BRAIN STIMULATION: Basic, Translational, and Clinical Research in Neuromodulation
Volume 4, Issue 1 , Pages 50-57 , January 2011

Fast estimation of transcranial magnetic stimulation motor threshold

  • Feng Qi

      Affiliations

    • Neuroscience, University of Southern California, Los Angeles, California
  • ,
  • Allan D. Wu

      Affiliations

    • Department of Neurology, University of California at Los Angeles, Los Angeles, California
  • ,
  • Nicolas Schweighofer

      Affiliations

    • Neuroscience, University of Southern California, Los Angeles, California
    • Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, California
    • Corresponding Author InformationCorrespondence: Nicolas Schweighofer, Neuroscience, University of Southern California, 1540 E. Alcazar, Los Angeles, CA 90089

Received 17 October 2009 ,Revised 7 June 2010 ,Accepted 7 June 2010.

References 

  1. Hallett M. Transcranial magnetic stimulation: a primer. Neuron. 2007;55(2):187–199
  2. Rossini PM, Barker AT, Berardelli A, et al. Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: basic principles and procedures for routine clinical application. Report of an IFCN committee. Electroencephalogr Clin Neurophysiol. 1994;91(2):79–92
  3. Rothwell JC, Hallett M, Berardelli A, Eisen A, Rossini P, Paulus W. Magnetic stimulation: motor evoked potentials. The International Federation of Clinical Neurophysiology. Electroencephalogr Clin Neurophysiol Suppl. 1999;52:97–103
  4. Mills KR, Nithi KA. Corticomotor threshold to magnetic stimulation: normal values and repeatability. Muscle Nerve. 1997;20(5):570–576
  5. Mishory A, Molnar C, Koola J, et al. 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 ECT. 2004;20(3):160–165
  6. Awiszus F. TMS and threshold hunting. Suppl Clin Neurophysiol. 2003;56:13–23
  7. MacKay DJC. Information-based objective functions for active data selection. Neural Computation. 1992;4:590–604
  8. Pentland A. Maximum likelihood estimation: the best PEST. Percept Psychophys. 1980;28(4):377–379
  9. Borckardt JJ, Nahas Z, Koola J, George MS. Estimating resting motor thresholds in transcranial magnetic stimulation research and practice: a computer simulation evaluation of best methods. J ECT. 2006;22(3):169–175
  10. Treutwein B. Adaptive psychophysical procedures. Vision Res. 1995;35(17):2503–2522
  11. Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006;
  12. Alcala-Quintana R, Garcia-Perez MA. Stopping rules in Bayesian adaptive threshold estimation. Spat Vis. 2005;18(3):347–374
  13. Watson AB, Pelli DG. QUEST: a Bayesian adaptive psychometric method. Percept Psychophys. 1983;33(2):113–120
  14. Stewart LM, Walsh V, Rothwell JC. Motor and phosphene thresholds: a transcranial magnetic stimulation correlation study. Neuropsychologia. 2001;39(4):415–419
  15. Treutwein B. YAAP: yet another adaptive procedure. Spat Vis. 1997;11(1):129–134
  16. Schluter ND, Rushworth MF, Passingham RE, Mills KR. Temporary interference in human lateral premotor cortex suggests dominance for the selection of movements: a study using transcranial magnetic stimulation. Brain. 1998;121(Pt 5):785–799

 This work was in part supported by National Science Foundation grant IIS 0535282 to NS.

PII: S1935-861X(10)00060-4

doi: 10.1016/j.brs.2010.06.002

BRAIN STIMULATION: Basic, Translational, and Clinical Research in Neuromodulation
Volume 4, Issue 1 , Pages 50-57 , January 2011