Functional connectivity in resting‐state networks relates to short‐term global cognitive functioning in cardiac arrest survivors
Bibliographic record
Abstract
Abstract Long‐term cognitive impairment is common in cardiac arrest survivors. Screening to identify patients at risk is recommended. Functional magnetic resonance brain imaging (fMRI) holds potential to contribute to prediction of cognitive outcomes. In this study, we investigated the possible value of early changes in resting‐state networks for predicting short and long‐term cognitive functioning of cardiac arrest survivors. We performed a prospective multicenter cohort study in cardiac arrest survivors in three Dutch hospitals. Resting‐state fMRI scans were acquired within a month after cardiac arrest. We primarily focused on functional connectivity within the default‐mode network (DMN) and salience network (SN), and additionally explored functional connectivity in seven other networks. Cognitive outcome was measured using the Montreal Cognitive Assessment (MoCA) during hospital admission and at 3 and 12 months, and by neuropsychological examination (NPE) at 12 months. We tested mixed effects models to evaluate the value of connectivity within the networks for predicting global cognitive outcomes at the three time points, and long‐term cognitive outcomes in the memory, attention, and executive functioning domains. We included 80 patients (age 60 ± 11 years, 72 (90%) male). MoCA scores increased significantly between hospital admission and 3 months (ΔMoCAhospital‐3M = 2.89, p < 0.01), but not between 3 and 12 months (ΔMoCA3M–12M = 0.38, p = 0.52). Connectivity within the DMN, SN, and dorsal attention network (DAN) was positively related to global cognitive functioning during hospital admission (βDMN = 0.85, p = 0.03; βSN = 1.48, p < 0.01; βDAN = 0.96, p = 0.01), but not at 3 and 12 months. Network connectivity was also unrelated to long‐term memory, attention, or executive functioning. Resting‐state functional connectivity in the DMN, SN, and DAN measured in the first month after cardiac arrest is related to short‐term global, but not long‐term global or domain‐specific cognitive performance of survivors. These results do not support the value of functional connectivity within these RSNs for prediction of long‐term cognitive performance after cardiac arrest.
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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 | 0.002 |
| 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) | 0.002 | 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".