Phase and power modulations on the amplitude of TMS-induced motor evoked potentials

Lukas Schilberg; Sanne Ten Oever; Teresa Schuhmann; Alexander T Sack

doi:10.1371/journal.pone.0255815

. 2021 Sep 16;16(9):e0255815. doi: 10.1371/journal.pone.0255815

Phase and power modulations on the amplitude of TMS-induced motor evoked potentials

Lukas Schilberg ^1,^#, Sanne Ten Oever ^1,^2,^3,^#, Teresa Schuhmann ^1,^4,⁵, Alexander T Sack ^1,^4,^5,^6,^*

Editor: Luigi Cattaneo⁷

PMCID: PMC8445484 PMID: 34529682

Abstract

The evaluation of transcranial magnetic stimulation (TMS)-induced motor evoked potentials (MEPs) promises valuable information about fundamental brain related mechanisms and may serve as a diagnostic tool for clinical monitoring of therapeutic progress or surgery procedures. However, reports about spontaneous fluctuations of MEP amplitudes causing high intra-individual variability have led to increased concerns about the reliability of this measure. One possible cause for high variability of MEPs could be neuronal oscillatory activity, which reflects fluctuations of membrane potentials that systematically increase and decrease the excitability of neuronal networks. Here, we investigate the dependence of MEP amplitude on oscillation power and phase by combining the application of single pulse TMS over the primary motor cortex with concurrent recordings of electromyography and electroencephalography. Our results show that MEP amplitude is correlated to alpha phase, alpha power as well as beta phase. These findings may help explain corticospinal excitability fluctuations by highlighting the modulatory effect of alpha and beta phase on MEPs. In the future, controlling for such a causal relationship may allow for the development of new protocols, improve this method as a (diagnostic) tool and increase the specificity and efficacy of general TMS applications.

Introduction

Transcranial magnetic stimulation (TMS) allows for a non-invasive investigation of corticospinal excitability. TMS-induced motor evoked potentials (MEPs) represent the excitability of the corticospinal tract, at which nerve fibers connect the cerebral motor cortex with the spinal cord for voluntary movement execution. Corticospinal excitability has become a frequently examined neurophysiological parameter in fundamental research as well as clinical studies [1, 2]. To serve as a valid and meaningful assessment tool that allows for veridical inferences about corticospinal excitability, TMS-induced MEP measures need to be stable and reliable. Whether they actually are reliable, however, has been subject for controversial debate, as concerns about high variability of trial-by-trial MEP amplitude have been a long known phenomenon [3–5] and specific biological and methodological factors contributing to the response variability of corticospinal excitability have been highlighted [6, 7].

A physiological cause for the variability of MEPs could be the influence of the naturally fluctuating state of neuronal activity in the brain at the time and location of assessment. Specifically, neuronal oscillations in the alpha and beta frequency range have been linked to sensorimotor processing [8–11]. Therefore, uncontrolled states of cortical excitability at the time and location of TMS could cause the reported variability of MEPs. Previous studies have demonstrated diverse associations of corticospinal excitability with preceding oscillation frequency power and phase. Reported findings include both the existence and absence of relationships between MEP amplitude and alpha or beta frequency power [12–18], phase [19–22] and phase-power interaction [23]. Most of these findings suggest that the dynamic state of the brain may influence the investigation of corticospinal excitability mechanisms with TMS. However, experimental paradigms and procedures differ considerably, which could explain the pronounced diversity of the reported results.

In this study, we aim to provide further insight into the relationship of the power and phase of alpha and beta oscillations with MEP amplitude as a representative measure of corticospinal excitability. For that we apply TMS over the primary motor cortex (M1) at suprathreshold intensities (120% of resting motor threshold (rMT)), while recording electroencephalography from the cortical area under stimulation. Moreover, we examine whether phase correlation with large MEPs cluster on consistent phases over participants. We hypothesize that the amplitude of induced MEPs is correlated to the power and particular phases of alpha and beta frequencies. We do not limit the phase analyses to either the peak or trough of the particular frequencies, but provide an elaborate investigation of the relationship between all frequency dependent phase angles and elevated cortical excitability.

Materials and methods

The EEG and EMG data analyzed for this study are part of a larger TMS study on the reliability of intermittent theta burst stimulation (iTBS)-induced neuroplasticity mechanisms that has been analyzed and published separately [24]. This larger study consisted of two identical experimental sessions in which iTBS was applied to participants at rest before MEP changes to baseline were measured for sixty minutes and one control visit in which iTBS was replaced with sham-iTBS. The data for the current study was collected during that single control session, which included one block of sham-iTBS, but no other form of active TMS in addition to the experimental single TMS pulses included in the analysis. Participants did not perform any tasks during the experiment. The study was approved by the Ethics Committee of the Faculty of Psychology and Neuroscience at Maastricht University (number: 04_06_2013).

Participants

Twenty-seven healthy participants (16 female; mean age (SD): 24.1 (3) years) were included in the study. All participants were right handed and of healthy cognition (Mini-Mental State Examination scores between 28 and 30). Participants were financially compensated for their participation.

TMS application

TMS was applied with a MagPro X100 stimulator (MagVenture A/S, Farum, Denmark) and a figure-of-eight coil (MC-B70). Pulses were biphasic with an anterior-posterior followed by posterior-anterior current direction in the brain. The coil was placed tangentially to the scalp on top of the EEG cap (Easycap, BrainProducts, Gilching, Germany) with the handle in posterior direction orienting 45° away from the midline. Neuronavigation (Brain Voyager, Brain Innovation B.V., Maastricht, The Netherlands) was used to ensure stability of target point stimulation throughout the session. A single pulse based cortical mapping procedure was applied over left M1 to determine the hotspot for TMS-induced muscle twitches of the FDI muscle from the dominant right hand. Single pulse TMS intensity was set at 120% of the individual rMT, defined as the lowest intensity necessary to induce an MEP with a greater peak-to-peak amplitude than 0.05 mV in 50% of the trials (five out of ten). TMS was applied manually with a minimum of seven seconds between single pulses.

EMG and EEG recording

Electromyography (EMG) signals were recorded with a Powerlab 4/34 connected to a BioAmp system (ADInstruments, Oxford, UK). EMG signals were amplified, sampled (4k/s), band-pass filtered (20-2000Hz), digitized and saved for online inspection and offline analysis with LabChart software (ADInstruments, Oxford, UK). Disposable adhesive surface electrodes (Plaquette^TM, Technomed Europe) were attached in a belly-tendon montage over the right FDI muscle. Resting EMG signals were continuously observed to keep the peak-to-peak amplitude below 0.05 mV.

