1. Neuroscience

Combined transcranial magnetic stimulation and electroencephalography reveals alterations in cortical excitability during pain

  1. Nahian Shahmat Chowdhury  Is a corresponding author
  2. Alan KI Chiang
  3. Samantha K Millard
  4. Patrick Skippen
  5. Wei-Ju Chang
  6. David A Seminowicz
  7. Siobhan M Schabrun
  1. Center for Pain IMPACT, Neuroscience Research Australia, Australia
  2. University of New South Wales, Australia
  3. School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Australia
  4. Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, Canada
  5. The Gray Centre for Mobility and Activity, University of Western Ontario, Canada
https://doi.org/10.7554/eLife.88567.3
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Cite this article
  1. Nahian Shahmat Chowdhury
  2. Alan KI Chiang
  3. Samantha K Millard
  4. Patrick Skippen
  5. Wei-Ju Chang
  6. David A Seminowicz
  7. Siobhan M Schabrun
(2023)
Combined transcranial magnetic stimulation and electroencephalography reveals alterations in cortical excitability during pain
eLife 12:RP88567.
https://doi.org/10.7554/eLife.88567.3
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Abstract

Transcranial magnetic stimulation (TMS) has been used to examine inhibitory and facilitatory circuits during experimental pain and in chronic pain populations. However, current applications of TMS to pain have been restricted to measurements of motor evoked potentials (MEPs) from peripheral muscles. Here, TMS was combined with electroencephalography (EEG) to determine whether experimental pain could induce alterations in cortical inhibitory/facilitatory activity observed in TMS-evoked potentials (TEPs). In Experiment 1 (n=29), multiple sustained thermal stimuli were administered to the forearm, with the first, second, and third block of thermal stimuli consisting of warm but non-painful (pre-pain block), painful (pain block) and warm but non-painful (post-pain block) temperatures, respectively. During each stimulus, TMS pulses were delivered while EEG (64 channels) was simultaneously recorded. Verbal pain ratings were collected between TMS pulses. Relative to pre-pain warm stimuli, painful stimuli led to an increase in the amplitude of the frontocentral negative peak ~45 ms post-TMS (N45), with a larger increase associated with higher pain ratings. Experiments 2 and 3 (n=10 in each) showed that the increase in the N45 in response to pain was not due to changes in sensory potentials associated with TMS, or a result of stronger reafferent muscle feedback during pain. This is the first study to use combined TMS-EEG to examine alterations in cortical excitability in response to pain. These results suggest that the N45 TEP peak, which indexes GABAergic neurotransmission, is implicated in pain perception and is a potential marker of individual differences in pain sensitivity.

eLife assessment

This valuable study provides convincing evidence that acute experimental pain induces changes of cortical excitability. Although the modality specificity of the findings is not fully clear, the findings will be of interest to researchers interested in the brain mechanisms of pain.

https://doi.org/10.7554/eLife.88567.3.sa0

Introduction

Pain is a complex subjective experience, and understanding how pain is processed remains a challenge (Apkarian, 2021). Several neuroimaging techniques have been applied to disentangle these complexities: functional magnetic resonance imaging has assisted in identifying brain structures implicated in pain processing (Reddan and Wager, 2018), while electroencephalography (EEG) has contributed to our understanding of the temporal sequence of pain processing (Ploner and May, 2018). Another useful technique that has been used to examine the role of inhibitory and facilitatory neural circuits in pain has been transcranial magnetic stimulation (TMS) delivered to the brain (Chang et al., 2018; Schabrun and Hodges, 2012). However, current applications of TMS to pain have involved recording the output of TMS from a muscle, a signal that could be influenced by many intermediate (subcortical, spinal, peripheral) factors, and which restricts investigations to the motor system only. Here, we used combined TMS-EEG measure to record output of TMS directly from the cortex and from multiple brain regions, in pain-free and tonic pain conditions.

When TMS is delivered over the primary motor cortex (M1), a magnetic pulse induces an electrical current in underlying cortical tissue that, if the intensity is sufficient, activates corticomotor pathways, inducing a motor evoked potential (MEP) in a target muscle. The magnitude of the MEP serves as an index of corticomotor excitability. Past systematic reviews on studies measuring MEPs during acute experimental pain (Bank et al., 2013; Burns et al., 2016; Chowdhury et al., 2022a; Rohel et al., 2021) have shown a reduction in MEP amplitude during pain and after pain resolution, with stronger reductions in MEP amplitude associated with lower acute pain severity (Chowdhury et al., 2022a). It has been hypothesized that this reduction in MEP amplitude is an adaptive mechanism that restricts movement in the pain-afflicted area, to protect the area from further pain and injury (Hodges and Tucker, 2011).

While previous findings show promise for the use of TMS to discover and validate potential biomarkers for pain, limitations exist when using TMS to measure MEPs. First, MEP responses to TMS reflect the net sum of cortical, spinal, and peripheral activity within the corticomotor pathway. This makes it unclear as to whether pain processes occur at the cortical, spinal, or peripheral level. Further, measurement of MEPs restricts investigations to M1. One way of overcoming these limitations is by combining TMS and EEG to measure TMS-evoked potentials (TEPs). TEPs index cortical excitability directly from the cortex (i.e. without influence of subcortical, spinal, and peripheral processes), as well as from regions outside M1 (Farzan et al., 2016). TEPs also provide an index of the activity of specific neurotransmitter circuits within the cortex. For example, TEP peaks that occur at ~45 ms and 100 ms post-stimulation are linked to GABAA and GABAB neurotransmission, respectively (Premoli et al., 2014), whereas the TEP peak ~60 ms post-stimulation is linked to glutamatergic neurotransmission (Belardinelli et al., 2021). Overall, TEPs provides additional spatial and temporal information about cortical activity over MEPs, making it ideal for understanding the brain mechanisms involved in pain perception.

TEPs have already shown potential to serve as a biomarker for the development and prognosis of various neurological and psychiatric conditions for reviews see (Kallioniemi and Daskalakis, 2022; Tremblay et al., 2019). However, the applicability of TEPs to pain research is yet to be established. While GABAergic processes indexed by TEPs have been hypothesized to be involved in pain (Barr et al., 2013), direct evidence is scarce. Two studies (Che et al., 2019; Ye et al., 2022) examined whether the potential analgesic effects of repetitive TMS (rTMS) over the dorsal prefrontal cortex are associated with plasticity in TEPs. These studies separately measured TEPs and ratings to painful stimuli, before and after rTMS, with one finding that increases in pain thresholds following rTMS were associated with changes in TEPs that index GABAergic processes (Ye et al., 2022). While these studies assist us in understanding whether TEPs might mediate rTMS-induced pain reductions, no study has investigated whether TEPs are altered in direct response to pain.

The aim of the present study was to use TMS-EEG to determine whether acute experimental pain induces alterations in cortical inhibitory and facilitatory peaks observed using TEPs. We used a tonic heat pain paradigm (Furman et al., 2020; Granot et al., 2006), in which multiple thermal stimuli were applied over the right extensor carpi radialis brevis (ECRB) muscle via a thermode. For each thermal stimulus, the temperature increased from a neutral baseline of 32 °C to either a warm non-painful or a painful (46 °C) temperature, with this temperature maintained for 40 s. During this time, TMS was administered to the left M1 with concurrent EEG to obtain TEPs from 63 scalp channels, and MEPs from the ECRB muscle (see Figure 1). Verbal pain ratings were obtained between pulses. It was hypothesized that TEP peaks that index GABAergic processes, including the peaks at ~45 and 100 ms after TMS, would increase in response to painful stimuli relative to warm non-painful stimuli.

Figure 1
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Schematic of experimental apparatus.

The apparatus consisted of transcranial magnetic stimulation (TMS) during concurrent electroencephalography (EEG) to simultaneously record motor-evoked potentials (MEPs) and TMS-evoked potentials (TEPs). MEPs were recorded using electromyographic (EMG) electrodes placed over the distal region of the extensor carpi radialis brevis (ECRB), while thermal pain was delivered over the proximal region of the ECRB.

Results

Experiment 1 – Does acute pain alter cortical excitability?

Design

In Experiment 1 (n=29), we determined whether painful thermal stimuli induced alterations in TEP peaks relative to a non-painful baseline. The protocol (Figure 2) consisted of three blocks of stimuli, in chronological order: pre-pain, pain, and post-pain blocks. The pre-pain and post-pain blocks each consisted of six 40 s thermal stimuli (20 s interstimulus interval) delivered at a non-painful temperature (calibrated to each participant’s warmth detection threshold), while the pain block consisted of six 40 s thermal stimuli delivered at 46 °C. The pre-pain/pain/post-pain design has been commonly used in the TMS-MEP pain literature, as many studies have demonstrated strong changes in corticomotor excitability that persist beyond the painful period. Indeed, in a systematic review, we showed effect sizes of 0.55–0.9 for MEP reductions 0–30 min after pain had resolved (Chowdhury et al., 2022a). As such, if we had used an alternative design with blocks of warm stimuli intermixed with blocks of painful stimuli, the warm stimuli blocks would not serve as a valid non-painful baseline. Based on a previous study (Dubé and Mercier, 2011) which also used sequences of painful (50 °C) and warm (36 °C) thermal stimuli, we did not anticipate that the stimulus in the pain block would entrain pain in the post-pain block.

Figure 2 with 2 supplements see all
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Schematic of the protocol for Experiment 1.

Participants experienced three blocks of thermal stimuli: a pre-pain, pain, and post-pain block, with each block consisting of multiple thermal stimuli delivered 40 s at a time, and during which TMS measurements (indicated by blue arrows) and verbal pain ratings were obtained. The pre-pain and post-pain blocks involved thermal stimuli delivered at the warm threshold (i.e. the temperature that leads to any perceived change in skin temperature from baseline). In the pain block, thermal stimuli were delivered at 46 °C.

Quantitative sensory testing

Prior to the test blocks, we measured warmth, cool, and pain detection thresholds to ascertain whether: (a) participants could perceive increases or decreases in the thermode temperature relative to a neutral baseline of 32 °C, and (b) the pain detection threshold was below 46 °C. All participants were able to detect increases or decreases in temperature from baseline. The mean (± SD) cool and warmth detection threshold was 28.6 ± 1.9°C and 35.1 ± 1.5°C, respectively. All participants reported a heat pain threshold that was above their warmth detection threshold and below the test temperature of 46 °C. The mean heat pain threshold was 41.2 ± 2.8°C.

Pain ratings

All participants reported 0/10 pain during the pre-pain and post-pain blocks, and pain ratings varying between 1 and 10 during the pain block. Figure 3 shows the mean pain ratings for the 10 pain measurements of each of the 6 painful stimuli delivered during the pain block (~4 s in between pain measurements). A 6 (stimulus number: 1–6) x 10 (timepoint:1–10) Bayesian repeated measures ANOVA revealed anecdotal evidence (i.e. no conclusive evidence) of a difference in pain between six thermal stimuli (BF10=2.86). However, there was very strong evidence for a difference in pain ratings between the 10 timepoints (BF10=6.130). There was also strong evidence of an interaction between stimulus number and timepoint, suggesting the time course of pain across the 40 s thermal stimulus differed across the six thermal stimuli of the pain block (BF10=19.6). Overall, although there was no conclusive evidence for pain differing between successive stimuli, there was evidence that pain fluctuated during each 40-s stimulus.

Figure 3 with 3 supplements see all
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No conclusive evidence of a difference in pain ratings between successive 46 °C 40 s thermal stimuli.

Mean (± SD) pain ratings (n = 29) during the 6 thermal stimuli delivered during the pain block (thermal stimuli delivered at 46 °C) of Experiment 1. Ten pain ratings were collected over each 40-s thermal stimulus ~every 4 s. A 6 (stimulus number: 1–6) x 10 (timepoint:1–10) Bayesian repeated measures ANOVA revealed anecdotal evidence (i.e. no conclusive evidence) of a difference in pain between six thermal stimuli (BF10=2.86), very strong evidence for a difference in pain ratings between the 10 timepoints (BF10=6.130) and strong evidence of an interaction between stimulus number and timepoint (BF10=19.6).

Motor-evoked potentials

The mean resting motor threshold (RMT) and test intensity of TMS was (mean ± SD) 70.7 ± 8.5% and 77.7 ± 9.2% of maximum stimulus output respectively. We note that the relatively high RMTs are likely due to aspects of the experimental setup that increased the distance between the TMS coil and the scalp, including the layer of foam placed over the coil, the EEG cap and relatively thick electrodes (6 mm). Three participants were excluded from the MEP analysis due to EMG software failure – these participants were still included in the TEP analysis. A Bayesian repeated-measures ANOVA was run to compare MEP amplitudes between pre-pain, pain, and post-pain conditions. There was anecdotal evidence of a difference in MEP amplitude between blocks (BF10=1.02; Figure 4A). A Bayesian correlation test was also run to determine whether the mean pain rating (across blocks and timepoints) was associated with the change in MEP amplitude during pain as a proportion of pre-pain. These MEP change values were log-transformed as they were not normally distributed according to a Shapiro-Wilk Test (W=0.58, p<0.001). There was strong evidence for a positive relationship (r26=0.54, BF10=11.17; Figure 4B). such that participants who showed a larger reduction in MEP amplitude during pain reported lower pain ratings.

Figure 4
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No conclusive evidence of MEP amplitude differences between conditions; however individual pain sensitivity was predicted by changes in MEP amplitude.

(A) Mean (± SD) MEP amplitude (n=26) during the pre-pain, pain, and post-pain blocks of Experiment 1. A Bayesian repeated-measures ANOVA revealed anecdotal evidence of a difference in MEP amplitude between blocks (BF10=1.02). (B) Individual-level Relationship between change in MEP amplitude during pain (proportion of pre-pain) and mean verbal pain rating provided by each participant. There was strong evidence for a positive relationship (r26=0.54, BF10=11.17).

TMS-evoked potentials

One participant was excluded from the TEP analysis due to failure to save the recording during the experiment, though this participant was still included in the MEP analysis. One participant had missing post-pain data as the TMS coil had overheated during this portion of the experiment – their data were still included for the pre-pain vs. pain comparison. Figure 5 shows the grand average TEPs for all 63 channels, across pre-pain, pain, and post-pain conditions, as well as the scalp topographies at timepoints where TEP peaks are typically observed – N15, P30, N45, P60, N100, and P180ms (Farzan et al., 2016). Source reconstruction using a co-registered template brain model was also conducted to characterize source activity at each timepoint (Figure 5). For the N15 and P30 peaks, there was higher current density in the left motor areas, consistent with previous studies suggesting that TMS evoked activity at 15 and 30ms after the TMS pulse reflect early excitation of motor areas ipsilateral to the stimulated region (Farzan and Bortoletto, 2022). For the N45 and P60 peaks, there was higher current density in the left motor and somatosensory areas at 45 and 60ms after the TMS pulse, consistent with previous studies showing a sensorimotor origin at these timepoints (Ahn and Fröhlich, 2021); however, higher current density was also present in the left parietal and right sensorimotor areas. For the N100 and P180 peaks, there was higher current density in the central regions, mostly contralateral to the stimulated cortex. Overall, we found consistencies in the source localization with previous studies, including a sensorimotor origin of early peaks from 15 to 60ms. However, we did not directly compare source activity between conditions due to the inaccuracies involved in source estimation in the absence of co-registered magnetic resonance imaging (MRI) scans (Brodbeck et al., 2011; Michel and Brunet, 2019) and EEG electrode location digitization (Shirazi and Huang, 2019).

