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Record W4292707928 · doi:10.3389/fphys.2022.955566

The effects of membrane potential oscillations on the excitability of rat hypoglossal motoneurons

2022· article· en· W4292707928 on OpenAlexaff
Qiang Zhang, Yue Dai, Junya Zhou, Renkai Ge, Yiyun Hua, Randall K. Powers, Marc D. Binder

Bibliographic record

VenueFrontiers in Physiology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of ChinaNational Institutes of HealthNational Science Foundation
KeywordsMembrane potentialBiophysicsElectrophysiologyDepolarizationChemistryNeuroscienceSodium channelTonic (physiology)CalciumResting potentialAfterhyperpolarizationPotassium channelNoise (video)Ion channelSodiumBiologyBiochemistryReceptor

Abstract

fetched live from OpenAlex

Oscillations in membrane potential induced by synaptic inputs and intrinsic ion channel activity play a role in regulating neuronal excitability, but the precise mechanisms underlying their contributions remain largely unknown. Here we used electrophysiological and modeling approaches to investigate the effects of Gaussian white noise injected currents on the membrane properties and discharge characteristics of hypoglossal (HG) motoneurons in P16-21 day old rats. We found that the noise-induced membrane potential oscillations facilitated spike initiation by hyperpolarizing the cells’ voltage threshold by 3.1 ± 1.0 mV and reducing the recruitment current for the tonic discharges by 0.26 ± 0.1 nA, on average (n = 59). Further analysis revealed that the noise reduced both recruitment and decruitment currents by 0.26 ± 0.13 and 0.33 ± 0.1 nA, respectively, and prolonged the repetitive firing. The noise also increased the slopes of frequency-current (F-I) relationships by 1.1 ± 0.2 Hz/nA. To investigate the potential mechanisms underlying these findings, we constructed a series of HG motoneuron models based on their electrophysiological properties. The models consisted of five compartments endowed with transient sodium (NaT), delayed-rectify potassium [K(DR)], persistent sodium (NaP), calcium-activated potassium [K(AHP)], L-type calcium (CaL) and H-current channels. In general, all our experimental results could be well fitted by the models, however, a modification of standard Hodgkin-Huxley kinetics was required to reproduce the changes in the F-I relationships and the prolonged discharge firing. This modification, corresponding to the noise generated by the stochastic flicker of voltage-gated ion channels (channel flicker, CF), was an adjustable sinusoidal function added to kinetics of the channels that increased their sensitivity to subthreshold membrane potential oscillations. Models with CF added to NaP and CaL channels mimicked the noise-induced alterations of membrane properties, whereas models with CF added to NaT and K(DR) were particularly effective in reproducing the noise-induced changes for repetitive firing observed in the real motoneurons. Further analysis indicated that the modified channel kinetics enhanced NaP- and CaL-mediated inward currents thus increasing the excitability and output of HG motoneurons, whereas they produced relatively small changes in NaT and K(DR), thus balancing these two currents and triggering variability of repetitive firing. This study provided insight into the types of membrane channel mechanisms that might underlie oscillation-induced alterations of neuronal excitability and motor output in rat HG motoneurons.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes1
Has abstractyes

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