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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".