Prediction of cortical excitability induced by 1 Hz rTMS

Kazuhisa Nojima, Keiji Iramina

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The aim of this study was to develop a model that predicts the effects of repetitive transcranial magnetic stimulation (rTMS), allowing stimulation of parameters for individual subjects. Modulation of cortical excitability induced by rTMS can be evaluated through motor evoked potential (MEP) amplitude. We establish a model that can predict how the MEP amplitude is modulated by entering rTMS intensity and number of pulses. First, MEPs are measured under various rTMS conditions of stimulus intensity and number of pulses. Then, cluster analysis is performed to classify the subjects, as rTMS affects individuals differently. Finally, a predictive model is created by applying multiple regression analysis to the data from each cluster. As a result, subjects are classified into two groups. For Cluster A, the inhibitive effect of rTMS is difficult to induce and the facilitative effect is induced depending on the stimulus condition. Then, the average predictive error is 46.19%. For Cluster B, the inhibitive effect is strongly induced by rTMS, and the average error is 20.25%. In the model, for both clusters, about 90% of measurement data is in the predictive interval. This paper describes the development of our prediction model and its efficiency.

Original languageEnglish
Pages (from-to)601-607
Number of pages7
JournalIEEJ Transactions on Electrical and Electronic Engineering
Volume12
Issue number4
DOIs
Publication statusPublished - Jul 2017

Fingerprint

Bioelectric potentials
Cluster analysis
Regression analysis
Modulation

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Prediction of cortical excitability induced by 1 Hz rTMS. / Nojima, Kazuhisa; Iramina, Keiji.

In: IEEJ Transactions on Electrical and Electronic Engineering, Vol. 12, No. 4, 07.2017, p. 601-607.

Research output: Contribution to journalArticle

@article{ab00093f1edb48e7b98775dc9490e9b3,
title = "Prediction of cortical excitability induced by 1 Hz rTMS",
abstract = "The aim of this study was to develop a model that predicts the effects of repetitive transcranial magnetic stimulation (rTMS), allowing stimulation of parameters for individual subjects. Modulation of cortical excitability induced by rTMS can be evaluated through motor evoked potential (MEP) amplitude. We establish a model that can predict how the MEP amplitude is modulated by entering rTMS intensity and number of pulses. First, MEPs are measured under various rTMS conditions of stimulus intensity and number of pulses. Then, cluster analysis is performed to classify the subjects, as rTMS affects individuals differently. Finally, a predictive model is created by applying multiple regression analysis to the data from each cluster. As a result, subjects are classified into two groups. For Cluster A, the inhibitive effect of rTMS is difficult to induce and the facilitative effect is induced depending on the stimulus condition. Then, the average predictive error is 46.19{\%}. For Cluster B, the inhibitive effect is strongly induced by rTMS, and the average error is 20.25{\%}. In the model, for both clusters, about 90{\%} of measurement data is in the predictive interval. This paper describes the development of our prediction model and its efficiency.",
author = "Kazuhisa Nojima and Keiji Iramina",
year = "2017",
month = "7",
doi = "10.1002/tee.22415",
language = "English",
volume = "12",
pages = "601--607",
journal = "IEEJ Transactions on Electrical and Electronic Engineering",
issn = "1931-4973",
publisher = "John Wiley and Sons Inc.",
number = "4",

}

TY - JOUR

T1 - Prediction of cortical excitability induced by 1 Hz rTMS

AU - Nojima, Kazuhisa

AU - Iramina, Keiji

PY - 2017/7

Y1 - 2017/7

N2 - The aim of this study was to develop a model that predicts the effects of repetitive transcranial magnetic stimulation (rTMS), allowing stimulation of parameters for individual subjects. Modulation of cortical excitability induced by rTMS can be evaluated through motor evoked potential (MEP) amplitude. We establish a model that can predict how the MEP amplitude is modulated by entering rTMS intensity and number of pulses. First, MEPs are measured under various rTMS conditions of stimulus intensity and number of pulses. Then, cluster analysis is performed to classify the subjects, as rTMS affects individuals differently. Finally, a predictive model is created by applying multiple regression analysis to the data from each cluster. As a result, subjects are classified into two groups. For Cluster A, the inhibitive effect of rTMS is difficult to induce and the facilitative effect is induced depending on the stimulus condition. Then, the average predictive error is 46.19%. For Cluster B, the inhibitive effect is strongly induced by rTMS, and the average error is 20.25%. In the model, for both clusters, about 90% of measurement data is in the predictive interval. This paper describes the development of our prediction model and its efficiency.

AB - The aim of this study was to develop a model that predicts the effects of repetitive transcranial magnetic stimulation (rTMS), allowing stimulation of parameters for individual subjects. Modulation of cortical excitability induced by rTMS can be evaluated through motor evoked potential (MEP) amplitude. We establish a model that can predict how the MEP amplitude is modulated by entering rTMS intensity and number of pulses. First, MEPs are measured under various rTMS conditions of stimulus intensity and number of pulses. Then, cluster analysis is performed to classify the subjects, as rTMS affects individuals differently. Finally, a predictive model is created by applying multiple regression analysis to the data from each cluster. As a result, subjects are classified into two groups. For Cluster A, the inhibitive effect of rTMS is difficult to induce and the facilitative effect is induced depending on the stimulus condition. Then, the average predictive error is 46.19%. For Cluster B, the inhibitive effect is strongly induced by rTMS, and the average error is 20.25%. In the model, for both clusters, about 90% of measurement data is in the predictive interval. This paper describes the development of our prediction model and its efficiency.

UR - http://www.scopus.com/inward/record.url?scp=85013996023&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85013996023&partnerID=8YFLogxK

U2 - 10.1002/tee.22415

DO - 10.1002/tee.22415

M3 - Article

AN - SCOPUS:85013996023

VL - 12

SP - 601

EP - 607

JO - IEEJ Transactions on Electrical and Electronic Engineering

JF - IEEJ Transactions on Electrical and Electronic Engineering

SN - 1931-4973

IS - 4

ER -