Brain topology and cognitive outcomes after cardiac arrest: A graph theoretical analysis of fMRI data
Bibliographic record
Abstract
Introduction: Half of all cardiac arrest patients experience long-term cognitive impairment. Identifying patients at risk is challenging and underlying mechanisms are incompletely understood. Since postanoxic encephalopathy is diffuse, measures of global network organization might contribute to identifying these patients at risk. We studied MRI-based whole-brain and subnetwork topology in relation to short- and long-term cognitive outcomes after cardiac arrest. Methods: We performed a multicenter prospective cohort study in cardiac arrest survivors. Patients underwent resting-state functional MRI during hospitalization (within one month after cardiac arrest). We analyzed 264 regions of interest using Power's atlas, extracting mean timeseries and calculating pairwise connectivity with Pearson's correlation. Overall functional connectivity, global efficiency, clustering coefficient, and modularity were calculated for the whole brain and five subnetworks. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) during hospitalization, at three, and twelve months, and using neuropsychological examination at twelve months. Mixed effects models were used to examine relationships between topology measures and cognitive outcomes. We corrected for multiple testing. Results: We included 80 patients (age 60 ± 11 years, 70 (90%) male). Our analyses showed consistent relations between various topology measures of the sensory/somatomotor network (SSN) and MoCA score during hospitalization and memory, attention, and executive functioning at twelve months follow up. After correction for multiple testing, we found no statistically significant relations between whole-brain and subnetwork graph measures and cognitive outcomes. Conclusion: Early whole brain functional topology was not related to short- or long-term cognitive outcome after cardiac arrest in this analysis. Potential relations between SSN topology and cognitive outcome point towards spatial heterogeneity of postanoxic encephalopathy. Possible predictive values of SSN structure or function need further investigation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".