Motor potentials evoked by transcranial magnetic stimulation: interpreting a simple measure of a complex system

Transcranial magnetic stimulation (TMS) is a non‐invasive technique that is increasingly used to study the human brain. One of the principal outcome measures is the motor‐evoked potential (MEP) elicited in a muscle following TMS over the primary motor cortex (M1), where it is used to estimate changes in corticospinal excitability. However, multiple elements play a role in MEP generation, so even apparently simple measures such as peak‐to‐peak amplitude have a complex interpretation. Here, we summarize what is currently known regarding the neural pathways and circuits that contribute to the MEP and discuss the factors that should be considered when interpreting MEP amplitude measured at rest in the context of motor processing and patients with neurological conditions. In the last part of this work, we also discuss how emerging technological approaches can be combined with TMS to improve our understanding of neural substrates that can influence MEPs. Overall, this review aims to highlight the capabilities and limitations of TMS that are important to recognize when attempting to disentangle sources that contribute to the physiological state‐related changes in corticomotor excitability

Using TMS-EEG to assess the effects of neuromodulation techniques: a narrative review

Over the past decades, among all the non-invasive brain stimulation (NIBS) techniques, those aiming for neuromodulatory protocols have gained special attention. The traditional neurophysiological outcome to estimate the neuromodulatory effect is the motor evoked potential (MEP), the impact of NIBS techniques is commonly estimated as the change in MEP amplitude. This approach has several limitations: first, the use of MEP limits the evaluation of stimulation to the motor cortex excluding all the other brain areas. Second, MEP is an indirect measure of brain activity and is influenced by several factors. To overcome these limitations several studies have used new outcomes to measure brain changes after neuromodulation techniques with the concurrent use of transcranial magnetic stimulation (TMS) and electroencephalogram (EEG). In the present review, we examine studies that use TMS-EEG before and after a single session of neuromodulatory TMS. Then, we focused our literature research on the description of the different metrics derived from TMS-EEG to measure the effect of neuromodulation

P153 Cerebellar-M1 connectivity (CBI): One or two different networks?

Introduction Recently it has been argued that two distinct interneuron networks in the primary motor cortex (M1) contribute distinctly to two varieties of physiological plasticity and motor behaviors (Hamada et al., 2014). Although one of the interneuron groups is thought to be dependent on cerebellar (CB) activity, direct physiological distinction regarding CB-M1 interactions (CBI) to these subpopulations remains poorly understood. Objectives This study assessed whether M1 coil orientation, thought to test different neuronal populations, affects CBI in the context of two motor behaviors that weight differently cerebellar vs. M1 contributions. Methods In experiment 1 (n = 10), we tested the effect of coil orientation (posterior–anterior, PA; anterior–posterior, AP) and inter-stimulus intervals (ISI: 3, 5 and 7 ms) on CBI; assessed with a conditioned TMS pulse over the cerebellum prior to TMS over the contralateral M1. In experiment 2 (n = 10), we tested how learning two distinct motor learning tasks (weighting sensorimotor calibration vs. a sequence task) affected AP- vs. PA-CBI measured at their preferential ISI. Results ANOVA-RM revealed a significant CBI effect for ISI (F(2,36) = 17.807; p < 0.01) and COIL ORIENTATION*ISI interaction (F(2,36) = 8.067; p = 0.01). Specifically, PA-CBI was prominent at 5 ms ISI (p = 0.02) and AP-CBI at 7 ms ISI (p = 0.01). To determine how learning affects AP- vs. PA-CBI at their preferential ISI, we compared CBI before, during and after training. ANOVA-RM revealed a significant effect of CBI for MOTOR TASK*TIME*ORIENTATION interaction (F(4,42) = 2.800; p = 0.04). When learning a sensorimotor calibration, PA-CBI changed only early during learning (p = 0.02), whereas AP-CBI changed only late (p = 0.01). Additionally, during sequence learning, PA-CBI also changed only early (p = 0.01), whereas AP-CBI was not modulated. Conclusion These findings indicate that CB-M1 interactions are different for the two M1 neural networks. This could be the result of either two independent CB-M1 pathways or distinct processing of cerebellar inputs within M1