The maximum-likelihood strategy for determining transcranial magnetic stimulation motor threshold, using parameter estimation by sequential testing is faster than conventional methods with similar precision

The resting motor threshold (rMT) is the basic unit of transcranial magnetic stimulation (TMS) dosing. Traditional methods of determining rMT involve finding a threshold of either visible movement or electromyography (EMG) motor-evoked potentials, commonly approached from above and below and then av...

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Veröffentlicht in:	The journal of ECT Jg. 20; H. 3; S. 160
Hauptverfasser:	Mishory, Alexander, Molnar, Christine, Koola, Jejo, Li, Xingbao, Kozel, F Andrew, Myrick, Hugh, Stroud, Zachary, Nahas, Ziad, George, Mark S
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.09.2004
Schlagworte:	Algorithms Analysis of Variance Differential Threshold Electromyography Evoked Potentials, Motor - physiology Humans Likelihood Functions Male Middle Aged Motor Cortex - physiology Software Transcranial Magnetic Stimulation
ISSN:	1095-0680
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Abstract	The resting motor threshold (rMT) is the basic unit of transcranial magnetic stimulation (TMS) dosing. Traditional methods of determining rMT involve finding a threshold of either visible movement or electromyography (EMG) motor-evoked potentials, commonly approached from above and below and then averaged. This time-consuming method typically uses many TMS pulses. Mathematical programs can efficiently determine a threshold by calculating the next intensity needed based on the prior results. Within our group of experienced TMS researchers, we sought to perform an illustrative study to compare one of these programs, the Maximum-Likelihood Strategy using Parameter Estimation by Sequential Testing (MLS-PEST) approach, to a modification of the traditional International Federation of Clinical Neurophysiology (IFCN) method for determining rMT in terms of the time and pulses required and the rMT value. One subject participated in the study. Five researchers determined the same subject's rMT on 4 separate days-twice using EMG and twice using visible movement. On each visit, researchers used both the MLS-PEST and the IFCN methods, in alternating order. The MLS-PEST approach was significantly faster and used fewer pulses to estimate rMT. For EMG-determined rMT, MLS-PEST and IFCN derived similar rMT, whereas for visible movement MLS-PEST rMT was higher than for IFCN. The MLS-PEST algorithm is a promising alternative to traditional, time-consuming methods for determining rMT. Because the EMG-PEST method is totally automated, it may prove useful in studies using rMT as a quickly changing variable, as well as in large-scale clinical trials. Further work with PEST is warranted.
AbstractList	The resting motor threshold (rMT) is the basic unit of transcranial magnetic stimulation (TMS) dosing. Traditional methods of determining rMT involve finding a threshold of either visible movement or electromyography (EMG) motor-evoked potentials, commonly approached from above and below and then averaged. This time-consuming method typically uses many TMS pulses. Mathematical programs can efficiently determine a threshold by calculating the next intensity needed based on the prior results. Within our group of experienced TMS researchers, we sought to perform an illustrative study to compare one of these programs, the Maximum-Likelihood Strategy using Parameter Estimation by Sequential Testing (MLS-PEST) approach, to a modification of the traditional International Federation of Clinical Neurophysiology (IFCN) method for determining rMT in terms of the time and pulses required and the rMT value.BACKGROUNDThe resting motor threshold (rMT) is the basic unit of transcranial magnetic stimulation (TMS) dosing. Traditional methods of determining rMT involve finding a threshold of either visible movement or electromyography (EMG) motor-evoked potentials, commonly approached from above and below and then averaged. This time-consuming method typically uses many TMS pulses. Mathematical programs can efficiently determine a threshold by calculating the next intensity needed based on the prior results. Within our group of experienced TMS researchers, we sought to perform an illustrative study to compare one of these programs, the Maximum-Likelihood Strategy using Parameter Estimation by Sequential Testing (MLS-PEST) approach, to a modification of the traditional International Federation of Clinical Neurophysiology (IFCN) method for determining rMT in terms of the time and pulses required and the rMT value.One subject participated in the study. Five researchers