Brain Glucose Metabolism Changes in Long‐COVID: A Brazilian Cohort Study
Bibliographic record
Abstract
Abstract Background The COVID‐19 pandemic is a public health crisis, and its lasting consequences are not yet fully understood. Epidemiological data suggest that low‐ and middle‐income countries, such as Brazil, will bear a considerable burden of COVID‐19‐related comorbidities. Individuals who have survived COVID‐19 often report persistent symptoms, including neurological manifestations such as brain fog. However, the underlying brain biological changes behind the neurological symptoms remain undefined. Here, our goal was to assess the influence of long‐COVID on brain metabolism in adults from a Brazilian cohort. Method Brazilian individuals (n=49) were recruited, examined through clinical and neuropsychological assessments, and divided into Control and Long‐COVID groups. Then, they underwent a brain [18F]FDG‐PET scan. Images were normalized by the global mean. Differences between groups were assessed through a [18F]FDG‐PET voxel‐wise linear regression accounting for age, sex and years of education (Table 1). The analysis was corrected for multiple comparisons using the cluster‐wise random field theory method (significant t < ‐3.28 and t > 3.28). Result Long‐COVID group exhibited recurring symptoms such as fatigue, memory complaints and lack of concentration (Table 1). They also exhibited hypometabolic clusters in the left superior parietal lobe (tmax=‐4.03; p < 0.001) and right insular cortex (tmax=‐3.31; p < 0.001) (Figure 1). Hypermetabolic clusters were found in the left whole cerebellum (tmax=4.92; p < 0.0001), right dorsolateral prefrontal cortex (tmax=3.83; p < 0.001), and right postcentral gyrus (tmax=4.24; p < 0.001) (Figure 2). Conclusion Our preliminary results demonstrate region‐dependent dual response on brain metabolism due to Long‐Covid. More specifically, glucose hypometabolism was found in regions associated with cognitive domains and affective modulation. By contrast, glucose hypermetabolism was observed in brain areas associated with motor coordination and sensory processing. Metabolic changes in these regions should be further evaluated to advance in the understanding of the pathophysiological mechanisms associated with persistent neurological manifestations seen in individuals presenting with Long COVID.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".