Figure 5
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Pain led to increased negative and positive amplitude in frontocentral and parietal-occipital sites respectively, 43–90ms after the TMS pulse.

(A) Grand-average TEPs (n = 28) during the pre-pain, pain, and post-pain blocks of Experiment 1. The gray-shaded area represents the window of interpolation around the TMS pulse. (B) Scalp topographies and estimated source activity at timepoints where TEP peaks are commonly observed, including the N15, P30, N45, P60, N100, and P180. A cluster plot is also shown on the right comparing signal amplitude between the pain and pre-pain conditions at a representative timepoint (48ms) between 43 and 90ms, which is where significant amplitude differences were observed. The black stars demonstrate the presence of significant positive (yellow) clusters or negative (blue) clusters.

Comparisons of TEP amplitude between conditions were based on the electrode-space data. However, as this is the first study investigating the effects of experimental pain on TEP amplitude, there were no a priori regions or timepoints of interest to compare between conditions. A statistically robust starting point in these situations is to use a cluster-based permutation analysis (Frömer et al., 2018). This analysis was used to compare amplitudes between pre-pain and pain, and pre-pain and post-pain, at each timepoint and for each electrode. We found that during pain relative to pre-pain, there was a significantly larger negative amplitude (p=0.021) at frontocentral electrodes and a significantly larger positive amplitude at parietal-occipital electrodes (p=0.028), specifically between 43 and 90ms after the TMS pulse. No significant differences in TEP amplitude were found when comparing the pre-pain and post-pain conditions, and pain and post-pain conditions. As such, the subsequent TEP peak analyses were focused on the pre-pain vs. pain comparison, while the pre-pain vs. post-pain and pain vs. postpain comparisons are presented in the figure supplements.

Figure 6A shows the grand average TEP waveform at the frontocentral electrodes ('AF3','AFz','AF4','F1','Fz','F2','F4','FC2','FC4') identified from the cluster analysis for the pre-pain vs. pain conditions (Figure 6—figure supplement 1 shows the post-pain comparisons). Two peaks at ~45 and 85 ms after the TMS pulse are visible in the time window where the significant cluster was detected. Given the approximate timing, these peaks are likely to be the N45 peak and an early N100 peak. The amplitude of these peaks was identified for each participant using the TESA peak function (Rogasch et al., 2017) with defined time windows of 40–70 and 75–95ms for the first and second peak respectively. These time windows were chosen to account for variation between participants in the latency of the first and second peak. Bayesian paired-sample t-tests showed very strong evidence that the first peak at ~45 ms (BF10=57.21) and moderate evidence that the second peak at ~85ms (BF10=6.77) had larger amplitude during the pain block compared to the pre-pain block. Figure 6B shows the individual level relationship between the mean pain rating, and the difference in N45 and N100 amplitude between pain and pre-pain. There was strong evidence that participants who reported higher pain ratings also showed a larger increase in N45 peak amplitude during the pain block (r26=0.52, BF10=10.64). There was anecdotal evidence for no association between pain ratings and changes in the N100 peak amplitude during pain (r26=0.24, BF10=0.48).

Figure 6C shows the mean TEP waveform of the parietal-occipital electrodes ('P1','PO3','O1','CPz','Pz','Pz','Oz','CP2','P2', 'PO4','O2','CP4','P4') identified from the cluster analysis for the pre-pain vs. pain conditions (Figure 6—figure supplement 1 shows the post-pain comparisons). One peak at ~50ms is visible in the time window where the significant cluster was detected. The approximate timing of this peak is consistent with the commonly identified P60. The amplitude of this peak was identified for each participant with a defined time window of 35–65ms. This time window was chosen to account for variation between participants in the latency of the peak. There was moderate evidence that the peak at ~50ms was stronger during the pain block compared to pre-pain block (BF10=5.56). Figure 6D shows the individual level relationship between the mean pain rating and the difference in the P60 amplitude between pain and pre-pain. There was anecdotal evidence in favour of no relationship between pain ratings and changes in P60 amplitude during the pain block (r26=0.21, BF10=0.407).

Figure 6 with 1 supplement see all
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Pain led to increases in N45, P60, and N100 peak amplitude, and individual pain sensitivity was predicted by changes in the N45 peak.

TEPs (n = 28) across pain and pre-pain condition for the frontocentral electrodes (A) and parietal-occipital electrodes (C) identified from the cluster analysis of Experiment 1. The grey shaded area represents the window of interpolation around the transcranial magnetic stimulation (TMS) pulse. For the frontocentral electrodes, there was evidence for stronger negative peaks at ~45 and 85ms post-TMS. For the parietal-occipital electrodes, there was evidence for a stronger positive peak was identified at ~50ms post-TMS. The astericks indicates at least moderate evidence for the alternative hypothesis that the peak amplitude is larger in pain vs. pre-pain (BF10 >3). Individual-level relationship between mean verbal pain ratings provided by each participant and change in peak amplitudes at ~45ms (N45),~85ms (N100) post-TMS (B), and ~50ms (P60) post-TMS (D). The astericks indicates at least moderate evidence for a relationship between change in peak amplitude, and verbal pain ratings (BF10 >3).

Relationship between changes in TEP peaks and MEP amplitude

Respectively, there was moderate and anecdotal evidence for no relationship between alterations in MEP amplitude and alterations in the N100 (r25=–.14, BF10=0.303) and P60 (r25=0.185, BF10=0.36) during pain. There was anecdotal evidence for a relationship between alterations in the N45 and MEP amplitude during pain (r25=–.387, BF10=1.40).

Experiment 2 – Does acute pain alter cortical excitability or sensory potentials?

Design

Several studies have shown that a significant portion of TEPs do not reflect the direct cortical response to TMS, but rather auditory potentials elicited by the ‘clicking’ sound from the TMS coil, and somatosensory potentials elicited by the ‘flicking’ sensation on the skin of the scalp (Biabani et al., 2019; Chowdhury et al., 2022b; Conde et al., 2019; Rocchi et al., 2021). Indeed, the signal at ~100ms post-TMS from Experiment 1 may reflect an auditory N100 response. As it is extremely challenging to isolate and filter these auditory- and somatosensory-evoked potentials using pre-processing pipelines, masking methods have been used to suppress these sensory inputs, (Ilmoniemi and Kicić, 2010; Massimini et al., 2005). However, recent studies have shown that even when these methods are used, sensory contamination of TEPs is still present, as shown by commonalities in the signal between active and sensory sham conditions that mimic the auditory/somatosensory aspects of real TMS (Biabani et al., 2019; Conde et al., 2019; Rocchi et al., 2021). This has led many leading authors (Biabani et al., 2019; Conde et al., 2019) to recommend the use of sham conditions to control for sensory contamination. To separate the direct cortical response to TMS from sensory evoked activity, Experiment 2 (n=10) included a sham TMS condition that mimicked the auditory/somatosensory aspects of active TMS to determine whether any alterations in the TEP peaks in response to pain were due to changes in sensory evoked activity associated with TMS, as opposed to changes in cortical excitability. A similar design (Figure 2—figure supplement 1) was used to Experiment 1, with the inclusion of a sham TMS condition within the pre-pain and pain blocks, and exclusion of the post-pain block, since the aim was to identify the source of the pain effect from Experiment 1. The sham TMS condition was similar to a recent study (Gordon et al., 2021), involving the delivery of the TMS coil rotated 90° to the scalp to simulate the auditory component associated with real TMS, and concurrent electrical stimulation beneath a sham coil to simulate the somatosensory component associated with real TMS (see Figure 7A).

Figure 7
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TMS-evoked potentials for Active and Sham TMS.

(A) Schematics showing the delivery of active and sham TMS of Experiment 2. Sham TMS involved scalp electrical stimulation (in red) beneath a sham coil (in dotted blue) to mimic somatosensory stimulation associated with active TMS, and concurrent delivery of active TMS 90° to the scalp (in shaded blue) to mimic auditory stimulation associated with TMS. (B) Left: TEPs (n = 10) during the pre-pain and pain blocks, for both active and sham stimulation. The grey shaded area represents the window of interpolation around the TMS pulse. Right: Scalp topographies and estimated source activity at timepoints where TEP peaks are commonly observed, including the N15, P30, N45, P60, N100, and P180.

Pain ratings

The mean cold and warm detection threshold was 29.6 ± 2.6°C and 36.0 ± 3.6°C, respectively. The mean heat pain threshold was 42.1 ± 3.6 °C. Figure 3—figure supplement 1 shows the pain ratings during the pain block for each condition.

TMS-evoked potentials

The mean RMT and test stimulus intensity was, respectively, 70.4 ± 4.3% and 77.7 ± 4.7% of maximum stimulus output. The mean test electrical stimulation intensity for the sham TMS condition was 4.4±2.5 mA. This intensity is comparable to previous studies using sham electrical stimulation and is insufficient to directly activate the cortex (Chowdhury et al., 2022b; Conde et al., 2019; Rocchi et al., 2021). Figure 7B shows the grand average TEPs, scalp topographies and estimated source activity for active and sham TMS, across pre-pain and pain conditions. Figure 8 shows the mean TEP waveform for the frontocentral and parietal-occipital clusters identified from Experiment 1, across active and sham conditions. There was moderate evidence that the amplitude of the N45 peak was increased during pain vs. pre-pain blocks for active TMS (BF10=3.26), and moderate evidence for no difference between pain and pre-pain blocks for sham TMS (BF10=0.309). When comparing pain and pre-pain blocks, there was, respectively, moderate and anecdotal evidence for no alterations in the frontocentral N100 for active (BF10=0.31) and sham TMS (BF10=0.42). There was anecdotal evidence for no alteration in the parietal occipital P60 for both active (BF10=0.786) and sham TMS (BF10=0.42). Overall, the results showed that the N45 peak was altered in response to pain for active but not sham TMS, suggesting the experience of pain led to an alteration in the excitability of the cortex, and not the auditory/somatosensory aspects of TMS.

Figure 8
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Pain led to an increase in the N45 peak amplitude during active TMS but not sham TMS.

TEPs (n = 10) during pain and pre-pain blocks, across active and sham TMS conditions of Experiment 2, for the frontocentral electrodes (left) and parietal-occipital electrodes (right) identified from the cluster analysis in the main experiment. A significantly stronger frontocentral negative peak was identified ~45ms post-TMS during pain compared to pre-pain, for the active TMS condition. The astericks indicates at least moderate evidence for the alternative hypothesis that the amplitude is larger in pain vs. pre-pain (BF10 >3).

Experiment 3 – Does acute pain alter cortical excitability or reafferent muscle activity?

Design

Previous studies have shown that a significant portion of the TEP peaks at 45 and 60ms post-TMS reflect reafferent feedback from the muscle twitch in response to suprathreshold TMS applied over M1. This comes from MRI-informed EEG studies showing source localization of the N45 and P60 peaks to the somatosensory areas, as well as correlations between MEP amplitude and N45/P60 amplitude (Ahn and Fröhlich, 2021; Petrichella et al., 2017). Indeed, Experiments 1 and 2 also showed localization of the N45 and P60 to sensorimotor areas. As such, Experiment 3 recruited a further ten participants to determine whether the pain-induced increase in the N45 peak was due to stronger reafferent feedback from muscle twitches. A design (Figure 2—figure supplement 2) similar to Experiment 1 was used, with the inclusion of a subthreshold TMS condition (90% RMT) within the pre-pain and pain blocks.

Pain ratings

The mean cold and warm detection threshold was 29.4 ± 2.0°C and 34.9 ± 1.4°C, respectively. The mean heat pain threshold was 42.9 ± 2.5°C. Figure 3—figure supplement 2 and Figure 3—figure supplement 3 shows, respectively, the warmth ratings during the pre-pain block for each condition, and the pain ratings during the pain block for each condition.

TMS-evoked potentials

The mean RMT, subthreshold and suprathreshold stimulus intensity was, respectively, 68.0 ± 8.3%, 61.0 ± 7.3% and 74.8 ± 9.1% of maximum stimulus output. Figure 9 shows the grand average TEPs, scalp topographies and estimated source activity for supra- and subthreshold TMS, across pre-pain and pain blocks. Figure 10 shows the mean TEP waveform for the frontocentral and parietal-occipital clusters identified from Experiment 1, across suprathreshold and subthreshold TMS conditions. When comparing the pain with pre-pain blocks, there was moderate evidence that the frontocentral N45 was increased during subthreshold TMS (BF10=3.05) and suprathreshold TMS (BF10=3.01). When comparing pain with pre-pain blocks, there was anecdotal evidence for no alterations in the frontocentral N100 peak during suprathreshold TMS (BF10=0.42) and subthreshold TMS (BF10=0.36). When comparing pain with pre-pain blocks, there was anecdotal evidence for an increase in the parietal occipital P60 peak during suprathreshold TMS (BF10=2.71) and anecdotal evidence for no alteration in the P60 peak during subthreshold TMS (BF10=0.72). Overall, there was evidence that that the N45 peak was altered in response to both supra- and subthreshold TMS, suggesting the pain-induced increase in the N45 peak was not a result of stronger reafferent feedback from the muscle twitches.

Figure 9
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TMS-evoked potentials for supra- and subthreshold TMS.

Left: TEPs (n = 10) during the pre-pain and pain blocks, for both supra- and subthreshold TMS of Experiment 3. The gray-shaded area represents the window of interpolation around the transcranial magnetic stimulation TMS pulse. Right: Scalp topographies and estimated source activity at timepoints where TEP peaks are commonly observed, including the N15, P30, N45, P60, N100, and P180.

Figure 10
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Pain led to an increase in the N45 peak amplitude for both suprathreshold and subthreshold TMS.

TEPs (n = 10) during pain and pre-pain blocks of Experiment 3, across supra- and subthreshold TMS conditions, for the frontocentral electrodes (left) and parietal occipital electrodes (right) identified from the cluster analysis in Experiment 1. A significantly stronger frontocentral negative peak was identified ~45ms post-TMS during pain compared to pre-pain for both supra- and subthreshold stimulation. The astericks indicates at least moderate Bayesian evidence for the alternative hypothesis that the amplitude is larger in pain vs. pre-pain (BF10 >3).

