Fast estimation of transcranial magnetic stimulation motor threshold

被引:39
作者
Qi, Feng [1 ]
Wu, Allan D. [2 ]
Schweighofer, Nicolas [1 ]
机构
[1] Univ So Calif, Los Angeles, CA 90089 USA
[2] Univ Calif Los Angeles, Dept Neurol, Los Angeles, CA 90024 USA
基金
美国国家科学基金会;
关键词
TMS; motor threshold; Bayesian method;
D O I
10.1016/j.brs.2010.06.002
中图分类号
R74 [神经病学与精神病学];
学科分类号
100204 [神经病学];
摘要
Background In Transcranial Magnetic Stimulation (TMS), the Motor Threshold (MT) is the minimum intensity required to evoke a liminal response in the target muscle. Because the MT reflects cortical excitability, the TMS intensity needs to be adjusted according to the subject's MT at the beginning of every TMS session. Objective Shorten the MT estimation process compared to existing methods without compromising accuracy. Methods We propose a Bayesian adaptive method for MT determination that incorporates prior MT knowledge and uses a stopping criterion based on estimation of MT precision. We compared the number of TMS pulses required with this new method with existing MT determination methods. Results The proposed method achieved the accuracy of existing methods with as few as seven TMS pulses on average when using a common prior and three TMS pulses on average when using subject-specific priors. Conclusions Our adaptive Bayesian method is effective in reducing the number of pulses to estimate the MT. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:50 / 57
页数:8
相关论文
共 17 条
[1]
Stopping rules in Bayesian adaptive threshold estimation [J].
Alcalá-Quintana, R ;
García-Pérez, MA .
SPATIAL VISION, 2005, 18 (03) :347-374
[2]
[Anonymous], 2006, Pattern recognition and machine learning
[3]
Awiszus F, 2003, SUPPL CLIN NEUROPHYS, V56, P13
[4]
Estimating resting motor thresholds in transcranial magnetic stimulation research and practice: A computer simulation evaluation of best methods [J].
Borckardt, Jeffrey J. ;
Nahas, Ziad ;
Koola, Jejo ;
George, Mark S. .
JOURNAL OF ECT, 2006, 22 (03) :169-175
[5]
Transcranial magnetic stimulation: A primer [J].
Hallett, Mark .
NEURON, 2007, 55 (02) :187-199
[6]
INFORMATION-BASED OBJECTIVE FUNCTIONS FOR ACTIVE DATA SELECTION [J].
MACKAY, DJC .
NEURAL COMPUTATION, 1992, 4 (04) :590-604
[7]
Mills KR, 1997, MUSCLE NERVE, V20, P570, DOI 10.1002/(SICI)1097-4598(199705)20:5<570::AID-MUS5>3.3.CO
[8]
2-F
[9]
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 [J].
Mishory, A ;
Molnar, C ;
Koola, J ;
Li, XB ;
Kozel, FA ;
Myrick, H ;
Stroud, Z ;
Nahas, Z ;
George, MS .
JOURNAL OF ECT, 2004, 20 (03) :160-165
[10]
MAXIMUM-LIKELIHOOD ESTIMATION - THE BEST PEST [J].
PENTLAND, A .
PERCEPTION & PSYCHOPHYSICS, 1980, 28 (04) :377-379