‘RMT-Finder’: An automated procedure to determine the Resting Motor Threshold for Transcranial Magnetic Stimulation
(2026) Journal of Neuroscience Methods — Vol. 433, p. 110787 (2026)
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- Abstract
- Background: Transcranial Magnetic Stimulation (TMS) studies identify the Resting Motor Threshold (RMT) to calibrate stimulation intensity. However, this procedure is time-consuming and subject to variability. We developed an automated procedure to improve the efficiency and standardization of RMT determination. New method: We developed an algorithm that measures MEP amplitudes and automatically adjusts stimulation intensity to determine the RMT. Experiment 1 compared this automated method with the manual procedure in terms of reliability and equivalence. Experiment 2 developed a "Fast" automated process, assessing it against both the manual and initial automated procedures. Results: Across both experiments the automated approach demonstrated excellent test-retest reliability and strong agreement with the manual method (Intraclass Correlation Coefficients ≥0.95), giving estimates of RMT statistically equivalent to those of manual measurements within ±3% MSO, with the majority of comparisons within ±2% MSO. Experiment 2 optimized the procedure, allowing empirical determination of the RMT in an average of < 3 min with only 33-34 pulses. Comparison with existing methods: 'RMT-Finder' provides a reliable and time-efficient alternative to manual approaches. This procedure can improve standardization and repro-ducibility in TMS studies. Conclusions: Automating RMT assessment allows rapid and highly reproducible assessment of this standard TMS measurement, making it viable for inclusion in routine clinical applications that require standardized procedures.
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Citations
Boidequin, L., Moreno-Verdú, M., Waltzing, B., Lambert, J., Van Caenegem, E., Truong, C., & Hardwick, R. (2026). ‘RMT-Finder’: An automated procedure to determine the Resting Motor Threshold for Transcranial Magnetic Stimulation. Journal of Neuroscience Methods, 433, 110787. https://doi.org/10.1016/j.jneumeth.2026.110787 (Original work published 2026)