Stability of N45 peaks across experiments

We conducted a supplementary investigation of the stability of the N45 TEP peaks for each experiment. The interclass correlation coefficient (Two-way fixed, single measure) for the N45 to active suprathreshold TMS across timepoints for each experiment was 0.90 for Experiment 1 (across pre-pain, pain, post-pain time points), 0.74 for Experiment 2 (across pre-pain and pain conditions), and 0.95 for Experiment 3 (across pre-pain and pain conditions). This suggests that even with the fluctuations in the N45 induced by pain, the N45 for each participant was stable across time, further supporting the reliability of our data. Data supporting the findings of this study are available on Open science framework https://osf.io/k3psu/.

Discussion

The present study determined whether acute experimental pain induces alterations in cortical inhibitory and/or facilitatory activity observed in TMS-evoked potentials. Across three experiments, there was Bayesian evidence (varying between moderate to very strong) for an increase in the amplitude of the N45 peak during painful stimuli compared to a non-painful baseline. Experiment 1 showed very strong evidence that a larger increase in the N45 peak in response to pain was correlated with higher pain ratings. Experiment 2 showed that the increase in the N45 peak during pain was not a result of alterations in sensory potentials associated with the TMS pulses, but rather, changes in cortical excitability. Experiment 3 showed that the increase in the N45 peak was not a result of stronger reafferent feedback from muscle twitches evoked by TMS during painful stimuli. While Experiment 1 showed moderate evidence for an increase in N100 and P60 peaks during pain relative to pre-pain baseline, this was not replicated in the follow-up experiments. Experiment 1 showed anecdotal evidence for group-level alterations in MEP amplitude during pain; however, there was very strong evidence that a larger reduction in MEP amplitude during pain was correlated with lower pain ratings.

Increased GABAergic activity during tonic pain

This study is the first to use TMS-EEG methodology to examine the direct cortical response to acute pain, extending previous studies that have used TMS to measure MEPs in response to pain (Chowdhury et al., 2022a). The key finding was an increase in the amplitude of the N45 peak in response to pain. This result was replicated across three experiments, providing robust evidence for the effect. Furthermore, we accounted for major confounds that have caused significant data interpretation issues in the TMS-EEG literature in recent years, namely the contamination of TEPs by sensory potentials associated with TMS pulses (Biabani et al., 2019; Chowdhury et al., 2022b) and the presence of reafferent feedback from muscle twitches (Ahn and Fröhlich, 2021).

The finding of a reliable increase in the amplitude of the N45 peak during pain suggests a role for GABAA neurotransmission in pain processing, as previous work has shown that the amplitude of the N45 peak is increased in response to GABAA agonists (Premoli et al., 2014). Our source reconstruction results suggest that around this timepoint, the current density was stronger in the sensorimotor area, consistent with the idea that the N45 peak reflects GABAergic activity within the sensorimotor cortex (Farzan and Bortoletto, 2022). While it has been shown that reafferent muscle activity also contributes to the N45 peak (Ahn and Fröhlich, 2021), Experiment 3 showed that pain increased the amplitude of the N45 peak even during subthreshold TMS. Taken together, these findings suggest that the increased amplitude of the N45 peak in response to pain reflects an increase in GABAergic activity within the sensorimotor cortex.

GABAergic neurons play a critical role in pain-related brain networks (Barr et al., 2013; Ong et al., 2019). They are involved in the generation of gamma oscillations (Buzsáki and Wang, 2012), which have been strongly implicated in pain perception (Barr et al., 2013; Li et al., 2023). Indeed, previous work has shown an increase in gamma oscillations in response to painful thermal stimuli comparable to the present study, across a wide range of brain regions such as the prefrontal (Schulz et al., 2015) and sensorimotor cortices (Gross et al., 2007). It is therefore possible that increases in the N45 peak during pain reflect increased sensorimotor gamma oscillations. Further multimodal work is required to confirm this finding.

While our findings are consistent with some studies that show increases in GABAergic activity in response to pain (Gross et al., 2007; Kupers et al., 2009; Schulz et al., 2015), other studies have also reported reduced GABAergic activity in response to experimental pain (Cleve et al., 2015; de Matos et al., 2017). Differences between studies can be attributed to the duration of the noxious stimulus (tonic pain lasting several seconds/minutes vs. transient pain stimuli lasting <1 s). Indeed, pooled data have shown that the cerebral response to pain is highly dependent on the duration of the painful stimuli, as the adaptive response (to suppress or increase cortical activity) changes depending on the duration of pain (Chowdhury et al., 2022a). This highlights the need for further work to replicate our findings using different durations of experimental pain and in chronic pain populations.

Another finding of Experiment 1 was the increase in the amplitude of N100 peak, a marker of GABAB neurotransmission (Premoli et al., 2014), and the parietal-occipital P60 peak, a marker of glutamatergic neurotransmission (Belardinelli et al., 2021). However, this was not replicated in Experiments 2 and 3, potentially due to the smaller sample size. Nonetheless, we encourage further investigations of alterations in these peaks during pain, particularly the P60 peak, as several magnetic resonance spectroscopy studies have reported increases in glutamate concentration during experimental pain (Archibald et al., 2020).

Predicting individual differences in pain using TEPs

Experimental pain models are useful tools to explore brain measures that may predict individual differences in pain sensitivity, with an ultimate goal of determining whether such measures explain why some people develop chronic pain. Experiment 1 showed that higher pain ratings were associated with a larger increase in the N45 peak during pain. This analysis was not conducted in Experiments 2 and 3 due to smaller sample sizes and given the primary aims of Experiments 2 and 3 were to isolate the source of the group-level effect. Nonetheless, our results suggest that the N45 peak is a potential marker of sensorimotor GABAergic activity and may be associated with individual differences in pain sensitivity. This is consistent with other studies measuring GABAergic responses to pain, showing associations between higher pain sensitivity and larger sensorimotor gamma oscillations (Barr et al., 2013) and higher left somatosensory cortical GABA laterality (Niddam et al., 2021). However, the direction of this relationship likely depends on the duration of pain (Chowdhury et al., 2022a). Our results have implications for understanding the development and maintenance of chronic pain. Further TMS-EEG studies are required to determine whether the N45 peak is altered in chronic pain populations and whether the N45 peak can explain why some individuals in the acute stages of pain transition to chronic pain.

The TEP vs. MEP response to pain

The present study showed that a larger reduction in MEP amplitude during pain was correlated with lower pain ratings, consistent with a recent systematic review (Chowdhury et al., 2022a) and the idea that reduced MEP amplitude is an adaptive mechanism that restricts movement in the pain-afflicted area, to protect the area from further pain and injury (Hodges and Tucker, 2011). The novelty of this study was the use of an experimental heat pain paradigm that has not yet been used in combination with TMS research, and a paradigm that controls for non-painful somatosensory stimulation.

The finding of a pain-induced increase in the amplitude of the N45 peak, which indexes GABAA receptor activity, is consistent with TMS research showing pain-induced increases in short-interval intracortical inhibition (SICI) (Salo et al., 2019; Schabrun and Hodges, 2012). SICI refers to the reduction in MEP amplitude to a TMS pulse that is preceded 1–5ms by a subthreshold pulse, with this reduction believed to be mediated by GABAA neurotransmission (Kujirai et al., 1993). Some studies have reported associations between SICI and the TEP N45 peak (Leodori et al., 2019; Rawji et al., 2019), suggesting the two may share common neurophysiological mechanisms. However, we also found that a larger reduction in MEP amplitude during pain was associated with less pain, while a larger increase in the TEP N45 peak during pain was associated with stronger pain ratings, suggesting that inhibitory processes mediating MEPs and the TEP N45 peak during pain are distinct. Further work is required to disentangle the relationship between corticomotor excitability measured by MEPs and cortical activity measured by TEPs.

Study limitations

Some methodological limitations should be noted. Firstly, while there was no conclusive evidence for a difference in pain ratings between the six thermal stimuli of the pain block, there was evidence for fluctuations in pain ratings during each painful stimulus. This suggests that the perceived pain intensity was not stable across 40 s, which may have introduced noise in the TEP data. Future studies could use pain paradigms that can more effectively maintain a constant level of pain, for example hypertonic saline infusion paradigms (Svensson et al., 2003). Secondly, the use of verbal pain ratings prevented the characterization of pain on a finer time scale. However, verbal ratings were used to eliminate potential contamination of MEPs introduced by using the hand for providing pain rating. Thirdly, the increased N45 peak amplitude in response to pain may reflect increased alertness/arousal during pain. However, a recent study showed that higher alertness is associated with reduced TEP amplitude (Noreika et al., 2020), suggesting the increase in the N45 peak amplitude is not related to pain-induced arousal. Lastly, future research should consider replicating our experiment using intermixed pain and no pain blocks, as opposed to fixed pre-pain and pain blocks, to control for order effects (the explanation that successive thermal stimuli applied to the skin results in an increase in the N45 peak, regardless of whether the stimuli are painful or not). However, we note that there was no conclusive evidence for a difference in N45 peak amplitude between pre-pain and post-pain conditions of Experiment 1 (Figure 6—figure supplement 1), suggesting it is unlikely that the observed effects were an artefact of time.

Conclusion

This study is the first to use TMS-EEG methodology to examine alterations in cortical activity in direct response to acute pain. Findings across three experiments suggest that tonic heat pain leads to an increase in the amplitude of the frontocentral TEP N45 peak (associated with GABAergic neurotransmission), and that larger increases in this peak are associated with higher pain ratings. The findings suggest that TEP indices of GABAergic neurotransmission have the potential to be used as predictive markers of pain severity.

Materials and methods

Participants

Experiment 1 consisted of 29 healthy participants (18 males, 11 females, mean age; 26.24±5.5). Participants were excluded if they had a history of chronic pain condition or any current acute pain, any contraindication to TMS such as pregnancy, mental implants in the skull, seizure, or if they reported a history of neurological or psychiatric conditions, or were taking psychoactive medication. Participants completed a TMS safety screen (Rossi et al., 2009). Procedures adhered to the Declaration of Helsinki and were approved by the human research ethics committee of UNSW (HC200328). All participants provided informed written consent.

The sample size calculation was done in G*power 3.1.9.7 with 80% power. As there were no prior TMS-EEG pain studies, we used pooled data from our systematic review on TMS studies (Chowdhury et al., 2022a) showing that the weighted effect size of changes in MEP amplitude in response to tonic experimental pain was 0.56. Using this value, 28 participants were required to detect a significant difference between pain and pre-pain blocks.

To determine the sample size of Experiments 2 and 3, we computed the effect sizes of the N45, P60 and N100 changes (pain vs. prepain) from Experiment 1 (Cohen’s dRM = 1.76, 0.99, and 0.83 for N45, P60, and N100, respectively). With a power of 80%, the required sample size was 4–11 participants to detect a significant difference. Experiment 2 recruited a further 10 healthy participants (four males, six females, mean age: 26.8±5.9) and Experiment 3 consisted of 10 healthy participants (four males, six females, mean age: 28±5.9).

Experimental protocol

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Participants were seated comfortably in a shielded room. They viewed a fixation cross to minimize eye movements. TMS was applied to left M1 while participants wore an EEG cap containing 63 scalp electrodes to record TEPs. Surface electromyographic (EMG) electrodes were placed over the distal region of the right ECRB to record MEPs. EMG signals were amplified (x 1000) and filtered (16–1000 Hz), and digitally sampled at 2000 Hz (Spike2, CED). A thermode was attached over the proximal region of the right ECRB in close proximity to the EMG electrodes (Figure 1). The TMS coil was covered in a layer of foam (5 mm thickness) to minimize decay artefacts (Rogasch et al., 2017). Participants also wore both foam earplugs and headphones to reduce any potential discomfort from the TMS click. Auditory masking was not used. Instead, auditory-evoked potentials resulting from the TMS click sound were controlled for in Experiment 2.

The protocol for Experiment 1 is illustrated in Figure 2 (Furman et al., 2020; Granot et al., 2006). In Experiment 1, participants experienced three blocks of thermal stimuli, in chronological order: pre-pain, pain, and post-pain block. Each block consisted of multiple thermal stimuli delivered 40 s at a time during which suprathreshold (110% RMT) TMS measurements and verbal pain ratings were obtained. The thermode commenced at a baseline temperature of 32 °C. The pre-pain and post-pain blocks consisted of six thermal stimuli delivered at the warm threshold (the temperature that led to any detectable change in skin temperature from baseline). In the pain block, six thermal stimuli were delivered at 46 °C, which has been shown to produce lasting pain with a mean rating of ~5/10 (Furman et al., 2020). Given we were interested in the individual relationship between pain and excitability changes, the fixed temperature of 46 °C ensured larger variability in pain ratings as opposed to calibrating the temperature of the thermode for each participant (Adamczyk et al., 2022). The inclusion of blocks with warm stimuli allowed for control for changes in cortical excitability due to non-painful somatosensory stimulation.

The protocol for Experiment 2 (Figure 2—figure supplement 1) and Experiment 3 (Figure 2—figure supplement 2) were identical to Experiment 1 with two differences: the exclusion of the post-pain block (as the aim was to disentangle the source of the pain vs. pre-pain effect from Experiment 1) and the inclusion of a sham TMS condition (Experiment 2) or subthreshold (90% RMT) TMS (Experiment 3) intermixed within both the pre-pain and pain blocks.

Electrical stimulation setup (Experiment 2 Only)

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Electrical stimulation was based on previous studies attempting to simulate the somatosensory component of active TMS (Chowdhury et al., 2022b; Gordon et al., 2021; Rocchi et al., 2021). Prior to EEG setup, 8 mm Ag/AgCl electrodes were placed directly over the scalp. ‘Snap on’ lead wires were then clipped in place and connected to the electrical stimulator (Digitimer DS7AH, Digitimer Ltd., UK). To keep the electrodes and lead wires firmly in position, participants were fitted with a tight netted wig cap, which sat on top of the electrodes but underneath the EEG cap. Consistent with previous research (Chowdhury et al., 2022b; Rocchi et al., 2021), and to minimize EEG artefacts caused by electrical stimulation, the stimulating electrodes were not placed directly underneath the EEG electrodes. Rather, stimulating electrodes were positioned in the middle of the EEG electrode cluster located in closest proximity to the motor hotspot. This roughly corresponded to an anode position between FC1 and FC3 and a cathode position between C1 and C3. Scalp electrical stimulation was delivered using a 200 µs square wave via with a compliance of 200 V.

Electroencephalography

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EEG was recorded using a DC-coupled, TMS-compatible amplifier (ActiChamp Plus, Brain Products, Germany) at a sampling rate of 25,000 Hz. Signals were recorded from 63 TMS-compatible active electrodes (6 mm height, 13 mm width), embedded in an elastic cap (ActiCap, Brain Products, Germany), in line with the international 10–10 system. Active electrodes result in similar TEPs (both magnitude and peaks) to more commonly used passive electrodes (Mancuso et al., 2021). There is also evidence that active electrodes have higher signal quality than passive electrodes at higher impedance levels (Laszlo et al., 2014). Recordings were referenced online to ‘FCz’ and the ground electrode placed on ‘FPz’. Electrolyte gel was used to reduce electrode impedances below ~5kOhms. Online TEP monitoring was not available with the EEG software.

