Machine learning approaches to predict whether MEPs can be elicited via TMS.
J Neurosci Methods
; 410: 110242, 2024 Oct.
Article
in En
| MEDLINE
| ID: mdl-39127350
ABSTRACT
BACKGROUND:
Transcranial 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, which after transmission along cortico-muscular pathways can be measured as motor-evoked potentials (MEPs). 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. NEWMETHOD:
We 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.RESULTS:
We obtained prediction accuracies of on average 77â¯% and 65â¯% with maxima up to up to 90â¯% and 72â¯% within and between subjects, respectively. Across the board, a bagging ensemble appeared to be the most suitable approach to predict the presence of MEPs.CONCLUSIONS:
Although within a subject the prediction of MEPs via TMS setup parameter-based machine learning might be feasible, the limited accuracy between subjects suggests that the transfer of this approach to experimental or clinical research comes with significant challenges.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Evoked Potentials, Motor
/
Transcranial Magnetic Stimulation
/
Machine Learning
/
Motor Cortex
Limits:
Adult
/
Female
/
Humans
/
Male
Language:
En
Journal:
J Neurosci Methods
Year:
2024
Document type:
Article
Affiliation country:
Netherlands