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

  1. Tilman Stephani
  2. Alice Hodapp
  3. Mina Jamshidi Idaji
  4. Arno Villringer
  5. Vadim V Nikulin

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    Evaluation 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 results are puzzling in an interesting way, and while the authors provide a nicely parsimonious explanation rooted in the underlying neurophysiology, editors and reviewers think this study has the potential to further motivate many lines of investigation. This manuscript will be of interest mainly to researchers using electrophysiological methods (EEG, MEG, ECoG etc.), as the authors have produced a very high-quality EEG data-set (including uncommon peripheral measurements).

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #2 agreed to share their names with the authors.)

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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.

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  1. Version published to 10.7554/elife.67838 on eLife
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    Author Response

    Reviewer #1 (Public Review):

    [...] My main technical concern lies in the choice of decomposition filter for SEP and alpha 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 alpha sources, with the logic being that this procedure extracts the alpha oscillation from the same sources (e.g., L359). I have no issues (or expertise) with using the CCA filter for the SEP, but if my understanding of the authors' intent is correct, then I don't agree with the logic that using the same filter isolate for alpha as well. The prestimulus alpha 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 alpha 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, alpha 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 alpha amplitude to partition the responses) is repeated using alpha derived from this procedure. Also, have the authors considered using individualized alpha filters given that alpha 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 alpha 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 alpha rhythm. Yet, we agree with the reviewer that it should be acknowledged that we may have missed or mixed-up effects of alpha 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 alpha 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 alpha 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 Supplementary Figure 1 for Peer Review). Also, using the binning approach to examine the relation or pre-stimulus alpha activity with SDT parameter criterion c, the effects were most pronounced around the right somatosensory regions (Supplementary Figure 2 for Peer Review), 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 alpha 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 alpha amplitude, t(31)=0.633, p=.531, CI95%=[-.083, .157]. Taken together, the findings we report in our original manuscript for pre-stimulus alpha 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 alpha 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.

    Addressing the question on filtering alpha 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 alpha range and slightly different filter ranges (adjusted with regards to the individual alpha peak frequency) are thus unlikely to lead to large differences in the estimation of pre-stimulus alpha amplitudes. Therefore, we refrained from using individualized frequency bands here and focused on the more generic approach using one common alpha band (8-13 Hz) for all participants, which should also facilitate direct comparisons with previous studies on pre-stimulus oscillatory effects.

    In the same vein, both alpha and N20 amplitude relate to perceptual judgement, and to each other. I believe this is nicely accounted for in the multivariate analysis using the SEM, but the analysis that partitions the behavioral responses using the 20% and 80% are done separately, which means that different behavioral trials are used to compute the effect of N20 and alpha on sensitivity and criterion. While this is not necessarily an issue given that there IS a multivariate analysis, I would like to know how many of those trials overlap between the two analyses.

    This is an interesting point indeed. We included both the binning analyses and the multivariate analyses in our manuscript as we believe they offer complimentary views on the data, and also allow a direct comparison to previous studies in the field (e.g., Iemi et al., 2017). In fact, the trial overlap between the extreme bins of the alpha and N20 data were rather small.

    Since the expected trial overlap is 20% when partitioning the data into quintiles randomly, the effect-driven increments and reductions in trial overlap in our data appear to be rather small. However, they showed the expected directions: Larger alpha amplitudes were associated with more negative N20 amplitudes (and vice versa). Presumably, these small differences in trial overlap reflect the rather small effect sizes we also observed in the multivariate analyses. We have added this information to our revised manuscript in the following way to give the reader a better picture of the underlying data for the binning analyses (page 9, lines 137 ff.): “(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 Fig. 2—figure supplement 2 for further details on the trial overlap.)”

    At multiple points, the authors comment that the covariation of N20 and alpha amplitude in the same direction is counterintuitive (e.g., L123-125), and it wasn't clear to me why that should be the case until much later on in the paper. My naive expectation (perhaps again being unfamiliar with the field) is that alpha amplitude SHOULD be positively correlated with SEP amplitude, due to the brain being in a general state of higher variability. It was explained later in the manuscript that lower alpha amplitude and higher SEP amplitude are associated with excitability, and hence should have the opposite directions. This could be explicitly stated earlier in the introduction, as well as the expected relationship between alpha 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 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).”