Transcranial magnetic stimulation

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Single, monophasic stimuli were delivered using a Magstim unit (Magstim Ltd., UK) and 70 mm figure-of-eight flat coil. The coil was oriented at 45° to the midline, inducing a current in the posterior-anterior direction. The scalp site that evoked the largest MEP measured at the ECRB (‘hotspot’) was determined and marked. The RMT was determined using the TMS motor thresholding assessment tool, which estimates the TMS intensity required to induce an MEP of 50 microvolts with a 50% probability using maximum likelihood parametric estimation by sequential testing (Awiszus, 2003; Awiszus and Borckardt, 2011). This method has been shown to achieve the accuracy of methods such as the Rossini-Rothwell method (Rossini et al., 1994; Rothwell et al., 1999) but with fewer pulses (Qi et al., 2011; Silbert et al., 2013). The test stimulus intensity was set at 110% RMT to concurrently measure MEPs and TEPs during pre-pain, pain and post-pain blocks.

Thermal pain

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Thermal stimuli were delivered over the proximal region of the right ECRB using a contact heat stimulator (27 mm diameter Medoc Pathway CHEPS Peltier device; Medoc Advanced Medical Systems Ltd). Pain ratings were obtained after each TMS pulse using a verbal rating scale (0=no pain, and 10=most pain imaginable). Verbal ratings were collected rather than pain ratings provided on the computer by hand to avoid contamination of MEP measures from motor processes of hand movements. Verbal pain ratings have been shown to yield excellent test-retest reliability (Alghadir et al., 2018).

Quantitative sensory testing

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Warmth, cold and pain thresholds were assessed in line with a previous study (Furman et al., 2020). With the baseline temperature set at a neutral skin temperature of 32 °C, participants completed three threshold tests: to report when they felt a temperature increase (warmth detection threshold; Furman et al., 2020), to report when they felt a temperature decrease (cool detection threshold; Furman et al., 2020); (3) to report when an increasing temperature first became painful (heat pain threshold; Furman et al., 2020). A total of three trials was conducted for each test to obtain an average, with an interstimulus interval of 6 s (Furman et al., 2020). The sequence of cold, warmth and pain threshold was the same for all participants. Participants provided feedback for each trial by pressing a button (with their left hand) on a hand-held device connected to the Medoc Pathway. Temperatures were applied with a rise/decrease rate of 1 °C/s and return rate of 2 °C/s (initiated by the button click).

Matching task (Experiment 2 only)

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As the aim of Experiment 2 was to perceptually match the somatosensory aspects of active and sham TMS, a 2-Alternative Forced Choice task was used to determine the electrical stimulation intensity that led to a similar flicking sensation to active TMS (Chowdhury et al., 2022b). Participants received either electrical stimulation or active TMS in a randomized order and were asked whether the first or second stimulus led to a stronger flick sensation. The electrical stimulation intensity was then increased or decreased until participants could no longer judge the first or second stimulus as stronger. This intensity was then applied during the test blocks.

Test blocks

Experiment 1

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The temperature of the thermode commenced at a neutral skin temperature (32 °C). Participants were exposed to 18 sustained thermal stimuli with a 20-s interstimulus interval. For each thermal stimulus, a single temperature (rise rate of 1 °C/s, return rate of 2 °C/s) was applied over the proximal region of the ECRB for 40 s. Thermal stimuli 1–6 (pre-pain block) were delivered at the participant’s individually determined warmth detection threshold, Thermal stimuli 7–12 at 46°, and Thermal stimuli 13–18 again at the participant’s warmth detection threshold. Participants were not informed of the order of the warm and painful stimuli to minimize the influence of expectation of pain on TEPs and MEPs. During each 40-s thermal stimulus, TMS pulses were manually delivered, with a verbal pain rating score (0=no pain, and 10=worst pain imaginable) obtained between pulses. To avoid contamination of TEPs by verbal ratings, the subsequent TMS pulse was not delivered until the verbal rating was complete, and the participant was cued by the experimenter to provide the pain rating after each pulse. As TMS was delivered manually, there was no set interpulse interval. However, the 40-s thermal stimulus duration allowed for 11 pulses for each thermal stimulus (hence 66 TMS pulses for each of the pre-pain, pain, and post-pain blocks), and 10 verbal pain ratings between each TMS pulse (~4 s in between pain ratings). Current recommendations (Hernandez-Pavon et al., 2023) suggest basing the number of TMS trials per condition on the key outcome measure (e.g. TEP peaks vs. frequency measures) and based on previous test-retest reliability studies. In our study the number of trials was based on a test-retest reliability study by (Kerwin et al., 2018) which showed that 60 TMS pulses (delivered in the same run) was sufficient to obtain reliable TEP peaks (i.e. sufficient within-individual concordance between the resultant TEP peaks of each trial).

Experiment 2

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Participants were exposed to 24 sustained thermal stimuli (40 s each). Thermal stimuli 1–6 and 7–12 consisted of warm stimuli (pre-pain block), while Thermal stimuli 13–18 and 19–24 consisted of stimuli delivered at 46 °C (pain block). Active or sham TMS was delivered during either thermal stimuli 1–6 or 7–12, with the order of active and sham randomly determined for each participant. The same applied for thermal stimuli 13–18 and 19–24. The active and sham TMS conditions were similar to that used in a recent TMS-EEG study (Gordon et al., 2021). Sham TMS involved the active TMS coil rotated 90° to the scalp, and a sham coil (identical in shape/weight) placed underneath the active coil and tangentially over the scalp. The active TMS coil was then triggered with the electrical stimulation unit to simultaneously simulate the auditory and somatosensory components of active TMS, respectively. Active TMS involved the delivery of the active TMS coil placed tangentially over the scalp, and the sham TMS coil above the active coil rotated 90° to the scalp (see Figure 7). The design allowed for 11 pulses for each thermal stimulus and ten pain ratings (hence 66 TMS pulses for active pre-pain, sham pre-pain, active pain and sham pain blocks).

Experiment 3

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Participants were exposed to 24 sustained thermal stimuli (40 s each). Thermal stimuli 1–6 and 7–12 consisted of warm stimuli (pre-pain block), while thermal stimuli 13–18 and 19–24 consisted of thermal stimuli delivered at 46 °C (pain block). Suprathreshold or subthreshold TMS (90% RMT) was delivered during either thermal stimuli 1–6 or 7–12, with the order of supra- and subthreshold TMS randomly determined for each participant. The same applied for thermal stimuli 13–18 and 19–24. In addition to the pain rating in between TMS pulses, we collected a second rating for warmth of the thermal stimulus (0=neutral, 10=very warm) to confirm that the participants felt some difference in sensation relative to baseline during the pre-pain block. Overall, the design allowed for 11 pulses for each thermal stimulus and 10 pain/warmth ratings (hence 66 TMS pulses for suprathreshold pre-pain, subthreshold pre-pain, suprathreshold pain and subthreshold pain blocks).

Data processing

Motor-evoked potentials

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The amplitude of each MEP was determined using a custom MATLAB script (https://github.com/Nahian92/TMS_EEG_Preprocessing/tree/main, copy archived at Chowdhury, 2023). The onsets and offsets of the MEPs were manually determined for each trial. In some participants, background EMG activity was observed due to placement of the thermode close to the EMG electrodes, which can influence MEP amplitude (Ruddy et al., 2018). To account for this, MEP amplitude was calculated by subtracting the root mean square (RMS) of background EMG noise from the RMS of the MEP window using a fixed window between 55 and 5ms before the TMS pulse (Chowdhury et al., 2023; Tsao et al., 2011).

TMS-evoked potentials

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Pre-processing of the TEPs was completed using EEGLAB (Delorme and Makeig, 2004) and TESA (Rogasch et al., 2017) in MATLAB (R2021b, The Math works, USA), and based on previously described methods (Chowdhury et al., 2022b; Mutanen et al., 2018; Rogasch et al., 2017). The script is available on available on https://github.com/Nahian92/TMS_EEG_Preprocessing/tree/main, (copy archived at Chowdhury, 2023). First, bad channels were removed. The mean number of channels removed across participants was 2.2±2.7 for Experiment 1, 3±2.2 for Experiment 2, and 6.1±2.64 for Experiment 3. The period between –5 and ~14ms after the TMS pulse was removed and interpolated using the ARFIT function for continuous data (Neumaier and Schneider, 2001; Schneider and Neumaier, 2001). The exact interval was based on the duration of decay artefacts. Data was epoched 1000ms before and after the TMS pulse, and baseline corrected between –1000 and –5ms before the TMS pulse. Noisy epochs were identified via the EEGLAB auto-trial rejection function (Delorme et al., 2007) and then visually confirmed. There was moderate Bayesian evidence for no difference in the mean number of epochs excluded between conditions for Experiment 1 (5.8±5.2, 5.1±5.0 and 5.3±4.1 for the pre-pain, pain, and post-pain conditions, respectively, BF10=0.145), Experiment 2 (5.1±3.0, 5.6±3.4, 8.2±5.2 and 4.6±4.4 for the active pre-pain, sham pre-pain, active pain, and sham pain conditions, respectively, BF10=0.27) and Experiment 3 (5.4±3.4, 8.1±6.5, 6.5±6.6 and 5.4±2.8 for the subthreshold pre-pain, suprathreshold pre-pain, subthreshold pain, suprathreshold pain, respectively, BF10=0.169). The fastICA algorithm with auto-component rejection was used to remove eyeblink and muscle artefacts (Rogasch et al., 2017). There was anecdotal or moderate Bayesian evidence for no difference in the mean number of rejected components between conditions for Experiment 1 (11.0±6.3, 11.7±8.4 and 10.25±8.3 for the pre-pain, pain, and post-pain block, respectively, BF10=0.19), Experiment 2 (11.4±8.4, 12.3±6.9, 10.1±3.4 and 12.9±9.5 for the active pre-pain, sham pre-pain, active pain, and sham pain conditions, respectively, BF10=0.181) and Experiment 3 (9.1±7.8, 7.6±7.1, 9.6±7.8 and 8.6±7.0, BF10=0.576). The source-estimation noise-discarding (SOUND) algorithm was applied (Mutanen et al., 2020; Mutanen et al., 2018), which estimates and supresses noise at each channel based on the most likely cortical current distribution given the recording of other channels. This signal was then re-referenced (to average). A band-pass (1–100 Hz) and band-stop (48–52 Hz) Butterworth filter was then applied. Any lost channels were interpolated.

Source localization

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Source localization of TEPs was conducted using Brainstorm (Tadel et al., 2011). A template brain model (ICBM 152) was co-registered with the TMS-EEG data. Noise estimation was used to determine sensor weighting and regularization parameter of the current density construction. The forward model involved use of the Symmetric Boundary Element Method with the head having 3 compartments of fixed conductivities, implemented in OpenMEEG software (Gramfort et al., 2010), and inverse model involved use of Minimum Norm Estimations.

Statistical analysis

Given we were interested in determining the evidence for pain altering TEP peaks in certain conditions (e.g. active TMS) and pain not altering TEP peaks in other conditions (sham TMS), we used a Bayesian approach as opposed to a frequentist approach, which considers the strength of the evidence for the alternative vs. null hypothesis. Bayesian inference was used to analyze the data using JASP software (Version 0.12.2.0, JASP Team, 2020). Bayes factors were expressed as BF10 values, where BF10’s of 1–3, 3–10, 10–30 and 30–100 indicated ‘weak’, ‘moderate’, ‘strong’ and ‘very strong’ evidence for the alternative hypothesis, while BF10’s of 1/3–1, 1/10-1/3, 1/30-1/10 and 1/100-1/30 indicated ‘anecdotal’, ‘moderate’, ‘strong’ and ‘very strong’ evidence in favour of the null hypothesis (van Doorn et al., 2021).

Pain ratings

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A 6 (thermal stimulus number: 1–6) x 10 (timepoint:1–10) Bayesian repeated measures ANOVA with default priors in JASP (r scale fixed effects = .5, r scale random effects = 1, r scale covariates = .354) was conducted on the pain ratings during the pain block. This was to assess differences in pain ratings between the six painful stimuli and whether pain ratings differed between the timepoints of each painful stimulus.

Motor-evoked potentials

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A Bayesian one-way repeated measures ANOVA with default priors in JASP was performed to assess differences in MEP amplitudes between pre-pain, pain, and post-pain blocks of Experiment 1. As there is now increasing emphasis placed on investigating the individual level relationship between changes in cortical excitability and pain and not only the group level effect (Chowdhury et al., 2022a; Seminowicz et al., 2018; Seminowicz et al., 2019; Summers et al., 2019), we also investigated the correlations between pain ratings and changes in MEP (and TEP) amplitude, A Bayesian correlation analysis with default priors in JASP (stretched Beta prior width = 1) was run to determine whether the change in mean MEP amplitude during pain (as a proportion of pre-pain) was associated with the mean verbal pain rating score. Data were checked for assumptions of normally distributed data using a Shapiro-Wilk test. Where assumptions were violated, data were log-transformed.

TMS-evoked potentials

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The grand-averaged signals for the pre-pain, pain, and post-pain condition were obtained. For Experiment 1, a cluster-based permutation analysis was used to compare amplitude levels between pre-pain and pain, and pre-pain and post-pain, at each time-point and for each electrode. For all experiments, the mean TEP waveform of any identified clusters from Experiment 1 were plotted, and peaks (e.g. N15, P30, N45, P60, N100) were identified using the TESA peak function (Rogasch et al., 2017). Any identified peaks were then compared between conditions using Bayes paired sample t-tests with default priors in JASP (Cauchy scale = .707). A Bayesian correlation analysis with default priors in JASP was performed to determine whether the difference in identified peaks between pre-pain and pain blocks was associated with the mean pain rating score. Data were checked for assumptions of normally distributed data using a Shapiro-Wilk test. Where assumptions were violated, data were log-transformed.

Data availability

Data supporting the findings of this study are available on Open science framework https://osf.io/k3psu/.

The following data sets were generated
    1. Chowdhury N
    (2023) Open Science Framework
    ID k3psu. Alterations in cortical excitability during pain: A combined TMS-EEG Study.
    https://osf.io/k3psu/

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    Magnetic stimulation: motor evoked potentials: the International Federation of Clinical Neurophysiology
    Electroencephalography and Clinical Neurophysiology. Supplement 52:97–103.

