over 3000 km.

Participants 983 adolescents (baseline Mage=12.6, SD=0.5, 54.8% girl) drawn from the control group of the Health4Life Study.

Primary outcomes The prevalence of physical inactivity, poor diet (insufficient fruit and vegetable intake, high sugar-sweetened beverage intake, high discretionary food intake), poor sleep, excessive recreational screen time, alcohol use and tobacco use.

Results The prevalence of excessive recreational screen time (prevalence ratios (PR)=1.06, 95% CI=1.03 to 1.11), insufficient fruit intake (PR=1.50, 95% CI=1.26 to 1.79), and alcohol (PR=4.34, 95% CI=2.82 to 6.67) and tobacco use (PR=4.05 95% CI=1.86 to 8.84) increased over the 2-year period, with alcohol use increasing more among girls (PR=2.34, 95% CI=1.19 to 4.62). The prevalence of insufficient sleep declined across the full sample (PR=0.74, 95% CI=0.68 to 0.81); however, increased among girls (PR=1.24, 95% CI=1.10 to 1.41). The prevalence of high sugar-sweetened beverage (PR=0.61, 95% CI=0.64 to 0.83) and discretionary food consumption (PR=0.73, 95% CI=0.64 to 0.83) reduced among those subjected to stay-at-home orders, compared with those not in lockdown.

Conclusion Lifestyle risk behaviors, particularly excessive recreational screen time, poor diet, physical inactivity and poor sleep, are prevalent among adolescents. Young people must be supported to find ways to improve or maintain their health, regardless of the course of the pandemic. Targeted approaches to support groups that may be disproportionately impacted, such as adolescent girls, are needed.

Trial registration number Australian New Zealand Clinical Trials Registry (ACTRN12619000431123)

Data availability statement

Data are available upon reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial.

Strengths and limitations of this study

  • A prospective cohort design was used to explore changes in a comprehensive set of health indicators among adolescents, from before (2019) to during (2021) the COVID-19 pandemic, and whether changes varied by gender and lockdown status.
  • The study included a large (n=983) and geographically diverse sample of adolescents across three Australian states (New South Wales, Queensland and Western Australia) spanning over 3000 km.
  • Limitations of the research include the reliance on self-report measures, and while the sample was diverse, it is not representative of the Australian population.

The global spread of COVID-19 and subsequent lockdown measures have presented challenges worldwide. While disease severity, hospital admissions and deaths have typically been lower among adolescents, compared with adults,1 government responses, such as movement restrictions and school closures, present further potential health ramifications due to the related changes in lifestyle behaviours. Critically, despite some studies demonstrating the significant physical and mental health consequences of lockdown measures on adolescents,2–4 research has typically focused on a few select behaviours, rather than a comprehensive set of health indicators. Given the unique presentation of COVID-19 across countries and differing government responses, there is a need to examine health-related changes from a variety of contexts to develop a better understanding of global health.

According to the Oxford COVID-19 Government Response Tracker,5 the strictness of lockdown restrictions since the first confirmed cases in January 2020 to October 2021 was similar in Australia, the USA and the UK, with average stringency indexes of 60/100, 59/100 and 61/100, respectively, despite much lower incidence and mortality rates in Australia.6 However, there can be substantial variation within countries.7 8 In Australia, for example, stringency index values varied between states and territories by as much as 68 during 2020.8 The strictest and most extensive lockdown restrictions have been implemented in Victoria and New South Wales (NSW), two of the most populous states that saw heightened case numbers during the January 2020 to October 2021 period, while other Australian states, such as Queensland (QLD) and Western Australia (WA), experienced far fewer cases and restrictions.9 10 The Australian context may therefore serve as a case study for understanding the impact of various levels of restrictions on adolescent health behaviours.

Previous research has highlighted the importance of six key lifestyle behaviours, including diet, physical activity, sleep, sedentary behaviour (including recreational screen time), alcohol use and smoking—collectively referred to as the ‘Big 6’—for the short-term and long-term health of adolescents.11–14 These behaviours are common among youth worldwide, with more than 80% of adolescents insufficiently physically active15 and screen time rapidly increasing.14 16 The Big 6 contribute significantly to global disease burden and are known predictors of chronic diseases, including cancer, cardiovascular disease and mental disorders.13 17

Research suggests that COVID-19 has impacted the Big 6, and in turn, the health of adolescents. For example, youth in Europe and Palestine have gained weight during the pandemic,18 19 which may be the result of increased consumption of discretionary food and sugar-sweetened beverages (SSB) during lockdown periods.18 20 However, some studies report improvements in dietary behaviours, including less SSB consumption among Colombian adolescents, higher fruit intake among Italian youth and higher vegetable intake among adolescents from Spain, Brazil and Chile.20 21 Among the few existing Australian studies, Munasinghe et al 3 found physical distancing measures implemented in the initial lockdown period (March–April 2020) were associated with a decline in fast food consumption among adolescents, but there were no changes in fruit and vegetable consumption. However, it is unknown whether these changes have been sustained, or whether other dietary behaviours changed.

The pandemic presents particular challenges for movement behaviours, including physical activity, sedentary behaviour and sleep. Typically, lockdowns are associated with lower levels of adolescent physical activity4 18 20 22 23 and increased screen time, both for remote learning and recreation, resulting in sedentary lifestyles.3 4 23 24 However, some research in Australia25 and Germany26 suggests physical activity increased.26 International studies also report an increase in adolescent sleep duration during lockdown periods,18 20 but higher prevalence of sleep problems, particularly among girls.27 Similarly, Australian adolescents perceived an increase in sleep difficulties and had increased sleep disturbance during the first lockdown.25 One study28 reported increased sleep duration among Australian adolescents who were engaged in remote learning; however, another3 found no changes.

Studies investigating the impact of the pandemic on adolescent alcohol and tobacco use have produced mixed findings. For example, alcohol use is reported to have increased among Canadian adolescents,29 reduced among Spanish adolescents,30 while there was no change in alcohol or tobacco use among adolescents from the USA.31 Further, European research suggests a reduction in adolescent tobacco use during the pandemic period,30 32 yet there has been an increase in Uganda.33 To date, changes in alcohol and tobacco use among Australian adolescents have not been examined.

Evidence suggests that the prevalence of the Big 6 varies by gender. For example, adolescent girls are more likely to be physically inactive, whereas adolescent boys are more likely to engage in high levels of recreational screen time, have a poor diet, and use alcohol and tobacco.15 34–38 However, less is known about whether changes in lifestyle behaviours over the pandemic period vary by gender.

To address these gaps in the literature, this study aims to examine changes in the prevalence of the Big 6 among a large, geographically diverse sample of adolescents, from before to during the COVID-19 pandemic, and explore whether differences over time are associated with gender and lockdown status.


Participants and procedure

The sample comprised participants from three Australian states (NSW, QLD and WA), spanning over 3000 km, who were randomly allocated to the control group of the Health4Life Study.39 Participants who provided written consent and had parental consent (passive, active written or active verbal, depending on approved procedures for the school type and region) completed self-report assessments in a supervised classroom setting. Only students who provided data prior to the beginning of the pandemic (between July and November 2019) and during the pandemic (approximately 24 months after baseline, between July and 10 October 2021) were included in this study. During the 2021 data collection period, Australia had strict border policies, restricting international travel and mandating hotel quarantine, while state-level and territory-level border policies for domestic travel varied.40 Greater Sydney, including the Central Coast, Shellharbour and Wollongong were subjected to lockdown restrictions under the NSW stay-at-home Public Health Order41; the most stringent of which included not being permitted to leave the home unless essential (eg, one person per household to shop for food or 1 hour of exercise per day), movement restricted to a 5 km radius of the home, closure of all non-essential retail (eg, hairdressers), home-based work and schooling requirements, curfews, and mandatory mask wearing, with a high police presence and large fines enforced for non-adherence. QLD, WA and areas outside of Greater Sydney were not subjected to extended stay-at-home lockdown restrictions.

Patient and public involvement

Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.


Sociodemographic characteristics

Participants self-reported their age and gender (male, female, non-binary/gender fluid, missing). A binary ‘lockdown’ variable was created reflecting participants who attended schools in the Greater Sydney region that were subjected to the stay-at-home Public Health Order in 2021 and those who were not.41


Dietary intake was assessed using items adapted from the NSW School Physical Activity and Nutrition Survey.42 Participants self-reported the number of metric cups of SSB usually consumed per week or day. A binary variable was created to reflect high (≥5–6 cups or more/week) and low consumption (≤4 cups/week). Participants reported how often they consume six discretionary food items (hot chips, French fries, wedges or fried potatoes; potato crisps or other salty snacks; snack foods, for example, sweet and savoury biscuits, cakes, doughnuts or muesli bars; confectionary; ice cream or ice blocks; and takeaway meals or snacks). High discretionary food consumption was defined as eating any of the items ‘2 or more times/day’, or eating at least two of the items ‘3–4 times/week’ or more often. Participants reported the number of serves of fruit and vegetables consumed per day, and in line with the Australian dietary guidelines,43 insufficient fruit and vegetable consumption was defined as <2 serves of fruit and <5 serves of vegetables per day, respectively.

Physical activity

A single item was used to assess the number of days over the past week that participants engaged in moderate-to-vigorous physical activity for at least 60 min.44 As per the Australian health guideline, insufficient physical activity was defined as engaging in <60 min of moderate-to-vigorous physical activity/day.45

Recreational screen time

The International Sedentary Assessment Tool46 was used to evaluate free time spent on a typical weekday and weekend day over the past 7 days watching television/DVDs/streaming services or using an electronic device. In line with the Australian health guideline,45 excessive recreational screen time was defined as >2 hours/day.


The Modified Sleep Habits Survey47 was used to assess sleep duration. Total sleep time was calculated by finding the difference between the time participants reported first attempting sleep, and the time they woke up in the morning, minus the reported time taken to fall asleep from first attempt, with a weighted average sleep duration calculated for school and weekend nights. Self-reported bedtime, waketime and sleep duration have been shown to be reliable and valid in adolescent populations.48 49 As per the Australian guidelines, insufficient sleep was defined as an average duration outside of 9–11 hours/night for those aged 11–13 years, or 8–10 hours/night for those aged 14–17 years.45

Alcohol and tobacco use

Alcohol and tobacco use were measured using two dichotomous (yes/no) items drawn from previous large scale trials and population based epidemiological surveys50 51: ‘Have you had a full standard alcoholic drink in the past 6 months?’ and ‘In the past 6 months, have you tried cigarette smoking, even one or two puffs?’

Statistical analysis

Generalised linear mixed models were used to investigate change over time in the Big 6. Owing to the high prevalence of outcomes, we used Robust Poisson methods to generate prevalence ratios (PR) and 95% CIs, to overcome some of the limitations of reporting ORs from logistic regressions, which may appear inflated.52 PR are interpreted as the estimated prevalence of an outcome in one group, compared with another, providing an indication of a change in prevalence, as opposed to risk or odds. All models included a random intercept at the student level and school level, Robust Poisson distribution and a log link function, where time is continuous and represents the prepandemic (2019) and mid-pandemic scores (2021). Group by time interactions were estimated to assess change in the prevalence of the Big 6 over time in relation to gender (female/male, given the low prevalence of the ‘non-binary/gender fluid’ (0.1%) and ‘prefer not to say’ (.5%) subgroups) and the presence of lockdown restrictions during the 2021 survey occasion. All analyses were conducted in Stata V.17.53


Descriptive statistics

The sample included 983 students (baseline Mage=12.6, SD=0.5, 54.8% girl) from 22 schools across NSW, QLD and WA (see table 1 for baseline characteristics). At the 2021 survey occasion, approximately one-third of the sample (32.7%) was under lockdown restrictions. Table 2 presents the prevalence of lifestyle risk behaviours over time.

Table 1

Sample characteristics

Table 2

Prevalence of lifestyle risk behaviours before and during the COVID-19 pandemic

Changes in lifestyle risk behaviours

Change over time in the prevalence of the Big 6 and differences based on lockdown status and gender are illustrated in figure 1, with PR and CIs detailed in online supplemental table 1.

Supplemental material


Figure 1

Figure 1

Change over time in the prevalence of the Big 6 and differences based on lockdown status and gender. a≤13 years old: 9 to 11 hours/night, 14-17years: 8 to 10 hours/night. MVPA, moderate-to-vigorous physical activity; SSB, sugar-sweetened beverage.

Dietary behaviours

SSB consumption

There was no significant change in the prevalence of high SSB consumption over time (PR=0.83 95% CI=0.58 to 1.18). However, the prevalence was 39% lower in individuals under lockdown (PR=0.61, 95% CI=0.64 to 0.83) over time, compared with those not in lockdown.

Discretionary food consumption

There was no significant change in the prevalence of high discretionary food consumption over time (PR=0.97, 95% CI=0.86 to 1.09). However, the prevalence was 27% lower for individuals living under lockdown (PR=0.73, 95% CI=0.64 to 0.83) over time, compared with those not in lockdown.

Fruit and vegetable intake

The prevalence of insufficient fruit intake increased by 50% over time (PR=1.50, 95% CI=1.26 to 1.79). There were no changes in the prevalence of insufficient vegetable intake over time (PR=1.01, 95% CI=0.97 to 1.06), and the presence of lockdown restrictions was not associated with a change in the prevalence of insufficient fruit or vegetable intake over time.

There were no gender-based differences in the prevalence of high SSB consumption, high discretionary food consumption or insufficient fruit/vegetable intake over time.


The prevalence of insufficient sleep decreased by 26% over time (PR=0.74 95% CI=0.68 to 0.81). Girls reported a higher prevalence of insufficient sleep over time, compared with boys (PR=1.24, 95% CI=1.10 to 1.41). The presence of lockdown restrictions was not associated with a change in the prevalence of insufficient sleep over time.

Recreational screen time

There was a 6% increase in the prevalence of excessive recreational screen time over time (PR=1.06, 95% CI=1.03 to 1.11). Gender and the presence of lockdown restrictions were not associated with a change in the prevalence of excessive recreational screen time over time.

Physical activity

There was no change in the prevalence of insufficient physical activity over time (PR=1.03, 95% CI=1.00 to 1.07). Neither gender nor the presence of lockdown restrictions was associated with change in the prevalence of insufficient physical activity over time.

Alcohol use

The prevalence of past 6-month alcohol use increased by 334% over time (PR=4.34, 95% CI=2.82 to 6.67). The prevalence of alcohol use increased more in girls compared with boys (PR=2.34, 95% CI=1.19 to 4.62). The presence of lockdown restrictions was not associated with change in the prevalence of past 6-month alcohol use over time.

Tobacco use

The prevalence of past 6-month tobacco use increased by 305% over time (PR=4.05 95% CI=1.86 to 8.84). Neither gender nor the presence of lockdown restrictions was associated with change in the prevalence of past 6-month tobacco use over time.


This study was the first to explore changes in all of the Big 6 lifestyle risk behaviours among a large, geographically diverse cohort of adolescents, from before (2019) to during (2021) the COVID-19 pandemic, and whether changes varied by gender and lockdown status. Over the 2-year period, the prevalence of excessive recreational screen time, insufficient fruit intake and alcohol and tobacco use increased, with alcohol use increasing among girls in particular. The prevalence of insufficient sleep reduced in the overall sample; yet, increased among girls. Being in lockdown was associated with improvements in SSB consumption and discretionary food intake.

These findings highlight the varied impact of the pandemic across countries. For example, consistent with other Australian findings,3 but in contrast to international research,18 20 the prevalence of discretionary food intake decreased among those in lockdown. Yet in line with some international findings,21 SSB intake reduced among adolescents in lockdown. This may reflect increased parental monitoring during lockdown and reduced opportunistic exposure to fast food due to not being with friends or commuting to school.54 55 As such, continued parental monitoring beyond the lockdown period and the promotion of healthy food options may be beneficial. However, improvements in healthy dietary behaviours were not observed. In fact, the prevalence of insufficient fruit intake increased among the full sample. This may relate to the higher cost of fresh fruit and vegetables in Australia during the pandemic, caused by labour shortages within the farming, wholesale and retail sectors due to fewer working holiday-makers.56 These findings support calls for governments to consider broader policy-level changes to improve diet, such as taxes and subsidies.57

The finding that sleep duration improved from before to during the pandemic is consistent with some Australian28 and international18 20 studies. This contrasts typical trends over adolescence58 59 and was despite an increase in the prevalence of excessive recreational screen time, which is often considered a primary contributor to poor sleep.60 It is posited that the time usually spent getting ready and commuting to school is instead spent getting additional sleep during periods of lockdown, leading to calls for delayed school start times28; however, we found no differences based on lockdown status to support this. The finding that insufficient sleep increased among girls is consistent with international research reporting increased sleep disorders among girls during the pandemic27 and may reflect the association between girl pubertal maturation and the emergence of insomnia symptoms.61 Targeted intervention approaches to address sleep among girls are needed.

Notably, in contrast to previous international and Australian research attributing increased screen time to lockdown and physical distancing measures,3 4 23 we found no difference in the prevalence of excessive recreational screen time between the lockdown and non-lockdown groups. This increase may instead reflect general trends of increasing screen time among adolescents.16 These findings highlight the value of assessing behaviours among adolescents both in lockdown and not in lockdown in the same period for comparability, and the need for effective interventions targeting screen time among adolescents.39

Contrasting typical trends over adolescence and previous pandemic research,4 23 the prevalence of insufficient physical activity did not change over time, nor did it increase more for those in lockdown. Previous studies have attributed reductions in physical activity during the pandemic to government responses, such as the cancellation of organised sport and closure of gyms and recreation centres.1 23 However, given the current data were from the 2021 lockdown, whereas previous studies focused on the initial lockdown in 2020, it may be that over time, adolescents have learnt to adapt to the rapidly changing situation and find other ways to achieve their physical activity goals (eg, replaced organised sport with outdoor gym sessions and training). It may also be that other forms of physical activity, such as light and incidental physical activity, have been more severely impacted. Future research would benefit from assessing how these different forms of physical activities changed throughout the pandemic.

Finally, although alcohol and tobacco use increased over time, the prevalence of these behaviours at the first timepoint, when participants were aged 12, was very low and remained relatively low 24 months later. This increase is to be expected among adolescents62; however, the greater increase in alcohol use among girls was unexpected. Considering this and the increase in prevalence of insufficient sleep, girls may be disproportionately impacted by the pandemic. This may reflect general patterns of higher prevalence and increasing trends of mental health problems among adolescent girls across the globe,63 64 which are often comorbid with poor sleep and substance use; as well as narrowing of the gender gap in alcohol use among more among recent cohorts.35 65 Links between these factors are complex66 and assessing changes in mental health alongside changes in the Big 6 may be a useful future research direction.

Key strengths of this study include having assessment occasions before and during the pandemic, rather than relying on retrospective accounts of perceived changes in behaviours, and a sample comprised of adolescents both in and not in lockdown at follow-up for comparability. However, we cannot rule out the potential impact of other factors, such as maturation or mental health, that could also be influencing the Big 6. Although the study builds on previous research that has focused on the early pandemic period, claims about behavioural shifts across the early and late pandemic periods need to be interpreted with caution. Other limitations include the reliance on self-report measures, and while the sample was more diverse than other Australian studies, it is limited to three Australian states and is therefore not representative of the entire Australian adolescent population.14


Lifestyle risk behaviours, particularly excessive recreational screen time, poor diet, physical inactivity and poor sleep, are prevalent among adolescents and should be addressed with effective behavior change interventions.39 With the pandemic remaining a continually evolving situation across the world, the impact on health behaviors is also likely to be dynamic and diverse. Supporting young people to improve or maintain their health behaviours, regardless of the course of the pandemic, is important, alongside targeted research and intervention efforts to support groups that may be disproportionately impacted, such as adolescent girls.