EEG was recorded with BrainAmp MR plus EEG amplifiers and BrainVision recorder (BrainProducts, Gilching, Germany). A 30-channel TMS compatible EEG-cap with Ag/AgCl electrodes (Easycap, BrainProducts, Gilching, Germany) was used with equally distributed electrode placement over the whole head (FP1, FP2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8, P7, P8, Fz, Cz, Pz, FC1, FC2, CP1, CP2, FC5, FC6, CP5, CP6, FT9, FT10, and left mastoid (A1)). Four additional channels for horizontal and vertical eye movement recordings were placed horizontally on the outside of both eyes and vertically below and above the left eye. The reference electrode was placed on the right mastoid (A2) and the ground electrode at AFz (10–20 EEG system). Data was recorded with a sampling rate of 2500Hz, a hardware bandpass filter of 0.1 – 1000Hz, a software low pass filter of 500Hz. Impedance was maintained under 10 kilo-ohm.

Procedure

Before the experiment, all participants provided written informed consent. Prior to the experimental session, participants underwent a thorough safety and eligibility screening for participating in a non-invasive brain stimulation study. After a participant was screened, EEG, EMG and neuronavigation were prepared, the individual FDI hotspot was mapped and MT was determined. In addition to the single TMS pulses necessary for the preparatory procedures, participants received stimulation during eight blocks that consisted of thirty experimental single TMS pulses at 120% of individual rMT over their FDI hotspot. Single pulses within each block were administered at jittered inter-pulse-intervals of at least seven seconds.

EMG and EEG preprocessing

EMG data were preprocessed with LabChart. Peak-to-peak MEPs and pre-pulse resting peak-to-peak EMG amplitude values were exported to Microsoft Excel. EEG data were stored with BrainVision recorder. Further analyses were performed with Matlab (MathWorks, 2017a), the FieldTrip toolbox [25], and circular statistics toolbox [26]. All trials in which no MEP was elicited were removed from the analyses (peak-to-peak MEP < 0.05 mV). To prevent any effects of pre-TMS muscle contraction on MEP amplitudes, all single trials with a pre-pulse peak-to-peak resting EMG amplitude that were further than 3 times away from the median absolute distance of every point to the median for a time window of 100ms prior to TMS, were excluded from the analysis [27]. The same criterium was held for MEP outliers and for power trials in the power analyses.

EEG data was demeaned and re-referenced to the average of all channels. Initial epoching was between -4 to 4 seconds around TMS pulse onset. Bad EEG channels as measured through visual inspection were interpolated with neighboring channels within four centimeter distance. Then, EEG was re-epoched to a time window from -2 seconds until TMS-pulse onset and resampled to 256 Hz. Eye blink correction was performed using the function ‘scrls_regression’ within the eeglab plugin AAR (filter order 3; forgetting factor 0.999; sigma 0.01; precision 50; [28]). The main analyses was repeated using a current source density approach using the CSDtoolbox [29].

The relationship between EEG oscillation power and phase with MEP amplitude

We extracted the logarithm of the power and phase for frequencies ranging between 2Hz to 30Hz in steps of 0.5Hz [30, 31]. A Fast Fourier transform was performed using Hanning tapers extracted over three oscillatory cycles before the TMS pulse onset (leading to different time windows and frequency resolution for different frequencies). We verified that with this analysis none of the participants displayed a phase bias (Rayleigh tests over all trials p > 0.05). For the analysis of oscillation phase, we calculated a circular-linear correlation between phase and MEP amplitude. For the analysis of power, we performed a Pearson linear correlation between power and MEP amplitude. To extract chance correlations, we calculated for each participant an estimated chance correlation by performing 1000 permutations of the correlation calculation using permuted labels for the MEP amplitudes. The number of trials included for further analyses were on average 205.44 (SD = 24.67) for the power analysis and 191.74 (SD = 24.33) for the phase analysis per participant. To get an estimation of the phase effect over time, we also extracted the Hilbert transform over the data (after applying a two-pass Butterworth filter of 8–12 Hz) and repeated the phase and power correlation analysis over time (up to -1.5 seconds prior to the TMS pulse).

The region of interest for all analyses was defined as C3 and all adjacent central electrodes (FC1, FC5, CP1, CP5) ipsilateral to the stimulation site. Correlation values at each of these channels were averaged for the initial analysis. We created a null distribution repeating the analysis using permuted labels. This null distribution reflects the expected average correlation based on chance. To statistically compare our observed values to the chance values, we compared the median of the permuted labels with the observed correlation values for power and phase separately at all frequency bins of 0.5Hz between 2Hz and 30Hz. To correct for multiple comparisons, we performed cluster statistics (clusterstatistics = ‘maxsum’, alpha = 0.05, clusteralpha = 0.05, n = 1000). Data points outside of the 95^th percentile of the null distribution of the monte-carlo simulation were entered in the second level cluster analysis. For the phase analysis, we report the one-sided p-value as circular correlations can only be positive. For the power analysis we report the corrected p-values. We deliberately avoid referring to μ-rhythms for the discussion of neuronal oscillations within the alpha frequency band that were measured over sensorimotor regions, because clear independence from oscillations originating in the parieto-occipital regions is not ascertained. We repeated this analysis for a different set of more posterior electrodes (O1, P3, P7, Pz, T7) to investigate the spatial specificity of this effect.

Next, we investigated whether the phase of maximal MEP size was consistent across participants. Therefore, we extracted for each participant the mean phase of the 50% highest MEPs (for all five EEG channels separately) at the alpha frequency with the highest correlation for that individual participant (limiting frequencies to frequencies within the significant cluster). Then, we performed for the circular average of the five channels a Rayleigh test to investigate the phase consistency over participants.

EEG power prior to the TMS pulse might be correlated to the activity of the EMG signal. To test this, we correlated the pre-pulse EMG activity (including all trials) with the EEG power for all tested frequency bins. Then we repeated the main power and phase analyses correcting for this correlation. For the power-MEP relation we took the residuals of the pre-pulse EMG activity and power correlation and correlated the residual power with the MEP size. For the phase-MEP relation we took the residuals of the pre-pulse EMG activity and MEP size and performed a circular-linear correlation between the phase and the residual-MEP size. Statistical tests were performed in the same manner as the main analyses.

For a final analysis we split the data in low and high power trials (median split) for each frequency bin separately and repeated the circular-linear correlations described above to investigate if the phase modulation was dependent on the oscillatory power [32]. Phase values for all figures represent the phase expected at time point ‘0’ in which ‘0’ corresponds to the peak and +/- pi to the trough.

Results

Effect of neuronal oscillation power on MEP amplitude

Averaged Pearson linear correlations from EEG channels C3, FC1, FC5, CP1 and CP5 were performed between logarithmic power and both MEP amplitudes and permutated MEP amplitudes for frequencies between 2Hz and 30Hz in steps of 0.5Hz. We found a significant cluster (Fig 1A and 1B) including the frequency 10.4Hz to 16.7Hz (p = 0.028, cluster-statistics = 0.3624). The reported significant correlation was 0.0297 (r² = 0.0009). When repeating the analyses for posterior channels we did not find a significant effect (no cluster found).

