Functional brain abnormalities in post COVID-19 condition and their relationship with cognition
Bibliographic record
Abstract
After COVID-19 infection, some patients develop a post-COVID condition (PCC) that is popularly referred to as long COVID. Among its symptoms is persistent cognitive dysfunction that is potentially linked to altered brain functional connectivity (FC). While research has explored functional reorganization in patients with PCC, the intra- and inter- network connectivity and its relationship with cognitive status and clinical outcomes remain unclear. In this study, we recruited 121 individuals with PCC (67 with, and 54 without, cognitive impairment), 20 months after infection, along with 37 non-infected healthy controls from the NAUTILUS Project (ClinicalTrials.gov IDs: NCT05307549 and NCT05307575). Participants underwent resting-state functional magnetic resonance imaging and comprehensive neuropsychological assessment. Resting-state networks were characterized using independent component analyses, dual regression and network modelling for individual FC characterization. Group differences in intra- and inter-network FC, and their associations with clinical and neuropsychological data, were studied. Significance was set at a corrected p-value of < 0.05. Patients with PCC showed increased intra-network FC in 10 cognitively relevant networks, including the default mode, salience, executive control, auditory and basal ganglia networks, correlating positively with general cognition (Montreal Cognitive Assessment scores), time since infection, fatigue and subjective memory failures. Increased inter-network FC between default mode and sensorimotor networks was also observed. Increases in FC may reflect an inefficient compensatory mechanism in patients with PCC, associated with fatigue, subjective memory complaints and persistence of PCC.
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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 | 0.000 | 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) | 0.001 | 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".