  1. MCRI COVID-19 Governance Group. COVID-19 and child and adolescent health. Victoria, Australia, 2021.Google Scholar
  2. Caroppo E , Mazza M , Sannella A , et al . Will nothing be the same again?: changes in lifestyle during COVID-19 pandemic and consequences on mental health. Int J Environ Res Public Health 2021;18:8433.doi:10.3390/ijerph18168433 Google Scholar
  3. Munasinghe S , Sperandei S , Freebairn L , et al . The impact of physical distancing policies during the COVID-19 pandemic on health and well-being among Australian adolescents. J Adolesc Health 2020;67:65361.doi:10.1016/j.jadohealth.2020.08.008 pmid: CrossRefPubMedGoogle Scholar
  4. Li SH , Beames JR , Newby JM , et al . The impact of COVID-19 on the lives and mental health of Australian adolescents. Eur Child Adolesc Psychiatry 2021.doi:doi:10.1007/s00787-021-01790-x. [Epub ahead of print: 28 Apr 2021].pmid: Google Scholar
  5. Hale T , Angrist N , Goldszmidt R , et al . A global panel database of pandemic policies (Oxford COVID-19 government response Tracker). Nat Hum Behav 2021;5:529–38.doi:10.1038/s41562-021-01079-8 PubMedGoogle Scholar
  6. Dong E , Du H , Gardner L . An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020;20:533–4.doi:10.1016/S1473-3099(20)30120-1 pmid: CrossRefPubMedGoogle Scholar
  7. Hallas L , Hatibie A , Majumdar S . Variation in US states’ responses to COVID-19 2.0. Blavatnik School of Government Working Paper, 2020.Google Scholar
  8. Australian Bureau of Statistics. State economies and the stringency of COVID-19 containment measures, December quarter 2020. Canberra,Australia: ABS, 2021.Google Scholar
  9. COVID-19-AU. COVID-19 in Australia real-time report, 2022. Available: [Accessed 28 Feb 2022].Google Scholar
  10. Australian Bureau of Statistics. National, state and territorypopulation. Canberra: ABS, 2021.Google Scholar
  11. Ding D , Rogers K , van der Ploeg H , et al . Traditional and emerging lifestyle risk behaviors and all-cause mortality in middle-aged and older adults: evidence from a large population-based Australian cohort. PLoSMed 2015;12:e1001917.doi:10.1371/journal.pmed.1001917 pmid: PubMedGoogle Scholar
  12. Lynch BM , Owen N . Too much sitting and chronic disease risk: steps to move the science forward. Ann Intern Med 2015;162:146–7.doi:10.7326/M14-2552 pmid: CrossRefPubMedGoogle Scholar
  13. Ezzati M , Riboli E . Behavioral and dietary risk factors for noncommunicable diseases. N Engl J Med Overseas Ed 2013;369:95464.doi:10.1056/NEJMra1203528 Google Scholar
  14. Champion KE , Chapman C , Gardner LA , et al . Lifestyle risks for chronic disease among Australian adolescents: a cross-sectional survey. Med J Aust 2022;216:156–7.doi:10.5694/mja2.51333 pmid: PubMedGoogle Scholar
  15. Guthold R ,Stevens GA , Riley LM , et al . Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc Health 2020;4:23–35.doi:10.1016/S2352-4642(19)303232 pmid: PubMedGoogle Scholar
  16. Yang L ,Cao C , Kantor ED , et al . Trends in sedentary behavior among the US population, 20012016. JAMA 2019;321:158797. doi:10.1001/jama.2019.3636  pmid: PubMedGoogle Scholar
  17. Australian Institute of Health and Welfare. Australia’s health 2018. Australia’s health series no. 16 AUS 221. Canberra: AIHW, 2018.Google Scholar
  18. Allabadi H , Dabis J , Aghabekian V . Impact of COVID-19 lockdown on dietary and lifestyle behaviours among adolescents in Palestine. Dyn Hum Health 2020;2020: 7.Google Scholar
  19. Stavridou A , Kapsali E , Panagouli E , et al . Obesity in children and adolescents during COVID-19 pandemic. Children 2021;8:135. doi:10.3390/children8020135  pmid: CrossRefPubMedGoogle Scholar
  20. Pietrobelli A , Pecoraro L , Ferruzzi A , et al . Effects of COVID-19 Lockdown on lifestyle behaviors in children with obesity living in Verona, Italy: a longitudinal study. Obesity 2020;28:1382–5.doi:10.1002/oby.22861  pmid:http://www.ncbi.nlm. PubMedGoogle Scholar
  21. Ruiz-Roso MB , de Carvalho Padilha P , Mantilla-Escalante DC , et al . Covid-19 Confinement and Changes of Adolescent’s Dietary Trends in Italy, Spain, Chile, Colombia and Brazil. Nutrients 2020;12:1807.doi:10.3390/nu12061807 Google Scholar
  22. Bates L , Zieff G , Stanford K , et al . COVID-19 impact on behaviors across the 24-hour day in children and adolescents: physical activity, sedentary behavior, and sleep. Children 2020;7:138.doi:10.3390/children7090138 pmid: PubMedGoogle Scholar
  23. Reece LJ , Owen K , Foley B , et al . Understanding the impact of COVID-19 on children’s physical activity levels in NSW, Australia. Health Promot J Austr 2021;32:365–6.doi:10.1002/hpja.436 pmid:http: // pubmed/33201543 CrossRefPubMedGoogle Scholar
  24. Lu C , Chi X , Liang K , et al . Moving more and sitting less as healthy lifestyle behaviors are protective factors for insomnia, depression, and anxiety among adolescents during the COVID-19 pandemic. Psychol Res Behav Manag 2020;13:1223–33.doi:10.2147/PRBM.S284103 pmid: PubMedGoogle Scholar
  25. Olive L , Sciberras E , Berkowitz TS . Child and parent physical activity, sleep and screen time during COVID-19 compared to pre-pandemic nationally representative data and associations with mental health 2020.Google Scholar
  26. Schmidt SCE , Anedda B , Burchartz A , et al . Physical activity and screen time of children and adolescents before and during the COVID-19 lockdown in Germany: a natural experiment. Sci Rep 2020;10:21780.doi:10.1038/s41598-020-78438-4 pmid: PubMedGoogle Scholar
  27. Fidancı İzzet , Aksoy H , Yengil Taci D , et al . Evaluation of the effect of the COVID-19 pandemic on sleep disorders and nutrition in children. Int J Clin Pract 2021;75:e14170.doi:10.1111/ijcp.14170 pmid: PubMedGoogle Scholar
  28. Stone JE , Phillips AJK , Chachos E , et al . In-person vs home schooling during the COVID-19 pandemic: differences in sleep, circadian timing, and mood in early adolescence. J Pineal Res 2021;71:e12757.doi:10.1111/jpi.12757  pmid: PubMedGoogle Scholar
  29. Dumas TM , Ellis W , Litt DM . What does adolescent substance use look like during the COVID-19 pandemic? Examining changes in frequency, social contexts, and Pandemic-Related predictors. J AdolescHealth 2020;67: 3541.doi:10.1016/j. jadohealth. 2020.06.018 pmid:http: //www.ncbi.nlm. PubMedGoogle Scholar
  30. Rogés J ,Bosque-Prous M , Colom J , et al . Consumption of alcohol, cannabis, and tobacco in a cohort of adolescents before and during COVID-19 confinement. Int J Environ Res Public Health 2021;18:7849 .doi:10.3390/ijerph18157849  pmid: PubMedGoogle Scholar
  31. Chaffee BW , Cheng J , Couch ET , et al . Adolescents’ substance use and physical activity before and during the COVID-19 pandemic. JAMA Pediatr 2021;175:715–22.doi:10.1001/jamapediatrics.2021.0541 pmid: PubMedGoogle Scholar
  32. Benschop A , van Bakkum F , Noijen J . Changing patterns of substance use during the coronavirus pandemic: self-reported use of tobacco, alcohol, cannabis, and other drugs. Front Psychiatry 2021;12:633551. doi:10.3389/fpsyt.2021.633551  pmid: PubMedGoogle Scholar
  33. Matovu JKB , Kabwama SN , Ssekamatte T , et al . COVID-19 awareness, adoption of COVID-19 preventive measures, and effects of COVID-19 Lockdown among adolescent boys and young men in Kampala, Uganda. J Community Health  2021;46:842–53.doi:10.1007/s10900-021-00961-w pmid:http://www. PubMedGoogle Scholar
  34. Active Healthy Kids Australia. Physical literacy: do our kids have all the tools? the 2016 active healthy kids Australia report card on physical activity for children and young people. Adelaide, South Australia: Active Healthy Kids Australia, 2016.Google Scholar
  35. Inchley J , Currie D , Vieno A . Adolescent alcohol-related behaviours: trends and inequalities in the who European region, 2002–2014: observations from the health behaviour in school-aged children (HBSC) who Collaborative cross-national study. viii. Copenhagen: World Health organization, Regional Office for Europe, 2018: + 83 p.Google Scholar
  36. Bucksch J ,Sigmundova D , Hamrik Z , et al . International trends in adolescent Screen-Time behaviors from 2002 to 2010. J Adolesc Health 2016;58:417–25.doi:10.1016/j.jadohealth.2015.11.014 pmid: PubMedGoogle Scholar
  37. Centers for Disease Control and Prevention (CDC). 1991-2019 high school youth risk behavior survey data, 2021.Google Scholar
  38. Morley B , Scully M , Niven P . National secondary students’ diet and activity (NaSSDA) survey, 2012-13. Melbourne: Cancer Council Victoria, 2014.Google Scholar
  39. Teesson M ,Champion KE , Newton NC , et al . Study protocol of the Health4Life initiative: a cluster randomised controlled trial of an eHealth school-based program targeting multiple lifestyle risk behaviours among young Australians. BMJ Open 2020;10:e035662.doi:10.1136/bmjopen-2019035662 pmid:http:// Abstract/FREE Full TextGoogle Scholar
  40. Bower M , Smout S , Ellsmore S . COVID-19 and Australia’s mental health: An overview of academic literature, policy documents, lived experience accounts, media and community reports. Sydney, NSW: Australia’s Mental Health Think Tank, 2021.Google Scholar
  41. NSW Health. Public health (COVID-19 temporary movement and gathering restrictions) order 2021 under the public health act 2010. NSW, Australia, 2021.Google Scholar
  42. Hardy LL , Mihrshahi S , Drayton BA . Nsw schools physical activity and nutrition survey (spans) 2015: full report. Sydney: NSW Department of Health, 2016.Google Scholar
  43. National Health and Medical Research Council. Australian dietary guidelines. Canberra: National Health and Medical Research Council, 2013.Google Scholar
  44. Active Healthy Kids Australia. Physical literacy: do our kids have all the tools? the 2016 report card on physical activity for children and young people. Adelaide, 2016.Google Scholar
  45. The Australian Government Department of Health. Australian 24-hour movement guidelines for children and young people (5 to 17 years): an integration of physical activity, sedentary behaviour, and sleep. Canberra: Commonwealth of Australia, 2019.Google Scholar
  46. Prince SA , LeBlanc AG , Colley RC , et al . Measurement of sedentary behaviour in population health surveys: a review and recommendations. PeerJ 2017;5: e4130.doi:10.7717/peerj.4130 pmid: CrossRefPubMedGoogle Scholar
  47. Short MA , Gradisar M , Lack LC , et al . Estimating adolescent sleep patterns: parent reports versus adolescent self-report surveys, sleep diaries, and actigraphy. Nat Sci Sleep 2013;5:23–6.doi:10.2147/NSS.S38369 pmid:http: // /pubmed/23620690 PubMedGoogle Scholar
  48. Golley RK , Maher CA , Matricciani L , et al . Sleep duration or bedtime? exploring the association between sleep timing behaviour, diet and BMI in children and adolescents. Int J Obes 2013;37:546–51.doi:10.1038/ijo.2012.212 pmid:http: // /pubmed/23295498 CrossRefPubMedGoogle Scholar
  49. Nascimento-Ferreira MV , Collese TS , de Moraes ACF , et al . Validity and reliability of sleep time questionnaires in children and adolescents: a systematic review and meta-analysis. Sleep Med Rev 2016;30:85–96.doi:10.1016/j.smrv.2015.11.006  pmid: PubMedGoogle Scholar
  50. CDC CfDCaP. National youth risk behavior survey. U.S. Department of Health and Human Services, 2019.Google Scholar
  51. Australian Institute of Health and Welfare. National drug strategy household survey 2016: detailed findings. Canberra: AIHW, 2017.Google Scholar
  52. Deddens JA , Petersen MR . Approaches for estimating prevalence ratios. Occup Environ Med 2008;65:501–6.doi:10.1136/oem.2007.034777 pmid: FREE Full TextGoogle Scholar
  53. StataCorp LLC. Stata Statistical Software: Release 17 [program]. College Station: TX: StataCorp LLC, 2021.Google Scholar
  54. Scully M , Morley B , Niven P , et al . Factors associated with frequent consumption of fast food among Australian secondary school students. Public Health Nutr 2020;23:1340–9.doi:10.1017/S1368980019004208 pmid : PubMedGoogle Scholar
  55. Haszard JJ , Skidmore PML , Williams SM , et al . Associations between parental feeding practices, problem food behaviours and dietary intake in New Zealand overweight children aged 4-8 years. Public Health Nutr 2015;18:1036–43.doi:10.1017/S1368980014001256 pmid: PubMedGoogle Scholar
  56. ABARES. Agricultural commodities: September quarter 2021. Canberra: Australian Bureau of Agricultural and Resource Economics and Sciences, 2021.Google Scholar
  57. Cobiac LJ , Tam K , Veerman L , et al . Taxes and subsidies for improving diet and population health in Australia: a cost-effectiveness modelling study. PLoS Med 2017;14:e1002232.doi:10.1371/journal.pmed.1002232 pmid: PubMedGoogle Scholar
  58. Keyes KM , Maslowsky J , Hamilton A , et al . The great sleep recession: changes in sleep duration among US adolescents, 19912012. Pediatrics 2015;135:460.doi:10.1542/peds.2014-2707 pmid: Abstract/FREE Full TextGoogle Scholar
  59. Olds T , Maher C , Blunden S , et al . Normative data on the sleep habits of Australian children andadolescents. Sleep 2010;33:13818.doi:10.1093/sleep/33.10.1381 pmid:http: // PubMedWeb of ScienceGoogle Scholar
  60. Xu F , Adams SK , Cohen SA , et al . Relationship between physical activity, screen time, and sleep quantity and quality in US adolescents aged 16–19. Int J Environ Res Public Health 2019;16:1524.doi:10.3390/ijerph16091524 Google Scholar
  61. Zhang J , Chan NY , Lam SP , et al . Emergence of sex differences in insomnia symptoms in adolescents: a large-scale school-based study. Sleep 2016;39:1563–70.doi:10.5665/sleep.6022 pmid: PubMedGoogle Scholar
  62. Guerin N , White V . ASSAD 2017 Statistics & Trends: Australian Secondary Students’ Use of Tobacco, Alcohol, Over-the-counter Drugs, and Illicit Substances. Cancer Council Victoria, 2018.Google Scholar
  63. Campbell OLK , Bann D , Patalay P . The gender gap in adolescent mental health: a cross-national investigation of 566,829 adolescents across 73 countries. SSM Popul Health 2021;13:100742.doi:10.1016/j.ssmph.2021.100742 pmid: PubMedGoogle Scholar
  64. Högberg B , Strandh M , Hagquist C . Gender and secular trends in adolescent mental health over 24 years – The role of school-related stress. Soc Sci Med 2020;250:112890.doi:10.1016/j.socscimed.2020.112890 pmid: PubMedGoogle Scholar
  65. Slade T , Chapman C , Swift W , et al . Birth cohort trends in the global epidemiology of alcohol use and alcohol-related harms in men and women: systematic review and metaregression. BMJ Open 2016;6:e011827.doi:10.1136/bmjopen-2016-011827 pmid: CrossRefPubMedGoogle Scholar
  66. Patalay P , Gage SH . Changes in millennial adolescent mental health and health-related behaviours over 10 years: a population cohort comparison study. Int J Epidemiol 2019;48:165064.doi:10.1093/ije/dyz006 pmid: CrossRefPubMedGoogle Scholar

Multivariate profile and acute-phase correlates of cognitive deficits in a COVID-19 hospitalized cohort

Authors: AdamHampshireaDoris A.ChatfieldbAnne ManktelowMPhilbAmyJollyaWilliamTrenderaPeter J.HellyeraMartina DelGiovaneaVirginia F.J.NewcombebJoanne G. Outtrimb BenWarneb JunaidBhattid LindaPointond AnneElmere NyarieSitholebf JohnBradleybgh NathalieKingston lStephen J.Sawceri Edward T.Bullmorecdj…David K.Menonbck1


Volume 47, May 2022, 101417



Preliminary evidence has highlighted a possible association between severe COVID-19 and persistent cognitive deficits. Further research is required to confirm this association, determine whether cognitive deficits relate to clinical features from the acute phase or to mental health status at the point of assessment, and quantify rate of recovery.


46 individuals who received critical care for COVID-19 at Addenbrooke’s hospital between 10th March 2020 and 31st July 2020 (16 mechanically ventilated) underwent detailed computerised cognitive assessment alongside scales measuring anxiety, depression and post-traumatic stress disorder under supervised conditions at a mean follow up of 6.0 (± 2.1) months following acute illness. Patient and matched control (N = 460) performances were transformed into standard deviation from expected scores, accounting for age and demographic factors using N = 66,008 normative datasets. Global accuracy and response time composites were calculated (G_SScore & G_RT). Linear modelling predicted composite score deficits from acute severity, mental-health status at assessment, and time from hospital admission. The pattern of deficits across tasks was qualitatively compared with normal age-related decline, and early-stage dementia.


COVID-19 survivors were less accurate (G_SScore=-0.53SDs) and slower (G_RT=+0.89SDs) in their responses than expected compared to their matched controls. Acute illness, but not chronic mental health, significantly predicted cognitive deviation from expected scores (G_SScore (p=​​0.0037) and G_RT (p = 0.0366)). The most prominent task associations with COVID-19 were for higher cognition and processing speed, which was qualitatively distinct from the profiles of normal ageing and dementia and similar in magnitude to the effects of ageing between 50 and 70 years of age. A trend towards reduced deficits with time from illness (r∼=0.15) did not reach statistical significance.


Cognitive deficits after severe COVID-19 relate most strongly to acute illness severity, persist long into the chronic phase, and recover slowly if at all, with a characteristic profile highlighting higher cognitive functions and processing speed.


This work was funded by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC), NIHR Cambridge Clinical Research Facility (BRC-1215-20014), the Addenbrooke’s Charities Trust and NIHR COVID-19 BioResource RG9402. AH is funded by the UK Dementia Research Institute Care Research and Technology Centre and Imperial College London Biomedical Research Centre. ETB and DKM are supported by NIHR Senior Investigator awards. JBR is supported by the Wellcome Trust (220258) and Medical Research Council (SUAG/051 G101400). VFJN is funded by an Academy of Medical Sciences/ The Health Foundation Clinician Scientist Fellowship. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Evidence before this study

A PubMed search for articles using the terms ‘COVID-19′, ‘chronic’ and ‘cognitive impairment’ returns 85 results between 2020 and 2022, reflecting growing concern that people may suffer persistent cognitive problems after SARS-CoV-2 infection. However, most of these studies have built on either subjective report of cognitive problems or brief pen-and paper assessment scales that lack sensitivity to mild deficits and precision regarding affected cognitive domains.

Added value of this study

Using precision computerised cognitive assessment tools, we observed that 46 COVID-19 patients matched for age, gender, education and first language, 6–10 months after admission for care at Addenbrookes hospital perform less well than controls in terms of cognition. Critically, the scale of their cognitive deficits correlated with acute illness severity as recorded during the hospital stay, but not fatigue or mental health status at the time of cognitive assessment.

Implications of all the available evidence

These results suggest that the patients who have recovered from severe COVID-19 may need longer term support for cognitive deficits that persist into the chronic phase. More research is required to understand the basis of these deficits. Future work will be focused on mapping these cognitive deficits to underlying neural pathologies and inflammatory biomarkers, and to longitudinally track recovery into the chronic phase.


There is growing evidence that COVID-19 can cause lasting cognitive and mental health problems. Recovered patients reporting psychological symptoms including fatigue, cognitive difficulties (“brain fog” and “problems finding the words”), sleep disturbances breathlessness and psychiatric disorders months after infection.1 In the UK alone, 13.7% of 20,000 individuals reported having symptoms inclusive of cognitive difficulties 12 weeks after a positive COVID-19 test (UK Office for National Statistics, April 2021). Mild cases can report persistent cognitive symptoms; however, prevalence is higher in severe cases,2 with ∼33–76% of patients suffering cognitive symptoms 3–6 months post hospitalisation.3,4

The neurobiological and psychological bases of these deficits remain unclear. Imaging biomarker studies indicate multiple likely candidates. Indeed, drawing parallels with serious acute respiratory syndrome (SARS), middle eastern respiratory syndrome (MERS) and post-critical illness/intensive care syndrome, a range of neurological/ central nervous system (CNS) complications can arise from infection.5,6 Most notably, encephalitis, ischaemia, haemorrhage, microstructural and functional changes and cerebrovascular disease (CVD) have been observed in COVID-19 patients, and more recently, evidence of brainstem inflammation using 7 Tesla magnetic resonance imaging (MRI) has been reported.7 There has been concern regarding whether cognitive deficits will remain for years as a chronic syndrome, and whether patients who develop CVD as a result of infection will experience neurodegeneration and dementia in the long-term,789 despite recovery of other acute and sub-acute symptoms.10

Key limitations for much of this early work include a reliance on self-report as opposed to objective assessment of cognitive deficits, the application of neuropsychological scales that lack sensitivity to detect subtle deficits in the formerly unimpaired or precision to differentiate deficits across cognitive domains, and uncertainty regarding longevity of deficits. Furthermore, depression, anxiety, fatigue and post-traumatic stress are elevated post COVID-19 illness,11 which might mediate the association with cognitive sequelae.

Recently, we provided preliminary results addressing some of these limitations. Specifically, we used computerised cognitive assessment technology,12,13 which has superior sensitivity and precision to gold-standard neuropsychological scales,14 to investigate objectively measurable deficits across multiple cognitive domains in a large online cohort15,16 that incidentally included people who reported infection with COVID-19 of varying severity.17 Higher cognitive functions such as spatial planning and analogical reasoning appeared to be disproportionately impaired, especially in hospitalised patients. However, our earlier analyses lacked clinical-record corroboration of self-reported illness severity or hospital treatment. Furthermore, participants primarily were in the early chronic phase ∼2–3 months post illness, which limited insight into the longevity of deficits.

Here, we use the same technology to assess patients at timepoints ranging from between ∼1 and 10 months post admission to hospital for severe COVID-19. We sought to determine whether (i) the finding of higher cognitive deficits after COVID-19 infection can be replicated in a hospital confirmed cohort, (ii) the cognitive deficits relate to features of acute illness vs. mood, anxiety, tiredness or post-traumatic stress disorder (PTSD) at the point of assessment, (iii) the deficits negatively correlate with time since illness and (iv) the scale and profile of deficits is qualitatively comparable to that observed in normal age-related decline or dementia.


Data collection

All patients admitted to Addenbrookes Hospital with COVID-19 between 10th March 2020 and 31st July 2020, who survived and consented to take part were eligible for this cohort study. This comprised 489 patients, of whom 49 were consented to the NIHR COVID-19 BioResource to participate in the study and were administered the follow up battery. The study was approved by the Cambridge Central Research Ethics Committee (17/EE/0025 0025 & IRAS ID: 220277). Of these, 46 patients (27 females, 19 males, age mean=51 years standard deviation (SD)=14 years, range 28–83 years) completed the study protocol adequately to allow analysis (Tables S1–3). Based on the effect size observed in our previous citizen science dataset,17 where people were assessed using the same technology, expected effect size for critically ill hospitalised patients would be >0.5 standard deviations. At n-46, power was sufficient to detect with one-tailed alpha at p < 0.05 a 0.5SD effect size difference as gauged by DfE scores from the linear model at 96% relative to zero and at 94% relative to the matched control group. There was statistical power of 95% to detect medium strength correlations of r = 0.50 at two tailed alpha p < 0.05. Participants completed a custom computerised cognitive assessment battery under supervised conditions via the Cognitron platform,17,18 comprising 8 tasks deployed on an iPad (Supplemental Methods), as well as standard mood, anxiety and post-traumatic stress scales, specifically, the Generalized Anxiety Disorder 7 (GAD-7),19 the Patient Health Questionnaire 9 (PHQ-9)20,21 and the PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders 5 (PCL-5)22 in a return visit to the hospital on average 179 days after illness onset (SD=62 inter-quartile range=81).

Statistical methods

All analyses were conducted in MATLAB R2020a. To enable correlation of deficit magnitude with clinical and mental health measures whilst accounting for population variables, accuracy and median reaction times were extracted for each task, comprising 16 measures (Table S4), were transformed to deviation from expected (DfE) scores (see below definition) relative to N = 66,008 normative datasets (Table S5), comprising individuals who had performed the same set of tasks. Specifically, to calculate DfE scores, linear models were trained to predict performance for each task within the normative dataset from age decade, sex (male, female, other), education level, handedness (left, right ambidextrous) and first language (English, other). The trained models were then applied to the patient demographics, to which they were naive, providing expected scores for each individual. DfE score was quantified as the difference between observed minus predicted score divided by the control standard deviation. Non-compliant individuals from the normative dataset already had been identified and removed based on responding unfeasibly fast given the response time distribution; applying the same threshold identified no non-compliant participants within the patient dataset. Four patients could not complete Verbal Analogies and one could not complete Spatial Planning as they found them too challenging. Control and patient datasets were concatenated, and composites were then calculated by taking the first unrotated principal component (Table S6) across the eight summary score measures (G_SScore), focused on accuracy, and across the eight response time scores (G_RT), focussing on speed of response. Component scores were calculated for each subject via regression of the component loadings matrix across the above measures, excluding any unavailable datapoints, and transformed to DfE score as described above. For further comparison, a set of matched controls was identified from within the normative database and processed in the same manner as the patients. Specifically, for each patient, ten unique control datasets were randomly selected who exactly matched them in terms of age decade, sex, handedness, first language and education level.

All statistical analyses applied a prior significance cut-off set to p < 0.05. T-tests, performed one-tailed with family wise error (FWE) correction for multiple comparisons, evaluated whether patient composite and individual task DfE scores were consistently poorer than expected relative to the matched normative group. Multiple regression determined whether G_SScore and G_RT DfE scores could be predicted from clinical features during the acute hospital stay or mental health measures at the time of assessment. Clinical features were World Health Organisation (WHO) COVID-19 severity score,23 highest C-reactive protein (CRP), mechanical ventilation, extrapulmonary support, days ventilated, tracheostomy, and highest D-dimer; as well as age, sex and time since illness. Mental health scores were the GAD7, PHQ9 and PCL5. Due to high correlations between some of these clinical features, the feature matrix was reduced by applying Principal Component Analysis with varimax rotation, where the number of components was defined according to the Kaiser convention of retaining components with eigenvalues >1. The relationship between G_SScore and G_RT to time since illness was further examined in isolation using bivariate correlations with one-tailed significance.

To qualitatively gauge whether the profile of COVID-19 related cognitive deficits was similar in pattern or scale to age-related decline, standard deviation differences were extracted from the normative models (that is, accounting for the other population variables listed above) for each task between people at ages aged 70–79 minus those 20–29 or 50–59 within the control dataset. For further qualitative comparison, performance data from a previously collected group of 28 early-mid stage dementia patients were submitted to the same DfE pipeline as described above and effect sizes plotted (clinical and demographic details provided in Table S7).

Role of the funding source

The funder of the study had no role in the design of the study, data collection, data analysis, interpretation or writing of the report. All authors had full access to all data within the study. The corresponding authors had final responsibility for the decision to submit for publication.


T-tests of global summary score and response time composites (Figure 1a) confirmed that participants who had been hospitalised due to COVID-19 scored significantly lower and were slower in their responses than would be expected given the control population as gauged by DfE scores (G_SScore estimate=-0.538 SDs, t=-4.214 p < 0.0001; G_RT estimate=0.726SDs, t = 4.507, p < 0.0001). Repeating the analysis for the 43 chronic-phase patients >90 days post symptom onset showed a similar result (G_SScore estimate=-0.524 SDs, t=-3.875 p = 0.0004; G_RT estimate=0.715SDs, t = 4.194, p < 0.0001). Contrasting the DfE scores directly against 460 precisely matched individuals (Figure 2), 10 per patient, from the control database reinforced this observation (mean difference in G_SScore estimate=-0.525SDs, t=-4.327, p < 0.0001; mean difference in G_RT estimate=0.887SDs, t = 5.803, p < 0.0001).

Fig 1
Fig 2

Application of Principal Component Analysis to the matrix of clinical and mental health features identified three components with eigenvalues >1 capturing 74% of the variance (Figure 1b). After varimax rotation, Component 1 captured variance pertaining to general severity of acute illness, including heavy positive loadings from WHO COVID-19 severity score, highest CRP, and requirement for mechanical ventilation, extrapulmonary organ support and days ventilated, moderate positive loading with age and requirement of tracheostomy and moderate negative loading for days since illness. Component 2 had heavy positive loading of requirement for tracheostomy and days ventilated, moderate positive loading for highest D-dimer and mechanical ventilation and extrapulmonary support, and moderate negative loading for females vs. males and time from illness onset. Component 3 had heavy positive loadings for the three mental health scales.

Multiple regression of the component scores onto DfE performance composites (Figure 1c) showed a significant negative correlation between G_SScore and Component 1 (Estimate=-0.346, F(1,42)= 9.392 p = 0.00380), but not Component 2 (Estimate=0.140, F(1,42)=1.841 p = 0.18208) or Component 3 (Estimate=-0.153, F(1,42)=1.855 p = 0.18041). There was also a threshold level negative correlation between G_RT and Component 1 (Estimate=0.305, F(1,42)=4.008 p=​ 0.05178), but not Component 2 (Estimate=-0.177, F(1,42)=1.791 p = 0.21044) or Component 3 (Estimate=0.111, F(1,42)=0.592 p = 0.46861).

Bivariate correlations (Table S8) showed significant associations between G_SScore and Severity WHO COVID-19 ordinal scale, mechanical ventilation, extrapulmonary organ dysfunction support and highest CRP during admission at the one tailed uncorrected threshold. However, the hypothesised trends towards reduced underperformance over time were of small effect size and were statistically non-significant (G_SScore r = 0.15 p = 0.1542, G_RT r=-0.16 p = 0.1486 one tailed and uncorrected). Reanalysing the data focusing exclusively on either those who were or were not ventilated relative to their respective controls showed significant cognitive deficits in both sub-groups (Fig. S1 & Table S9).

Finally, DfE scores were examined at the individual task level. There was a broad pattern of reduced accuracy and slowed response compared to the 460 matched controls (Table 1Figure 2a), with multiple tasks surviving the p < 0.05 one-tailed and family wise error (FWE) corrected for multiple comparisons threshold. As predicted,17 underperformance was more substantial for tasks challenging higher cognitive functions such as Analogical Reasoning (score -0.85SDs RT +1.34SDs) and Spatial Planning (score +0.28SDs RT +0.89SDs), as well as 2D Manipulations (score -0.58SDs RT +0.57SD) and word recall (immediate score -0.43SDs RT +0.43SDs delayed score -0.051SDs RT +0.46SDs).

