Asynchronous Action Potential Discharge in Human Muscle Sympathetic Nerve Activity
Bibliographic record
Abstract
Pulse‐rhythmic bursts formed by synchronous action potential (AP) discharge characterises the firing behaviour of muscle sympathetic nerve activity (MSNA). However, complex AP discharge patterns related to multiple sites of control warrant an investigation of synchronicity in the human sympathetic system. Therefore, this study quantified the synchronicity of muscle sympathetic AP discharge in eight healthy individuals (4 females, 23 – 31 years, 170 ± 7 cm, 69 ± 13 kg, 60 ± 7 bpm, 89 ± 6 mmHg). MSNA (microneurography) was measured during baseline (BSL), −10 mmHg lower body negative pressure (LBNP), −40 mmHg LBNP, and an end‐expiratory apnea (APN; 30 ± 7 s). MSNA APs were detected from the filtered raw signal using a wavelet deconstruction approach and binned in clusters based on peak‐to‐peak amplitude (normalized within‐participant). An AP was considered to fire synchronously: 1) if its occurrence corresponded with a burst in the integrated MSNA signal (i.e., within ± 0.4 s of the burst peak), or 2) if it was one of ≥ 2 visible APs in the filtered microneurographic signal firing within ± 0.4 s of an ECG R‐wave, after adjusting for the conduction delay. All other APs were considered to fire asynchronously. At BSL, 33 ± 12 % of total AP activity was asynchronous (99 ± 48 asynchronous APs/100beats) and a pattern emerged whereby the probability of asynchronous AP firing decreased logarithmically (R 2 = 0.80, P < 0.01) as AP cluster size increased. Compared to BSL (20 ± 6 bursts/min, 42 ± 7 AU), MSNA burst frequency increased on going to −10 mmHg LBNP (24 ± 6 bursts/min), −40 mmHg LBNP (35 ± 8 bursts/min), and APN (35 ± 9 bursts/min), while burst amplitude increased only with −40 mmHg LBNP (57 ± 8 AU) and APN (64 ± 14 AU) (both repeated measures analysis of variance [RM ANOVA]: P < 0.05, all P post‐hoc < 0.05). Compared with BSL, within‐burst AP discharge increased across all conditions (BSL: 144 ± 69 AP/min, 7 ± 2 AP/burst; −10 mmHg LBNP: 211 ± 87 AP/min, 8 ± 3 AP/burst; −40 mmHg LBNP: 410 ± 207 AP/min, 11 ± 4 AP/burst; APN: 397 ± 160 AP/min, 11 ± 3 AP/burst) (both RM ANOVA: P < 0.05, all P post‐hoc < 0.05). Compared to BSL, increased burst amplitude with −40 mmHg LBNP and APN was attributed to an increase in the within‐burst firing of previously active APs (BSL: 4 ± 1 clusters/burst, −40 mmHg LBNP: 5 ± 2 clusters/burst, APN: 5 ± 1 clusters/burst; RM ANOVA: P < 0.05, both P post‐hoc < 0.05) more so than the recruitment of new AP clusters (BSL: 14 ± 4 total clusters; −40 mmHg LBNP: 18 ± 7 total clusters; APN: 16 ± 6 total clusters; RM ANOVA: P > 0.05). However, compared to BSL (33 ± 12 %), the probability of asynchronous AP firing was reduced with −10 mmHg LBNP (26 ± 10 %), −40 mmHg LBNP (16 ± 7 %), and APN (14 ± 7 %; RM ANOVA: P < 0.05, all P post‐hoc < 0.05). The probability of asynchronous AP discharge was inversely and linearly related to burst frequency (β = −0.98, r 2 = 0.67) and amplitude (β = −0.41, r 2 = 0.20; both P < 0.01). This study suggests that: 1) integrated bursts of MSNA formed by synchronous AP firing are not separated by ‘neural silence’ but rather, by asynchronous AP discharge, and 2) smaller sympathetic APs are more likely to exhibit asynchronous discharge. Support or Funding Information This work was funded by a Natural Sciences and Engineering Research Council of Canada Discovery Grant. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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".