Background: Latencies of motor evoked potentials (MEP) can provide insights into the motor neuronal pathways activated by transcranial magnetic stimulation (TMS). Notwithstanding its clinical relevance, accurate, unbiased methods to automatize latency detection are still missing. Objective: We present a novel open-source algorithm suitable for MEP onset/latency detection during resting state that only requires the post-stimulus electromyography signal and exploits the approximation of the first derivative of this signal to find the time point of initial deflection of the MEP. Methods: The algorithm has been benchmarked, using intra-class coefficient (ICC) and effect sizes, to manual detection of latencies done by three researchers independently on a dataset comprising almost 6500 MEP trials from healthy participants (n=18) and stroke patients (n=31) acquired during rest. The performance was further compared to currently available automatized methods, some of which created for active contraction protocols. Results: The unstandardized effect size between the human raters and the present method is smaller than the sampling period for both healthy and pathological MEPs. Moreover, the ICC increases when the algorithm is added as a rater. Conclusion: The present algorithm is comparable to human expert decision and outperforms currently available methods. It provides a promising method for automated MEP latency detection under physiological and pathophysiological conditions.

Nociceptive stimuli disrupt sleep, but may, or may not, entail an arousal. While arousal reactions go along with the activation of a widespread cortical network, the factors enabling such activation remain unknown. Here we used intracranial EEG in humans to test the relation between the cortical activity immediately preceding a noxious stimulus and the capacity of such a stimulus to trigger arousal. Intracranial EEG signals were analyzed during all-night sleep in 14 epileptic patients (4 women), who received laser stimuli slightly above their individual pain threshold. During 5 s preceding each stimulus, the functional correlation (spectral phase-coherence) between the main spinothalamic sensory area (posterior insula) and 12 other brain regions, grouped in four networks, as well as their spectral contents, were contrasted according to the presence of a stimulus-induced arousal, and then fed into a logistic regression model to assess their predictive value. Enhanced prestimulus phase-coherence between the sensory posterior insula and neocortical and limbic areas increased significantly the probability of arousal to nociceptive stimuli, in both slow-wave (N2) and rapid eye movements/paradoxical sleep. Furthermore, during N2 sleep, arousal was facilitated by stimulus delivery in periods of attenuated slow-wave activity. Together, these data indicate that sleep micro-states with enhanced interareal communication facilitate information transfer from sensory to higherorder cortical areas, and hence physiological arousal.

Objective: Sources of heterogeneity in non-invasive brain stimulation literature can be numerous, with underlying brain states and protocol differences at the top of the list. Yet, incoherent results from brain-state-dependent stimulation experiments suggest that there are further factors adding to the variance. Hypothesizing that different signal processing pipelines might be partly responsible for heterogeneity; we investigated their effects on brain-state forecasting approaches. Approach: A grid-search was used to determine the fastest and most-accurate combination of preprocessing parameters and phase-forecasting algorithms. The grid-search was applied on a synthetic dataset and validated on electroencephalographic (EEG) data from a healthy (n=18) and stroke (n=31) cohort. Main results: Differences in processing pipelines led to different results; the grid-search chosen pipelines significantly increased the accuracy of published forecasting methods. The accuracy achieved in healthy was comparably high in stroke patients. Significance: This systematic offline analysis highlights the importance of the specific EEG processing and forecasting pipelines used for online state-dependent setups where precision in phase prediction is critical. Moreover, successful results in the stroke cohort pave the way to test state-dependent interventional treatment approaches.