Evidence map›Paper›PMID 37274205›Full record

ReviewFrontiers in neuroscience2023

Alignment of magnetic sensing and clinical magnetomyography.

Negin Ghahremani Arekhloo, Hossein Parvizi, Siming Zuo, Huxi Wang, Kianoush Nazarpour, Justus Marquetand, Hadi Heidari

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
5.5field-weighted citation impact, top 3% of its field
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

15 citing papers in PubMed, 27 citations in OpenAlex.

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  12. Medium density EMG armband for gesture recognition.Frontiers in neurorobotics · 2025
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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

7 authors at 3 institutions in 2 countries.

Negin Ghahremani ArekhlooMicroelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom.
Hossein ParviziMicroelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom.
Siming ZuoMicroelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom.
Huxi WangMicroelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom.
Kianoush NazarpourNeuranics Ltd., Glasgow, United Kingdom.
Justus MarquetandDepartment of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
Hadi HeidariMicroelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom.
University of Glasgow · GBHertie Institute for Clinical Brain Research · DEUniversity of Edinburgh · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuromuscular diseases are a prevalent cause of prolonged and severe suffering for patients, and with the global population aging, it is increasingly becoming a pressing concern. To assess muscle activity in NMDs, clinicians and researchers typically use electromyography (EMG), which can be either non-invasive using surface EMG, or invasive through needle EMG. Surface EMG signals have a low spatial resolution, and while the needle EMG provides a higher resolution, it can be painful for the patients, with an additional risk of infection. The pain associated with the needle EMG can pose a risk for certain patient groups, such as children. For example, children with spinal muscular atrophy (type of NMD) require regular monitoring of treatment efficacy through needle EMG; however, due to the pain caused by the procedure, clinicians often rely on a clinical assessment rather than needle EMG. Magnetomyography (MMG), the magnetic counterpart of the EMG, measures muscle activity non-invasively using magnetic signals. With super-resolution capabilities, MMG has the potential to improve spatial resolution and, in the meantime, address the limitations of EMG. This article discusses the challenges in developing magnetic sensors for MMG, including sensor design and technology advancements that allow for more specific recordings, targeting of individual motor units, and reduction of magnetic noise. In addition, we cover the motor unit behavior and activation pattern, an overview of magnetic sensing technologies, and evaluations of wearable, non-invasive magnetic sensors for MMG.

Indexed as

electromyographymagnetomyographymotor unit decompositionoptically pumped magnetometerspintronic sensorssuperconducting quantum interference devicestunnel magnetoresistancewearable sensors

Identifiers

PMID37274205
PMCPMC10232862
OpenAlexW4377103036

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.