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Neural excitability and sensory input determine intensity perception with opposing directions in initial cortical responses

  1. Tilman Stephani  Is a corresponding author
  2. Alice Hodapp
  3. Mina Jamshidi Idaji
  4. Arno Villringer
  5. Vadim V Nikulin  Is a corresponding author
  1. Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Germany
  2. International Max Planck Research School NeuroCom, Germany
  3. Machine Learning Group, Technical University of Berlin, Germany
  4. Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Germany
  5. Clinic for Cognitive Neurology, University Hospital Leipzig, Germany
  6. Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Russian Federation
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Cite this article as: eLife 2021;10:e67838 doi: 10.7554/eLife.67838

Abstract

Perception of sensory information is determined by stimulus features (e.g., intensity) and instantaneous neural states (e.g., excitability). Commonly, it is assumed that both are reflected similarly in evoked brain potentials, that is, larger amplitudes are associated with a stronger percept of a stimulus. We tested this assumption in a somatosensory discrimination task in humans, simultaneously assessing (i) single-trial excitatory post-synaptic currents inferred from short-latency somatosensory evoked potentials (SEPs), (ii) pre-stimulus alpha oscillations (8–13 Hz), and (iii) peripheral nerve measures. Fluctuations of neural excitability shaped the perceived stimulus intensity already during the very first cortical response (at ~20 ms) yet demonstrating opposite neural signatures as compared to the effect of presented stimulus intensity. We reconcile this discrepancy via a common framework based on the modulation of electro-chemical membrane gradients linking neural states and responses, which calls for reconsidering conventional interpretations of brain potential magnitudes in stimulus intensity encoding.

Introduction

Even for the very same stimulus, the brain’s response differs from moment to moment. This has been explained by ever-changing neural states (Arieli et al., 1996), with behaviorally relevant consequences (Waschke et al., 2021). Specifically, these changes of neural states are often conceptualized as fluctuations of cortical excitability (Jensen and Mazaheri, 2010; Klimesch et al., 2007; Romei et al., 2008). In the human brain, a commonly hypothesized marker of cortical excitability is oscillatory activity in the alpha band (8–13 Hz), which can be measured with electro- and magnetoencephalography (EEG/MEG). This marker has been associated with modulations of a stimulus’ percept in various sensory domains including the visual (Busch et al., 2009; Iemi et al., 2017), auditory (Müller et al., 2013), and somatosensory domain (Baumgarten et al., 2016; Craddock et al., 2017; Forschack et al., 2020). According to the baseline sensory excitability model (BSEM; Samaha et al., 2020), higher alpha activity preceding a stimulus indicates a generally lower excitability level of the neural system, resulting in smaller stimulus-evoked responses, which are in turn associated with a lower detection rate of near-threshold stimuli but no changes in the discriminability of sensory stimuli (since neural noise and signal are assumed to be affected likewise). On a cellular level, such excitability modulations may be reflected in changes of membrane potentials (Castro-Alamancos, 2009), which may occur in an oscillatory manner (Lakatos et al., 2005) and shift the threshold for incoming sensory information to be processed further downstream in the neural system. This notion has been further supported by monkey studies showing that higher oscillatory activity within the alpha band is associated with a lower neural firing rate (Bollimunta et al., 2011; Haegens et al., 2011).

However, it remains unclear up to now whether the influence of instantaneous excitability on perceptual processes can be generalized to the intensity perception of stimuli per se (i.e., beyond the sensory threshold) – which would have far-reaching implications for a wide variety of studies in the field of perception. Moreover, if such modulation indeed occurs, the question remains: At which stage of the neural response cascade do instantaneous excitability changes begin to interact with the sensory input in order to shape the brain’s response in a behaviorally relevant way?

A unique opportunity to non-invasively measure instantaneous excitability changes of neurons involved in the first cortical response to sensory stimuli in humans is offered by the N20 component of the somatosensory evoked potential (SEP) as measured with EEG: The N20 component, a negative deflection after around 20 ms at centro-parietal electrode sites in response to median nerve stimulation, reflects excitatory post-synaptic potentials (EPSPs) of the first thalamo-cortical volley (Bruyns-Haylett et al., 2017; Peterson et al., 1995; Wikström et al., 1996) which are generated in the anterior wall of the postcentral gyrus, Brodmann area 3b (Allison et al., 1991). Thus, the N20 directly reflects the intensity of a given stimulus. However, when keeping the sensory input constant, the amplitude of this early part of the SEP only depends on the excitability of the involved, well-defined neuronal population in the primary somatosensory cortex, and therefore represents an excellent instantaneous probe thereof. Notably, amplitude fluctuations of the N20 component have recently been found to relate to pre-stimulus alpha activity both at a given instance and through their long-term temporal dynamics, which suggests that both measures reflect a common modulating factor, that is, cortical excitability (Stephani et al., 2020).

In the current study, we set out to examine the implications of instantaneous excitability fluctuations at initial cortical processing – as measured by both pre-stimulus alpha activity and N20 amplitudes – on the perceived intensity of somatosensory stimuli. We used a binary intensity rating task in which participants were to discriminate supra-threshold median nerve stimuli of two intensities in a continuous stimulation sequence (Figure 1a). Our results show that both pre-stimulus alpha activity and N20 amplitudes are associated with a bias in the perceived intensity of somatosensory stimuli. Thus, instantaneous excitability fluctuations affect sensory brain responses already at earliest possible cortical processing with behaviorally relevant consequences. Counter-intuitively, elevated neural excitability and stronger stimulus intensity resulted in reverse effects on short-latency SEP amplitudes, which in turn may offer further insights into the neural mechanisms of excitability regulation through resting membrane potentials.

Experimental paradigm and main electrophysiological measures.

(a) The relationships between pre-stimulus alpha oscillations, stimulus-evoked responses, and perceived intensity of somatosensory stimuli were examined in a continuous sequence of median nerve stimuli of two intensities with inter-stimulus intervals of ISI = 1513 ± 50 ms. After every stimulus, participants were to rate the perceived intensity as either ‘strong’ or ‘weak’ as fast as possible by button press. The raster plots represent the data of an exemplary subject with the rows corresponding to single trials. Displayed from left to right: Average pre-stimulus alpha amplitude, intensity of the presented stimuli (red = strong; blue = weak intensity), short-latency somatosensory evoked potentials (SEPs), and the perceived intensity as reported by the participants (red = strong; blue = weak intensity). Alpha activity and the SEP were both retrieved from the same tangentially oriented canonical correlation analysis (CCA) component (displayed in panels b–e) and hence reflect activity of the same neuronal sources. (b) Grand average of the SEP (N = 32) in sensor space (electrodes F4, CP4, and P4) and for the tangential CCA component as derived from the single-trial extraction approach using CCA. (c) Activation pattern of the tangential CCA component displaying a tangential dipole contralateral to stimulation site over the central sulcus which is typical for the N20-P35 complex of the SEP. Averaged across participants (N = 32). (d) Neuronal sources (absolute values) underlying the activation pattern of the tangential CCA component, reconstructed using eLoreta inverse modeling. Averaged across participants (N = 32). (e) Same as d but applying an amplitude threshold of 95% in order to indicate the strongest generators of neural activity (displayed on a smoothed cortex surface).

Results

Behavioral results

Participants discriminated the weak and the strong stimuli with an average accuracy of accmean = 69.72% (SD = 7.94%; CI95%: [66.86%, 72.58%]), suggesting a moderate to high task difficulty. As confirmed by permutation tests, every individual participant performed better than chance level, all p < 0.05 (Bonferroni-corrected). The average discrimination sensitivity was d’mean = 1.14 (SD = 0.46; CI95%: [0.98, 1.31]) and the average criterion cmean = 0.01 (SD = 0.22; CI95%: [–0.066, 0.094]), according to signal detection theory (Green and Swets, 1966). Participants pressed the respective response button with an average reaction time of RTmean = 642.42 ms (SD = 95.79 ms; CI95%: [607.89 ms, 676.96 ms]).

Extraction of single-trial SEPs

Single-trial activity of the early SEP was extracted using a variant of canonical correlation analysis (CCA; Scheer et al., 2013; Waterstraat et al., 2015), as previously reported for a similar paradigm examining the fluctuation of single-trial SEPs in response to stimuli with constant intensity (Stephani et al., 2020). This variant of CCA extracts a number of spatially distinct components based on a pattern matching between average SEP and single trials. Similar to Stephani et al., 2020, a prominent CCA component was identified in all subjects, which showed a clear peak at around 20 ms post-stimulus (Figure 1b) and displayed the pattern of the typical N20 tangential dipole (Figure 1c). Furthermore, neuronal sources of this CCA component were primarily located in the anterior wall of the post-central gyrus (Brodmann area 3b) in the primary somatosensory cortex (Figure 1d and e). Single-trial SEPs from this – as is referred to in the following – tangential CCA component are displayed for an exemplary subject in Figure 1a.

Pre-stimulus alpha amplitude is associated with a bias in perceived stimulus intensity

To assess whether pre-stimulus neural states modulated the perception of upcoming somatosensory stimuli, we related oscillatory activity in the alpha band (8–13 Hz) before stimulus onset to the participants’ reports of perceived stimulus intensity. Alpha band activity was measured from the same neural sources as the SEP, applying the spatial filters of the tangential CCA component. Figure 2a shows the envelope of pre-stimulus alpha activity depending on the behavioral responses of the participants. Pre-stimulus alpha amplitude was higher when participants rated the stimulus to be weak rather than strong (regardless of the actual stimulus intensity). This observation was further quantified with signal detection theory (SDT; Green and Swets, 1966) in order to differentiate the ability to discriminate stimulus intensities, as measured by sensitivity d’, from a response bias toward either strong or weak perceived intensity, as measured by criterion c. In correspondence with a recent study in the visual domain (Iemi et al., 2017), these SDT-derived parameters were statistically compared between the 20% of trials with the lowest and the 20% of trials with the highest pre-stimulus alpha amplitudes (as averaged in a time window from 200 to 10 ms before the stimulus onset; Figure 2b). A paired-sample t-test confirmed a difference regarding criterion c, t(31) = –2.777, p = 0.009, Cohen’s d = –0.491, with average criterions of clowest20% = –0.009 and chighest20% = 0.060 (CI95% of difference: [–0.119, –0.018]). No difference was found for sensitivity d’, t(31) = –1.425, p = 0.164, Cohen’s d = –0.252, with average sensitivities d’lowest20% = 1.058 and d’highest20% = 1.142 (CI95% of difference: [–0.204, 0.036]). Thus, higher pre-stimulus alpha amplitude was associated with a higher threshold to rate the stimulus as ‘strong’, corresponding to a bias to generally report lower stimulus intensities, whereas the discriminability between the stimulus categories appeared unaffected.

Figure 2 with 3 supplements see all
Bivariate relationships between pre-stimulus alpha amplitude, N20 peak amplitude, and perceived stimulus intensity.

(a) Time course of the amplitude of pre-stimulus alpha band activity (8–13 Hz) displayed by behavioral response categories. Note that for statistical analyses, pre-stimulus epochs were cut at –5 ms relative to stimulus onset before filtering the data in the alpha band (8–13 Hz), in order to prevent contamination of the pre-stimulus window by stimulus-related activity. (b) Change in perception bias (i.e., signal detection theory [SDT] parameter criterion c) from the lowest to the highest alpha amplitude quintile (as measured between –200 and –10 ms). (c) Somatosensory evoked potential (SEP) derived from the tangential component of the canonical correlation analysis (CCA), sorted with respect to pre-stimulus alpha amplitude quintiles. Alpha quintiles were sorted in ascending order (i.e., first quintile = lowest alpha amplitude). (d) SEP (tangential CCA component) sorted according to behavioral response categories. (e) Change in perception bias (i.e., SDT parameter criterion c) from the most to the least negative N20 peak amplitude quintile. All panels show the grand average across all participants (N = 32). Shaded areas in panels a, c, and d, as well as error bars in panels b and e correspond to the standard errors of the mean based on the within-subject variances (Morey, 2008). Transparent circles in panels b and e reflect data of individual participants while black lines reflect the arithmetic mean on group level. Please refer to Figure 2—figure supplement 1 for a schematic of SDT, to Figure 2—source data 1 for details on the trial overlap between SDT analyses of pre-stimulus alpha activity and N20 amplitudes, to Figure 2—figure supplement 2 for time-frequency representations of the observed effects, and to Figure 2—figure supplement 3 for further control analyses using simulated SEP data.

Figure 2—source data 1

Trial overlap between extreme bins of N20 and pre-stimulus alpha amplitudes used for the SDT analyses.

Trial overlap between extreme bins of N20 and pre-stimulus alpha amplitudes used for the SDT analyses.

https://cdn.elifesciences.org/articles/67838/elife-67838-fig2-data1-v1.pdf

Pre-stimulus alpha amplitude is associated with single-trial N20 amplitudes

Following the hypothesis that both pre-stimulus alpha band activity and the N20 component of the SEP reflect changes in instantaneous cortical excitability, a covariation of these two measures should be expected (Stephani et al., 2020). Indeed, higher pre-stimulus alpha amplitudes were associated with larger (i.e., more negative) N20 peak amplitudes (Figure 2c), as statistically tested with a random-slope linear-mixed-effects model, βfixed = –0.023, t(32.17) = −2.969, p = 0.006 (CI95% of βfixed: [–0.040, –0.007]). Notably, the direction of this effect may appear counter-intuitive at first sight but can be explained by the physiological basis of EEG generation, which offers important insights into the functional link between pre-stimulus alpha activity and SEP (see Discussion section Opposing signatures of presented stimulus intensity and excitability in the early SEP).

Single-trial N20 amplitudes are associated with a bias in perceived stimulus intensity

Given the relationships of pre-stimulus alpha activity with perceived stimulus intensity and N20 peak amplitudes, we tested whether the latter also related to the SDT parameters of the behavioral performance. In parallel to the analyses of the effect of pre-stimulus alpha activity, sensitivity d’ and criterion c were statistically compared between the 20% of trials with the most negative and the 20% of trials with the least negative N20 peak amplitudes. (Please note that this procedure resulted in a different trial selection as compared to the SDT analysis of pre-stimulus alpha activity. Please refer to Figure 2—source data 1 for further details on the trial overlap.) Again, a significant difference was found for criterion c, t(31) = 2.306, p = 0.028, Cohen’s d = 0.408, with average criterions of cmost neg.20% = 0.054 and cleast neg.20% = −0.001 (CI95% of difference: [0.006, 0.104]), as assessed with a paired-sample t-test. No effect emerged for sensitivity d’, t(31) = −1.747, p = 0.091, Cohen’s d = −0.309, with d’leastneg.20% = 1.213 and d’mostneg.20% = 1.142 (CI95% of difference: [–0.154, 0.012]). Thus, criterion c was lower for smaller than for larger N20 peak amplitudes (Figure 2e). This indicates that participants were more likely to rate a stimulus as ‘strong’ rather than ‘weak’ when the magnitude of the N20 potential was smaller (i.e., less negative), after taking into account the stimulus’ actual intensity, as it also becomes evident from the SEPs sorted by the behavioral response categories (Figure 2d). Interestingly, the relationship between N20 amplitudes and perceptual outcome appeared to be driven mainly by differences within the strong stimulus category (Figure 2d). This may reflect the naturally higher signal-to-noise ratio (SNR) of SEPs in response to stronger stimuli, or it could also point out that there was a ‘floor effect’ for the modulation of SEPs in response to the weaker stimuli.