Fig 1 — A) Mean power averaged from the EEG channels C3, FC1, FC5, CP1 and CP5. The inset shows the (unfiltered) ERPs for four different equally spaced phase bins in channel CP1 for alpha. This figure demonstrates that there is no phase bias in our estimation. B) Averaged power-MEP correlation for the five selected channels (blue) and the average permutation (black). C) Average circular-linear phase-MEP correlation for the five selected channels (blue) and the related permutation (black). Red dots indicate significance at alpha = 0.05 (cluster corrected). D) Phase-MEP relation extracting the instantaneous alpha phase via the Hilbert transform displaying the time course of the effect. Color coding identical to C). E) Phase correlation topography based on the Hilbert analysis. The strongest correlation was present at CP1. All shaded areas indicate the standard error of the mean. F) and G) are identical to D) and E) but for the beta frequency range (15–25 Hz).

A-priori, we were interested in both alpha and beta effects, therefore we also performed an RM ANOVA directly testing for a possible interaction between alpha and beta power modulation. The RM ANOVA was a 2*2 ANOVA with the factors data (observed vs median permuted data) and frequency (alpha (8–12 Hz) and beta (15–25 Hz)). No interaction was found (F(1,26) = 1.45. p = 0.239). Since there was no interaction, we could not provide any evidence that the alpha power effect is be stronger than the beta power effect.

Effect of neuronal oscillations phase on MEP amplitude

To investigate the relationship between neuronal oscillation phase and MEP amplitude, we compared correlations between phase of oscillation frequency and both measured and permuted MEP amplitude values for all frequencies between 2Hz and 30Hz. Correlations differed significantly for alpha frequencies ranging from 6.5Hz to 11.8Hz (p = 0.003). Statistics were corrected for multiple comparisons using cluster methods (Cluster-statistics = 0.1625; Fig 1C). The reported significant correlation was 0.1004 (r² = 0.0101). No effects were found when repeating the analyses using the CSD transform (p > 0.05). When repeated for posterior channels we did not find a significant effect (p > 0.05).

Also for phase we performed a RM ANOVA analysis to investigate alpha-beta interactions. The interaction showed a trend (F(1,26) = 3.917, p = 0.059). This suggests that the effect of alpha is stronger, but that this cannot be fully corroborated with the statistic as it was borderline significant. Consistent with analyses in the main script, alpha showed a significant effect (t(1,26) = 3.57, p = 0.001), and beta did not show any effect (p>0.1).

Inter-individual phase consistency related to high MEP amplitude

The phase consistency of the averaged individual dominant alpha frequency phase related to the 50% highest TMS-induced MEP amplitudes was analyzed for the five central EEG channels ipsilateral to the stimulation site. We did not find any phase consistency over participants (p > 0.1). We would like to note that using a different outlier criterion (standard deviations instead of a distance measure) did lead to significant phase consistency. However, as this was not consistent across outlier criteria we do not believe this to be a robust effect.

Time course of alpha phase and power modulation

The time course extracted by the angle of the Hilbert transform of the filtered alpha data showed significant correlations between alpha phase and MEP amplitude (Fig 1D and 1E). Two clusters were identified (cluster 1: clusterstat = 0.887, -0.25–0 sec, p = 0.003; cluster 2: clusterstat = 0.673, -0.539 - -0.2773 sec, p = 0.018). The abrupt drop of phase correlation just before TMS onset is a consequence of edge effects of the filter (as the data was cut at zero). Still, it is evident that there is an increase in the correlation prior to the TMS pulse onset. We repeated the analyses for alpha power, we found one significant cluster (clusterstat = 2.185, -0.637 - -0.359 sec, p = 0.044). When repeating the analyses for posterior channels, we did not find a significant effect (p > 0.05 for both phase and power).

Post-hoc analysis on time course of beta phase and power modulation

Besides using FFTs to estimate phase at stimulus onset, instantaneous phase has also often been estimated using the Hilbert transform. Therefore, as a post-hoc analyses, we analyzed the beta frequency window using the Hilbert transform to extract a time course around the beta phase modulation. For this analysis we found significant correlations between beta phase and MEP amplitude (Fig 1F and 1G). Four clusters were identified (cluster 1: clusterstat = 0.5045, -0.109–0 sec, p = 0.003; cluster 2: clusterstat = 0.488, -1.34 - -1.23 sec, p = 0.003; cluster 3: clusterstat = 0.361, -1.020 - -0.910 sec, p = 0.010; cluster 4: clusterstat = 0.291, -0.215 - -0.145 sec, p = 0.0270). We found no effect for beta power (all p > 0.1). To investigate the frequency specificity of the Hilbert phase analyses, we additionally ran the Hilbert analysis on filtered data at frequency ranges in which we did not expect any phase modulation (30–50 Hz, i.e. the theta band). No significant clusters were found (p > 0.05). When repeating the analyses for posterior channels we did not find a significant effect (p > 0.05 for both phase and power).

Correction for pre-pulse EMG activity

The correlation between pre-pulse EMG activity and EEG showed a negative correlation for low frequencies (Fig 2A). This correlation did not survive correction for multiple comparison, but was significant uncorrected between 2.99 Hz (p = 0.036) and 9.37 Hz (p = 0.035). Note that not all datapoints within this frequency range were significant. When we corrected for the correlation, we only found small differences. For the power correlation, no cluster was found, but uncorrected p-values did show an effect, suggesting a trend (Fig 2B; uncorrected p-values of p = 0.044). For the phase-MEP relation, we found a significant cluster between 8.93 and 11.82 Hz (p = 0.032, clusterstatistics = 0.0711; Fig 2C).

Phase correlation for high alpha power

Previous findings have shown stronger phase effects when power values are high [32]. To investigate if this was also true in our sample, we extracted the phase modulation again, but splitting the data for low and high power trials (median split for each frequency bin). We found a significant alpha phase modulation for the high (p = 0.03), but not the low power trials (p > 0.05). Comparing the high and low power effect directly (averaging over the significant high frequency cluster) revealed a significant difference between the high and low power trials (t(26) = 2.299, p = 0.030 (one-sided)). For the Hilbert transform, we repeated the same analyses averaging across the cluster closest to TMS pulse onset. Here, we did not find a significant difference between low and high power for either alpha or beta power (p > 0.1).

Discussion

We tested whether corticospinal excitability is related to oscillations in the sensorimotor cortex. We report that the amplitude of MEPs induced by TMS at suprathreshold intensity (120% MT) is dependent on the instantaneous phase of ongoing alpha and beta frequency oscillations at the time and site of stimulation. Moreover, the alpha power also correlated with MEP amplitudes. In contrast to our expectations, we did not find any phase consistency linked to the 50% largest MEPs across participants.