    Furthermore, I have a concern with the interpretation here that's rooted in the same issue as the assumption that they are from the same sources: the authors' physiological interpretation makes sense if alpha and N20 originated from the same sources, but that is not necessarily the case. In fact, the population driving the alpha oscillation could hypothetically have a modulatory effect on the (separate) population that eventually encodes the sensory representation of the stimulus, in which case the explanation the authors provide would not be wrong per se, just not applicable. A comment on this would be appreciated in the revision.

    Our extensive additional analyses suggest that the sources of behaviorally relevant alpha and N20 activity were located at very similar cortical sites. Nevertheless, this is not a proof that exactly the same neuronal populations were involved (for example, alpha and N20 effects could originate from different cortical layers). Therefore, we have added this potential limitation to our revised manuscript in the following way (page 19, lines 379 ff.): “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.”

    In addition, given how closely related the investigation of these two quantities are in this specific study, I think it would be relevant to discuss the perspective that SEPs are potentially oscillation phase resets. Even though the SEP is extracted using an entirely different filter range, it could nevertheless be possible that when averaged over many trials, small alpha residues (or other low freq components) do have a contribution in the SEP. If the authors are motivated enough, a simulation study could be done to check this, but is not necessary from my point of view if there is an adequate discussion on this point.

    Indeed, the phase reset mechanism may be a possible alternative explanation for relations between oscillations and later parts of the ERP. However, the N20 potential reflects the very first excitation of the cortex in response to a somatosensory stimulus and should therefore represent a textbook example of an additive response (EPSPs are added to ongoing background activity). Moreover, the N20 response should be over long before a possible phase reset in lower frequencies (such as alpha frequencies) would start to play a role (Hanslmayr et al., 2007; Sauseng et al., 2007). Nevertheless, we ran additional control analyses (including a simulation study) in order to exclude that some odd combination of phase-locking and filter residues led to the present findings: Please see Essential Revision #4 for details and how we included these considerations in our revised manuscript.

    **Reviewer #2 (Public Review): **

    [...] The main weaknesses of the manuscript becomes 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.". I am not really sure if there are many cognitive neuroscientists, that would actually 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.

    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.

    Also it should be considered that with regards to the analysis approach using CCA, the claims are mainly restricted to BA3b: i.e. while I also think that this is a strength of the current study, one 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, we have further specified these aspects in the Discussion.

    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 alpha 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.)

    “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.)

    “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 alpha 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: […]”

    I have some concerns whether the relationship between large alpha power and more negative N20s could be driven by more trivial factors rather than the model explanations the authors develop in the discussion. Concretely the question whether phase locking of large alpha 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 alpha influences the N20 amplitudes as well as the perceptual reports.

    Indeed, potential phase-locking of alpha 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 alpha activity and the N20 potential of the SEP. Although such pre-stimulus alpha 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 alpha cycle), we have run the following control analyses to fully exclude this possibility:

    First, we analyzed whether pre-stimulus alpha phase values were distributed uniformly and whether these phase distributions differed between high and low alpha amplitudes as well as between high and low N20 amplitudes. The phase of pre-stimulus alpha 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 Supplementary Figure 3 for Peer Review, pre-stimulus alpha phases were distributed uniformly across all five quintiles of both alpha 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 alpha amplitude or N20 amplitude bin, we found evidence for a non-uniform distribution of alpha phase, all p>.367 (after Bonferroni correction for multiple testing). Thus, there was no phase-locking of pre-stimulus alpha activity that could serve as a trivial alternative explanation of the relationship between pre-stimulus alpha amplitude and N20 amplitude.

    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 alpha 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 alpha 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 alpha 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). The resulting average SEPs did neither show a visually detectable difference between the five alpha amplitude quintiles nor indicated a random-slope linear-mixed-effects model any relation between pre-stimulus alpha 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 alpha 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 alpha 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 Supplementary Figure 5 for Peer Review, the time-frequency representations of the effects on N20 amplitudes indeed indicated a specific role of the alpha band, with its effects (i.e., already 200 ms before stimulus and in the upper alpha 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 alpha band. Of note, there were also strong effects in the beta band. These may be interesting to study further in future studies – in particular, whether they reflect independent physiological processes or rather harmonics of the alpha 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 alpha activity and N20 amplitudes is not due to technical issues (such as filter leakage and phase-locking) but rather reflects genuine neurophysiological effects of alpha oscillations on SEPs.

