A novel non‐invasive method to measure sympathetic activity and autoregulation in humans
Bibliographic record
Abstract
Current methods to assess sympathetic nervous system activity are associated with technical difficulty, invasiveness (microneurography) and poor temporal resolution ([plasma norepinephrine]). Use of heart rate variability overcomes most of these issues but is controversial because of limited accuracy. We have developed a quick, easy and non‐invasive approach based on spectral domain optical coherence tomography (OCT) that also allows investigation of arterial autoregulation. Here, we compare OCT and heart rate variability to microneurography, the gold standard of sympathetic assessment. We recruited 6 healthy males (26±3y) and 5 healthy females (23±1y), instrumented them for ECG and muscle sympathetic nerve microneurography and exposed them to respiratory and sympathetic provocations. Choroid vascular perfusion density (VPD) decreases with cold pressor test, inhaled hypoxia and breath‐hold, and increases with hyperoxia and hyperventilation suggesting that sympathetic activity dominates choroid responses. In contrast, retinal pigment epithelium VPD was unaffected by the cold pressor test, increased with hypoxia and breath hold and decreases with hyperoxia and hyperventilation, suggesting autoregulation dominates the retinal pigment epithelium. With regards to integrated muscle sympathetic nerve activity, heart rate variability had low predictive power whereas OCT was strongly (inversely) correlated (R= −0.91; p<0.01). These data suggest OCT may provide a novel approach to assessing autoregulation and sympathetic activity with superior predictability compared to heart rate variability. Given that sympathetic nervous system activity is a main determinant of autonomic function, sympathetic excitation is associated with serious cardiorespiratory/metabolic diseases and autoregulation is critical for brain health, we suggest that this new OCT technique will be of broad use to clinicians and researchers alike. Support or Funding Information This study was supported by Canadian Institute for Health Research (RJAW & WT). RJAW is an Alberta Innovates Health Solutions Senior Scholar. NGJ received postdoctoral salary support for this project from Alberta Innovates Health Solutions and the T Chen Fong Foundation. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".