While within participant there was a systematic relation between alpha phase and MEP, across participants we did not find any evidence of phase consistency. This finding is inconsistent with previous reports showing that the peak or trough of the signal should reflect the most excitable phase of the oscillation. Recent reports have emphasized the modulatory effect of μ-alpha phase on corticospinal excitability by demonstrating that larger MEPs are evoked during troughs compared with peaks of the μ-alpha waves [21, 22, 33, 34]. To address the question whether the phasic modulation of corticospinal excitability by μ-alpha reflects symmetric or asymmetric pulse facilitation or inhibition, Bergmann and colleagues [19] used real-time EEG-triggered single-pulse TMS to measure corticospinal excitability and paired-pulse TMS to assess short-latency intracortical inhibition (SICI). They found that MEP amplitudes were facilitated during μ-alpha troughs and rising slopes, but not during peaks and falling slopes. In addition, μ-alpha power and phase were not linked to intracortical inhibition. Therefore, sensorimotor alpha was related to pulsed facilitation, but not inhibition, of motor cortex excitability. Assuming that the trough of the oscillation wave measured at the scalp reflects the strongest local neuronal depolarization, facilitated MEP responses linked to the rising slope of sensorimotor alpha oscillations could reflect the responsiveness of the targeted neuronal ensembles to a synaptic input following rhythmic inhibition after the last neuronal population spike [35]. It is important to note here that the exact generators of EEG are unknown and consist of a summation of currents from many directions [36] and only direct in-vivo recordings of the local excitability can provide evidence for a link to depolarization of the underlying neuronal generators [37]. It is therefore possible that scalp measurements of phase are not consistent across participants (see e.g. [38] for a study which also shows on inconsistent phase estimations across participants in an EEG study). However, while we do not report phase consistency, previous studies stimulating at a specific phase do find effects [19, 39].

Based on our results and on previous findings, it appears that the sensorimotor alpha oscillation provides a cyclic modulation of excitability. Additionally, we found an effect of beta phase on MEP amplitudes. In a previous study from our lab, we found the rising slope phase relative to transcranial alternating current stimulation (tACS) in the beta frequency range to yield MEPs with the largest amplitudes [18]. This finding has been replicated by others [35]. It is interesting that the FFT analyses did not result in a significant beta phase effect, but the Hilbert analyses did. This could be a consequence of the general wider spread of individual beta frequency versus alpha peak [40] as the Hilbert analysis is robust against frequency variations.

We found a correlation between MEP amplitude and alpha power (Fig 1B). There have been conflicting results on whether alpha power and MEP size relate, some do report an effect [12, 13, 15–17] and other do not [41, 42]. These divergent findings reported in the literature show that a more mechanistic explanation about the exact origin of the measured μ/alpha generators is needed to understand the complex pattern that leads to spontaneous MEP modulations.

Several studies have investigated the relationship between neuronal oscillations and corticospinal excitability with varying methodological parameters, such as TMS intensity, TMS pulse waveform, inter pulse intervals, number of TMS pulses, targeted hand muscle, muscle contraction, channels for EEG recording or data analysis. One parameter that may be of particular interest for the discussion of the results presented here is TMS intensity. In contrast to the suprathreshold stimulation intensity (120% rMT) we applied, many previous studies applied TMS at individual MT intensity (100%; [12, 14, 15, 42], at low suprathreshold intensity (110% MT; [16, 17]) or at a pre-defined stimulation intensity that would lead to reliable MEPs of 1mV amplitude [13]. A systematical comparison of effects at different stimulation intensities found stronger effects for weaker intensities [22]. Note that in this study, the analysis focused on comparing peak versus trough stimulations, so it is possible that the optimal stimulation phase could be more consistent at lower intensities. We chose to apply TMS at suprathreshold intensities, as this ensures the induction of action potentials in the pyramidal neurons involved in the elicitation of the MEP and it allows for the assessment of facilitatory or inhibitory neuronal oscillation effects on successfully depolarized pyramidal neurons in form of increased or reduced MEP amplitude. Of course, with this approach a large neuronal extend of neighboring regions will also be stimulated this way, which can exhibit indirect effects on the MEP amplitude. In contrast, threshold stimulation limits the stimulation extend. At MT stimulation one can explore whether successful TMS induction of MEPs is dependent on ongoing neuronal oscillations. This is different to the investigation of the influence of neuronal oscillations on the actual magnitude of corticospinal excitability. One major disadvantage of investigating stimulation effects at MT is that many trials (by definition 50%) elicit no MEP. On these trials oscillatory modulations acting on cortical neurons are overlooked as only the downstream motor output is investigated. This can induce a non-linearity in the result that is not present in the cortex.

In our study we could only demonstrate a beta phase relationship when applying a Hilbert transform and not with the FFT approach. This adds to previous literature reporting differences in frequency relationships with phase modulation [16, 33, 42–45]. These differences could be explained by the strength of oscillatory power during TMS stimulation. In our study, TMS was applied during self-controlled muscle relaxation. Elevated levels of EEG beta activity over the motor and somatosensory cortex are usually linked to motor performance. However, the power of the ongoing resting EEG beta activity measured here is relatively low (Fig 1A), because participants are not performing any active motor task. As such, revealing any ongoing beta phase effect is challenging as phase is difficult to estimate, possibly leading to false negatives. Indeed, in a study by Torrecillos and colleagues [44], MEP size was modulated by beta phase when restricting TMS triggering to trials with high beta power. This is similar to other online-triggered TMS studies in which TMS is often only applied when power reaches a certain threshold [19, 33]. tACS does induce phase modulations at beta ranges [18, 39, 46], likely because those oscillatory beta patterns are induced by the stimulation itself. Other EEG studies do report beta-phase effects when using different TMS stimulation parameters, such as threshold stimulation [16, 42]. For future studies, it is critical to verify the exact mechanisms behind this variation: Is the absence of phase modulation during low power a consequence of the inability to reliably estimate oscillatory phase or a physiological change in how phase modulates neuronal responses? Mechanistic explanations of varying TMS parameters are lacking, as well as a good control on how the state of the circuitry might have a direct effect on the reported frequency ranges.

Previous work on the variability of MEP responses have also focused on the level of contraction of relevant muscle groups at the time of TMS stimulation [47]. This literature reported more variability at lower muscle contraction levels [47–49] and overall stronger MEPs at higher muscle pre-contraction [50]. Here, we showed that the contraction variations did not vary consistently in the frequency ranges of the significant effects. Moreover, correcting between EEG power and pre-pulse EMG activity did also not change our effects. In other words, any pre-pulse EMG activity that is directly related to EEG power cannot explain our effect.