    We have added the time-frequency analysis, as well as the SEP simulation analysis 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 alpha band and (b) not due to any data processing-related artifacts.

    It is important to emphasize that the model develop is a post-hoc one, i.e. the authors do not develop already in the discussion various alternative scenario results based on different model predictions. Therefore there is no strong evidence in support of the specific one advanced in the discussion.

    Thank you for raising this issue. Indeed, we cannot prove with the current findings that our proposed physiological model of the relation between alpha oscillations and the SEP is the correct model (or that it is at least the best one out of a selection of possible alternative models). To do so, future studies would be needed that can actually directly measure and/or manipulate differences in membrane potentials and trans-membrane currents. Rather, we aimed with the present study to associate a physiological meaning with the concept of excitability changes in the human EEG – offering a hypothesis that may be worthwhile to be studied (and either confirmed or rejected) in future studies. We have tried to make this motivation more explicit in the Discussion section (page 20, lines 384 ff.): “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.”

  3. eLife

    Reviewer #2 (Public Review):

    It is well established in diverse sensory modalities that fluctuating excitability of cortical regions is likely reflected in ongoing alpha activity in these respective areas. However, how this oscillatory activity relates to "intensities" of neural (~evoked) responses and perception following supra-threshold stimulation is not well established. Building up and extending also their own previous work in the somatosensory domain (Stephani et al., 2020), this is the main goal of the authors.

    To achieve their goals the authors implement a straight-forward somatosensory discrimination task while recording EEG. The study builds up on very high quality data as well as analysis approaches and along with a decent sample size allows draw conclusions with respect to the aforementioned questions. Using CCA to analyse ongoing and stimulus (single-trial) evoked responses from a (for the non-invasive researcher world) well-circumscribed brain region is a clear strength, when studying the inter-relationships between these brain activity features. The displayed results of the structural equation model (Figure 4) is a great summary of the main effects of the results and an important contribution to the field. In particular, I really appreciate the inclusion of peripheral responses, that convincingly make the case that the non-trivial relationship between stimulus and perceptual intensity on the one hand side and early evoked response (N20) on the other hand side indeed emerges at a brain level.

    However there are also some weaknesses that need to be mentioned:

    • The main weaknesses of the manuscript becomes 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.". I am not really sure if there are many cognitive neuroscientists, that would actually 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.

    • Also it should be considered that with regards to the analysis approach using CCA, the claims are mainly restricted to BA3b: i.e. while I also think that this is a strength of the current study, one should refrain from over-interpreting 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 readability of results and especially the comparison of effects would profit from a more coherent approach.

    • I have some concerns whether the relationship between large alpha power and more negative N20s could be driven by more trivial factors rather than the model explanations the authors develop in the discussion. Concretely the question whether phase locking of large alpha 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 alpha influences the N20 amplitudes as well as the perceptual reports.

    • It is important to emphasize that the model develop is a post-hoc one, i.e. the authors do not develop already in the discussion various alternative scenario results based on different model predictions. Therefore there is no strong evidence in support of the specific one advanced in the discussion.

  4. eLife

    Reviewer #1 (Public Review):

    In this study, Stephani et al. addresses 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. Specifically, pre-stimulus alpha oscillation amplitude and the N20 component of the SEP are used as a readout of cortical excitability, while signal detection theory quantities - sensitivity and criterion - derived from participant response are used as the behavioral correlates. The authors find that 1) higher prestimulus alpha amplitude is associated with a higher criterion, i.e., participants tend to rate stimuli as "weaker" regardless of the actual intensity, while there was no effect on sensitivity; 2) larger N20 amplitude (more negative) is associated with stronger stimulus intensity; 3) conditioned on actual stimulus intensity, larger N20 amplitude is associated with a higher criterion, similar to prestim alpha; 4) the above effects are confirmed using a multi-level structural equation model while also accounting for peripheral control measures; and finally 5) that the thalamic response, as measured in very early components, have no association with perceptual response and previous findings on later SEP components (N140) is reproduced in this data. The authors offer a physiological interpretation that explains the seemingly contradictory result by accounting for the recruitment level of cortical neurons and their membrane depolarization in excitable stages.