determined the same subject's rMT on 4 separate days-twice using EMG and twice using visible movement. On each visit, researchers used both the MLS-PEST and the IFCN methods, in alternating order.METHODSOne subject participated in the study. Five researchers determined the same subject's rMT on 4 separate days-twice using EMG and twice using visible movement. On each visit, researchers used both the MLS-PEST and the IFCN methods, in alternating order.The MLS-PEST approach was significantly faster and used fewer pulses to estimate rMT. For EMG-determined rMT, MLS-PEST and IFCN derived similar rMT, whereas for visible movement MLS-PEST rMT was higher than for IFCN.RESULTSThe MLS-PEST approach was significantly faster and used fewer pulses to estimate rMT. For EMG-determined rMT, MLS-PEST and IFCN derived similar rMT, whereas for visible movement MLS-PEST rMT was higher than for IFCN.The MLS-PEST algorithm is a promising alternative to traditional, time-consuming methods for determining rMT. Because the EMG-PEST method is totally automated, it may prove useful in studies using rMT as a quickly changing variable, as well as in large-scale clinical trials. Further work with PEST is warranted.CONCLUSIONSThe MLS-PEST algorithm is a promising alternative to traditional, time-consuming methods for determining rMT. Because the EMG-PEST method is totally automated, it may prove useful in studies using rMT as a quickly changing variable, as well as in large-scale clinical trials. Further work with PEST is warranted. The resting motor threshold (rMT) is the basic unit of transcranial magnetic stimulation (TMS) dosing. Traditional methods of determining rMT involve finding a threshold of either visible movement or electromyography (EMG) motor-evoked potentials, commonly approached from above and below and then averaged. This time-consuming method typically uses many TMS pulses. Mathematical programs can efficiently determine a threshold by calculating the next intensity needed based on the prior results. Within our group of experienced TMS researchers, we sought to perform an illustrative study to compare one of these programs, the Maximum-Likelihood Strategy using Parameter Estimation by Sequential Testing (MLS-PEST) approach, to a modification of the traditional International Federation of Clinical Neurophysiology (IFCN) method for determining rMT in terms of the time and pulses required and the rMT value. One subject participated in the study. Five researchers determined the same subject's rMT on 4 separate days-twice using EMG and twice using visible movement. On each visit, researchers used both the MLS-PEST and the IFCN methods, in alternating order. The MLS-PEST approach was significantly faster and used fewer pulses to estimate rMT. For EMG-determined rMT, MLS-PEST and IFCN derived similar rMT, whereas for visible movement MLS-PEST rMT was higher than for IFCN. The MLS-PEST algorithm is a promising alternative to traditional, time-consuming methods for determining rMT. Because the EMG-PEST method is totally automated, it may prove useful in studies using rMT as a quickly changing variable, as well as in large-scale clinical trials. Further work with PEST is warranted.
Author	Stroud, Zachary George, Mark S Molnar, Christine Li, Xingbao Myrick, Hugh Kozel, F Andrew Nahas, Ziad Mishory, Alexander Koola, Jejo
Author_xml	– sequence: 1 givenname: Alexander surname: Mishory fullname: Mishory, Alexander email: mishory@musc.edu organization: Brain Stimulation Laboratory, Department of Psychiatry, Medical University of South Carolina, Charleston 29425, USA. mishory@musc.edu – sequence: 2 givenname: Christine surname: Molnar fullname: Molnar, Christine – sequence: 3 givenname: Jejo surname: Koola fullname: Koola, Jejo – sequence: 4 givenname: Xingbao surname: Li fullname: Li, Xingbao – sequence: 5 givenname: F Andrew surname: Kozel fullname: Kozel, F Andrew – sequence: 6 givenname: Hugh surname: Myrick fullname: Myrick, Hugh – sequence: 7 givenname: Zachary surname: Stroud fullname: Stroud, Zachary – sequence: 8 givenname: Ziad surname: Nahas fullname: Nahas, Ziad – sequence: 9 givenname: Mark S surname: George fullname: George, Mark S
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SubjectTerms	Algorithms Analysis of Variance Differential Threshold Electromyography Evoked Potentials, Motor - physiology Humans Likelihood Functions Male Middle Aged Motor Cortex - physiology Software Transcranial Magnetic Stimulation
Title	The maximum-likelihood strategy for determining transcranial magnetic stimulation motor threshold, using parameter estimation by sequential testing is faster than conventional methods with similar precision
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