In the current study, the individually dominant frequencies were measured over sensorimotor areas. The location of the alpha effect was directly underlying the stimulation site, which prompts us to cautiously speculate that we are indeed extracting the influence of underlying excitability changes on the μ-rhythms known to be critical for motor processing [51]. The main results presented here concern a common reference approach in which data is re-referenced to the average of all EEG channels. The oscillatory activity measured in this way is more sensitive to global activity patterns and less sensitive to local patterns. These patterns are more specifically picked up by spatial transforms such as the CSD transforms. Indeed, a common way to online-triggered TMS on the motor system is to use such a Hjorth or CSD transformation [19, 22, 33]. It is therefore puzzling that we do not find a phase modulation in our FFT analyses when applying a CSD transformation. However, we did not find alpha phase effects for more posterior and occipital channels, which are the core generators of posterior alpha. It is therefore unlikely that posterior alpha is driving the effect. The discrepancy between our results and previous reports maybe due to the specifics of the stimulation (stimulation at higher alpha power values for the online-triggered stimulation). Although higher local specificity of the oscillatory activity might seem as a benefit, the downside of this transformation is that any activity patterns shared over a larger brain area could be dampened, missing the more global effects as we report here.

Conclusion

We show that MEP amplitude is dependent on the phase of dominant sensorimotor alpha and beta frequency oscillations and alpha power at the time and site of TMS. While our results support the notion for a relationship between particular neuronal oscillation patterns and TMS induced MEPs, more research is needed for an accurate understanding of how frequency phase and power are related to cortical excitability. This is crucial considering that the reported correlations are very low and therefore not clinically relevant at this stage. Eventually, we will be able to incorporate these neurophysiological measures into TMS protocols for more specific applications and more dependable outcome measures. Advanced knowledge about the functional mechanisms of cortical and corticospinal excitability will help to develop efficient closed-loop protocols combining online neurophysiological measures with TMS [33, 52]. Ultimately, such information based TMS protocols will have great potential to provide reliable and well controlled results for research purposes and they could be further developed to serve as specified monitoring and diagnostic tools or allow for individualized treatment applications in clinical settings.

Supporting information

S1 Dataset

(ZIP)

Click here for additional data file.^{(40.9MB, zip)}

Data Availability

The data underlying this study are available within the manuscript and Supporting Information files and from the Dataverse repository (https://doi.org/10.34894/KW3YKN).

Funding Statement

This work was funded by the research program NWO VICI “The rhythms of cognition: Using simultaneous TMS-fMRI-EEG to integrate brain-wide network and oscillatory communication mechanisms for enhancing human cognition” (awarded to ATS) with project number 453-15-008, which is financed by the Netherlands Organization for Scientific Research (NWO).

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PLoS One. doi: 10.1371/journal.pone.0255815.r001

Decision Letter 0

Luigi Cattaneo

7 Apr 2021

PONE-D-21-07587

Oscillatory phase modulates the amplitude of TMS-induced motor evoked potentials

PLOS ONE

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Reviewer #1: The authors performed an experiment with the aim to associate power and phase of 2-30 Hz frequencies of the EEG recordings, in a pre-TMS window, with the amplitude of the evoked-TMS MEPs. They found that a significant correlation exists only between phase and MEP amplitude at alpha and beta frequencies. This correlation survives the exclusion of pre-TMS EMG background contraction, They aimed to show that a specific range of phases (correlating with MEP amplitude), was consistent across the participants, and eventually that phase and power in the alpha range interact, with only high power alpha showing significant correlation with MEPs amplitude.

While this is an interesting paper and topic, the manuscript presents more than one methodological concern and I would appreciate if the authors clarify some choices

1) It is not clear what the task of the participants was. Moreover, it would be more transparent (and in general easier for the reader) if the authors (briefly) described the experiment within which the sessions described in the present manuscript were recorded (other than just pointing the reader to the references).

2) line 114 pg 6: I believe that relying on SD to exclude trials is a sub-optimal approach. From Jones 2019, "sample mean and variance are easily distorted by extreme values, meaning that more distant outliers may ‘mask’ lesser ones" (Jones 2019, https://doi.org/10.3758/s13414-019-01726-3). Other more robust measures, such as "Sn", that can be applied also to skewed distributions (see the reference which offers also a MATLAB function for this purpose).

Moreover, is there a reason for excluding the lowest pre-TMS contractions, assuming that the authors are looking for neurophysiological relationships while participants are at rest?

2a) line 119: again, assuming that the authors are looking for a relationship while participants are at rest, it is reasonable that trials showing extreme pre-TMS contractions were excluded. However, I do not see the reason of excluding high amplitude MEPs. I understand removing trials in which no MEP is detected, because it might add noise to the data (maybe in that trial the coil has been moved from the hot spot, maybe it was not properly tilted and tangentially held for example), not knowing if the peak-to-peak measures are actually come from very small MEPs or not. Ideally the best solution would be to use a more robust correlation metric such as the spearman correlation without excluding data (at least for the power analyses). I would like the authors to explain the rationale behind their choice.

3) line 130 , pg 7: it is not entirely clear to me why the logarithm of the power has been chosen as a measure of EEG frequency amplitude. One could have chosen the amplitude, the power, or other transformations. This choice implies that the relationship between log(power EEG freqs) vs MEP amplitude is linear. Elaborate on this point please.

Again, if no linear relationship is reasonably assumed, a spearman correlation should be considered: indeed, given the assumed monotonic relationship between EEG power and MEP amplitudes, should provide a positive/negative correlation even if the the relationship is not linear.

4) line 145 pg 8: the authors have chosen to average 5 channels around C3. This should be consistent throughout the analyses: either they chose the average of the channels or each channel should be analysed separately (or they can be taken into consideration in a cluster based statistics which includes also the position of the channels, see fieldtrip website). Specifically I am referring to line 159 about the consistency across participants, where the channels have been treated separately and corrected by Bonferroni. Elaborate on this issue please if there is a rationale behind this choice, otherwise adopt only one criterion.

5) In general however, given the EEG volume conduction problems, there is no sufficient evidence in my opinion that effects found arise from central sources. Since the authors have chosen to average 5 "central" electrodes, they should compare the effects not only against a permutation, but also against other 5 occipito-parietal sensors on the same side of the stimulation. Indeed, as acknowledged by authors, a prominent alpha rhythm is present also in more posterior brain sites. Even for Beta analyses, such a comparison would provide a better evidence that the correlation effects do not arise caused by occipito-parietal.

6) line 147: why was the median used for the permuted data and the average for the actual data?

7) line 150 pg 8: this is just a check of whether the authors have used the error 1 threshold correctly, given that fieldtrip might be misleading concerning this issue:

https://www.fieldtriptoolbox.org/faq/why_should_i_use_the_cfg.correcttail_option_when_using_statistics_montecarlo/

(the authors might specify in the manuscript the cfg.correcttail used, so that the solution adopted is more transparent)

8) line 189 pg 9: the authors state that CP1 has the strongest correlation. Is this comparison tested statistically?

