This paper describes real-time electroencephalography-triggered transcranial magnetic stimulation to study and modulate human brain networks.
The effect of a stimulus to the brain depends not only on the parameters of the stimulus but also on the dynamics of brain activity at the time of the stimulation. The combination of electroencephalography (EEG) and transcranial magnetic stimulation (TMS) in a real-time brain state-dependent stimulation system allows the study of relations of dynamics of brain activity, cortical excitability, and plasticity induction. Here, we demonstrate a newly developed method to synchronize the timing of brain stimulation with the phase of ongoing EEG oscillations using a real-time data analysis system. This real-time EEG-triggered TMS of the human motor cortex, when TMS is synchronized with the surface EEG negative peak of the sensorimotor µ-alpha (8-14 Hz) rhythm, has shown differential corticospinal excitability and plasticity effects. The utilization of this method suggests that real-time information about the instantaneous brain state can be used for efficacious plasticity induction. Additionally, this approach enables personalized EEG-synchronized brain stimulation which may lead to the development of more effective therapeutic brain stimulation protocols.
TMS is a well-established method for noninvasive brain stimulation and enables the specific modulation of ongoing network dynamics and studies of corticocortical and corticospinal neural pathways with high spatiotemporal precision1. When stimulating the primary motor cortex (M1), the neural response can be quantified as motor evoked potentials (MEPs), as well as TMS-evoked EEG potentials. MEPs can be recorded by electromyography (EMG) of target muscles, and their amplitude reflects corticospinal excitability when stimulating the primary motor cortex2.
Despite the unique potential of noninvasive brain stimulation as a scientific tool to investigate and modulate brain networks in healthy study participants and in patients, TMS studies suffer from large trial-to-trial and intra- and interindividual variability of evoked responses3,4,5. Specifically, in TMS studies of corticospinal excitability and plasticity, MEP responses, as well as induced long-term potentiation (LTP)- or long-term depression (LTD)-like plasticity, exhibit high intrinsic variability, even when the stimulus parameters are carefully controlled3,4. However, evidence from animal studies indicates that the observed variability of responses is not attributable to "random noise" but is instead related to the fluctuating brain states at the time of stimulation6. Accordingly, by combining TMS with EEG in a real-time brain-state-dependent stimulation paradigm (i.e., EEG-triggered TMS), the fluctuating instantaneous brain state can be used to optimize stimulus timing7,8,9,10.
Several studies have related the instantaneous phase of ongoing neural oscillations to neuronal excitability using TMS-compatible EEG systems11,12. Modern EEG amplifiers can handle the large electromagnetic TMS artifacts, and increasingly well-established experimental protocols exist for the combination of EEG with TMS13,14 and the post hoc removal of TMS-related EEG artifacts15,16. While the influence of the prestimulus brain state as assessed by EEG on TMS-evoked responses can be assessed with randomly applied TMS stimuli that are sorted post hoc17,18, the repetitive application of TMS in a predefined brain state requires real-time EEG-triggered TMS11,19.
Here, a custom millisecond-resolution EEG-triggered TMS setup is used to synchronize TMS pulses with a predetermined phase of ongoing brain oscillations11, demonstrating that the negative EEG deflection of the µ-alpha rhythm corresponds to a higher cortical excitability state (leading to larger MEP amplitudes) as compared to the positive EEG deflection8,11,12,20. In this manuscript, we present a method for conducting real-time EEG-triggered TMS protocols to study human brain networks.
All experimental procedures described in the following sections have been approved by the Institutional Ethics Committee following the guidelines of the Declaration of Helsinki, and all participants provided written informed consent prior to study enrollment.
