Jaqueline H. Becker, PhD1; Tracy D. Vannorsdall, PhD2; Sara L. Weisenbach, PhD3, JAMA Psychiatry. 2023;80(11):1085-1086. doi:10.1001/jamapsychiatry.2023.2820
SARS-CoV-2 infection is associated with increased rates of postillness cognitive dysfunction, colloquially referred to as “brain fog,”1 that may portend significant consequences for patient functioning and quality of life. Post–COVID-19 cognitive dysfunction is 1 of approximately 200 symptoms of post–COVID-19 condition (PCC), defined by the World Health Organization as developing within 3 months of an initial SARS-CoV-2 infection, lasting at least 2 months, and cannot be explained by an alternative diagnosis. A pooled analysis of 54 studies and 1.2 million individuals found that 3.2% of patients’ self-reported cognitive problems 3 months after symptomatic infection,1 while other studies have shown objective evidence of cognitive dysfunction in approximately 24% of patients nearly 1 year later.2 Accumulating evidence also supports the hypothesis that COVID-19 may increase risk for later neurodegeneration3 and exacerbate preexisting cognitive dysfunction.4 As one of the most common symptoms of PCC and one for which affected individuals may seek accommodations and disability benefits in accordance with the Americans With Disabilities Act, it is imperative that we use more rigorous studies of cognitive outcomes. Accordingly, the following recommendations have been generated by members of the NeuroCOVID International Neuropsychology Taskforce based on initial guidelines.5
Recommendation 1
The first recommendation is for rigorous assessment of post–COVID-19 cognitive dysfunction. Early post–COVID-19 cognitive studies were understandably limited by methodologic shortcomings. To some degree, these limitations were expected given the rapid onset of the pandemic and associated practical and safety limitations related to assessing cognition. We argue that 3 years into the pandemic, it is both feasible and necessary to change our approach if we wish to understand the accurate prevalence, incidence, trajectory, and underlying mechanisms of post–COVID-19 cognitive dysfunction as well as develop treatment interventions that are appropriately tailored to patient phenotypes.
First, studies relying on self-report instruments of cognitive functions, which are distal measures of brain functioning, have skewed perceptions of the frequency of post–COVID-19 cognitive dysfunction, as it is well known that subjective and objective cognitive data are frequently misaligned.6 Performance of 1.5 or more SDs below the mean of the normative sample has historically been used as a metric for cognitive dysfunction. However, given that neurologically healthy people often produce low cognitive scores, studies should assess whether rates of low cognitive scores exceed statistical expectation.7 Second, while brief dementia screeners provide objective data, they are insensitive to subtle cognitive dysfunction and have been found to be inadequate in the evaluation of post–COVID-19 cognition.8 A battery of cognitive tests that assesses the landscape of cognitive functioning should be used.5 Third, existing studies have questionable generalizability. That is, despite the disproportionate effects of COVID-19 on racial and ethnic minority populations, these populations remain underrepresented in cognitive studies. Furthermore, a selection bias exists whereby studies frequently report rates of cognitive dysfunction in patients specifically seeking care for PCC. While relevant, results of these studies may not provide an accurate reflection of the true prevalence of objective cognitive dysfunction in the general population. Thus, we recommend that studies specify to whom their results are most likely to generalize and include diverse samples when possible.
Relatedly, we recommend that studies on post–COVID-19 cognitive dysfunction include appropriate control groups and, when possible, comparison of pre- and postpandemic data. More sensitive, multidomain objective instruments are needed to identify patterns of cognitive weakness reflecting dysfunctional brain regions and networks after COVID-19. Inclusive of this, performance validity tests and measures of premorbid functioning should be incorporated in cognitive batteries to rule out suboptimal effort and make more conclusive determinations of likely cognitive decline. Longitudinal analyses to determine varying cognitive trajectories are greatly needed and would ideally be performed annually using alternate forms or standardized regression-based norms.9 Finally, normative data that are demographically representative of the population under study are critical for the most accurate interpretation of results. As cognitive data can help to inform the underlying neural networks and trajectory of cerebral dysfunction, this multifaceted approach can help inform specific illness pathophysiology and will be crucial to the development of efficacious interventions.
Recommendation 2
The second recommendation is examination of clinical phenotypes. There is considerable heterogeneity in the clinical presentation and underlying mechanisms of PCC. Similarly, post–COVID-19 cognitive dysfunction is unlikely to reflect a unitary construct, as the nature and severity of cognitive sequelae are varied. As such, the underlying mechanisms for post–COVID-19 cognitive dysfunction may be distinct for different patient groups, and risk may be distinct for different cognitive trajectories. To that end, it would be beneficial to delineate phenotypes by studying symptoms and trajectories of patients stratified by the following factors: (1) COVID-19 severity, as there appear to be meaningful differences in the experiences of hospitalized vs nonhospitalized patients2; (2) age, as older patients may have more preexisting risk factors and comorbidities and, consequently, elevated risk for postacute sequelae of SARS-CoV-2 infection10; (3) family history of dementia or mental health disorders, as these may be associated with cognitive dysfunction in patients; and (4) preexisting cognitive and/or psychiatric disorders. Other phenotypes may exist based on COVID-19 variants, vaccination status, and history of other viral illnesses or preexisting autoimmune conditions.
Recommendation 3
The third recommendation is assessment of psychosocial contributions. There is considerable controversy surrounding post–COVID-19 cognitive dysfunction, including skepticism of its existence and disagreement on its cause and maintenance. This controversy is familiar to neuropsychologists, who frequently evaluate patients with similarly controversial conditions, such as myalgic encephalomyelitis, or chronic fatigue syndrome. Perhaps because psychiatric disorders can co-occur with these multifaceted conditions, many have dismissed these conditions as being psychosomatic with nonbiologic underpinnings. This broad dismissal is contrary to scientific evidence and can be harmful for patients and communities affected. From a biopsychosocial perspective, it is possible that in some individuals, the development and maintenance of symptoms may most strongly reflect an interplay between an initial insult or illness and psychological and social factors. For others, cognitive dysfunction may be associated with a postviral syndrome, whereby persistent inflammation or vascular dysfunction directly affected the central nervous system. Researchers must appropriately address the range of biological, psychological, and social factors and the myriad of other factors known to affect cognitive performance. This can be done through comprehensive assessment and appropriate statistical modeling of the multiple variables that might affect cognition.
Conclusions
While existing studies have been invaluable in unmasking the postacute neuropsychological symptoms of COVID-19, it is imperative that we begin to use more rigorous methodologic approaches across diverse samples to improve our understanding of post–COVID-19 cognitive dysfunction. We argue that accurate assessment of post–COVID-19 cognitive functioning requires (1) comprehensive, culturally appropriate neuropsychological evaluations administered by a skilled examiner in a well-controlled environment (in person or via telehealth); (2) evaluation of performance validity and premorbid functioning; (3) use of appropriate normative data in test interpretation; (4) consideration of biopsychosocial factors and potentially differing phenotypes; and (5) appropriate longitudinal follow-up. Future studies should also include adequate sample sizes, improve inclusion of marginalized populations, use appropriate comparison groups, reduce selection bias, and adjust for other potential confounding factors. It will be important to consider that results from studies using clinical data will likely differ from those of large cohort studies with more representative samples. Together, these data will allow improved clarity regarding the pathophysiology of post–COVID-19 cognitive dysfunction and factors that contribute to symptom persistence. Ultimately, this will create opportunities for the development of effective treatment interventions using a personalized medicine approach.