Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance
Stroke severity is associated with the presence or absence of motor-evoked potentials (MEPs) induced by transcranial magnetic stimulation (TMS). However, there is limited evidence regarding the relationship between MEP waveforms, post-stroke motor impairment, and functional performance. This study a...
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Elsevier
2025-01-01
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| Series: | Informatics in Medicine Unlocked |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914825000310 |
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| author | Chun-Ren Phang Shintaro Uehara Sachiko Kodera Akiko Yuasa Shin Kitamura Yohei Otaka Akimasa Hirata |
| author_facet | Chun-Ren Phang Shintaro Uehara Sachiko Kodera Akiko Yuasa Shin Kitamura Yohei Otaka Akimasa Hirata |
| author_sort | Chun-Ren Phang |
| collection | DOAJ |
| description | Stroke severity is associated with the presence or absence of motor-evoked potentials (MEPs) induced by transcranial magnetic stimulation (TMS). However, there is limited evidence regarding the relationship between MEP waveforms, post-stroke motor impairment, and functional performance. This study aimed to evaluate the predictive value of inter-trial correlation (ITC), a novel metric reflecting waveform consistency, along with MEP amplitude and resting motor threshold (rMT), in estimating post-stroke motor outcomes. Thirty-eight stroke participants were enrolled, and TMS was applied to the hotspot of the first dorsal interosseous muscle in the ipsilesional or contralesional hemisphere to elicit MEPs. MEP amplitude, ITC, and rMT were analyzed in 20 participants with detectable MEPs. Pearson correlation coefficient (PCC) analysis assessed the relationships between MEP features and motor outcomes, including the Stroke Impairment Assessment Set (SIAS), Fugl-Meyer Assessment (FMA), and Action Research Arm Test (ARAT). A linear support vector machine (SVM) was trained using leave-one-subject-out cross-validation to predict the motor outcomes. Participants without detectable MEPs (n = 18) had significantly lower motor scores than those with detectable MEPs did. MEP amplitude from the contralesional side was positively correlated with SIAS, FMA, and ARAT (PCC = 0.51, 0.47, and 0.55, respectively), whereas LICI amplitude and ITC from the ipsilesional side were negatively correlated with motor scores. The SVM model predicted motor outcomes with an R2 of 0.42 and a normalized root mean square error of 0.26. A Gaussian classifier achieved 75 % accuracy in classifying motor outcome improvements. These findings suggest that bilateral MEP features, particularly those from the contralesional hemisphere, offer valuable prognostic information. This study proposes a practical framework for post-stroke motor outcome prediction based on MEP analysis with potential utility in individualized rehabilitation planning. |
| format | Article |
| id | doaj-art-49207d21616e4d1ebea483ded1e26c7d |
| institution | Kabale University |
| issn | 2352-9148 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj-art-49207d21616e4d1ebea483ded1e26c7d2025-08-20T03:49:31ZengElsevierInformatics in Medicine Unlocked2352-91482025-01-015510164310.1016/j.imu.2025.101643Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performanceChun-Ren Phang0Shintaro Uehara1Sachiko Kodera2Akiko Yuasa3Shin Kitamura4Yohei Otaka5Akimasa Hirata6Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Aichi, Japan; Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya, Aichi, JapanFaculty of Rehabilitation, Fujita Health University School of Health Sciences, Toyoake, Aichi, JapanDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Aichi, Japan; Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya, Aichi, JapanDepartment of Rehabilitation Medicine, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; The Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo, JapanFaculty of Rehabilitation, Fujita Health University School of Health Sciences, Toyoake, Aichi, JapanDepartment of Rehabilitation Medicine, Fujita Health University School of Medicine, Toyoake, Aichi, JapanDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Aichi, Japan; Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya, Aichi, Japan; Corresponding author. Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya, Aichi, 466-8555, Japan.Stroke severity is associated with the presence or absence of motor-evoked potentials (MEPs) induced by transcranial magnetic stimulation (TMS). However, there is limited evidence regarding the relationship between MEP waveforms, post-stroke motor impairment, and functional performance. This study aimed to evaluate the predictive value of inter-trial correlation (ITC), a novel metric reflecting waveform consistency, along with MEP amplitude and resting motor threshold (rMT), in estimating post-stroke motor outcomes. Thirty-eight stroke participants were enrolled, and TMS was applied to the hotspot of the first dorsal interosseous muscle in the ipsilesional or contralesional hemisphere to elicit MEPs. MEP amplitude, ITC, and rMT were analyzed in 20 participants with detectable MEPs. Pearson correlation coefficient (PCC) analysis assessed the relationships between MEP features and motor outcomes, including the Stroke Impairment Assessment Set (SIAS), Fugl-Meyer Assessment (FMA), and Action Research Arm Test (ARAT). A linear support vector machine (SVM) was trained using leave-one-subject-out cross-validation to predict the motor outcomes. Participants without detectable MEPs (n = 18) had significantly lower motor scores than those with detectable MEPs did. MEP amplitude from the contralesional side was positively correlated with SIAS, FMA, and ARAT (PCC = 0.51, 0.47, and 0.55, respectively), whereas LICI amplitude and ITC from the ipsilesional side were negatively correlated with motor scores. The SVM model predicted motor outcomes with an R2 of 0.42 and a normalized root mean square error of 0.26. A Gaussian classifier achieved 75 % accuracy in classifying motor outcome improvements. These findings suggest that bilateral MEP features, particularly those from the contralesional hemisphere, offer valuable prognostic information. This study proposes a practical framework for post-stroke motor outcome prediction based on MEP analysis with potential utility in individualized rehabilitation planning.http://www.sciencedirect.com/science/article/pii/S2352914825000310Transcranial magnetic stimulationMotor-evoked potentialMotor outcomeMachine learningPrediction |
| spellingShingle | Chun-Ren Phang Shintaro Uehara Sachiko Kodera Akiko Yuasa Shin Kitamura Yohei Otaka Akimasa Hirata Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance Informatics in Medicine Unlocked Transcranial magnetic stimulation Motor-evoked potential Motor outcome Machine learning Prediction |
| title | Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance |
| title_full | Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance |
| title_fullStr | Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance |
| title_full_unstemmed | Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance |
| title_short | Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance |
| title_sort | statistical analysis and machine learning of tms induced meps for predicting poststroke motor impairment and performance |
| topic | Transcranial magnetic stimulation Motor-evoked potential Motor outcome Machine learning Prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2352914825000310 |
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