Active and resting motor threshold are efficiently obtained with adaptive threshold hunting

Transcranial magnetic studies typically rely on measures of active and resting motor threshold (i.e. AMT, RMT). Previous work has demonstrated that adaptive threshold hunting approaches are efficient for estimating RMT. To date, no study has compared motor threshold estimation approaches for measure...

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Vydáno v:PloS one Ročník 12; číslo 10; s. e0186007
Hlavní autoři: Ah Sen, Christelle B., Fassett, Hunter J., El-Sayes, Jenin, Turco, Claudia V., Hameer, Mahdiya M., Nelson, Aimee J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 05.10.2017
Public Library of Science (PLoS)
Témata:
Adolescent
Adult
Algorithms
Biology and Life Sciences
Brain research
Computer simulation
Electromyography
Engineering and Technology
Female
Frequency estimation
Handedness
Humans
Hunting
Kinesiology
Magnetic brain stimulation
Magnetic fields
Magnetic studies
Male
Maximum likelihood estimation
Medicine and Health Sciences
Methods
Motor Cortex - physiology
Muscles
Parameter estimation
Pests
Physical Sciences
Research and Analysis Methods
Spinal cord
Studies
Transcranial magnetic stimulation
Transcranial Magnetic Stimulation - methods
Young Adult
Ontario Canada
Canada
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:Transcranial magnetic studies typically rely on measures of active and resting motor threshold (i.e. AMT, RMT). Previous work has demonstrated that adaptive threshold hunting approaches are efficient for estimating RMT. To date, no study has compared motor threshold estimation approaches for measures of AMT, yet this measure is fundamental in transcranial magnetic stimulation (TMS) studies that probe intracortical circuits. The present study compared two methods for acquiring AMT and RMT: the Rossini-Rothwell (R-R) relative-frequency estimation method and an adaptive threshold-hunting method based on maximum-likelihood parameter estimation by sequential testing (ML-PEST). AMT and RMT were quantified via the R-R and ML-PEST methods in 15 healthy right-handed participants in an experimenter-blinded within-subject study design. AMT and RMT estimations obtained with both the R-R and ML-PEST approaches were not different, with strong intraclass correlation and good limits of agreement. However, ML-PEST required 17 and 15 fewer stimuli than the R-R method for the AMT and RMT estimation, respectively. ML-PEST is effective in reducing the number of TMS pulses required to estimate AMT and RMT without compromising the accuracy of these estimates. Using ML-PEST to estimate AMT and RMT increases the efficiency of the TMS experiment as it reduces the number of pulses to acquire these measures without compromising accuracy. The benefits of using the ML-PEST approach are amplified when multiple target muscles are tested within a session.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0186007