A substantial proportion of persons who develop COVID-19 experience postacute sequelae of SARS-CoV-2 infection (PASC). This article reports baseline findings from an ongoing longitudinal cohort study that seeks to characterize the risk factors, clinical findings, laboratory features, and natural history of PASC.
Background: The COVID 19 pandemic led to dramatic threats to health and social life. Study objectives are to develop a prediction model leveraging subsample of known Patient/Controls and evaluate the relationship of predicted mental health status to clinical outcome measures and pandemic-related psychological and behavioral responses during lockdown (spring/summer 2020). Methods: Online cohort study conducted by National Institute of Mental Health Intramural Research Program. Convenience sample of English speaking adults (enrolled 4/4 to 5/16/20; n=1,992). Enrollment measures: demographics, clinical history, functional status, psychiatric and family history, alcohol/drug use. Outcome measures (enrollment and q2 weeks/6 months): distress, loneliness, mental health symptoms, and COVID 19 survey. NIMH IRP Patient/Controls survey responses informed assignment of Patient Probability Scores (PPS) for all participants. Regression models analyzed the relationship between PPS and outcome measures. Outcomes: Mean age 46.0, female (82.4%), white (88.9 %). PPS correlated with distress, loneliness, depression, and mental health factors. PPS associated with negative psychological responses to COVID 19. Worry about mental health (OR 1.46) exceeded worry about physical health (OR 1.13). PPS not associated with adherence to social distancing guidelines but was with stress related to social distancing and worries about infection of self/others. Interpretation: Mental health status (PPS) was associated with concurrent clinical ratings and COVID 19 specific negative responses. A focus on mental health during the pandemic is warranted, especially among those with mental health vulnerabilities. We will include PPS when conducting longitudinal analyses of mental health trajectories and risk and resilience factors that may account for differing clinical outcomes. Funding: NIMH (ZIAMH002922); NCCIH (ZIAAT000030)
Findings that the brain is capable of plasticity up until old age have led to interest in the use of cognitive training as a potential intervention to delay the onset of dementia. However, individuals participating in training regimens differ greatly with respect to their outcomes, demonstrating the importance of considering individual differences, in particular age and baseline performance in a cognitive domain, when evaluating the effectiveness of cognitive training. In this review, we summarize existing literature on cognitive training in adults across the domains of episodic memory, working memory and the task-switching component of executive functioning to clarify the picture on the impact of age and baseline performance on cognitive training-related improvements. Studies targeting episodic memory induced greater improvements in younger adults with more intact cognitive abilities, explained in part by factors specific to episodic memory training. By contrast, older, lower baseline performance adults improved most in several studies targeting working memory in older individuals as well as in the majority of studies targeting executive functioning, suggesting the preservation of neural plasticity in these domains until very old age. Our findings can have important implications for informing the design of future interventions for enhancing cognitive functions in individuals at the prodromal stage of Alzheimer’s Disease and potentially delaying the clinical onset of Alzheimer’s Disease. Future research should more clearly stratify individuals according to their baseline cognitive abilities and assign specialized, skill-specific cognitive training regimens in order to directly answer the question of how individual differences impact training effectiveness.
The NIMH Healthy Research Volunteer Dataset is a collection of phenotypic data characterizing healthy research volunteers using clinical assessments such as assays of blood and urine, mental health assessments, diagnostic and dimensional measures of mental health, cognitive and neuropsychological functioning, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG). In addition, blood samples of healthy volunteers are banked for future analyses. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. There are currently few open access MEG datasets, and multimodal neuroimaging datasets are even more rare. Due to its depth of characterization of a healthy population in terms of brain health, this dataset may contribute to a wide array of secondary investigations of non-clinical and clinical research questions.
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