1. Study Participants
2. Setup Preparation
3. Conducting the Experiment
The utilization of the real-time EEG-triggered TMS in the human primary motor cortex reveals differential corticospinal excitability and plasticity effects. Using the protocol described above, real-time EEG-TMS was applied, synchronizing TMS with the ongoing EEG oscillatory phase of the endogenous sensorimotor µ-rhythm in three trigger conditions (positive peak, negative peak, and random phase) in randomized order. A Laplacian EEG montage was used to extract the sensorimotor µ-rhythm by referencing the EEG electrode C3 to the average of four surrounding electrodes (FC1, FC5, CP1, and CP5). Figure 2a shows the average prestimulus EEG signal in the 400 ms before the TMS pulse for the three predefined conditions. The average elicited MEPs recorded from the right-hand muscles are depicted in Figure 2b. These results show that the negative EEG deflection of the µ-rhythm corresponds to a higher cortical excitability state (leading to larger MEP amplitudes) as compared to the positive EEG deflection, with low intertrial variability of the noted corticospinal excitability effects, presented in Figure 2c.
Figure 1: Brain-state-dependent EEG-phase-triggered TMS. Scalp EEG raw data derived from a five channel Laplacian montage centered on the C3 electrode over the left sensorimotor cortex was acquired sample-by-sample by a real-time digital signal processing system. (a) A 500-ms sliding window of EEG data was processed by the algorithm every 2 ms. (b) The signal after band-pass filtering and removal of the edge artifacts. (c) The forward-predicted signal (red trace) based on an autoregressive forecasting model that was calculated from the window of data. The phase at time zero ("right now") was estimated using a Hilbert transform, the spectral power was estimated from the window of data. The TMS stimulator was triggered when a predefined phase and spectral amplitude condition were met. TMS over the left primary motor cortex resulted in MEPs in right-hand muscles recorded with surface EMG. Please click here to view a larger version of this figure.
Figure 2: Data from one exemplary subject who received real-time EEG-triggered TMS over the left M1, targeting the phase of the 10 Hz sensorimotor μ-rhythm. A hundred stimuli each were applied according to three phase-trigger conditions (positive peak, negative peak, and random phase) in combination with a constant minimum 10 Hz spectral power threshold condition, in randomized order, with an intertrial interval of approximately 3 s. A Laplacian EEG montage was used to extract the sensorimotor µ-rhythm by referencing the EEG electrode C3 to the average of four surrounding electrodes (FC1, FC5, CP1, and CP5). (a) Average prestimulus EEG signal in the 400 ms before the TMS pulse for the three conditions. (b) Average EMG trace of the motor evoked potential (MEP) recorded from the right abductor pollicis brevis muscle for each condition. (c) Peak-to-peak MEP amplitude (in microvolts) of each trial over time, per trigger condition. Note that the MEPs are largest in the negative peak condition, smallest in the positive peak condition, and intermediate in the random phase condition. (d) The mean MEP amplitude in each condition is shown with error bars illustrating the standard error of the mean. Note that a participant with a particularly clear effect has been selected for illustration purposes and that this effect size is not representative for the group average. Please click here to view a larger version of this figure.
Brain-state-dependent EEG-triggered TMS is a novel method with unique perspectives with respect to effectiveness and consistency of the ensuing brain-stimulation effects8,9,31. The main advantage of the method is that a functionally relevant endogenous brain state may be specifically targeted to trigger the TMS pulse, inducing potentially less variable and longer-lasting brain responses11. Real-time EEG-triggered repetitive TMS in the negative phase of the sensorimotor µ-rhythm of human M1 (i.e., the state of increased corticospinal excitability, Figure 2) induced significantly stronger LTP-like plasticity (a long-term increase of MEP amplitude) compared to brain-state-independent TMS11,20. In addition to its scientific utility, the application of real-time EEG-TMS to cortical areas, such as the dorsolateral prefrontal cortex (DLPFC), has the potential to increase the effectiveness of current therapeutic brain stimulation protocols.