Table 1. T-tests contrasting patients vs. 460 matched controls (one-tailed and FWE corrected for multiple comparisons).

Empty CellEmpty CellEffect size (DfE)tp (corrected)
AccuracyVerbal analogies-0.854-6.205<0.00001
2D manipulation-0.575-4.2210.00026
Words immediate-0.432-2.8690.03863
Spatial span-0.405-3.6050.00309
Target detection-0.176-1.4501.32876
3D rotation-0.076-0.9962.87946
Words delayed-0.051-0.4585.82405
Spatial planning0.2831.5101.18614
LatencySpatial span0.2311.5431.11135
Words immediate0.4313.0350.02276
Target detection0.4442.5680.09468
Words delayed0.4632.9420.03070
2D manipulation0.5703.8790.00107
3D rotation0.6203.5220.00421
Spatial planning0.8884.7790.00002
Verbal analogies1.3377.018<0.00001

For comparison (Figure 2), the pattern of mean age-related differences in performance of people in their 70s minus 20s or 70s minus 50s was quite distinct, with age related differences being most pronounced for 2D Manipulations, Spatial Span and Target Detection as opposed to Spatial Planning or Verbal Analogies. Furthermore, the 28 dementia patients who undertook 6 of the tasks showed the greatest DfE score on the Word Memory task with notably higher effect size.


Individuals who survive severe COVID-19 illness have objectively measurable cognitive deficits, lasting many months, with respect to age- and demographic-adjusted norms.17,24252627282930 Taking Cohen’s notion of effect sizes as a gauge, the scale of those deficits was large; on average the 0.52SD and 0.89SD levels of underperformance on global accuracy and response time composite measures span the medium to large effect size range. For individuals who required mechanical ventilation, both composites were in the large range at 0.90SDs and 1.0 SDs, respectively, which is somewhat larger than our previous online study using the same assessment tools.17 The deficits within specific cognitive domains were even greater, e.g., Verbal Analogies response times were 1.3SDs longer on average for all patients and 1.7SDs for those who had required mechanical ventilation. Notably, when analysing only those individuals for whom English was the native language, the same pattern of deficits was still evident. Furthermore, our analyses accounted for both first language and education level. These results accord with self-reported problems ‘finding words’31 and neuropsychological case studies indicating verbal fluency deficits in severe COVID-19 patients post recovery.32

By using a large pre-existing normative dataset to correct for normal population variability in cognitive performance, we were able to begin the process of disentangling potential contributors to cognitive deficits post COVID-19. In particular, measures of mood, post-traumatic stress and mental health at the point of assessment were sufficiently dissociable from acute illness severity to be evaluated within the predictor matrix. This distinction is critical, because it is now well established that people who have recovered from severe COVID-19 illness can have a broad spectrum of symptoms of poor mental health11 as do those suffering from Long Covid,1 which could conceivably contribute to both self-perceived and objectively measured cognitive deficits. These include problems with depression, anxiety post-traumatic stress, low motivation, fatigue, low mood, and disturbed sleep. Here, it was clearly the case that acute illness severity was the better predictor of objectively measurable global cognitive deficits during the chronic phase. At the level of individual clinical features, WHO COVID-19 severity score, highest CRP and the requirement for mechanical ventilation and multiple organ support were predictive of poorer cognitive performance.

All patients were recruited from the same hospital and following illness within a narrow timeframe, which given differences in patient treatment and virus variants across time limits our confidence when generalising these results. We believe that this limitation is somewhat mitigated by the concordance between the results presented here and our previous citizen science dataset, published in this journal.17 Nonetheless, future research should seek to determine the relationship between variants, treatment strategies and cognitive outcomes at larger scale.

Regarding how representative the cohort was, the recruited population were younger, and more frequently female, and with a higher proportion of critical care admissions (WHO Ordinal Scale >6) than those who came through the centre (Tables S10–S14). A significant proportion, though not all, of these differences is attributable to the mortality of 24% in the overall admitted population, since non-survivors were older (median age=80 inter quartile range =73–87), more often male (64%), and may have included patients in whom treatment limitation decisions may have been in place.

Our analysis of fatigue post COVID-19 illness was not in the original analysis plan. However, scores capturing self-report of fatigue in the months post illness were available for 38 patients (Tables S1–3) from the Post-Intensive Care Unit Presentation Screen, a brief functional screening tool to inform the rehabilitation needs after treatment in intensive care settings. 28 of them endorsed some level of fatigue. Fatigue score correlated robustly with the mental health composite score (r=-0.45 p = 0.005) but not with the acute illness composite score (r = 0.03 p = 0.852) or either of the cognitive composite scores (G_ACC r = 0.19 p = 0.240; G_RT r=-0.16 p = 0.343). These results indicate that although both fatigue and mental health are prominent chronic sequalae of COVID-19, their severity is likely to be somewhat independent from the observed cognitive deficits.

A further limitation was that the acute clinical features were too highly correlated with each other to dissociate. All but two of the participants requiring mechanical ventilation also required multiple organ support, and the requirement for mechanical ventilation correlated with highest CRP, a measure of inflammation, at r∼=0.8. The observed correlation with a marker of acute inflammation may reflect a causal relationship beyond the severity of respiratory problems; however, given the high correlation to other clinical features of the acute phase, work seeking to disentangle underlying clinical causes of the observed cognitive deficits will require either substantial sized cohorts with sufficient power to delineate highly correlated predictors or additional data types, such as brain imaging in order to detect associations with markers in specific types of neuropathology.

Some previous studies have observed significant recovery across time in terms of cognitive symptoms18 and imaging measures of brain function.33 In accordance with these studies, we did observe slow and non-significant trends towards reduced deficits in both accuracy and response latency as a function of time from illness. We conclude that any recovery in cognitive faculties is at best likely to be slow. It also is important to consider that trajectories of cognitive recovery may vary across individuals depending on illness severity and the neurological or psychological underpinnings, which are likely complex. Plotting recovery trajectories and untangling their multivariate relationships to clinical features will require multi-timepoint studies in larger cohorts.

At a finer multivariate grain, the profile of deficits replicates our previous report in an online cohort of disproportionate underperformance within certain cognitive domains. In concordance with a previous large scale online study this pattern includes tasks designed to assess performance accuracy of attention, memory, difficult word-based reasoning and planning.17 However, we also observed slowed processing speed. On a neurological level, this pattern of impairment aligns with the observation of sub-acute phase hypometabolism within frontoparietal systems after COVID-19 illness26 that are known to be recruited in different combinations and configurations during the performance of these tasks.12,13,34

In this latter respect, the application of an assessment battery that provides a dimensional profile spanning multiple cognitive domains is of value when offering interoperability across studies. Indeed, it was informative to note that this profile of cognitive dysfunction was quite distinct to the normal pattern of age-related decline and to the pattern of deficits observed in early-stage dementia patients. On average, the scale of deficits was most similar to that observed in normal cognitive decline between the ages of 50–70; however, when examined in more detail the pattern of cognitive deficits was most pronounced for different tasks than either age-related decline or the dementia group. These more detailed results highlight the potential value of cross comparing multivariate profile of COVID-19 cognitive deficits to a wider variety of populations in order to identify potential similarities to other neurological conditions. Future work should also expand the repertoire of disorders, especially populations who have recovered from other critical illnesses, and cross relate these detailed cognitive profiles to imaging and blood biomarker measures of neuropathology and tracking recovery and decline trajectories over a longer temporal scale.

In summary, severe COVID-19 illness is associated with significant objectively measurable cognitive deficits that persist into the chronic phase. The scale of the deficits correlates with clinical severity during the acute phase as opposed to mental health status at the time of assessment, shows at best a slow recovery trajectory and the multivariate profile of deficits is consistent with higher cognitive dysfunction as opposed to accelerated ageing or dementia.


This work was funded by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC), NIHR Cambridge Clinical Research Facility (BRC-1215-20014), the Addenbrooke’s Charities Trust and NIHR COVID-19 BioResource RG9402. AH is funded by the UK Dementia Research Institute Care Research and Technology Centre and Imperial College London Biomedical Research Centre. ETB and DKM are supported by NIHR Senior Investigator awards. JBR is supported by the Wellcome Trust (220258) and Medical Research Council (SUAG/051 G101400). VFJN is funded by an Academy of Medical Sciences/ The Health Foundation Clinician Scientist Fellowship. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.


AH and DKM had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: AH, JB, ETB, JBR, DKM

Acquisition, analysis, or interpretation of data: AH, DAC, AM, AJ, WT, PH, MDG, VFJN, JGG, JB, LP, AE, NS, JB, NK, SJS, DKM.

Drafting of the manuscript: AH.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: AH.

Obtained funding: JB, JBR, ETB, DKM.

Supervision: AH, ETB, JBR, DKM.

Data sharing statement

Requests for data should directed to the corresponding authors. Data will be available upon reasonable request.

Declaration of Interests

Dr. Hampshire reports grants from UK Dementia Research Institute, grants from NIHR Imperial Biomedical Research Centre, and grants from NIHR, outside the submitted work; and is Co-director and owner of H2 Cognitive Designs Ltd and director and owner of Future Cognition Ltd, which support online cognitive studies and develop custom cognitive assessment software, respectively. Ms. Chatfield has nothing to disclose. Ms. Manktelow has nothing to disclose. Dr. Jolly has nothing to disclose. Mr. Trender has nothing to disclose. Dr. Hellyer reports being Chief Executive of H2 Cognitive Designs LTD, which provides a platform for online cognitive tests for remote assessment and receives remuneration for role. Ms. Del Giovane has nothing to disclose. Dr. Newcombe reports grants from Academy of Medical Sciences / The Health Foundation Clinician Scientist Fellowship during the conduct of the study. Ms. Outrim has nothing to disclose. Mr. Warne has nothing to disclose. Mr. Bhatti has nothing to disclose. Ms. Pointon has nothing to declare. Ms. Elmer has nothing to disclose. Dr. Sithole has nothing to disclose. Dr. Bradley reports grants from Funding for NIHR BioResource (IS-BRC-1215-20014) during the conduct of the study. Dr. Kingston has nothing to disclose. Dr. Sawcer has nothing to disclose. Dr. Bullmore reports personal fees from GlaxoSmithKline, personal fees from Sosei Heptares, outside the submitted work; and is Honorary Treasurer and member of Council for the Academy of Medical Sciences. Dr. Rowe reports grants from Wellcome Trust, grants from NIHR, grants from Medical Research Council, during the conduct of the study. Dr. Menon reports grants from Lantmannen AB, grants from GlaxoSmithKline Ltd, personal fees from Calico LLC, personal fees from GlaxoSmithKline Ltd, personal fees from Lantmannen AB, other from Integra Neurosciences, outside the submitted work; and reports leadership and fiduciary roles for Queens’ College, Cambridge, Intensive Care National Audit and Research Centre, London, and European Brain Injury Consortium.


We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centres, NHS Trusts and staff for their contribution. We thank the National Institute for Health Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

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1A. Nalbandian, K. Sehgal, A. Gupta, et al.Post-acute COVID-19 syndromeNat Med, 27 (4) (2021), pp. 601-615 View PDFCrossRefView Record in ScopusGoogle Scholar2D. Huang, X. Lian, F. Song, et al.Clinical features of severe patients infected with 2019 novel coronavirus: a systematic review and meta-analysisAnn Transl Med, 8 (9) (2020), p. 576 View PDFCrossRefView Record in ScopusGoogle Scholar3C. Huang, Y. Wang, X. Li, et al.Clinical features of patients infected with 2019 novel coronavirus in Wuhan, ChinaLancet, 395 (10223) (2020), pp. 497-506ArticleDownload PDFGoogle Scholar4V. Chopra, S.A. Flanders, M. O’Malley, A.N. Malani, H.C. PrescottSixty-day outcomes among patients hospitalized with COVID-19Ann Intern Med, 174 (4) (2021), pp. 576-578 View PDFCrossRefView Record in ScopusGoogle Scholar5S. Inoue, J. Hatakeyama, Y. Kondo, et al.Post-intensive care syndrome: its pathophysiology, prevention, and future directionsAcute Med Surg, 6 (3) (2019), pp. 233-246 View PDFCrossRefView Record in ScopusGoogle Scholar6M.H. Lam, Y.K. Wing, M.W. Yu, et al.Mental morbidities and chronic fatigue in severe acute respiratory syndrome survivors: long-term follow-upArch Intern Med, 169 (22) (2009), pp. 2142-2147 View PDFCrossRefView Record in ScopusGoogle Scholar7S. Miners, P.G. Kehoe, S. LoveCognitive impact of COVID-19: looking beyond the short termAlzheimers Res Ther, 12 (1) (2020), p. 170View Record in ScopusGoogle Scholar8D. Bougakov, K. Podell, E. GoldbergMultiple neuroinvasive pathways in COVID-19Mol Neurobiol, 58 (2) (2021), pp. 564-575 View PDFCrossRefView Record in ScopusGoogle Scholar9E. Goldberg, K. Podell, D.K. Sodickson, E. FieremansThe brain after COVID-19: compensatory neurogenesis or persistent neuroinflammation?EClinicalMedicine, 31 (2021), Article 100684ArticleDownload PDFView Record in ScopusGoogle Scholar10L.A. Jason, M.F. Islam, K. Conroy, et al.COVID-19 symptoms over time: comparing long-haulers to ME/CFSFatigue Biomed Health Behav (2021)Google Scholar11S.R. Chamberlain, J.E. Grant, W. Trender, P. Hellyer, A. HampshirePost-traumatic stress disorder symptoms in COVID-19 survivors: online population surveyBJPsych Open, 7 (2) (2021), p. e47View Record in ScopusGoogle Scholar12E. Soreq, I.R. Violante, R.E. Daws, A. HampshireNeuroimaging evidence for a network sampling theory of individual differences in human intelligence test performanceNat Commun, 12 (1) (2021), p. 2072View Record in ScopusGoogle Scholar13A. Hampshire, R.R. Highfield, B.L. Parkin, A.M. OwenFractionating human intelligenceNeuron, 76 (6) (2012), pp. 1225-1237ArticleDownload PDFView Record in ScopusGoogle Scholar14H. Brooker, G. Williams, A. Hampshire, et al.FLAME: a computerized neuropsychological composite for trials in early dementiaAlzheimers Dement, 12 (1) (2020), p. e12098(Amst)View Record in ScopusGoogle Scholar15Hampshire A. Great British intelligence test protocol. Research Square. 2020.Google Scholar16A. Hampshire, P.J. Hellyer, E. Soreq, et al.Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United KingdomNat Commun, 12 (1) (2021), p. 4111View Record in ScopusGoogle Scholar17A. Hampshire, W. Trender, S.R. Chamberlain, et al.Cognitive deficits in people who have recovered from COVID-19EClinicalMedicine (2021), Article 101044ArticleDownload PDFView Record in ScopusGoogle Scholar18Zhao S, Shibata K, Hellyer PJ. et al. Rapid vigilance and episodic memory decrements in COVID-19 survivors. medrXiv 2021.Google Scholar19R.L. Spitzer, K. Kroenke, J.B. Williams, B. LoweA brief measure for assessing generalized anxiety disorder: the GAD-7Arch Intern Med, 166 (10) (2006), pp. 1092-1097 View PDFCrossRefView Record in ScopusGoogle Scholar20K. Kroenke, R.L. Spitzer, J.B. Williams, B. LoweThe patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic reviewGen Hosp Psychiatry, 32 (4) (2010), pp. 345-359ArticleDownload PDFView Record in ScopusGoogle Scholar21R.L. Spitzer, K. Kroenke, J.B. WilliamsValidation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary care evaluation of mental disorders. Patient health questionnaireJAMA, 282 (18) (1999), pp. 1737-1744Google Scholar22C.A. Blevins, F.W. Weathers, M.T. Davis, T.K. Witte, J.L. DominoThe posttraumatic stress disorder checklist for DSM-5 (PCL-5): development and initial psychometric evaluationJ Trauma Stress, 28 (6) (2015), pp. 489-498 View PDFCrossRefGoogle Scholar23WHO Working Group on the Clinical Characterisation and Management of COVID-19 infectionA minimal common outcome measure set for COVID-19 clinical researchLancet Infect Dis, 20 (8) (2020), p. e192-e7Google Scholar24V. Beaud, S. Crottaz-Herbette, V. Dunet, et al.Pattern of cognitive deficits in severe COVID-19J Neurol Neurosurg Psychiatry, 92 (5) (2021), pp. 567-568 View PDFCrossRefView Record in ScopusGoogle Scholar25J. Hellmuth, T.A. Barnett, B.M. Asken, et al.Persistent COVID-19-associated neurocognitive symptoms in non-hospitalized patientsJ Neurovirol, 27 (1) (2021), pp. 191-195 View PDFCrossRefView Record in ScopusGoogle Scholar26J.A. Hosp, A. Dressing, G. Blazhenets, et al.Cognitive impairment and altered cerebral glucose metabolism in the subacute stage of COVID-19Brain, 144 (4) (2021), pp. 1263-1276 View PDFCrossRefView Record in ScopusGoogle Scholar27A. Jaywant, W.M. Vanderlind, G.S. Alexopoulos, C.B. Fridman, R.H. Perlis, F.M. GunningFrequency and profile of objective cognitive deficits in hospitalized patients recovering from COVID-19Neuropsychopharmacology (2021)Google Scholar28K.W. Miskowiak, S. Johnsen, S.M. Sattler, et al.Cognitive impairments four months after COVID-19 hospital discharge: pattern, severity and association with illness variablesEur Neuropsychopharmacol, 46 (2021), pp. 39-48ArticleDownload PDFView Record in ScopusGoogle Scholar29B. Raman, M.P. Cassar, E.M. Tunnicliffe, et al.Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital dischargeEClinicalMedicine, 31 (2021), Article 100683ArticleDownload PDFView Record in ScopusGoogle Scholar30M.S. Woo, J. Malsy, J. Pottgen, et al.Frequent neurocognitive deficits after recovery from mild COVID-19Brain Commun, 2 (2) (2020), p. fcaa205View Record in ScopusGoogle Scholar31J. Seessle, T. Waterboer, T. Hippchen, et al.Persistent symptoms in adult patients one year after COVID-19: a prospective cohort studyClin Infect Dis (2021)Google Scholar32D.M. Whiteside, V. Oleynick, E. Holker, E.J. Waldron, J. Porter, M. KasprzakNeurocognitive deficits in severe COVID-19 infection: case series and proposed modelClin Neuropsychol, 35 (4) (2021), pp. 799-818 View PDFCrossRefView Record in ScopusGoogle Scholar33G. Blazhenets, N. Schroeter, T. Bormann, et al.Slow but evident recovery from neocortical dysfunction and cognitive impairment in a series of chronic COVID-19 patientsJ Nucl Med, 62 (7) (2021), pp. 910-915 View PDFCrossRefView Record in ScopusGoogle Scholar34A.E. Jolly, G.T. Scott, D.J. Sharp, A.H. HampshireDistinct patterns of structural damage underlie working memory and reasoning deficits after traumatic brain injuryBrain, 143 (4) (2020), pp. 1158-1176 View PDFCrossRefView Record in ScopusGoogle Scholar

COVID-19 infection could age brain by 20 years, lower IQ significantly

Authors: Chris Melore Study Finds May 3, 2022

A severe coronavirus infection could leave patients with the brain of a 70-year-old, lowering someone’s IQ by 10 points, according to a new study. Researchers from the University of Cambridge and Imperial College London found that COVID patients are dealing with levels of cognitive impairment which are similar to the decline a healthy person sees between the ages of 50 and 70. Disturbingly, the team warns that this damage may never fully heal.

Long-term cognitive and mental health problems have been a growing issue during the pandemic. Even after the infection passes, a large number of patients continue to experience “brain fog,” problems recalling words, sleep issues, PTSD, and dozens of other symptoms for months — a condition doctors call long COVID. A recent study found that up to six in 10 recovering patients develop long COVID.

Even a mild case of the virus can lead to lingering cognitive issues. Study authors say as many as three-quarters of hospitalized COVID patients could suffer from some level of brain damage and cognitive decline.

Brain aging 20 years — in 6 months

Their new study examined 46 coronavirus patients entering the hospital or an intensive care unit between March and July 2020. Sixteen of these individuals ended up needing mechanical ventilation due to severe infection.

Six months later, the team conducted a series of computerized cognitive tests using the Cognitron platform. The system measures different aspects of brain health, including memory, attention, and reasoning skills. Researchers also examined each person’s levels of anxiety, depression, and PTSD following their infection.

In comparison to over 66,000 healthy people from the general public, results show severe COVID patients were less accurate and had slower response times on cognitive tests. These deficits were even worse among patients needing ventilation while in the hospital.

Estimates show that the damage from a COVID-19 infection led to the same amount of cognitive decline that the average person sees after 20 years of aging — between ages 50 and 70. That’s also the equivalent of losing 10 IQ points during their illness and the months following.

Specifically, COVID survivors did poorly on tasks involving verbal analogical reasoning — which translates to problems with finding the right words in conversation. The patients also displayed slower processing speeds, which the team says connects to the decreases in brain glucose consumption within the frontoparietal network of the brain doctors are seeing during the pandemic. This area of the brain controls a person’s attention span and complex problem-solving skills. It’s also important to their working memory.

“Cognitive impairment is common to a wide range of neurological disorders, including dementia, and even routine ageing, but the patterns we saw – the cognitive ‘fingerprint’ of COVID-19 – was distinct from all of these,” Professor David Menon says in a university release.

Some COVID-19 patients will ‘never fully recover’

Although studies continue to find ties between long COVID and lingering mental health issues, the study authors say they’ve discovered a clearer link between the severity of someone’s infection and cognitive decline. Concerningly, the team found that patients are only gaining some of these skills back over time.

“We followed some patients up as late as ten months after their acute infection, so were able to see a very slow improvement. While this was not statistically significant, it is at least heading in the right direction, but it is very possible that some of these individuals will never fully recover,” Prof. Menon adds.

As for what’s causing COVID to take such a toll on the human brain, researchers say there are a number of possibilities. COVID could be directly infecting the brain, but the team notes this is not a major cause. It’s more likely that a combination of inadequate oxygen or blood supply to the brain, blood clots, and microscopic bleeds are all contributing to the brain damage in coronavirus patients.

There is also growing evidence that COVID-19 produces inflammation which is similar to what people experience while developing Alzheimer’s disease.

“Around 40,000 people have been through intensive care with COVID-19 in England alone and many more will have been very sick, but not admitted to hospital. This means there is a large number of people out there still experiencing problems with cognition many months later. We urgently need to look at what can be done to help these people,” concludes Professor Adam Hampshire from the Department of Brain Sciences at Imperial College London.

The study is published in the journal eClinicalMedicine.The contents of this website do not constitute advice and are provided for informational purposes only.

Alzheimer’s-like signaling in brains of COVID-19 patients

Authors: Steve Reiken,Leah Sittenfeld,Haikel Dridi,Yang Liu,Xiaoping Liu,Andrew R. Marks First published: 03 February 2022



The mechanisms that lead to cognitive impairment associated with COVID-19 are not well understood.


Brain lysates from control and COVID-19 patients were analyzed for oxidative stress and inflammatory signaling pathway markers, and measurements of Alzheimer’s disease (AD)-linked signaling biochemistry. Post-translational modifications of the ryanodine receptor/calcium (Ca2+) release channels (RyR) on the endoplasmic reticuli (ER), known to be linked to AD, were also measured by co-immunoprecipitation/immunoblotting of the brain lysates.


We provide evidence linking SARS-CoV-2 infection to activation of TGF-β signaling and oxidative overload. The neuropathological pathways causing tau hyperphosphorylation typically associated with AD were also shown to be activated in COVID-19 patients. RyR2 in COVID-19 brains demonstrated a “leaky” phenotype, which can promote cognitive and behavioral defects.


COVID-19 neuropathology includes AD-like features and leaky RyR2 channels could be a therapeutic target for amelioration of some cognitive defects associated with SARS-CoV-2 infection and long COVID.


1.1 Contextual background

Patients suffering from COVID-19 exhibit multi-system organ failure involving not only pulmonary1 but also cardiovascular,2 neural,3 and other systems. The pleiotropy and complexity of the organ system failures both complicate the care of COVID-19 patients and contribute, to a great extent, to the morbidity and mortality of the pandemic.4 Severe COVID-19 most commonly manifests as viral pneumonia-induced acute respiratory distress syndrome (ARDS).5 Respiratory failure results from severe inflammation in the lungs, which arises when SARS-CoV-2 infects lung cells. Cardiac manifestations are multifactorial and include hypoxia, hypotension, enhanced inflammatory status, angiotensin-converting enzyme 2 (ACE2) receptor downregulation, endogenous catecholamine adrenergic activation, and direct viral-induced myocardial damage.67 Moreover, patients with underlying cardiovascular disease or comorbidities, including congestive heart failure, hypertension, diabetes, and pulmonary diseases, are more susceptible to infection by SARS-CoV-2, with higher mortality.67

In addition to respiratory and cardiac manifestations, it has been reported that approximately one-third of patients with COVID-19 develop neurological symptoms, including headache, disturbed consciousness, and paresthesias.8 Brain tissue edema, stroke, neuronal degeneration, and neuronal encephalitis have also been reported.2810 In a recent study, diffuse neural inflammatory markers were found in >80% of COVID-19 patient brains, processes which could contribute to the observed neurological symptoms.11 Furthermore, another pair of frequent symptoms of infection by SARS-CoV-2 are hyposmia and hypogeusia, the loss of the ability to smell and taste, respectively.3 Interestingly, hyposmia has been reported in early-stage Alzheimer’s disease (AD),3 and AD type II astrocytosis has been observed in neuropathology studies of COVID-19 patients.10

Systemic failure in COVID-19 patients is likely due to SARS-CoV-2 invasion via the ACE2 receptor,9 which is highly expressed in pericytes of human heart8 and epithelial cells of the respiratory tract,12 kidney, intestine, and blood vessels. ACE2 is also expressed in the brain, especially in the respiratory center and hypothalamus in the brain stem, the thermal center, and cortex,13 which renders these tissues more vulnerable to viral invasion, although it remains uncertain whether SARS-CoV-2 virus directly infects neurons in the brain.14 The primary consequences of SARS-CoV-2 infection are inflammatory responses and oxidative stress in multiple organs and tissues.1517 Recently it has been shown that the high neutrophil-to-lymphocyte ratio observed in critically ill patients with COVID-19 is associated with excessive levels of reactive oxygen species (ROS) and ROS-induced tissue damage, contributing to COVID-19 disease severity.15

Recent studies have reported an inverse relationship between ACE2 and transforming growth factor-β (TGF-β). In cancer models, decreased levels of ACE2 correlated with increased levels of TGF-β.18 In the context of SARS-CoV-2 infection, downregulation of ACE2 has been observed, leading to increased fibrosis formation, as well as upregulation of TGF-β and other inflammatory pathways.19 Moreover, patients with severe COVID-19 symptoms had higher blood serum TGF-β concentrations than those with mild symptoms,20 thus further implicating the role of TGF-β and warranting further investigation.

