Predicting rTMS effect for deciding stimulation parameters

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Repetitive transcranial magnetic stimulation (rTMS) is used in the medical field to modulate cortical excitability. However, when applied in this setting, rTMS stimulation parameters are not usually decided objectively. The aim of this study is to make a model that predicts the rTMS effect, allowing stimulation parameters (intensity and pulse number) to be easily determined before use. First, we investigated the relationship between stimulation condition and rTMS outcome. rTMS delivered at 1 Hz was applied with stimulation intensities of 85%, 100%, or 115% resting motor threshold (RMT) over the primary motor cortex in the left hemisphere. Motor-evoked potentials (MEPs) were measured before rTMS and after every 200 rTMS pulses. Eighteen hundred pulses were applied for each stimulation condition. Results showed that more pulses and stronger intensities lead to a larger decrease in MEP amplitude. An initial prediction model was then made by applying multiple regression analysis over the experimental data. We then adjusted the model depending on the size of the initial MEP amplitude before rTMS, and confirmed the improvement.

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages6369-6372
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: Jul 3 2013Jul 7 2013

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Country/TerritoryJapan
CityOsaka
Period7/3/137/7/13

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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