Edited by:
Reviewed by:
*Correspondence:
†
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Inter- and intra-subject variability of the motor evoked potentials (MEPs) to TMS is a well-known phenomenon. Although a possible link between this variability and ongoing brain oscillations was demonstrated, the results of the studies are not consistent with each other. Exploring this topic further is important since the modulation of MEPs provides unique possibility to relate oscillatory cortical phenomena to the state of the motor cortex probed with TMS. Given that alpha oscillations were shown to reflect cortical excitability, we hypothesized that their power and variability might explain the modulation of subject-specific MEPs to single- and paired-pulse TMS (spTMS, ppTMS, respectively). Neuronal activity was recorded with multichannel electroencephalogram. We used spTMS and two ppTMS conditions: intracortical facilitation (ICF) and short-interval intracortical inhibition (SICI). Spearman correlations were calculated within and across subjects between MEPs and the pre-stimulus power of alpha oscillations in low (8–10 Hz) and high (10–12 Hz) frequency bands. Coefficient of quartile variation was used to measure variability. Across-subject analysis revealed no difference in the pre-stimulus alpha power among the TMS conditions. However, the variability of high-alpha power in spTMS condition was larger than in the SICI condition. In ICF condition pre-stimulus high-alpha power variability correlated positively with MEP amplitude variability. No correlation has been observed between the pre-stimulus alpha power and MEP responses in any of the conditions. Our results show that the variability of the alpha oscillations can be more predictive of TMS effects than the commonly used power of oscillations and we provide further support for the dissociation of high and low-alpha bands in predicting responses produced by the stimulation of the motor cortex.
While single-pulse TMS (spTMS) allows studying corticospinal excitability (
Compared to rather large number of TMS-EEG studies dedicated to spTMS (
Another important question is whether the degree of oscillatory neuronal variability by itself can relate with MEP produced by sp and ppTMS. During the last several years a number of studies specifically focused on the role of brain activity fluctuations and their functional relevance to behavioral and clinical outcomes (
Given all the considerations presented above, in the present study we hypothesized that: (1) Not only power but also the variability of alpha oscillations would be predictive of TMS responses. (2) Pre-stimulus alpha oscillations would relate more closely to MEP variability in ppTMS than in spTMS protocols.
Seventeen right-handed healthy volunteers (six females) between 19 and 34 years of age (mean: 24 ± 4, SD) participated in the experiment after giving a written informed consent in accordance with the Declaration of Helsinki. Subjects were screened for contraindications to TMS (
A MagPro X100 (MagVenture) stimulator connected to a water-cooled MCF-B65 induction coil with 75-mm wing radius was used to produce biphasic TMS pulses. A frameless TMS navigation system (Localite TMS Navigator, Localite GmbH) was used for MRI-guided navigation, which ensures consistent coil position and orientation in a 3D space through the sequence of stimulations. TMS coil position was optimized accordingly to individual MR scan (1.5 T MRI scanner; T1 weighted; 1 mm thickness; sagittal orientation; acquisition matrix 256 × 256; MR-scanner Siemens Magnetom Avanto). Stimulation targeted the left primary motor cortex [i.e., motor knob (
Three blocks of stimuli (101–114 trials each) were delivered: single-pulse (SP) TMS and two ppTMS protocols: short-interval cortical inhibition (SICI) and ICF. The inter-stimulus intervals (ISIs) for the paired-pulse stimuli were set at 2 ms or 12 ms for SICI and ICF protocols, respectively. The intensity of 110% of RMT was used for spTMS pulses and for the test pulses (S2) in ICF and SICI protocols. Conditioning pulses (S1) for both paired-pulse paradigms had 90% RMT intensity. The interval between the stimuli (or pairs of stimuli) varied randomly from 3 to 10 s and the inter-condition interval varied between 1 to 5 min. We provide the following reasons for determining inter-stimulus intervals. Firstly, we aimed at having a large variability of the intervals in order to avoid the anticipation effect. Secondly, as we used the same distribution for delays in all subjects, the results are unlikely to be due to the chosen inter-stimulus delays but rather would reflect genuine impact of oscillatory activity on cortical excitability. The three blocks were performed in a random order across participants. During all the conditions the coil was held by the operator who constantly monitored its position and orientation with respect to the target using navigation system. Subjects were seated in a comfortable armchair with elbows flexed at 90° and prone hands in a relaxed position, eyes were open and fixed at the mark on the opposite wall.
