Response inhibition is among the core constructs of cognitive control. It is notoriously difficult to quantify from overt behavior, since the outcome of successful inhibition is the lack of a behavioral response. Currently, the most common measure of action stopping, and by proxy response inhibition, is the model-based stop signal reaction time (SSRT) derived from the stop signal task. Recently, partial response electromyography (prEMG) has been introduced as a complementary physiological measure to capture individual stopping latencies. PrEMG refers to muscle activity initiated by the go signal that plummets after the stop signal before its accumulation to a full response. Whereas neither the SSRT nor the prEMG is an unambiguous marker for neural processes underlying response inhibition, our analysis indicates that the prEMG peak latency is better suited to investigate brain mechanisms of action stopping. This study is a methodological resource with a comprehensive overview of the psychometric properties of the prEMG in a stop signal task, and further provides practical tips for data collection and analysis. Response inhibition, or the ability to stop actions, is a core construct of cognitive control and is widely studied among cognitive and clinical neuroscientists. Most of our knowledge about inhibitory capability is derived from a single behavioral measure, the stop signal reaction time (SSRT), which quantifies the time the stopping process needs to countermand an already initiated action. SSRTs are slower in patients with attention deficit/hyperactivity disorder, obsessive compulsive disorder, drug abuse, schizophrenia, Parkinson’s disease, and Tourette syndrome among others . HoweverThe stop signal task is among the most common experimental tasks used to study inhibition, with more than 7000 publications using the task in the last years . The priThe SSRT estimation is based on the independent race model (or horse race model), in which go and stop processes independently race against each other, and whichever process wins this race, determines whether a planned action will be executed or not . The theA notable exception is the proposition to derive a more direct measure of the stopping latency from EMG. Specifically, EMG recorded at the effector muscles that generate the response can be used to capture the time-point when the motor activity begins to decline in the muscles. In many successfully stopped trials , surface EMG recordings still capture small bursts of muscle activity. These bursts are presumably responses initiated after the go signal that are stopped before the muscle activity culminates in a button press. We have termed these responses partial response EMG  in trials where go EMG was interrupted early, before reaching the motor pool. However, they used a stop signal task with unusually short SSDs from −100 to 100 ms relative to the go signal. Dissociation between these response types is unlikely in a more typical stop signal task with SSDs >100 ms.Partial error. This term is consistently used in tasks in which an erroneous automatic response needs to be overridden by a correct response  from 46 healthy young adults who performed a standard stop signal task. The primary task was a choice reaction time task where participants had to respond to a go signal either with a left or right hand button press . All three methods also correlated with the SSRT (We compared the three methods and found a good correspondence between them (Spearman 24) and that prEMG onset latencies were similar to the EMG onset latencies in go trials (BF = 0.28). When time-locked to the EMG onset (29) and unsuccessful stop trials (BF = 1.91 × 1027). Interestingly, EMG amplitudes in unsuccessful stop trials were also smaller than those in go trials (BF = BF = 2.32 × 107), suggesting that the former were affected by stop signal processing despite the registered button press.Averaged EMG waveforms for each trial type are shown in MG onset , the ris12). Speculatively, the steep decline of the falling flank seen in unsuccessful stop trials may suggest that unsuccessful stop trials were also affected by the processes triggered by the stop signal around the same time as the prEMG decline in successful stop trials.When time-locked to stop signal onset, a very clear prEMG waveform appeared , which s6; replication BF = 3.93 × 105). Altogether, these results indicate different response dynamics in unsuccessful stop trials and go trials, suggesting that unsuccessful stop trials may be affected by the stop process as well. However, this may be contingent on task procedure and even on the response device mechanics. Here, we used a response device with very little resistance to produce a button press, resulting in very small motor times and low proportion of trials with prEMG. Nonetheless, the replication dataset with longer motor times and a higher prEMG proportion still indicates different response dynamics in go and unsuccessful stop trials.We additionally quantified motor time, that is the difference between the EMG onset and the RT, and the rise time, that is the duration of the EMG burst from its onset to peak . Even though the differences are numerically small , the motIn many studies, the SSRT has been reported as a single behavioral outcome measure of the stop signal task, purportedly reflecting the speed of response inhibition. However, recent evidence indicates that both SSRTs and prEMG peak latencies are related to go RTs, potentially due to behavioral adaptations beyond response inhibition . We therr = −0.61, p = 1.62 × 10−5) and moderately with the prEMG latencies . Secondly, goRTs correlated moderately with the prEMG detection frequency . The relationships between prEMG frequency and stopping latency indices were not significant . In sum, persons who responded fast had more prEMG trials and longer stopping latencies, likely due to a strategic trade-off between going and stopping. Noticeably, this trade-off seems to be best captured by the associations between the goRT and the SSRT.Firstly, goRTs correlated strongly with the SSRTs  = −2.65, p = 0.012), as was the smaller association between the go RTs and prEMG peak latencies  = −2.21, p = 0.049). Similarly, studies with faster go RTs reported higher prEMG detection frequencies  = −2.96, p = 0.010). Interestingly, while every single study reported a positive correlation between the prEMG and the SSRT with an average correlation of r = 0.64  = 2.29, p = 0.044). Most importantly, while prEMG detection frequency varied considerably across studies , it was not associated with its peak latency  = 0.91, p = 0.375).Next, we quantified these relationships across 21 different experiments or conditions as reported in 10 studies . Despite5, BEESTS BF = 2.6 × 109). Both SSRTs correlated moderately with the prEMG peak latency .There are several different ways to calculate the SSRT. The two state-of-the-art techniques are the integration method with replacement of go omissions , and theHaving established the basic summary characteristics and their (non-)correspondence with the SSRT, we set out to investigate how the prEMG relates to the framework of the horse race model of going and stopping. A feature of the stop signal task is that the SSD varies, most commonly due to an online tracking algorithm that adapts the SSDs to each participant’s performance. The purpose of this is to capture the SSD where participants manage to stop their response for about 50% of the time. A key assumption of this approach is that the probability of unsuccessful stopping (p(button press|stop)) is low (approaching zero) at short SSDs, increases steeply at mid-SSDs, and approaches one at long SSDs. With EMG, this would correspond to the probability of producing EMG at any given stop trial ), as this implies that the go process was at least initiated before stopping process. This probability thus includes all stop trials with detectable EMG, and is conceptually the sum of unsuccessful stop trials and successful stop trials with prEMG. Thus, by definition, the function of p(EMG|stop) should be shifted to the left from the function of p(button press|stop). In addition, assuming that the successful stop trials with prEMG are drawn uniformly from the distribution of successful stop trials, we would expect the two functions to run in parallel. However, preliminary evidence suggests that prEMG occurs more often at short SSDs . Given tR2 = 0.64); this model thus also allows for the assessment of the interaction of response type  and SSD. The effect of SSD was significant , as was the parametric effect of response type . Further, the interaction term was significant , as the difference between detecting a button press compared to an EMG response was larger at smaller SSDs. Since p(EMG|stop) contains p(button press|stop) as well successful stop trials with prEMG, these effects indicate that prEMG was more common in trials with short SSDs.The empirical inhibition functions are depicted on A key assumption of the horse race model is that the go and stop processes are independent of each other. In particular, the context independence assumption states that the stop process does not affect the primary go process, thus the distribution of the go-finishing times (estimated by go RTs) is identical in the go and stop trials . We appl24) and EMG onset latencies (BF = 1.32 × 1024).The most common test compares the mean go RT with the mean RT of unsuccessful stop trials. If response activation is identical in the go and stop trials, the unsuccessful stop trial distribution is censored at the right side; it thus follows that the mean RTs should be shorter in unsuccessful stop trials than in go trials. This was true in our data both for RTs  gives an estimate of the distribution of trials for a given SSD in which the go process would or would not escape the stop process. Specifically, the portion at the left of this division are the fast go trials that would escape inhibition, and the portion on the right are slow go trials that would be stopped. We found that the mean unsuccessful stop trial RTs were not equivalent to the mean of the left side of the go-RT distribution . Since the means of the fast go-RT portions may misrepresent the central values, we repeated the analysis using median values instead. Here, we did find the participant-specific medians of the left side of the go-RT distribution equivalent to the unsuccessful stop trial RTs in the discovery set (BF = 0.53), but not in the replication dataset .This test has been extended to the EMG onset latencies, deriving an additional division line using the percentile corresponding to p . Figure 9). Secondly, the average prEMG onsets were faster than the corresponding average onset of the middle partition of the go-EMG distribution (BF = 2.17 × 104), which contrasts the prediction that these should be roughly equivalent. Lastly, the slow go-EMG onsets occurred later than the prEMG onsets, as expected (BF = 2.81 × 1021). These patterns remained the same if medians were used instead of means. To confirm that the results are not affected by sampling differences due to having more go trials than stop trials, we repeated this analysis by randomly sampling matching numbers of fast go and medium go trials for each participant and doing t-tests on these subsamples  1000 times. The pattern of results stayed the same every single time. These results indicate that extending the horse race model to the EMG is not applicable and, consequently, estimating the SSRT based on go-EMG distributions should be avoided.The EMG onset latencies, averaged over all trials within each partition of the go-EMG distribution, are listed in . The datEven though the EMG onset patterns do not seem to be in accordance with the assumption of the horse race model, it is important to determine the prEMG characteristics across all SSDs to establish whether stopping estimates based on the prEMG are representative of the overall stopping behavior. Based on previous findings, we expected the prEMG frequency to decline and its