Peer review

Reviewer #1 (Public Review):

The objective of this investigation was to determine whether experimental pain could induce alterations in cortical inhibitory / facilitatory activity observed in TMS-evoked potentials (TEPs). Previous TMS investigations of pain perception had focused on motor evoked potentials (MEPs), which reflect a combination of cortical, spinal, and peripheral activity, as well as restricting the focus to M1. The main strength of this investigation is the combined use of TMS and EEG in the context of experimental pain. More specifically, Experiment 1 investigated whether acute pain altered cortical excitability, reflected in the modulation of TEPs. The main outcome of this study is that relative to non-painful warm stimuli, painful thermal stimuli led to an increase on the amplitude of the TEP N45, with a larger increase associated with higher pain ratings. Because it has been argued that a significant portion of TEPs could reflect auditory potentials elicited by the sound (click) of the TMS, Experiment 2 constituted a control study that aimed to disentangle the cortical response related to TMS and auditory activity. Finally, Experiment 3 aimed to disentangle the cortical response to TMS and reafferent feedback from muscular activity elicited by suprathreshold TMS applied over M1. The fact that the authors accompanied their main experiment with two control experiments strengthens the conclusion that the N45 TEP peak could be implicated in the perception of painful stimuli. Perhaps, the addition of a highly salient but non-painful stimulus (i.e. from another modality) would have further ruled out that the effects on the N45 are not predominantly related to intensity / saliency of the stimulus rather than to pain per se.

https://doi.org/10.7554/eLife.88567.3.sa1

Reviewer #2 (Public Review):

The authors have used transcranial magnetic stimulation (TMS) and motor evoked potentials (MEPs) and TMS-electroencephalography (EEG) evoked potentials (TEPs) to determine how experimental heat pain could induce alterations these metrics.In Experiment 1 thermal stimuli were administered over the forearm, with the first, second and third block of stimuli consisting of warm but non painful (pre-pain block), painful heat (pain block) and warm but non-painful (post-pain block) temperatures respectively. Painful stimuli led to an increase in the amplitude of the fronto-central N45, with a larger increase associated with higher pain ratings. Experiments 2 and 3 studied the correlation between the increase in the N45 in pain and the effects of a sham stimulation protocol/higher stimulation intensity. They found that the centro-frontal N45 TEP was decreased in acute pain. While their results are in line with reductions seen in motor evoked responses during pain and effort was made to address possible confounding factors (study 2 and 3). This study opens the way for the use exploration of cortical excitability outside M1 in acute pain, and potentially in chronic pain instances. While there is still open discussion on the best strategy to handle auditory and mechanical tactile noise, technological and methodological improvements seen in the last years have greatly improved the signal to noise ratio of TMS-EEG.

https://doi.org/10.7554/eLife.88567.3.sa2

Reviewer #3 (Public Review):

The present study aims to investigate whether pain influences cortical excitability. To this end, heat pain stimuli are applied to healthy human participants. Simultaneously, TMS pulses are applied to M1 and TMS-evoked potentials (TEPs) and pain ratings are assessed after each TMS pulse. TEPs are used as measures of cortical excitability. The results show that TEP amplitudes at 45 msec (N45) after TMS pulses are higher during painful stimulation than during non-painful warm stimulation. Control experiments indicate that auditory, somatosensory, or proprioceptive effects cannot explain this effect. Considering that the N45 might reflect GABAergic activity, the results suggest that pain changes GABAergic activity. The authors conclude that TEP indices of GABAergic transmission might be useful as biomarkers of pain sensitivity.

Pain-induced cortical excitability changes is an interesting, timely, and potentially clinically relevant topic. The paradigm and the analysis are sound, the results are convincing, and the interpretation is adequate. The findings will be of interest to researchers interested in the brain mechanisms of pain.

https://doi.org/10.7554/eLife.88567.3.sa3

Author response

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

The objective of this investigation was to determine whether experimental pain could induce alterations in cortical inhibitory/facilitatory activity observed in TMS-evoked potentials (TEPs). Previous TMS investigations of pain perception had focused on motor evoked potentials (MEPs), which reflect a combination of cortical, spinal, and peripheral activity, as well as restricting the focus to M1. The main strength of this investigation is the combined use of TMS and EEG in the context of experimental pain. More specifically, Experiment 1 investigated whether acute pain altered cortical excitability, reflected in the modulation of TEPs. The main outcome of this study is that relative to non-painful warm stimuli, painful thermal stimuli led to an increase on the amplitude of the TEP N45, with a larger increase associated with higher pain ratings. Because it has been argued that a significant portion of TEPs could reflect auditory potentials elicited by the sound (click) of the TMS, Experiment 2 constituted a control study that aimed to disentangle the cortical response related to TMS and auditory activity. Finally, Experiment 3 aimed to disentangle the cortical response to TMS and reafferent feedback from muscular activity elicited by suprathreshold TMS applied over M1. The fact that the authors accompanied their main experiment with two control experiments strengthens the conclusion that the N45 TEP peak could be implicated in the perception of painful stimuli.

Perhaps, the addition of a highly salient but non-painful stimulus (i.e. from another modality) would have further ruled out that the effects on the N45 are not predominantly related to intensity/saliency of the stimulus rather than to pain per se.

We thank the reviewer for their comment on the possibility of whether stimulus intensity influences the N45 as opposed to pain per se. We agree that the ideal experiment would have included multiple levels of stimulation. We would argue, however, that that in Experiment 1, despite the same level of stimulus intensity for all participants (46 degrees), individual differences in pain ratings were associated with the change in the N45 amplitude, suggesting that the results cannot be explained by stimulus intensity, but rather by pain intensity.

Reviewer #2 (Public Review):

The authors have used transcranial magnetic stimulation (TMS) and motor evoked potentials (MEPs) and TMS-electroencephalography (EEG) evoked potentials (TEPs) to determine how experimental heat pain could induce alterations in these metrics.In Experiment 1 (n = 29), multiple sustained thermal stimuli were administered over the forearm, with the first, second, and third block of stimuli consisting of warm but non-painful (pre-pain block), painful heat (pain block) and warm but non-painful (post-pain block) temperatures respectively. Painful stimuli led to an increase in the amplitude of the fronto-central N45, with a larger increase associated with higher pain ratings. Experiments 2 and 3 studied the correlation between the increase in the N45 in pain and the effects of a sham stimulation protocol/higher stimulation intensity. They found that the centro-frontal N45 TEP was decreased in acute pain. The study comes from a very strong group in the pain fields with long experience in psychophysics, experimental pain, neuromodulation, and EEG in pain. They are among the first to report on changes in cortical excitability as measured by TMS-EEG over M1. While their results are in line with reductions seen in motor-evoked responses during pain and effort was made to address possible confounding factors (study 2 and 3), there are some points that need attention. In my view the most important are:

1. The method used to calculate the rest motor threshold, which is likely to have overestimated its true value : calculating highly abnormal RMT may lead to suprathreshold stimulations in all instances (Experiment 3) and may lead to somatosensory "contamination" due to re-afferent loops in both "supra" and "infra" (aka. less supra) conditions.

The method used to assess motor threshold was the TMS motor threshold Assessment Tool (MTAT) which estimates motor threshold using maximum likelihood parametric estimation by sequential testing (Awiszus et al., 2003; Awiszus and Borckardt, 2011). This was developed as a quicker alternative for calculating motor threshold compared to the traditional Rossini-Rothwell method which involves determining the lowest intensity that evokes at least 5/10 MEPs of at least 50 microvolts. The method has been shown to achieve the same accuracy of determining motor threshold as the traditional Rossini-Rothwell method, but with fewer pulses (Qi et al., 2011; Silbert et al., 2013).

We have now made this clearer in the manuscript:

“The RMT was determined using the TMS motor thresholding assessment tool, which estimates the TMS intensity required to induce an MEP of 50 microvolts with a 50% probability using maximum likelihood parametric estimation by sequential testing (Awiszus, 2003; Awiszus & Borckardt, 2011). This method has been shown to achieve the accuracy of methods such as the Rossini-Rothwell method (Rossini et al., 1994; Rothwell et al., 1999) but with fewer pulses (Qi, Wu, & Schweighofer, 2011; Silbert, Patterson, Pevcic, Windnagel, & Thickbroom, 2013). The test stimulus intensity was set at 110% RMT to concurrently measure MEPs and TEPs during pre-pain, pain and post-pain blocks.”

Therefore, the high RMTs in our study cannot be explained by the threshold assessment method. Instead, they are likely explained by aspects of the experimental setup that increased the distance between the TMS coil and the scalp, including the layer of foam placed over the coil, the EEG cap and the fact that the electrodes we used had a relatively thick profile. This has been explained in the paper:

“We note that the relatively high RMTs are likely due to aspects of the experimental setup that increased the distance between the TMS coil and the scalp, including the layer of foam placed over the coil, the EEG cap and relatively thick electrodes (6mm)”

Awiszus, F. (2003). TMS and threshold hunting. In Supplements to Clinical neurophysiology (Vol. 56, pp. 13-23). Elsevier.

Qi, F., Wu, A. D., & Schweighofer, N. (2011). Fast estimation of transcranial magnetic stimulation motor threshold. Brain stimulation, 4(1), 50-57.

Silbert, B. I., Patterson, H. I., Pevcic, D. D., Windnagel, K. A., & Thickbroom, G. W. (2013). A comparison of relative-frequency and threshold-hunting methods to determine stimulus intensity in transcranial magnetic stimulation. Clinical Neurophysiology, 124(4), 708-712.

1. The low number of pulses used for TEPs (close to ⅓ of the usual and recommended)

We agree that increasing the number of pulses can increase the signal to noise ratio. During piloting, participants were unable to tolerate the painful stimulus for long periods of time and we were required to minimize the number of pulses per condition.

We note that there is no set advised number of trials in TMS-EEG research. According to the recommendations paper, the number of trials should be based on the outcome measure e.g., TEP peaks vs. frequency domain measures vs. other measures and based on previous studies investigating test-retest reliability (Hernandez-Pavon et al., 2023). The choice of 66 pulses per condition was based on the study by Kerwin et al., (2018) showing that optimal concordance between TEP peaks can be found with 60-100 TMS pulses delivered in the same run (as in the present study). The concordance was particularly higher for the N40 peak at prefrontal electrodes, which was the key peak and electrode cluster in our study. We have made this clearer:

“Current recommendations (Hernandez-Pavon et al., 2023) suggest basing the number of TMS trials per condition on the key outcome measure (e.g., TEP peaks vs. frequency measures) and based on previous test-retest reliability studies. In our study the number of trials was based on a test-retest reliability study by (Kerwin, Keller, Wu, Narayan, & Etkin, 2018) which showed that 60 TMS pulses (delivered in the same run) was sufficient to obtain reliable TEP peaks (i.e., sufficient within-individual concordance between the resultant TEP peaks of each trial).”

Further supporting the reliability of the TEP data in our experiment, we note that the scalp topographies of the TEPs for active TMS at various timepoints (Figures 5, 7 and 9) were similar across all three experiments, especially at 45 ms post-TMS (frontal negative activity, parietal-occipital positive activity).

In addition to this, the interclass correlation coefficient (Two-way fixed, single measure) for the N45 to active suprathreshold TMS across timepoints for each experiment was 0.90 for Experiment 1 (across pre-pain, pain, post-pain time points), 0.74 for Experiment 2 (across pre-pain and pain conditions), and 0.95 for Experiment 3 (across pre-pain conditions). This suggests that even with the fluctuations in the N45 induced by pain, the N45 for each participant was stable across time, further supporting the reliability of our data. These ICCs are now reported in the supplementary material (subheading: Test-retest reliability of N45 Peaks).

Hernandez-Pavon, J. C., Veniero, D., Bergmann, T. O., Belardinelli, P., Bortoletto, M., Casarotto, S., ... & Ilmoniemi, R. J. (2023). TMS combined with EEG: Recommendations and open issues for data collection and analysis. Brain Stimulatio, 16(3), 567-593

Kerwin, L. J., Keller, C. J., Wu, W., Narayan, M., & Etkin, A. (2018). Test-retest reliability of transcranial magnetic stimulation EEG evoked potentials. Brain stimulation, 11(3), 536-544.

Lack of measures to mask auditory noise.

In TMS-EEG research, various masking methods have been proposed to suppress the somatosensory and auditory artefacts resulting from TMS pulses, such as white noise played through headphones to mask the click sound (Ilmoniemi and Kičić, 2010), and a thin layer of foam placed between the TMS coil and EEG cap to minimize the scalp sensation (Massimini et al., 2005). However, recent studies have shown that even when these methods are used, sensory contamination of TEPs is still present, as shown by studies that show commonalities in the signal between active and sensory sham conditions that mimic the auditory/somatosensory aspects of real TMS (Biabani et al., 2019; Conde et al., 2019; Rocchi et al., 2021). This has led many authors (Biabani et al., 2019; Conde et al., 2019) to recommend the use of sham conditions to control for sensory contamination. To separate the direct cortical response to TMS from sensory evoked activity, Experiment 2 included a sham TMS condition that mimicked the auditory/somatosensory aspects of active TMS to determine whether any alterations in the TEP peaks in response to pain were due to changes in sensory evoked activity associated with TMS, as opposed to changes in cortical excitability. Therefore, the lack of auditory masking does not impact the main conclusions of the paper.

We have made this clearer:

“… masking methods have been used to suppress these sensory inputs, (Ilmoniemi and Kičić, 2010; Massimini et al., 2005). However recent studies have shown that even when these methods are used, sensory contamination of TEPs is still present, as shown by commonalities in the signal between active and sensory sham conditions that mimic the auditory/somatosensory aspects of real TMS (Biabani et al., 2019; Conde et al., 2019; Rocchi et al., 2021). This has led many leading authors (Biabani et al., 2019; Conde et al., 2019) to recommend the use of sham conditions to control for sensory contamination.”

Ilmoniemi, R. J., & Kičić, D. (2010). Methodology for combined TMS and EEG. Brain topography, 22, 233-248.

Massimini, M., Ferrarelli, F., Huber, R., Esser, S. K., Singh, H., & Tononi, G. (2005). Breakdown of cortical effective connectivity during sleep. Science, 309(5744), 2228-2232.

Biabani, M., Fornito, A., Mutanen, T. P., Morrow, J., & Rogasch, N. C. (2019). Characterizing and minimizing the contribution of sensory inputs to TMS-evoked potentials. Brain stimulation, 12(6), 1537-1552.

Conde, V., Tomasevic, L., Akopian, I., Stanek, K., Saturnino, G. B., Thielscher, A., ... & Siebner, H. R. (2019). The non-transcranial TMS-evoked potential is an inherent source of ambiguity in TMS-EEG studies. Neuroimage, 185, 300-312.

Rocchi, L., Di Santo, A., Brown, K., Ibáñez, J., Casula, E., Rawji, V., ... & Rothwell, J. (2021). Disentangling EEG responses to TMS due to cortical and peripheral activations. Brain stimulation, 14(1), 4-18.

