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SPARC: Parameters of Electrical Excitation and Block of the Rat Vagus Nerve

2020· article· en· W3016480487 on OpenAlexaboutno aff
Nicole A. Pelot, Warren M. Grill

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsnot available
Fundersnot available
KeywordsVagus nerveVagus nerve stimulationRheobaseStimulationElectrophysiologyNerve conduction velocityPulse (music)BurstingNeuroscienceAutonomic nervous systemChemistryMedicineAnatomyPhysicsInternal medicineBiologyHeart rateBlood pressure

Abstract

fetched live from OpenAlex

There is growing interest in bioelectronic medicines, where diseases are treated through electrical stimulation and block of the peripheral autonomic nervous system. Given the widespread connections of the vagus nerve from the brainstem to most truncal organs, applications include treating epilepsy, depression, obesity, heart failure, and rheumatoid arthritis. However, designing and programming the parameters of bioelectronic medicines require understanding the biophysical responses of small myelinated and unmyelinated autonomic fibers. Using in vivo electrophysiology, we quantified strength‐duration properties, activity‐dependent slowing (ADS), and responses to kilohertz frequency (KHF) signals for rapidly conducting (>2 m/s) and slowly conducting (<2 m/s) fibers in the rat vagus nerve. We stimulated the cervical vagus nerve and recorded compound action potential (CAP) input‐output curves from the abdominal vagus nerve for pulse widths from 20 to 1000 μs. Thresholds were largest for the slowest fibres (0.5 to 1 m/s), especially at shorter pulse widths. Fits to the data using standard strength‐duration equations were qualitatively similar, but the estimates of chronaxie and rheobase varied substantially. ADS describes the slowed conduction in peripheral axons resulting from persistent low frequency activity. Using a novel cross‐correlation CAP‐based analysis method, we measured ADS of ~2.3% after 3 min of 2 Hz stimulation, approximately constant across fiber conduction speeds. This is comparable to that reported for sympathetic efferents in somatic nerves, but much smaller than ADS in cutaneous nociceptors. We found greater ADS with higher stimulation frequency and non‐monotonic changes in conduction speed in select cases. KHF signals can block neural conduction in peripheral axons, and we used CAP recordings to quantify the effects of KHF signals from 10 to 80 kHz on small autonomic fibers. Block thresholds were higher for more slowly conducting fibers, and block thresholds increased monotonically with frequency, in contrast to published findings indicating that block thresholds of unmyelinated axons vary non‐monotonically with frequency. Further, there are varied reports on the time for recovery of neural conduction after KHF block; we found that the carryover effect could last tens of seconds following 25 s of KHF signal. The quantification of mammalian autonomic nerve responses to conventional and kilohertz frequency signals provides essential information for development of bioelectronic medical devices and for understanding mechanisms of action. Support or Funding Information This work was supported by Fulbright Canada (15122811), the Natural Sciences and Engineering Research Council of Canada (PGS M‐425353‐2012 and PGS D3‐437918‐2013), and Duke University (University Scholars Program, James B. Duke Fellowship, and Pratt School of Engineering Faculty Discretionary Fund).

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.304
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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