amplitude to increase with increasing SSD. Further, as go RTs tend to correlate positively with the SSDs, we expected that the prEMG onset latencies  would increase with SSDs as well. Note that the prEMG peak latency, in contrast, is calculated relative to the stop signal and thus reflects the speed of the stop process. Critically, it has never been asked whether the prEMG peak latency remains stable at different SSDs.F = 24.28, p = 2.47 × 10−10, R2 = 0.09). Similarly, we found increased EMG onsets with increasing SSD . The prEMG peak latencies decreased with SSD , indicating that stopping may be slower at short SSDs. PrEMG amplitudes increased slightly with longer SSDs, with the smooth term at the border of significance .We fit four GAMMs to predict the prEMG probability, onsets, amplitudes, and peak latencies from the SSDs . As expeR2 = 0.04), but we found no evidence that the trial order would affect prEMG peak latency . We also tested whether the trial order affected prEMG frequency by predicting the existence of prEMG in a given trial from the trial order using logistic regression (while controlling for the SSD), but this model yielded no trial order effects either . Note that these results do not exclude that stopping may be automatic to begin with nor that automatism affects underlying processing  are associated with delayed stopping. This would result in a negative relationship between prEMG onset and peak latencies and between the onset latencies and AUCs, but a positive relationship between the peak latencies and the AUCs.r = 0.88). To account for this, we predicted onsets from the SSDs for each participant and used the residuals instead of original onset values. AUCs were log transformed since the original values were extremely skewed toward low values.We tested these scenarios by fitting GAMMs predicting (1) peak latency from onset latency, (2) peak latency from AUC, and (3) onset latency from AUC. Onset latencies and SSDs correlated highly at the single-trial level . There was also a small negative association between onset latencies and the AUCs . In the discovery dataset, there was no relationship between peak latencies and AUCs , while the replication dataset showed a very small negative association . As such, there was strong evidence for dependencies between going and stopping at the single-trial level, where delayed going was associated with delayed stopping. Note that none of these relationships were significant at the group level , thus these results apply for single-trial data only.The data showed evidence for scenario 2  with a sUntil now, most previous studies focused on a single estimate of the peak latency of the prEMG. However, prEMG allows for the calculation of the standard deviation of the peak latency for each individual; we can therefore empirically capture the variability of single-trial stopping latencies for each individual . A caveaTo understand the variability measure better, we first considered whether the between-participant variability could be driven by nuisance factors, such as the number of prEMG trials per person or the amount of baseline noise in the EMG. Secondly, it is possible that the variability of the stopping latency simply reflects variability in general processing, which can simply be assessed through the analysis of plain go responses without any need for a stopping variability measure. Therefore, we correlated the variability of the prEMG peak latency with the variability of the go RTs, where a low correlation would indicate that stopping variability gives information beyond the variability seen in go responses. The correlation matrix is depicted in r = 0.30, p = 0.05), but showed no associations with baseline EMG noise  or go-RT variability . Lastly, we replicated the moderate positive correlation reported by r = 0.40, p = 0.01). The correlational structure did not change if four outliers were included in the data, nor when the analysis was confined to participants with more than 10 prEMG trials. The data thus suggest that an individual’s standard deviation of the prEMG peak latency provides complementary information on individual differences in stopping behavior.The individual variability in prEMG peak latency correlated moderately with the number of prEMG trials  were included. Thus, excluding participants based on low number of available trials is not strictly necessary and may cause more harm by increasing the group-level standard errors and thereby reducing statistical power.We analyzed prEMG from a standard stop signal task to investigate its utility and reliability as a marker of action stopping. We compared it thoroughly to the best-established marker of stopping latency, the SSRT. Further, we evaluated whether prEMG supports the horse race model that underlies the SSRT estimation in nearly all studies using the stop signal task. We also provided practical tips for EMG data acquisition and analysis . We founBox 3.Recording systemsEMG can be recorded by dedicated systems, but also by using EEG equipment that many human neurophysiology labs already own. In fact, if simultaneous EEG–EMG measures are of interest, it is preferable to record them by the same system so that both signals are synchronized on acquisition. Many EEG systems include a limited number of dedicated channel pairs that allow for the recording of activity referenced against each other instead of a referencing against the common EEG reference. Those channels can be used to record from electrodes placed on the skin above muscles to capture EMG. Alternatively, EMG electrodes can be referenced against the common EEG reference during acquisition and then re-referenced offline to get a single bipolar EMG channel. Note though that a major caveat of this procedure is that a larger distance between the active EMG electrodes and the EEG reference increases the chance of amplifier saturation, rendering the EMG signal useless.Muscle choice and movement directionpollicis brevis), index fingers , or a combination of both, although head, neck, and biceps muscles have been used as well  and behavioral (SSRT) measures may reflect the final ballistic component of the movement, the so-called point of no return, after which initiated movements are unaffected by any higher-order control mechanism  optimizing task design to produce more prEMG trials, for example by encouraging fast responding, using a response device with stiff buttons, or using an anticipated response task that discourages response slowing ; (2) reqAnother unsettled question regarding the generalizability is whether prEMG trials could be qualitatively different from other successful stop trials. Finally, does prEMG latency generalize across different contexts? While it has been shown that the SSRT becomes increasingly unreliable in more complex stop signal tasks and its use is thus discouraged , prEMG cWhile prEMG can be considered an assumption-free physiological measure in its implementation, it is not free from assumptions in its interpretation. The most urgent unresolved question is whether prEMG captures the latency of inhibitory mechanisms in the brain. On the one hand, prEMG decline unequivocally indicates stopping of muscle activity, so it is safe to presume that the peak latency represents the latency of action stopping. On the other hand, the actual mechanisms that cause this EMG decline cannot be derived from prEMG alone. Alternative explanations to cortical active inhibition mechanisms include activation of antagonist muscles triggering peripheral lateral inhibition, an initial subthreshold activation of the go response, or cessation of the invigoration of the go response by the motor system. Several studies have investigated whether stopping of specific muscle activity could be caused by the activation of antagonist muscles and found that agonist stopping occurs before antagonist activation; thus, the former cannot be caused by the latter . It is aBased on the synthesis of previous work together with a comprehensive analysis of prEMG characteristics in an independent dataset, we conclude that prEMG is a useful and reliable measure of action stopping. PrEMG is not limited to specific muscles, stimulus modalities, or task contexts, so it provides a comparative physiological measure for a range of different scenarios. Even though there are some similarities between the SSRT and the prEMG, these two measures should not be used interchangeably, but rather complementarily. The SSRT benefits from decades of research and is therefore well characterized across different contexts and sample populations. Yet, it is founded on assumptions that are not always met in empirical data, and it seems to be more strongly affected by behavioral strategies than the prEMG. In contrast, prEMG is directly observable and provides action stopping latencies at a single-trial level. Its peak latency is relatively stable even though design choices may limit the number of available trials. Importantly, neither the SSRT nor the prEMG peak latency is an unambiguous marker of response inhibition in the brain, but since the prEMG is directly observable, it has stronger potential for guiding research on brain mechanisms underlying action stopping.Data were analyzed from 46 healthy young adults . Initially, data were collected from 52 participants, but four were discarded due to technical issues with the response device and another two were discarded as statistical outliers (above two interquartile ranges) based on EMG onset and peak latencies. Participants gave written informed consent prior to data collection, and everyone received monetary compensation for their participation. The study was conducted in accordance with the Helsinki declaration, and was approved by the internal review board of the University of Oslo (ref. 1105078).All participants performed a choice-reaction time task and a stop signal task presented in alternating blocks . Task presentation was controlled by E-prime 2.0 . For this analysis, we focus on the stop signal task exclusively. The primary task was to respond with a button press using the thumb of the left or right hand to an arrow pointing to the left or right, correspondingly. In 24% of the trials, a stop signal appeared after a variable SSD, instructing participants to stop their initiated response. In total, there were 684 go trials and 216 stop trials, equally distributed between left- and right-hand trials.Each trial started with a black fixation cross (duration jittered between 500 and 1000 ms), followed by a colored arrow as a go signal, presented for 100 ms. In stop trials, a second arrow of a different color was presented after the SSD. The SSD could vary between 100 and 800 ms and was adjusted based on an adaptive tracking procedure collapsed across both hands: The initial SSD was set to 250 ms, and then increased or decreased by 50 ms following successful and unsuccessful stop trials, respectively. Responses were collected up to 1000 ms after the go stimulus and potential responses after this window were treated as omissions. All stimuli were presented centrally against a gray background and the stimulus color assignments  were counterbalanced across participants. Trial order within a block was randomized, except for the first block, which always started with a minimum of 10 go trials. Short pauses (9 s) were given after every 75 trials, and a longer break with a duration of roughly 5 min was allowed halfway through the task.Prior to the experiment, the participants completed a short training block  with computerized trial-by-trial feedback. In addition, participants were told that it was important to be both fast and accurate, that they were not supposed to wait for the stop signal, and that it was not possible to successfully stop in all stop trials. Automated feedback was presented on screen after every 75th trial. Here, participants were instructed to be faster if their average goRT was above 600 ms, and to be more accurate if the average stop accuracy was below 40%. If neither of these applied, they received the feedback ‘Well done!’