Active and resting motor threshold are efficiently obtained with adaptive threshold hunting

Area of Science:

• Neuroscience
• Neuromodulation
• Motor Control

Background:

• Transcranial magnetic stimulation (TMS) commonly uses active motor threshold (AMT) and resting motor threshold (RMT) measures.
• Adaptive threshold hunting is efficient for RMT estimation, but AMT estimation methods lack comparative studies.
• AMT is crucial for TMS studies investigating intracortical circuits.

Purpose of the Study:

• To compare the Rossini-Rothwell (R-R) method with maximum-likelihood parameter estimation by sequential testing (ML-PEST) for AMT and RMT estimation.
• To evaluate the efficiency and accuracy of ML-PEST for acquiring motor thresholds in TMS.

Main Methods:

• A within-subject, experimenter-blinded study design was used.
• AMT and RMT were quantified using both the R-R and ML-PEST methods in 15 healthy participants.
• Motor thresholds were measured using transcranial magnetic stimulation.

Main Results:

• Both R-R and ML-PEST methods yielded comparable AMT and RMT estimates, showing strong intraclass correlation and good agreement.
• ML-PEST required significantly fewer TMS stimuli (17 fewer for AMT, 15 fewer for RMT) compared to the R-R method.
• The efficiency gains with ML-PEST are particularly beneficial when multiple muscles are targeted in a single TMS session.

Conclusions:

• ML-PEST is an effective and efficient method for estimating AMT and RMT in TMS.
• This adaptive approach reduces the number of TMS pulses without sacrificing accuracy.
• Implementing ML-PEST enhances TMS experimental efficiency, especially for multi-muscle assessments.