1. A supra-stimulus heat stimulus not based on individual HPT, that oscillates during the experiment and that lead to large variations in pain intensity across participants is unfortunate.

The choice of whether to calibrate or fix stimulus intensity is a contentious question in experimental pain research. A recent discussion by Adamczyk et al., (2022) explores the pros and cons of each approach and recommends situations where one method may be preferred over the other. That paper suggests that the choice of the methodology is related to the research question – when the main outcome of the research is objective (neurophysiological measures) and researchers are interested in the variability in pain ratings, the fixed approach is preferrable. Given we explored the relationship between MEP/N45 modulation by pain and pain intensity, this question is better explored by using the same stimulus intensity for all participants, as opposed to calibrating the intensity to achieve a similar level of pain across participants.

We have made this clearer:

“Given we were interested in the individual relationship between pain and excitability changes, the fixed temperature of 46ºC ensured larger variability in pain ratings as opposed to calibrating the temperature of the thermode for each participant (Adamczyk et al., 2022).”.

Adamczyk, W. M., Szikszay, T. M., Nahman-Averbuch, H., Skalski, J., Nastaj, J., Gouverneur, P., & Luedtke, K. (2022). To calibrate or not to calibrate? A methodological dilemma in experimental pain research. The Journal of Pain, 23(11), 1823-1832.

So is the lack of report on measures taken to correct for a fortuitous significance (multiple comparison correction) in such a huge number of serial paired tests.

Note that we used a Bayesian approach for all analyses as opposed to the traditional frequentist approach. In contrast to the frequentist approach, the Bayesian approach does not require corrections for multiple comparisons (Gelman et al., 2000) given that they provide a ratio representing the strength of evidence for the null vs. alternative hypotheses as opposed to accepting or rejecting the null hypothesis based on p-values. As such, throughout the paper, we frame our interpretations and conclusions based on the strength of evidence (e.g. anecdotal/weak, moderate, strong, very strong) as opposed to referring to the significance of the effects.

Gelman A, Tuerlinckx F. (2000). Type S error rates for classical and Bayesian single and multiple comparison procedures. Computational statistics, 15(3):373-90.

Reviewer #3 (Public Review):

The present study aims to investigate whether pain influences cortical excitability. To this end, heat pain stimuli are applied to healthy human participants. Simultaneously, TMS pulses are applied to M1 and TMS-evoked potentials (TEPs) and pain ratings are assessed after each TMS pulse. TEPs are used as measures of cortical excitability. The results show that TEP amplitudes at 45 msec (N45) after TMS pulses are higher during painful stimulation than during non-painful warm stimulation. Control experiments indicate that auditory, somatosensory, or proprioceptive effects cannot explain this effect. Considering that the N45 might reflect GABAergic activity, the results suggest that pain changes GABAergic activity. The authors conclude that TEP indices of GABAergic transmission might be useful as biomarkers of pain sensitivity.

Pain-induced cortical excitability changes is an interesting, timely, and potentially clinically relevant topic. The paradigm and the analysis are sound, the results are mostly convincing, and the interpretation is adequate. The following clarifications and revisions might help to improve the manuscript further.

1. Non-painful control condition. In this condition, stimuli are applied at warmth detection threshold. At this intensity, by definition, some stimuli are not perceived as different from the baseline. Thus, this condition might not be perfectly suited to control for the effects of painful vs. non-painful stimulation. This potential confound should be critically discussed.

In Experiment 3, we also collected warmth ratings to confirm whether the pre-pain stimuli were perceived as different from baseline. This detail has been added to them methods:

“In addition to the pain rating in between TMS pulses, we collected a second rating for warmth of the thermal stimulus (0 = neutral, 10 = very warm) to confirm that the participants felt some difference in sensation relative to baseline during the pre-pain block. This data is presented in the supplementary material”.

We did not include these data in the initial submission but have now included it in the supplemental material. These data showed warmth ratings were close to 2/10 on average. This confirms that the non-painful control condition produced some level of non-painful sensation.

1. MEP differences between conditions. The results do not show differences in MEP amplitudes between conditions (BF 1.015). The analysis nevertheless relates MEP differences between conditions to pain ratings. It would be more appropriate to state that in this study, pain did not affect MEP and to remove the correlation analysis and its interpretation from the manuscript.

The interindividual relationship between changes in MEP amplitude and individual pain rating is statistically independent from the overall group level effect of pain on MEP amplitude. Therefore, conclusions for the individual and group level effects can be made independently.

It is also important to note that in the pain literature, there is now increasing emphasis placed on investigating the individual level relationship between changes in cortical excitability and pain as opposed to the group level effect (Seminowicz et al., 2019; Summers et al., 2019). As such, it is important to make these results readily available for the scientific community.

We have made this clearer:

‘As there is now increasing emphasis placed on investigating the individual level relationship between changes in cortical excitability and pain and not only the group level effect, (Chowdhury et al., 2022; Seminowicz et al., 2018; Seminowicz, Thapa, & Schabrun, 2019; Summers et al., 2019) we also investigated the correlations between pain ratings and changes in MEP (and TEP) amplitude”

Chowdhury, N. S., Chang, W. J., Millard, S. K., Skippen, P., Bilska, K., Seminowicz, D. A., & Schabrun, S. M. (2022). The Effect of Acute and Sustained Pain on Corticomotor Excitability: A Systematic Review and Meta-Analysis of Group and Individual Level Data. The Journal of Pain, 23(10), 1680-1696.

Summers, S. J., Chipchase, L. S., Hirata, R., Graven-Nielsen, T., Cavaleri, R., & Schabrun, S. M. (2019). Motor adaptation varies between individuals in the transition to sustained pain. Pain, 160(9), 2115-2125.

Seminowicz, D. A., Thapa, T., & Schabrun, S. M. (2019). Corticomotor depression is associated with higher pain severity in the transition to sustained pain: a longitudinal exploratory study of individual differences. The Journal of Pain, 20(12), 1498-1506.

1. Confounds by pain ratings. The ISI between TMS pulses is 4 sec and includes verbal pain ratings. Considering this relatively short ISI, would it be possible that verbal pain ratings confound the TEP? Moreover, could the pain ratings confound TEP differences between conditions, e.g., by providing earlier ratings when the stimulus is painful? This should be carefully considered, and the authors might perform control analyses.

It is unlikely that the verbal ratings contaminated the TEP response as the subsequent TMS pulse was not delivered until the verbal rating was complete and given that each participant was cued by the experimenter to provide the pain rating after each pulse (rather than the participant giving the rating at any time). As such, it would not be possible for participants to provide earlier ratings to more painful stimuli.

We have made this clearer:

"To avoid contamination of TEPs by verbal ratings, the subsequent TMS pulse was not delivered until the verbal rating was complete, and the participant was cued by the experimenter to provide the pain rating after each pulse.”

1. Confounds by time effects. Non-painful and painful conditions were performed in a fixed order. Potential confounds by time effects should be carefully considered.

Previous research suggests that pain alters neural excitability even after pain has subsided. In a recent meta-analysis (Chowdhury et al., 2022) we found effect sizes of 0.55-0.9 for MEP reductions 0-30 minutes after pain had resolved. As such, we avoided intermixing pain and warm blocks given subsequent warm blocks would not serve as a valid baseline, as each subsequent warm block would have residual effects from the previous pain blocks.

Chowdhury, N. S., Chang, W. J., Millard, S. K., Skippen, P., Bilska, K., Seminowicz, D. A., & Schabrun, S. M. (2022). The Effect of Acute and Sustained Pain on Corticomotor Excitability: A Systematic Review and Meta-Analysis of Group and Individual Level Data. The Journal of Pain, 23(10), 1680-1696.

At the same time, given there was no conclusive evidence for a difference in N45 amplitude between pre-pain and post-pain conditions of Experiment 1 (Supplementary Figure 1), it is unlikely that the effect of pain was an artefact of time i.e., the explanation that successive thermal stimuli applied to the skin results an increase in the N45, regardless of whether the stimuli are painful or not. We will make this point in our next revision.

We have discussed this issue:

“Lastly, future research should consider replicating our experiment using intermixed pain and no pain blocks, as opposed to fixed pre-pain and pain blocks, to control for order effects i.e., the explanation that successive thermal stimuli applied to the skin results an increase in the N45 peak, regardless of whether the stimuli are painful or not. However, we note that there was no conclusive evidence for a difference in N45 peak amplitude between pre-pain and post-pain conditions of Experiment 1 (Supplementary Figure 1), suggesting it is unlikely that the observed effects were an artefact of time.”

1. Data availability. The authors should state how they make the data openly available.

We have uploaded the MEP, TEP and pain data on the Open science framework https://osf.io/k3psu/

Reviewer #1 (Recommendations For The Authors):

I think the study is quite solid and I only have very minor recommendations for the authors:

  • Introduction, p. 3: "Functional magnetic resonance imaging has helped us understand where in the brain pain is processed". This is an overstatement. fMRI provides us with potential biomarkers (e.g. "the pain signature"), but the specificity of these responses for pain is debated and we still do not know where in the brain pain is processed.

We have amended to:

“functional magnetic resonance imaging has assisted in the localization of brain structures implicated in pain processing”

  • Introduction, p. 5: "neural baseline" should be "neutral baseline"?

We thank the reviewer for identifying this – this has now been amended.

Reviewer #2 (Recommendations For The Authors):

INTRODUCTION

The introduction mentions how important extra-motor areas can be explored by TMS-EEG, then the effects of DLPFC rTMS on TEPs ... but you do not explore the DLPFC... Perhaps the introduction should be reframed.

The current work explores cortical excitability throughout the brain (as shown in our cluster-based permutation and source localization analyses), so our investigations are in line with the introductions statement about the importance of studying non-motor areas.

The reference to DLPFC rTMS was to highlight current existing research that has applied TMS-EEG to understand pain. It was not used as a methodological rationale to investigate the DLPFC in the present study. To make the research gap clearer, we state:

“While these studies assist us in understanding whether TEPs might mediate rTMS-induced pain reductions, no study has investigated whether TEPs are altered in direct response to pain”

Lignes 63-65 the term "TMS" is used to refer to motor corticospinal excitability measures, in contrast to TMS-EEG measures of TEPs. Then the authors come back to TMS-EEG and then again back to MEPs. This is rather confusing: TMS means TMS... the concept of MEP/ motor corticospinal excitability measures is not intuitive when using the term "TMS". I suggest using motor corticospinal excitability measures when referring to MEP/MEP-based measures of cortical excitability... and M1TMS-EEG-evoked potentials (usually abbreviated to TEPs) to refer to TMS-EEG responses as measured here.

Throughout the manuscript, we now use the term TEPs when referring to TMS-EEG measures, and MEPs when referring to TMS-EMG measure. The use of TEPs vs. MEPs will make it easier for readers to follow which measures we are referring to.

Line 83: "As such, the precise origin of the pain mechanism cannot be localized." Please rephrase, the sentence conveys the idea that it is indeed possible to localize the origin of a pain mechanism with a different approach, and we know this is not currently possible, irrespective of the methodological setup.

We have replaced this with:

“This makes it unclear as to whether pain processes occur at the cortical, spinal or peripheral level.”

How can one predetermine the temperature that will be perceived as painful by someone else, and not base it on individual HPT? This is against principles of psychophysics. Please comment. Attesting all participants had HPT below 46 is important, but then being stimulated at 46C when our HPT is 45C is different from when our HPT is 39C. Please explain why the pain intensity was not standardised based on individual HPT.

Please refer to our response to the public review related to the issue

Line 38: "if we had used an alternative design with blocks of warm stimuli intermixed with blocks of painful stimuli, the warm stimuli blocks would not serve as a valid non-painful baseline". I do not understand why it is not possible to have a pain-free baseline, followed by a pain/warm sequence.

In our study, we had the choice of either intermixing blocks or to use a fixed sequence. Previous research suggests that pain alters neural excitability even after pain has subsided. In a recent meta-analysis (Chowdhury et al., 2022) we found effect sizes of 0.55-0.9 for MEP reductions 0-30 minutes after pain had resolved. As such, we avoided intermixing pain and warm blocks given subsequent warm blocks would not serve as a valid baseline, as each subsequent warm block would have residual effects from the previous pain blocks.

We have updated the manuscript to be clearer about why we used a fixed sequence:

“The pre-pain/pain/post-pain design has been commonly used in the TMS-MEP pain literature, as many studies have demonstrated strong changes in corticomotor excitability that persist beyond the painful period. Indeed, in a systematic review, we showed effect sizes of 0.55-0.9 for MEP reductions 0-30 minutes after pain had resolved (Chowdhury et al., 2022). As such, if we had used an alternative design with blocks of warm stimuli intermixed with blocks of painful stimuli, the warm stimuli blocks would not serve as a valid non-painful baseline”

Chowdhury, N. S., Chang, W. J., Millard, S. K., Skippen, P., Bilska, K., Seminowicz, D. A., & Schabrun, S. M. (2022). The Effect of Acute and Sustained Pain on Corticomotor Excitability: A Systematic Review and Meta-Analysis of Group and Individual Level Data. The Journal of Pain, 23(10), 1680-1696.

Please explain, and provide evidence that stimulation of people with predetermined temperatures is able to create warm/pain/warm sensations, without entraining pain in the last warm stimulation.

A previous study by Dube et al. (2011) used sequences of warm (36°C), painful and neutral (32° C) and found that participants did not experience pain at any time when the temperature was at a warm temperature of 36°C. We have now cited this study:

“Based on a previous study (Dubé & Mercier, 2011) which also used sequences of painful (50ºC) and warm (36°C) thermal stimuli, we did not anticipate that the stimulus in the pain block would entrain pain in the post-pain block”

Dubé, J. A., & Mercier, C. (2011). Effect of pain and pain expectation on primary motor cortex excitability. Clinical neurophysiology, 122(11), 2318-2323.

METHODS

It is not clear if participants with chronic pain, present in 20% of the general population, were excluded. If they were, please provide "how" in methods.

We excluded participants with a history or presence of acute/chronic pain. This has now been clarified:

“Participants were excluded if they had a history of chronic pain condition or any current acute pain”

Line 489: the definition of warm detection threshold is unusual, please provide a reference.

We used an identical method to Furman et al., (2020). We have made the reference to this clearer:“Warmth, cold and pain thresholds were assessed in line with a previous study (Furman et al., 2020)”

Furman, A. J., Prokhorenko, M., Keaser, M. L., Zhang, J., Chen, S., Mazaheri, A., & Seminowicz, D. A. (2020). Sensorimotor peak alpha frequency is a reliable biomarker of prolonged pain sensitivity. Cerebral Cortex, 30(12), 6069-6082.

In Experiment 2, please explain how the lack of randomisation between "pre-pain" and "pain" may have influenced results.

Given we tried to replicate Experiment 1’s methodology as close as possible (to isolate the source of the effect from Experiment 1) we chose to repeat the same sequence of blocks as Experiment 1: pre-pain followed by pain.