.The following behavioral variables were extracted based on task performance: go accuracies, error, and omission rates, go reaction times, stop accuracies, unsuccessful stop reaction times, average SSD, and SSRTs. These were calculated using the integration method, where corresponding go reaction time distributions included choice errors, and omissions were replaced by the maximum reaction time .μ and τ parameters and a standard deviation of σ2 + τ2. As this model only fits one go process, choice errors were removed prior to analysis. We also removed outlier trials, defined as trials with RTs ± 3 SDs away from each participant’s mean. Estimation was run using the Dynamic Models of Choice toolbox (μgo and μstop), 200 ms (σgo and τgo), and 100 ms (σstop and τstop), and the standard deviations to 1000ms. The priors for go and trigger failures had a mean of 1.5  with a standard deviation of 1. We ran 24 chains, 3 for each estimated parameter. All rhat-values were <1.1, indicating chain convergence. Posterior distributions were updated well, suggesting that priors were largely uninformative. Plots showing the prior relative to posterior distributions, chains, and posterior predictive model checks can be found in the accompanying materials on OSF  with an online low-pass filter of 1000 Hz, a sampling rate of 5000 Hz, and a 0.5 µV resolution.Two surface EMG electrodes were placed on the skin above the MATLAB, version 2018a) and eeglab (https://osf.io/rqnuj/). The EMG signal was bandpass filtered between 20 and 250 Hz using a second-order butterworth filter and resampled to 500 Hz. Data epochs were extracted for all trials from −0.2 to 1.6 s relative to go signal onset. Trials were rejected at this stage if the average baseline activity was larger than 100 μV .EMG preprocessing and variable extractions were performed with custom scripts in MATLAB . For each participant and each hand, epochs were then concatenated and EMG bursts were identified in a trial if any data point exceeded a threshold of 1.2 . This threshold was confirmed visually and has been held constant across several recent studies . The peatidyverse  varied across SSDs. To account for individual variability in SSDs, we centralized the SSDs per participant instead of using the absolute values. The mode of each participant’s SSD distribution was treated as zero and we extracted the values ±3 SSD steps around the mode. The prEMG characteristics were averaged per SSD, so that each participant had one value per SSD, given that they had any trials with prEMG at that specific SSD.gamm4 , they were orthogonalized for the SSD by predicting onsets from the SSD for each participant and using the residuals instead of original values. AUCs were log transformed since the original values were extremely skewed toward low values. Additional data cleaning was applied, in which trials were excluded if the peak latency occurred before the stop signal or if the onsets (go-locked) or peaks (stop-locked) exceeded two interquartile ranges of the individual participant’s data. Three separate GAMMs were performed predicting peak latency from onset latency, AUC from peak latency, and AUC from onset latency.Single-trial analysis. The single-trial analysis was done on prEMG  only, using prEMG onset latencies, peak latencies, and AUC from onset to peaks. These were calculated as a cumulative sum of the amplitudes from onset to peak. AUC thus integrates the duration and amplitude of the prEMG bursts before their decline. As the bivariate correlations with the SSD at a single-trial level were high for the onset latencies  p values were used to identify relationships between prEMG variability and the number of prEMG trials per participant, go reaction time standard deviations, average root mean square amplitudes during the intertrial interval of −20 to 0 ms before go stimulus, and standard deviation of the SSRT calculated by the BEESTS method. The scatterplots with the outliers included are in Reliability. We estimated the internal consistency of the different prEMG metrics  using a permutation-based split-half approach . After tInitially, all trials from all participants were used for the reliability estimates. Next, we repeated the analysis iteratively using an equal number of trials per participant and excluding all participants who did not have enough trials available for a specific iteration. We started with 6 trials per participant  and increased the number of trials per iteration by one until we were left with 32 trials per participant (with 12 participants remaining). We repeated this process 1000 times, thus getting a distribution of reliability estimates at each trial count.z-transformed data, and then retransformed into correlation coefficients. The associations between the behavioral and EMG measures were tested with general mixed models with ‘study’ as a random variable, using the R package lme4  were searched with keywords (‘EMG’ OR ‘electromyography’) AND  on 11/21/2021. The abstracts of 158 entries were screened and included in the meta-analysis if they reported prEMG peak or offset latencies relative to the stop signal onset. Most studies reported data from more than one experiment or experimental condition. All experiments/conditions were included as separate entries, except for age lme4 . The sprage lme4 . The authors propose that the covert latency of the stopping process, which allows for the stopping of movements in tasks like the stop-signal paradigm, can be measured through the offset latency of bursts of electromyographic (EMG) activity that is observable on trials in which no overt response  was produced. The investigation is extensive and systematic and will provide a helpful methodological resource that rigorously specifies this alternative measure of stopping. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Inge Leunissen (Reviewer #2).Thank you for submitting the paper "Partial response electromyography as a marker of the individual stopping latency" for consideration by Comments to the Authors:eLife.We are sorry to say that, after consultation with the reviewers, we have decided that this work will not be considered further for publication by eLife. I have included the full reviewer comments appended below to convey the basis for this decision.Both reviewers found the work to be important and timely, given the growing use of pmEMG as a measure of stopping. However, in review and discussion, it was not clear to all reviewers that the data fully support the conclusion that prEMG is an unambiguous marker of stopping latency. Though one reviewer was more positive in their review, both reviewers highlighted concerns that were potential serious. For example, there was agreement that only a small percentage of trials show the bursting effect. This raised concern in consultation about the conclusions. Indeed, one reviewer commented that these results could have led to the opposite conclusion, namely that much is still unknown about prEMG and its relationship to stopping, and so it is premature to declare it a viable marker of stopping latency. In light of these ambiguities, it was decided that the paper was not suitable for Reviewer #1:The current article proposes the usage of "partial response electromyography (prEMG)" to measure inhibitory control processing in the context of the stop-signal task. Specifically, the idea is to use single-trial EMG to detect subthreshold activity (and its subsequent reduction) on trials in which no overt response was made . According to the authors' proposal, this would allow for a single-trial quantification of the latency of the stopping process.This possibility is attractive for obvious reasons, chiefly the fact that the latency of the stopping process in the stop-signal paradigm is not directly or overtly observable from behavioral data alone. While this "partial response EMG" approach has been used in several prominent papers in the 90s, it has recently seen a resurgence, largely due to prior work by the current group of investigators, as well as others . This resurgence is largely due to the explicit proposal that the offset latency of the prEMG in particular could be used as a proxy of the latency of the stopping process on individual trials.Here, the authors perform an extensive investigation of several psychometric properties of prEMG as well as its relationship to behavior and other aspects of the stop-signal task.Based on this, the authors propose that prEMG  is a "unique and reliable measure of stopping" and "encourage the widespread us of prEMG to investigate the underlying mechanisms of response inhibition".Interestingly, based on the results of their current investigation, which is thorough and extensive in many ways, I actually arrive at a different conclusion.Indeed, while I completely agree that there is utility to prEMG as a measurement, I think there are many theoretical and practical considerations that severely limit that utility. Furthermore, while the authors may turn out to be right that prEMG is a "reliable measure of stopping", I am quite unconvinced that this has been conclusively shown yet, including in the current paper. Indeed, some of the data presented here casts even more doubt on that purported association.Chiefly, my concerns are as follows:– First, I see no definitive reason to assume that the reduction of the EMG that constitutes the prEMG measurement is actually due to an inhibitory process. While this may well be the case, it also seems entirely possible that the observed reduction of the EMG is merely due to a cessation of the invigoration of the go-response by the motor system, which could happen entirely in the absence of inhibitory processing. Furthermore, alternatively, it could also reflect an initial subthreshold activation of the go response in those trials. At this point, as much as the authors  may prefer it, there seems to be little evidence to favor the inhibition-based interpretation over the others – at least to me.– Second, as the authors themselves note (to their credit), the correlation between prEMG and SSRT is only very moderate – as low as ~10% variance explained, depending on the study. I understand and agree that SSRT has problems as a measurement, but it is also true that relationships to SSRT have been used as the ultimate benchmark when evaluating the utility of a physiological measurement as a potential "biomarker" of stopping – including by the current authors at several points of their manuscript. At face value, the low correlation to SSRT further casts doubt on the proposed association between prEMG and inhibition / stopping behavior.– Third, while again I agree with the authors that SSRT has been the subject of recent (justified) criticism, I am not aware of the fact that anybody is arguing for abolishing it altogether. Of course, work by investigators like Matzke, Bissett, Verbruggen and others have outlined boundary conditions under which SSRT estimates can become imprecise (though corrections exist in most cases). But the same is of course true of the EMG measurement, which is subject to considerable noise, as well as crosstalk from other muscles – including, notably, the antagonist of the responding muscle, etc. Hence, it is at least doubtful to me that prEMG is inherently superior in regard to its associated principled problems (some of which I will get into further below).– Fourth, this leaves the single-trial argument, which I agree is quite compelling: SSRT is a compound measure, whereas prEMG carries some degree of single-trial information . However, this glosses over the highly pertinent aspect that only a remarkably low fraction of successful stop-trials even shows a prEMG burst (23% in the current study!). As such, there is a huge problem in that trials on which prEMG can be measured do not represent the typical stop-signal trial (or even their majority), and instead only reflect a very minor portion of the trials. The proposition that we can learn something general and universal about the behavior in a certain task by only looking at a small fraction of atypical trials in the critical condition seems like a huge deal to me, which is not really reflected in the considerations presented in this article at all. This problem is amplified by the fact that I imagine that there is some degree of dispersion around that mean, meaning that there will be subjects in which the fraction of prEMG trials is even lower than this 23%. How much are we really gaining in terms of an 'overt' measure of the stopping process if it is only present in such a small subset of trials?