    Overall, I find this study to be very nicely done, well-written, and with informative figures. My expertise in signal detection theory and awareness of the SEP literature are limited, and the following comments will probably reflect that. Considering that, the introduction was very concise yet informative regarding the state of the field, and nicely motivates why suprathreshold stimulation is an interesting question to investigate, and was overall just a pleasure to read. The data and analyses seem convincing in supporting the authors' conclusions. The results are indeed puzzling (in an interesting way), and while the authors provide a nicely parsimonious explanation rooted in the underlying neurophysiology, I think this study has the potential to further motivate many lines of investigation, especially considering that the majority of works done in this field looks at the effect of ongoing neural activity on the detection of near-threshold sensory stimuli (as far as I know). I have some major concerns broadly regarding the interplay between alpha oscillation and the N20 (detailed below), the rest are mostly clarifying comments/questions that I believe may help the authors improve this paper, as well as other interesting points to consider in the discussion to relate to the broader literature.

    N20 and alpha oscillation

    My main technical concern lies in the choice of decomposition filter for SEP and alpha 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 alpha sources, with the logic being that this procedure extracts the alpha oscillation from the same sources (e.g., L359). I have no issues (or expertise) with using the CCA filter for the SEP, but if my understanding of the authors' intent is correct, then I don't agree with the logic that using the same filter isolate for alpha as well. The prestimulus alpha 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 alpha 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, alpha 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 alpha amplitude to partition the responses) is repeated using alpha derived from this procedure. Also, have the authors considered using individualized alpha filters given that alpha frequency vary across individuals? Why or why not?

    In the same vein, both alpha and N20 amplitude relate to perceptual judgement, and to each other. I believe this is nicely accounted for in the multivariate analysis using the SEM, but the analysis that partitions the behavioral responses using the 20% and 80% are done separately, which means that different behavioral trials are used to compute the effect of N20 and alpha on sensitivity and criterion. While this is not necessarily an issue given that there IS a multivariate analysis, I would like to know how many of those trials overlap between the two analyses.

    At multiple points, the authors comment that the covariation of N20 and alpha amplitude in the same direction is counterintuitive (e.g., L123-125), and it wasn't clear to me why that should be the case until much later on in the paper. My naive expectation (perhaps again being unfamiliar with the field) is that alpha amplitude SHOULD be positively correlated with SEP amplitude, due to the brain being in a general state of higher variability. It was explained later in the manuscript that lower alpha amplitude and higher SEP amplitude are associated with excitability, and hence should have the opposite directions. This could be explicitly stated earlier in the introduction, as well as the expected relationship between alpha amplitude and behavior.

    Furthermore, I have a concern with the interpretation here that's rooted in the same issue as the assumption that they are from the same sources: the authors' physiological interpretation makes sense if alpha and N20 originated from the same sources, but that is not necessarily the case. In fact, the population driving the alpha oscillation could hypothetically have a modulatory effect on the (separate) population that eventually encodes the sensory representation of the stimulus, in which case the explanation the authors provide would not be wrong per se, just not applicable. A comment on this would be appreciated in the revision.

    In addition, given how closely related the investigation of these two quantities are in this specific study, I think it would be relevant to discuss the perspective that SEPs are potentially oscillation phase resets. Even though the SEP is extracted using an entirely different filter range, it could nevertheless be possible that when averaged over many trials, small alpha residues (or other low freq components) do have a contribution in the SEP. If the authors are motivated enough, a simulation study could be done to check this, but is not necessary from my point of view if there is an adequate discussion on this point.

  5. eLife

    Evaluation 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 results are puzzling in an interesting way, and while the authors provide a nicely parsimonious explanation rooted in the underlying neurophysiology, editors and reviewers think this study has the potential to further motivate many lines of investigation. This manuscript will be of interest mainly to researchers using electrophysiological methods (EEG, MEG, ECoG etc.), as the authors have produced a very high-quality EEG data-set (including uncommon peripheral measurements).

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #2 agreed to share their names with the authors.)

  6. Version published to 10.1101/2020.11.27.401430v3 on bioRxiv
  7. Version published to 10.1101/2020.11.27.401430v2 on bioRxiv
  8. Version published to 10.1101/2020.11.27.401430v1 on bioRxiv