Structural equation modeling of effect paths

Importantly, Figure 2d also suggests that N20 amplitudes were generally larger (i.e., more negative) for higher stimulus intensities – thus showing an effect of opposite direction on N20 amplitudes as compared to instantaneous cortical excitability. In order to disentangle these effects of excitability and stimulus intensity, we examined their respective contributions in a two-level structural equation model, with stimulus intensity, pre-stimulus alpha amplitude, and N20 peak amplitude as predictors of perceived stimulus intensity on level 1 (within subjects), and random intercepts as well as their variances on level 2 (between subjects), including all single trials. Furthermore, we added the measures of compound nerve action potentials of the median nerve (CNAP; Figure 3a and b) and compound muscle action potentials of the M. abductor pollicis brevis (CMAP; Figure 3c and d) to the model, in order to control for peripheral variability.

Measures to control for peripheral nerve variability.

(a) Single trials of the compound nerve action potential (CNAP) in response to the median nerve stimuli, measured at the inner side of the ipsilateral upper arm (shown for an exemplary subject). (b) Grand average across participants (N = 32) of the CNAP, displayed by stimulus and response types. (c) Single trials of the compound muscle action potential (CMAP), measured at the M. abductor pollicis brevis (shown for an exemplary subject). (d) Grand average across participants (N = 32) of the CMAP, displayed by stimulus and response types. Shaded areas in panels b and d correspond to the standard errors of the mean based on the within-subject variances (Morey, 2008).

On the one hand, both these peripheral measures should relate to stimulus intensity. On the other, there should be no effect of CNAP and CMAP on N20 amplitudes, when statistically controlling for stimulus intensity if the hypothesized fluctuations of excitability emerge on a cortical level. Yet, stimulus-induced thumb twitches may influence the participants’ intensity ratings of the stimuli (even though the stimulated hand was covered with a paper box). The resulting two-level structural equation model (SEM 1; Figure 4) indicated statistical significance of all hypothesized effect paths, all pβ ≤ 0.003, with model fit indices of Akaike information criterion (AIC) = 278,788.5, Bayesian information criterion (BIC) = 278,972.2, and log-likelihood = −139,372.2.

Multi-level structural equation model of the interplay between pre-stimulus alpha activity, the initial cortical response (N20 component of the somatosensory evoked potential [SEP]), intensity of the presented stimuli, the peripheral control measures of the compound muscle action potential (CMAP) of the M. abductor pollicis brevis and the compound nerve action potential (CNAP) of the median nerve, as well as the perceived intensity as reported by the participants (referred to as SEM 1).

Effect paths were estimated between the manifest variables on level 1 (within participants). Latent variables on level 2 served to estimate the respective random intercepts as well as their between-subject variances according to the latent variable approach for multi-level models as implemented in Mplus.

To evaluate the model fit, we compared a list of alternative models including or excluding relevant effect paths (Table 1). As indicated by chi-square difference tests, the log-likelihood of SEM 1 did not differ from those of SEMs 2–4, 9, and 10. Seeking model parsimony, SEM 1 is preferred over SEMs 2–4, 9, and 10 since the alternative models included one more parameter each, while fitting the data equally well. In comparison to SEMs 5–8, SEM 1 showed a significantly higher log-likelihood suggesting a better model fit than these more parsimonious models. This is further supported by the AIC and BIC values which were altogether lowest for SEM 1. Hence, we conclude that SEM 1 fitted our empirical data best.

Table 1
Model comparison of structural equation models (SEMs).

The original SEM (1) was compared to the alternative models (2–10) using Akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood (LL), and the chi-square difference test based on the LL (with corresponding p-value; p-values < 0.05 are indicated by bold print). Differences in AIC, BIC, LL, and degrees of freedom (df) were derived by the subtraction alternative SEM minus SEM 1. A better model fit is indicated by lower AIC and/or BIC as well as higher LL. The χ2 difference tests correspond to the comparisons model with fewer parameters minus model with more parameters.

Model fit indices
AIC diff.BIC diff.LL diff.χ2 diff.df diff.p-value
(1) Original SEM (‘SEM 1’)
(2) SEM incl. N20 ~ CNAP1.81310.1660.0930.146–10.702
(3) SEM incl. N20 ~ CMAP0.0888.4410.9560.799–10.371
(4) SEM incl. perceived_int ~ CNAP1.96710.3200.0160.019–10.890
(5) SEM excl. perceived_int ~ prestim8.002–0.351–5.00111.4151<0.001
(6) SEM excl. N20 ~ prestim15.0536.701–8.5278.08710.005
(7) SEM excl. N2047.09922.040–26.55031.0953<0.001
(8) SEM excl. CMAP9586.9069570.200–4795.45387.0302<0.001
(9) SEM incl. CMAP ~ prestim1.4049.7570.2970.220–10.639
(10) SEM incl. CNAP ~ prestim–0.1158.2391.0572.342–10.126

The estimated path coefficients (Figure 4) correspond well with above reported bivariate relationships: When controlling for stimulus intensity, both higher pre-stimulus alpha amplitudes and larger (i.e., more negative) N20 amplitudes were associated with a lower perceived intensity (equivalent to a response bias as reflected in criterion c), as well as higher pre-stimulus alpha amplitudes co-occurred with larger (i.e., more negative) N20 amplitudes. In addition, the SEM further dissociated the effects of stimulus intensity on early electrophysiological measures and their respective effects on perceived stimulus intensity. Higher stimulus intensity was associated with larger N20 amplitudes, which constitutes an effect of opposite direction as compared to the N20-related excitability effect on perceived intensity. Furthermore, higher stimulus intensity also led to larger amplitudes of CMAP and CNAP, due to the physical difference in stimulation strength, as could be expected a priori. Additionally, larger CMAP amplitudes resulted in a higher perceived intensity, while no such effect was observed for CNAP. Importantly, neither CMAP nor CNAP related to N20 amplitudes when controlling for stimulus intensity. Thus, fluctuations in cortical processing were not driven by peripheral variability. Also, peripheral activity in CMAP and CNAP did not show any association with pre-stimulus alpha activity. Finally, a substantial effect on the perceived intensity was found for stimulus intensity. This was expected as the overall accuracy in the discrimination task was about 70%.

Taken together, the SEM confirms the hypothesized influences of instantaneous fluctuations of early SEPs as well as pre-stimulus oscillatory activity on the consciously accessible percept of a stimulus. Moreover, this analysis demonstrates that stimulus intensity and cortical excitability, which in turn determines the perceived stimulus intensity, show opposing effects on the amplitude of the early SEP.

Reconstruction of the observed effects in source space

In order to investigate whether the observed effects of pre-stimulus alpha activity on N20 amplitudes and the perceived stimulus intensity were specific only to the generator regions of the SEP, we repeated the SDT analysis as well as the linear-mixed-effects models for these relations in source space (i.e., separately for every source estimated based on individual head models; see Materials and methods). As visible from Figure 5, the effects of pre-stimulus alpha amplitude on both N20 amplitude and perceived stimulus intensity were indeed most pronounced around the hand region of the right primary somatosensory cortex, the same region which we identified as source for the tangential CCA component used in the analyses above. The effects in source space using the SDT approach did not reach significance after the correction for multiple comparisons. Yet, a region of interest (ROI) analysis within the hand region of the right primary somatosensory cortex did confirm the observations from our previous analyses: There was an effect of pre-stimulus alpha amplitude on SDT parameter criterion c, t(31) = –2.951, p = 0.006, CI95% = [–0.173, –0.032], but no effect on sensitivity d’, t(31) = 0.633, p = 0.531, CI95% = [–0.083, 0.157]. (Please refer to Figure 5—figure supplement 1 for the distribution of the SDT effects across the whole cortex.) Taken together, the relationships between pre-stimulus alpha activity, N20 potential of the SEP, and perceived stimulus intensity appear to be attributable to neural activity from the same (or at least very similar) sources in the right primary somatosensory cortex.

Figure 5 with 1 supplement see all
Relations of pre-stimulus alpha amplitude with N20 amplitudes and perceived stimulus intensity as analyzed with linear-mixed-effects models in source space.

(a) Effects of pre-stimulus alpha amplitudes on N20 amplitudes (uncorrected t values). (b) Same as (a) but corrected for multiple comparisons (FDR-corrected; p < 0.01). (c) Effects of pre-stimulus alpha amplitudes on perceived stimulus intensity (uncorrected z values). (d) Same as (c) but corrected for multiple comparisons (FDR-corrected; p < 0.01).

Variability in thalamus-related activity is not related to behavioral responses

To examine further whether the observed neuronal effects on the perceived stimulus intensity were of a cortical origin, we analyzed the EEG responses prior to the N20 potential. In a sub-sample of 13 participants, the CCA decomposition provided a component that showed a clear peak at 15 ms, characterized by a spatial pattern that suggested a deep, medial source (Figure 6a). Most likely, this CCA component thus corresponds to the P15 potential of the SEP, which is thought to reflect activity in the thalamus (Albe-Fessard et al., 1986). The amplitude of this P15 component did not relate to the perceived stimulus intensity, as examined with a random-intercept linear-mixed-effects model with perceived intensity as dependent variable and P15 amplitude and stimulus intensity as predictors, βP15 = 0.008, z = 0.394, p = 0.694 (CI95% of βP15: [–0.031, 0.047]). As expected, stimulus intensity however showed a significant effect on perceived intensity, βstim_int = 1.851, z = 46.463, p < 0.001 (CI95% of βstim_int: [1.773, 1.929]). Additionally, we calculated the statistical power of finding an effect of P15 amplitude in the linear-mixed-effects model, using Monte Carlo simulations (Green et al., 2016) and assuming an effect size comparable to the observed N20 effect on perceived intensity. The post hoc power analysis revealed a statistical power of 71.9%. In addition, the SDT analysis based on binning of the P15 amplitudes into quintiles neither suggested a relation with criterion c nor sensitivity d’, t(12) = 1.201, p = 0.253, and t(12) = –0.201, p = 0.844, respectively. Therefore, we conclude that it is unlikely that the effect of N20 amplitudes on perceived stimulus intensity was driven by thalamic variability and that the modulation of perceived stimulus intensity emerges rather on the cortical level, reflecting instantaneous changes of cortical excitability. However, P15 amplitudes did also not differ between different presented stimulus intensities, as tested with a random-intercept linear-mixed-effects model, βstim_int = 0.009, t(12721.82) = 0.531, p = 0.596 (CI95% of βstim_int: [–0.025, 0.043]), which may indicate that this EEG-based measure of thalamic activity is generally not very sensitive to differences between experimental conditions. For completeness, we also tested for the effect of pre-stimulus alpha amplitude on P15 amplitude, which was also not significant, βprestim = –0.007, t(12210.50) = –0.635, p = 0.526 (CI95% of βprestim: [–0.028, 0.014]).

Thalamic activity and later somatosensory evoked potential (SEP) components.

(a) Grand average (N = 13) of the thalamic component derived from canonical correlation analysis (CCA), showing a clear P15 potential which did not differ across behavioral response categories. (b) Activation pattern of the thalamic CCA component (average across subjects). (c) Grand average (N = 32) of later SEP components (extracted with the tangential-CCA filter in the frequency range from 0.5 to 45 Hz). The N140 is visible as a negative peak at around 149 ms. Larger N140 amplitudes are associated with higher perceived intensities. Shaded areas correspond to the standard errors of the mean based on the within-subject variances (Morey, 2008).

Effects in later SEP components conform with previous studies

In order to relate our novel findings in early SEPs to the existing literature on somatosensory processing at later stages, we additionally examined a well-studied later component of the SEP, the N140. For this SEP component, a larger amplitude has typically been associated with a stronger percept of the presented stimulus (e.g., Al et al., 2020; Schröder et al., 2021; Schubert et al., 2006). Indeed, a comparable effect of N140 amplitude on perceived intensity was also present in our data (Figure 6c), as statistically tested with a random-slope linear-mixed-effects model, βfixed = –0.058, z = −3.387, p < 0.001 (CI95% of βfixed: [–0.093, –0.024]). Also, the presented stimulus intensity was related to the perceived intensity, βfixed = 1.876, z = 14.015, p < 0.001 (CI95% of βfixed: [1.605, 2.145]), which was expected given the participants’ discrimination performance being above chance level. These findings were in line with a separate SDT analysis: N140 amplitudes were associated with an effect on criterion c, t(31) = –3.010, p = 0.005, but no effect on sensitivity d’ emerged, t(31) = 0.246, p = 0.807. In addition, a second random-slope linear-mixed-effects model indicated that N140 amplitudes were in turn modulated by the presented stimulus intensity, βfixed = 0.030, t(30.92) = 2.275, p = 0.030 (CI95% of βfixed: [0.004, 0.056]), as well as by pre-stimulus alpha activity, βfixed = 0.021, t(31.70) = 2.73, p = 0.030 (CI95% of βfixed: [0.002, 0.040]).

Taken together, our results are thus consistent with previous studies on the relation between pre-stimulus state and somatosensory processing at later stages, while demonstrating opposing effects for the very first cortical response.

Discussion

Using a somatosensory discrimination paradigm, we examined the modulation of perceived stimulus intensity by instantaneous fluctuations of cortical excitability at initial cortical processing. Both pre-stimulus alpha band activity and initial cortical evoked responses were associated with a bias in intensity discrimination, suggesting that a lower cortical excitability reduces the perceived intensity of sensory stimuli. Furthermore, we rule out that variability in peripheral nerve activity accounted for these effects, in line with the notion of instantaneous excitability changes being intrinsic to cortical brain dynamics. Intriguingly, elevated excitability and higher presented stimulus intensity resulted in opposing amplitude effects on the initial stimulus-related response in the cortex, the N20 component of the SEP. Based on the neurophysiological principles of the EEG generation, this finding may be explained by a mechanistic link between pre-stimulus alpha activity and initial cortical EPSPs through modulations of resting membrane potentials.