In this manuscript, we presented the methodological steps for the implementation of real-time EEG-TMS. Fundamental requirements for the conduction of experiments with this method are, first, the use of a TMS-compatible EEG system with a real-time digital out option and, second, the use of real-time signal processing with the implementation of a phase-detection algorithm24, which extracts the desired brain rhythm (e.g., sensorimotor µ-rhythm) from the recorded EEG signal using spatial filters (e.g., C3-centered Laplacian filter) and applies stimulation when preselected conditions (i.e., phase and power of the targeted brain rhythm) are met. The performance and accuracy of the algorithm depend strongly on the SNR of the EEG recording20. Thus, the EEG preparation steps of the protocol are crucial to achieve a high SNR and ensure accurate triggering of the TMS, and a preselection of participants may need to be considered if the respective target oscillation is not sufficiently observable with EEG in every individual. Furthermore, the use of mechanical support arms for the coils and vacuum pillows to immobilize the participant's head is advisable, in order to minimize artifacts due to the varying pressure of the coil on the electrodes.
Regarding the application of the real-time EEG-TMS method in experimental paradigms, the selection of the brain rhythm of interest may vary. Thus, adjustments of the filtering are advisable to facilitate the identification of the targeted brain activity. Recently, several spatial filtering methods have been proposed to optimally extract a functionally relevant brain state (e.g., in channel space19, with current source density13, with local spatial filters11,28, and with individualized filters using, for example, spatial-spectral decomposition29). Yet, so far, no unequivocal method exists to extract from surface EEG signals (sensor space) the real brain-oscillation phase (source space). Future studies that assess the correspondence of surface and source-space signals are warranted to improve the precision of real-time EEG algorithms.
Whereas in this protocol we have focused on the 8-14-Hz sensorimotor µ-rhythm to demonstrate the influence of the instantaneous phase of this oscillation on corticospinal excitability, other oscillations (e.g., beta, theta, or infraslow oscillations) may also play a role. This method can, in principle, be used to target the phase for any oscillation that can be isolated with a sufficient SNR, including multiple superimposed oscillations (e.g., a negative cycle of alpha and a simultaneous positive peak of gamma).
One main limitation of the real-time EEG-TMS experiments is that the spatiotemporal resolution with respect to the brain sources is strongly dependent on artifact occurrence and consistency of the stimulation. Therefore, a critical prerequisite of the protocol is the monitoring of the performance of the algorithm (i.e., ensuring that stimulation occurs upon the detection of neuronal and not artifactual activity throughout the experiment). Furthermore, the utilization of neuronavigation for optimal and consistent positioning of the stimulation coil (especially in experimental paradigms using stimulation sites such as the DLPFC) is helpful for reducing response variability due to variability in coil position. Note also, as a further limitation, that specifically selected and configured EEG/EMG, TMS, and real-time processing devices are required, along with experience in preparing and conducting the experiments in such a way as to minimize external sources of response variability that may mask the effect of instantaneous brain-state.
In conclusion, we demonstrated a standard protocol for conducting real-time EEG-TMS experiments and introduced a novel method for utilizing the endogenous brain states of interest (i.e., preselected phases and power of a targeted endogenous brain oscillation) to trigger brain stimulation. Further research using the real-time EEG-TMS method will allow methodological improvements and facilitate the development of effective protocols for the study and modulation of human brain networks.
The authors have nothing to disclose.
C.Z. acknowledges support from the Clinician Scientist Program of the Faculty of Medicine, University of Tübingen. U.Z. acknowledges support from the German Research Foundation (grant ZI 542/7-1). T.O.B. acknowledges support from the German Research Foundation (grant BE 6091/2-1). J.O.N. acknowledges support from the Academy of Finland (Decisions No. 294625 and 306845). The authors acknowledge support by the Open Access Publishing Fund of the University of Tübingen.
EEG and EMG recording systems | |||
EEG/EMG amplifier | NeurOne with Real-time Digital Out, Bittium Biosignals Ltd., Finland | ||
TMS device | MAG & More Research 100, MAG & More GmbH, Munich, Germany | ||
Software | Mathworks Simulink Real-Time (Mathworks Ltd, USA) | ||
Stereo infrared camera neuronavigation system including reflective head tracker, pointer tool, head tracker | |||
Experimental control PC that is connected to the EEG system, the TMS stimulator, the real-time device and the neuronavigation system | |||
EEG electodes, EMG electrodes, syringes, abrasive and conductive gel | |||
Plastic wrap and adhesive tape |