Interestingly, reduced angiotensin/ACE2 activity has been associated with tau hyperphosphorylation and increased amyloid beta (Aβ) pathology in animal models of AD.2122 The link between reduced ACE2 activity and increased TGF-β and tau signaling in the context of SARS-CoV-2 infection needs further exploration.

Our laboratory has shown that stress-induced ryanodine receptor (RyR)/intracellular calcium release channel post-translational modifications, including oxidation and protein kinase A (PKA) hyperphosphorylation related to activation of the sympathetic nervous system and the resulting hyper-adrenergic state, deplete the channel stabilizing protein (calstabin) from the channel complex, destabilizing the closed state of the channel and causing RyR channels to leak Ca2+ out of the endoplasmic/sarcoplasmic reticulum (ER/SR) in multiple diseases.2329 Increased TGF-β activity can lead to RyR modification and leaky channels,30 and SR Ca2+ leak can cause mitochondrial Ca2+ overload and dysfunction.29 Increased TGF-β activity31 and mitochondrial dysfunction32 are also associated with SARS-CoV-2 infection.

Here we show that SARS-CoV-2 infection is associated with adrenergic and oxidative stress and activation of the TGF-β signaling pathway in the brains of patients who have succumbed to COVID-19. One consequence of this hyper-adrenergic and oxidative state is the development of tau pathology normally associated with AD. In this article, we investigate potential biochemical pathways linked to tau hyperphosphorylation. Based on recent evidence that has linked tau pathology to Ca2+ dysregulation associated with leaky RyR channels in the brain,333 we investigated RyR2 biochemistry and function in COVID-19 patient brains.


  1. Systematic review: The authors reviewed the literature using PubMed. While the mechanisms that lead to cognitive impairment associated with COVID-19 are not well understood, there have been recent reports studying SARS-CoV-2 infection and brain biochemistry and neuropathology. These relevant citations are appropriately cited.
  2. Interpretation: Our findings link the inflammatory response to SARS-CoV-2 infection with the neuropathological pathways causing tau hyperphosphorylation typically associated with Alzheimer’s disease (AD). Furthermore, our data indicate a role for leaky ryanodine receptor 2 (RyR2) in the pathophysiology of SARS-CoV-2 infection.
  3. Future directions: The article proposes that the alteration of cellular calcium dynamics due to leaky RyR2 in COVID-19 brains is associated with the activation of neuropathological pathways that are also found in the brains of AD patients. Both the cortex and cerebellum of SARS-CoV-2–infected patients exhibited a reduced expression of the Ca2+ buffering protein calbindin. Decreased calbindin could render these tissues more vulnerable to cytosolic Ca2+ overload. Ex vivo treatment of the COVID-19 brain using a Rycal drug (ARM210) that targets RyR2 channels prevented intracellular Ca2+ leak in patient samples. Future experiments will explore calcium channels as a potential therapeutic target for the neurological complications associated with COVID-19.

1.2 Study conclusions and disease implications

Our results indicate that SARS-CoV-2 infection activates inflammatory signaling and oxidative stress pathways resulting in hyperphosphorylation of tau, but normal amyloid precursor protein (APP) processing in COVID-19 patient cortex and cerebellum. There was reduced calbindin expression in both cortex and cerebellum rendering both tissues vulnerable to Ca2+-mediated pathology. Moreover, COVID-19 cortex and cerebellum exhibited RyR Ca2+ release channels with the biochemical signature of ‘‘leaky’’ channels and increased activity consistent with pathological intracellular Ca2+ leak. RyR2 were oxidized, associated with increased NADPH oxidase 2 (NOX2), and were PKA hyperphosphorylated on serine 2808, both of which cause loss of the stabilizing subunit calstabin2 from the channel complex promoting leaky RyR2 channels in COVID-19 patient brains. Furthermore, ex vivo treatment of COVID-19 patient brain samples with the Rycal drug ARM210, which is currently undergoing clinical testing at the National Institutes of Health for RyR1-myopathy ( Identifier: NCT04141670), fixed the channel leak. Thus, our experiments demonstrate that SARS-CoV-2 infection activates biochemical pathways linked to the tau pathology associated with AD and that leaky RyR Ca2+ channels may be a potential therapeutic target for the neurological complications associated with COVID-19.

The molecular basis of how SARS-CoV-2 infection results in ‘‘long COVID’’ is not well understood, and questions regarding the role of defective Ca2+ signaling in the brain in COVID-19 remain unanswered. A recent comprehensive molecular investigation revealed extensive inflammation and degeneration in the brains of patients that died from COVID-19,34 including in patients with no reported neurological symptoms. These authors also reported overlap between marker genes of AD and genes that are upregulated in COVID-19 infection, consistent with the findings of increased tau pathophysiology reported in the present study. We propose a potential mechanism that may contribute to the neurological complications caused by SARS-CoV-2: defective intracellular Ca2+ regulation and activation of AD-like neuropathology.

TGF-β belongs to a family of cytokines involved in the formation of cellular fibrosis by promoting epithelial-to-mesenchymal transition, fibroblast proliferation, and differentiation.35 TGF-β activation has been shown to induce fibrosis in the lungs and other organs by activation of the SMAD-dependent pathway. We have previously reported that TGF-β/SMAD3 activation leads to NOX2/4 translocation to the cytosol and its association with RyR channels, promoting oxidization of the channels and depletion of the stabilizing subunit calstabin in skeletal muscle and in heart.2830 Alteration of Ca2+ signaling may be particularly crucial in COVID-19-infected patients with cardiovascular/neurological diseases due, in part, to the multifactorial RyR2 remodeling after the cytokine storm, increased TGF-β activation, and increased oxidative stress. Moreover, SARS-CoV-2–infected patients exhibited a hyperadrenergic state. The elevated expression of glutamate carboxypeptidase 2 (GCPII) in COVID-19 brains reported in the present study could also contribute directly to increased PKA signaling of RyR2 by reducing PKA inhibition via metabotropic glutamate receptor 3 (mGluR3).36 Hyperphosphorylation of RyR2 channels can promote pathological remodeling of the channel and exacerbate defective Ca2+ regulation in these tissues. The increased Ca2+/cAMP/PKA signaling could also open nearby K+ channels which could potentially weaken synaptic connectivity, reduce neuronal firing,36 and could activate Ca2+ dependent enzymes.

Interestingly, both the cortex and cerebellum of SARS-CoV–2-infected patients exhibited a reduced expression of the Ca2+ buffering protein calbindin. Decreased calbindin could render these tissues more vulnerable to the cytosolic Ca2+ overload. This finding is in accordance with previous studies showing reduced calbindin expression levels in Purkinje cells and the CA2 hippocampal region of AD patients3739 and in cortical pyramidal cells of aged individuals with tau pathology.3340 In contrast to the findings in the brains of COVID-19 patients in the present study, calbindin was not reduced in the cerebellum of AD patients, possibly protecting these cells from AD pathology.3941

Leaky RyR channels, leading to increased mitochondrial Ca2+ overload and ROS production and oxidative stress, have been shown to contribute to the development of tau pathology associated with AD.3232933 Recent studies of the effects of COVID-19 on the central nervous system have found memory deficits and biological markers similar to those seen in AD patients.4243 Our data demonstrate increased activity of enzymes responsible for phosphorylating tau (pAMPK, pGSK3β), as well as increased phosphorylation at multiple sites on tau in COVID-19 patient brains. The tau phosphorylation observed in these samples exhibited some differences from what is typically observed in AD, occurring in younger patients and in areas of the brain, specifically the cerebellum, that usually do not demonstrate tau pathology in AD patients. Taken together, these data suggest a potential contributing mechanism to the development of tau pathology in COVID-19 patients involving oxidative overload-driven RyR2 channel dysfunction. Furthermore, we propose that these pathological changes could be a significant contributing factor to the neurological manifestations of COVID-19 and in particular the “brain fog” associated with long COVID, and represent a potential therapeutic target for ameliorating these symptoms. For example, tau pathology in the cerebellum could explain the recent finding that 74% of hospitalized COVID-19 patients experienced coordination deficits.44 The data presented also raise the possibility that prior COVID-19 infection could be a potential risk factor for developing AD in the future.

The present study was limited to the use of existing autopsy brain tissues at the Columbia University Biobank from SARS-CoV-2–infected patients. The number of subjects is small and information on their cognitive function as well as their brain histopathology and levels of Aβ in cerebrospinal fluid and plasma are lacking. Furthermore, we did not have access to a suitable animal model of SARS-CoV-2 infection in which to test whether the observed biochemical changes in COVID-19 brains and potential cognitive and behavioral deficits associated with the brain fog of long COVID could be reversed or attenuated by therapeutic interventions. The design of future studies should include larger numbers of subjects that are age- and sex-matched. The cognitive function of SARS-CoV-2–infected patients who presented cognitive symptoms should be assessed and regularly monitored. Moreover, it is important to know whether the observed neuropathological signaling is unique to SARS-CoV-2 infection or are common to all other viral infections. Previous studies have reported cognitive impairment in Middle East respiratory syndrome45 as well as Ebola4647 patients. Retrospective studies comparing the incidence and the magnitude of cognitive impairments caused by these different viral infections would improve our understanding of these neurological complications of viral infections.


There were increased markers of oxidative stress (glutathione disulfide [GSSG]/ glutathione [GSH]) in the cortex (mesial temporal lobe) and cerebellum (cerebellar cortex, lateral hemisphere) of COVID-19 tissue. Kynurenic acid, a marker of inflammation, was increased in COVID-19 cortex and cerebellum brain lysates compared to controls, is in accordance with recent studies showing a positive correlation between kynurenic acid and cytokines and chemokine levels in COVID-19 patients.4850

To determine whether SARS-CoV-2 infection also increases tissue TGF-β activity, we measured SMAD3 phosphorylation, a downstream signal of TGF-β, in control and COVID-19 tissue lysates. Phosphorylated SMAD3 (pSMAD3) levels were increased in COVID-19 cortex and cerebellum brain lysates compared to controls, indicating that SARS-CoV-2 infection increased TGF-β signaling in these tissues. Interestingly, brain tissues from COVID-19 patients exhibited activation of the TGF-β pathway, despite the absence of the detectable (by immunohistochemistry and polymerase chain reaction, data not shown) virus in these tissues. These results suggest that the TGF-β pathway is activated systemically by SARS-CoV-2, resulting in its upregulation in the brain, as well as other organs. In addition to oxidative stress, COVID-19 brain tissues also demonstrated increased PKA and calmodulin-dependent protein kinase II association domain (CaMKII) activity, most likely associated with increased adrenergic stimulation. Both PKA and CaMKII phosphorylation of tau have been reported in tauopathies.5152

The hallmarks of AD brain neuropathology are the formation of Aβ plaques from abnormal APP processing by BACE1, as well as tau ‘‘tangles’’ caused by tau hyperphosphorylation.53 Brain lysates from COVID-19 patients’ autopsies demonstrated normal BACE1 and APP levels compared to controls. The patients analyzed in the present study were grouped by age (young ≤ 58 years old, old ≥ 66 years old) to account for normal, age-dependent changes in APP and tau pathology. Abnormal APP processing was only observed in brain lysates from patients diagnosed with AD. However, AMPK and GSK3β phosphorylation were increased in both the cortex and cerebellum in COVID-19 brains. Activation of these kinases in SARS-CoV-2–infected brains leads to a hyperphosphorylation of tau consistent with AD tau pathology in the cortex. COVID-19 brain lysates from older patients showed increased tau phosphorylation at S199, S202, S214, S262, and S356. Lysates from younger COVID-19 patients showed increased tau phosphorylation at S214, S262, and S356, but not at S199 and S202, demonstrating increased tau phosphorylation in both young and old individuals and suggesting a tau pathology similar to AD in COVID-19–affected patients. Interestingly, both young and old patient brains demonstrated increased tau phosphorylation in the cerebellum, which is not typical of AD.

RyR channels may be oxidized due to the activation of the TGF-β signaling pathway.30 NOX2 binding to RyR2 causes oxidation of the channel, which activates the channel, manifested as an increased open probability that can be assayed using 3[H]ryanodine binding.54 When the oxidization of the channel is at pathological levels, there is destabilization of the closed state of the channel, resulting in spontaneous Ca2+ release or leak.2730 To determine the effect of the increased TGF-β signaling associated with SARS-CoV-2 infection on NOX2/RyR2 interaction, RyR2 and NOX2 were co-immunoprecipitated from brain lysates of COVID-19 patients and controls. NOX2 associated with RyR2 in brain tissues from SARS-CoV-2–infected individuals were increased compared to controls.

Given the increased oxidative stress and increased NOX2 binding to RyR2 seen in COVID-19 brains, RyR2 post-translational modifications were investigated. Immunoprecipitated RyR2 from brain lysates demonstrated increased oxidation, PKA phosphorylation on serine 2808, and depletion of the stabilizing protein subunit calstabin2 in SARS-CoV-2–infected tissues compared to controls. This biochemical remodeling of the channel is known as the ‘‘biochemical signature’’ of leaky RyR2235556 that is associated with destabilization of the closed state of the channel. This leads to SR/ER Ca2+ leak, which contributes to the pathophysiology of a number of diseases including AD.232426305557 RyR channel activity was determined by binding of 3[H]ryanodine, which binds only to the open state of the channel. RyR2 was immunoprecipitated from tissue lysates and ryanodine binding was measured at both 150 nM and 20 μM free Ca2+. RyR2 channels from SARS-CoV-2–infected brain tissue demonstrated abnormally high activity (increased ryanodine binding) compared to channels from control tissues at physiologically resting conditions (150 nM free Ca2+), when channels should be closed. Interestingly, cortex and cerebellum of SARS-CoV-2–infected patients also exhibited a reduced expression of the Ca2+ binding protein calbindin. Calbindin is typically not reduced in the cerebellum of AD patients, possibly providing some protection against AD pathology. The low calbindin levels in the cerebellum of COVID-19 brains could contribute to the observed tau pathology in this brain region. An additional atypical finding in the COVID-19 brains studied in this investigation is an increased level of GCPII. This could contribute to the observed RyR PKA phosphorylation by increasing cAMP and inhibiting the metabotropic glutamate receptor type 3.36


3.1 Methods

3.1.1 Human samples

De-identified human heart, lung, and brain tissue were obtained from the COVID BioBank at Columbia University. The cortex samples were from the mesial temporal lobe and the cerebellum samples were from the cerebellar cortex, lateral hemisphere. The Columbia University BioBank functions under standard operating procedures, quality assurance, and quality control for sample collection and maintenance. Age- and sex-matched controls exhibited absence of neurological disorders and cardiovascular or pulmonary diseases. Sex, age, and pathology of patients are listed in Table 1.TABLE 1. Sex, age, and pathology of COVID-19 patients

Patient NumberSexAgePathology
1Male57Acute hypoxic-ischemic injury in the hippocampus, pons, and cerebellum.
2Female38Hypoxic ischemic encephalopathy, severe, global.
3Male58Hypoxic/ischemic injury, global, widespread astrogliosis/microgliosis.
4Male84Dementia. Beta-amyloid plaques are noted in cortex and cerebellum.
5Female80Severe hypoxic ischemic encephalopathy, severe. Global astrogliosis and microgliosis. Mild Alzheimer-type pathology.
6Female74Acute hypoxic-ischemic encephalopathy, global, moderate to severe. Arteriolosclerosis, mild. Metabolic gliosis, moderate
7Male66Left frontal subacute hemorrhagic infarct. Multifocal subacute infarcts in pons and left cerebral peduncle. Global astrogliosis and microgliosis (see microscopic description). Alzheimer’s pathology.
8Female76Hypoxic ischemic encephalopathy, moderate. Alzheimer’s pathology. Atherosclerosis, moderate. Arteriolosclerosis, moderate
9Male72Hypoxic/ischemic injury, acute to subacute, involving hippocampus, medulla and cerebellum. Mild atherosclerosis. Mild arteriolosclerosis
10Male71Hypoxic-ischemic encephalopathy, acute, global, mild to moderate. Diffuse Lewy body disease, neocortical type, consistent with Parkinson disease dementia. Atherosclerosis, severe. Arteriolosclerosis, mild.

Lysate preparation and Western blots

Tissues (50 mg) were isotonically lysed using a Dounce homogenizer in 0.25 ml of 10 mM Tris maleate (pH 7.0) buffer with protease inhibitors (Complete inhibitors from Roche). Samples were centrifuged at 8000 × g for 20 minutes and the protein concentrations of the supernatants were determined by Bradford assay. To determine protein levels in tissue lysates, tissue proteins (20 μg) were separated by 4% to 20% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and immunoblots were developed using the following antibodies: pSMAD3 (Abcam, 1:1000), SMAD3 (Abcam, 1:1000), AMPK (Abcam, 1:1000), tau (Thermo Fisher, 1:1000), pTauS199 (Thermo Fisher, 1:1000), pTauS202/T205 (Abcam, 1:1000), pTauS262 (Abcam, 1:1000), GSK3β (Abcam, 1:1000), pGSK3βS9 (Abcam, 1:1000), pGSK3βT216 (Abcam, 1:1000), APP (Abcam, 1:1000), BACE1 (Abcam, 1:1000), GAPDH (Santa Cruz Biotech, 1:1000), CTF-β (Santa Cruz Biotechnology, Inc., 1:1000), Calbindin (Abcam, 1:1000), and GCPII (Thermo Fisher, 1:4000).

Analyses of ryanodine receptor complex

Tissue lysates (0.1 mg) were treated with buffer or 10 μM Rycal (ARM210) at 4°C. RyR2 was immunoprecipitated from 0.1 mg lung, heart, and brain using an anti-RyR2 specific antibody (2 μg) in 0.5 ml of a modified radioimmune precipitation assay buffer (50 mm Tris-HCl, pH 7.2, 0.9% NaCl, 5.0 mm NaF, 1.0 mm Na3VO4, 1% Triton X-100, and protease inhibitors; RIPA) overnight at 4°C. RyR2-specific antibody was an affinity-purified polyclonal rabbit antibody using the peptide CKPEFNNHKDYAQEK corresponding to amino acids 1367–1380 of mouse RyR2 with a cysteine residue added to the amino terminus. The immune complexes were incubated with protein A-Sepharose beads (Sigma) at 4°C for 1 hour, and the beads were washed three times with RIPA. The immunoprecipitates were size-fractionated on SDS-PAGE gels (4%–20% for RyR2, calstabin2, and NOX2) and transferred onto nitrocellulose membranes for 1 hour at 200 mA. Immunoblots were developed using the following primary antibodies: anti-RyR2 (Affinity BioReagents, 1:2500), anti-phospho-RyR-Ser(pS)-2808 (Affinity BioReagents 1:1000), anti- calstabin2 (FKBP12 C-19, Santa Cruz Biotechnology, Inc., 1:2500), and anti-NOX2 (Abcam, 1:1000). To determine channel oxidation, the carbonyl groups in the protein side chains were derivatized to DNP by reaction with 2,4-dinitrophenylhydrazine. The DNP signal associated with RyR2 was determined using a specific anti-DNP antibody according to the manufacturer using an Odyssey system (LI-COR Biosciences) with infrared-labeled anti-mouse and anti-rabbit immunoglobulin G (IgG; 1:5000) secondary antibodies.

Ryanodine binding

RyR2 was immunoprecipitated from 1.5 mg of tissue lysate using an anti-RyR2 specific antibody (25 μg) in 1.0 ml of a modified RIPA buffer overnight at 4°C. The immune complexes were incubated with protein A-Sepharose beads (Sigma) at 4°C for 1 hour, and the beads were washed three times with RIPA buffer, followed by two washes with ryanodine binding buffer (10 mM Tris-HCl, pH 6.8, 1 M NaCl, 1% CHAPS, 5 mg/ml phosphatidylcholine, and protease inhibitors). Immunoprecipitates were incubated in 0.2 ml of binding buffer containing 20 nM [3H] ryanodine and either of 150 nM and 20 μm free Ca2+ for 1 hour at 37°C. Samples were diluted with 1 ml of ice-cold washing buffer (25 mm Hepes, pH 7.1, 0.25 m KCl) and filtered through Whatman GF/B membrane filters pre-soaked with 1% polyethyleneimine in washing buffer. Filters were washed three times with 5 ml of washing buffer. The radioactivity remaining on the filters is determined by liquid scintillation counting to obtain bound [3H] ryanodine. Nonspecific binding was determined in the presence of 1000-fold excess of non-labeled ryanodine.

GSSG/GSH ratio measurement and SMAD3 phosphorylation

Approximately 20 mg of tissue suspended in 200 μL of ice-cold phosphate-buffered saline/0.5% NP-40, pH6.0 was used for lysis. Tissue was homogenized with a Dounce homogenizer with 10 to 15 passes. Samples were centrifuged at 8000 × g for 15 minutes at 4°C to remove any insoluble material. Supernatant was transferred to a clean tube. Deproteinizing of the samples was accomplished by adding 1 volume ice-cold 100% (w/v) trichloroacetic acid (TCA) into five volumes of sample and vortexing briefly to mix well. After incubating for 5 minutes on ice, samples were centrifuged at 12,000 × g for 5 minutes at 4°C and the supernatant was transferred to a fresh tube. The samples were neutralized by adding NaHCO3 to the supernatant and vortexing briefly. Samples were centrifuged at 13,000 × g for 15 minutes at 4°C and supernatant was collected. Samples were then deproteinized, neutralized, TCA was removed, and they were ready to use in the assay. The GSSG/GSH was determined using a ratio detection assay kit (Abcam, ab138881). Briefly, in two separate assay reactions, GSH (reduced) was measured directly with a GSH standard and Total GSH (GSH + GSSG) was measured by using a GSSG standard. A 96-well plate was set up with 50 μL duplicate samples and standards with known concentrations of GSH and GSSG. A Thiol green indicator was added, and the plate was incubated for 60 minutes at room temperature (RT). Fluorescence at Ex/Em = 490/520 nm was measured with a fluorescence microplate reader and the GSSG/GSH for samples were determined comparing fluorescence signal of samples with known standards.

Kynurenic acid assay

Kynurenic acid (KYNA) concentration in brain lysates was determined using an enzyme-linked immunosorbent assay (ELISA) kit for KYNA (ImmuSmol). Briefly, samples (50 μl) were added to a microtiter plate designed to extract the KCNA from the samples. An acylation reagent was added for 90 minutes at 37°C to derivatize the samples. After derivatization, 50 μl of the prepared standards and 100 μl samples were pipetted into the appropriate wells of the KYNA microtiter plate. KYNA Antiserum was added to all wells and the plate was incubated overnight at 4°C. After washing the plate four times, the enzyme conjugate was added to each well. The plate was incubated for 30 minutes at RT on a shaker at 500 rpm. The enzyme substrate was added to all wells and the plate was incubated for 20 minutes at RT. Stop solution was added to each well. A plate reader was used to determine the absorbance at 450 nm. The sample signals were compared to a standard curve.

PKA activity assay

PKA activity in brain lysates was determined using a PKA activity kit (Thermo Fisher, EIAPKA). Briefly, samples were added to a microtiter plate containing an immobilized PKA substrate that is phosphorylated by PKA in the presence of ATP. After incubating the samples with ATP at RT for 2 hours, the plate was incubated with the phospho-PKA substrate antibody for 60 minutes. After washing the plate with wash buffer, goat anti-rabbit IgG horseradish peroxidase (HRP) conjugate was added to each well. The plate was aspirated, washed, and TMB substrate was added to each well, which was then incubated for 30 minutes at RT. A plate reader was used to determine the absorbance at 450 nm. The sample signals were compared to a standard curve.

CaMKII activity assay

CaMKII activity in brain lysates was determined using the CycLex CaM kinase II Assay Kit (MBL International). Briefly, samples were added to a microtiter plate containing an immobilized CaMKII substrate that is phosphorylated by CaMKII in the presence of Mg2+ and ATP. After incubating the samples in kinase buffer containing Mg2+ and ATP at RT for 1 hour, the plate was washed and incubated with the HRP conjugated anti-phospho-CaMKII substrate antibody for 60 minutes. The plate was aspirated, washed, and TMB substrate was added to each well, which was then incubated for 30 minutes at RT. A plate reader was used to determine the absorbance at 450 nm. The sample signals were compared to a standard curve.


Group data are presented as mean ± standard deviation. Statistical comparisons between the two groups were determined using an unpaired t-test. Values of P < .05 were considered statistically significant. All statistical analyses were performed with GraphPad Prism 8.0.