Fluctuations of cortical excitability affect the perceived stimulus intensity

In line with previous studies on the modulatory role of alpha oscillations on perceptual processes (Craddock et al., 2017; Iemi et al., 2017), we found higher pre-stimulus alpha amplitudes to be associated with a lower perceived intensity of somatosensory stimuli. This was indicated both by an increased threshold (criterion c) of reporting a higher stimulus intensity according to SDT (Green and Swets, 1966) and by the negative relationship between pre-stimulus alpha amplitude and reported stimulus intensity in the structural equation model. Moreover, sensory processing appeared to be modulated by ongoing oscillatory activity already during initial cortical responses, as suggested by the relations between pre-stimulus alpha activity and N20 amplitude, as well as N20 amplitude and perceived stimulus intensity. The N20 component of the SEP reflects initial stimulus-related excitatory activity (i.e., EPSPs) resulting from the first thalamo-cortical volley to the primary somatosensory cortex (Bruyns-Haylett et al., 2017; Peterson et al., 1995; Wikström et al., 1996) and thus represents a direct measure of cortical excitability (when keeping the stimulus intensity constant). The modulation of perceived stimulus intensity therefore relates to a sensory bias at earliest possible cortical processing, reflecting fluctuations of instantaneous neural excitability.

Furthermore, these findings demonstrate that effects of pre-stimulus oscillatory activity on the processing of sensory stimuli are not restricted to near-threshold stimuli where a detection threshold is assumed to be shifted by ongoing brain activity (Iemi et al., 2017; Samaha et al., 2020). Instead, our findings suggest that cortical excitability can affect the representation of stimulus features in supra-threshold perception, too. Importantly, our criterion-free discrimination paradigm of two neutral response alternatives (‘strong’ or ‘weak’) precluded the potential confounding effect of perceptual confidence, which has recently been considered as an alternative explanation for pre-stimulus alpha effects on perceptual biases (Benwell et al., 2017; Samaha et al., 2017). In our forced-choice paradigm, different levels of perceptual confidence could not have influenced the intensity ratings since the task was to distinguish two clearly perceptible stimuli, and not to report whether a stimulus was perceived or not (as done in near-threshold paradigms). Thus, the current findings unequivocally indicate – to the best of our knowledge for the first time – that pre-stimulus alpha oscillations affect the behavioral outcome via a modulation of the internally represented stimulus intensity.

Opposing signatures of presented stimulus intensity and excitability in the early SEP

Following the hypothesis of higher alpha activity being associated with lower cortical excitability (Jensen and Mazaheri, 2010; Klimesch et al., 2007; Samaha et al., 2020), it may seem counter-intuitive that higher alpha amplitudes were associated with larger (i.e., more negative) N20 amplitudes in our data. However, as we proposed recently (Stephani et al., 2020), this relationship may be explained by the neurophysiological mechanisms of EEG generation. The generated voltage on the scalp, U, in our case relating to the N20 potential, can be defined in the following way (Ilmoniemi and Sarvas, 2019; Kandel et al., 2000; Lopes da Silva, 2004):

UINneuronsLF,

where I denotes the sum of local primary post-synaptic currents due to the activation of a given neuron, Nneurons the number of involved neurons, and LF the lead field coefficient projecting source activity to the electrodes on the scalp. Since the spatial arrangement of the neural generators and the EEG sensors was stable across stimulation events, LF reflects a constant in the measurement of the N20 potential. In contrast, Nneurons should increase with stimulus intensity since more nerve fibers are excited at stimulation site when applying stimuli of higher currents. This should lead to an increase of SEP amplitude with stimulus intensity, as reported in previous studies (Jousmäki and Forss, 1998; Klostermann et al., 1998) and as was observed in the current dataset for cortical (Figure 2d) as well as peripheral responses (Figure 3b and d). For constant stimulus intensity, however, Nneurons is expected to stay approximately constant and amplitudes in the EEG should primarily depend on I, reflecting excitatory post-synaptic currents (EPSCs) in case of the N20 component. Crucially, EPSCs directly depend on the electro-chemical driving forces produced by the membrane potential. When moving the membrane potential toward depolarization – a state of higher excitability – the electro-chemical driving force for further depolarizing inward trans-membrane currents is decreased (Castro-Alamancos, 2009), which leads to smaller EPSCs (Deisz et al., 1991), and should in turn result in smaller amplitudes of the scalp EEG. Assuming an inverse relationship between the amplitude of alpha oscillations and neuronal excitability (as for example indicated by a lower neural firing rate during higher alpha activity; Haegens et al., 2011), one should hence rather expect decreased N20 amplitudes following low pre-stimulus alpha activity. This is what was observed in our data when controlling for stimulus intensity (Figure 4). Moreover, the notion of smaller (i.e., less negative) N20 amplitudes reflecting a state of higher excitability is corroborated by the behavioral data: When controlling for stimulus intensity, we found smaller N20 amplitudes to be associated with higher perceived stimulus intensity. Notably, the attenuation of early SEPs during high excitability states is in fact in line with previous observations from biophysically realistic modeling of oscillatory activity in the somatosensory cortex as well as its interplay with stimulus-evoked responses (Jones et al., 2009).

Taken together, our findings thus demonstrate that the intensity of the presented stimulus and the degree of instantaneous neural excitability are jointly reflected in the early SEP but with opposing signatures: While stronger stimulus intensity increases the N20 potential, decreased N20 amplitudes appear to be associated with an increase in excitability (which in turn lead to a higher perceived stimulus intensity). This challenges previous assumptions that the amplitude of brain potentials, especially at early processing stages, reflects the coding of the perceived stimulus intensity. Rather, our findings call for a more differentiated view. Although the amplitude of early event-related potentials may indeed reflect the size of the input (e.g., a stronger or weaker somatosensory stimulus), the neural evaluation of this input (i.e., the perceived intensity), however, further depends on internal neural states, such as neural excitability, which may even reverse the amplitude effects of the input already at the earliest cortical processing stages. Crucially, our data are at the same time consistent with previous studies on somatosensory processing at later stages, where larger EEG potentials are typically associated with a stronger percept of a given stimulus (e.g., Al et al., 2020; Schröder et al., 2021; Schubert et al., 2006), as both our SDT and linear-mixed-effects analyses of the N140 component showed. Thus, the present findings of opposing signatures of neural excitability and sensory input appear to be a distinct characteristic of early cortical potentials, involving the first bottom-up sensory processing. In this context, it should be further emphasized that our proposed physiological model may be directly applicable to well-isolated neural signals only (such as the N20 component of the SEP). The physiological interpretation of amplitudes of later EEG potentials, such as the N140, however, is not as straightforward as described above, since several distinct SEP components may interact (Auksztulewicz et al., 2012), and excitatory and inhibitory contributions cannot be readily distinguished. Furthermore, with the present data, we cannot unambiguously conclude that the observed relation between pre-stimulus alpha activity and initial SEP indeed involved the very same neuronal populations – which may represent a limitation of the hypothesized mechanism. However, all approaches to localize these effects pointed to very similar cortical regions (as discussed in the following section). Also, we would like to emphasize that the presented mechanism reflects a hypothesized model, which shall be further supported or falsified with more targeted studies, for example, directly quantifying membrane potentials and trans-membrane currents in relation to different excitability states in somatosensation.

Origin of excitability fluctuations

To further narrow down the neuronal sources that eventually led to fluctuations of the perceptual outcome, we controlled for peripheral nerve variability, extracted spatially well-defined EEG potentials (i.e., as reflected in the tangential components of the single-trial CCA), re-analyzed the effects of interest in source space, and examined subcortical activity.

Variability in afferent peripheral activity, as measured by CNAP at the upper arm, did not influence the perceived stimulus intensity when controlling for stimulus intensity. However, a robust effect on the perceived stimulus intensity was observed for efferent peripheral activity, as measured by CMAP of the M. abductor pollicis brevis. This may be explained by differences in proprioceptive sensations associated with the thumb twitches elicited by the stimulation, whose extent may depend on changes of the prevailing muscle tonus. Importantly, neither the CNAP nor the CMAP related to cortical excitability as measured by the N20 component. Thus, the excitability effects in the early SEP are distinct from variability in peripheral nerve activity.

Furthermore, pre-stimulus alpha band activity and the N20 component of the SEP were retrieved from the same neuronal sources, which – as indicated by source reconstruction of the tangential CCA components – were localized in the primary sensory cortex, centered around the hand region of Brodmann area 3b. An additional, SEP-independent analysis of the effects of pre-stimulus alpha amplitude on N20 amplitude and perceived stimulus intensity confirmed these findings in source space. Although one should bear in mind the limited spatial resolution of EEG, this further supports the notion of excitability fluctuations in primary sensory regions of the cortex, being reflected in both ongoing and evoked neural activity.

Another possibility is that already subcortical sources – particularly in the thalamus – may play a role in modulating sensory excitability and hence shape the perceptual outcome (Kosciessa et al., 2021). Yet, neither our SDT analyses nor the linear-mixed-effects models of the thalamus-related P15 component supported this notion. Although we estimated an acceptable statistical power of these analyses, it should be noted that we could also not observe an effect of presented stimulus intensity on P15 amplitudes. Therefore, these results should be interpreted with some caution as this measure may lack the required SNR to detect rather subtle experimental effects of interest such as of the intensity difference of two very similar stimuli. Nevertheless – and also taking into account our analyses in source space – we conclude that the findings of the present study are most consistent with the idea that the modulation of perceived intensity had its origins at the cortical level.

However, it remains an open question whether the observed excitability changes reflect local or global neural dynamics. Although there is initial evidence that cortical excitability may be organized temporally in a scale-free manner (Stephani et al., 2020), which may reflect an embedding into global critical-state dynamics (Avramiea et al., 2020; Beggs and Plenz, 2003; Palva et al., 2013), future work has to examine the spatial organization of excitability more specifically across different somatotopic projections in primary sensory areas as well as across diverse brain regions.

In addition, it is unclear at this point how exactly the observed fluctuations of initial cortical responses are integrated in later, downstream neural processes. In principle, changes in early sensory processing should provide the ground for later neural activity involved in the perceptual decision making, and finally shape the behavioral outcome (as observed in the current study). However, with our data, we cannot unambiguously tell whether the modulation of alpha oscillations – associated with excitability changes at the earliest cortical level – may in turn reflect a top-down regulated signal, which could enable the neural system to account for ongoing fluctuations of excitability and even benefit from a certain degree of variability. Although more and more evidence indeed suggest an adaptive, functional role of neural ‘noise’ (e.g., Findling and Wyart, 2021), further studies are needed to better understand how this concept may pertain also to such fundamental neural properties as the initial cortical excitability to external sensory stimuli.

Conclusions

Both ongoing oscillatory alpha activity and amplitude fluctuations of the first cortical response shape the perceived intensity of somatosensory stimuli. These effects most likely reflect instantaneous changes of cortical excitability in the primary somatosensory regions of the cortex, leading to a sensory bias which manifests already during the very first cortical response. Further questioning previous assumptions of how the evaluation of stimulus intensity is reflected in brain potentials, cortical excitability and the presented stimulus intensity were associated with opposing effects on the early SEP. We argue that this disparity may be explained by a mechanistic link between ongoing oscillations and stimulus-evoked activity through membrane potential alterations. This sheds new light on the neural correlates of the intensity encoding of somatosensory stimuli, which may well apply to other sensory domains, too.

Materials and methods

Participants

A total of 32 participants (all male, mean age = 27.0 years, SD = 5.0) were recruited from the database of the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. As assessed using the Edinburgh Handedness Inventory (Oldfield, 1971), all participants were right-handed (lateralization score, M = + 93.1, SD = 11.6). No participant reported any neurological or psychiatric disease. All participants gave informed consent and were reimbursed monetarily. The study was approved by the local ethics committee (Ethical Committee at the Medical Faculty of Leipzig University, Leipzig, Germany). The chosen sample size was based on previous, similar studies which yielded an appropriate statistical power. (Please also note here that the estimates of the within-subject effects analyzed in the current study were based on the analysis of single trials [nearly 1000 per participant], ensuring a sufficiently high statistical power.)

Stimuli

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Somatosensory stimuli were applied using electrical stimulation of the median nerve. A non-invasive bipolar stimulation electrode was positioned on the left wrist (cathode proximal). The electrical stimuli were designed as squared pulses of a 20 µs duration and applied using a DS-7 constant-current stimulator (Digitimer, Hertfordshire, United Kingdom). Stimuli of two intensities were presented, in the following referred to as weak and strong stimulus. The intensity of the weak stimulus was set to 1.2 times the motor threshold, leading to a clearly visible thumb twitch for every stimulus. The individual motor threshold was determined as the lowest intensity for which a thumb twitch was visible to the experimenter, as determined by a staircase procedure. The intensity of the strong stimulus was adjusted during training blocks prior to the experiment so that it was barely above the just-noticeable difference, corresponding to a discrimination sensitivity of about d’ = 1.5 according to Signal Detection Theory (SDT; Green and Swets, 1966). Thus, the stimulation intensities of the two stimuli were only barely distinguishable (despite both being clearly perceivable), with average intensities of 6.60 mA (SD = 1.62) and 7.93 mA (SD = 2.06), for the weak and strong stimulus, respectively.

Procedure

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During the experiment, participants were seated comfortably in a chair their hands extended in front of them in the supinate position on a pillow. The left hand and wrist, to which the stimulation electrodes were attached, was covered with a cardboard box in order to prevent the participants to judge the stimulus intensity visually by the extent of thumb twitches elicited by the stimulation. Weak and strong stimuli were presented with an equal probability in a continuous, pseudo-randomized sequence with inter-stimulus intervals (ISI) ranging from 1463 to 1563 ms (randomly drawn from a uniform distribution; ISIaverage = 1513 ms). In total, 1000 stimuli were applied, divided into five blocks of 200 stimuli each with short breaks in between. Participants were to indicate after each stimulus whether it was the weak or strong stimulus, by button press with their right index and middle fingers as fast as possible. The button assignment for weak and strong stimulus was balanced across participants. Furthermore, every sequence started with a weak stimulus in order to provide an anchor point for the intensity judgments (participants were informed about this). While performing the discrimination task, participants were instructed to fixate their gaze on a cross on a computer screen in front of them.

Prior to the experiment, training blocks of 15 stimuli each were run in order to familiarize the participants with the task and to individually adjust the intensity of the strong stimulus so that a discrimination sensitivity of about d’ = 1.5 resulted (the intensity of the weak stimulus was set at 1.2 times the motor threshold for all participants). On average across participants, this procedure comprised 10.5 training blocks (SD = 5.8). During these training blocks, participants were provided with visual feedback of their response accuracy. No information on task performance was given during the experimental blocks.

