RESUMO
BACKGROUND: Neurological and psychological symptoms are increasingly realized in the post-acute phase of COVID-19. PURPOSE: To examine and characterize cognitive and related psychosocial symptoms in adults (21-75 years) who tested positive for or were treated as positive for COVID-19. METHODS: In this cross-sectional study, data collection included a cognitive testing battery (Trails B; Digit Symbol; Stroop; Immediate and Delayed Verbal Learning) and surveys (demographic/clinical history; self-reported cognitive functioning depressive symptoms, fatigue, anxiety, sleep disturbance, social role performance, and stress). Results were compared with published norms, rates of deficits (more than 1 standard deviation (SD) from the norm) were described, and correlations were explored. RESULTS: We enrolled 52 participants (mean age 37.33 years; 78.85% female) who were, on average, 4 months post illness. The majority had a history of mild or moderate COVID-19 severity. Forty percent of participants demonstrated scores that were 1 SD or more below the population norm on one or more of the cognitive tests. A subset had greater anxiety (21.15%), depressive symptoms (23.07%), and sleep disturbance (19.23%) than population norms. Age differences were identified in Stroop, Digit Symbol, and Trails B scores by quartile ( p < .01), with worse performance in those 28-33 years old. CONCLUSIONS: Cognitive dysfunction and psychological symptoms may be present in the weeks or months after COVID-19 diagnosis, even in those with mild to moderate illness severity. IMPLICATIONS FOR PRACTICE: Clinicians need to be aware and educate patients about the potential late/long-term cognitive and psychological effects of COVID-19, even in mild to moderate disease.
RESUMO
Converging evidence suggests that human cognition and behavior emerge from functional brain networks interacting on local and global scales. We investigated two information-theoretic measures of functional brain segregation and integration-interaction complexity C I (X), and integration I(X)-as applied to electroencephalographic (EEG) signals and how these measures are affected by choice of EEG reference. CI(X) is a statistical measure of the system entropy accounted for by interactions among its elements, whereas I(X) indexes the overall deviation from statistical independence of the individual elements of a system. We recorded 72 channels of scalp EEG from human participants who sat in a wakeful resting state (interleaved counterbalanced eyes-open and eyes-closed blocks). CI(X) and I(X) of the EEG signals were computed using four different EEG references: linked-mastoids (LM) reference, average (AVG) reference, a Laplacian (LAP) "reference-free" transformation, and an infinity (INF) reference estimated via the Reference Electrode Standardization Technique (REST). Fourier-based power spectral density (PSD), a standard measure of resting state activity, was computed for comparison and as a check of data integrity and quality. We also performed dipole source modeling in order to assess the accuracy of neural source CI(X) and I(X) estimates obtained from scalp-level EEG signals. CI(X) was largest for the LAP transformation, smallest for the LM reference, and at intermediate values for the AVG and INF references. I(X) was smallest for the LAP transformation, largest for the LM reference, and at intermediate values for the AVG and INF references. Furthermore, across all references, CI(X) and I(X) reliably distinguished between resting-state conditions (larger values for eyes-open vs. eyes-closed). These findings occurred in the context of the overall expected pattern of resting state PSD. Dipole modeling showed that simulated scalp EEG-level CI(X) and I(X) reflected changes in underlying neural source dependencies, but only for higher levels of integration and with highest accuracy for the LAP transformation. Our observations suggest that the Laplacian-transformation should be preferred for the computation of scalp-level CI(X) and I(X) due to its positive impact on EEG signal quality and statistics, reduction of volume-conduction, and the higher accuracy this provides when estimating scalp-level EEG complexity and integration.
