EP 142

• Published in: Clinical neurophysiology Vol. 127; no. 9; pp. e301 - e302
• Main Authors: Bathe-Peters, R, Rönnefarth, M, Robert, F, Arvid, K, Brandt, S.A, Schmidt, S
• Format: Journal Article
• Language: English
• Published: 01.09.2016
• Subjects: Neurology
• ISSN: 1388-2457
• DOI: 10.1016/j.clinph.2016.05.181

Objective A major challenge in the interpretation of data in experiments with transcranial magnetic stimulation (TMS) is the variability of measures of cortico-spinal excitability (CSE). Background activity in the electromyogram (EMG), called preinnervation, is a critical confounder in TMS data and has so far been controlled by audio-visual monitoring and subjectively minimizing the tone in a target muscle group (Rossini et al. 2015). Here we suggest and validate a rater-independent alternative by measuring and correcting for EMG-activity preceding a TMS stimulus based on a regression model (Schmidt et al. 2015, Bathe-Peters et al., NBS 2013). The validity of different algorithms for the quantification and correction of preinnervation is unknown. The purpose of this study was to compare the predictive validity of differentially defined measures of preinnervation, using linear and non-linear methods, in a self-paced isometric contraction task versus a “resting” condition. We hypothesized that, in the “active” condition, a non-linear correction for preinnervation is superior to a linear method. Secondly, longer time bins of pre-stimulus EMG-activity should increase validity. In the “resting” condition, where the muscle tone was visually minimized, linear and non-linear modeling of CSE independent of possible left-over preinnervation should yield similar results (Darling et al. 2006). Methods Trains of single pulses of navigated TMS (nTMS) were applied to the dominant “hot-spot” of the first dorsal interosseous muscle (FDI) in healthy volunteers (4 f., 3 m.). In the “active” condition, subjects performed an isometric flexion of the index finger with three pre-defined force levels in a randomized order. In the “resting” condition, relaxation was visually monitored in the surface EMG of the FDI. Preinnervation was defined by the area-under-the-curve in 100, 200 and 300 ms time bins in the EMG prior to a stimulus. The amount of variability of motor evoked potentials explained by these measures of preinnervation was assessed with a simple linear and a non-linear regression using a sigmoidal fit. Results In the “active” condition, the predictive validity of preinnervation steadily increased with longer time bins, starting at about 60% (p < 0.001) using a 100 ms time bin and a linear fit to about 70% ( p < 0.001) using a 300 ms time bin and a sigmoidal fit. In subjects at rest, no significant difference between the fitting algorithm and different time bins was found. Conclusions In line with previous studies, preinnervation validly predicted CSE in both linear (Schmidt et al. 2015) and non-linear (Darling et al. 2006) regression. Across different levels of muscle activity, a sigmoidal fit might reflect the input-output properties of the stimulated cortico-spinal networks and thus yield higher predictive validity (Devanne et al. 1997). In the resting motor state, we suggest linear regression as a valid method to measure and correct for preinnervation.