Age-Related Changes in Cortical Connectivity During Surgical Anesthesia
Bibliographic record
Abstract
An advanced understanding of the neurophysiologic changes that occur with aging may help improve perioperative care for older, vulnerable patients. The objective of this study was to determine age-related changes in cortical connectivity patterns during surgical anesthesia. This was a substudy analysis of a prospective, observational study characterizing cortical connectivity during surgical anesthesia in adult patients (n=45) via whole-scalp (16-channel) electroencephalography. Functional connectivity was estimated using weighted phase lag index, which was classified into a discrete set of states through k-means analysis. Temporal dynamics were quantified by occurrence rate and state transition probabilities. The mean global, connectivity state transition probability (13.4% [±8.1]) was not correlated with age (ρ = 0.100, P=0.513). Increasing age was inversely correlated with prefrontal-frontal alpha-beta connectivity (ρ = -0.446, P=0.002) and positively correlated with frontal-parietal theta connectivity (ρ = 0.414, P=0.005). After adjusting for anesthetic-related confounders, prefrontal-frontal alpha-beta connectivity remained significantly associated with age (β = -0.625, 95% CI -0.99 to -0.26; P=0.001), while frontal-parietal theta connectivity was no longer significant (β = 0.436, 95% CI -0.03 to 0.90; P=0.066). Specific transition states were also examined. Between frontal-parietal connectivity states, transitioning from theta-alpha to theta-dominated connectivity positively correlated with age (ρ = 0.545, P=0.001). Exploratory analysis in subsets of relatively young and old patients also demonstrated an increased propensity for frontal-parietal theta connectivity with older age and reduced prefrontal-frontal alpha-beta connectivity. Dynamic connectivity states during surgical anesthesia, particularly involving alpha and theta bandwidths, may be an informative measure to assess neurophysiologic changes that occur with aging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".