3.2 Results

3.2.1 Oxidative stress and TGF-β, PKA, and CaMKII activation

Oxidative stress levels were determined in brain tissues (cortex, cerebellum) from COVID-19 patient autopsy tissues and controls by measuring the ratio of GSSG to GSH by an ELISA kit. COVID-19 patients exhibited significant oxidative stress with a 3.8- and 3.2-fold increase in GSSG/GSH ratios in cortex (Ctx) and cerebellum (CB) compared to controls, respectively (Figure 1A). High circulating levels of kynurenine have been reported in COVID-19.4850 However, the expression of KYNA in COVID-19 brain tissue has not been examined. Levels in the Ctx and CB were measured using an ELISA kit. COVID-19 brains had a significant increase in the Ctx and CB compared to controls (Figure 1A). An additional marker of tissue inflammation is increased cytokine expression. SMAD3 phosphorylation, a downstream signal of TGF-β, was increased in COVID-19 Ctx and CB tissue lysates compared to controls (Figure 1B and 1C). Increased adrenergic activation in the brain of patients infected with SARS-CoV-2 was also demonstrated by measuring PKA activity in the Ctx and CB and CaMKII activity was increased as well (Figure 1D).

Details are in the caption following the image
FIGURE 1Open in figure viewerIncreased oxidative stress, inflammatory and adrenergic signaling in brains of COVID-19 patients. A, Bar graph depicting the glutathione disulfide (GSSG)/ glutathione (GSH) ratio and kynurenic acid (KYNA) enzyme-linked immunsorbent assay signal from control (n = 6) and COVID-19 (n = 6) tissue lysates. CB, cerebellum; Ctx, cortex. Data are mean ± standard deviation (SD). *P < .05 control versus COVID-19. B, Western blots showing phospho-SMAD3 and total SMAD3 from control (n = 4) and COVID-19 (n = 7) brain lysates. C, Bar graphs depicting quantification of pSMAD3/SMAD3 from Western blot signals in B. D, Calmodulin-dependent protein kinase II association domain (CaMKII) and protein kinase A (PKA) activity of brain tissue lysates. Data are mean ± SD. *P < .05 control versus COVID-19

Activation of AD-linked signaling

Both PKA and CaMKII have been directly implicated in the increased phosphorylation of tau associated with AD.5152 Because COVID-19 brain lysates had increased PKA and CaMKII activity, AD-linked biochemistry was evaluated in the COVID-19 brain lysates. Normal APP processing was observed in COVID-19 brain lysates as demonstrated by normal BACE1 and APP levels compared to controls (Figure 2A and B). Abnormal APP processing was only observed in brain lysates from patients diagnosed with AD (see Table 1 for patient details). However, phosphorylation/activation of AMPK and GSK3β was observed in SARS-CoV-2–infected patient brain lysates. Activation of these kinases along with the activation of PKA and CaMKII (Figure 1) leads to a hyperphosphorylation of tau at multiple residues (Figure 2C and D). Tau hyperphosphorylation in the cerebellum is not typical of AD pathology. The CB tau pathology demonstrated in COVID-19 warrants further investigation.

Details are in the caption following the image
FIGURE 2Open in figure viewerHyperphosphorylation of tau but normal amyloid precursor protein (APP) processing in COVID-19 brains. A, Brain (CB, cerebellum; Ctx, cortex) lysates were separated by 4% to 20% polyacrylamide gel electrophoresis. Immunoblots were developed for pAMPK, AMPK, GSK3β, pGSK3β (T216), APP, BACE1, and GAPDH loading control. The numbers (1–10) above immunoblots refer to patient numbers listed in Table 1. B, Bar graphs showing quantification of pAMPK, pGSK3β, APP/GAPDH, and BACE1/GAPDH from Western blots in (A). Data are mean ± standard deviation (SD). *P < .05 control versus COVID-19; **P < .05 CB versus Ctx; #P < .05 COVID (Young) versus COVID (Old). C, Immunoblots of brain lysates showing total tau and tau phosphorylation on residues S199, S202/T205, S214, S262, and S356. D, Bar graphs showing quantification phosphorylated tau at the residues shown on Western blots in (C). Data are mean ± SD. *P < .05 control versus COVID-19; **P < .05 CB versus Ctx; #P < .05 COVID (Young) versus COVID (Old)

RyR2 channel oxidation and leak

RyR2 biochemistry was investigated to determine whether RyR2 in COVID-19 brain tissues demonstrated a “leaky” phenotype. Increased NOX2/RyR2 binding was shown in Ctx and CB lysates from SARS-CoV-2–infected individuals compared to controls using co-immunoprecipitation (Figure 3A and B). In addition, RyR2 from SARS-CoV-2–infected brains had increased oxidation, increased serine 2808 PKA phosphorylation, and depletion of the stabilizing protein subunit calstabin2 compared to controls (Figure 3A and B). RyR channels exhibiting these characteristics can be inappropriately activated at low cytosolic Ca2+ concentrations resulting in a pathological ER/SR Ca2+ leak. 3[H]Ryanodine binding to immunoprecipitated RyR2 was measured at both 150 nM and 20 μM free Ca2+. Because ryanodine binds only to the open state of the channel under these conditions, 3[H]Ryanodine binding may be used as a surrogate measure of channel open probability. The total amount of RyR immunoprecipitated was the same for control and COVID-19 samples (data not shown). Increased RyR2 channel activity at resting conditions (150 nM free Ca2+) was observed in COVID-19 channels compared to controls (Figure 3C). Under these conditions, RyR channels should be closed. Rebinding of calstabin2 to RyR2, using a Rycal, has been shown to reduce SR/ER Ca2+ leak, despite the persistence of the channel remodeling. Indeed, calstabin2 binding to RyR2 was increased when COVID-19 patient brain tissue lysates were treated ex vivo with the Rycal drug ARM210 (Figure 3A and B). Abnormal RyR2 activity observed at resting Ca2+ concentration was also decreased by Rycal treatment (Figure 3C).

Details are in the caption following the image
FIGURE 3Open in figure viewerDysregulation of calcium-handling proteins in COVID-19 brains. A, Western blots depicting ryanodine receptor 2 (RyR2) oxidation, protein kinase A (PKA) phosphorylation, and calstabin2 or NADPH oxidase 2 (NOX2) bound to the channel from brain (CB, cerebellum; Ctx, cortex) lysates. B, Bar graphs quantifying DNP/RyR2, pS2808/RyR2, and calstabin2 and NOX2 bound to the channel from the Western blots. Data are mean ± standard deviation (SD). *P < .05 control versus COVID-19; # P < .05 COVID-19 versus COVID-19+ARM210. C, 3[H]ryanodine binding from immunoprecipitated RyR2. Bar graphs show ryanodine binding at 150 nM Ca2+ as a percent of maximum binding (Ca2+ = 20 μM). Data are mean ± SD. *P < .05 control versus COVID-19; #P < .05 COVID-19 versus COVID-19+ARM210. D, Western blots showing the levels of glutamate carboxypeptidase 2 (GCPII), calbindin, and GAPDH loading control in brain (Ctx, CB). E, Bar graphs quantifying GCPII/GAPDH and calbindin/GAPDH from the western blots. Data are mean ± SD. *P < .05 control versus COVID-19

An interesting finding concerning the tau phosphorylation in brain lysates from SARS-CoV-2 patients was the increase of phosphorylation at multiple sites in the cerebellum. This is atypical of AD. One potential mechanism to explain this finding is the significantly decreased levels of calbindin expressed in COVID-19 cerebellum (Figure 3D3E). The decreased cerebellar calbindin levels could make this area of the brain more susceptible to Ca2+-induced activation of enzymes upstream of tau phosphorylation. Moreover, increased GCPII expression was observed in COVID-19 cortex and cerebellar lysates (Figure 3D3E), which would reduce mGluR3 inhibition of PKA signaling and could contribute to the PKA hyperphosphorylation of RyR2.

Model for the role for leaky RyR2 in the pathophysiology of SARS-CoV-2 infection

Our data indicate a role for leaky RyR2 in the pathophysiology of SARS-CoV-2 infection (Figure 4). In addition to the brain of COVID-19 patients, we observed increased systemic oxidative stress and activation of the TGF-β signaling pathway in lung, and heart, which correlates with oxidation-driven biochemical remodeling of RyR2 (Figure 3 and S1 in supporting inormation). This RyR2 remodeling results in intracellular Ca2+ leak, which can play a role in heart failure progression, pulmonary insufficiency, as well as cognitive dysfunction.232628 The alteration of cellular Ca2+ dynamics has also been implicated in COVID-19 pathology.5859 Taken together, the present data suggest that leaky RyR2 may play a role in the long-term sequelae of COVID-19, including the “brain fog” associated with SARS-CoV-2 infection which could be a forme fruste of AD,60 and could predispose long COVID patients to developing AD later in life. Leaky RyR2 channels may be a therapeutic target for amelioration of some of the persistent cognitive deficits associated with long COVID.

Details are in the caption following the image
FIGURE 4Open in figure viewerSARS-CoV-2 infection results in leaky ryanodine receptor 2 (RyR2) that may contribute to cardiac, pulmonary, and cognitive dysfunction. SARS-CoV-2 infection targets cells via the angiotensin-converting enzyme 2 (ACE2) receptor, inducing inflammasome stress response/activation of stress signaling pathways. This results in increased transforming growth factor-β (TGF-β) signaling, which activates SMAD3 (pSMAD) and increases NADPH oxidase 2 (NOX2) expression and the amount of NOX2 associated with RyR2. Increased NOX2 activity at RyR2 oxidizes the channel, causing calstabin2 depletion from the channel macromolecular complex, destabilization of the closed state, and ER/SR calcium leak that is known to contribute to cardiac dysfunction,55 arrhythmias,61 pulmonary insufficiency,2325 and cognitive and behavioral abnormalities associated with neurodegenreation.2426 Decreased calbindin in COVID-19 may render brain more susceptible to tau pathology. Rycal drugs fix the RyR2 channel leak by restoring calstabin2 binding and stabilizing the channel closed state. Fixing leaky RyR2 may improve cardiac, pulmonary, and cognitive function in COVID-19.

Warning to anyone who’s had Covid over ‘irreversible’ damage to the brain

Authors: Vanessa Chalmers, Digital Health Reporter  Apr 11 2022

COVID survivors have been warned that the brain could be irreversibly harmed by the virus.

The major organ has been shown in dozens of studies to be damaged in even the mildest forms of Covid illness.

‘Brain fog’, difficulty concentrating and memory problems have all been reported, with some encouraging studies suggesting most people see improvements in six to nine months.

The new study, by researchers at the University of Oxford, looked at people in the UK over the age of 50 who had a mild case of Covid.

All 785 participants were in the UK Biobank, a large database for medical research, and had two brain scans 38 months aparts.

A total of 401 participants had tested positive for Covid in between the two scans.

The study found a number of effects on the brain, on average 4.5 months following infection.

Covid survivors had a greater reduction in grey matter thickness and tissue damage in regions of the brain associated with smell.

They had a reduction in whole brain size and, after performing a number of tests, showed a drop in cognitive function.

The effects ranged from 0.2 to 2 per cent additional change compared with the participants who had not been infected.

Professor Gwenaëlle Douaud, lead author on the study, said: “Despite the infection being mild for 96 per cent of our participants, we saw a greater loss of grey matter volume, and greater tissue damage in the infected participants.

“They also showed greater decline in their mental abilities to perform complex tasks, and this mental worsening was partly related to these brain abnormalities. 

“All these negative effects were more marked at older ages. 

“A key question for future brain imaging studies is to see if this brain tissue damage resolves over the longer term.”

It is not clear at this stage if the effects on the brain are reversible.

Professor Stephen Smith, senior author on the study, said: “The fact that we have the pre-infection scan helps us distinguish brain changes related to the infection from differences that may have pre-existed in their brains.”

The evidence is stacking up

The study, published in the journal Nature in March, echoes the findings of a number of others.

Researchers at Tulane University reported findings last week based on studying primates, which are used in studies for the likeness to humans. 

They found severe brain swelling and injury linked to reduced blood flow or oxygen to the brain.

They also found evidence of small bleeds, neuron damage and death – even in primates that didn’t have a severe illness.

Lead investigator Dr Tracy Fischer said: “Because the subjects didn’t experience significant respiratory symptoms, no one expected them to have the severity of disease that we found in the brain.

“But the findings were distinct and profound, and undeniably a result of the infection.”

Meanwhile, researchers – including from the universities of Imperial College London and Cambridge – found that Covid can cause a “substantial drop” in intelligence.

The findings came from a series of tests on memory, reasoning, planning and problem solving on more than 81,300 people.

People who had been on a ventilator during their Covid sickness were most likely to see a decline in scores.

In a classic intelligence test, they would have lost the equivalent of seven points in IQ, the team claimed.

The study said: “These results accord with reports of long-Covid, where ‘brain fog’, trouble concentrating and difficulty finding the correct words are common.

“The deficits were of substantial effect size for people who had been hospitalised.”

Another study reassured that “brain fog” shouldn’t persist for more than a year.

Covid patients scored significantly worse in episodic memory and in their ability to sustain attention on a task over time.

However, Professor Masud Husain, of Oxford University, said it was “encouraging” that most people’s attention and memory return “largely to normal in six to nine months”.

He said: “We still do not understand the mechanisms that cause these cognitive deficit.”

Explain this…

A team in the US suggested brain fog symptoms were the result of the organ being starved of oxygen.

After autopsying Covid victims, scientists at Johns Hopkins University School of Medicine found that large cells called megakaryocytes were taking up space and leaving less room for blood to pass through the brain freely.

According to Professor James Goodwin, the Director of Science and Research Impact at the Brain Health Network, it is thought that Covid gets into the brain through tightly sealed blood vessels which surround the organ.

But there is another explanation, he wrote in The Telegraph, and our own immune systems are to blame.

Sometimes the immune system goes into overdrive in response to a virus, releasing too many inflammatory molecules called cytokines.

This phenomenon, known as a cytokine storm, can injure healthy organs, including the brain, as well as the lungs and heart. 

It has led to the death of many Covid victims, and those who survive may have long-term damage.

The cytokine storm is typically more common in people who are unhealthy, have a long-term illness, are older or who have a high viral load, Prof Goodwin said.

COVCOG 1: Factors Predicting Physical, Neurological and Cognitive Symptoms in Long COVID in a Community Sample. A First Publication From the COVID and Cognition Study

Authors: Panyuan Guo1Alvaro Benito Ballesteros1Sabine P. Yeung1Ruby Liu1Arka Saha1Lyn Curtis2Muzaffer Kaser3,4Mark P. Haggard1 and Lucy G. Cheke1*

Since its first emergence in December 2019, coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has evolved into a global pandemic. Whilst often considered a respiratory disease, a large proportion of COVID-19 patients report neurological symptoms, and there is accumulating evidence for neural damage in some individuals, with recent studies suggesting loss of gray matter in multiple regions, particularly in the left hemisphere. There are a number of mechanisms by which COVID-19 infection may lead to neurological symptoms and structural and functional changes in the brain, and it is reasonable to expect that many of these may translate into cognitive problems. Indeed, cognitive problems are one of the most commonly reported symptoms in those experiencing “Long COVID”—the chronic illness following COVID-19 infection that affects between 10 and 25% of patients. The COVID and Cognition Study is a part cross-sectional, part longitudinal, study documenting and aiming to understand the cognitive problems in Long COVID. In this first paper from the study, we document the characteristics of our sample of 181 individuals who had experienced COVID-19 infection, and 185 who had not. We explore which factors may be predictive of ongoing symptoms and their severity, as well as conducting an in-depth analysis of symptom profiles. Finally, we explore which factors predict the presence and severity of cognitive symptoms, both throughout the ongoing illness and at the time of testing. The main finding from this first analysis is that that severity of initial illness is a significant predictor of the presence and severity of ongoing symptoms, and that some symptoms during the initial illness—particularly limb weakness—may be more common in those that have more severe ongoing symptoms. Symptom profiles can be well described in terms of 5 or 6 factors, reflecting the variety of this highly heterogenous condition experienced by the individual. Specifically, we found that neurological/psychiatric and fatigue/mixed symptoms during the initial illness, and that neurological, gastrointestinal, and cardiopulmonary/fatigue symptoms during the ongoing illness, predicted experience of cognitive symptoms.


Manifestations of coronavirus 2 (SARS-CoV-2) infection vary in severity ranging from asymptomatic to fatal. In the acute stage, symptomatic patients—at least in the early variants—typically experience respiratory difficulties that can result in hospitalization and require assisted ventilation (Baj et al., 2020Heneka et al., 2020Jain, 2020). While COVID-19 is primarily associated with respiratory and pulmonary challenge, 35% of patients report neurological symptoms including headache and dizziness (e.g., Mao et al., 2020). In severe illness, neurological symptoms can be seen in 50–85% of patients (e.g., Pryce-Roberts et al., 2020Romero-Sánchez et al., 2020). Indeed, alteration in taste or smell (anosmia/dysgeusia) is reported in over 80% of cases (e.g., Lechien et al., 2020), is often the first clinical symptom (Mao et al., 2020Romero-Sánchez et al., 2020) and regularly persists beyond resolution of respiratory illness (Lechien et al., 2020).

Accumulating evidence suggests that many COVID-19 patients experiencing severe illness show evidence of neural damage (Helms et al., 2020Kandemirli et al., 2020) and unusual neural activity (Galanopoulou et al., 2020). There are a number of postulated mechanisms linking COVID-19 infection with neurological problems (Bougakov et al., 2021). For example, based on the behavior of previous SARS viruses, SARS-CoV-2 may attack the brain directly perhaps via the olfactory nerve (Lechien et al., 2020Politi et al., 2020) causing encephalitis. Severe hypoxia from respiratory failure or distress can also induce hypoxic/anoxic-related encephalopathy (Guo et al., 2020). There is considerable evidence that COVID-19 is associated with abnormal blood coagulation, which can increase risk of acute ischemic and hemorrhagic cerebrovascular events (CVAs) (Beyrouti et al., 2020Li et al., 2020Wang et al., 2020Kubánková et al., 2021) leading to more lasting brain lesions. Indeed, ischemic or hemorrhagic lesions have been found in COVID-19 patients in multiple studies (Le Guennec et al., 2020Matschke et al., 2020Moriguchi et al., 2020Poyiadji et al., 2020). A recent study using the United Kingdom Biobank cohort comparing structural and functional brain scans before and after infection with COVID-19 identified significant loss of gray matter in the parahippocampal gyrus, lateral orbitofrontal cortex and insula, notably concentrated in the left hemisphere in patients relative to controls (Douaud et al., 2021).

A key candidate mechanism is dysfunctional or excessive immune response to infection. For example, excessive cytokine release (“cytokine storm”) and immune-mediated peripheral neuropathy (e.g., Guillain-Barre syndrome) are both linked with neurological and sensory-motor issues (Alberti et al., 2020Das et al., 2020Poyiadji et al., 2020Whittaker et al., 2020Zhao et al., 2020). In addition to acute effects, chronic inflammation has also been associated with neural and cognitive dysfunction, particularly in the hippocampus—a key area responsible for memory (Ekdahl et al., 2003Monje et al., 2003Jakubs et al., 2008Belarbi et al., 2012). Considerable rodent evidence links inflammatory cytokines with cognitive impairments (e.g., IL-1β: Thirumangalakudi et al., 2008Beilharz et al., 20142018Che et al., 2018Mirzaei et al., 2018; TNF-α: Thirumangalakudi et al., 2008Beilharz et al., 2014Almeida-Suhett et al., 2017). These findings are broadly reflected in human studies, wherein circulating cytokines have been associated with reduced episodic memory (e.g., Kheirouri and Alizadeh, 2019) and chronic neuroinflammation has been heavily implicated in the pathophysiology of neurodegenerative diseases (McGeer and McGeer, 2010Zotova et al., 2010Chen et al., 2016Bossù et al., 2020). Given the volume of reports of excessive immune response to COVID-19 infection (Mehta et al., 2020Tay et al., 2020), and evidence for neuroinflammation from postmortem reports (Matschke et al., 2020) research into cognitive sequalae is highly implicated.

Given the evidence for widespread neural symptoms and demonstrable neural damage, it could be expected that COVID-19 infection would be associated with cognitive deficits. Indeed, there is some early evidence linking neural changes following COVID-19 and cognitive deficits. Hosp et al. (2021) found that evidence of frontoparietal hypometabolism in older patients presenting with post-COVID-19 neurological symptoms via positron emission tomography (PET) was associated with lower neuropsychological scores, particularly in tests of verbal memory and executive functions.

Many forms of neuropathology would be unlikely to be present uniquely as cognitive deficits, but would be associated with a range of related symptoms. Some of these symptoms may be neurological (e.g., disorientation, headache, numbness) while others may reflect systemic/multisystem involvement (e.g., reflecting the symptom profile of chronic inflammatory or autoimmune diseases). It may therefore be possible to gain information as to the mechanism of neurological involvement via investigation of symptomatology. If it is possible to identify groups of symptoms (such as neurological, respiratory, systemic) during either the acute or post-acute phase of illness that predict cognitive problems, this may aid in the identification of patients that are at risk of developing cognitive deficits. In a highly heterogenous condition, in which up to 200 symptoms have been suggested (Davis et al., 2021), reduction of dimensionality is essential to allow meaningful associations to be drawn between experienced symptoms and relevant outcomes.

The United Kingdom Office for National Statistics [ONS] (2021) has estimated that around 21% of those experiencing COVID-19 infection still have symptoms at 5 weeks, and that 10% still have these symptoms at 12 weeks from onset. These figures may not tell the full story, being based on a list of 12 physical symptoms which does not include neurological or cognitive manifestations (e.g., Alwan and Johnson, 2021Ziauddeen et al., 2021). Other calculations suggest that around 1 in 3 non-hospitalized COVID-19 patients have physical or neurological symptoms after 2–6 weeks from disease onset (Sudre et al., 2020Tenforde et al., 2020Nehme et al., 2021) and that 11–24% still have persisting physical, neurological or cognitive symptoms 3 months after disease onset (Cirulli et al., 2020Ding et al., 2020). A community-based study reported that around 38% symptomatic people experienced at least one physical or neurological symptom lasting 12 weeks or more from onset and around 15% experienced three or more of these symptoms (Whitaker et al., 2021). Ongoing symptoms seem to occur regardless of the severity of the initial infection, with even asymptomatic patients sometimes going on to develop secondary illness (FAIR Health, 2021Nehme et al., 2021), however, initial severity may impact severity of ongoing issues (e.g., Whitaker et al., 2021).

The National Institute for Health and Care Excellence (NICE) guidelines describe “post-COVID-19 syndrome” as “Signs or symptoms that develop during or after infection consistent with COVID-19, continue for more than 12 weeks and are not explained by an alternative diagnosis” (National Institute for Health and Care Excellence [NICE], 2020). One difficulty with this definition is that the “signs or symptoms” that qualify for the diagnosis are not specified (e.g., Alwan and Johnson, 2021Ziauddeen et al., 2021) thus many patients could go uncounted and unrecognized clinically, or conversely over-liberal inclusion may lead to overcounting. The patient-created term “Long COVID” has increasingly been used as an umbrella term to describe the highly heterogenous condition experienced by many people following COVID-19 infection (Callard and Perego, 2021).

Emerging evidence suggests that Long COVID is a debilitating multisystem illness that affects multiple organ systems and there have been some attempts to characterize “phenotypes.” An online survey involved in 2,550 non-hospitalized participants detected two clusters within both initial and ongoing symptoms. Initial symptoms showed a majority cluster with cardiopulmonary symptoms predominant, and a minority cluster with multisystem symptoms that did not align specifically with any one organ system. Similarly, ongoing symptoms were clustered into a majority cluster with cardiopulmonary, cognitive symptoms and exhaustion, and a minority cluster with multisystem symptoms. Those with more related symptoms in the initial major cluster were more likely to move into ongoing multisystem cluster, and this movement can be predicted by gender and age, with higher risk in women, those younger than 60, and those that took less rest during the initial illness (Ziauddeen et al., 2021).

“Long COVID” research has repeatedly identified cognitive dysfunction as one of the most common persistent symptoms (after fatigue), occurring in around 70% of patients (Cirulli et al., 2020Bliddal et al., 2021Davis et al., 2021Ziauddeen et al., 2021). Indeed, brain fog and difficulty concentrating are more common than cough is at many points in the Long COVID time course (Assaf et al., 2020). Ziauddeen et al. (2021) report nearly 40% of participants endorsing at least one cognitive symptom during the initial 2 weeks of illness, with this persisting in the long term. However around 30% of participants also reported developing cognitive symptoms—particularly brain fog and memory problems—later. Indeed, Davis et al. (2021) demonstrate that brain fog, memory problems and speech and language problems were more commonly reported at week 8 and beyond than they were during initial infection. Furthermore, strenuous cognitive activity was found to be one of the most common triggers leading to relapse/exacerbation of existing symptoms (Davis et al., 2021Ziauddeen et al., 2021). Crucially, 86% of participants indicated that cognitive dysfunction and/or memory impairment was impacting their ability to work, with nearly 30% reporting being “severely unable to work” and only 27% working as many hours as they had pre-COVID-19 (Davis et al., 2021). These figures suggest that the cognitive sequelae of COVID-19 have the potential for long-term consequences not just for individuals but also—given the prevalence of Long COVID—for the economy and wider society.