Given there was no conclusive evidence for a difference in N45 amplitude between pre-pain and post-pain conditions of Experiment 1 (Supplementary Figure 1), it is unlikely that the effect of pain was an order effect i.e., the explanation that successive thermal stimuli applied to the skin results an increase in the N45, regardless of whether the stimuli are painful or not.

We now discuss the issue of randomization:

“Lastly, future research should consider replicating our experiment using intermixed pain and no pain blocks, as opposed to fixed pre-pain and pain blocks, to control for order effects i.e. the explanation that successive thermal stimuli applied to the skin results an increase in the N45 peak, regardless of whether the stimuli are painful or not. However, we note that there was no conclusive evidence for a difference in N45 peak amplitude between pre-pain and post-pain conditions of Experiment 1 (Supplementary Figure 1), suggesting it is unlikely that the observed effects were an artefact of time”

Also, in Methods in general, disclose how pain intensity was assessed, and how.

Pain intensity was assessed using a verbal rating scale (0 = no pain, and 10 = most pain imaginable). We have provided more detail:

“During each 40 second thermal stimulus, TMS pulses were manually delivered, with a verbal pain rating score (0 = no pain, and 10 = worst pain imaginable) obtained between pulses. To avoid contamination of TEPs by verbal ratings, the subsequent TMS pulse was not delivered until the verbal rating was complete, and the participant was cued by the experimenter to provide the pain rating after each pulse”

Please explain how auditory masking was made during data collection.

Auditory masking noise was not played through the headphones, given that Experiment 2 controlled for auditory evoked potentials. We have made this clearer:

“Auditory masking was not used. Instead, auditory evoked potentials resulting from the TMS click sound were controlled for in Experiment 2”

Please explain if online TEP monitoring was used during data collection

Online TEP monitoring was not available with our EEG software. We have made this clearer in the manuscript:

“Online TEP monitoring was not available with the EEG software”

Line 499: what is subthreshold TMS here? You are measuring TEPs, and not MEPs initially, so you may have a threshold for MEPs and TEPs, which are not the same.

The intensity was calibrated relative to the MEP response (rather than TEP response) - this has now been clarified:

“… and the inclusion of a subthreshold TMS (90% of resting motor threshold) condition intermixed within both the pre-pain and pain blocks.”

Please provide a reference and a figure to illustrate the electric stimulation used in the sham procedure in Study 2

The apparatus for the electrical stimulation is shown in Figure 7A, and was based on previous papers using electrical stimulation over motor cortex to simulate the somatosensory aspect of real TMS (Chowdhury et al., 2022; Gordon et al., 2022; Rocchi et al., 2021). We have made this clearer:

“Electrical stimulation was based on previous studies attempting to simulate the somatosensory component of active TMS (Chowdhury et al., 2022; Gordon et al., 2022; Rocchi et al., 2021)”

Gordon, P. C., Jovellar, D. B., Song, Y., Zrenner, C., Belardinelli, P., Siebner, H. R., & Ziemann, U. (2021). Recording brain responses to TMS of primary motor cortex by EEG–utility of an optimized sham procedure. Neuroimage, 245, 118708.

Chowdhury, N. S., Rogasch, N. C., Chiang, A. K., Millard, S. K., Skippen, P., Chang, W. J., ... & Schabrun, S. M. (2022). The influence of sensory potentials on transcranial magnetic stimulation–Electroencephalography recordings. Clinical Neurophysiology, 140, 98-109.

Rocchi, L., Di Santo, A., Brown, K., Ibánez, J., Casula, E., Rawji, V., ... & Rothwell, J. (2021). Disentangling EEG responses to TMS due to cortical and peripheral activations. Brain stimulation, 14(1), 4-18.

It is not so common to use active electrodes for TMS-EEG. Please confirm the electrodes used and if they are c-ring TMS compatible and provide reference if otherwise (or actual papers recommending active ones)

To be more specific about the electrode type we have indicated:

“Signals were recorded from 63 TMS-compatible active electrodes (6mm height, 13mm width), embedded in an elastic cap (ActiCap, Brain Products, Germany), in line with the international 10-10 system”

A paper directly comparing TEPs between active and passive electrodes found no difference between the two and concluded TEPs can be reliably obtained using active electrodes (Mancuso et al., 2021). There is also evidence that active electrodes have better signal quality than passive electrodes at higher impedance levels (Laszlo et al., 2014).

This information has now been added to the paper:

“Active electrodes result in similar TEPs (both magnitude and peaks) to more commonly used passive electrodes (Mancuso et al., 2021). There is also evidence that active electrodes have higher signal quality than passive electrodes at higher impedance levels (Laszlo, Ruiz-Blondet, Khalifian, Chu, & Jin, 2014).”

There is a growing literature showing that monophonic pulses are not reliable for TEPs when compared to biphasic ones, please provide references. https://doi.org/10.1016/j.brs.2023.02.009

The reference provided by the reviewer states that biphasic and monophasic pulses both have advantages and disadvantages, rather than stating “monophonic pulses are not reliable for TEPs”. While there is some evidence that the artefacts resulting from monophasic pulses are larger than biphasic pulses, the EEG signal still returns to baseline levels within 5ms of the TMS pulse (Rogasch et al., 2013). Moreover, one paper (Casula et al. 2018) found that the resultant TEPs evoked by monophasic pulses are larger than those resulting from biphasic pulses. The authors postulated that monophasic pulses are more effective at activating widespread cortical areas than biphasic pulses. Ultimately the reference provided by the reviewer concludes that “effect of pulse shape on TEPs has not been systematically investigated and more studies are needed”.

Rogasch, N. C., Thomson, R. H., Daskalakis, Z. J., & Fitzgerald, P. B. (2013). Short-latency artifacts associated with concurrent TMS–EEG. Brain stimulation, 6(6), 868-876.

Casula, E. P., Rocchi, L., Hannah, R., & Rothwell, J. C. (2018). Effects of pulse width, waveform and current direction in the cortex: A combined cTMS-EEG study. Brain stimulation, 11(5), 1063-1070.

In most heads, a pulse in the PA direction is not obtained by a coil oriented 45o to the midline. The later induced later-medial pulses, good to obtain MEPs

We followed previous studies measuring MEPs from the ECRB elbow muscle (Schabrun et al., 2016; de Martino et al., 2019) whereby the TMS coil handle was angled at 45 degrees relative to the midline in order to induce a posterior-anterior current. We are not aware of literature that shows that the 45 degrees orientation does not induce a posterior anterior current in most heads.

Schabrun, S. M., Christensen, S. W., Mrachacz-Kersting, N., & Graven-Nielsen, T. (2016). Motor cortex reorganization and impaired function in the transition to sustained muscle pain. Cerebral Cortex, 26(5), 1878-1890.

De Martino, E., Seminowicz, D. A., Schabrun, S. M., Petrini, L., & Graven-Nielsen, T. (2019). High frequency repetitive transcranial magnetic stimulation to the left dorsolateral prefrontal cortex modulates sensorimotor cortex function in the transition to sustained muscle pain. Neuroimage, 186, 93-102.

The definition of RMT is (very) unusual. RMT provides small 50microV MEPs in 50% of times. If you obtain MEPs at 50microV you are supra threshold!

The TMS motor threshold assessment tool calculates threshold in the same manner as other threshold tools – it calculates the intensity that elicits an MEP of 50 microvolts, 50% of the time. We have made this clearer:

“The RMT was determined using the TMS motor thresholding assessment tool, which estimates the TMS intensity required to induce an MEP of 50 microvolts with a 50% probability using maximum likelihood parametric estimation by sequential testing (Awiszus and Borckardt, 2011). This method has been shown to achieve the accuracy of methods such as the Rossini-Rothwell method (Rossini et al., 1994; Rothwell et al., 1999) but with fewer pulses (Qi et al., 2011; Silbert et al., 2013).”

Please inform the inter TMS pulse interval used of TEPs and whether they were randomly generated.

The pulses were delivered manually – the interval was not randomly generated – as stated:

“As TMS was delivered manually, there was no set interpulse interval. However, the 40 second stimulus duration allowed for 11 pulses for each heat stimulus …. (~ 4 seconds in between …)”

Why have you stimulated suprathreshold on M1 when assessing TEP´s? The whole idea is that large TEPs can be obtained at lower intensities below real RMT and that prevents re-entering loops of somatosensory and joint movement inputs that insert "noise" to the TEPs.

The suprathreshold intensity was used to concurrently measure MEPs during pre-pain, pain and post-pain blocks.

We have made this clearer:

“The test stimulus intensity was set at 110% RMT to concurrently measure MEPs and TEPs during pre-pain, pain and post-pain blocks.”

The influence of re-afferent muscle activity was controlled for in Experiment 3.

Did you assess pain intensity after each of the TEP pulses? Please discuss how such a cognitive task may have influenced results

Pain intensity was assessed after each TMS pulse, as stated:

“TMS pulses were manually delivered, with a verbal pain rating score (0 = no pain, and 10 = most pain imaginable) obtained between pulses”

Reviewer 3 also brought up a concern of whether the verbal rating task might have influenced the TEPs. However, it is unlikely that the task contaminated the TEP response as the subsequent TMS pulse was not delivered until the verbal rating was complete and given that each participant was cued by the experimenter to provide the pain rating after each pulse (rather than the participant giving the rating at any time). We have made this clearer where we state:

“To avoid contamination of TEPs by verbal ratings, the subsequent TMS pulse was not delivered until the verbal rating was complete, and the participant was cued by the experimenter to provide the pain rating after each pulse”

The QST approach is unusual. Please confirm the sequence of CDT, WDT and HPT were not randomised and that no interval beyond 6sec were used. Proper references are welcome.

In line with a previous study (Furman et al., 2020), the sequence of the CPT, WDT and HPT were not randomized, and the interval was not more than 6 seconds.

We have made this clearer:

“A total of three trials was conducted for each test to obtain an average, with an interstimulus interval of six seconds. The sequence of cold, warmth and pain threshold was the same for all participants (Furman et al. 2020)”

Performing 60 pulses for TEPs is unusual, and against the minimum number in recommendations

Please explain and comment.https://doi.org/10.1016/j.brs.2023.02.009

Please refer to our previous response to this concern in the public reviews.

Line 578: when you refer to "heat" the reader may confound warm/heat with heat meaning suprathreshold. Please revise the wording.

We have now replaced the word heat stimulus with thermal stimulus.

Why were Bayesian statistics used instead as frequentist ones?

We have made this clearer:

“Given we were interested in determining the evidence for pain altering TEP peaks in certain conditions (e.g., active TMS) and pain not altering TEP peaks in other conditions (sham TMS), we used a Bayesian approach as opposed to a frequentist approach, which considers the strength of the evidence for the alternative vs. null hypothesis”

RESULTS

There is a huge response with high power after 100ms- Please discuss if you believe auditory potentials may have influenced it.

It is indeed possible that auditory potentials were present at 100ms. We now state:

“Indeed, the signal at ~100ms post-TMS from Experiment 1 may reflect an auditory N100 response”

The presence of auditory contamination does not impact the main conclusions of the paper given this was controlled for in Experiment 2.

Please discuss how pain ranging from 3-10 may have influenced results in the "PAIN" situation,

It is anticipated that the fixed thermal stimulus intensity approach would lead to large variations in pain ratings (Adamczyk et al., 2022). This is a recommended approach when the aim of the research is to determine relationships between neurophysiological measures and individual differences in pain sensitivity (Adamczyk et al., 2022). Indeed, we were interested in whether alterations in neurophysiological measures were associated with pain intensity, and we found that higher pain ratings were associated with smaller reductions in MEP amplitude and larger increases in N45 amplitude.

Adamczyk, W. M., Szikszay, T. M., Nahman-Averbuch, H., Skalski, J., Nastaj, J., Gouverneur, P., & Luedtke, K. (2022). To calibrate or not to calibrate? A methodological dilemma in experimental pain research. The Journal of Pain, 23(11), 1823-1832.

Please indicate if any participants offered pain after warm stimulation ( possible given secondary hyperalgesia after so many plateaux of heat stimulation).

As stated in the results “All participants reported 0/10 pain during the pre-pain and post-pain blocks”.

Please discuss the potential effects of having around 10% of "bad channels In average per experiment per participants, its impacts in source localisation and in TEP measurement. Same for >5 epochs excluded by participant.

The number of bad channels has been incorrectly stated by the reviewer as being 10% on average per experiment per participant, whereas the correct number of reported bad channels was 3%, 4.7% and 9.8% for Experiment 1, 2 and 3 respectively (see supplementary material). These numbers are below the accepted number of bad channels to interpolate (10%) in EEG pipelines (e.g., Debnath et al., 2020; Kayhan et al., 2022), so it is unlikely that our channel exclusions significantly influenced the quality of our source localization an TEP data.

Debnath, R., Buzzell, G. A., Morales, S., Bowers, M. E., Leach, S. C., & Fox, N. A. (2020). The Maryland analysis of developmental EEG (MADE) pipeline. Psychophysiology, 57(6), e13580.

Kayhan, E., Matthes, D., Haresign, I. M., Bánki, A., Michel, C., Langeloh, M., ... & Hoehl, S. (2022). DEEP: A dual EEG pipeline for developmental hyperscanning studies. Developmental cognitive neuroscience, 54, 101104.

The number of excluded epochs is unlikely to have influenced the results given there was evidence for no difference in the number of rejected epochs between conditions (E1 BF10 = 0.145, E2 BF10 = 0.27, E3 BF10 = 0.169 – these BFs have now been reported in the supplementary material), and given the reliability of the N45 was high (see response to previous comment on the number of trials per condition).

HPT of 42.9 {plus minus} 2.5{degree sign}C means many participants had HPT close to 46oC. Please discuss

While some participants did indeed have pain thresholds close to 46 degrees, they nonetheless reported pain during the test blocks. While such participants may have reported less pain compared to others, we aimed for larger variations in pain ratings, given one of the research questions was to determine why pain intensity differs between individuals (given the same noxious stimulus). Indeed, we showed that this variation was meaningful (pain intensity was related to alterations in N45 and MEP amplitude).

Please explain the sentence : line 139 "As such, if we had used an alternative design with blocks of warm stimuli intermixed with blocks of painful stimuli, the warm stimuli blocks would not serve as a valid non-painful baseline." I cannot see why.

Please refer to our previous point on why the fixed sequence was included.

And on the top of that heat was not individualised according to HPT.

Please refer to our previous point on why we used a fixed stimulus approach.

Sequences of warm/heat were not randomised.Please refer to our previous point on the why the sequence of blocks was not randomized.