Data acquisition

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EEG data were recorded from 60 Ag/AgCl electrodes at a sampling rate of 5000 Hz using an 80-channel EEG system (NeurOne Tesla, Bittium, Oulu, Finland). A built-in band-pass filter in the frequency range from 0.16 to 1250 Hz was used. Electrodes were mounted in an elastic cap (EasyCap, Herrsching, Germany) at the international 10–10 system positions FP1, FPz, FP2, AF7, AF3, AFz, AF4, AF8, F7, F5, F3, F1, Fz, F2, F4, F6, F8, FT9, FT7, FT8, FT10, FC5, FC3, FC1, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, CP5, CP3, CP1, CPz, CP2, CP4, CP6, T7, T8, TP7, TP8, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO3, PO4, PO8, O1, and O2, with FCz as the reference and POz as the ground. For the purpose of source reconstruction, the electrode positions were measured in 3D space individually for each subject using the Polhemus Patriot motion tracker (Polhemus, Colchester, Vermont). In order to record the electrooculogram, four additional electrodes were positioned at the outer canthus and the infraorbital ridge of each eye. The impedances of all electrodes were kept below 10 kΩ. For source reconstruction, EEG electrode positions were measured in 3D space individually for each subject using Polhemus Patriot (Polhemus, Colchester, Vermont). Additionally, the compound nerve action potential (CNAP) of the median nerve and the compound muscle action potential (CMAP) of the M. abductor pollicis brevis were measured. For the CNAP, two bipolar electrodes were positioned on the inner side of the left upper arm along the path of the median nerve, at a distance of about 1 cm (reference electrode distal). The CMAP was measured from two bipolar electrodes placed on the stimulated hand, one on the muscle belly of the M. abductor pollicis brevis and the other on the second joint of the thumb (reference electrode).

Structural T1-weighted MRI scans (MPRAGE) of all participants, but two were obtained from the database of the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, acquired within the same year of the experiment or up to 3 years earlier on a 3T Siemens Verio, Siemens Skyra, or Siemens Prisma scanner (Siemens, Erlangen, Germany).

EEG pre-processing

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Stimulation artifacts were cut out and interpolated between –2 and 4 ms relative to stimulus onset using Piecewise Cubic Hermite Interpolating Polynomials (MATLAB function pchip). The EEG data were band-pass filtered between 30 and 200 Hz, sliding a fourth-order Butterworth filter forward and backward over the data to prevent phase shift (MATLAB function filtfilt). As outlined in a previous study (Stephani et al., 2020), this filter allowed to specifically focus on the N20-P35 complex of the SEP, which emerges from frequencies above 35 Hz, and to omit contributions of later (slower) SEP potentials of no interest. Additionally, this filter effectively served as baseline correction of the SEP since it removed slow trends in the data, reaching an attenuation of 30 dB at 14 Hz, thus ensuring that fluctuations in the SEP did not arise from fluctuations within slower frequencies (e.g., alpha band activity). Subsequently, segments of the data that were distorted by muscle or non-biological artifacts were removed by visual inspection. After re-referencing to an average reference, eye artifacts were removed using independent component analysis (Infomax ICA) whose weights were calculated on the data band-pass filtered between 1 and 45 Hz (fourth-order Butterworth filter applied forward and backward). For SEP analysis, the data were segmented into epochs from –100 to 600 ms relative to stimulus onset, resulting in about 995 trials on average per participant. EEG pre-processing was performed using EEGLAB (Delorme and Makeig, 2004), and custom-written scripts in MATLAB (The MathWorks Inc, Natick, MA).

Single-trial extraction using CCA

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Single-trial SEPs were extracted using canonical correlation analysis (CCA), as proposed by Waterstraat et al., 2015, and in the same way applied as described in Stephani et al., 2020, for a similar dataset.

CCA finds the spatial filters wx and wy for multi-channel signals X and Y by solving the following optimization problem for maximizing the correlation:

max wx,wycorr(wxTX,wyTY),

where X is a multi-channel signal constructed from concatenating all the epochs of a subject’s recording, that is, X=[x1, x2,, xN] with xiRchannel×time being the multi-channel signal of a single trial and N the total number of trials. Additionally, Y=[x¯,...,x¯]Ntimes with x¯=1Ni=1Nxi denoting the grand average of all trials. Since averaging cancels the background noise and recovers the shared morphology of the SEP of interest among all the trials, the CCA procedure resembles a template matching between the single-trial signals and the template time signature of the SEP of interest. The spatial filter wx provides us with a vector of weights for mixing the channels of each single trial (i.e., xi,CCA=wxTxi) and recovering their underlying SEP. Therefore, wx can be interpreted as the spatial signature of the SEP of interest across all single trials. The optimization problem of CCA can be solved using eigenvalue decomposition. Therefore, multiple CCA spatial components can be extracted for each subject, being the eigenvectors of the corresponding eigenvalue decomposition. Since we are mainly interested in the early portion of the SEP, the two signal matrices X and Y were constructed using shorter segments from 5 to 80 ms post-stimulus. The extracted CCA spatial filter was, however, applied to the whole-length epochs from –100 to 600 ms. The signal resulting from mixing the single trial’s channels using the CCA spatial filter wx , that is, xi,CCA=wxTxi , is called a CCA component of that trial.

The spatial activity pattern of each CCA component was computed by multiplying the spatial filters wx by the covariance matrix of X, as cov(X)wx , in order to take the noise structure of the data into account (Haufe et al., 2014). The CCA components whose spatial patterns showed a pattern of a tangential dipole over the central sulcus (typical for the N20-P35 complex) were selected for further analyses and referred to as tangential CCA components. Such a tangential CCA component was present in all subjects among the first two CCA components with the maximum canonical correlation coefficients. Since CCA solutions are insensitive to the polarity of the signal, we standardized the resulting tangential CCA components by multiplying the spatial filter by a sign factor, in the way that the N20 potential always appeared as a negative peak in the SEP.

Furthermore, in a sub-sample of 13 subjects, a CCA component could be identified among the first four CCA components, which showed a peak at around 15 ms post-stimulus (presumably the P15 component of the SEP) and a spatial pattern that was characterized by a central, outspread activation (in the following referred to as thalamic CCA component). Also here, the CCA components were standardized so that the P15 always appeared as a positive peak.

In order to additionally evaluate the later time course of the SEP (i.e., the lower and later frequency content), the spatial filter of the tangential CCA component was applied to EEG data temporally filtered between 0.5 and 45 Hz (apart from this, pre-processed in the same way as described above).

SEP peak amplitudes and pre-stimulus oscillatory activity

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N20 peak amplitudes were defined as the minimum value in single-trial SEPs of the tangential CCA components ± 2 ms around the latency of the N20 in the within-subject average SEP. P15 amplitudes were measured from the thalamic CCA components as the average amplitude in a time window ±1 ms around the latency of the P15 in the within-subject average SEP. N140 amplitudes were measured from the low-frequency-filtered EEG (0.5–45 Hz), after application of the tangential CCA filter, as the average voltage in a time window between 140 and 160 ms after stimulus onset.

To estimate the average amplitude of pre-stimulus alpha band activity, the data were segmented from –500 to –5 ms relative to stimulus onset and band-pass filtered between 8 and 13 Hz, using a fourth-order Butterworth filter (applied forward and backward). In order to avoid filter-related edge artifacts, the data segments were mirrored before filtering to both sides (symmetric padding). Segmenting the data before filtering prevented any leakage from post-stimulus signals to the pre-stimulus time window. In order to examine pre-stimulus alpha band activity of the same sources as of the SEP, the spatial filter of the tangential CCA component was also applied to the pre-stimulus alpha data. Subsequently, the amplitude envelope of the extracted alpha oscillations was computed by taking the absolute value of the analytic signal, using Hilbert transform of the real-valued signal. To derive one pre-stimulus alpha metric for every trial, amplitudes of the alpha envelope were averaged in the pre-stimulus time window of interest between –200 and –10 ms and log-transformed for subsequent statistical analyses in order to approximate a normal distribution.

EEG source reconstruction

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Sources of the EEG signal were reconstructed using lead field matrices based on individual brain anatomies and individually measured electrode positions. Structural T1-weighted MRI images (MPRAGE) were segmented using the Freesurfer software (http://surfer.nmr.mgh.harvard.edu/), and a three-shell boundary element model based on the segmented MRI was used to compute the lead field matrix with OpenMEEG (Gramfort et al., 2010; Kybic et al., 2005). A template brain anatomy (ICBM152; Fonov et al., 2009) was used for two subjects for whom no individual MRI scans were available. Additionally, standard electrode positions were used for one subject for whom the 3D digitization of the electrode positions was corrupted. The lead field matrices, constrained to sources perpendicular to the cortex surface, were inverted using the eLORETA method (Pascual-Marqui, 2007), and sources were reconstructed for the spatial patterns of the tangential CCA component of every subject, as well as for pre-stimulus alpha activity (on a single-trial level). For group-level analysis, we projected the individual source estimates onto the ICBM152 template anatomy using the spherical co-registration with the FSAverage template (Fischl et al., 1999) derived from Freesurfer. Subsequently, the source estimates were averaged across subjects. Brainstorm (Tadel et al., 2011) was used for building individual head models and visualizing the source space data. The MATLAB implementation of the eLORETA algorithm was derived from the MEG/EEG Toolbox of Hamburg (METH; https://www.uke.de/english/departments-institutes/institutes/neurophysiology-and-pathophysiology/research/research-groups/index.html).

Processing of peripheral electrophysiological data (median nerve CNAP and thumb CMAP)

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Analogously to the EEG data, stimulation artifacts were cut out and interpolated between –2 and 4 ms relative to stimulus onset using Piecewise Cubic Hermite Interpolating Polynomials. To achieve a sufficient SNR of the short latency CNAP peak of only a few milliseconds duration on single-trial level, the data were high-pass filtered at 70 Hz (fourth-order Butterworth filter applied forward and backward). For the CMAP, no further filtering was necessary given the naturally high SNR of muscle potentials (mV range). Here, only a baseline correction was performed from –20 to –5 ms to account for slow potential shifts. For the CNAP, single-trial peak amplitudes were extracted as the maximum amplitude ±1 ms around the participant-specific latency of the CNAP peak that was found between 5 and 9 ms in the within-participant averages. The CMAP was evaluated regarding its peak-to-peak amplitude, which was defined as the difference between the minimum and maximum amplitude measured ±1 ms around the participant-specific latencies of the negative and positive peaks of the biphasic CMAP response (which were found between 5 and 11 ms as well as 10 and 20 ms in the within-participant averages, respectively).

Signal Detection Theory

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In order to separate the discrimination ability of the two stimulus intensities from a general response bias, we applied Signal Detection Theory (SDT; Green and Swets, 1966; Kingdom and Prins, 2016). The ability to discriminate the two stimulus intensities was quantified using sensitivity d’, as calculated in the following way:

d=ϕ1(p(strong§quot;|strong))ϕ1(p(strong§quot;|weak)),

where p(§quot;strong§quot;|weak) corresponds to the inverse of the cumulative normal distribution, p(strong§quot;|strong) to the probability of strong stimuli being rated as strong stimuli, and p(strong§quot;|weak) to the probability of weak stimuli being rated as strong stimuli. Response probabilities were calculated as the number of responses divided by the number of stimuli of the respective categories. The response bias, criterion c, was calculated as follows:

c=0.5(ϕ1(p(strong§quot;|strong))+ϕ1(p(strong§quot;|weak))).

According to SDT, sensitivity d’ here represents the distance between the distributions of the internal responses of the two stimuli, and thus reflects the discriminability between strong and weak stimulus intensity. Criterion c reflects the internal threshold above which a stimulus is rated as strong stimulus and below which a stimulus is rated as weak stimulus, thus representing a general response bias. With respect to our data, a higher criterion c therefore indicates a general tendency to report lower stimulus intensities.

Statistical analyses

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To confirm that task accuracy was above chance level, we ran non-parametric permutation tests (Crowley, 1992). Within each participant, we derived a null distribution of chance-level performance by randomly remapping the behavioral responses with the presented stimuli 100,000 times (Combrisson and Jerbi, 2015). The p-value of the empirical task accuracy was calculated as the proportion of higher accuracy values in the surrogate data. Bonferroni correction was applied to account for the multiple tests across participants.

The effects of the EEG measures pre-stimulus alpha amplitude, N20 peaik amplitude, P15 mean amplitude, and N140 mean amplitude on the SDT measures sensitivity d’ and criterion c were examined using a binning approach: First, trials were sorted according to the amplitudes of the EEG measures. Next, the SDT measures corresponding to the first and fifth quintile of the sorted trials were compared using paired-sample t-tests. To quantify effect sizes, Cohen’s d was calculated as the mean difference between the dependent samples divided by the standard deviation of differences between the dependent samples.

The relationship between pre-stimulus alpha activity and the N20 component was tested using a random-slope linear-mixed effects model with pre-stimulus alpha amplitude as predictor of N20 peak amplitude, and subject as random factor:

N20 peak amplitude1+pre-stimulus alpha+(1+pre-stimulus alpha | subject).

The relationship between thalamus-related activity and intensity perception was tested using a random-intercept linear-mixed-effects model with P15 amplitude and presented stimulus intensity as predictors of perceived stimulus intensity, as well as subject as random factor:

Perceived stimulus intensity1+P15 amplitude+presented stimulus intensity+(1 | subject).

Here, a logit link function was used to account for the dichotomous scale of perceived stimulus intensity (note that we refrained from estimating a random slope for P15 amplitude here given the small sample size of available data for thalamic activity). Furthermore, we tested the association between presented stimulus intensity and P15 amplitude as well as pre-stimulus alpha amplitude:

P15 amplitude1+pre-stimulus alpha+presented stimulus intensity+(1 | subject).

Analogously, we analyzed the effect of N140 amplitude on perceived stimulus intensity, however now including random slopes for N140 amplitude and presented stimulus intensity:

Perceived stimulus intensity1+N140 amplitude+presented stimulus intensity+(1+N140 amplitude+presented stimulus intensity | subject).

The dependence of the N140 on presented stimulus intensity and pre-stimulus alpha activity was examined using the following model:

Furthermore, we conducted a post hoc power analysis to evaluate the probability of finding an effect of P15 amplitude on perceived stimulus intensity if it was existent. For this, we used Monte Carlo simulations with 1000 permutations based on the empirical dataset (Green et al., 2016), assuming an effect size of β = 0.05, which is in the range of the observed effect of N20 amplitude on perceived stimulus intensity.

In addition, the interrelation of pre-stimulus alpha activity, the N20 component of the SEP, peripheral nerve activity as measured by CNAP and CMAP, the presented stimulus intensity, as well as the perceived stimulus intensity were examined using confirmatory path analysis based on multi-level structural equation modeling as implemented in the general latent variable framework of Mplus (Muthén and Muthén, 2017). Pre-stimulus alpha amplitude and presented stimulus intensity were included as exogenous variables, N20 peak amplitude, CNAP amplitude, CMAP amplitude, and perceived stimulus intensity as endogenous variables. The relationships contained in the hypothesized model (‘SEM 1’) are summarized in Table 2. Trials with no behavioral response were excluded from the analysis. In total, 31,347 single trials were included in the SEM, with 979.6 trials on average per participant. Model parameters were estimated using the MLR estimator provided by Mplus, a maximum-likelihood estimator robust to violations of the assumption of normally distributed data. A logit link function was used to account for the dichotomous scale of perceived stimulus intensity. The fit of the hypothesized model was examined comparing it to alternative models constructed by stepwise including or excluding relevant effect paths (Table 1). Model comparisons were evaluated using χ2 difference tests (based on the log-likelihood; Muthén, 2004), the AIC, and the BIC. (Note that no other fit indices, such as CFI, RMSEA, or SRMR, are available for our type of model with a multi-level structure and a dichotomous outcome variable.)