9) line 196: again, I do not see the reason for treating the channels separately when they have been averaged for the previous power/phase-MEP analyses.

10) line 323: beta power is relatively low with respect to what? in this case the hilbert should be performed again separately for high and low power data? (the alpha as well, given the obtained results between power and phase)

11) can the authors interpret the time-course of the correlation between the hilbert of the beta phases and MEP amplitude? for example, a significant correlation has been found more than one second before TMS pulse; is it something the authors expected? Also the sudden jumping of the correlations looks peculiar, especially considering the very short significant period just before the TMS pulse (in contrast to the alpha hilbert, which shows a longer period of correlation before the TMS).

12) Figure 1A: it is not clear what the inset is, please explain more in detail. Moreover, the inset cannot be apporciated given the resolution of the figure (see comment 13)

13) Figure 1B: it looks like the image is lacking the error bar for the permuted data (grey). (the image on the PDF is very blurry, likely not authors' fault)

14) Please in figure 1 F and H indicate the 5 target sensors position

Reviewer #2: In their manuscript (MS), Schilberg and colleagues employ single pulse Transcranial Magnetic Stimulation (TMS) over the primary motor cortex (M1), together with simultaneous Electroencephalography (EEG) and Electromyography (EMG) to show that the amplitude of motor evoked potentials (MEPs) correlates with the phase of the ongoing EEG activity in the alpha and beta frequency band.

The present MS is not very novel per se, as the state-dependency of MEPs and TEPs (but also the occurrence of phosphines when stimulation V1 see for example Romei J Neurosc 2010) is a topic of debate since early nineties (Kiers Clin Neurophys 1993) and has been tackled by many different authors in the last 30 years (some of them cited in the MS, ref from 8 to 23). Nevertheless, this study is methodologically solid and it reports few new results that might be useful for the scientific community.

I have just few major and minor concerns that, in my opinion, need to be addressed in order to guarantee a better interpretation of the results. I have detailed my comments below:

MAJORS

1) The authors focus on alpha and beta rhythms because “alpha and beta frequency range have been linked to sensorimotor processing (8-11)”. They also report previous works showing “…diverse associations of corticospinal excitability with preceding oscillation frequency power and phase…. Reported findings include both the existence and absence of relationships between MEP amplitude and alpha or beta frequency power (12-18), phase (19-22) and phase-power interaction (23)”. Many of the reported works refer to mu-band that, however, is by definition “multispectral” (Tihonen 1998). This means that it includes frequencies in the alpha (8-12Hz) and beta (12.5-30Hz), which may overlap and interact. In line with this, the authors correctly analysed separately alpha and beta. I think that it would be interesting to see whether there is any statistical interactions between alpha and beta, both in power and in phase (e.g. multivariate regression analysis?).

2) When analyzing the inter-individual phase consistency related to high MEP amplitude the authors found significant results only in CP1 and not in the other considered channels (after corrections). Even if the number of EEG contact is limited, perhaps, moving from the voltage space to the source space (MNI could be ok, if the MRI of single subjects are not available) could lead to more solid results.

3) When considering the inter-individual phase consistency related to high MEP amplitude the authors focused on the 50% amplitude of the MEP. Please justify this a priori selection.

4) In Figure 1F and H the topographies are not very clear. Perhaps I would saturate the colour-scale and/or I would set to zero the non-significant values.

5) - This is optional - Fecchio et al. (Plos One, 2017) showed that the EEG responses to TMS (TEPs) are different in presence/absence of MEP. Since the authors collect the EEG during TMS, It would very be interesting to test whether not only the MEPs, but also the TEPs are modulated by the phase of the ongoing activity.

MINORS

1) LINE 104 ”The EEG and EMG data analyzed for this study is part of a larger TMS study that has been analyzedand published separately (24). The data was collected during a single control session of that largerstudy, which included one block of repetitive sham TMS, but no other form of real TMS in addition to the experimental single TMS pulses included in the analysis.” This sentence is reported in the analysis section, I would report that in the procedure. I would also clearly indicate a the beginning of the Methods that these data have been collected in the context of a previous study.

2) Please report the protocol number for the Ethical approval and indicate whether (I hope so) the participants signed any informed consent.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Sep 16;16(9):e0255815. doi: 10.1371/journal.pone.0255815.r002

Author response to Decision Letter 0

27 May 2021

We thank the reviewers for their useful comments. We have answered all concerns point by point below. Changes in the manuscript have been highlighed in yellow.

While this is an interesting paper and topic, the manuscript presents more than one methodological concern and I would appreciate if the authors clarify some choices

We thank the reviewer for this valuable suggestion which we gladly implanted in the revised version of the manuscript. We now describe the experiment in which this data was recorded in more detail in order to provide the broader context of the experimental session here analyzed. We also explicitly stated now that measurements were done with participants being at rest and thus without the execution of a specific task. Based on a comment by another reviewer, we moved this paragraph to the beginning of the Methods section to further increase clarity of this general aspect of study design and experimental context.

Moreover, is there a reason for excluding the lowest pre-TMS contractions, assuming that the authors are looking for neurophysiological relationships while participants are at rest?

We believe that in general a proper outlier detection is important to ensure robust estimation of any effect of interest. If we do not remove statistical outliers according to standard and accepted outlier analysis criteria, our findings are likely inappropriately affected by individual values. We therefore originally decided to conduct an outlier analysis following standard statistical criteria, excluding outlier values for both high power as well as MEPs since both measurements are used in the calculated correlation.

Regarding the outlier criteria for pre-TMS contractions, we indeed excluded trials with pre-TMS contractions that were 3.5 times higher than the standard deviation from the average of all pre-pulse EMG peak-to-peak values for a time window of 100ms prior to TMS. There were no exclusions of trials based on low pre-TMS contractions. However, we did exclude trials with MEP amplitudes lower than 0.05mV, as those do not represent an elicited MEP. We clarified this in the manuscript.

Indeed, there might be different ways to deal with outlier criteria. One thing the reviewer suggests instead of using the standard deviation, to rather rely on Spearman correlation without outlier removal. While this is possible for the power-MEP correlation, we are not aware of a circular correlation that can also perform non-parametric correlation measures such as the Spearman correlation. The only option would be to use permutations to estimate the circular correlation as expected by chance. But this is already the approach we had taken in the current manuscript. (We calculate the correlation both for the observed data as well as permuted values of this approach and then use cluster statistics to investigate whether the chance permutation is higher than the observed correlation).

To still ensure that none of our results are due to specific outlier criteria, we re-run the analysis in two different ways (as suggested by the reviewer):

- Perform no outlier correction for high power or high MEP and perform a Spearman correlation (for the phase correlation still a circular correlation was used).

- Perform the outlier correction with an Sn approach.