– Fifth, the trial-wise variation of prEMG latency is quite considerable. While the authors argue that there is some informational value in that intra-subject standard deviation , I find it very hard to believe that the true stopping process within a given subject varies by as much as a standard deviation as large as 143ms would indicate. Indeed, the BEESTS method estimates the SD of SSRT to be <50ms at the largest, as is evident from Figure 7B. The comparably enormous variation of the prEMG measure seems therefore underappreciated.– Sixth, how does it make sense that prEMG occurs most prominently at short SSDs? Intuitively, wouldn't one presume that prEMG is most likely to occur when stopping happens "at the last second" – i.e., after cortico-spinal volleys have already been fired and the responding muscle has begun to contract? If so, shouldn't that be _least_ likely when SSDs are short?– Seventh, the authors cite the recent Tatz et al. paper as an independent replication of the association between prEMG and SSRT. However, they never discuss what seems to be the core thrust of that study, which is that reductions of EMG are not specific to stopping and appear to occur after other events as well.– Finally, the theoretical framing of the paper seems a bit unclear to me. The authors initially frame prEMG as an "alternative to SSRT" and "a more direct measure of the stopping latency" (p. 3f). However, they then argue that prEMG and SSRT are "partially independent" . In the Conclusions, the authors then unequivocally state that "prEMG is a unique and reliable measure of stopping". So, if I understand the take home message correctly, the authors suggest that prEMG is a measure of stopping, which is  independent from SSRT, which, however, is also a "behavioral measure" of stopping (p. 23). In other words, the proposed relationship between prEMG, SSRT, and stopping should be more explicit. Do they both measure stopping? If so, how is that sensible? Or does only one of them? Should we discard SSRT in favor of prEMG? If the latter is to be the argument, I would put forward the above concerns, and reiterate that an explicit proof that prEMG reflects an inhibitory effort is still outstanding. If both SSRT and prEMG measure stopping, then how can they be only very moderately correlated and occur on different time scales?In summary, I remain rather unconvinced that prEMG is as of yet established as an "unambiguous marker of the stopping latency", which is the study's primary claim. It is also not clear to me if the additional set of problems associated with this measure makes it a viable "alternative to SSRT", as is implied. Hence, while I appreciate that two flawed measures (SSRT and prEMG) may be superior to only one, even if their relationship is unclear, I still believe it's too early to "encourage the widespread us of prEMG to investigate the underlying mechanisms of response inhibition".Reviewer #2:Raud et al. explore the possibility of using partial electromyography responses on successful stop trials as a physiological measure to capture individual stopping latencies (prEMG). They provide an extensive overview of the measure in a large stop signal task data set, how it compares to traditional measures, how it relates to the assumptions underlying the horse-race model and its variability / reliability. In addition, it provides practical tips on how to collect the EMG data and to extract the partial responses. Overall, the data analysis is rigorous, and the conclusions are justified by the data. The main limitation of the approach is that partial responses were only detected on 23% of all successful stop trials. Previous literature has reported incidence of ~50%, but it remains a question whether this portion of the data is representative of all stopping behavior. The conclusion that the prEMG does not support the independent horse race model could have severe implications. It is not the first account of violations of the independence assumption of the model, which is widely used for the estimation of inhibition speed. It is vital to investigate the degree of violation in both response times and partial EMG responses under different task conditions to get an idea of the degree of the violation and its impact on the stopping latency estimations. I commend the efforts of the authors and would very much like the field to adopt the inclusion of these EMG recordings.1. The percentage of successful stops with a partial EMG response seems to be quite low (~23%). In my own experience the percentage was more around 40 (with a choice reaction time version of the SST) to 50% (with an anticipated response version of the SST) . Also in the paper of Coxon et al. 2006 and Jana et al. 2020 the percentage seems to be quite a bit higher (for the latter even 67%). I wonder whether the focus on the go task (and not slowing down here) can explain these differences. The RTs reported in your experiment are fairly long. Providing more feedback on the go task might help to increase the percentage of successful stops with a partial response. This would be important for at least two reasons: it is unclear how well 23% of all successful stops represent stopping behavior. Moreover, most researchers do not present 216 stop trials, but rather only 80-100. Leaving one with only ~10 partial responses per participant. The willingness to adopt such a lengthy protocol in addition to the EMG might be low, therefore I think it would be good to reflect on this.2. APB vs FDI: You report the partial EMG responses from a downwards thumb movement in the APB. Of course, this makes sense for this movement direction, but in my experience it is much easier to get a good signal to noise ratio in the EMG for the FDI (which would require and abduction of the index finger or a different hand position). See Coxon et al. 2006 J Neurophysiology Figure 1 and 3 for a comparison of the EMG traces of the APB and FDI. Having a better SNR might also help in identifying the partial responses in a greater number of trials.3. You show a dependency between the go and stop process based on the EMG analyses. It might be of added value to check the level of dependency in the response times as well. For example based on the BEEST approach outlined by Dora Matzke in this pre-print (https://psyarxiv.com/9h3v7/). It would be interesting to see if there is agreement between the two approached with respect to the (lack) of independence. It would also be nice to be able to compare the RTs on unsuccessful stops and the fast go-RT portion .The conclusion that there is dependency between the go and stop process seems to hinge on the finding that the average EMG onset in unsuccessful stop trials was slower than the average EMG onset in fast go trials. Could a difference in power perhaps produce this result? You have many more go trials than unsuccessful stop trials, would the full distribution simply not have been sampled in the unsuccessful stop trials?4. In my opinion the discussion could state a bit more clearly what the benefit of using the EMG peak latency on successful stop trials is compared to a parametric approach of analyzing the response times for example. Should it really be EMG or could one also simply use a force censor to obtain a similar measure? What are possible disadvantages of EMG? For example, in clinical populations one will find that the background EMG is much higher, making it more difficult to identify the partial responses.5. I think it would be helpful to include a visualization of the stop signal task used.6. Please include a figure that visualizes how the different latencies are determined.7. Rise time is defined as EMG onset to peak latency. But peak latency was the time between the stop signal and the peak amplitude in the EMG, obviously there is no peak latency in go trials. Please redefine for clarity.8. Motor time was longer in unsuccessful stop trials than in go trials. Was this true for all portions of go trials in the distribution? Response force on go trials tends to increase with the increasing likely hood of a stop signal appearing (i.e. participants wait and then respond very quickly and forcefully) . Those trials might have a higher RT but a very short motor time.9. Could the average areas under the curve be added to table 2? This would be helpful for understanding the analysis on the different dynamics of going and stopping.10. Page 24, line 370-371. Comment regarding SSRT being associated with a speed-accuracy trade off suggests that this is (possibly) not the case for prEMG. Give that this speed-accuracy tradeoff reflects the strategic choices of the participant I assume that this should also be reflected in the  of the prEMG. [Editors’ note: The authors appealed the original decision. What follows is the authors’ response to the first round of review.]Reviewer #1:[…]Chiefly, my concerns are as follows:– First, I see no definitive reason to assume that the reduction of the EMG that constitutes the prEMG measurement is actually due to an inhibitory process. While this may well be the case, it also seems entirely possible that the observed reduction of the EMG is merely due to a cessation of the invigoration of the go-response by the motor system, which could happen entirely in the absence of inhibitory processing. Furthermore, alternatively, it could also reflect an initial subthreshold activation of the go response in those trials. At this point, as much as the authors  may prefer it, there seems to be little evidence to favor the inhibition-based interpretation over the others – at least to me.We agree with this point. Importantly, it is not our goal to ‘sell’ prEMG as an unambiguous marker of response inhibition – we agree with the reviewer that there is no evidence for this. Yet, by definition, the decline of prEMG is a marker of stopping already initiated actions. Correspondingly, we recognize prEMG as an extremely useful tool, as it allows us to address whether action stopping is achieved by an active inhibitory process or other mechanisms in the brain. This is what motivated us to submit the manuscript in the ‘tools and methods’ section of the journal, not as a separate empirical finding.We had addressed this point the original submission’s discussion in a section entitled ‘Outstanding question 1: Is prEMG peak latency a marker of individual inhibition capacity. Given that we changed the outline of the discussion, this point is now discussed in paragraph ‘Response inhibition and prEMG’ in the revised manuscript. As this point is critical to the interpretation of the prEMG, we have further elaborated on it in the introduction (lines 87-93), changed the title and abstract correspondingly, and screened the manuscript carefully for ambiguities in wording, so that in no place we would indicate that prEMG decline is a biomarker of inhibition as such.