Line 197: "However, as this is the first study investigating the effects of experimental pain on TEPsamplitude, there were no a priori regions or timepoints of interest to compare betweenconditions". This is not clear. It means you have not measured the activity (size of the N45) under the electrode closest to the TMS coil? The TEP is supposed to by higher under the stimulated target/respective corresponding electrode…

We are not aware of any current recommendations that state that the region of interest should be based on the site of stimulation. The advantage of TMS-EEG is that it allows characterisation of cortical excitability changes throughout the brain, not just the site of stimulation. We based our region of interest on a cluster-based permutation analysis, as recommended by Frömer, Maier, & Abdel Rahman, (2018)

Frömer, R., Maier, M., & Abdel Rahman, R. (2018). Group-level EEG-processing pipeline for flexible single trial-based analyses including linear mixed models. Frontiers in neuroscience, 12, 48.

Please explain where N45 values came from.

The N45 was calculated using the TESA peak function (Rogasch et al., 2017) which identifies a data point which is larger/smaller than +/- 5 data points within a specified time window (e,g, 40-70ms post-TMS as in the present study). Where multiple peaks are found, the amplitude of the largest peak is returned. Where no peak is found, the amplitude at the specified latency is returned.

Rogasch, N. C., Sullivan, C., Thomson, R. H., Rose, N. S., Bailey, N. W., Fitzgerald, P. B., ... & Hernandez-Pavon, J. C. (2017). Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software. Neuroimage, 147, 934-951.

If only the cluster assessment was made please provide the comparison between P45 from the target TMS channel location in pre pain vs pain.

We assume the reviewer is referring to the N45 rather than P45, and that by “target” TMS channel they are referring to the stimulated region.

We first clarify that there is no “target” channel given the motor hotspot differs between individuals and so the channel that is closest to the site of stimulation will always differ.

Secondly, as stated above, we are not aware of any current recommendations in TMS-EEG research that states that the region of interest for TEP analysis should be based on the site of stimulation. The advantage of TMS-EEG is that it allows characterisation of cortical excitability throughout the brain, not just the site of stimulation. If we based our ROI on the target channel only, we would lose valuable information about excitability changes occurring in other brain regions.

Lastly, the N45 was localized at frontocentral electrodes, which is also where the cluster differences emerged. As such, we do not believe it would be informative to compare N45 peak amplitude at the region of stimulation.

Also explain how correction for multiple comparisons was made

Please refer to our response to the public review related to this issue.

And report data from pain vs post-pain.

The pain vs. post-pain comparisons are now reported in the Supplementary material.

There is a strong possibility the response at N85 is an auditory /muscle signal. Please provide the location of this response.

We have opted not to include the topography at 85ms in the main paper as it would introduce too much clutter into the figures (which are already very dense), and because the topography was very similar to the topography at 100ms. As an example, for the reviewer, in Author response image 1 we have shown the topography for the pre-pain condition of Experiment 1.

Author response image 1
Download asset Open asset

Experiment 2: I have a strong impression both active TEPs and sham TEPs were contaminated by auditory (and muscle) noise. Please explain.

While it possible that auditory noise may have influenced TEPs in the active and sham groups, it does not impact the main conclusions of the paper, given that the purpose of the sham condition was to control for auditory and somatosensory stimulation resulting from TMS.

While muscle activity may also affect have influenced the TEPs in active and sham conditions, we used fastICA in all conditions to suppress muscle activity. The fastICA algorithm (Rogasch et al., 2017) runs an independent component analysis on the data, and classifies components as neural, TMS-evoked muscle, eye movements and electrode noise, based on a set of heuristic thresholding rules (e.g., amplitude, frequency and topography of the components). Components classified as TMS-evoked muscle/other muscle artefacts are then removed. In the supplementary material, we further report that the number of components removed did not differ between conditions, suggesting the impact of muscle artefacts are not larger in some conditions vs. others.

Rogasch, N. C., Sullivan, C., Thomson, R. H., Rose, N. S., Bailey, N. W., Fitzgerald, P. B., ... & Hernandez-Pavon, J. C. (2017). Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software. Neuroimage, 147, 934-951.

Experiment 3: One interpretation can be that both supra and sub-threshold TMS were leading to somatosensory re-afferent responses, based on the way RMT was calculated, which hyper estimate the RMT and delivers in reality 2 types of supra-threshold stimulations. Please discuss

Please refer to our response to the public review related to this issue.

Please provide correlation between N45 size and MEPs amplitudes.

This has now been included:

“There was no conclusive evidence of any relationship between alterations in MEP amplitude during pain, and alterations in N100, N45 and P60 amplitude during pain (see supplementary material).”

The supporting statistics for these analyses have been included in the supplementary material.

DISCUSSION

Line 303: " The present study determined whether acute experimental pain induces alterations in cortical inhibitory and/or facilitatory activity observed in TMS-evoked potentials".

Well, no. The study assessed the N45, and was based on it. It did not really explore other metrics in a systematic fashion. P60 and N100 changes were not replicated in experiments 2 and 3..

We assume the reviewer is stating that we did not assess other TEP peaks (such as the N15, P30 and P180). However, we did indeed assess these peaks in a systematic fashion. First, we identified the ROI by using a cluster-based analysis. This is a recommended approach when the ROI is unclear (Frömer, Maier, & Abdel Rahman, 2018). We then analysed the TEP representing the mean voltage across the electrodes within the cluster, and then identified any differences in all peaks between conditions (not just the N45). This has been made clearer in the manuscript.

This has now been included:

“For all experiments, the mean TEP waveform of any identified clusters from Experiment 1 were plotted, and peaks (e.g., N15, P30, N45, P60, N100) were identified using the TESA peak function (Rogasch et al., 2017)”

Frömer, R., Maier, M., & Abdel Rahman, R. (2018). Group-level EEG-processing pipeline for flexible single trial-based analyses including linear mixed models. Frontiers in neuroscience, 12, 48.

And the N45 is not related to facilitatory or inhibitory activity, it is a measure of an evoked response indicating excitability

Evidence suggests the N45 is mediated by GABAAergic neurotransmission (inhibitory activity), as drugs which increase GABAA receptor activity increase the amplitude of the N45 (Premoli et al., 2014) and drugs which decrease GABAA receptor activity decrease the amplitude of the N45 (Darmani et al., 2016). As such, we and various other empirical papers (e.g., Bellardinelli et al., 2021; Noda et al., 2021; Opie at 2019 ) and review papers (Farzan & Bortoletto, 2022; Tremblay et al., 2019) have interpreted changes in the N45 peak as reflecting changes in cortical inhibitory/GABAA mediated activity.

Premoli, I., Castellanos, N., Rivolta, D., Belardinelli, P., Bajo, R., Zipser, C., ... & Ziemann, U. (2014). TMS-EEG signatures of GABAergic neurotransmission in the human cortex. Journal of Neuroscience, 34(16), 5603-5612.

Belardinelli, P., König, F., Liang, C., Premoli, I., Desideri, D., Müller-Dahlhaus, F., ... & Ziemann, U. (2021). TMS-EEG signatures of glutamatergic neurotransmission in human cortex. Scientific reports, 11(1), 8159.

Darmani, G., Zipser, C. M., Böhmer, G. M., Deschet, K., Müller-Dahlhaus, F., Belardinelli, P., ... & Ziemann, U. (2016). Effects of the selective α5-GABAAR antagonist S44819 on excitability in the human brain: a TMS–EMG and TMS–EEG phase I study. Journal of Neuroscience, 36(49), 12312-12320.

Noda, Y., Barr, M. S., Zomorrodi, R., Cash, R. F., Lioumis, P., Chen, R., ... & Blumberger, D. M. (2021). Single-pulse transcranial magnetic stimulation-evoked potential amplitudes and latencies in the motor and dorsolateral prefrontal cortex among young, older healthy participants, and schizophrenia patients. Journal of Personalized Medicine, 11(1), 54.

Farzan, F., & Bortoletto, M. (2022). Identification and verification of a'true'TMS evoked potential in TMS-EEG. Journal of neuroscience methods, 378, 109651.

Opie, G. M., Foo, N., Killington, M., Ridding, M. C., & Semmler, J. G. (2019). Transcranial magnetic stimulation-electroencephalography measures of cortical neuroplasticity are altered after mild traumatic brain injury. Journal of Neurotrauma, 36(19), 2774-2784.

Tremblay, S., Rogasch, N. C., Premoli, I., Blumberger, D. M., Casarotto, S., Chen, R., ... & Daskalakis, Z. J. (2019). Clinical utility and prospective of TMS–EEG. Clinical Neurophysiology, 130(5), 802-844.

Line 321: why have you not measured SEPs in experiment 3?

It is not possible to directly measure the somatosensory evoked potentials resulting from a TMS pulse, given that the TMS pulse produces a range of signals including cortical activity, muscle/eye blink responses, auditory responses, somatosensory responses and other artefacts. While some researchers attempt to isolate the SEP from TMS using pre-processing methods such as ICA, others use control conditions such as sensory sham conditions (to control for the “tapping” artefact) or subthreshold intensity conditions (to control for reafferent muscle activity), as we have done in Experiment 2 and 3 of our study.

We have now stated this in the manuscript:

“As it is extremely challenging to isolate and filter these auditory and somatosensory evoked potentials using pre-processing pipelines, masking methods have been used to suppress these sensory inputs, (Ilmoniemi and Kičić, 2010; Massimini et al., 2005). However recent studies have shown that even when these methods are used, sensory contamination of TEPs is still present, as shown by commonalities in the signal between active and sensory sham conditions that mimic the auditory/somatosensory aspects of real TMS (Biabani et al., 2019; Conde et al., 2019; Rocchi et al., 2021). This has led many leading authors (Biabani et al., 2019; Conde et al., 2019) to recommend the use of sham conditions to control for sensory contamination”

Line 365: SICI is dependent on GABAa activity. But the way the text is written if conveys the idea that TMS pulses "activate" GABA receptors, which is weird...Please rephrase.

This has now been reworded.

“SICI refers to the reduction in MEP amplitude to a TMS pulse that is preceded 1-5ms by a subthreshold pulse, with this reduction believed to be mediated by GABAA neurotransmission (Chowdhury et al., 2022)”

Reviewer #3 (Recommendations For The Authors):

-Key references Ye et al., 2022 and Che et al., 2019 need to be included in the reference list.

These references have now been included in the reference list.

-Heat pain stimuli and TMS stimuli are applied simultaneously. Sometimes the term "stimulus" is used without specifying whether it refers to TMS pulses or heat pain stimuli. Clarifying this whenever the word "stimulus" is used would enhance clarity for the reader.

We have now clarified the use of the word “stimulus” throughout the paper.

-Panels A-D in Figure 6 should be correctly labeled in the text and the figure legend.

Figure 6 Panel labels have now been amended.

https://doi.org/10.7554/eLife.88567.3.sa4

Article and author information

Author details

  1. Nahian Shahmat Chowdhury

    1. Center for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
    2. University of New South Wales, Sydney, Australia
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    n.chowdhury@neura.edu.au
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6357-0746

  2. Alan KI Chiang

    1. Center for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
    2. University of New South Wales, Sydney, Australia
    Contribution
    Conceptualization, Investigation, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9156-0534

  3. Samantha K Millard

    1. Center for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
    2. University of New South Wales, Sydney, Australia
    Contribution
    Conceptualization, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1409-8179

  4. Patrick Skippen

    Center for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
    Contribution
    Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared

  5. Wei-Ju Chang

    1. Center for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
    2. School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, Australia
    Contribution
    Conceptualization, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0524-4883

  6. David A Seminowicz

    1. Center for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
    2. Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Canada
    Contribution
    Conceptualization, Resources, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3111-3756

  7. Siobhan M Schabrun

    1. Center for Pain IMPACT, Neuroscience Research Australia, Sydney, Australia
    2. The Gray Centre for Mobility and Activity, University of Western Ontario, London, Canada
    Contribution
    Conceptualization, Resources, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared

Funding

National Institutes of Health (1R61NS113269-01)

  • David A Seminowicz
  • Siobhan M Schabrun

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by 1R61NS113269-01 from The National Institutes of Health to DAS, SMS, and The Four Borders Foundation to DAS.

Ethics

Procedures adhered to the Declaration of Helsinki and were approved by the human research ethics committee of UNSW (HC200328). All participants provided informed written consent.

Senior Editor

  1. Christian Büchel, University Medical Center Hamburg-Eppendorf, Germany

Reviewing Editor

  1. Markus Ploner, Technische Universität München, Germany

Version history

  1. Preprint posted: April 21, 2023 (view preprint)
  2. Sent for peer review: May 16, 2023
  3. Preprint posted: July 7, 2023 (view preprint)
  4. Preprint posted: October 30, 2023 (view preprint)
  5. Version of Record published: November 15, 2023 (version 1)

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.88567. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2023, Chowdhury et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Nahian Shahmat Chowdhury
  2. Alan KI Chiang
  3. Samantha K Millard
  4. Patrick Skippen
  5. Wei-Ju Chang
  6. David A Seminowicz
  7. Siobhan M Schabrun
(2023)
Combined transcranial magnetic stimulation and electroencephalography reveals alterations in cortical excitability during pain
eLife 12:RP88567.
https://doi.org/10.7554/eLife.88567.3

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    Associative memory neurons of encoding multi-modal signals are recruited by neuroligin-3-mediated new synapse formation

    Yang Xu, Tian-liang Cui ... Jin-Hui Wang
    Research Article

    The joint storage and reciprocal retrieval of learnt associated signals are presumably encoded by associative memory cells. In the accumulation and enrichment of memory contents in lifespan, a signal often becomes a core signal associatively shared for other signals. One specific group of associative memory neurons that encode this core signal likely interconnects multiple groups of associative memory neurons that encode these other signals for their joint storage and reciprocal retrieval. We have examined this hypothesis in a mouse model of associative learning by pairing the whisker tactile signal sequentially with the olfactory signal, the gustatory signal, and the tail-heating signal. Mice experienced this associative learning show the whisker fluctuation induced by olfactory, gustatory, and tail-heating signals, or the other way around, that is, memories to multi-modal associated signals featured by their reciprocal retrievals. Barrel cortical neurons in these mice become able to encode olfactory, gustatory, and tail-heating signals alongside the whisker signal. Barrel cortical neurons interconnect piriform, S1-Tr, and gustatory cortical neurons. With the barrel cortex as the hub, the indirect activation occurs among piriform, gustatory, and S1-Tr cortices for the second-order associative memory. These associative memory neurons recruited to encode multi-modal signals in the barrel cortex for associative memory are downregulated by neuroligin-3 knockdown. Thus, associative memory neurons can be recruited as the core cellular substrate to memorize multiple associated signals for the first-order and the second-order of associative memories by neuroligin-3-mediated synapse formation, which constitutes neuronal substrates of cognitive activities in the field of memoriology.

    1. Chromosomes and Gene Expression
    2. Neuroscience

    Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer’s disease dataset

    Alan E Murphy, Nurun Fancy, Nathan Skene
    Research Article

    Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer’s disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.