Changes in Electrical Brain Activity and Cognitive Functions Following Mild to Moderate COVID-19: A one-Year Prospective Study After Acute Infection
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) can disrupt various brain functions. Over a one-year period, we aimed to assess brain activity and cognitive function in 53 COVID-19 patients and 30 individuals without COVID-19 (or asymptomatic). The Montreal Cognitive Assessment, Trail Making Test Parts A and B (TMT-A and B), and Digit Span Test were used to assess cognitive function. Cognitive variables and electroencephalography (EEG) data (activity, mobility, and complexity) were compared between the groups at rest and during cognitive demand (F3-F7, Fz-F3, Fz-F4, and F4-F8). There was a reduction in F3-F7 activity during the TMT-B in the COVID-19 group at 6-12 months compared to the controls (p = 0.01) at baseline (p = 0.03), a reduction in signal complexity at F3-F7 at rest in the COVID-19 group at baseline and 6-12 months compared to the controls (p < 0.001), and a reduction in Fz-F4 activity at rest from 6-12 months in the post-COVID group compared to baseline (p = 0.02) and 3-6 months (p = 0.04). At 6-12 months, there was a time increase in TMT-A in the COVID-19 group compared to that in the controls (p = 0.04). Some correlations were found between EEG data and cognitive test in both groups. In conclusion, there was a reduction in brain activity at rest in the Fz-F4 areas and during high cognitive demands in the F3-F7 areas. A reduction in signal complexity in F3-F7 at rest was found in the COVID-19 group at 6-12 months after acute infection. Furthermore, individuals with COVID-19 experience long-term changes in cognitive function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".