Altered corticostriatal connectivity in long-COVID patients is associated with cognitive impairment
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had a significant impact on the health of millions of people worldwide, and many manifest new or persistent symptoms long after the initial onset of the infection. One of the leading symptoms of long-COVID is cognitive impairment, which includes memory loss, lack of concentration, and brain fog. Understanding the nature and underlying mechanisms of cognitive impairment in long-COVID is important for developing preventive and therapeutic interventions. METHODS: Our present study investigated functional connectivity (FC) changes in patients with long-COVID and their associations with cognitive impairment. Resting-state functional MRI data from 60 long-COVID patients and 52 age- and sex-matched healthy controls were analyzed using seed-based functional connectivity analysis. RESULTS: We found increased FC between the right caudate nucleus and both the left and right precentral gyri in long-COVID patients compared with healthy controls. In addition, elevated FC was observed between the right anterior globus pallidus and posterior cingulate cortex as well as the right temporal pole in long-COVID patients. Importantly, the magnitude of FC between the caudate and the left precentral gyrus showed a significant negative correlation with Montreal Cognitive Assessment (MoCA) scores and a negative correlation with Trail Making Test B performance in the patient group. CONCLUSION: Patients with long-COVID present enhanced FC between the caudate and the left precentral gyrus. Furthermore, those FC alterations are related to the severity of cognitive impairment, particularly in the domain of executive functions.
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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 | 0.001 |
| 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) | 0.002 | 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".