Machine learning approaches to predicting whether muscles can be elicited via TMS

Jin, F.; Bruijn, S.; Daffertshofer, A.

doi:10.1101/2023.12.14.571623

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bioRxiv · 10.1101/2023.12.14.571623

Machine learning approaches to predicting whether muscles can be elicited via TMS

Jin, F.·Bruijn, S.·Daffertshofer, A.

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Abstract

BackgroundTranscranial magnetic stimulation (TMS) is a valuable technique for assessing the function of the motor cortex and cortico-muscular pathways. TMS activates the motoneurons in the cortex, and this activation is transmitted through the cortico-muscular pathway, after which it can be measured as a motor evoked potential (MEP) in the muscles. The position and orientation of the TMS coil and the intensity used to deliver a TMS pulse are considered central TMS setup parameters influencing the presence/absence of MEPs. New MethodWe sought to predict the presence of MEPs from TMS setup parameters using machine learning. We trained different machine learners using either within-subject or between-subject designs. ResultsWe obtained prediction accuracies of on average 77% and 65% with maxima up to up to 90% and 72% within and between subjects, respectively. Across the board, a bagging ensemble appeared to be the most suitable approach to predict the presence of MEPs, although a comparably simple logistic regression model also performed well. ConclusionsWhile the prediction between subjects clearly leaves room for improvement, the within-subject performance encourages to supplement TMS by machine learning to improve its diagnostic capacity with respect to motor impairment.

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Jin, F., Bruijn, S., Daffertshofer, A.. 2023-12-14. Machine learning approaches to predicting whether muscles can be elicited via TMS. https://doi.org/10.1101/2023.12.14.571623

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