– Second, as the authors themselves note (to their credit), the correlation between prEMG and SSRT is only very moderate – as low as ~10% variance explained, depending on the study. I understand and agree that SSRT has problems as a measurement, but it is also true that relationships to SSRT have been used as the ultimate benchmark when evaluating the utility of a physiological measurement as a potential "biomarker" of stopping – including by the current authors at several points of their manuscript. At face value, the low correlation to SSRT further casts doubt on the proposed association between prEMG and inhibition / stopping behavior.The associations between the SSRT and the prEMG vary greatly in the studies, which is now communicated better via the added meta-analysis in which we found a mean effect size of r=0.64 (lines 188-192). Interestingly, most individual studies consider this correlation as evidence for the prEMG latency and the SSRT to capture the same process, but at different time-points . The time-difference between the two also varies greatly between the studies but is typically ‘explained away’ by the electromechanical delay, that is the delay between muscle contraction/relaxation and behavioral response.We agree with the reviewer that this across-study variability indicates weak evidence that the SSRT and the prEMG latency captures the same underlying process. However, we disagree that this somehow invalidates the prEMG measure.The fact that the SSRT has been used as a benchmark stopping-measure is of course true, but it does not mean that one should dismiss new evidence suggesting that this approach is not as straight-forwards as it was believed to be. We find it counter-intuitive that a new measure should be rejected on the grounds that it does not fully correspond to an existing flawed measure. We have now added a paragraph directly discussing the interpretation of the prEMG and the SSRT (lines 431-465) and listed the advantages and disadvantages of the SSRT and prEMG in the conclusion (lines 552-561).– Third, while again I agree with the authors that SSRT has been the subject of recent (justified) criticism, I am not aware of the fact that anybody is arguing for abolishing it altogether. Of course, work by investigators like Matzke, Bissett, Verbruggen and others have outlined boundary conditions under which SSRT estimates can become imprecise (though corrections exist in most cases). But the same is of course true of the EMG measurement, which is subject to considerable noise, as well as crosstalk from other muscles – including, notably, the antagonist of the responding muscle, etc. Hence, it is at least doubtful to me that prEMG is inherently superior in regard to its associated principled problems (some of which I will get into further below).Abolishing the SSRT and replacing it with the prEMG is not our goal. We also do not propose that prEMG is inherently superior to the SSRT, precisely because we think they capture complementary aspects of stopping behavior. However, we do propose to re-evaluate the interpretation of the SSRT, as it has meanwhile been shown that it is not ‘un-ambiguous’ marker of stopping either . Whereas it is true that physiological raw signals are noisy, prEMG’s split-half reliability of 0.87 (and 0.92 in the replication dataset) for its peak latency showcases excellent reliability.– Fourth, this leaves the single-trial argument, which I agree is quite compelling: SSRT is a compound measure, whereas prEMG carries some degree of single-trial information . However, this glosses over the highly pertinent aspect that only a remarkably low fraction of successful stop-trials even shows a prEMG burst (23% in the current study!). As such, there is a huge problem in that trials on which prEMG can be measured do not represent the typical stop-signal trial (or even their majority), and instead only reflect a very minor portion of the trials. The proposition that we can learn something general and universal about the behavior in a certain task by only looking at a small fraction of atypical trials in the critical condition seems like a huge deal to me, which is not really reflected in the considerations presented in this article at all. This problem is amplified by the fact that I imagine that there is some degree of dispersion around that mean, meaning that there will be subjects in which the fraction of prEMG trials is even lower than this 23%. How much are we really gaining in terms of an 'overt' measure of the stopping process if it is only present in such a small subset of trials?We approach this issue with an empirical and a theoretical argument.Starting with the empirical: based on our experience with different response devices, we suspect that the low number of prEMG trials in this study was caused by a low resistance of the response device buttons, so that even minor thumb movements resulted in a button press. This may limit the generalizability of prEMG across studies, so we performed several extra analyses to gauge the extent of this issue.1) First, we looked at the associations of the prEMG frequency with the goRT, SSRT, and the prEMG peak latency across participants in the current study (lines 162-177). We found a moderate negative correlation with the goRT, indicating that faster participants had more prEMG trials. The correlations of the prEMG detection frequency with the prEMG peak latency and SSRT were small and not significant. This is in context of a strong negative correlation between the goRT and the SSRT. As such, there is evidence for trade-off between fast going and slow stopping. This relationship seems to some degree affect prEMG frequency, but less so its latency, which supports the usability of the prEMG latency as a measure of action stopping.2) Second, we quantified the same associations across studies via a meta-analysis . The strong negative association between goRT and the SSRT prevailed also across studies, as did the relationship between goRT and prEMG frequency. The prEMG peak latencies were relatively stable across different experiments and were not associated with prEMG detection frequency.3) Third, this still leaves us with the question whether the psychometric properties of the prEMG, as quantified in the current sample, generalize to a sample with more prEMG trials. We therefore applied our pipeline to a previously published dataset from a different lab with ~60% of stop trials with prEMG . Most of the results replicated and many of the trends were even more clearly visible in the replication dataset. The few instances that did not replicate are now reported in the manuscript . The full replication results are available in Appendix 1.Now to the theoretical argument. The reviewer argues that the prEMG potentially does not reflect a ‘typical stop trial’. This implies that a ‘typical stop trial’ exists in the first place, reflecting the long tradition of conceptualizing stopping in the horse race model framework. The reviewer agrees that the horse race model assumptions are a limiting factor for the SSRT and mentions that corrections for the violation of its underlying assumptions exist for most cases. However, these correction methods rely on the removal of participants and/or trials that do not fit said assumptions, or the replacement of such non-typical trials based on further assumptions.This inherently implies that there exists a ‘typical stop trial’ which uses the ‘correct’ inhibition process, while trials/participants that deviate from an expected pattern are simply discarded without any further explanation. Notably, this limits the applicability of the SSRT as well as its interpretation, as violations of the assumptions of the horse race model may be common in-patient groups.With EMG, we can quantify the variability of stopping latencies across trials, impose trial classifications to address potential sub-processes of action stopping  and complement such analyses with other physiological measurements.In addition, more elaborate task designs can be used to acquire even more information, e.g. on antagonist muscle movements , partial errors in compound movements , or the contraction and relaxation times of different muscle groups . In sum, whether there exists a ‘typical stop-trial’ is an empirical question, and it is one that the prEMG can provide answers to.We have now included a comprehensive discussion on the generalizability of the prEMG in the discussion (lines 467-524).– Fifth, the trial-wise variation of prEMG latency is quite considerable. While the authors argue that there is some informational value in that intra-subject standard deviation , I find it very hard to believe that the true stopping process within a given subject varies by as much as a standard deviation as large as 143ms would indicate. Indeed, the BEESTS method estimates the SD of SSRT to be <50ms at the largest, as is evident from Figure 7B. The comparably enormous variation of the prEMG measure seems therefore underappreciated.The standard deviations of prEMG are right-skewed with a few participants having very large values. Given that we used Spearman’s rank-ordered correlations for the variability analysis, we were not concerned about the outliers affecting our results. In hindsight, this was not the best choice, as it indeed gives an impression of implausibly large variability across participants. We have now excluded 4 outliers who exceeded two inter-quartile ranges from the mean. . The corrected standard deviation is now 31ms .Omitting these outliers did not have major impact on the correlation analysis see . We addiThe scatter plots for the prEMG variability data can be found in Appendix 2. We overlayed the outliers in red to illustrate the potential effects on the results.– Sixth, how does it make sense that prEMG occurs most prominently at short SSDs? Intuitively, wouldn't one presume that prEMG is most likely to occur when stopping happens "at the last second" – i.e., after cortico-spinal volleys have already been fired and the responding muscle has begun to contract? If so, shouldn't that be _least_ likely when SSDs are short?This is an alternative hypothesis, which would indicate dependence of the stopping process on the activation of cortico-spinal volleys, which occurs more often at longer SSDs. In contrast, our hypothesis, based on previous literature , was that stop process is triggered by the stop signal independently of the status of the cortico-spinal volleys. Instead, the decreased prEMG frequency at higher SSDs would result from the fact that it is more difficult to stop a response with a high SSD, thus many of these trials end up being classified as unsuccessful stop trials instead. This is indeed supported by our data.– Seventh, the authors cite the recent Tatz et al. paper as an independent replication of the association between prEMG and SSRT. However, they never discuss what seems to be the core thrust of that study, which is that reductions of EMG are not specific to stopping and appear to occur after other events as well.The paper by Tatz et al. (2021) is an excellent demonstration of the usefulness of the EMG approach to study the neural mechanisms of inhibitory control. In the authors words “the current results are the first demonstration that this [prEMG] signature is not uniquely indicative of inhibitory control during action-stopping, but again common to all salient events, just like the CSE suppression. This is further in line with the assertion that the rapid invocation of inhibitory control is a stereotypic consequence of stimulus-driven attentional capture .” We have now referenced this article as a good example for investigating mechanisms of cognitive control using EMG in the manuscript lines 521-523.