Evidence map›Paper›PMID 38865093›Full record

ArticleChaos (Woodbury, N.Y.)2024

A novel mechanism for ramping bursts based on slow negative feedback in model respiratory neurons.

Sushmita R John, Ryan S Phillips, Jonathan E Rubin

Abstract read
In one paragraph

Article in Chaos (Woodbury, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sushmita R JohnDepartment of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA.ORCID 0009-0006-4685-0811
Ryan S PhillipsSeattle Children's Research Institute, Seattle, Washington 98109, USA.ORCID 0000-0002-8570-2348
Jonathan E RubinDepartment of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA.ORCID 0000-0002-1513-1551

Funding

Dissociating respiratory depression and analgesia via a data-driven model of interacting respiratory and pain networksK01DA058543 · NIDA · SEATTLE CHILDREN'S HOSPITAL · PI Ryan Sean Phillips · 2023 to 2026
$403k
NIDA NIH HHS K01 DA058543
6 · The paper itself

Abstract

Recordings from pre-Bötzinger complex neurons responsible for the inspiratory phase of the respiratory rhythm reveal a ramping burst pattern, starting around the time that the transition from expiration to inspiration begins, in which the spike rate gradually rises until a transition into a high-frequency burst occurs. The spike rate increase along the burst is accompanied by a gradual depolarization of the plateau potential that underlies the spikes. These effects may be functionally important for inducing the onset of inspiration and hence maintaining effective respiration; however, most mathematical models for inspiratory bursting do not capture this activity pattern. Here, we study how the modulation of spike height and afterhyperpolarization via the slow inactivation of an inward current can support various activity patterns including ramping bursts. We use dynamical systems methods designed for multiple timescale systems, such as bifurcation analysis based on timescale decomposition and averaging over fast oscillations, to generate an understanding of and predictions about the specific dynamic effects that lead to ramping bursts. We also analyze how transitions between ramping and other activity patterns may occur with parameter changes, which could be associated with experimental manipulations, environmental conditions, and/or development.

Indexed as

Models, NeurologicalNeuronsAction PotentialsAnimalsFeedback, Physiological

Identifiers

PMID38865093
PMCPMC11191356

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.