Association between resting-state brain functional connectivity and muscle sympathetic burst incidence
Bibliographic record
Abstract
The insula (IC) and cingulate are key components of the central autonomic network and central nodes of the salience network (SN), a set of spatially distinct but temporally correlated brain regions identified with resting-state (task free) functional MRI (rsMRI). To examine the SN's involvement in sympathetic outflow, we tested the hypothesis that individual differences in intrinsic connectivity of the SN correlate positively with resting postganglionic muscle sympathetic nerve activity (MSNA) burst incidence (BI) in subjects without and with obstructive sleep apnea (OSA). Overnight polysomnography, 5-min rsMRI, and fibular MSNA recording were performed in 36 subjects (mean age 57 yr; 10 women, 26 men). Independent component analysis (ICA) of the entire cohort identified the SN as including bilateral IC, pregenual anterior cingulate cortex (pgACC), midcingulate cortex (MCC), and the temporoparietal junction (TPJ). There was a positive correlation between BI and the apnea-hypopnea index (AHI) (P < 0.001), but dual-regression analysis identified no differences in SN functional connectivity between subjects with no or mild OSA (n = 17) and moderate or severe (n = 19) OSA. Correlation analysis relating BI to the strength of connectivity within the SN revealed large (i.e., spatial extent) and strong correlations for the left IC (P < 0.001), right pgACC/MCC (P < 0.006), left TPJ (P < 0.004), thalamus (P < 0.035), and cerebellum (P < 0.013). Indexes of sleep apnea were unrelated to BI and the strength of SN connectivity. There were no relationships between BI and default or sensorimotor network connectivity. This study links connectivity within the SN to MSNA, demonstrating several of its nodes to be key sympathoexcitatory regions.
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 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 | 0.000 | 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) | 0.001 | 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".