In order to measure EEG, we used BrainAmp DC (Brain Products, Germany) amplifier. 91-electrode BrainCap Fast’n Easy Standard Electrode Cap (Brain Products) was used with TMS-compatible electrodes. The reference electrode was at the bridge of the nose, and ground electrode was placed on the left cheekbone. Three electrooculographic (EOG) electrodes were placed above the nasion and below the outer canthi of the eyes as indicated in (
MEPs to single- and paired-pulse TMS (SP, SICI, ICF) were recorded from the right APB muscle with surface bipolar EMG, using Ag–AgCl bipolar electrodes in a belly–tendon montage and were also recorded with the BrainAmp DC amplifier.
EMG activity was high-pass filtered with a fourth order Butterworth high-pass filter (cut-off frequency at 10 Hz) and with the band-stop filter at 50 Hz to remove power-line noise. MEP peaks were identified within the range of 20–62 ms from the onset of the TMS stimulus for the three conditions (SP, SICI, and ICF). This range was sufficient to include MEPs in all subjects. Peak-to-peak measures of the largest positive–negative deflection were used as the MEP amplitudes. Visual inspection was also performed in parallel to remove possible artifactual trials.
Electroencephalographic recordings were segmented using TMS event marker. Pre-stimulus segment was 1200 ms (-1210 ms to -10 ms). Channels with excessive amount of noise were excluded from further analysis (maximum 15 channels per subject). Blink-related artifacts in EEG were removed with fastICA algorithm (
Pre-stimulus alpha power was estimated from the spectrum calculated with Fast Fourier Transform (FFT), Hanning window (duration 1000-ms immediately preceding TMS pulse in spTMS or conditioning pulse in ppTMS). For the single-trial analysis the power was calculated separately for each pre-stimulus interval. For the across-subjects analysis, the power was averaged across all trials in each condition, electrode and subject. As has been performed in previous studies (
The variability was estimated from single trials both for the amplitude of MEPs and for the pre-stimulus alpha power obtained in each subject, electrode and condition. It was quantified with the coefficient of quartile variation (CQV) (
In (1), Q1 and Q3 denote the first (lower) and third (upper) quartiles of the data, respectively. Quartiles are the points that divide any ranked data set into four equal groups. Each group contains a quarter of the data. Q3 - Q1 is defined as the interquartile range and it is a measure of the spread. Let vectors
We calculated a repeated measures analysis of variance (ANOVA) in order to compare the CQV of MEP amplitudes among the three TMS conditions. We computed Wilcoxon signed rank test for the comparison of pre-stimulus alpha power among the three TMS conditions. Using the same test, we compared the CQV of pre-stimulus alpha power and CQV of MEP amplitudes between the TMS conditions.
We computed a Spearman correlation of MEP amplitudes between conditions. Besides, we computed a Spearman correlation between alpha power and the amplitude of MEPs for each EEG channel and condition within and across subjects. Within-subject correlations are based on single trial analysis where the power of alpha oscillations is correlated with the corresponding MEP amplitude in each channel, subject, and condition. Moreover, in order to address a relationship between cortical and peripheral variabilities, correlations were computed between the CQV of alpha power versus CQV of MEP amplitudes for each EEG channel and condition across subjects (see above the description of CQV).
In order to take into account multiple comparisons (calculation of tests for many channels), for both Wilcoxon signed rank test and Spearman correlations across subjects, a significance was estimated using cluster-based permutation statistics (
The analysis was performed with custom scripts implemented in Matlab (The MathWorks Inc., Natick, MA, USA).
ppTMS protocols led to robust SICI and ICF phenomena across subjects. ICF protocol resulted in the increase of MEPs amplitudes compared to MEPs amplitudes during SP condition (ICF/SP mean 2.34 ± 0.29). Likewise, SICI protocol resulted in the significant attenuation of MEPs compared to the SP (SICI/SP mean: 0.49 ± 0.07,
The variability of MEPs was quantified with CQV. In
Repeated measures ANOVA showed significant differences among conditions (
CQV of MEP amplitudes between the conditions showed significant positive correlations across subjects between SP and SICI conditions only: SP-ICF (
We compared pre-stimulus alpha power between the three conditions using Wilcoxon signed rank test with cluster-based permutation statistics (see Materials and Methods). There were no differences of the alpha power between any of the TMS conditions (SP vs. SICI vs. ICF).
Variability (CQV) of the alpha power differed between SP and SICI conditions.