Here we report on the first stage of a mixed cross-sectional/longitudinal investigation—The COVID and Cognition Study (COVCOG)—aimed at understanding cognition in post-acute COVID-19. The aims of this current paper are threefold: First, to provide a detailed demographic profile of our sample, comparing those who had experienced COVID-19 infection to those who had not, and those who recovered to those who continued to experience COVID-19 symptoms after acute phase of illness. Second, we aim to contribute to the understanding of phenotypes of Long COVID by using a rigorous factor analytic approach to identify groups of symptoms that tend to co-occur. We investigate symptom profiles both during and following initial infection in those that had experienced COVID-19. This allows investigation of symptoms during initial illness that may be predictive of ongoing symptoms, as well as exploring the nature of those ongoing symptoms themselves. These phenotypes may, through future studies, be directly linked to disease profiles and mechanisms. In an application of this second aim, a third objective is to use the symptom factors extracted (such as those incorporating neurological symptoms) to investigate predictors of self-reported cognitive deficits. Due to the novel character of both the virus and the subsequent ongoing illness at the time of study creation, this study was designed not to test specific hypotheses but to map the terrain, generating hypotheses for future, more targeted investigation.

Materials and Methods


A total of 421 participants aged 18 and over were recruited through word of mouth, student societies and online/social media platforms such as the Facebook Long COVID Support Group (over 40K members). Of these, 163 participants were recruited through the Prolific recruitment site, targeting participants with demographic profiles otherwise underrepresented in our sample. Specifically, recruitment through Prolific was limited to those with low socioeconomic status and levels of education below a bachelor’s degree. As the study was conducted in English, participants were recruited from majority English speaking countries (the United Kingdom, Ireland, United States, Canada, Australia, New Zealand, or South Africa). Informed consent to use of anonymized data was obtained prior to starting.

Data collection for this stage of the study took place between October 2020 and March 2021, and recorded data on infections that occurred between March 2020 and February 2021. As such, all participants with experience of COVID-19 infection were likely to have been infected with either Wild-Type or Alpha-variant SARS-CoV-2, as the later-emerging variants (e.g., Delta, Omicron) were not common in the study countries at that time. Study recruitment started before the roll out of vaccinations, thus we do not have confirmed vaccination status for all participants. Once vaccination became available, the questionnaire was revised to ask about vaccination status. Of the 33 participants who were tested after this point, 11 (2 in the No COVID group, 9 in the COVID group) reported being vaccinated. Among them, 8 had received the first dose and 3 had had two doses. The majority (over 80%) had the vaccine within the last 7 days to last month. All received Pfizer (BNT162b2) except 1 (COVID group) who received AstraZeneca (AZD1222).


The study was reviewed by University of Cambridge Department of Psychology ethics committee (PRE.2020.106, 8/9/2020). The current paper is part of a larger, mixed cross-sectional/longitudinal online study (“COVCOG”) conducted using the online assessment platform Gorilla.1 The COVCOG study consists of a baseline assessment of characteristics and cognition in samples of individuals who had or had not experienced COVID-19 infection. Both groups completed questionnaire and a range of cognitive tasks and were then followed up at regular intervals. The results reported here are for the questionnaire section of the baseline session only. The questionnaire covered demographics, previous health and experience of COVID-19.

Participants answered questions relating to their age, sex, education level, country of permanent residence, ethnicity, and profession. They were then asked a series of questions relating to their medical history and health-related behaviors. These included self-reporting their height and weight—which were used to calculate body mass index (BMI), and their usual diet intake, use of tobacco and alcohol, and physical activity (before the illness if infected) on a 6-point frequency scale from “Never” to “Several times daily.” Following this, they were asked for details of their experience of COVID-19. Because many of the participants in this study contracted COVID-19 before confirmatory testing of infection state was widely available, both those with (“Confirmed”) and without test confirmation (“Unconfirmed”) were included in the “COVID” group. Those that didn’t think they had had COVID-19 but had experienced an illness that could have been COVID-19 were assigned an “Unknown” infection status. Those that confirmed that they had not had COVID-19, nor any illness that might have been COVID-19, were included in the “No COVID” group. The procedure for grouping and progression through the baseline session is detailed in Figure 1.FIGURE 1

Figure 1. Study procedural flow.

Participants in the “COVID” group indicated the number of weeks since infection on a drop-down menu. Those that reported being within the first 3 weeks of infection proceeded straight to debriefing and were followed up 2 weeks later, once the initial infection was passed. Apart from this delay, they proceeded with the experiment in the same way as the rest of the COVID group. Participants then answered questions on the severity of the initial illness and whether they were experiencing ongoing symptoms. Finally, participants were asked to give details on a large number of individual symptoms during three time periods: initial illness (first 3 weeks), ongoing illness (“since then,” i.e., the time since initial infection), and currently (past 1–2 days). When reporting on initial symptoms, participants gave an indication of severity on a scale of 1–3 from “Not at all” to “Very severe.” When reporting symptoms over the period “since then” they reported on both severity and regularity of symptoms on a scale of 1–5 from “Not at all” to “Very severe and often.” When reporting on symptoms in the past 1–2 days, they reported the presence or absence of the symptoms dichotomously (i.e., check the box of the symptom if present). These symptom lists were developed based on currently available medical literature reporting symptoms experienced by COVID-19 patients and through consulting medical doctors and COVID-19 patients from the Long COVID Support Group. Participants in the “No COVID” Group were not asked their experience of COVID-19.

Data Processing and Analysis

Analyses were conducted using IBM SPSS Statistics for Windows, Version 23.0. We describe quantitative variables using means and standard deviations, and numbers and percentages for qualitative variables. Sidak’s correction for multiple comparisons was employed. All p-values are reported uncorrected, and the Sidak-corrected alpha is quoted where appropriate.

We investigated differences in the first group of variables: sociodemographic, medical history, and health behaviors, concerning two COVID group classifications. First dividing the sample into two groups (COVID/No COVID), second subdividing the COVID group by symptom longevity and severity (Recovered, Ongoing mild infection, and Ongoing severe infection). Where parametric analysis was not appropriate, we employed the Pearson’s chi-square (χ2) test for categorical variables and the Mann-Whitney and Kruskal-Wallis test for continuous variables depending on the number of COVID groups. To investigate differences between groups (COVID/No COVID; Recovered/Ongoing mild/Ongoing severe), we employed Mann-Whitney and ANOVA/Kruskal-Wallis. To examine whether these variables and initial symptoms predicted degrees of ongoing illness, we ran independent multinomial logistic regression, using forward stepwise method to identify what items within these variables were significant predictors while controlling for demographics including sex, age, education, and country of residence. Next, to determine suitable groups of symptoms, we employed exploratory principal component analysis (PCA) with varimax rotation. Based on our high number of items (Nunnally, 1978) and the novelty of the subject (Henson and Roberts, 2006), we performed two PCAs, one for the initial symptoms and another one symptoms experienced since the initial phase. We then used the high-loading items on the “since then” symptom factors to calculate profiles for currently experienced symptoms. To explore what symptom factors were associated with infection or ongoing symptoms, we employed various independent multinomial logistic regression with backward elimination of variables p > 0.05 to identify the best fitted models. Data analyzed in relation to our study aims are depicted in Figure 2.FIGURE 2

Figure 2. Data analyzed in relation to our study aims.


Sample Characteristics

No COVID (NCn = 185) vs. COVID (Cn = 181)

Distributions of demographics including sex, age, education level, country, and ethnicity of the two groups (NC/C) are shown in Table 1. The majority of participants were from the United Kingdom and were of White (Northern European) ethnicity (over 70% in both groups). Pearson’s chi-square tests showed that the groups did not significantly differ in sex, but differed in age [χ2(5) = 19.08, p = 0.002, V = 0.228] and level of education [χ2(5) = 56.86, p < 0.001, V = 0.394], with the COVID group tending to fall into the older age ranges and higher education level more than the No COVID group.TABLE 1

Table 1. Distribution of demographics in No COVID and COVID groups.


Supplementary Table 1 shows the distributions of pre-pandemic profession and employment status. To adjust for multiple comparisons, Sidak corrections were applied and alpha levels were adjusted to 0.003 for profession and 0.007 for employment status. The COVID group had significantly more people working in healthcare [χ2(1) = 12.77, p < 0.001, V = 0.187] and engaging in full-time work before the pandemic [χ2(1) = 21.19, p < 0.001, V = 0.241]. In contrast, the No COVID group were more likely not to be in paid work [Profession “Not in paid work” χ2(1) = 27.72, p < 0.001, V = 0.275; Employment status “Not Working” χ2(1) = 13.18, p < 0.001, V = 0.190], and they were more likely to be students [χ2(1) = 8.91, p = 0.003, V = 0.156].

Health and Medical History

Supplementary Table 2 compares medical history and health behaviors across the COVID and No COVID groups, which may be informative as to vulnerabilities. Sidak correction adjusted the alpha level to 0.003 for medical history and 0.008 for health behaviors. Pearson’s chi-square tests showed that inflammatory or autoimmune diseases [χ2(1) = 9.81, p = 0.002, V = 0.164] were found more commonly in the COVID group than the No COVID group. Mann-Whitney U-tests showed that the COVID group consumed more fruit and vegetables (U = 13,525, p = 0.001) and had higher level of physical activity (U = 13,752, p = 0.002) than the No COVID group, while the No COVID group consumed sugary (U = 14168.5, p = 0.008) food more than the COVID group. ANOVA showed that the COVID group (M = 26.71, SD = 7.26) had higher BMI than the No COVID group (M = 25.15, SD = 5.64), [F(1, 361) = 5.24, p = 0.023]. However this effect was not significant after controlling for sex, age, education and country [F(1, 357) = 1.57, p = 0.211].

Characteristics of Those Experiencing Ongoing Symptoms

To understand the potential association between the progression of COVID-19 and various potential risk factors at baseline, including demographics, medical history and health behaviors, and the severity of initial illness and initial symptoms, we further divided the COVID group into three duration subgroups: (i) those who, at the time of test, had recovered from COVID-19 (“Recovered group,” Rn = 42), (ii) those who continued to experience mild or moderate ongoing symptoms [“Ongoing (Mild/Moderate) group,” C + ; n = 53], and (iii) those who experienced severe ongoing symptoms [“Ongoing (Severe) group,” C + + ; n = 66]. Those who were still at their first 3 weeks of COVID-19 infection (n = 17) or those who reported “it is too soon” to comment on their ongoing symptoms (n = 3) were not included in the following analyses. Participants in all groups ranged between 3 and 31 + weeks since symptom-onset, and a majority (81.5%) of those with ongoing symptoms reporting after more than 6 months since infection.

Figure 3 shows the distribution of demographic variables across the COVID-19 duration subgroups (further details available in Supplementary Table 3). In each, more than half of the participants were from the United Kingdom (54.8–92.4%) and were of White (Northern European) ethnicity (69–93.9%). Pearson’s chi-square tests suggested that age [χ2(10) = 53.41, p < 0.001, V = 0.407] and education level [χ2(10) = 20.03, p = 0.029, V = 0.249], but not sex, significantly differed between subgroups. In terms of age, the R subgroup tended to fall more in the younger age ranges (see Figure 3A). In terms of education level, the R subgroup tended to have lower education level (GCSE or below and A level), but the C + + (Severe) subgroup clustered more in higher education level (bachelor’s degree) (see Figure 3B). The subgroups also differed in the time elapsed since infection at the time of completing the study [χ2(6) = 19.64, p = 0.003, V = 0.247]. The R subgroup were more likely to be in their first 10 weeks of infection, while the C + + (Severe) subgroup were more likely to be at their 31 weeks or above (Figure 3C).FIGURE 3

Figure 3. Distributions of (A) age, (B) education level, (C) weeks since infection, and (D) severity of initial illness in Recovered, Ongoing (Mild/Moderate) and Ongoing (Severe) subgroups.

A multinomial logistic regression indicated that only age, but not sex or education, was significantly associated with COVID-19 progression [χ2(10) = 43.6, p < 0.001]. People in the age ranges of 18–20 and 21–30 years were more likely to recover from COVID-19 than to progress into mild/moderate (ps = 0.02–0.03) or severe (p = 0.002) ongoing symptoms.

We examined whether medical history and health behaviors were different between COVID-19 duration subgroups. Table 2 shows the descriptive statistics of these factors in RC +, and C + + subgroups for medical history and pre-pandemic health behaviors. None of the listed health conditions significantly differed between subgroups (against Sidak α = 0.003). There were, however, significant group differences (Sidak α = 0.008) in fruit and vegetables consumption [H(2) = 15.92, p < 0.001] and fatty food consumption [H(2) = 36.54, p < 0.001]. Both ongoing symptom subgroups ate more fruit and vegetables (C + + : U = 810, p < 0.001; C + : U = 808, p = 0.016) and less fatty food (C + : U = 773.5, p = 0.005; C + + : U = 552.5, p < 0.001) than the R subgroup. The C + (Mild/Moderate) subgroup also consumed more fatty food than the C + + (Severe) subgroup (U = 1142, p < 0.001). The subgroups did not significantly differ in BMI [F(2, 157) = 0.085, p = 0.919].TABLE 2

Table 2. Distribution of medical history and health behaviors (1 = Never–6 = Several times daily; higher scores indicating higher frequency) in COVID subgroups: Recovered (R), Ongoing (Mild/Moderate) (C+) and Ongoing (Severe) (C++).

After controlling for sex, age, education, and country, a forward stepwise multinomial logistic regression indicated that no medical history variables were associated with COVID-19 progression, however, health behaviors including fatty food consumption [χ2(2) = 23.25, p < 0.001], physical activity [χ2(2) = 10.31, p = 0.006], and alcohol consumption [χ2(2) = 8.18, p = 0.017] were all significantly associated with COVID-19 progression. In our sample, people consuming more fatty food had a higher chance of having recovered from COVID-19 (p < 0.001) or having developed mild/moderate ongoing symptoms (p < 0.001) than progressing into severe ongoing symptoms. Higher levels of physical activity were associated with reduced chance of recovery relative to progression onto mild/moderate (p = 0.002) or severe ongoing symptoms (p = 0.034). Those drinking alcohol more frequently were more likely to recover from COVID-19 than to develop severe ongoing symptoms (p = 0.007).

Severity of Initial Illness

The severity of illness in the first 3 weeks of infection was associated with subsequent symptom longevity. Multinomial logistic regression showed that severity of initial illness was significantly associated with COVID-19 progression [χ2(2) = 24.44, p < 0.001], with higher initial severity associated with more severe subsequent ongoing symptoms (ps < 0.001–0.02). This effect was maintained after controlling for sex, age, education, and country [χ2(2) = 12.28, p = 0.002; C + + > C + : p = 0.048; C + + > Rp = 0.001]. Those with severe ongoing symptoms experienced more severe initial illness than those whose ongoing symptoms were mild/moderate (U = 1,258, p = 0.005, Figure 3D) and those who were fully recovered (U = 658.5, p < 0.001). The severity difference between the C + (Mild/Moderate) subgroup and the R subgroup was also significant (U = 842, p = 0.034).

Supplementary Table 4 shows the relative frequencies of particular diagnoses received during the initial illness. Of the 109 participants who sought medical assistance, the most common diagnoses received were hypoxia (14.7%), blood clots (5.5%), and inflammation (4.6%).

Symptoms During Initial Illness

Symptoms that appeared in less than 10% of participants were excluded. Kruskal-Wallis H-tests (Sidak α = 0.001) showed significant duration-group differences in 11/33 symptoms in terms of the severity experienced (see Figure 4, more information in Supplementary Table 5). In post hoc analysis (Sidak α = 0.017), muscle/body pains, breathing issues and limb weakness showed gradation, with the C + + (Severe) subgroup having experienced the most severe symptoms, followed by the C + (Mild/Moderate) subgroup, and the R subgroup experiencing the least (p ranges < 0.001–0.012). Some symptoms did not show gradation with severity of ongoing symptoms, but were reliably higher in those with ongoing symptoms. Both the ongoing symptoms subgroups reported more severe symptoms of fatigue, brain fog and chest pain/tightness during the initial illness than those that recovered (ps < / = 0.001) but did not differ from one another. Those with severe ongoing symptoms experienced more severe nausea and blurred vision than those with mild/moderate or who recovered (p ranges < 0.001–0.009). Finally, the C + + (Severe) subgroup experienced more abdominal pain, altered consciousness and confusion during the initial illness than the R subgroup (ps < / = 0.001).FIGURE 4

Figure 4. Severity of different symptoms during the initial (left) and ongoing (right) illness among those who recovered or had ongoing mild or severe illness. Higher scores indicate higher severity.

After controlling for sex, age, education, and country, a forward stepwise multinomial logistic regression suggested that six initial symptoms were significantly associated with COVID-19 progression. These were: limb weakness [χ2(2) = 25.92, p < 0.001], brain fog [χ2(2) = 13.82, p = 0.001], chest pain or tightness [χ2(2) = 10.81, p = 0.005], dizziness [χ2(2) = 7.82, p = 0.02], cough [χ2(2) = 7.74, p = 0.021], and breathing difficulties [χ2(2) = 6.98, p = 0.031]. People initially experiencing more severe limb weakness were more likely to experience severe ongoing symptoms than to recover (p < 0.001) or develop mild/moderate ongoing symptoms (p < 0.001). More severe initial breathing issues (p = 0.014) and dizziness (p = 0.037) were associated with greater likelihood of severe than mild/moderate ongoing symptoms, but people with more severe initial dizziness (p = 0.02) and cough (p = 0.009) were more likely to recover rather than to develop mild/moderate ongoing symptoms. More severe initial brain fog and chest pain/tightness were associated with more progression into mild/moderate than either severe ongoing symptoms (brain fog: p = 0.029; chest pain: p = 0.026) or recovery (brain fog: p = 0.001; chest pain: p = 0.007).

Symptoms During Ongoing Illness

Excluding those who reported being totally asymptomatic throughout or feeling completely better very quickly after initial illness (who did not report on ongoing symptoms, n = 15), the COVID subgroups were asked to report on their ongoing experience of a list of 52 symptoms. Symptoms that appeared in less than 10% of participants were excluded. The duration-groups differed significantly in 27/47 symptoms (Sidak α = 0.001; see Figure 4 and Supplementary Table 6). Post hoc tests (Sidak α = 0.017) showed that the C + + (Severe) subgroup reported higher levels of severity than the R subgroup in all 27 symptoms (ps < 0.001–0.017) and then the C + (Mild/Moderate) subgroup in all except two (altered consciousness and eye-soreness; ps < 0.001–0.017). The C + (Mild/Moderate) subgroup also reported experiencing higher severity in 16 symptoms (including fatigue, difficulty concentrating, brain fog, and forgetfulness) than the R subgroup (ps < 0.001–0.016; see Figure 4 and Supplementary Table 6; see also Supplementary Table 7 for similar analysis of current symptoms).

Symptoms in Those With Confirmed or Suspected COVID-19 vs. “Other” Illnesses

As much of our sample experienced infection early in the pandemic before widespread testing was available, not all cases included in our COVID group were confirmed by a polymerase chain reaction (PCR) test (infection statuses: “Confirmed” COVID, “Unconfirmed” COVID). Meanwhile, a significant minority of participants had an illness during the pandemic period that they did not think was COVID-19 (infection status: “Unknown”) (see Figure 1). We compared symptom prevalence across these three groups (Unknown, n = 55; Unconfirmed, n = 96; Confirmed, n = 65) for both the initial 3 weeks of illness, and the time since then. Those who were still at their first 3 weeks of COVID-19 infection (n = 17) and who reported “it is too soon” to comment on their ongoing symptoms (n = 3) were not included in this analysis.

The groups significantly differed in 14 out of 31 symptoms during the initial illness (Sidak α = 0.0016; Supplementary Table 8). Both Confirmed and Unconfirmed groups reported higher severity than the Unknown group on 13 symptoms (including fatigue, muscle/body pains and loss of smell/taste; p ranges < 0.001–0.014; Sidak α = 0.017). Additionally, the Unconfirmed group reported more severe blurred vision than the Unknown group (p < 0.001), and the Unknown group reported more severe sore throat/hoarseness than the Confirmed group (p < 0.001). As for the differences within those with COVID-19, the Confirmed group experienced greater loss of smell/taste than the Unconfirmed group (p = 0.002), while the Unconfirmed group reported higher levels of breathing issues, chest pain/tightness, sore throat/hoarseness, and blurred vision than the Confirmed group (ps = 0.004–0.015).

Of these participants, 177 (Unknown group: n = 31; Unconfirmed group: n = 88; Confirmed group: n = 58) reported experiencing ongoing symptoms after the 3 weeks of illness. Significant group differences were found in 11/47 ongoing symptoms (Sidak α = 001; see Figure 5 and Supplementary Table 9). Post hoc tests (Sidak α = 0.017) showed that, compared with the Unknown group, both the Confirmed and Unconfirmed groups reported higher levels of fatigue, difficulty concentrating, brain fog, tip-of-the-tongue (ToT) problems, muscle/body pains, fast/irregular pulse, semantic disfluency, chest pain/tightness, limb weakness, and loss of smell/taste (ps < / = 0.001). The Unconfirmed group also experienced higher level of night waking (p = 0.001) than the Unknown group. There were no significant differences in ongoing symptoms between the Confirmed and the Unconfirmed groups.FIGURE 5

Figure 5. Experience of ongoing symptoms in Unknown, Unconfirmed COVID, and Confirmed COVID groups.

Characterizing Symptom Profiles

While data on individual symptoms are useful in identifying highly specific predictors, these are too numerous for more systematic analysis, which require data-reduction. A stated aim of this study was to identify symptom profiles that may be informative as to underlying pathology.

Initial Symptom Factors

To group the initial symptoms, we included 34 symptoms in the PCA after excluding paralysis and seizures (experienced by less than 10% of the participants). A total of 164 participants reported on their symptoms during the first 3 weeks of illness (the factor analysis coded here as 1 = Very severe, 3 = Not at all). The Kaiser-Meyer-Olkin (KMO) test (value 0.861) and Bartlett’s test of sphericity [χ2(528) = 2,250, p < 0.001] showed the data were suitable for factor analysis. We employed the varimax rotation. Initially, nine factors were obtained with eigenvalue > 1.0, which was reduced to five via Cattell’s Scree test (Kline, 2013). Assessments were conducted of 4, 5, and 6 factor solutions for interpretability and robustness. The ratio of rotated eigenvalue to unrotated eigenvalue was higher for the 5-factor solution than for the 4- or 6-factor solutions, and this structure was also the most interpretable. We thus proceeded with a 5-factor solution, which explained 50.59% of item variance with last rotated eigenvalue of 1.998.

We labeled the new components as “F1: Neurological/Psychiatric,” “F2: Fatigue/Mixed,” “F3: Gastrointestinal,” “F4: Respiratory/Infectious,” and “F5: Dermatological” (see Table 3 for factor loadings). We computed the factor scores using the regression method (see Supplementary Table 10 for factor scores).TABLE 3

Table 3. Factors and loadings from the “Initial Symptoms” PCA.

People who went on to experience ongoing symptoms showed higher factor scores in the Fatigue/Mixed symptom factor during the initial illness [F(2, 158) = 23.577, p < 0.001], but did not differ in any other initial symptom factor. Pairwise analysis revealed that those who recovered were significantly less likely to experience Fatigue/Mixed symptoms than those with mild/moderate (p < 0.001) or severe (p < 0.001) ongoing symptoms (Figure 6).FIGURE 6

Figure 6. Severity of Fatigue/Mixed symptom factor during initial illness among those who went on to full recover, or have ongoing mild or severe symptoms.

Ongoing Symptom Factors

We performed a second PCA using the symptoms experienced since the initial phase (after the first 3 weeks), including 45 symptoms. Paralysis and seizures were excluded (experienced by less than 10% of the participants). A total of 149 participants reported on their symptoms over the time since the first 3 weeks of illness (the factor analysis coded here as 1 = Very severe and often, 5 = Not at all). The KMO test (value 0.871) and Bartlett’s test of sphericity [χ2(861) = 3,302, p < 0.001] showed suitability for factor analysis. We employed the varimax rotation. PCA showed 11 components with eigenvalues > 1.0, and this was reduced to 6 via inspection of the eigenvalue gradient (scree plot). The ratio of rotated eigenvalue to unrotated eigenvalue was higher for the 7-factor solution, followed by the 6-factor. The 6- and 7-factor solutions were differentiated by subdivision of the second factor, reducing the degree of cross-loading. However, the 7-factor solution was less interpretable and less robust to removal to cross-loaders (the presence of which can be accepted from a pathology perspective, given that multiple mechanisms can produce the same symptom). As such, we proceeded with the 6-factor solution, which explained 54.17% of item variance and had a last rotated eigenvalue of 2.227.

We labeled the new components as “F1: Neurological,” “F2: Gastrointestinal/Autoimmune,” “F3: Cardiopulmonary/Fatigue,” “F4: Dermatological/Fever,” “F5: Appetite Loss,” and “F6: Mood” (see Table 4 for factor loadings). We computed the factor scores using the regression method (see Supplementary Table 11 for factor scores).TABLE 4

Table 4. Factors and loadings from the exploratory factor analysis of ongoing “since then” symptoms PCA.

In order for cognitive symptoms [brain fog, forgetfulness, tip-of-the-tongue (ToT) problems, semantic disfluency and difficulty concentrating] to be used as a dependent variable, these were isolated and a PCA run separately. A single component emerged, with all the cognitive symptoms loading homogeneously highly (see Supplementary Table 12). The KMO test (value 0.886) and Bartlett’s test of sphericity [χ2(10) = 564, p < 0.001] indicated suitability for factor analysis, and the single 5-item factor explained 76.86% of variance.