– Finally, the theoretical framing of the paper seems a bit unclear to me. The authors initially frame prEMG as an "alternative to SSRT" and "a more direct measure of the stopping latency" (p. 3f). However, they then argue that prEMG and SSRT are "partially independent" . In the Conclusions, the authors then unequivocally state that "prEMG is a unique and reliable measure of stopping". So, if I understand the take home message correctly, the authors suggest that prEMG is a measure of stopping, which is  independent from SSRT, which, however, is also a "behavioral measure" of stopping (p. 23). In other words, the proposed relationship between prEMG, SSRT, and stopping should be more explicit. Do they both measure stopping? If so, how is that sensible? Or does only one of them? Should we discard SSRT in favor of prEMG? If the latter is to be the argument, I would put forward the above concerns, and reiterate that an explicit proof that prEMG reflects an inhibitory effort is still outstanding. If both SSRT and prEMG measure stopping, then how can they be only very moderately correlated and occur on different time scales?In summary, I remain rather unconvinced that prEMG is as of yet established as an "unambiguous marker of the stopping latency", which is the study's primary claim. It is also not clear to me if the additional set of problems associated with this measure makes it a viable "alternative to SSRT", as is implied. Hence, while I appreciate that two flawed measures (SSRT and prEMG) may be superior to only one, even if their relationship is unclear, I still believe it's too early to "encourage the widespread us of prEMG to investigate the underlying mechanisms of response inhibition".The reviewer rightly points to some inconsistencies in our wording that muffle the main message. We are conceptualizing prEMG as an independent measure of action stopping that is not bounded by the assumptions of the horse race model. However, we do acknowledge that previous research on inhibition heavily relied on the SSRT and the horse race model, and we are thus interested in comparing prEMG against the existing framework. Further, prEMG and the SSRT share some variance, and thus our unfortunate wording that they are only ‘partially independent’. Guided by the reviewer’s helpful questions, we have now changed the discussion and conclusion of the manuscript considerably, emphasizing the points brought up by the reviewer particularly in the paragraphs entitled ‘Comparison of the prEMG peak latency and the SSRT’ and ‘Conclusions’.Reviewer #2:Raud et al. explore the possibility of using partial electromyography responses on successful stop trials as a physiological measure to capture individual stopping latencies (prEMG). They provide an extensive overview of the measure in a large stop signal task data set, how it compares to traditional measures, how it relates to the assumptions underlying the horse-race model and its variability / reliability. In addition, it provides practical tips on how to collect the EMG data and to extract the partial responses. Overall, the data analysis is rigorous, and the conclusions are justified by the data. The main limitation of the approach is that partial responses were only detected on 23% of all successful stop trials. Previous literature has reported incidence of ~50%, but it remains a question whether this portion of the data is representative of all stopping behavior. The conclusion that the prEMG does not support the independent horse race model could have severe implications. It is not the first account of violations of the independence assumption of the model, which is widely used for the estimation of inhibition speed. It is vital to investigate the degree of violation in both response times and partial EMG responses under different task conditions to get an idea of the degree of the violation and its impact on the stopping latency estimations. I commend the efforts of the authors and would very much like the field to adopt the inclusion of these EMG recordings.1. The percentage of successful stops with a partial EMG response seems to be quite low (~23%). In my own experience the percentage was more around 40 (with a choice reaction time version of the SST) to 50% (with an anticipated response version of the SST) . Also in the paper of Coxon et al. 2006 and Jana et al. 2020 the percentage seems to be quite a bit higher (for the latter even 67%). I wonder whether the focus on the go task (and not slowing down here) can explain these differences. The RTs reported in your experiment are fairly long. Providing more feedback on the go task might help to increase the percentage of successful stops with a partial response. This would be important for at least two reasons: it is unclear how well 23% of all successful stops represent stopping behavior. Moreover, most researchers do not present 216 stop trials, but rather only 80-100. Leaving one with only ~10 partial responses per participant. The willingness to adopt such a lengthy protocol in addition to the EMG might be low, therefore I think it would be good to reflect on this.We agree that a higher number of prEMG trials would have been preferable. We assume that the low number of prEMG trials was caused by a response device buttons with very low resistance. However, specifics in the task design that caused long goRTs may indeed also have contributed to the low number of prEMG trials.We therefore investigated the association between the prEMG frequency and goRTs across the participants in our sample, as well as across different studies by a meta-analysis . Indeed, we found a negative relationship between the goRTs and the prEMG frequency, suggesting that shorter goRTs are associated with more prEMG trials (as hypothesized by the reviewer). This analysis suggests that specific task designs may help to increase the prEMG frequency (added in discussion lines 488-497). It would be particularly interesting to see whether the prEMG peak latency is in a similar range in tasks with less slowing, like the anticipated response task. While we know several studies that have analyzed the prEMG in this task, we are not aware that any of them have reported the prEMG latencies relative to the stop signal. Of note, most of the studies used in the meta-analysis had less stop trials available than in the current study, yet produced similar estimates of the prEMG peak latencies. Thus, lengthy protocols do not seem to be necessary.2. APB vs FDI: You report the partial EMG responses from a downwards thumb movement in the APB. Of course, this makes sense for this movement direction, but in my experience it is much easier to get a good signal to noise ratio in the EMG for the FDI (which would require and abduction of the index finger or a different hand position). See Coxon et al. 2006 J Neurophysiology Figure 1 and 3 for a comparison of the EMG traces of the APB and FDI. Having a better SNR might also help in identifying the partial responses in a greater number of trials.The meta-analysis indicates that about half of the prEMG-latency studies report EMG from the APB, while the rest reports from the FDI (or a combination of both). We have experienced that the signal to noise ratio is highly dependent on the movement direction and electrode placement. After some experimenting with both index finger (FDI) and thumbs (APB), we found better signal-to-noise ratio for thumbs in our setting, but we (and others) have also achieved good signal from FDI with different set-up and response device. We have now elaborated on the choice of the recorded muscle in Box 3.3. You show a dependency between the go and stop process based on the EMG analyses. It might be of added value to check the level of dependency in the response times as well. For example based on the BEEST approach outlined by Dora Matzke in this pre-print (https://psyarxiv.com/9h3v7/). It would be interesting to see if there is agreement between the two approached with respect to the (lack) of independence. It would also be nice to be able to compare the RTs on unsuccessful stops and the fast go-RT portion .The conclusion that there is dependency between the go and stop process seems to hinge on the finding that the average EMG onset in unsuccessful stop trials was slower than the average EMG onset in fast go trials. Could a difference in power perhaps produce this result? You have many more go trials than unsuccessful stop trials, would the full distribution simply not have been sampled in the unsuccessful stop trials?Thank you for the reference to the procedure proposed by Matzke et. al., as this is an interesting addition to the context independence analysis. We ran the suggested analysis for the RT data and generalized it to the EMG data. . The cumulative density functions (CDFs) and corresponding delta plots are visualized in While we find this analysis very interesting, we decided against including it to the manuscript. This is because the theoretical predictions about the prEMG are not well worked out in this framework. Further, according to Matzke et. al., the observed CDFs may only detect severe violations compared with the BEESTS-based CDFs, but no modelling approach exists for the prEMG. Lastly, to explain this analysis sufficiently would lengthen the already dense results section considerably, whereas the results do not add much new information, as the results that are currently included already point to a similar conclusion - trials with prEMG seem to be those with medium response times. Critically, the context dependence test we had already included is more stringent than the one based on the CDFs, as it directly compares the equivalence of the corresponding observable proportions of the go and stop trial distributions. We have now included this test also for the RTs, as suggested by the reviewer (lines 254-259).To assess whether differences in the sampling of EMG in unsuccessful stop and go trials could drive the reported EMG onset difference, we ran a control analysis. First, for each participant, we randomly drew a matching number of fast go and medium go trials . Second, we did two paired t-tests: comparing the sub-sample of fast go trials with the unsuccessful stop trials, and medium go trials with the stop trials with prEMG. Third, we repeated this procedure 1000 times to obtain t- and p-value distribution. As seen in 4. In my opinion the discussion could state a bit more clearly what the benefit of using the EMG peak latency on successful stop trials is compared to a parametric approach of analyzing the response times for example. Should it really be EMG or could one also simply use a force censor to obtain a similar measure? What are possible disadvantages of EMG? For example, in clinical populations one will find that the background EMG is much higher, making it more difficult to identify the partial responses.This is an excellent point. We do think that other parametric approaches are viable options as well, but we chose not to dwell on this point not to diverge from the main message about EMG properties. We have now indicated that alternative parametric approaches, such as force sensors, may outperform EMG in some contexts , if properly validated (lines 493-497).5. I think it would be helpful to include a visualization of the stop signal task used.6. Please include a figure that visualizes how the different latencies are determined.These are now depicted in Figure 1 and Box 2 for point 5 and 6, respectively.7. Rise time is defined as EMG onset to peak latency. But peak latency was the time between the stop signal and the peak amplitude in the EMG, obviously there is no peak latency in go trials. Please redefine for clarity.We have clarified that for the rise time calculation, the onset and peak latency are extracted relative to the same signal, i.e. go signal (line 143 and Table 2).8. Motor time was longer in unsuccessful stop trials than in go trials. Was this true for all portions of go trials in the distribution? Response force on go trials tends to increase with the increasing likely hood of a stop signal appearing (i.e. participants wait and then respond very quickly and forcefully) . Those trials might have a higher RT but a very short motor time.That is an interesting observation and merits further investigation. The appropriate analysis would then be to test the equivalence of motor times in fast go trials and unsuccessful stop trials. We calculated the motor times for the three parts of the go-RT distribution, as outlined in the section of contextual independence testing. In contrast to the reviewer’s prediction, we found increasing motor times with increasing RTs, and that slow go trial motor times roughly correspond to unsuccessful stop motor times. We added this analysis to the manuscript (lines 145-161), but avoided too much speculation on the implications, as the replication analysis on a different dataset showed slight deviation from the overall pattern.9. Could the average areas under the curve be added to table 2? This would be helpful for understanding the analysis on the different dynamics of going and stopping.These have now been added to the table 2.10. Page 24, line 370-371. Comment regarding SSRT being associated with a speed-accuracy trade off suggests that this is (possibly) not the case for prEMG. Give that this speed-accuracy tradeoff reflects the strategic choices of the participant I assume that this should also be reflected in the  of the prEMG.Yes, there is evidence for some sort of trade-off between going and stopping. Previously, we mentioned trade-off between fast going and accurate stopping based on previous literature. Here, we do not actually look at stopping accuracies, but do find evidence for a trade-off between fast going and slow stopping, or vice versa. Interestingly, this appears to strongly affect the SSRTs and prEMG detection frequency, but to a lesser degree prEMG peak latency. The correlation analysis is reported on lines 171-177, 184-187, and we have briefly included it in the discussion lines 456-462.ReferencesJ Neurosci 38:4367–4382. doi:10.1523/JNEUROSCI.1745-17.2018Atsma J, Maij F, Gu C, Medendorp WP, Corneil BD. 2018. 