Table 2
Relationships included in the hypothesized structural equation model (‘SEM 1’).

Level 1 equations reflect the within-participant effects between variables of interest. On level 2, only intercepts and variances of each variable were modeled; apart from stimulus intensity which only varied within participants by experimental design.

Level 1 (within participants):
  N20 amplitude ~ 1 + stimulus intensity + pre-stimulus alpha
  CNAP ~ 1 + stimulus intensity
  CMAP ~ 1 + stimulus intensity
  Perceived intensity ~ 1 + stimulus intensity + N20 amplitude + pre-stimulus alpha + CMAP
Level 2 (between participants):
  N20 amplitude ~~ N20 amplitude
  CNAP ~~ CNAP
  CMAP ~~ CMAP
  Perceived intensity ~~ perceived intensity
  Pre-stimulus alpha ~~ pre-stimulus alpha

Moreover, the association between pre-stimulus alpha activity, the N20 potential and perceived intensity were examined in source space, independently from the SEP-derived spatial CCA filter. For that, the sources of pre-stimulus alpha activity were reconstructed as described under EEG source reconstruction, and subjected to the following linear-mixed-effects models (in close correspondence to the sub-equations of the SEM approach):

N20 peak amplitude1+presented stimulus intensity+pre-stimulus alphavertex i+(1 | subject),

and

Perceived stimulus intensity1+presented stimulus intensity+pre-stimulus alphavertex i+(1 | subject),

where vertex i refers to one of 5003 modelled cortical sources. We used FDR-correction (p < 0.01) to account for the multiple comparisons. Analogously, we repeated the SDT analysis for every single source as well as for the averaged activity of a region of interest (ROI) that included 19 sources covering the hand region of the right primary somatosensory cortex (Figure 5—figure supplement 1).

For all analyses (apart from the FDR correction for multiple comparisons), the statistical significance level was set to p = 0.05 (two-sided). Correspondingly, two-sided confidence intervals were calculated with a confidence level of 0.95 (CI95%). The permutation-based analyses and t-tests were performed in MATLAB (version 2019b, The MathWorks Inc, Natick, MA). For both the linear-mixed-effects model and the structural equation models, all continuous variables (i.e., pre-stimulus alpha, N20 amplitude, CNAP, and CMAP) were z-transformed prior to statistics. The linear-mixed-effects models were calculated in R (version 3.5.3, R Development Core Team, 2018) with the lmer function of the lme4 package (version 1.1–23, Bates et al., 2015), estimating the fixed-effect coefficients based on maximum likelihood. To derive a p-value for the fixed-effect coefficients, the denominator degrees of freedom were adjusted using Satterthwaite’s method (Satterthwaite, 1946) as implemented in the R package lmerTest (version 3.1–2, Kuznetsova et al., 2017). Structural equation modeling was performed in Mplus (version 8.6, Base Program and Combination Add-On; Muthén and Muthén, 2017) using the MplusAutomation package in R for scripting (Hallquist and Wiley, 2018). Post hoc statistical power analyses were performed using the R package simr (Green et al., 2016).

Data availability

The data used for generating the figures are openly accessible at: https://osf.io/v9xa6/. The raw EEG data cannot be made available in a public repository due to the privacy policies for human biometric data according to the European General Data Protection Regulation (GDPR). Further preprocessed data can be obtained from the corresponding author (TS; stephani@cbs.mpg.de) upon reasonable request and as far as the applicable data privacy regulations allow it.

The following data sets were generated
    1. Stephani T
    2. Hodapp A
    3. Jamshidi Idaji M
    4. Villringer A
    5. Nikulin VV
    (2021) Open Science Framework
    ID v9xa6. Neural excitability and sensory input determine intensity perception with opposing directions in initial cortical responses.

References

  1. Book
    1. Green DM
    2. Swets JA
    (1966)
    Signal Detection Theory and Psychophysics
    Wiley.
  2. Book
    1. Ilmoniemi R
    2. Sarvas J
    (2019)
    Brain Signals: Physics and Mathematics of MEG and EEG
    The MIT Press.
  3. Book
    1. Kandel ER
    2. Schwartz JH
    3. Jessel TM
    (2000)
    Principles of Neural Science (4th edn)
    New York, NY: McGraw-Hill Health Professions Division.
  4. Book
    1. Kingdom FAA
    2. Prins N
    (2016)
    Psychophysics: A Practical Introduction
    London: Elsevier Academic Press.
  5. Book
    1. Muthén BO
    (2004)
    Mplus Technical Appendices
    Muthén & Muthén.
  6. Book
    1. Muthén LK
    2. Muthén BO
    (2017)
    Mplus User´s Guide
    Muthén & Muthén.
  7. Software
    1. R Development Core Team
    (2018) R: A Language and Environment for Statistical Computing
    R Foundation for Statistical Computing, Vienna, Austria.
  8. Conference
    1. Scheer HJ
    2. Burghoff M
    3. Waterstraat G
    4. Nikulin VV
    5. Curio G
    (2013) em ID= proceedings: IEEE engineering in medicine and bio’’F45473da-49confeproceedings:IEEengineeriin24medicineandconferbiologysociety
    Distinction between added-energy and phase-resetting mechanisms in non-invasively detected somatosensory evoked responses. pp. 1688–1691.
    https://doi.org/10.1109/EMBC.2013.6609843

Decision letter

  1. Jonas Obleser
    Reviewing Editor; University of Lübeck, Germany
  2. Floris P de Lange
    Senior Editor; Radboud University, Netherlands
  3. Richard Gao
    Reviewer; University of California, San Diego, United States
  4. Nathan Weisz
    Reviewer; University of Salzburg, Austria

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Acceptance summary:

Stephani et al., address the question of how ongoing fluctuations in neuronal excitability, as well as stimulus strength, impact the perception of above-threshold tactile stimuli and the subsequent stimulus-evoked brain activity. The study builds up on very high quality data as well as analysis approaches and, alongside a decent sample size and a host of additional peripheral measures and a simulation study, allows the authors to challenge common interpretations of brain potential magnitudes in stimulus-intensity encoding.

Decision letter after peer review:

Thank you for submitting your article "Neural excitability and sensory input determine intensity perception with opposing directions in initial cortical responses" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Floris de Lange as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Richard Gao (Reviewer #1); Nathan Weisz (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) The main weaknesses of the manuscript become most apparent with respect to the stated impact that "The widespread belief that a larger brain response corresponds to a stronger percept of a stimulus may need to be revisited.". We are not sure that there are many cognitive neuroscientists who would subscribe to such a simplistic relationship between evoked responses and perception and that temporal differentiation (early vs late responses) and the biasing influence of prestimulus activity patterns are becoming increasingly recognized. So rather than actually changing a dominant paradigm, this work is an (excellent) contribution to a paradigm shift that is already taking place. Unneccesary claims of controiversiality and novelty thus should be toned down. See many specific hints to this in the two individual reviews below.

2) A main technical concern lies in the choice of decomposition filter for SEP and α oscillations, and the conclusions the authors draw from that. Specifically, a CCA spatial filter is optimized here for the N20 component, which is then identically applied to isolate for α sources, with the logic being that this procedure extracts the α oscillation from the same sources (e.g., L359). If our understanding of the authors' intent is correct, then the majority of us does not agree with the logic that using the same filter will isolate for α as well. The prestimulus α oscillation can have arbitrary source configurations that are different from the SEP sources, which may hypothetically have a different association with the behavioral responses when it's optimally isolated. In other words, just because one uses the same spatial filter, it does not imply that one is isolating α from the same source as the SEP, but rather simply projecting down to the same subspace – looking at a shadow on the same wall, if you will.

To show that they are from the same sources, α should be isolated independently of the SEP (using CCA, ICA, or other methods), and compared against the SEP topology. If the topology is similar, then it would strengthen the authors' current claims, but ideally the same analyses (e.g., using the 1st and 5th quintile of α amplitude to partition the responses) is repeated using α derived from this procedure. Also, have the authors considered using individualized α filters given that α frequency vary across individuals? Why or why not?

3) It should be considered that with regards to the analysis approach using CCA, the claims are mainly restricted to BA3b: The authors should refrain from overinterpreting the results in a very generalized manner. The authors do include some "thalamus" and "late" evoked response patterns as well, however that presentation of the results is somewhat changed now as compared to the N20 (e.g. using LMEs rather than comparison of extremes; not using SEMs). The readablity of results and especially the comparison of effects would profit from a more coherent approach.

4) Concerns arose whether the relationship between large α power and more negative N20s could be driven by more trivial factors rather than the model explanations the authors develop in the discussion. Put in concrete terms, the question is whether phase locking of large α power along with >30 Hz high pass filtering could produce a similar finding as shown e.g. in Figure 2c. This is an important issue, as prestimulus α influences the N20 amplitudes as well as the perceptual reports. See also our point #1 above.

5) At multiple points, the authors comment that the covariation of N20 and α amplitude in the same direction is counterintuitive (e.g., L123-125). It is being explained later in the manuscript that lower α amplitude and higher SEP amplitude are associated with excitability, and hence should have the opposite directions. This should be explicitly stated earlier in the introduction, as well as the expected relationship between α amplitude and behavior.

Reviewer #1 (Recommendations for the authors):

General comments for improving clarity and other discussion points

– Figure 1 is a really nice schematic of the study. Personally, it would have been extremely helpful to have an additional panel that shows the quintile analysis, and perhaps a visual representation of the quantities extracted (i.e., sensitivity and criterion). A slightly adapted version of a generic SDT schematic would be sufficient for the naive reader such as myself (as I had to Google around a bit), but could be unnecessary given the target audience of the paper

– What is the rationale for choosing the top and bottom 20% of α / SEP amplitude for partitioning? Is this just an arbitrary choice? If so, is the result robust to using, say, quartiles?

– α amplitude was higher in general when response was "weak", but SEP amplitude was larger for all stronger stimuli, and only larger when response was "weak" conditioned on the stimulus strength. In fact, it looks like the SEP effect is really driven by a difference within the strong stimuli (Figure 2d). I may have missed this, but a comment on this and why it might be would be great.

– I'm not sure if this was by design to emphasize the counterintuitiveness of the findings, but at multiple points in the manuscript, the description of the α and SEP effects are reversed with double negatives. For example, L100-102 says α amplitude was higher when participants rated the stim to be "weaker", while L140-141 says SEP was smaller when participants rated the stim to be "stronger". If I'm not mistaken, these mean the same thing, especially considering that N20 negativity is also intuitively an amplitude, in which case, both amplitude measures correlate with a bias towards "weaker" ratings. Same with the arrangement of figure 2b and 2e, where it would be natural to put higher amplitude (i.e., more negative N20) on the right. Unless this was done very intentionally, I think it would improve the clarity of the manuscript to make those descriptions and figures the same.

– Which is SEM1? Is that represented in Figure 4? I don't think this is labeled anywhere, and in the table SEM1 is just "original model"

– I might have misunderstood something here, but it was very surprising that the thalamic component does not vary as a function of stimulus intensity? Is there an explanation for this?

– Just to reiterate the earlier point on α, L312-317 is a really nice explanation of the model the authors have in mind, though I had a totally different one reading through the paper, that basically α amp and SEP amp have a shared origin, and hence both reflect synchronous neuronal activity, potentially of the same population. My reading of the author's interpretation is that α is a readout of excitability, and the lower the α amp the higher the excitability, which leads to more depolarized neurons and hence smaller EPSCs in a different (population). If this is correct, my suggestion would be to hint at this earlier on, so people don't go down the same rabbit hole as I did (though I am unfamiliar with this literature)

– Related to the above, I had to really wrap my head around the fact that the N20 is both a function of the true stimulus intensity (i.e. post-stim readout), as well as a marker for prestim (or ongoing) neural excitability (i.e., pre-stim readout). I know this is the whole point of the paper, but a sentence explicitly stating that very early on might help the naive reader to orient themselves to the subsequent findings.

– L328-330: nice emphasis on the significance, would do the same earlier as well if not already.

– I think a brief discussion on how the brain then uses the information represented by the SEP downstream to "perform the perceptual rating" would be great, i.e., does it account for the ongoing fluctuation in excitability?

– L351-355: CNAP (sensory readout) variability did not influence perceptual decision, but CMAP (muscle output) variability does – both are within the first 10ms, so there must be another loop from the motor reaction / reflex readout to the brain, and when does the brain / subject make the decision? Does the motor percept also feed back into the N20?

– I was looking for an explanation as to why criterion might be affected here but not sensitivity, maybe a brief discussion speculating on this point would be appropriate?

Again, great paper – it was a joy to read!

Reviewer #2 (Recommendations for the authors):

Overall I found the manuscript very interesting and my recommendations pretty much concern the discussed weaknesses.

– The authors should reflect more clearly the impact of the study along the lines mentioned above. I pretty much have the impression that the results are not a "game changer" in the field but fits well with increasingly dominant views in the field.

– I would strongly advise to check the possibility whether the α-N20-link could be caused by high-pass-filtering of phase locked α responses. Along these lines a time frequency depiction of results in figure 1a would be helpful (i.e. showing low to high frequencies of non-high pass filtered data). Also since the link between N20 and perception appears "counterintuitive", it may be also useful to test the relationship within α bins (i.e. high-low N20 responses stratified for α power) and see whether the relationship still holds.

– Furthermore, it would improve readability if the authors used a streamlined statistical approach also for the "thalamic" and "late" evoked responses.

– The authors convincingly show that evoked peripheral responses are not linked to N20 amplitude. It would be interesting to know whether variability in ongoing peripheral activity is linked to ongoing α activity (i.e. in prestimulus period).

– At the moment the value of the physiological model developed in the discussion is not really clear (see previous comments). It also seems to be particularly geared to the N20 results and leaves open the question whether it also encompasses the "thalamus" and "late" results.

https://doi.org/10.7554/eLife.67838.sa1

Author response

Essential revisions:

1) The main weaknesses of the manuscript become most apparent with respect to the stated impact that "The widespread belief that a larger brain response corresponds to a stronger percept of a stimulus may need to be revisited.". We are not sure that there are many cognitive neuroscientists who would subscribe to such a simplistic relationship between evoked responses and perception and that temporal differentiation (early vs late responses) and the biasing influence of prestimulus activity patterns are becoming increasingly recognized. So rather than actually changing a dominant paradigm, this work is an (excellent) contribution to a paradigm shift that is already taking place. Unneccesary claims of controiversiality and novelty thus should be toned down. See many specific hints to this in the two individual reviews below.

Thank you for this feedback. We agree that the paradigm shift away from simplistic assumptions about the relationship between variability of neural responses and perception is already taking place and that this is already being appreciated by many scientists in the field. Also, we agree that the present study contributes more evidence to this emerging notion rather than changing the whole field. However, we do think that particularly the observation of opposite amplitude modulations of initial somatosensory evoked responses associated with presented stimulus intensity on the one hand and pre-stimulus excitability state on the other, provides a novel perspective for our understanding of how fundamental features of sensory stimuli are processed at initial cortical levels. Following your suggestions to tone down claims about the controversiality as well as to avoid over-generalization, we have therefore adjusted the impact statement of this manuscript to:

“Larger evoked responses during initial cortical processing may reflect states of lower excitability.”