Overall, our general results remained, with some notable additions and differences we now also report in the revised manuscript. Please see the table below.

For the FFT alpha power, the previously trend-significant effect reached significance in the Sn outlier approach (p = 0.028). This also was the case for the alpha Hilbert analyses in which previously the cluster was not significant, but now reached significance (p=0.04).

Original; No outliers removal + Spearman; Sn outlier approach

FFT phase: p = 0.028; p = 0.029; p = 0.003

FFT power: No cluster found; No cluster found; p = 0.028

Hilbert alpha phase: p = 0.002; p = 0.003; p = 0.003

Hilbert alpha power: p > 0.1; p > 0.05; p = 0.04

Hilbert beta phase p < 0.001 p = 0.002 p = 0.003

Hilbert beta power p > 0.1 p > 0.1 P > 0.1

As the reviewer pointed out, the Sn outlier approach might be the most appropriate for outlier detection. Therefore, we now report all results using this approach. Besides the change of the alpha power effect, we also saw a change in the consistency of the phase estimation across subjects. Both for the “no outlier removal” and “Sn outlier removal” approach this effect did not reach significance. We therefore decided to report this transparently stating that this effect depends on the outlier criteria chosen, but further do not dedicate a figure to it.

Changes in the manuscript are:

- The title changed and now reads: “Phase and power modulations on the amplitude of TMS-induced motor evoked potentials”.

- All results reflecting the phase consistency have been removed. The results section regarding the manuscript now reads: “The phase consistency of the averaged individual dominant alpha frequency phase related to the 50% highest TMS-induced MEP amplitudes was analyzed for the five central EEG channels ipsilateral to the stimulation site. We did not find any phase consistency over participants (p > 0.1). We would like to note that using a different outlier criterion (standard deviations instead of a distance measure) did lead to significant phase consistency. However, as this was not consistent across outlier criteria we do not believe this to be a robust effect. “

- We now report on the significant alpha power effect in the discussion and the abstract.

The log transform is the most commonly accepted EEG power transform to ensure transform of the skewed data to a more normal distribution (see e.g. (Gasser, Bächer, & Möcks, 1982; Smulders, Ten Oever, Donkers, Quaedflieg, & van de Ven, 2018)). We did show a significant effect based on the suggested Sn outlier approach, which was absent (or uncorrected trend-significant) using the Spearman correlation. This suggest that the relation is indeed linear between the log distribution of the alpha power and the MEP power.

We agree with the reviewer. We first went and redid the analyses across all five channels. The phase consistency was significant and consistent (Z=3.37, p=0.03). However, as we report in comment to point #2, the phase consistency did not survive the application of a different outlier criterion and we were therefore decided not to conclusively report on this effect. We mention that we don’t find strong evidence for phase consistency in the results section.

We have repeated the analyses for 5 occipito-parietal sensors (O1, P3, P7, Pz, T7). For the FFT analyses we found no effect (for phase p > 0.05; for power no cluster was found). For the alpha Hilbert we also did not find any significant effect for phase or power in alpha or beta (cluster p-value > 0.05). We have included this analysis in the main manuscript. All results have been updated to add this information. In addition, we report in the discussion on this finding by stating: “...we did not find alpha phase effects for more posterior and occipital channels, which are the core generators of posterior alpha. It is therefore unlikely that posterior alpha is driving the effect.”

6) line 147: why was the median used for the permuted data and the average for the actual data?

The mean refers to the mean of the five channels. This was performed for both the actual and the permuted data. The median refers to the median of the 1000 permutations we performed which we use to contrast the actual data to. For each of these permutations the mean of the five channels was still used. We clarified this now in the manuscript. It now reads: “Correlation values at each of these channels were averaged for the initial analysis. We created a null distribution repeating the analysis using permuted labels. This null distribution reflects the expected average correlation based on chance. To statistically compare our observed values with the chance values, we compared the median of the permuted labels with the observed correlation values for power and phase separately at all frequency bins of 0.5Hz between 2Hz and 30Hz”.

7) line 150 pg 8: this is just a check of whether the authors have used the error 1 threshold correctly, given that fieldtrip might be misleading concerning this issue:

https://www.fieldtriptoolbox.org/faq/why_should_i_use_the_cfg.correcttail_option_when_using_statistics_montecarlo/

(the authors might specify in the manuscript the cfg.correcttail used, so that the solution adopted is more transparent)

We regret not being fully transparent and for this oversight. Indeed, fieldtrip by default corrects the alpha of the values and not the probabilities and we used this default. While for the phase correlations this does not change anything (as these correlations can only be positive so a-priori only positive clusters are expected), for the power correlations this does have an influence. For consistency across the phase and power effects, we now report all the corrected p-values which changes the power p-values throughout the manuscript. We also state this in the manuscript. It reads: “For the phase analysis we report the one-sided p-value as circular correlations can only be positive. For the power analysis we report the corrected p-values.” We thank the reviewer for this attentive comment.

8) line 189 pg 9: the authors state that CP1 has the strongest correlation. Is this comparison tested statistically?

Good point. We did not test this statistically and thus removed this description from the text.

9) line 196: again, I do not see the reason for treating the channels separately when they have been averaged for the previous power/phase-MEP analyses.

We agree and changed this accordingly.

The beta power was low in our study in comparison with other studies in which participants were actively required to perform a motor task. This is also demonstrated by the FFT that does not show a clear beta peak (see Fig. 1A). To make it clearer that the EEG beta power measured here is rather low, because there was no motor task involved, we updated the text to: “In our study, TMS was applied during self-controlled muscle relaxation. Elevated levels of EEG beta activity over the motor and somatosensory cortex are usually linked to motor performance. However, the power of the ongoing resting EEG beta activity measured here is relatively low (Fig. 1A), because participants are not performing any active motor task.”

As the reviewer suggested, we repeated the analyses for low and high alpha/beta power for the Hilbert analyses (averaged of the cluster closed to TMS pulse onset). We did not find a significant difference between low and high power trials (p>0.1). This is now reported in the manuscript.

We do not have a clear interpretation for this effect. As indicated in the manuscript the beta Hilbert analyses was a post-hoc analyses we only performed after we also performed the alpha Hilbert analyses. Our a-priori analyses focused on the FFT. But after some review rounds we also decided to post-hoc include the beta Hilbert transform, which in contrast to the FFT analyses does show an effect. We agree that the finding in the beta is rather peculiar and difficult to explain. It is possible that this is related to some form of cross-frequency coupling by which the beta power is coupled to lower frequency phase. This has been reported in the literature (Axmacher et al., 2010; Canolty et al., 2006). Unfortunately, at this moment this is rather speculative and we do not have a satisfying answer to why this is the case.