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The mechanisms for this exquisite Ca2+ selectivity have not been defined. Here, using a reconstituted system, we study the electrical properties of the channel’s minimal Ca2+-conducting complex, MCU-EMRE, from Tribolium castaneum to probe ion selectivity mechanisms. The wild-type TcMCU-EMRE complex recapitulates hallmark electrophysiological properties of endogenous Uniporter channels. Through interrogation of pore-lining mutants, we find that a ring of glutamate residues, the “E-locus,” serves as the channel’s selectivity filter. Unexpectedly, a nearby “D-locus” at the mouth of the pore has diminutive influence on selectivity. Anomalous mole fraction effects indicate that multiple Ca2+ ions are accommodated within the E-locus. By facilitating ion-ion interactions, the E-locus engenders both exquisite Ca2+ selectivity and high ion throughput. Direct comparison with structural information yields the basis for selective Ca2+ conduction by the channel.The mitochondrial calcium uniporter, which regulates aerobic metabolism by catalyzing mitochondrial Ca Electrical measurements of MCU-EMRE reveal mechanisms for selective Ca2+ conduction by the mitochondrial Ca2+ uniporter. The selectivity of the Uniporter for Ca2+ is of paramount importance—K+ and other cations outnumber Ca2+ by approximately 1 million–fold. Without high selectivity for Ca2+, substantial influx of K+ would reduce the proton motive force used for ATP synthesis.The Uniporter serves important biological functions. Mitochondrial calcium uptake through it regulates adenosine triphosphate (ATP) production to meet energy demands, shapes cytosolic calcium signals, and controls a mitochondrial permeability transition that, when activated, leads to mitochondrial stress and cell death (2+ uptake (b (2+-dependent activation to the channel whereby these subunits reduce Ca2+ flux at low resting concentrations of Ca2+ (below 1 μM) by occluding the ion conduction pathway and set a threshold for Ca2+-dependent activation  structures of metazoan MCU-EMRE complexes or of fungal MCU complexes have been determined  of <2 nM, indicating that the Uniporter contains a high-affinity binding site for Ca2+ (2+ until much higher (~mM) levels of Ca2+ are present, and Ca2+ currents do not saturate until approximately 80 mM , show that, similar to some other cellular Ca2+-activated chloride channels in their membranes, which could, for example, cloud the effects of introduced mutations that alter ion selectivity. Nevertheless, it has been shown using two-electrode voltage clamp experiments that mutation of the E-locus to alanine in an MCU-EMRE fusion construct eliminates the ability of the channel to conduct Ca2+ , current rectification, and selectivity for Ca2+ following the conductivity sequence: Ca2+ ~ Sr2+ >> Mn2+ ~ Ba2+ > Mg2+  of 0.22 ± 0.06  or voltage-dependent potassium (KV) channels, multiple interacting ions have been shown to overcome the seeming paradox of tight ion binding and high flux . Lower macroscopic Ca2+ currents in comparison to Na+ could be due to reduced flow of ions through each open channel (lower single-channel conduction), reduced probability of the channels adopting an open state (lower open probability), or a combination of these. We observed a substantial amount of current fluctuation  in the macroscopic current traces for Na+ , which is another indication of a change in the open-probability and/or single-channel conductance of the channel when Ca2+ is the charge carrier in comparison to Na+ (fig. S3). We used analyses of such fluctuations to estimate the single-channel properties of TcMCU-EMRE, as described later.The macroscopic Ca for Na+ . The flu2+ channels . The strong preference for Ca2+ over Ba2+ is unusual among Ca2+ channels. For example, CaV channels, the calcium release–activated calcium channel, the inositol triphosphate receptor, and the ryanodine receptor exhibit higher conductance for Ba2+ compared to Ca2+  curve is sigmoidal  of approximately 40 nM . Mutation of the D-locus to alanine (D261A) or the E-locus to aspartic acid (E264D) . These properties are further indications that the structure of the mutants generally corresponds to the wild-type channel.After determining that purified  (E264D) , which w by Ca2+ . Similar2+, we found that Ca2+ blocked the Na+ currents observed for wild-type TcMCU-EMRE at −80 mV with a Kd value of <2 nM (+ currents (Kd < 2 nM) (2+ (Kd ~ 42 nM) (By titrating the amount of Caof <2 nM . The D→A < 2 nM) . The E→D~ 42 nM) .2+ to block Na+ currents  voltages resulted in a lower extent of block, whereas voltages closer to neutral yielded more block (2+ (and presumably a Ca2+) ion bound in the blocking site experiences a force due to an electric field across the membrane.We found that the potency of block of the wild-type channel by a concentration of Srre block . The obs2+ and Sr2+ suggest that the blocking site within the E-locus is unperturbed by the D→A mutation at the mouth of the pore. The E→D mutant, on the other hand, displayed relatively modest voltage dependence of block, which provides further evidence that the high-affinity blocking site present in the wild-type and D→A mutant channels is the E-locus , or Mn2+ and Mg2+, with smaller ionic radii , are much less conductive. The E→D mutant displayed a different divalent conductivity sequence from the wild-type pore, in which Ba2+ is more conductive than Ca2+ (Ba2+ > Ca2+ ~ Sr2+ >> Mn2+ ~ Mg2+)  . The inc+ on one side and a monovalent test cation on the other, TcMCU-EMRE exhibited a permeability sequence of Li+ > Na+ > K+ > Cs+ as determined by reversal potentials (+ and Na+) are more permeable than larger ones; that is, smaller ones more easily enter the pore  is less, and larger K+ and Cs+ ions  are restricted. This size-based selection is consistent with the divalent cation selectivity results, in which Ca2+ and Sr2+, with ionic radii of 1.00 and 1.13 Å, respectively, are highly conductive, whereas larger or smaller ions are much less so  effects on ion selectivity, whereas mutation of the E-locus causes marked changes.For the E→D mutant, by contrast, we observed a different monovalent permeability sequence, Li2+ ion has been observed within the E-locus in all x-ray or cryo-EM structures of MCU channels, suggesting that this region has an important role in selectivity  and the probability that the channel adopts an open conformation (PO) can be deduced  and nonconductive (closed) states in a stochastic manner. The fluctuations provided an experimentally tractable means to estimate the single-channel Na+ current (iNa). The magnitude of observed fluctuations  is given by σ2 = nPO(1 − PO)i2. One can appreciate that the variance will be greatest when PO = 0.5; in this case, each channel has a 50% probability of conducting ions as it stochastically switches between conductive and nonconductive states. On the other hand, when PO is close to one or approaching zero, the variance will be minimized.Considering our observations that the E-locus serves as the selectivity filter of the channel, we sought to address what mechanisms would allow for rapid ion conduction through it and what, if any, influence the D-locus has on the ion conduction rate. To address these questions, we investigated the single-channel properties of wild-type and mutant channels using nonstationary noise analysis. Using this technique, the amount of current flowing through a single channel    depict gmate iNa  (61). Do± 0.4 pS . This va carrier .iNa for the mutants. Using the same approach as for wild-type protein, we find that the E→D mutation has a similar PO to the wild-type channel (0.72±0.05) but that it has approximately fivefold lower iNa (−0.44 ± 0.02 pA at −120 mV) and single-channel conductance (3.7 ± 0.2 pS) relative to it  is a potent blocker of Ca2+ currents through the channel. The measured apparent affinity of TcMCU-EMRE for La3+, in the presence of 40 mM Ca2+, was 150 ± 29 nM , estimated at 20 to 40 fS  , but our data do not exclude the possibility of interactions with more than two Ca2+ ions. For example, molecular dynamics simulations suggest that three Ca2+ ions sometimes interact with a ring of acidic amino acids in the selectivity filter of the Ca2+-selective TRPV6 channel  collectively conduct ions at approximately 10% of the rate observed for Ca2+ or Sr2+ alone. The magnitude of the AMFE for the channel is among the greatest observed for an ion channel and indicates that strong energetic coupling between ions underlies selective ion permeation in the Uniporter. Our mutational studies indicate that this ion-ion coupling does not depend on the D-locus and indicate that it occurs in the E-locus.The AMFE that we observe is substantial. Typical effects in other ion channels result in less than 10% perturbation in ion conduction (2+ (Kd < 2 nM). Cryo-EM densities from structures of the Uniporter are consistent with a single Ca2+ ion in the E-locus, but our AMFE experiments indicate that multiple Ca2+ ions can be accommodated in it. We sought to reconcile these observations. As observed previously   relative to the K1/2 of saturation, density consistent with a single calcium ion is positioned between the two rings, where it would be directly coordinated by all four glutamate residues (2+ and the lower ring (2+ ion and the coordinating oxygens (and the possibility that the ion density could represent two ions) are limited by the resolutions of the structures, which are determined at up to 3.1 Å resolution and have coordinate errors of 1.5 Å or more. Nevertheless, the data presented in this work indicate that the density for a Ca2+ ion within the E-locus represents the high-affinity site that blocks monovalent current through the channel. The observation that blockade is voltage dependent indicates that this site is located within the electric field. We find that the E→D mutation, which would increase the diameter of the pore in this region by approximately 2 Å, produces a channel with lower ion selectivity for Ca2+. Furthermore, we observe that the conductivity ratios for monovalent and divalent cations in the wild-type pore are correlated with ionic radii. The results suggest that the dimensions of the selectivity filter formed by the E-locus are crucial for Ca2+ selectivity. More precisely defined interatomic distances garnered by higher-resolution structures would provide further insight.Each glutamate residue of the E-locus is positioned such that its two oxygen atoms line the ion conduction pore with one closer to the IMS side and one closer to the matrix side . In the residues   indicate that interaction with Camediated . Unlike 2+ selectivity or iNa, the D-locus is highly conserved among MCU channels. A possible explanation for this conservation may be the interaction of the D-locus with MICU regulatory complexes (2+ in the IMS is at a low resting level (below approximately 1 μM)  with which Ca2+ blocks monovalent current. Kd is related to the off-rate (koff) and the on-rate (kon) by the following relationship: Kd = koff/kon. If one considers a hypothetical hemispherical binding site that acts as a sink for ions at the mouth of the pore, then the fastest kon from bulk solution is limited by diffusion and is approximately 2 × 109 M−1 s−1  of one ion approximately every 10 s. Even the fastest-releasing 2 nM affinity binding site is approximately 10 million times slower than the observed rate of ~106 ions/s at which Ca2+ ions can flow through the Uniporter. The Uniporter is an extreme example among highly selective ion channels that ion binding affinity alone cannot underlie selective ion conduction at the observed rates.That the Uniporter achieves exquisite selectivity for Ca+ channels or by Eisenman et al.  