Furthermore, we have adjusted similar statements throughout the manuscript accordingly:

“This challenges previous assumptions that the amplitude of brain potentials, especially at early processing stages, reflects the coding of the perceived stimulus intensity.” (page 19, lines 360 ff.)

And

“Further questioning previous assumptions of how the evaluation of stimulus intensity is reflected in brain potentials, cortical excitability and the presented stimulus intensity were associated with opposing effects on the early SEP.” (page 22, lines 449 ff.)

2) A main technical concern lies in the choice of decomposition filter for SEP and α oscillations, and the conclusions the authors draw from that. Specifically, a CCA spatial filter is optimized here for the N20 component, which is then identically applied to isolate for α sources, with the logic being that this procedure extracts the α oscillation from the same sources (e.g., L359). If our understanding of the authors' intent is correct, then the majority of us does not agree with the logic that using the same filter will isolate for α as well. The prestimulus α oscillation can have arbitrary source configurations that are different from the SEP sources, which may hypothetically have a different association with the behavioral responses when it's optimally isolated. In other words, just because one uses the same spatial filter, it does not imply that one is isolating α from the same source as the SEP, but rather simply projecting down to the same subspace – looking at a shadow on the same wall, if you will.

To show that they are from the same sources, α should be isolated independently of the SEP (using CCA, ICA, or other methods), and compared against the SEP topology. If the topology is similar, then it would strengthen the authors' current claims, but ideally the same analyses (e.g., using the 1st and 5th quintile of α amplitude to partition the responses) is repeated using α derived from this procedure. Also, have the authors considered using individualized α filters given that α frequency vary across individuals? Why or why not?

Indeed, applying the same spatial filter to EEG signals with different spatial arrangements of the sources can lead to the extraction of neuronal activity which does not originate from the very same sources. We had chosen our approach, as it is well known that the generators of the early SEP components and the generators of the prominent somatosensory α rhythm co-reside at similar sites in the primary somatosensory cortex (e.g., Haegens et al., 2015). Therefore, we considered our approach appropriate to specifically focus on neural activity from the somatosensory region both in the frequency band of the SEP as well as of the α rhythm. Yet, we agree with the reviewer that it should be acknowledged that we may have missed or mixed-up effects of α activity from other sources by using this procedure (which might have led to different conclusions otherwise). In order to account for this, we repeated our analyses with an SEP-independent reconstruction of the oscillatory effects in source space (“whole brain analysis”). For this, we first reconstructed the sources of α activity using eLORETA and head models based on participant-specific MRI scans, and estimated the respective effects independently for all sources across the cortex using both linear-mixed effects models (LME) as well as a binning approach for the Signal Detection Theory (SDT) parameters sensitivity d’ and criterion c (consistent with the previous analyses in our manuscript). In the LME analyses, both the effects of pre-stimulus α activity on N20 amplitudes as well as on perceived stimulus intensity were strongest in the right primary somatosensory cortex – in accordance with the sources of the originally extracted tangential CCA component of the SEP (see Figure 5). Also, using the binning approach to examine the relation or pre-stimulus α activity with SDT parameter criterion c, the effects were most pronounced around the right somatosensory regions (see Figure 5—figure supplement 1), yet these effects did not survive statistical correction for multiple comparisons (FDR-correction with p<.01). However, when performing the same binning analysis for our region of interest (ROI), the hand area in BA 3b of the right somatosensory cortex, a significant effect or pre-stimulus α on criterion c was indeed confirmed, t(31)=-2.951, p=.006, CI95%=[-.173, -.032]. Furthermore, in line with our previous CCA results, for sensitivity d’, neither the whole brain analysis nor the ROI analysis showed effects of pre-stimulus α amplitude, t(31)=0.633, p=.531, CI95%=[-.083,.157].

Taken together, the findings we report in our original manuscript for pre-stimulus α activity obtained with the spatial CCA filter can thus be replicated with a SEP-uninformed source reconstruction, both using LMEs for a “whole-brain analysis” as well as SDT analyses in a ROI-based approach. We therefore conclude that the relationships between pre-stimulus α activity, N20 potential of the SEP, and perceived stimulus intensity can indeed be attributed to neural activity from the same (or at least very similar) sources in the primary somatosensory cortex.

We have added the following sections to our manuscript where we describe these analyses in more detail:

Results (page 12, lines 206 ff.):

“Reconstruction of the observed effects in source space

In order to investigate whether the observed effects of pre-stimulus α activity on N20 amplitudes and the perceived stimulus intensity were specific only to the generator regions of the SEP, we repeated the SDT analysis as well as the linear-mixed-effects models for these relations in source space (i.e., separately for every source estimated based on individual head models; see Methods). As visible from Figure 5, the effects of pre-stimulus α amplitude on both N20 amplitude as well as perceived stimulus intensity were indeed most pronounced around the hand region of the right primary somatosensory cortex, the same region which we identified as source for the tangential CCA component used in the analyses above. The effects in source space using the SDT approach did not reach significance after the correction for multiple comparisons. Yet, a ROI analysis within the hand region of the right primary somatosensory cortex did confirm the observations from our previous analyses: There was an effect of pre-stimulus α amplitude on SDT parameter criterion c, t(31)=-2.951, p=.006, CI95%=[-.173, -.032], but no effect on sensitivity d’, t(31)=0.633, p=.531, CI95%=[-.083,.157]. (Please refer to Figure 5—figure supplement 1 for the distribution of the SDT effects across the whole cortex.) Taken together, the relationships between pre-stimulus α activity, N20 potential of the SEP, and perceived stimulus intensity appear to be attributable to neural activity from the same (or at least very similar) sources in the right primary somatosensory cortex.”

Discussion (page 20, lines 406 ff.):

“An additional, SEP-independent analysis of the effects of pre-stimulus α amplitude on N20 amplitude and perceived stimulus intensity confirmed these findings in source space.”

Methods:

“[…] sources were reconstructed for the spatial patterns of the tangential CCA component of every subject, as well as for pre-stimulus activity filtered in the α band (8 to 13 Hz; extracted on a single-trial level).” (page 30, lines 631 ff.)

and:

“Moreover, the association between pre-stimulus α activity, the N20 potential and perceived intensity were examined in source space, independently from the SEP-derived spatial CCA filter. For that, the sources of pre-stimulus α activity were reconstructed as described under EEG source reconstruction, and subjected to the following linear-mixed-effects models (in close correspondence to the sub-equation of the SEM approach):

N20 peak amplitude ~ 1 + presented stimulus intensity + pre-stimulus alphavertex i + (1 | subject),

and

Perceived stimulus intensity ~ 1 + presented stimulus intensity + pre-stimulus alphavertex i + (1 | subject)

where vertex i refers to one of 5003 modelled cortical sources. We used FDR-correction (p<.01) to account for the multiple comparisons. Analogously, we repeated the SDT analysis for every single source as well as for the averaged activity of a region of interest (ROI) that included 19 sources covering the hand region of the right primary somatosensory cortex (Figure 5—figure supplement 1).” (page 34, lines 735 ff.)

Addressing the question on filtering α activity in individualized frequency bands, we considered this option, too. However, the rather short length of our pre-stimulus window (-200 to -10 ms) constitutes a natural limit for the frequency resolution in the α range and slightly different filter ranges (adjusted with regards to the individual α peak frequency) are thus unlikely to lead to large differences in the estimation of pre-stimulus α amplitudes. Therefore, we refrained from using individualized frequency bands here and focused on the more generic approach using one common α band (8-13 Hz) for all participants, which should also facilitate direct comparisons with previous studies on pre-stimulus oscillatory effects.

3) It should be considered that with regards to the analysis approach using CCA, the claims are mainly restricted to BA3b: The authors should refrain from overinterpreting the results in a very generalized manner. The authors do include some "thalamus" and "late" evoked response patterns as well, however that presentation of the results is somewhat changed now as compared to the N20 (e.g. using LMEs rather than comparison of extremes; not using SEMs). The readablity of results and especially the comparison of effects would profit from a more coherent approach.

We agree that our findings indeed have the specific focus on the N20 component and thus on its generators in BA3b. We did not intend to suggest that the effects we observed for this initial cortical response can be readily generalized to other (later) ERP components, too. However, we do believe (and hypothesize) that similar mechanisms may be in place for corresponding initial cortical responses in other sensory modalities, too – yet it is clear that we cannot test this generalization with the current study. To avoid misunderstandings of these interpretations and their limitations and also relating to the last comment of reviewer #2, we have further specified these aspects in the Discussion; page 19, lines 373 ff.:

“In this context, it should be further emphasized that our proposed physiological model may be directly applicable to well isolated neural signals only (such as the N20 component of the SEP). The physiological interpretation of amplitudes of later EEG potentials, such as the N140, however, is not as straightforward as described above, since several distinct SEP components may interact (Auksztulewicz et al., 2012), and excitatory and inhibitory contributions cannot be readily distinguished.”

Regarding our analyses of the later SEP (i.e., N140 component) and thalamus-related activity (i.e., P15 component), we initially decided to use linear-mixed effects models as they are mathematically equivalent to the way the sub-equations of the structural equation model were constructed (Table 2 in the manuscript). Nevertheless, we have now additionally run binning analyses to make a direct comparison also with Signal Detection Theory (SDT) parameters possible: For the N140 component, there was a significant effect on criterion c, t(31)=-3.010, p=.005, but no effect on sensitivity d’, t(31)=0.246, p=.807. For the P15 component, no effects emerged either for criterion c or sensitivity d’, t(12)=1.201, p=.253, and t(12)=-0.201, p=.844, respectively. These findings correspond well to the previous LME analyses and may indeed further facilitate the comparison with the findings for the N20 potential and pre-stimulus α activity. Therefore, we have added these complimentary analyses to our manuscript in the following way:

Results:

“In addition, the SDT analysis based on binning of the P15 amplitudes into quintiles neither suggested a relation with criterion c nor with sensitivity d’, t(12)=1.201, p=.253, and t(12)=-0.201, p=.844, respectively.” (page 14, lines 241 ff.)

and

“These findings were in line with a separate SDT analysis: N140 amplitudes were associated with an effect on criterion c, t(31)=-3.010, p=.005, but no effect on sensitivity d’ emerged, t(31)=0.246, p=.807.” (page 15, lines 263 ff.)

Discussion:

“Crucially, our data are at the same time consistent with previous studies on somatosensory processing at later stages, where larger EEG potentials are typically associated with a stronger percept of a given stimulus (e.g., Al et al., 2020; Schröder et al., 2021; Schubert et al., 2006), as both our SDT and LME analyses of the N140 component showed.” (page 19, lines 367 ff.)

and

“Yet, neither our SDT analyses nor the LME models of the thalamus-related P15 component supported this notion.” (page 21, lines 414 ff.)

Methods (page 32, lines 681 ff.):

“The effects of the EEG measures pre-stimulus α amplitude, N20 peak amplitude, P15 mean amplitude, and N140 mean amplitude on the SDT measures sensitivity d’ and criterion c were examined using a binning approach: […]”

4) Concerns arose whether the relationship between large α power and more negative N20s could be driven by more trivial factors rather than the model explanations the authors develop in the discussion. Put in concrete terms, the question is whether phase locking of large α power along with >30 Hz high pass filtering could produce a similar finding as shown e.g. in Figure 2c. This is an important issue, as prestimulus α influences the N20 amplitudes as well as the perceptual reports. See also our point #1 above.

Indeed, potential phase-locking of α oscillations to stimulus onset and filter-related effects are important issues that could potentially offer an alternative explanation for the observed relationship between amplitudes of pre-stimulus α activity and the N20 potential of the SEP. Although such pre-stimulus α locking is rather unlikely in a paradigm with jittered stimulus onsets (in our case uniformly distributed between -50 ms and +50 ms; corresponding to a whole α cycle), we have run the following control analyses to fully exclude this possibility:

First, we analyzed whether pre-stimulus α phase values were distributed uniformly and whether these phase distributions differed between high and low α amplitudes as well as between high and low N20 amplitudes. The phase of pre-stimulus α activity was obtained from a Fast-Fourier transform in the pre-stimulus time window from -200 to -10 ms, applied to unfiltered, but otherwise identically pre-processed data as in the original manuscript (i.e., applying the spatial filter of the tangential CCA component). For the FFT, we used zero padding (extending the pre-stimulus data segments to 2048 data points each) in order to obtain an interpolated frequency resolution of around 3 Hz. The phase was extracted at the frequency 9.766 Hz (i.e., the closest available frequency to 10 Hz). As visible from Author response image 1, pre-stimulus α phases were distributed uniformly across all five quintiles of both α and N20 amplitudes. This observation was confirmed by the Rayleigh test (testing for deviations from a uniform distribution; Berens, 2009): Neither in the concatenated phase data of all participants, z=1.130, p=.323, nor in single-participant analyses within every α amplitude or N20 amplitude bin, we found evidence for a non-uniform distribution of α phase, all p>.367 (after Bonferroni correction for multiple testing). Thus, there was no phase-locking of pre-stimulus α activity that could serve as a trivial alternative explanation of the relationship between pre-stimulus α amplitude and N20 amplitude.

Author response image 1
Phase distributions of pre-stimulus alpha band activity, separately displayed for quintiles of alpha amplitudes (panel a), and N20 amplitudes (panel b).

Alpha phase estimates were based on Fast-Fourier transforms in the pre-stimulus time windows from -200 to -10 ms. For visualization, data were concatenated across all participants (N=32).

Second, in order to examine whether the combination of our temporal filters (30 to 200 Hz band-pass for the SEP, and 8 to 13 Hz band-pass for α activity) could have led to the present findings, we additionally re-ran our analysis pipeline with simulated data: We mixed exemplary SEP responses with constant amplitudes (unfiltered; derived from within-participant averages), with simulated α band activity with randomized amplitude fluctuations, and pink noise, reflecting neural background activity as is typical for the human EEG. The SEP onsets were chosen according to our original experimental paradigm with inter-stimulus intervals of 1513 ms and a jitter of ±50 ms. Next, we filtered these mixed signals between 30 and 200 Hz in order to extract the single-trial SEPs, and estimated the pre-stimulus α amplitudes between -200 and -10 ms in the same way as was done in the original manuscript (i.e., by filtering the mixed signal between 8 and 13 Hz). This procedure was repeated for 32 generated data streams, containing 1000 SEPs each (corresponding to our empirical dataset of 32 participants). As can be seen from Figure 2—figure supplement 3, the resulting average SEPs did neither show a visually detectable difference between the five α amplitude quintiles nor indicated a random-slope linear-mixed-effects model any relation between pre-stimulus α amplitude and N20 amplitude on a single-trial level, βfixed=-.0005, t(255.16)=-.094, p=.925. Therefore, our findings cannot be explained by filter artifacts or residual activity leaking from the α frequency band to the frequency band of the N20 potential.