In single-trial analysis Spearman correlations between EEG alpha power (8–10, 10–12 Hz) and the amplitude of MEPs did not reveal consistently similar channels with significant correlations across subjects. In
We computed a Spearman correlation between oscillatory pre-stimulus power and the amplitude of MEPs across subjects using cluster-based permutation statistics. No correlation of the alpha power (averaged across all trials separately for each subject and condition) with the MEP amplitudes across subjects was detected in any TMS condition (SP, SICI, ICF) or in alpha frequency sub-bands. In addition, no correlation of the alpha power was found with the MEPs variability (i.e., CQV of MEP amplitudes during three TMS conditions).
Finally we assessed a relationship between the variability at both cortical and peripheral levels using cluster-based permutation statistics. Only for ICF condition we found a significant positive correlation between both CQVs (
Although our main intention for the study was to investigate the relevance of the alpha oscillations for cortical excitability, we also calculated correlations between power of delta (1–3 Hz) and theta (4–7 Hz) oscillations and parameters of MEPs. There were no significant correlations across subjects between power in these bands and MEPs amplitudes or between oscillatory-power CQV and MEP CQV.
There were four main findings of this study. Firstly, we showed that trial-to-trial variability of the MEP amplitudes differed significantly among three TMS conditions. Secondly, we also found a significant difference of trial-to-trial variability of the high-alpha (10–12 Hz) power in the 1000-ms pre-stimulus EEG for SP-SICI comparison (SP > SICI). At the same time, for alpha power no difference among TMS conditions was observed. Thirdly, neither single trial analysis, nor across-subjects approach revealed any significant correlation between pre-stimulus alpha-power and MEP amplitudes. Finally, in the ICF condition larger variability of the high-alpha power in the pre-stimulus EEG was positively correlated with higher variability of MEP amplitudes.
Below we discuss possible explanations of these findings and their significance for the understanding of the within- and across-subject variability of the motor responses in TMS studies.
Both SICI and ICF were pronounced across subjects but also demonstrated considerable variability, which agrees with previous studies in healthy volunteers (
We have not observed significant differences in the pre-stimulus alpha power across conditions, indicating that there were no changes in the overall generation of alpha oscillations often observed for different experimental conditions including attention (
One of the goals of the present study was to investigate possible relationships between pre-stimulus power of oscillations and MEP amplitudes during prolonged spTMS and ppTMS sessions. Such dependencies are relevant for the development of brain-state triggered stimulation (
It is interesting to observe that the variability of MEPs on a single trial level is in contrast to reproducible average MEP responses in ppTMS paradigms (
One of the most remarkable findings of the study is a relationship between two levels of trial-to-trial variability in ICF condition: variability of the high-alpha power over central and right parieto-occipital areas correlated positively with MEP amplitudes variability. Firstly, such a link between central and peripheral levels of the variability corresponds to the previous research connecting brain signal variability with the behavioral variability of both afferent (
Interestingly, all our significant results were found for the upper alpha (10–12 Hz) frequency band. In general this agrees with the findings indicating that low (8–10 Hz) and high-alpha (10–12 Hz) sub-bands may be associated with different neuronal processes (
While we did not find a considerable evidence for the previously reported link between the pre-stimulus EEG alpha power and MEP amplitudes either within-or across-subjects, we were able to demonstrate an importance of a pre-stimulus alpha-power variability for ppTMS phenomena, thus, providing a further support for the hypothesis that ongoing neuronal variability may modulate cortical motor output.
For both studied ppTMS phenomena only one ISI (2 ms for SICI and 12 for ICF) among several commonly used (
Because of the residual scalp EMG in some of our subjects, we were not able to investigate beta oscillations which are known to be relevant for motor processing. However, our results on alpha rhythm already provide a novel link between the variability in the cortical oscillations and motor responses as tested with ppTMS.
At this stage of the study we did not investigate TMS-EEG responses. Firstly, it was due to our original intention to investigate a relationship between ongoing neuronal oscillations and cortical excitability as probed with MEPs in ppTMS phenomena. Secondly, since our amplifiers did not have a technical possibility to be gated during the TMS pulse, we observed considerable artifacts in the post-stimulus interval thus preventing us from analyzing TMS-evoked responses.
ZI processed and analyzed the data, and wrote the manuscript. MN designed the study, collected the data and wrote the manuscript. ZI and MN contributed equally to the study. TF wrote codes for the pre-processing and analysis of the data. EB designed the study and collected the data. VN designed and supervised the study, wrote the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer PL declared a past collaboration with one of the authors (MN) to the handling Editor, who ensured that the process met the standards of a fair and objective review.