Association between brain metabolism and anxiety in long COVID in an underrepresented cohort
Bibliographic record
Abstract
Abstract Background Long COVID is an under‐characterized disorder that affects a wide range of individuals after COVID‐19 resolution. Long COVID individuals report persistent neurological manifestations, such as anxiety. Understanding its effects in the brain might help uncover the actual burden imposed by the pandemic sequelae and either define or discard long COVID as a risk factor for neurodegenerative diseases. Here, we aim to identify whether there is an association between brain metabolism and anxiety in an underrepresented population. Method Community‐dwelling individuals, above 50 years old, from Porto Alegre, Brazil, were divided into long COVID (n = 39) and control groups (n = 10) were evaluated with a battery of neuropsychological testing, including the GAD‐7 scale of anxiety. Then, they underwent a brain [18F]FDG‐PET scan (images normalized by the pons). We conducted a voxel‐wise linear regression testing the association between [18F]FDG metabolism and GAD‐7, and corrected for education, sex, and age. The analysis was corrected for multiple comparisons using the cluster‐wise random field theory method (significant t←3.34 and t>3.34, p<0.001, df = 35). Result We found that GAD‐7 score presented a widespread negative association with [18F]FDG metabolism in multiple gray and white matter regions (Figure 1). Specifically, hippocampus (tmax = ‐3.34, p = 0.002), amygdala (tmax = ‐3.82, p = 0.0005), cerebellum (tmax = ‐4.28, p = 0.0001), and lateral occipitotemporal gyrus (tmax = ‐5.26, p = 0.0001) had the most relevant associated clusters in gray matter, while temporal lobe (tmax = ‐3.9, p = 0.0004) and frontal lobe (tmax = ‐4.33, p = 0.0001) presented the most relevant associated clusters in white matter. Conclusion Anxiety symptoms are a highly self‐reported symptom in long COVID. Here we show that anxiety is widely associated with reduced brain glucose metabolism in crucial areas for the limbic system and cognition, such as the hippocampus and amygdala. The peculiar associations between anxiety and FDG metabolism in white matter may suggest inflammatory responses triggered by long COVID. These data provide new insights into the mechanisms underlying long COVID symptoms in the brain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".