12) Figure 1A: it is not clear what the inset is, please explain more in detail. Moreover, the inset cannot be apporciated given the resolution of the figure (see comment 13)

The inset refers to the ERPs related to different phase bins to provide a visualization of the different alpha phases we estimated ensuring that the phases are not biased. We have changed the figure legend to make this clearer.

13) Figure 1B: it looks like the image is lacking the error bar for the permuted data (grey). (the image on the PDF is very blurry, likely not authors' fault)

We ensure that the error bars are there. However, they are very similar across participants (correlations by chance are close to zero, and circular-linear correlations by chance are very similar when trial amounts are comparable).

14) Please in figure 1 F and H indicate the 5 target sensors position

We have added the sensor positions to the figure.

I have just few major and minor concerns that, in my opinion, need to be addressed in order to guarantee a better interpretation of the results. I have detailed my comments below:

MAJORS

Thank you for this excellent suggestion. Indeed, we extracted alpha (8-12 Hz) and beta (15-25 Hz) phase correlation from the FFT and performed a repeated measures anova with factors frequency (alpha versus beta) and permutation (original data versus permutation data). The interaction showed a trend (F(1,26) = 3.917 = 0.059). This suggests that the effect of alpha is stronger, but that this cannot be fully corroborated with the statistic. Consistent with analyses in the main script, alpha showed a significant effect (t(1,26) = 3.57, p = 0.001), and beta did not show any effect (p>0.1). For power we did not find an interaction (F(1,26)=1.45, p =0.239). We report this analysis in the updated manuscript.

The absence of an interaction is not strange considering that beta phase likely does have an influence on the MEP size. In our FFT analyses this did not come out, but for the Hilbert analysis, the phase correlation in beta did show an effect. We also repeated the interaction analysis for the Hilbert transform across time, but did not find any effects here.

In our response to a comment from reviewer 1 with regard to the outlier analysis criteria, we repeated the analyses using an alternative outlier criterium which revealed that our finding of phase consistency measure was not as robust as the previous statistics seemed to suggest. While the effect seemed quite strong, especially on CP1 (even correcting for multiple comparisons using Bonferonni), using a different outlier criterion removed the effect completely. This surprised us and we decided the best way forward is to report on this.

The manuscript now reads: “The phase consistency of the averaged individual dominant alpha frequency phase related to the 50% highest TMS-induced MEP amplitudes was analyzed for the five central EEG channels ipsilateral to the stimulation site. We did not find any phase consistency over participants (p > 0.1). We would like to note that using a different outlier criterion (standard deviations instead of a distance measure) did lead to significant phase consistency. However, as this was not consistent across outlier criteria we do not believe this to be a robust effect.”

Considering we don’t find a robust effect, it also seems unnecessary to repeat the analysis in source space.

3) When considering the inter-individual phase consistency related to high MEP amplitude the authors focused on the 50% amplitude of the MEP. Please justify this a priori selection.

In order to look at phase consistency across participants we need a means to estimate the phase at which the MEP is strongest (as a circular-linear correlation does not provide you the mean angle). The approach that seemed most appropriate was to split the data to extract a phase angle. The most straightforward approach would be a median split, this is why the 50% was chosen. From these top 50% MEP it was possible to estimate a phase angle and to subsequently look at phase consistency.

4) In Figure 1F and H the topographies are not very clear. Perhaps I would saturate the colour-scale and/or I would set to zero the non-significant values.

We have changed the color scheme to make it clearer. As we analyzed the data with a-priori channels we cannot differentiate values from being significant or not as this has only been quantified across the topography. We now highlight the five channels that we analyzed.

We would love to perform these analyses, however during data collection we did not optimize recordings to be able to look at the TEPs. Unfortunately, in the early TEP components which are commonly analyzed in relation to MEPs we have a lot of artifacts that make it impossible to look at those components.

MINORS

We thank the reviewer for his comment. We moved the paragraph on the context of this experimental session as part of a larger study to the beginning of the Methods section and we added a brief description on the larger study setup for clarification.

2) Please report the protocol number for the Ethical approval and indicate whether (I hope so) the participants signed any informed consent.

We added the protocol number for the approval of the Ethics Committee at Maastricht University. The signing of the informed consent is mentioned in the participant description of the Methods section.

References:

Axmacher, N., Henseler, M. M., Jensen, O., Weinreich, I., Elger, C. E., & Fell, J. (2010). Cross-frequency coupling supports multi-item working memory in the human hippocampus. Proceedings of the National Academy of Sciences, 107(7), 3228-3233.

Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., . . . Knight, R. T. (2006). High gamma power is phase-locked to theta oscillations in human neocortex. science, 313(5793), 1626-1628.

Gasser, T., Bächer, P., & Möcks, J. (1982). Transformations towards the normal distribution of broad band spectral parameters of the EEG. Electroencephalography and clinical neurophysiology, 53(1), 119-124.

Smulders, F. T., Ten Oever, S., Donkers, F. C., Quaedflieg, C. W., & van de Ven, V. (2018). Single‐trial log transformation is optimal in frequency analysis of resting EEG alpha. European Journal of Neuroscience.

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PLoS One. doi: 10.1371/journal.pone.0255815.r003

Decision Letter 1

Luigi Cattaneo

26 Jul 2021

Phase and power modulations on the amplitude of TMS-induced motor evoked potentials

PONE-D-21-07587R1

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6. Review Comments to the Author

Reviewer #1: MINOR

line 92: "While within participant there was a systematic relation between alpha phase and MEP,...."

since this point has not been explored statistically I would avoid it. Statistical data analyses have been conducted checking the statistical consistency of the relationship between phase and MEP amplitudes across participants (and not within participants).

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PLoS One. doi: 10.1371/journal.pone.0255815.r004

Acceptance letter

Luigi Cattaneo

8 Sep 2021

PONE-D-21-07587R1

Phase and power modulations on the amplitude of TMS-induced motor evoked potentials

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PERMALINK

Phase and power modulations on the amplitude of TMS-induced motor evoked potentials

Lukas Schilberg

Sanne Ten Oever

Teresa Schuhmann

Alexander T Sack

Roles

Abstract

Introduction

Materials and methods

Participants

TMS application

EMG and EEG recording

Procedure

EMG and EEG preprocessing

The relationship between EEG oscillation power and phase with MEP amplitude

Results

Effect of neuronal oscillation power on MEP amplitude

Fig 1. Results for the power and phase analysis.

Effect of neuronal oscillations phase on MEP amplitude

Inter-individual phase consistency related to high MEP amplitude

Time course of alpha phase and power modulation

Post-hoc analysis on time course of beta phase and power modulation

Correction for pre-pulse EMG activity

Fig 2. Analysis corrected for pre-TMS EMG activity and median split on power.

Phase correlation for high alpha power

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Luigi Cattaneo

Roles

Author response to Decision Letter 0

Decision Letter 1

Luigi Cattaneo

Roles

Acceptance letter

Luigi Cattaneo

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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