Current Symptoms

The current symptoms assessed were the same as the ongoing symptoms, but rated dichotomously as either currently present or absent. To estimate the degree to which current symptoms aligned with the factors established for the ongoing period, we generated a quasi-continuously distributed variable according to how many of the high loading (> / = 0.5) items from the ongoing factors were recorded as present currently. Using this sum scores by factor method (Tabachnick et al., 2007Hair, 2009), each score was subsequently divided by the number of items in that factor producing quasi “factor scores” that were comparable and indicative of “degree of alignment” of current symptoms to established factors.

To assess the stability and specificity of symptom profiles between these periods, serial correlations were conducted for corresponding and non-corresponding factors. Correlations of the same factor across time points were materially higher (> 0.2) from the next highest correlation among the 5 non-corresponding factors, with Williams tests (Steiger, 1980) giving the narrowest gap at p = 0.003 (Neurological: r = 0.676, t = 5.712; Gastrointestinal/Autoimmune: r = 0.531, t = 3.778; Cardiopulmonary/Fatigue: r = 0.678, t = 7.272; Dermatological/Fever: r = 0.523, t = 3.364; Appetite Loss: r = 0.591, t = 5.017; Mood: r = 0.490, t = 4.803). This consistency suggests that while particular symptoms may fluctuate, the profile of symptoms—once grouped into an adequately supported factor—is moderately stable for individuals, and can be relatively well represented by a “snapshot” of current symptoms. For completeness, an additional factor analysis was conducted on the current symptoms, which are reported in Supplementary Table 13.

One symptom factor showed change over time since infection, suggesting higher severity in those who had been ill for longer: Number of weeks since infection (positive test/first symptoms) was positive correlated with severity of ongoing severity of Cardiopulmonary/Fatigue symptoms [r(147) = 0.271, p < 0.001; Figure 7] and, to a weaker extent, current alignment with the same factor [r(147) = 0.206, p = 0.012], however, only the former association survived correction for multiple comparisons (Sidak α = 0.0085).FIGURE 7

Figure 7. Association between number of weeks since infection and severity of (top) Cardiopulmonary/Fatigue Symptoms and (bottom) cognitive symptoms in the entire period since the initial infection (left) and the past 1–2 days (right). Higher scores indicate higher symptom severity.

Cognitive Symptoms

Within those currently experiencing symptoms (n = 126), 77.8% reported difficulty concentrating, 69% reported brain fog, 67.5% reported forgetfulness, 59.5% reported tip-of-the-tongue (ToT) word finding problems and 43.7% reported semantic disfluency (saying or typing the wrong word).

Symptoms experienced during the initial illness significantly predicted both ongoing and current cognitive symptoms (Figure 8). A linear regression with backward elimination found that the best model contained the Neurological/Psychiatric, Fatigue/Mixed, Gastrointestinal, and Respiratory/Infectious symptom factors and explained 20% of variance (Radj2 = 0.2, p < 0.001). Table 5 shows that the Fatigue/Mixed symptoms factor (η′p2 = 0.129) was the better predictor followed by the Neurological/Psychiatric symptom factor (η′p2 = 0.092). For current cognitive symptoms, the best model contained both the Neurological/Psychiatric and Fatigue/Mixed symptom factors, together explaining 13.9% of variance (p < 0.001). Of the two, the Fatigue/Mixed factor was the better predictor (η′p2 = 0.110). No interactions between factors contributed significantly and were thus not included in the final models.FIGURE 8

Figure 8. Association between combined regression model predicted value for (A) initial symptom factors and ongoing cognitive symptoms; (B) initial symptom factors and current cognitive symptoms; (C) ongoing symptom factors and ongoing cognitive symptoms; (D) ongoing symptom factors and current cognitive symptoms; and (E) current symptom factors and current cognitive symptoms.TABLE 5

Table 5. Regression models predicting variation in the cognitive symptom factor (ongoing and current) from non-cognitive symptom factors (initial, ongoing, and current).

A similar, but much stronger, pattern emerged when considering the predictive value of ongoing (non-cognitive) symptoms (Figure 8). Using backward elimination to factors with significance (p < 0.05), all factors except Dermatological/Fever remained in the model, which explained over 55% of variance (Radj2 = 0.558, p < 0.001). The effect size (η′p2) for each factor is given in Table 5. The Gastrointestinal/Autoimmune and Cardiopulmonary/Fatigue factors were the biggest contributors to the model. Indeed, in an extreme elimination model in which contributing factors were limited to two or fewer, these two factors alone explained 38% of variance retaining strong significance (p < 0.001). No interactions between factors contributed significantly and were thus not included in the final models. Ongoing symptoms also predicted current cognitive symptoms. The best model explained 36% of the variance (p < 0.001) and included the Neurological, Gastrointestinal/Autoimmune and Cardiopulmonary/Fatigue factors and an interaction between the Gastrointestinal/Autoimmune and Cardiopulmonary/Fatigue factors. Of these, Cardiopulmonary/Fatigue symptoms were the strongest predictor (η′p2 = 0.208), with Neurological (η′p2 = 0.118) and Gastrointestinal/Autoimmune (η′p2 = 0.115) being relatively equal.

Current symptom factors also strongly predicted current cognitive symptoms (Figure 8). The backward elimination model left three contributing factors: Neurological, Cardiopulmonary/Fatigue and Appetite Loss. Together these explained around 50% of variance (Radj2 = 0.494). Of these, Cardiopulmonary/Fatigue was the stronger predictor (η′p2 = 0.306). Indeed, when the model was limited to just this factor, this model still explained 43% of the variance.

There was a significant association between degree of cognitive symptoms and duration of illness. Those who had been ill for longer were more likely to report having had cognitive symptoms throughout the ongoing illness [r(147) = 0.262, p = 0.001] and to be experiencing them at the time of test [r(147) = 0.179, p = 0.03] (Figure 7).

Experiences and Impact of Long COVID

Here we limited analysis to all those who reported some degree or period of ongoing symptoms following COVID-19 [i.e., excluding those who reported being totally asymptomatic throughout or feeling completely better very quickly after initial illness (n = 15)]. Of the remaining 146 participants, 108 (74%) self-identified as experiencing or having experienced “Long COVID.”

We examined the impact and experiences of ongoing illness (Table 6). In most cases, the nature and degree of negative experience of ongoing symptoms scaled with perceived severity. The change in symptoms over time differed between severity subgroups [χ2(6) = 37.52, p < 0.001, V = 0.367]. The C + + (Severe) subgroup were more likely to report that symptoms were consistent over time, while those with mild/moderate ongoing symptoms were more likely to report improvement in symptoms. As might be expected, the R subgroup were alone in reporting complete resolution of symptoms after recovery from the initial illness (Supplementary Table 14).TABLE 6

Table 6. Experiences and impact of Long COVID in different ongoing symptom severity groups.

Long COVID has significant impact on individuals’ lives. Over 54.6% of those with ongoing symptoms had experienced long periods unable to work and 34.5% had lost their job due to illness, 63.9% reported difficulty coping with day-to-day activities, 49.6% had had difficulty getting medical professionals to take their symptoms seriously, and 43.7% felt that they had experienced a trauma, while 17.6% had experienced financial difficulty as a result of illness. These impacts scaled with symptom severity. Those with severe ongoing symptoms were more likely to report being unable to work for a long period due to illness [χ2(2) = 46.42, p < 0.001, V = 0.564], having difficulty coping with day-to-day requirements [χ2(2) = 20.23, p < 0.001, V = 0.372], having difficulty getting medical professionals to take their symptoms seriously [χ2(2) = 23.05, p < 0.001, V = 0.397], and losing their job due to illness [χ2(2) = 24.39, p < 0.001, V = 0.409]. In contrast, the R subgroup tended to report experiencing none of the above [χ2(2) = 52.73, p < 0.001, V = 0.601].

We further compared job-loss with the No COVID group (n = 185). Those with ongoing symptoms were more likely to have lost their job than those who had not experienced COVID-19 [χ2(1) = 26.74, p < 0.001, V = 0.297]. The most common reason for job-loss among those with ongoing symptoms was illness [χ2(1) = 56.85, p < 0.001, V = 0.432], while the most common reason in the No COVID group was economy [χ2(1) = 7.67, p = 0.006, V = 0.159].


Nature of Illness and Symptom Profiles

Here we report the initial findings from a cross-sectional/longitudinal study investigating cognition post-COVID-19. One aim of this first publication was to characterize the “COVID and Cognition Study” (COVCOG) sample. Within the COVID group, we recruited specifically to get good representation of those who were experiencing or had experienced ongoing symptoms. Indeed, 74% identified with the term “Long COVID.” Our final sample had a relatively even spread of those that had fully recovered at the time of test (42), or had mild/moderate (53) or severe (66) ongoing symptoms. Medical history did not differ between those experiencing ongoing symptoms and those who recovered. However, in terms of health behaviors, those with ongoing symptoms were in general “healthier,” being more likely to have previously been consuming less fatty food and more fruits and vegetables. This result is counterintuitive and may reflect insufficient controls for confounding demographic variables relating to socio-economic status. Nonetheless potential links between lifestyle and nutrition and COVID-19 recovery warrant further investigation.

The nature of the initial illness was found to have a significant impact on the likelihood and severity of ongoing symptoms. Despite this sample almost entirely comprised of non-hospitalized patients, those with more severe initial illness were more likely to have ongoing symptoms, and for those symptoms to be more severe. This suggests even in “community” cases, initial infection severity is a predictor of vulnerability to Long COVID. In an analysis of all symptoms experienced during the initial illness, there were several that were predictive of presence or severity of ongoing symptoms. In particular, individuals with severe ongoing symptoms were significantly more likely to have experienced limb weakness during the initial illness than those that recovered. However, some differences in severity ratings between ongoing subgroups were small despite being statistically significant, which warrant caution in interpreting the results.

We asked participants to retrospectively report on symptoms over three time periods: initial illness, ongoing illness, and currently experienced. Given the highly heterogenous nature of Long COVID, we used principal component analysis (PCA) with the aim to ascertain whether there may be different phenotypes of the condition within our sample—that is to say, that there may be certain types of symptoms that tend to (or not to) co-occur. For both the initial and ongoing illness, the symptom factors resemble those found in previous studies (e.g., Davis et al., 2021Whitaker et al., 2021Ziauddeen et al., 2021), with some quite coherent cardiopulmonary clusters, and other less specific “multisystem” profiles which may reflect more systemic issues such as inflammation, circulation, or endocrine function.

Predictors of Cognitive Difficulties

A large proportion of our sample reported cognitive difficulties. We isolated the cognitive symptoms for the ongoing and current illness and computed a single factor including only these. Using this, we investigated which (non-cognitive) symptom factors during both the initial and ongoing illness explained significant variance in severity of cognitive symptoms.

Together, the Fatigue/Mixed, Neurological/Psychiatric, Gastrointestinal and Respiratory/Infectious symptom factors during the initial illness explained around 20% of variance in ongoing (“since then”) cognitive symptoms, and a similar model (containing only Neurological/Psychiatric and Fatigue/Mixed symptom factors) explained nearly 14% of variance in current cognitive symptoms. These findings strongly suggest that experience of neurological symptoms during the initial illness are significant predictors of self-reported cognitive impairment. While only one factor is named “Neurological” both this and the Fatigue/Mixed factor contain clear elements of neurological involvement. Indeed, headache, dizziness, and brain fog all loaded more highly on the Fatigue/Mixed factor than on the Neurological/Psychiatric factor (which was more characterized by disorientation, visual disturbances, delirium, and altered consciousness). This suggests different types of neurological involvement, potentially reflecting neuroinflammation (the Fatigue/Mixed factor) and encephalitis (the Neurological/Psychiatric factor), respectively. It is of note then that both these factors independently predicted subjective cognitive problems. Both inflammation and encephalitis have been proposed as mechanisms through which COVID-19 may impact the brain (Bougakov et al., 2021) and the presence of indications of neuro-inflammation have been found in post-mortem studies (Matschke et al., 2020). It will be an important next step in the investigation to explore whether the neurological and (possible) inflammatory symptom factors explain variance in performance in cognitive tests.

Participants’ experience of ongoing Neurological, Cardiopulmonary/Fatigue, Gastrointestinal/Autoimmune, Mood and Appetite Loss symptom factors all predicted current cognitive symptoms, together explaining around over 55% of variance. Unlike the initial symptom factors, the vast majority of neurological symptoms were contained within the Neurological factor for ongoing symptoms, with only headache and dizziness loading more strongly into the Gastrointestinal/Autoimmune factor. This latter factor was instead more characterized by symptoms associated with systemic illness—potentially endocrine, or reflecting thyroid disruption—including diarrhea, hot flushes and body pains. An additional predictor here was Cardiopulmonary/Fatigue symptoms, a factor which was quite narrowly characterized by symptoms associated with breathing difficulties. Alone, the Gastrointestinal/Autoimmune and Cardiopulmonary/Fatigue factors explained a large proportion of the variance (36%), suggesting these were the biggest contributor to individual differences in cognitive symptoms. These findings suggest that the symptoms linked with cognitive issues are not so specifically neurological as during the initial illness, but may also incorporate problems with heart and lung function (potentially implying hypoxia, which can induce hypoxic/anoxic-related encephalopathy; Guo et al., 2020) and with other ongoing ill health that is harder to label (resembling symptoms of the menopause, Crohn’s disease, hypothyroidism, and a number of other conditions), but may imply systemic inflammation. Again, these associations align with previous findings, in which cardiopulmonary and cognitive systems clustered in the same factor (Ziauddeen et al., 2021).

In terms of current symptoms, the Cardiopulmonary/Fatigue factor again emerged as a significant predictor, this time paired with Neurological and Appetite Loss symptom factors and explaining nearly 50% of variance. It is potentially notable that both the cognitive and Cardiopulmonary/Fatigue factors showed positive correlation with length of illness, suggesting either that the same disease process underpinning both increases in severity over time, or that the relationship between the two may be the result of both being symptoms more commonly still experienced in those with longer-lasting illness. Longitudinal investigation within individuals would be necessary to disambiguate this.

Impact of Long COVID

Of those experiencing Long COVID, more than half (and 75% of those with severe symptoms) reported long periods unable to work due to illness. These findings chime with evidence from other studies on Long COVID (e.g., Davis et al., 2021Ziauddeen et al., 2021). Notably, Davis et al. (2021) found that in their sample 86% of participants reported that it was the cognitive dysfunction in particular that was impacting their work (30% severely so). The reported experiences of those with Long COVID—many of whom were at least 6 months into their illness at the time of completing the study—suggest that in addition to broader economic challenges associated with the pandemic, society will face a long “tail” of workforce morbidity. It is thus of great importance—not just for individuals but for society—to be able to prevent, predict, identify and treat issues associated with Long COVID, and including treatment for cognitive symptoms as part of this policy.

A major roadblock to progress in management and treatment of Long COVID is that clinicians do not have the appropriate information or experience. A significant number (over 50% of those with severe symptoms) of our sample reported struggling to get medical professionals to take their symptoms seriously. Part of this issue will be the nature of the symptoms experienced. Patients whose symptoms cannot be, or are not routinely, clinically measured (such as cognitive symptoms; Kaduszkiewicz et al., 2010) are at greater risk of “testimonial injustice”—that is, having their illness dismissed by medical professionals (De Jesus et al., 2021). The novel and heterogenous nature of Long COVID also provides a particular challenge for clinicians dealing with complex and undifferentiated presentations and “medically unexplained symptoms” (Davidson and Menkes, 2021). The data presented here demonstrate that cognitive difficulties reported by patients can be predicted by severity and pattern of symptoms during the initial stages of infection, and during the ongoing illness. These findings should provide the foundation for clinicians to assess the risk of long-term (6 months +) cognitive difficulties, as well as for researchers to investigate the underlying mechanism driving these deficits. In our next paper, we will explore the association between general and cognitive symptoms and performance on cognitive tasks, with the aim of establishing whether self-reported cognitive issues translate into “objective” deficits on cognitive evaluations.

Some have argued that cognitive changes following COVID-19 infection may reflect changes related to experience of lockdown or social isolation (perhaps via development of depression or anxiety). There is indeed some evidence that pandemic-related changes in lifestyle impact cognition (e.g., Fiorenzato et al., 2021Okely et al., 2021). However, many of these studies did not record COVID-19 infection history (Okely et al., 2021Smirni et al., 2021) so it is difficult to ascertain to what degree these findings may have been related to COVID-19 infection. One study that did control for this (Fiorenzato et al., 2021) identified significant declines in self-reported attention and executive function, however, showed reduced reports of forgetfulness compared with pre-lockdown. Our results show that, compared to individuals who experienced a (probable) non-COVID-19 illness during the pandemic, those with suspected or confirmed COVID-19 infection experienced greater levels of fatigue, difficulty concentrating, brain fog, tip-of-the-tongue (ToT) word finding problems and semantic disfluency, but did not differ in levels of anxiety and depression. Meanwhile there was little difference between those that did and did not have biological confirmation of their COVID-19 infection. This strongly suggests that self-reported cognitive deficits reported in our sample are associated with COVID-19 infection, rather than the experience of illness, or pandemic more generally.

Limitations and Future Research

While the findings of this study are notable, there are a number of limitations in design and execution which warrant caution in interpreting the results.

Being unable to bring participants into the lab for clinical assessment, this study relied on online retrospective self-report of symptoms sometimes experienced some months previously. We thus must be cognizant of potential issues of misremembering and that questionnaires may not have been completed in an environment conducive to concentration and reflection. The manner of reporting symptoms differed between different reporting times, with a longer list and more reporting options (reflecting both severity and regularity) for the “ongoing” period. In particular, our binary present/absent reporting approach for currently experienced symptoms was not able to reflect current severity and did not lend itself to factor analysis. Using the sum scores by factor method (Tabachnick et al., 2007Hair, 2009) to calculate alignment of currently experienced symptoms with the symptom factors got around some of these issues, future studies should keep lists consistent to allow for direct comparison of symptom profiles at the different time points. A similar issue is that symptoms information was not collected for the “No COVID” group, or (in terms of current symptoms) for those that reported having recovered. This would have been highly useful in order to establish the degree to which symptoms (particularly those which might be expected to be exacerbated by lockdowns, such as depression, anxiety, fatigue) were more common in those that had previously experienced COVID-19 than those that had not. It would also be useful to ask both the COVID and No COVID groups about their living situation at the time of completing the study, such as whether lockdown or any social restrictions were taking place and how much these measures were affecting their physical and psychological health. It would also have been useful to assess whether people who reported having “recovered” showed symptomatology similar to the “No COVID” group, or remained distinct.

Due to the intensive performance focus of the current investigation, our study had a relatively smaller sample size than is feasible in an epidemiological cohort. Characterizing the sample, we found that those who had experienced COVID-19 infection—and within these, those with more severe ongoing symptoms—tended to be older and more educated. We do not believe that these features reflect vulnerabilities toward COVID-19 or Long COVID, but rather the biases in our recruitment and target populations. Our sample was recruited from English speaking countries (the United Kingdom, Ireland, United States, Canada, Australia, New Zealand, or South Africa) and the majority were from the United Kingdom, which may not be representative of people from other parts of the world. Where possible, we controlled for age, sex, education, and country of residence, which should mitigate some of these biases, however, these sampling discrepancies should be kept in mind. We furthermore specifically targeted our recruitment to those self-identifying as experiencing Long COVID, and we advertised the study as investigating memory and cognition in this group. Our sample may thus have been biased toward those individuals with more severe symptoms and cognitive symptoms in particular (as these individuals may be more motivated to take part). Overrepresentation of Long COVID sufferers is not a serious issue outside of prevalence studies, however, our reported rates of cognitive symptoms within the Long COVID cohort should be treated with caution. It is reassuring, however, that the figures for these symptoms within our cohort are comparable to those seen in much larger studies not explicitly investigating cognition (e.g., Davis et al., 2021Ziauddeen et al., 2021).

Finally, much of the analysis in this study was necessarily exploratory, as too little was known at the time of study design to form many clear hypotheses. To handle this, multiple comparisons were conducted, for which the alpha adjustments entailed that only the very strongest effects survived at conventional statistical thresholds. This high type 2 error rate means that it is likely that more than just these findings would be confirmed on replication, and because a stated aim of this study was to generate hypotheses that could be tested in later, more targeted research, we have additionally reported the uncorrected results. Similarly, in terms of investigating symptom profiles, we did not aim to present a “definitive” set of factors, but to provide stratifiers and covariates for future analysis, particularly of cognitive test performance, and changes over time. While this study is not able to identify a specific mechanism, it may be able to lay the groundwork with sufficient breadth and detail to inform future mechanistic investigation.


The COVID and Cognition study is a cross-sectional/longitudinal study assessing symptoms, experiences and cognition in those that have experience COVID-19 infection. Here we present the first analysis in this cohort, characterizing the sample and investigating symptom profiles and cognitive symptoms in particular. We find that particular symptom-profiles—particularly neurological symptoms—during both the initial infection and ongoing illness were predictive of experience of cognitive dysfunction. The symptoms and experiences reported by our sample appear to closely resemble those reported in previous work on Long COVID (e.g., Davis et al., 2021Ziauddeen et al., 2021) which suggests that our, smaller, sample might be generally representative of the larger Long COVID patient community. The participants in this study are being followed up over the course of the next 1–2 years, and it is hoped that future publications with this sample will provide valuable information as to the time-course of this illness.

The severity of the impact of “Long COVID” on everyday function and employment reported in our sample appear to reflect previous studies (e.g., Davis et al., 2021) and is notable, particularly given the large proportion of healthcare and education staff in our sample. All of these issues should be of interest to policy makers, particularly when considering the extent to which large case numbers should be a concern in the context of reduced hospitalizations and deaths due to vaccination. While we do not yet know the impact of vaccination on Long COVID numbers, there are reasons to believe that high levels of infection among relatively young, otherwise healthy individuals may translate into considerable long-term workforce morbidity.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.


Alberti, P., Beretta, S., Piatti, M., Karantzoulis, A., Piatti, M. L., and Santoro, P. (2020). Guillain-Barré syndrome related to COVID-19 infection. Neurol. Neuroimmunol. Neuroinflam. 7:741.

Google Scholar

Almeida-Suhett, C. P., Graham, A., Chen, Y., and Deuster, P. (2017). Behavioral changes in male mice fed a high-fat diet are associated with IL-1β expression in specific brain regions. Physiol. Behav. 169, 130–140. doi: 10.1016/j.physbeh.2016.11.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Alwan, N. A., and Johnson, L. (2021). Defining long COVID: Going back to the start. Med 2, 501–504. doi: 10.1016/j.medj.2021.03.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Assaf, G., Davis, H., McCorkell, L., Wei, H., Brooke, O., Akrami, A., et al. (2020). An Analysis of the Prolonged COVID-19 Symptoms Survey by Patient-led Research Team. Available online at: (accessed 15 November 2020).

Google Scholar

Baj, J., Karakuła-Juchnowicz, H., Teresiński, G., Buszewicz, G., Ciesielka, M., Sitarz, E., et al. (2020). COVID-19: specific and non-specific clinical manifestations and symptoms: the current state of knowledge. J. Clin. Med. 9:1753. doi: 10.3390/jcm9061753

PubMed Abstract | CrossRef Full Text | Google Scholar

Beilharz, J., Kaakoush, N., Maniam, J., and Morris, M. (2018). Cafeteria diet and probiotic therapy: cross talk among memory, neuroplasticity, serotonin receptors and gut microbiota in the rat. Mol. Psychiatry 23, 351–361. doi: 10.1038/mp.2017.38

PubMed Abstract | CrossRef Full Text | Google Scholar

Beilharz, J. E., Maniam, J., and Morris, M. J. (2014). Short exposure to a diet rich in both fat and sugar or sugar alone impairs place, but not object recognition memory in rats. Brain Behav. Immun. 37, 134–141. doi: 10.1016/j.bbi.2013.11.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Belarbi, K., Arellano, C., Ferguson, R., Jopson, T., and Rosi, S. (2012). Chronic neuroinflammation impacts the recruitment of adult-born neurons into behaviorally relevant hippocampal networks. Brain Behav. Immun. 26, 18–23. doi: 10.1016/j.bbi.2011.07.225

PubMed Abstract | CrossRef Full Text | Google Scholar

Beyrouti, R., Adams, M. E., Benjamin, L., Cohen, H., Farmer, S. F., Goh, Y. Y., et al. (2020). Characteristics of ischaemic stroke associated with COVID-19. J. Neurol. Neurosurg. Psychiatry 91, 889–891. doi: 10.1136/jnnp-2020-323586

PubMed Abstract | CrossRef Full Text | Google Scholar

Bliddal, S., Banasik, K., Pedersen, O. B., Nissen, J., Cantwell, L., Schwinn, M., et al. (2021). Acute and persistent symptoms in non-hospitalized PCR-confirmed COVID-19 patients. Sci. Rep. 11, 1–11. doi: 10.1038/s41598-021-92045-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Bossù, P., Toppi, E., Sterbini, V., and Spalletta, G. (2020). Implication of aging related chronic neuroinflammation on COVID-19 pandemic. J. Personal. Med. 10:102. doi: 10.3390/jpm10030102

PubMed Abstract | CrossRef Full Text | Google Scholar

Bougakov, D., Podell, K., and Goldberg, E. (2021). Multiple neuroinvasive pathways in COVID-19. Mol. Neurobiol. 58, 564–575. doi: 10.1007/s12035-020-02152-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Callard, F., and Perego, E. (2021). How and why patients made Long Covid. Soc. Sci. Med. 268:113426.

