Evidence map›Paper›PMID 39472924›Full record

ArticleJournal of neuroengineering and rehabilitation2024

Instrumented assessment of lower and upper motor neuron signs in amyotrophic lateral sclerosis using robotic manipulation: an explorative study.

D J L Stikvoort García, B T H M Sleutjes, W Mugge, J J Plouvier, H S Goedee, A C Schouten, F C T van der Helm, L H van den Berg

Abstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 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

8 authors.

D J L Stikvoort GarcíaDepartment of Neurology, F02.230, Brain Center Utrecht, University Medical Center Utrecht, P.O. Box 855000, Utrecht, 3508 GA, The Netherlands.
B T H M SleutjesDepartment of Neurology, F02.230, Brain Center Utrecht, University Medical Center Utrecht, P.O. Box 855000, Utrecht, 3508 GA, The Netherlands. b.sleutjes@umcutrecht.nl.
W MuggeLaboratory for Neuromuscular Control, Department of Biomechanical Engineering, Mechanical Engineering, Delft University of Technology, Delft, 2628CD, The Netherlands.
J J PlouvierLaboratory for Neuromuscular Control, Department of Biomechanical Engineering, Mechanical Engineering, Delft University of Technology, Delft, 2628CD, The Netherlands.
H S GoedeeDepartment of Neurology, F02.230, Brain Center Utrecht, University Medical Center Utrecht, P.O. Box 855000, Utrecht, 3508 GA, The Netherlands.
A C SchoutenLaboratory for Neuromuscular Control, Department of Biomechanical Engineering, Mechanical Engineering, Delft University of Technology, Delft, 2628CD, The Netherlands.
F C T van der HelmLaboratory for Neuromuscular Control, Department of Biomechanical Engineering, Mechanical Engineering, Delft University of Technology, Delft, 2628CD, The Netherlands.
L H van den BergDepartment of Neurology, F02.230, Brain Center Utrecht, University Medical Center Utrecht, P.O. Box 855000, Utrecht, 3508 GA, The Netherlands.

Funding

Stichting ALS Nederland AV20180012
6 · The paper itself

Abstract

backgroundAmyotrophic lateral sclerosis (ALS) is a lethal progressive neurodegenerative disease characterized by upper motor neuron (UMN) and lower motor neuron (LMN) involvement. Their varying degree of involvement results in a clinical heterogenous picture, making clinical assessments of UMN signs in patients with ALS often challenging. We therefore explored whether instrumented assessment using robotic manipulation could potentially be a valuable tool to study signs of UMN involvement.

methodsWe examined the dynamics of the wrist joint of 15 patients with ALS and 15 healthy controls using a Wristalyzer single-axis robotic manipulator and electromyography (EMG) recordings in the flexor and extensor muscles in the forearm. Multi-sinusoidal torque perturbations were applied, during which participants were asked to either relax, comply or resist. A neuromuscular model was used to study muscle viscoelasticity, e.g. stiffness (k) and viscosity (b), and reflexive properties, such as velocity, position and force feedback gains (kv, kp and kf, respectively) that dominated the responses. We further obtained clinical signs of LMN (muscle strength) and UMN (e.g. reflexes, spasticity) dysfunction, and evaluated their relation with the estimated neuromuscular model parameters.

resultsOnly force feedback gains (kf) were elevated in patients (p = 0.033) compared to controls. Higher kf, as well as the resulting reflexive torque (Tref), were both associated with more severe UMN dysfunction in the examined arm (p = 0.040 and p < 0.001). Patients with UMN symptoms in the examined arm had increased kf and Tref compared to controls (both p = 0.037). Neither of these measures was related to muscle strength, but muscle stiffness (k) was lower in weaker patients (p = 0.012). All these findings were obtained from the relaxed test. No differences were observed during the instructions comply and resist.

conclusionsThis findings are proof-of-concept that instrumented assessment using robotic manipulation is a feasible technique in ALS, which may provide quantitative, operator-independent measures relating to UMN symptoms. Elevated force feedback gains, driving larger reflexive muscle torques, appear to be particularly indicative of clinically established levels of UMN dysfunction in the examined arm.

Indexed as

Amyotrophic Lateral SclerosisElectromyographyMotor NeuronsMuscle, SkeletalRoboticsAdultAgedFemaleHumansMaleMiddle AgedMuscle StrengthTorqueWrist JointAmyotrophic lateral sclerosisBiomarkersEMGRobotic manipulationUpper motor symptoms

Identifiers

PMID39472924
PMCPMC11520903

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LicenceCC BY-NC-ND
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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.