Ca2+ ions would be present within the E-locus.Although our experiments do not exclude the possibility that more than two Ca3+ potently blocks Ca2+ current through TcMCU-EMRE. On the basis of its high-affinity interaction, its similar ionic radius (1.05 Å) to that of Ca2+ (1.00 Å), and that lanthanides (La3+ or Gd3+) often bind approximately where calcium ions do in the selectivity filters of Ca2+ channels  or +4 (two Ca2+ ions) is less favorable. This situation is reminiscent of the selectivity filter of canonical potassium channels, exemplified in studies of KcsA, which suggest that the filter accommodates more than two, but less than three, K+ ions on average  on the Ca2+ ion. The force would cause displacement of the Ca2+ ion toward the matrix, where it would be closer to the lower ring of oxygen atoms than the upper ring. If the field were strong enough, the force on the Ca2+ ion could cause it to be pulled from the E-locus (a punch-through effect). Evidence that punch-through occurs is that the extent of block for a given concentration of Ca2+ decreases as the voltage is increased to more negative values. In the model, downward displacement of the Ca2+ would create space for a second ion to bind, presumably where it would interact with the upper ring of oxygen atoms. We have shown that the apparent affinity for loading of multiple Ca2+ ions into the E-locus is approximately seven orders of magnitude lower than the Kd for the blocking Ca2+. The loading of multiple ions is strongly dependent on the applied voltage such that at −40 mV, K1/2 is approximately 62 mM, whereas at the typical mitochondrial voltage of −160 mV, K1/2 is approximately 1 mM. The average positions of the Ca2+ ions in the E-locus would be due to a combination of the attractive forces with the coordinating oxygens, electrostatic forces due to the membrane voltage, and repulsive forces between the ions. When two Ca2+ ions are present within the E-locus, we hypothesize that neither ion would occupy the blocking site. Rather, both ions would flank the blocking site, with one above and one below it. A negative voltage would bias the release of the lower Ca2+ into the remainder of the pore, thus resulting in Ca2+ conduction into the matrix. We suspect that the apparent K1/2 that we measure for Ca2+ saturation represents the overall affinity of multiple ions for the conductive configuration.We propose a model for selective Cakon) would be approximately 2 × 109 M−1 s−1 , the off-rate of one of the two ions (the ion conduction rate) would be approximately 2 × 106 s−1. This value is satisfyingly consistent with the observed conduction rate of approximately 1.4 × 106 calcium ions per second (at −160 mV and saturating Ca2+) from the noise analysis experiments. In the context of the negative mitochondrial potential (approximately −160 mV) applying a downward force, the lower ion would be more likely to dissociate. With the release of the lower Ca2+ ion, the upper Ca2+ ion would be drawn toward the blocking site by coordination with the glutamate oxygens , returning the situation to the original configuration and priming the E-locus for interaction with another Ca2+ ion. If one considers the maximal ion throughput rate of the channel (approximately 106 per ion per second) in comparison to an estimated rate of diffusion to the mouth of the pore of 2 × 109 M−1 s−1 and that ion conduction occurs as a result of a multi-ion pore, then it becomes apparent that with sufficient [Ca2+] for conduction (~1 mM at −160 mV), the rate for filling by the second ion is similar to the rate of release. This suggests that the presence of two ions is transient: As soon as the second ion arrives, the first one leaves.Continuing with the model, if access to the E-locus is limited by diffusion, then the maximum on-rate of the second ion  and purified as described previously , and the cells were incubated at 37°C for 16 hours, after which point 10 mM sodium butyrate (Sigma-Aldrich) was added and the cells were cultured at 30°C for an additional 48 hours. Cells were harvested by centrifugation (800g at 4°C for 20 min), washed once with phosphate-buffered saline, flash-frozen in liquid nitrogen, and stored at −80°C until use for protein purification.The N-methyl-d-glucamine) (pH 7.6), 200 mM NaCl, deoxyribonuclease I (0.1 mg/ml), leupeptin (1.5 μg/ml), pepstatin A (1.5 μg/ml), 1 mM 4-benzenesulfonyl fluoride hydrochloride, 1 mM benzamidine, 1 mM phenylmethylsulfonyl fluoride (PMSF), and 1:500 dilution of aprotinin; Sigma-Aldrich, catalog number A6279] and lysed by sonication . The crude membrane fraction was pelleted by centrifugation , resuspended in 30 ml of resuspension buffer , flash-frozen using liquid nitrogen, and stored at −80°C until further use.Crude cell membranes were prepared in the following manner. All steps were performed at 4°C unless otherwise noted. The cell pellet from 1 liter of culture was thawed in a bath of cool water. The pellet was then resuspended in 100 ml of lysis buffer  was added to the dried lipids to yield a total lipid concentration of 20 mg/ml. The lipids were incubated with end-over-end mixing for 1 hour, and the mixture was sonicated in a water bath for a total of 5 min in 1-min on/off intervals. The lipids were subsequently solubilized by adding 2% DDM (w/v) from powder. Following an approximately 1-hour incubation (with end-over-end mixing at room temperature), this DDM-solubilized PE:PG:cardiolipin mixture was used immediately for reconstitution.All lipids were obtained from Anatrace. A mixture of lipids was prepared by combining chloroform solutions containing 30 mg of 1-palmitoyl-2-oleoyl-g, 30 min, 4°C). After discarding the supernatant, the proteoliposomes were resuspended in reconstitution buffer [20 mM Hepes-NMDG (pH 7.6) and 450 mM NaCl] to yield an approximate lipid concentration of 20 mg/ml, sonicated briefly, flash-frozen using liquid nitrogen in 20-μl aliquots, and stored at −80°C until use in bilayer recordings.For reconstitution into liposomes, freshly prepared protein was concentrated to ~100 μl at ~1 mg/ml using a 100-kDa MWCO concentrator (Amicon Ultra-2) and mixed with the DDM-solubilized PE:PG:cardiolipin lipids to yield a 1:20 protein-to-lipid ratio . After mixing end-over-end overnight at 4°C, an equal volume of 100 mM methyl-β cyclodextrin  was added every 8 hours for 24 hours to remove detergent (n-decane [3:1 (w/w) ratio at 20 mg/ml]. Liposomes were fused under an osmotic gradient across the bilayer , filtered at 1 kHz, and digitized at 5 kHz using the Clampex 10.4 program (Axon Instruments). Data were analyzed using Clampfit 10.4 (Axon Instruments). In all cases, currents from bilayers without channels are subtracted. Error bars represent the SEM of at least three experiments, each using a different bilayer. We define the side to which the vesicles are added as the cis side and the opposite trans side as electrical ground, so that the transmembrane voltage is reported as +, Li+, and Cs+) as a chloride salt under divalent-free conditions . The voltage protocols are outlined in the figure legends.For experiments aimed at studying the channel properties of monovalent cations, the trans chamber was perfused with approximately 35 ml of a solution containing 150 mM test cation  was subsequently added to both chambers. For experiments aimed at studying the channel properties of different divalent cations, the trans chamber was then perfused with approximately 35 ml of a solution  containing 40 mM test cation  as a chloride salt. We chose not to estimate relative permeability ratios among divalent cations because the high selectivity for Ca2+ makes reversal potentials difficult to estimate . Relative conductances (γX/γCa) were determined by dividing the mean current at −160 mV for a given test ion by the mean current observed at −160 mV for Ca2+ from the same membrane.To record CaX represents the concentration of RuRedFor experiments using RuRed (Sigma-Aldrich), 1 μM RuRed was added to the cis chamber to inhibit channels with their IMS side facing that chamber. To assess the binding affinity, RuRed was then titrated into the trans chamber from 2 nM to 1 μM. For final concentrations less than 10 nM, a 2 μM stock of RuRed in dimethyl sulfoxide (DMSO) was used; for final concentrations up to 1 μM, a 200 μM stock in DMSO was used. Currents were normalized by dividing by the maximum current recorded at −160 mV in the absence of RuRed. The dose response was fit in GraphPad Prism 9 to the “log(inhibitor) vs. normalized response -- variable slope” equation, in which 2+ or Sr2+ to inhibit Na+ currents, CaCl2 or SrCl2 was added into standard divalent-free bath solutions to obtain the desired concentrations of [Ca2+]free and [Sr2+]free, calculated using MaxChelator (https://somapp.ucdmc.ucdavis.edu/pharmacology/bers/maxchelator/webmaxc/webmaxcE.htm). A range of [Ca2+]free and [Sr2+]free from approximately 2 nM to 6.3 μM was selected, appreciating that 2 nM is the lower limit of chelation for EDTA and EGTA. The dose response was fit in GraphPad Prism 9 to X representing the concentration of the divalent cation.To assess the ability of Ca2+ saturation experiments were performed by varying [Ca2+]free from 1 μM to 40 mM using CaCl2. The dose-response data were fit to the equation “specific binding with hill slope,” as implemented in GraphPad Prism 9I represents the mean current at a given concentration of Ca2+, Imax represents the mean current at 40 mM Ca2+, maxB represents the saturation value, and X represents the concentration of Ca2+.Ca+ current properties were performed by titrating [Sr2+]free from ~2 nM to 6.3 μM. Mean current values, ⟨I⟩, and variances, σ2, for a given test voltage and concentration of [Sr2+]free were calculated by segmenting the pulse (time window: 0.7 to 2.5 s) into 200-ms intervals using Clampfit 10.4. The data points in each noise analysis plot originate from one bilayer. Single-channel current, iNa, and the number of channels, n, were estimated by fitting a plot of σ2 versus ⟨I⟩ to the following equation using GraphPad Prism 9Nonstationary noise analyses to estimate NaPO, of the unblocked channel at a given test voltage was subsequently estimated from the following equation, with ⟨I⟩ representing the mean current of the unblocked channelOpen probability, iNa for TcMCU-EMRE and the mutants.Noise analysis was performed using at least three independent experiments (using different bilayers) to estimate g), iNa values deduced from multiple noise analysis experiments were plotted as a function of voltage and fit with a linear regressionTo estimate single-channel conductance . The mean current at −160 mV for each condition was plotted versus the mole fraction of Sr2+AMFE experiments were performed as follows. Starting from symmetric 40 mM Ca2+ currents through the channel by La3+ was determined by generating currents in 40 mM CaCl2 and titrating La(NO3)3 to the trans chamber (which had been perfused to remove chelators). Mean currents (at −160 mV) recorded in the presence of La3+were normalized by dividing by mean currents observed in its absence. The dose response was fit in GraphPad Prism 9 to X representing the concentration of La3+. Nonstationary noise analyses to estimate Ca2+ current properties were performed in the same manner as for Na+ currents.The inhibition of Ca