Third, we re-analyzed our empirical EEG data in time-frequency space to obtain a more detailed view of the effects of pre-stimulus α activity on N20 amplitudes. For this, we decomposed our pre-processed but unfiltered data with wavelet transformation (complex Morlet wavelets) and calculated linear-mixed effects models on the relation between signal amplitudes in the time-frequency domain and single-trial N20 amplitudes as obtained from our original analyses. As shown in Figure 2—figure supplement 2, the time-frequency representations of the effects on N20 amplitudes indeed indicated a specific role of the α band, with its effects (i.e., already 200 ms before stimulus and in the upper α frequency range) separated from the time- and frequency range of the N20 potential of the SEP (i.e., from ~20 ms after stimulus onwards and above ~20 Hz). In addition, we ran the same analysis for the behavioral effect (i.e., perceived stimulus intensity). Also here, pre-stimulus effects were predominantly visible in the α band. Of note, there were also strong effects in the β band. These may be interesting to study further in future studies – in particular, whether they reflect independent physiological processes or rather harmonics of the α band. Furthermore, these time-frequency representations suggest that the studied pre-stimulus effects might have been even more pronounced if we had analyzed the data in pre-stimulus time windows from -300 to -10 ms. However, in order to avoid inflating effect sizes by post-hoc data digging (“p-hacking”), we prefer to keep the original, a priori chosen time window for the main analyses of the manuscript. Yet, these onsets of pre-stimulus effects at around -300 ms may be of interest for future work. Taken together, these time-frequency analyses further support the notion that the observed relation between pre-stimulus α activity and N20 amplitudes is not due to technical issues (such as filter leakage and phase-locking) but rather reflects genuine neurophysiological effects of α oscillations on SEPs.

We have added the time-frequency analysis (Figure 2 Supplement 3), as well as the SEP simulation analysis (Figure 2 Supplement 4) as figure supplements to Figure 2 in our revised manuscript (page 8) since we believe that these control analyses comprehensively show that the observed effects were (a) specific to the α band and (b) not due to any data processing-related artifacts.

5) At multiple points, the authors comment that the covariation of N20 and α amplitude in the same direction is counterintuitive (e.g., L123-125). It is being explained later in the manuscript that lower α amplitude and higher SEP amplitude are associated with excitability, and hence should have the opposite directions. This should be explicitly stated earlier in the introduction, as well as the expected relationship between α amplitude and behavior.

Thank you for pointing out this unclarity. We have now made this rationale more explicit already at an early point in the introduction (page 3, lines 26 ff.):

“According to the baseline sensory excitability model (BSEM; Samaha et al., 2020), higher α activity preceding a stimulus indicates a generally lower excitability level of the neural system, resulting in smaller stimulus-evoked responses, which are in turn associated with a lower detection rate of near-threshold stimuli but no changes in the discriminability of sensory stimuli (since neural noise and signal are assumed to be affected likewise).”

Reviewer #1 (Recommendations for the authors):

General comments for improving clarity and other discussion points

– Figure 1 is a really nice schematic of the study. Personally, it would have been extremely helpful to have an additional panel that shows the quintile analysis, and perhaps a visual representation of the quantities extracted (i.e., sensitivity and criterion). A slightly adapted version of a generic SDT schematic would be sufficient for the naive reader such as myself (as I had to Google around a bit), but could be unnecessary given the target audience of the paper.

Good idea! We have added a SDT schematic as Figure 2—figure supplement 1.

– What is the rationale for choosing the top and bottom 20% of α / SEP amplitude for partitioning? Is this just an arbitrary choice? If so, is the result robust to using, say, quartiles?

The choice of partitioning the data into quintiles was made a priori, based on previous studies in the field (e.g., Iemi et al., 2017). In addition, we repeated the analysis with quartiles which resulted in very similar observations: There were significant effects for criterion c for both the pre-stimulus α and N20 analysis, t(31)=-3.238, p=.003, and t(31)=2.434, p=.021, respectively (always comparing first vs. fourth bin, sorted in ascending order). However, no effects emerged for sensitivity d’ either for α or N20 amplitude, t(31)=-.672, p=.507, or N20 amplitude, t(31)=.001, p>.999. Therefore, our findings appear robust also with other ways of partitioning the data.

– α amplitude was higher in general when response was "weak", but SEP amplitude was larger for all stronger stimuli, and only larger when response was "weak" conditioned on the stimulus strength. In fact, it looks like the SEP effect is really driven by a difference within the strong stimuli (Figure 2d). I may have missed this, but a comment on this and why it might be would be great.

Indeed, the SEP effect on behavior seemed to be driven by trials in which the strong stimulus intensity was presented (although the effect direction was the same also for weak stimuli). As this may be an important observation to be considered for the design of future studies, we have added this aspect with some tentative explanations (page 9, lines 148 ff.):

“Interestingly, the relationship between N20 amplitudes and perceptual outcome appeared to be driven mainly by differences within the strong stimulus category (Figure 2d). This may reflect the naturally higher signal-to-noise ratio of SEPs in response to stronger stimuli, or it could also point out that there was a “floor effect” for the modulation of SEPs in response to the weaker stimuli.”

– I'm not sure if this was by design to emphasize the counterintuitiveness of the findings, but at multiple points in the manuscript, the description of the α and SEP effects are reversed with double negatives. For example, L100-102 says α amplitude was higher when participants rated the stim to be "weaker", while L140-141 says SEP was smaller when participants rated the stim to be "stronger". If I'm not mistaken, these mean the same thing, especially considering that N20 negativity is also intuitively an amplitude, in which case, both amplitude measures correlate with a bias towards "weaker" ratings. Same with the arrangement of figure 2b and 2e, where it would be natural to put higher amplitude (i.e., more negative N20) on the right. Unless this was done very intentionally, I think it would improve the clarity of the manuscript to make those descriptions and figures the same.

Thank you for this feedback. We have unified the wording throughout the manuscript so that there are no “double negatives” anymore, which will hopefully make the meaning easier to grasp. The arrangement of Figure 2b and 2e, however, was indeed intentional since we wanted to emphasize the unexpected direction of the N20 effects, and also, to keep the order of data partitions (“bins”) the same across panels (i.e., in ascending order). We have made the following changes:

“Thus, criterion c was lower for smaller than for larger N20 peak amplitudes (Figure 2e). This indicates that participants were more likely to rate a stimulus as “strong” rather than “weak” when the magnitude of the N20 potential was smaller (i.e., less negative), after taking into account the stimulus´ actual intensity, as is it also becomes evident from the SEPs sorted by the behavioral response categories (Figure 2d).” (page 9, lines 143 ff.)

and:

“When controlling for stimulus intensity, both higher pre-stimulus α amplitudes and larger (i.e., more negative) N20 amplitudes were associated with a lower perceived intensity (equivalent to a response bias as reflected in criterion c), as well as higher pre-stimulus α amplitudes co-occurred with larger (i.e., more negative) N20 amplitudes.” (page 11, lines 184 ff.)

as well as:

“Moreover, the notion of smaller (i.e., less negative) N20 amplitudes reflecting a state of higher excitability is corroborated by the behavioral data: When controlling for stimulus intensity, we found smaller N20 amplitudes to be associated with higher perceived stimulus intensity.” (page 18, lines 349 ff.)

– Which is SEM1? Is that represented in Figure 4? I don't think this is labeled anywhere, and in the table SEM1 is just "original model".

Yes, “SEM1” is the “original model”. Thank you for spotting this inconsistency. We have clarified it throughout the whole manuscript (see Figure 4 (page 10), Table 1 (page 12), and Table 2 (page 34)).

– I might have misunderstood something here, but it was very surprising that the thalamic component does not vary as a function of stimulus intensity? Is there an explanation for this?

Good point – indeed, Figure 5a does not suggest any effect of stimulus intensity on P15 amplitude, which is puzzling. We confirmed this by a random-intercept linear-mixed-effects model (i.e., P15 amplitude as dependent variable and stimulus intensity as predictor), which could not find an effect of stimulus intensity on P15 amplitude, βstim_int=.009, t(12721.82)=.531, p=.596 (CI95% of βstim_int: [-.025,.043]). (As a side note: Here, it did also not make a noteworthy difference whether pre-stimulus α amplitude was included as a co-variate or not.) The lack of this (expected) effect of stimulus intensity on the P15 may be associated with the nucleus-like structure of the thalamus where the electrical signals from the neurons are not summed up as effectively as in the laminar structures of the cortex, resulting in a lower signal-to-noise ratio in the EEG. In addition, the deep location of the thalamus may have led to a considerable attenuation of the corresponding signals thus further decreasing the SNR. Furthermore, the difference between the weak and strong stimulus intensities was very small (adjusted according to the least-noticeable difference), making it even more challenging to detect difference effects in the EEG. Nevertheless, the lack of this intensity effect poses an important limitation for the interpretation of the thalamic control analysis, which we have added to the manuscript in the following way:

Results (page 14, lines 246 ff.):

“However, P15 amplitudes did also not differ between different presented stimulus intensities, as tested with a random-intercept linear-mixed-effects model, βstim_int=.009, t(12721.82)=.531, p=.596 (CI95% of βstim_int: [-.025,.043]), which may indicate that this EEG-based measure of thalamic activity is generally not very sensitive to differences between experimental conditions. For completeness, we also tested for the effect of pre-stimulus α amplitude on P15 amplitude, which was also not significant, βprestim=-.007, t(12210.50)=-.635, p=.526 (CI95% of βprestim: [-.028,.014]).”

Discussion (page 21, lines 415 ff.):

“Although we estimated an acceptable statistical power of these analyses, it should be noted that we could also not observe an effect of presented stimulus intensity on P15 amplitudes. Therefore, these results should be interpreted with some caution as this measure may lack the required signal-to-noise ratio to detect rather subtle experimental effects of interest such as of the intensity difference of two very similar stimuli. Nevertheless – and also taking into account our analyses in source space – we conclude that the findings of the present study are most consistent with the idea that the modulation of perceived intensity had its origins at the cortical level.”

Methods (page 32, lines 699 ff.):

“Furthermore, we tested the association between presented stimulus intensity and P15 amplitude as well as pre-stimulus α amplitude:

P15 amplitude ~ 1 + presented stimulus intensity + pre-stimulus α + (1 | subject).”

– Just to reiterate the earlier point on α, L312-317 is a really nice explanation of the model the authors have in mind, though I had a totally different one reading through the paper, that basically α amp and SEP amp have a shared origin, and hence both reflect synchronous neuronal activity, potentially of the same population. My reading of the author's interpretation is that α is a readout of excitability, and the lower the α amp the higher the excitability, which leads to more depolarized neurons and hence smaller EPSCs in a different (population). If this is correct, my suggestion would be to hint at this earlier on, so people don't go down the same rabbit hole as I did (though I am unfamiliar with this literature).

Thank you for this suggestion. We have now added a more detailed rationale behind this idea already at an early point in the introduction (in line with our response to Essential Revision #5; page 3, lines 26 ff.):

“According to the baseline sensory excitability model (BSEM; Samaha et al., 2020), higher α activity preceding a stimulus indicates a generally lower excitability level of the neural system, resulting in smaller stimulus-evoked responses, which are in turn associated with a lower detection rate of near-threshold stimuli but no changes in the discriminability of sensory stimuli (since neural noise and signal are assumed to be affected likewise).”

– Related to the above, I had to really wrap my head around the fact that the N20 is both a function of the true stimulus intensity (i.e. post-stim readout), as well as a marker for prestim (or ongoing) neural excitability (i.e., pre-stim readout). I know this is the whole point of the paper, but a sentence explicitly stating that very early on might help the naive reader to orient themselves to the subsequent findings.

Yes, this duality of the meaning of the N20 amplitude is exactly what we wanted to convey. We have made this idea more explicit already in the introduction in the following way:

“According to the baseline sensory excitability model (BSEM; Samaha et al., 2020), higher α activity preceding a stimulus indicates a generally lower excitability level of the neural system, resulting in smaller stimulus-evoked responses, which are in turn associated with a lower detection rate of near-threshold stimuli but no changes in the discriminability of sensory stimuli (since neural noise and signal are assumed to be affected likewise).” (page 3, lines 26 ff.)

as well as:

“The N20 component, a negative deflection after around 20 ms at centro-parietal electrode sites in response to median nerve stimulation, reflects excitatory post-synaptic potentials (EPSPs) of the first thalamo-cortical volley (Wikström et al., 1996; Nicholson Peterson et al., 1995; Bruyns-Haylett et al., 2017) which are generated in the anterior wall of the postcentral gyrus, Brodmann area 3b (Allison et al., 1991). Thus, the N20 directly reflects the intensity of a given stimulus. However, when keeping the sensory input constant, the amplitude of this early part of the SEP only depends on the excitability of the involved, well-defined neuronal population in the primary somatosensory cortex, and therefore represents an excellent instantaneous probe thereof.” (page 4, lines 46 ff.)

– L328-330: nice emphasis on the significance, would do the same earlier as well if not already.

Thank you for the suggestion. In line with Essential Revision #1, we have now toned down “unnecessary claims of controversiality” throughout the whole manuscript and would therefore be reluctant to put the mentioned statement at a more prominent place in the manuscript without a more differentiated discussion (which follows on page 19, lines 360 ff.):

“This challenges previous assumptions that the amplitude of brain potentials, especially at early processing stages, reflects the coding of the perceived stimulus intensity. Rather, our findings call for a more differentiated view. Although the amplitude of early event-related potentials may indeed reflect the size of the input (e.g., a stronger or weaker somatosensory stimulus), the neural evaluation of this input (i.e., the perceived intensity), however, further depends on internal neural states, such as neural excitability, which may even reverse the amplitude effects of the input already at the earliest cortical processing stages.”

– I think a brief discussion on how the brain then uses the information represented by the SEP downstream to "perform the perceptual rating" would be great, i.e., does it account for the ongoing fluctuation in excitability?

Exciting question! From the present study, we probably cannot tell in detail how the information at early stages is integrated in later, more complex neural processing (or whether there are even compensating mechanisms for variability of initial cortical activation). However, changes in early sensory processing should in principle provide the ground for these later processes: Regardless of how neural processing occurs at later stages, the only possibility for the neural system to discriminate between two stimuli is to use the initial sensory responses. We have added this discussion together with a slightly broader perspective on the integration of “noise” in neural systems in the following way (page 21, lines 432 ff.):

“In addition, it is unclear at this point how exactly the observed fluctuations of initial cortical responses are integrated in later, downstream neural processes. In principle, changes in early sensory processing should provide the ground for later neural activity involved in the perceptual decision making, and finally shape the behavioral outcome (as observed in the current study). However, with our data, we cannot unambiguously tell whether the modulation of α oscillations – associated with excitability changes at the earliest cortical level – may in turn reflect a top-down regulated signal, which could in principle enable the neural system to account for ongoing fluctuations of excitability and even benefit from a certain degree of variability. Although more and more evidence indeed suggest an adaptive, functional role of neural “noise” (e.g., Findling and Wyart, 2021), further studies are needed to better understand how this concept may pertain also to such fundamental neural properties as the initial cortical excitability to external sensory stimuli.”

