Neuroimaging Correlates of Post-COVID-19 Symptoms: A Functional MRI Approach
Bibliographic record
Abstract
BACKGROUNDS AND PURPOSE: Post-COVID syndrome is characterized by persistent symptoms, including fatigue and cognitive impairment. These symptoms may be experienced by up to 80% of patients. We aimed to identify possible patterns of brain activation underlying post-COVID fatigue. METHODS: The study used functional MRI (Siemens MAGNETOM Prisma 3T scanner with a specially created protocol) of the brain in 30 patients with post-COVID fatigue syndrome and 20 healthy volunteers. Task functional MRI (fMRI) was performed using a cognitive paradigm (modified Stroop test). Eligible patients included adults aged 18-50 years with a >12 weeks before enrolment (less than 12 months) prior history of documented COVID-19 with symptoms of fatigue not attributable to any other cause, and with MFI-20 score > 30 and MoCA at first visit. Healthy control participants had no prior history of COVID-19 and negative tests for severe acute coronavirus respiratory syndrome with MFI-20 score < 30 and MoCA at first visit. Task fMRI data were processed using the SPM12 software package based on MATLAB R2022a. RESULTS: Cognitive task fMRI analysis showed significantly higher activation in the post-COVID group versus healthy volunteers' group. Between-group analysis showed significant activation differences. Using a threshold of T > 3 we identified eight clusters of statistically significant activation: supramarginal gyri, posterior cingulate cortex, opercular parts of precentral gyri and cerebellum posterior lobe bilaterally. CONCLUSIONS: Post-COVID fatigue syndrome associated with subjective cognitive impairment could show changes in brain functional activity in the areas connected with information processing speed and quality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".