Evidence map›Paper›PMID 30983977›Full record

ArticleFrontiers in systems neuroscience2019

Peripheral Nerve Activation Evokes Machine-Learnable Signals in the Dorsal Column Nuclei.

Alastair J Loutit, Mohit N Shivdasani, Ted Maddess, Stephen J Redmond, John W Morley, Greg J Stuart, Ingvars Birznieks, Richard M Vickery, Jason R Potas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in systems neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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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

9 authors.

Alastair J LoutitSchool of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia.
Mohit N ShivdasaniGraduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia.
Ted MaddessEccles Institute of Neuroscience, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.
Stephen J RedmondGraduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia.
John W MorleySchool of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia.
Greg J StuartEccles Institute of Neuroscience, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.
Ingvars BirznieksSchool of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia.
Richard M VickerySchool of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia.
Jason R PotasSchool of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The brainstem dorsal column nuclei (DCN) are essential to inform the brain of tactile and proprioceptive events experienced by the body. However, little is known about how ascending somatosensory information is represented in the DCN. Our objective was to investigate the usefulness of high-frequency (HF) and low-frequency (LF) DCN signal features (SFs) in predicting the nerve from which signals were evoked. We also aimed to explore the robustness of DCN SFs and map their relative information content across the brainstem surface. DCN surface potentials were recorded from urethane-anesthetized Wistar rats during sural and peroneal nerve electrical stimulation. Five salient SFs were extracted from each recording electrode of a seven-electrode array. We used a machine learning approach to quantify and rank information content contained within DCN surface-potential signals following peripheral nerve activation. Machine-learning of SF and electrode position combinations was quantified to determine a hierarchy of information importance for resolving the peripheral origin of nerve activation. A supervised back-propagation artificial neural network (ANN) could predict the nerve from which a response was evoked with up to 96.8 ± 0.8% accuracy. Guided by

Indexed as

gracile nucleilateralizationmachine learningneural prosthesisproprioceptionsomatosensorytactile

Identifiers

PMID30983977
PMCPMC6448039

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.