Google Scholar

Che, Y.-Y., Xia, X.-J., He, B.-P., Gao, Y.-Y., Ren, W.-B., Liu, H.-T., et al. (2018). A corn straw-based diet increases release of inflammatory cytokines in peripheral blood mononuclear cells of dairy cows. J. Zhejiang Univ. Sci. B 19, 796–806. doi: 10.1631/jzus.B1700571

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, W. W., Zhang, X., and Huang, W. J. (2016). Role of neuroinflammation in neurodegenerative diseases. Mol. Med. Rep. 13, 3391–3396. doi: 10.3892/mmr.2016.4948

PubMed Abstract | CrossRef Full Text | Google Scholar

Cirulli, E., Barrett, K. M. S., Riffle, S., Bolze, A., Neveux, I., Dabe, S., et al. (2020). Long-term COVID-19 symptoms in a large unselected population. medrxiv doi: 10.1101/2020.10.07.20208702

CrossRef Full Text | Google Scholar

Das, G., Mukherjee, N., and Ghosh, S. (2020). Neurological insights of COVID-19 pandemic. ACS Chem. Neurosci. 11, 1206–1209.

Google Scholar

Davidson, S. L., and Menkes, D. B. (2021). Long covid: reshaping conversations about medically unexplained symptoms. BMJ 374:1857. doi: 10.1136/bmj.n1859

PubMed Abstract | CrossRef Full Text | Google Scholar

Davis, H. E., Assaf, G. S., Mccorkell, L., Wei, H., Low, R. J., Re’em, Y., et al. (2021). Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine 38:101019 doi: 10.1016/j.eclinm.2021.101019

PubMed Abstract | CrossRef Full Text | Google Scholar

De Jesus, V. V. A., Alwan, N., Callard, F., and Berger, Z. (2021). Listening to Long COVID: Epistemic Injustice and COVID-19 morbidity. medrxiv doi: 10.31219/

CrossRef Full Text | Google Scholar

Ding, X., Xu, J., Zhou, J., and Long, Q. (2020). Chest CT findings of COVID-19 pneumonia by duration of symptoms. Eur. J. Radiol. 127:109009. doi: 10.1016/j.ejrad.2020.109009

PubMed Abstract | CrossRef Full Text | Google Scholar

Douaud, G., Lee, S., Alfaro-Almagro, F., Arthofer, C., Wang, C., Lange, F., et al. (2021). Brain imaging before and after COVID-19 in UK Biobank. medRxiv doi: 10.1101/2021.06.11.21258690

PubMed Abstract | CrossRef Full Text | Google Scholar

Ekdahl, C. T., Claasen, J.-H., Bonde, S., Kokaia, Z., and Lindvall, O. (2003). Inflammation is detrimental for neurogenesis in adult brain. Proc. Nat. Acad. Sci. 100, 13632–13637. doi: 10.1073/pnas.2234031100

PubMed Abstract | CrossRef Full Text | Google Scholar

FAIR Health (2021). A Detailed Study of Patients with Long-Haul COVID. Available online at:–An%20Analysis%20of%20Private%20Healthcare%20Claims–A%20FAIR%20Health%20White%20Paper.pdf

Google Scholar

Fiorenzato, E., Zabberoni, S., Costa, A., and Cona, G. (2021). Cognitive and mental health changes and their vulnerability factors related to COVID-19 lockdown in Italy. PLoS One 16:e0246204. doi: 10.1371/journal.pone.0246204

PubMed Abstract | CrossRef Full Text | Google Scholar

Galanopoulou, A. S., Ferastraoaru, V., Correa, D. J., Cherian, K., Duberstein, S., Gursky, J., et al. (2020). EEG findings in acutely ill patients investigated for SARS-CoV-2/COVID-19: a small case series preliminary report. Epilepsia Open 5, 314–324. doi: 10.1002/epi4.12399

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, Y.-R., Cao, Q.-D., Hong, Z.-S., Tan, Y.-Y., Chen, S.-D., Jin, H.-J., et al. (2020). The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak–an update on the status. Milit. Med. Res. 7, 1–10. doi: 10.1186/s40779-020-00240-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Hair, J. F. (2009). Multivariate data analysis. New York, NY: Springer. doi: 10.1002/9781118887486.ch1

CrossRef Full Text | Google Scholar

Helms, J., Kremer, S., Merdji, H., Clere-Jehl, R., Schenck, M., Kummerlen, C., et al. (2020). Neurologic features in severe SARS-CoV-2 infection. N. Engl. J. Med. 382, 2268–2270. doi: 10.1056/NEJMc2008597

PubMed Abstract | CrossRef Full Text | Google Scholar

Heneka, M. T., Golenbock, D., Latz, E., Morgan, D., and Brown, R. (2020). Immediate and long-term consequences of COVID-19 infections for the development of neurological disease. Alzheimer’s Res. Ther. 12, 1–3. doi: 10.1186/s13195-020-00640-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Henson, R. K., and Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Edu. Psychol. Measure. 66, 393–416.

Google Scholar

Hosp, J. A., Dressing, A., Blazhenets, G., Bormann, T., Rau, A., Schwabenland, M., et al. (2021). Cognitive impairment and altered cerebral glucose metabolism in the subacute stage of COVID-19. Brain 144, 1263–1276. doi: 10.1093/brain/awab009

PubMed Abstract | CrossRef Full Text | Google Scholar

Jain, R. (2020). Evolving neuroimaging findings during COVID-19. Am. Soc. Neuroradiol. 41, 1355–1356. doi: 10.3174/ajnr.A6658

PubMed Abstract | CrossRef Full Text | Google Scholar

Jakubs, K., Bonde, S., Iosif, R. E., Ekdahl, C. T., Kokaia, Z., Kokaia, M., et al. (2008). Inflammation regulates functional integration of neurons born in adult brain. J. Neurosci. 28, 12477–12488. doi: 10.1523/JNEUROSCI.3240-08.2008

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaduszkiewicz, H., Zimmermann, T., Van Den Bussche, H., Bachmann, C., Wiese, B., Bickel, H., et al. (2010). Do general practitioners recognize mild cognitive impairment in their patients? J. Nutrit. Health Aging 14, 697–702. doi: 10.1007/s12603-010-0038-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Kandemirli, S. G., Dogan, L., Sarikaya, Z. T., Kara, S., Akinci, C., Kaya, D., et al. (2020). Brain MRI findings in patients in the intensive care unit with COVID-19 infection. Radiology 297, E232–E235. doi: 10.1148/radiol.2020201697

PubMed Abstract | CrossRef Full Text | Google Scholar

Kheirouri, S., and Alizadeh, M. (2019). Dietary inflammatory potential and the risk of neurodegenerative diseases in adults. Epidemiol. Rev. 41, 109–120. doi: 10.1093/epirev/mxz005

PubMed Abstract | CrossRef Full Text | Google Scholar

Kline, P. (2013). Handbook of psychological testing. Milton Park: Routledge.

Google Scholar

Kubánková, M., Hohberger, B., Hoffmanns, J., Fürst, J., Herrmann, M., Guck, J., et al. (2021). Physical phenotype of blood cells is altered in COVID-19. Biophys. J. 120, 2838–2847. doi: 10.1016/j.bpj.2021.05.025

PubMed Abstract | CrossRef Full Text | Google Scholar

Le Guennec, L., Devianne, J., Jalin, L., Cao, A., Galanaud, D., Navarro, V., et al. (2020). Orbitofrontal involvement in a neuroCOVID-19 patient. Epilepsia 61, e90–e94. doi: 10.1111/epi.16612

PubMed Abstract | CrossRef Full Text | Google Scholar

Lechien, J. R., Chiesa-Estomba, C. M., De Siati, D. R., Horoi, M., Le Bon, S. D., Rodriguez, A., et al. (2020). Olfactory and gustatory dysfunctions as a clinical presentation of mild-to-moderate forms of the coronavirus disease (COVID-19): a multicenter European study. Eur. Arch. Oto-Rhino-Laryngol. 277, 2251–2261. doi: 10.1007/s00405-020-05965-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Y., Li, M., Wang, M., Zhou, Y., Chang, J., Xian, Y., et al. (2020). Acute cerebrovascular disease following COVID-19: a single center, retrospective, observational study. Stroke Vascul. Neurol. 5:431. doi: 10.1136/svn-2020-000431

PubMed Abstract | CrossRef Full Text | Google Scholar

Mao, L., Jin, H., Wang, M., Hu, Y., Chen, S., He, Q., et al. (2020). Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan. China JAMA Neurol. 77, 683–690. doi: 10.1001/jamaneurol.2020.1127

PubMed Abstract | CrossRef Full Text | Google Scholar

Matschke, J., Lütgehetmann, M., Hagel, C., Sperhake, J. P., Schröder, A. S., Edler, C., et al. (2020). Neuropathology of patients with COVID-19 in Germany: a post-mortem case series. Lancet Neurol. 19, 919–929. doi: 10.1016/S1474-4422(20)30308-2

PubMed Abstract | CrossRef Full Text | Google Scholar

McGeer, E. G., and McGeer, P. L. (2010). Neuroinflammation in Alzheimer’s disease and mild cognitive impairment: a field in its infancy. J. Alzheimer’s Dis. 19, 355–361. doi: 10.3233/JAD-2010-1219

PubMed Abstract | CrossRef Full Text | Google Scholar

Mehta, P., Mcauley, D. F., Brown, M., Sanchez, E., Tattersall, R. S., and Manson, J. J. (2020). COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 395, 1033–1034. doi: 10.1016/S0140-6736(20)30628-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Mirzaei, F., Khazaei, M., Komaki, A., Amiri, I., and Jalili, C. (2018). Virgin coconut oil (VCO) by normalizing NLRP3 inflammasome showed potential neuroprotective effects in Amyloid-β induced toxicity and high-fat diet fed rat. Food Chem. Toxicol. 118, 68–83. doi: 10.1016/j.fct.2018.04.064

PubMed Abstract | CrossRef Full Text | Google Scholar

Monje, M. L., Toda, H., and Palmer, T. D. (2003). Inflammatory blockade restores adult hippocampal neurogenesis. Science 302, 1760–1765. doi: 10.1126/science.1088417

PubMed Abstract | CrossRef Full Text | Google Scholar

Moriguchi, T., Harii, N., Goto, J., Harada, D., Sugawara, H., Takamino, J., et al. (2020). A first case of meningitis/encephalitis associated with SARS-Coronavirus-2. Int. J. Infect. Dis. 94, 55–58. doi: 10.1016/j.ijid.2020.03.062

PubMed Abstract | CrossRef Full Text | Google Scholar

National Institute for Health and Care Excellence [NICE] (2020). COVID-19 Rapid Guideline: Managing the Long-Term Effects of COVID-19. London: National Institute for Health and Care Excellence.

Google Scholar

Nehme, M., Braillard, O., Alcoba, G., Aebischer Perone, S., Courvoisier, D., Chappuis, F., et al. (2021). COVID-19 symptoms: longitudinal evolution and persistence in outpatient settings. Ann. Int. Med. 174, 723–725. doi: 10.7326/M20-5926

PubMed Abstract | CrossRef Full Text | Google Scholar

Nunnally, J. C. (1978). Psychometric Theory, 2nd Edn. New York, NY: McGraw-Hill.

Google Scholar

Office for National Statistics [ONS] (2021). Prevalence of Ongoing Symptoms Following Coronavirus (COVID-19) Infection in the UK: 1 April 2021. Available online at: (accessed April 1, 2021).

Google Scholar

Okely, J. A., Corley, J., Welstead, M., Taylor, A. M., Page, D., Skarabela, B., et al. (2021). Change in physical activity, sleep quality, and psychosocial variables during COVID-19 lockdown: Evidence from the Lothian Birth Cohort 1936. Int. J. Environ. Res. Public Health 18:210. doi: 10.3390/ijerph18010210

PubMed Abstract | CrossRef Full Text | Google Scholar

Politi, L. S., Salsano, E., and Grimaldi, M. (2020). Magnetic resonance imaging alteration of the brain in a patient with coronavirus disease 2019 (COVID-19) and anosmia. JAMA Neurol. 77, 1028–1029. doi: 10.1001/jamaneurol.2020.2125

PubMed Abstract | CrossRef Full Text | Google Scholar

Poyiadji, N., Shahin, G., Noujaim, D., Stone, M., Patel, S., and Griffith, B. (2020). COVID-19–associated acute hemorrhagic necrotizing encephalopathy: imaging features. Radiology 296, E119–E120. doi: 10.1148/radiol.2020201187

PubMed Abstract | CrossRef Full Text | Google Scholar

Pryce-Roberts, A., Talaei, M., and Robertson, N. (2020). Neurological complications of COVID-19: a preliminary review. J. Neurol. 267, 1870–1873. doi: 10.1007/s00415-020-09941-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Romero-Sánchez, C. M., Díaz-Maroto, I., Fernández-Díaz, E., Sánchez-Larsen, A., Layos-Romero, A., García-García, J., et al. (2020). Neurologic manifestations in hospitalized patients with COVID-19: the ALBACOVID registry. Neurology 95, e1060–e1070. doi: 10.1212/WNL.0000000000009937

PubMed Abstract | CrossRef Full Text | Google Scholar

Smirni, D., Garufo, E., Di Falco, L., and Lavanco, G. (2021). The playing brain. the impact of video games on cognition and behavior in pediatric age at the time of lockdown: a systematic review. Pediatric Rep. 13, 401–415. doi: 10.3390/pediatric13030047

PubMed Abstract | CrossRef Full Text | Google Scholar

Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychol. Bull. 87:245.

Google Scholar

Sudre, C. H., Murray, B., Varsavsky, T., Graham, M. S., Penfold, R. S., Bowyer, R. C., et al. (2020). Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App. Medrxiv doi: 10.1101/2020.10.19.20214494

CrossRef Full Text | Google Scholar

Tabachnick, B. G., Fidell, L. S., and Ullman, J. B. (2007). Using multivariate statistics. Boston, MA: Pearson.

Google Scholar

Tay, M. Z., Poh, C. M., Rénia, L., Macary, P. A., and Ng, L. F. (2020). The trinity of COVID-19: immunity, inflammation and intervention. Nat. Rev. Immunol. 20, 363–374. doi: 10.1038/s41577-020-0311-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Tenforde, M. W., Kim, S. S., Lindsell, C. J., Rose, E. B., Shapiro, N. I., Files, D. C., et al. (2020). Symptom duration and risk factors for delayed return to usual health among outpatients with COVID-19 in a multistate health care systems network—United States. Morb. Mortal. Week. Rep. 69:993.

Google Scholar

Thirumangalakudi, L., Prakasam, A., Zhang, R., BimonteNelson, H., Sambamurti, K., Kindy, M. S., et al. (2008). High cholesterol-induced neuroinflammation and amyloid precursor protein processing correlate with loss of working memory in mice. J. Neurochem. 106, 475–485. doi: 10.1111/j.1471-4159.2008.05415.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Z., Yang, Y., Liang, X., Gao, B., Liu, M., Li, W., et al. (2020). COVID-19 associated ischemic stroke and hemorrhagic stroke: incidence, potential pathological mechanism, and management. Front. Neurol. 11:1152. doi: 10.3389/fneur.2020.571996

PubMed Abstract | CrossRef Full Text | Google Scholar

Whitaker, M., Elliott, J., Chadeau-Hyam, M., Riley, S., Darzi, A., Cooke, G., et al. (2021). Persistent symptoms following SARS-CoV-2 infection in a random community sample of 508,707 people. medRxiv doi: 10.1101/2021.06.28.21259452

CrossRef Full Text | Google Scholar

Whittaker, A., Anson, M., and Harky, A. (2020). Neurological manifestations of COVID-19: a systematic review and current update. Acta Neurol. Scand. 142, 14–22. doi: 10.1111/ane.13266

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhao, H., Shen, D., Zhou, H., Liu, J., and Chen, S. (2020). Guillain-Barré syndrome associated with SARS-CoV-2 infection: causality or coincidence? Lancet Neurol. 19, 383–384.

Google Scholar

Ziauddeen, N., Gurdasani, D., O’hara, M. E., Hastie, C., Roderick, P., Yao, G., et al. (2021). Characteristics of Long Covid: findings from a social media survey. medRxiv doi: 10.1101/2021.03.21.21253968

CrossRef Full Text | Google Scholar

Zotova, E., Nicoll, J. A., Kalaria, R., Holmes, C., and Boche, D. (2010). Inflammation in Alzheimer’s disease: relevance to pathogenesis and therapy. Alzheimers Res. Ther. 2, 1–9. doi: 10.1186/alzrt24

PubMed Abstract | CrossRef Full Text | Google Scholar

COVID virus linked with headaches, altered mental status in hospitalized kids

Authors: UNIVERSITY OF PITTSBURGH Peer-Reviewed Publication

PITTSBURGH, Jan. 21, 2022 – Of hospitalized children who tested or were presumed positive for SARS-CoV-2, 44% developed neurological symptoms, and these kids were more likely to require intensive care than their peers who didn’t experience such symptoms, according to a new study led by a pediatrician-scientist at UPMC and the University of Pittsburgh School of Medicine

The most common neurologic symptoms were headache and altered mental status, known as acute encephalopathy. Published in Pediatric Neurology, these preliminary findings are the first insights from the pediatric arm of GCS-NeuroCOVID, an international, multi-center consortium aiming to understand how COVID-19 affects the brain and nervous system. 

“The SARS-CoV-2 virus can affect pediatric patients in different ways: It can cause acute disease, where symptomatic illness comes on soon after infection, or children may develop an inflammatory condition called MIS-C weeks after clearing the virus,” said lead author Ericka Fink, M.D., pediatric intensivist at UPMC Children’s Hospital of Pittsburgh, and associate professor of critical care medicine and pediatrics at Pitt. “One of the consortium’s big questions was whether neurological manifestations are similar or different in pediatric patients, depending on which of these two conditions they have.” 

To answer this question, the researchers recruited 30 pediatric critical care centers around the world. Of 1,493 hospitalized children, 1,278, or 86%, were diagnosed with acute SARS-CoV-2; 215 children, or 14%, were diagnosed with MIS-C, or multisystem inflammatory syndrome in children, which typically appears several weeks after clearing the virus and is characterized by fever, inflammation and organ dysfunction. 

The most common neurologic manifestations linked with acute COVID-19 were headache, acute encephalopathy and seizures, while youths with MIS-C most often had headache, acute encephalopathy and dizziness. Rarer symptoms of both conditions included loss of smell, vision impairment, stroke and psychosis.  

“Thankfully, mortality rates in children are low for both acute SARS-CoV-2 and MIS-C,” said Fink. “But this study shows that the frequency of neurological manifestations is high—and it may actually be higher than what we found because these symptoms are not always documented in the medical record or assessable. For example, we can’t know if a baby is having a headache.” 

The analysis showed that neurological manifestations were more common in kids with MIS-C compared to those with acute SARS-CoV-2, and children with MIS-C were more likely than those with acute illness to have two or more neurologic manifestations. 

According to Fink, the team recently launched a follow up study to determine whether acute SARS-CoV-2 and MIS-C—with or without neurologic manifestations—have lasting effects on children’s health and quality of life after discharge from hospital.  

“Another long-term goal of this study is to build a database that tracks neurological manifestations over time—not just for SARS-CoV-2, but for other types of infections as well,” she added. “Some countries have excellent databases that allow them to easily track and compare children who are hospitalized, but we don’t have such a resource in the U.S.” 

This study was partly funded by the Neurocritical Care Society Investing in Clinical Neurocritical Care Research (INCLINE) grant. 

Other researchers who contributed to the study include Courtney L. Robertson, M.D., Johns Hopkins Children’s Center; Mark S. Wainwright, M.D., Ph.D., University of Washington and Seattle Children’s Hospital; Juan D. Roa, M.D., Universidad Nacional de Colombia and Fundación Universitaria de Ciencias de la Salud; Michelle E. Schober, M.D., University of Utah, and other GCS-NeuroCOVID Pediatrics investigators who are listed in the paper. 

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Pediatric Neurology




Observational study




Prevalence and Risk Factors of Neurologic Manifestations in Hospitalized Children Diagnosed with Acute SARS-CoV-2 or MIS-C



Some COVID-19 patients have brain complications, study suggests

Authors: Mary Van Beusekom | News Writer | CIDRAP News  | Jun 26, 2020

Some COVID-19 patients, including those younger than 60 years old, appear to develop neurologic and neuropsychiatric complications such as stroke, brain inflammation, psychosis, and dementia-like symptoms, according to a study published yesterday in The Lancet Psychiatry.

The early-stage study of 153 hospitalized patients with confirmed, probable, or possible COVID-19 in the United Kingdom (UK) from Apr 2 to 26 identified 125 patients with complete data, of whom 77 (62%) had a stroke.

Of 125 patients, 114 (92%) had confirmed coronavirus infection, 5 (4%) had probable infection, and 5 (4%) were classified as possibly infected.

Stroke, encephalopathy, psychiatric diagnoses

Fifty-seven of 77 stroke patients (74%) had an ischemic stroke caused by a blood clot in the brain, 9 (12%) had a stroke caused by a brain hemorrhage, and 1 (1%) had a stroke caused by inflammation in the brain’s blood vessels. Sixty-one of the 77 stroke patients for whom age was available (82%) were older than 60 years.

Thirty-nine of 125 patients (31%) had behavioral changes indicative of an altered mental state, of whom 9 (23%) had unspecified brain dysfunction known as encephalopathy, and 7 (18%) had brain inflammation, or encephalitis.

The remaining 23 patients with altered mental states had psychiatric diagnoses, including 10 with new-onset psychosis, 7 with depression or anxiety, and 6 with a dementia-like syndrome. Only 2 patients (9%) had exacerbations of a chronic mental illness, although the authors noted that they cannot exclude the possibility that cases classified as new were simply undiagnosed before the pandemic.

Of the 37 of 39 COVID-19 patients with an altered mental state for whom age was available, 18 (49%) were younger than 60 years, which could be because they were more likely to be referred to a psychiatrist or other specialist, while physicians may be likely to attribute confusion or behavioral changes in older patients to delirium without further investigation, the authors said.

Altered mental states in younger patients

While altered mental states are not uncommon in hospitalized patients with infections, especially those requiring intensive care, they occur most often in older patients.

“In this study, we observed a disproportionate number of neuropsychiatric presentations in younger patients and a predominance of cerebrovascular complications in older patients, which might reflect the state of health of the cerebral vasculature and associated risk factors, exacerbated by critical illness in older patients,” the authors said.

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Stress-Related Growth in Adolescents Returning to School After COVID-19 School Closure

Authors: Lea Waters,1,*Kelly-Ann Allen,1,2 and Gökmen Arslan3,4


The move to remote learning during COVID-19 has impacted billions of students. While research shows that school closure, and the pandemic more generally, has led to student distress, the possibility that these disruptions can also prompt growth in is a worthwhile question to investigate. The current study examined stress-related growth (SRG) in a sample of students returning to campus after a period of COVID-19 remote learning (n = 404, age = 13–18). The degree to which well-being skills were taught at school (i.e., positive education) before the COVID-19 outbreak and student levels of SRG upon returning to campus was tested via structural equation modeling. Positive reappraisal, emotional processing, and strengths use in students were examined as mediators. The model provided a good fit [χ2 = 5.37, df = 3, p = 0.146, RMSEA = 0.044 (90% CI = 0.00–0.10), SRMR = 0.012, CFI = 99, TLI = 0.99] with 56% of the variance in SRG explained. Positive education explained 15% of the variance in cognitive reappraisal, 7% in emotional processing, and 16% in student strengths use during remote learning. The results are discussed using a positive education paradigm with implications for teaching well-being skills at school to foster growth through adversity and assist in times of crisis.


Novel coronavirus (COVID-19) spread rapidly across the globe in 2020, infecting more than 70 million people and causing more than 1.5 million deaths at the time of submitting this paper (December 8, 2020; World Health Organization, 2020a). The restrictions and disruptions stemming from this public health crisis have compromised the mental health of young people (Hawke et al., 2020UNICEF, 2020Yeasmin et al., 2020Zhou et al., 2020). A review assessing the mental health impact of COVID-19 on 6–21-year-olds (n = 51 articles) found levels of depression and anxiety ranging between 11.78 and 47.85% across China, the United States of America, Europe, and South America (Marques de Miranda et al., 2020). Researchers have also identified moderate levels of post-traumatic stress disorder (PTSD) in youth samples during the COVID-19 pandemic (Guo et al., 2020Liang et al., 2020Wang et al., 2020).

Adolescence is a critical life stage for identity formation (Allen and McKenzie, 2015Crocetti, 2017) where teenagers strive for mastery and autonomy (Featherman et al., 2019), individuate from their parents (Levpuscek, 2006), and gravitate toward their peer groups to have their social and esteem needs met (Allen and Loeb, 2015). The pandemic has drastically curtailed the conditions for teens to meet their developmental needs (Loades et al., 2020). Gou et al. (2020, p. 2) argue that adolescents are “more vulnerable than adults to mental health problems, in particular during a lockdown, because they are in a transition phase… with increasing importance of peers, and struggling with their often brittle self-esteem.”

In addition to the researching psychological distress arising from COVID-19, it is also important to identify positive outcomes that may arise through this pandemic. Dvorsky et al. (2020) caution that research focused only on distress may create a gap in knowledge about the resilience processes adopted by young people. In line with this, Bruining et al. (2020, p. 1) advocate for research to keep “an open scientific mind” and include “positive hypotheses.” Waters et al. (2021) argue that researching distress during COVID-19 need not come at the expense of investigating how people can be strengthened through the pandemic. Hawke et al. (2020), for example, found that more than 40% of their teen and early adult sample reported improved social relationships, greater self-reflection, and greater self-care.

Focusing on adolescents and adopting positive hypotheses, the current study will examine the degree to which a positive education intervention taught at school prior to the COVID-19 outbreak had an influence on three coping approaches during remote learning (i.e., positive reappraisal, emotional processing, and strengths use) and on student levels of stress-related growth (SRG) upon returning to school.

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