– L351-355: CNAP (sensory readout) variability did not influence perceptual decision, but CMAP (muscle output) variability does – both are within the first 10ms, so there must be another loop from the motor reaction / reflex readout to the brain, and when does the brain / subject make the decision? Does the motor percept also feed back into the N20?

The thumb twitch in response to supra-threshold median nerve stimulation results from the excitation of motor axons at the site of stimulation (in our study the left wrist), thus activating muscle fibers via efferent neurons. Therefore, the CMAP is not necessarily related to the CNAP, which reflects the excitation of afferent neurons in response to the median nerve stimulation. Indeed, the afferent (i.e., proprioceptive) information of the thumb twitch itself should also travel along the median nerve – but this should happen markedly after the initial CNAP (and presumably in a less synchronized manner leading to a much lower measurable signal). This CMAP-related afferent signal then most likely influenced the perceived stimulus intensity as processed in the brain – however, in turn only at a later stage than the initial N20 response. Therefore, there is no possibility that the motor percept feeds back into the N20 signal. This also becomes evident from our SEM analyses where we did not observe a relation between N20 and CMAP amplitudes.

– I was looking for an explanation as to why criterion might be affected here but not sensitivity, maybe a brief discussion speculating on this point would be appropriate?

The idea of the baseline sensory excitability model (BSEM; Samaha et al., 2020) is that a generally lower excitability level already at early sensory processing stages results in a lower probability of reporting the presence of a stimulus no matter whether the stimulus was actually shown or not (i.e., decreasing both the numbers of hits and false alarms). This would correspond to a change in SDT parameter criterion c. The discriminability, that is SDT parameter sensitivity d’, however, should stay constant since the perception of noise and signal are changed by the excitability level in the same way. Thank you for pointing out this missing link which we have now added to the introduction (page 3, lines 26 ff.):

“According to the baseline sensory excitability model (BSEM; Samaha et al., 2020), higher α activity preceding a stimulus indicates a generally lower excitability level of the neural system, resulting in smaller stimulus-evoked responses, which are in turn associated with a lower detection rate of near-threshold stimuli but no changes in the discriminability of sensory stimuli (since neural noise and signal are assumed to be affected likewise).”

Again, great paper – it was a joy to read!

Reviewer #2 (Recommendations for the authors):

Overall I found the manuscript very interesting and my recommendations pretty much concern the discussed weaknesses.

– The authors should reflect more clearly the impact of the study along the lines mentioned above. I pretty much have the impression that the results are not a "game changer" in the field but fits well with increasingly dominant views in the field.

Yes, we fully agree. We have tried to incorporate this in our changes in relation to Essential Revision #1, adjusting the impact statement as well as similar statements throughout the manuscript:

Impact statement:

“Larger evoked responses during initial cortical processing may reflect states of lower excitability.”

Statements in the manuscript text:

“This challenges previous assumptions that the amplitude of brain potentials, especially at early processing stages, reflects the coding of the perceived stimulus intensity.” (page 19, lines 360 ff.)

and

“Further questioning previous assumptions of how the evaluation of stimulus intensity is reflected in brain potentials, cortical excitability and the presented stimulus intensity were associated with opposing effects on the early SEP.” (page 22, lines 449 ff.)

– I would strongly advise to check the possibility whether the α-N20-link could be caused by high-pass-filtering of phase locked α responses. Along these lines a time frequency depiction of results in figure 1a would be helpful (i.e. showing low to high frequencies of non-high pass filtered data). Also since the link between N20 and perception appears "counterintuitive", it may be also useful to test the relationship within α bins (i.e. high-low N20 responses stratified for α power) and see whether the relationship still holds.

Thank you for these suggestions. We have included a time-frequency depiction under Essential Revision #4 (please refer to our response there for more details). We have added these analyses also as Figure 2—figure supplement 3 in our revised manuscript. In addition, we analyzed the effect of N20 amplitudes on perceived stimulus intensity also within α quintiles (“N20 amplitudes stratified for α power”). Since Signal Detection Theory requires further partitioning within pre-stimulus α quintiles, only few trials would remain for the calculation of the individual sensitivity d’ and criterion c values (e.g., around 66 trials when stratifying for α amplitudes with 5 bins and comparing the first and last tertile (i.e., three bins) of N20 amplitudes within each α quintile, or 40 trials when using quintiles for N20 amplitudes as well). This means that on average across participants only 9.38 trials of the category “weak stimulus perceived as strong stimulus” could be included in such stratified SDT analysis. Since this trial number appears rather small to obtain robust statistical results, we instead used linear-mixed-effects models in close correspondence to the sub-models used in our SEM analysis: Within every pre-stimulus α quintile, we regressed perceived stimulus intensity on N20 amplitude while including the presented stimulus intensity as covariate and modelling random intercepts for every participant. Across α quintiles, the effect of N20 amplitude on the perceived intensity showed a consistent direction, however significance was reached only in the fourth quintile, while the second and fifth quintile showed trends, and the first and third quintile did not show any effect, zbin1=1.045, pbin1=.296, zbin2=1.744, pbin2=.081, zbin3=0.476, pbin3=.634, zbin4=2.01, pbin4=.045, zbin5=1.792, pbin5=.073, from first to fifth quintile, respectively. Taken together, these results suggest that a similar relation between N20 amplitudes and behavioral responses can be qualitatively observed across different levels of pre-stimulus α amplitude. However, statistical power seems to be lacking to detect these rather small effects when only analyzing subsets of the data (in this case quintiles). In addition, we would like to mention that our SEM approach actually already reflected a similar rationale: Although we did not stratify for different α amplitude levels there, we did include α amplitude as a co-predictor in the analysis of the effect of N20 amplitude on perceived stimulus intensity (see Table 2, fourth equation). Therefore, the N20 effects in the SEM reflected additional effects beyond the effects of pre-stimulus α amplitude, or, in other words, the N20 effects remained when partialing out effects of pre-stimulus α amplitude.

– Furthermore, it would improve readability if the authors used a streamlined statistical approach also for the "thalamic" and "late" evoked responses.

In line with Essential Revision #3, we have additionally run binning analyses for the “later” SEP (i.e., N140 component) and the thalamic SEP (i.e., P15 component), to make a direct comparison also with Signal Detection Theory (SDT) parameters possible: For the N140 component, there was a significant effect on criterion c, t(31)=-3.010, p=.005, but no effect on sensitivity d’, t(31)=0.246, p=.807. For the P15 component, no effects emerged either for criterion c or sensitivity d’, t(12)=1.201, p=.253, and t(12)=-0.201, p=.844, respectively. These findings correspond well to the previous LME analyses and may indeed further facilitate the comparison with the findings for the N20 potential and pre-stimulus α activity. Therefore, we have added these complimentary analyses to our manuscript in the following way:

Results:

“In addition, the SDT analysis based on binning of the P15 amplitudes into quintiles neither suggested a relation with criterion c nor with sensitivity d’, t(12)=1.201, p=.253, and t(12)=-0.201, p=.844, respectively.” (page 14, lines 241 ff.)

and

“These findings were in line with a separate SDT analysis: N140 amplitudes were associated with an effect on criterion c, t(31)=-3.010, p=.005, but no effect on sensitivity d’ emerged, t(31)=0.246, p=.807.” (page 15, lines 263 ff.)

Discussion:

“Crucially, our data are at the same time consistent with previous studies on somatosensory processing at later stages, where larger EEG potentials are typically associated with a stronger percept of a given stimulus (e.g., Al et al., 2020; Schröder et al., 2021; Schubert et al., 2006), as both our SDT and LME analyses of the N140 component showed.” (page 19, lines 367 ff.)

and

“Yet, neither our SDT analyses nor the LME models of the thalamus-related P15 component supported this notion.” (page 21, lines 414 ff.)

Methods (page 32, lines 681 ff.):

“The effects of the EEG measures pre-stimulus α amplitude, N20 peak amplitude, P15 mean amplitude, and N140 mean amplitude on the SDT measures sensitivity d’ and criterion c were examined using a binning approach: […]”

– The authors convincingly show that evoked peripheral responses are not linked to N20 amplitude. It would be interesting to know whether variability in ongoing peripheral activity is linked to ongoing α activity (i.e. in prestimulus period).

Indeed, this is an interesting aspect and an important sanity check of our dataset. We examined this by two random-slope linear-mixed-effects models with pre-stimulus α amplitude as predictor of CNAP and CMAP amplitude, respectively, while including presented stimulus intensity in both models as covariate. Neither for CNAP nor CMAP, we found a significant effect, t(30.296)=-1.334, p=.192, and t(29.918)=-0.377, p=.709, respectively. Additionally, we compared two more alternative SEMs – including the effects CMAP ~ prestim, and CNAP ~ prestim, respectively – to our original SEM (“SEM 1”). Also here, the model comparisons suggested that the original model without the relations between pre-stimulus α activity and CMAP or CNAP was to be preferred, as indicated by non-significant χ2-difference tests (but higher model complexities), χ2diff=.220, p=.639, and χ2diff=2.342, p=.126, respectively. Therefore, ongoing oscillatory brain activity in the α range was not related to our measures of peripheral nerve activity. We have added the SEM comparisons and their interpretation to our revised manuscript in Table 1 (page 12), as well as in the text accordingly:

“To evaluate the model fit, we compared a list of alternative models in- or excluding relevant effect paths (Table 1). As indicated by Chi-Square Difference Tests, the log-likelihood of SEM 1 did not differ from those of SEMs 2-4, 9, and 10. Seeking model parsimony, SEM 1 is preferred over SEMs 2-4, 9, and 10 since the latter alternative models included one more parameter each, while fitting the data equally well.” (page 11, lines 174 ff.)

and:

“Also, peripheral activity in CMAP and CNAP did not show any association with pre-stimulus α activity.” (page 11, lines 197 ff.)

– At the moment the value of the physiological model developed in the discussion is not really clear (see previous comments). It also seems to be particularly geared to the N20 results and leaves open the question whether it also encompasses the "thalamus" and "late" results.

Thank you for pointing out that this aspect was not clear: We aimed to address with our physiological model only the initial cortical portion of the SEP (i.e., the N20 response) because only this SEP component is thought to correspond to “pure” excitation (excitatory post-synaptic potentials). For later SEP components, there will almost always be a mixture of both excitatory and inhibitory contributions whose mutual effects on the SEP amplitude are difficult to disentangle then. Yet, the proposed mechanism of reduced or increased trans-membrane currents leading to smaller or larger EEG amplitudes may be applicable also to other scenarios. Nevertheless, we would like to be careful with such further assumptions as our data is only consistent with the proposed model for N20 responses. We have tried to clarify these considerations in the following way in the Discussion (page 19, lines 373 ff.):

“In this context, it should be further emphasized that our proposed physiological model may be directly applicable to well isolated neural signals only (such as the N20 component of the SEP). The physiological interpretation of amplitudes of later EEG potentials, such as the N140, however, is not as straightforward as described above, since several distinct SEP components may interact (Auksztulewicz et al., 2012), and excitatory and inhibitory contributions cannot be readily distinguished.”

References

Berens, P. (2009). CircStat A MATLAB Toolbox for Circular Statistics. Journal of Statistical Software, 31.

Haegens, S., Barczak, A., Musacchia, G., Lipton, M. L., Mehta, A. D., Lakatos, P., and Schroeder, C. E. (2015). Laminar Profile and Physiology of the α Rhythm in Primary Visual, Auditory, and Somatosensory Regions of Neocortex. The Journal of Neuroscience, 35, 14341–14352.

Hanslmayr, S., Klimesch, W., Sauseng, P., Gruber, W., Doppelmayr, M., Freunberger, R., Pecherstorfer, T., and Birbaumer, N. (2007). Α phase reset contributes to the generation of ERPs. Cerebral cortex (New York, N.Y. 1991), 17, 1–8.

Iemi, L., Chaumon, M., Crouzet, S. M., and Busch, N. A. (2017). Spontaneous Neural Oscillations Bias Perception by Modulating Baseline Excitability. The Journal of Neuroscience, 37, 807–819.

Morey, R. D. (2008). Confidence Intervals from Normalized Data: A correction to Cousineau (2005). Tutorial in Quantitative Methods for Psychology, 4, 61–64.

Samaha, J., Iemi, L., Haegens, S., and Busch, N. A. (2020). Spontaneous Brain Oscillations and Perceptual Decision-Making. Trends in cognitive sciences, 24, 639–653.

Sauseng, P., Klimesch, W., Gruber, W. R., Hanslmayr, S., Freunberger, R., and Doppelmayr, M. (2007). Are event-related potential components generated by phase resetting of brain oscillations? A critical discussion. Neuroscience, 146, 1435–1444.

https://doi.org/10.7554/eLife.67838.sa2

Article and author information

Author details

  1. Tilman Stephani

    1. Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    2. International Max Planck Research School NeuroCom, Leipzig, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review and editing
    For correspondence
    stephani@cbs.mpg.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3323-3874
  2. Alice Hodapp

    Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    Present address
    Now at Department of Psychology, University of Potsdam, Potsdam, Germany
    Contribution
    Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7886-0049
  3. Mina Jamshidi Idaji

    1. Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    2. International Max Planck Research School NeuroCom, Leipzig, Germany
    3. Machine Learning Group, Technical University of Berlin, Berlin, Germany
    Contribution
    Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1593-3201
  4. Arno Villringer

    1. Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    2. Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
    3. Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
    Contribution
    Funding acquisition, Resources, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2604-2404
  5. Vadim V Nikulin

    1. Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    2. Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russian Federation
    Contribution
    Conceptualization, Investigation, Project administration, Resources, Supervision, Writing – review and editing
    For correspondence
    nikulin@cbs.mpg.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6082-3859

Funding

National Research University Higher School of Economics

  • Vadim V Nikulin

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

Acknowledgements

VVN was supported in part by the Basic Research Program at the National Research University Higher School of Economics. We thank Sylvia Stasch for participant recruitment and help with data collection.

Ethics

Human subjects: All participants gave informed consent and consent to publish. The study was approved by the local ethics committee (Ethical Committee at the Medical Faculty of Leipzig University, 04006 Leipzig, Germany).

Senior Editor

  1. Floris P de Lange, Radboud University, Netherlands

Reviewing Editor

  1. Jonas Obleser, University of Lübeck, Germany

Reviewers

  1. Richard Gao, University of California, San Diego, United States
  2. Nathan Weisz, University of Salzburg, Austria

Publication history

  1. Received: February 24, 2021
  2. Accepted: August 20, 2021
  3. Version of Record published: October 5, 2021 (version 1)

Copyright

© 2021, Stephani 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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