Editor's evaluation: Right inferior frontal gyrus damage is associated with impaired initiation of inhibitory control, but not its implementation

Field: Neuroscience (Life Sciences)

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract Inhibitory control is one of the most important control functions in the human brain. Much of our understanding of its neural basis comes from seminal work showing that lesions to the right inferior frontal gyrus (rIFG) increase stop-signal reaction time (SSRT), a latent variable that expresses the speed of inhibitory control. However, recent work has identified substantial limitations of the SSRT method. Notably, SSRT is confounded by trigger failures: stop-signal trials in which inhibitory control was never initiated. Such trials inflate SSRT, but are typically indicative of attentional, rather than inhibitory deficits. Here, we used hierarchical Bayesian modeling to identify stop-signal trigger failures in human rIFG lesion patients, non-rIFG lesion patients, and healthy comparisons. Furthermore, we measured scalp-EEG to detect β-bursts, a neurophysiological index of inhibitory control. rIFG lesion patients showed a more than fivefold increase in trigger failure trials and did not exhibit the typical increase of stop-related frontal β-bursts. However, on trials in which such β-bursts did occur, rIFG patients showed the typical subsequent upregulation of β over sensorimotor areas, indicating that their ability to implement inhibitory control, once triggered, remains intact. These findings suggest that the role of rIFG in inhibitory control has to be fundamentally reinterpreted. Editor's evaluation This study takes a fresh view of the hypothesis that right inferior frontal gyrus is critical in inhibitory control in humans, as assessed by the widely used stop-signal task. It applies recent development in modeling and EEG measures in patients with focal brain damage, yielding causal insights. The findings are important and the evidence is convincing. https://doi.org/10.7554/eLife.79667.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Humans have remarkable cognitive control abilities, which allow them to safely navigate complex everyday situations. For example, when crossing a street, humans can rapidly stop themselves from continuing to walk when they suddenly notice a rapidly approaching car. The process underlying this ability to stop an already-initiated action is inhibitory control. In the laboratory, inhibitory control is typically tested in the stop-signal task (Verbruggen et al., 2019), in which an initial go-signal (i.e., a cue to initiate a movement) is sometimes followed by a subsequent stop-signal, prompting the cancellation of that movement. The processes underlying behavior in the stop-signal task are described in a well-characterized cognitive model – the horse-race model (Logan and Cowan, 1984). This model purports that on each trial, the inhibitory process triggered by the stop-signal races with the movement-initiation process triggered by the go-signal, thereby determining whether an action can be successfully stopped. The assumptions of the horse-race model allow the calculation of stop-signal reaction time (SSRT) – a latent variable that expresses the speed of the stopping process, which is not otherwise overtly observable (as successful stopping is defined by the absence of a response). In a seminal study on the neural basis of inhibitory control, Aron et al., 2003 showed that lesions to the right inferior frontal gyrus (rIFG) are associated with an elongation of SSRT, prompting the proposal that 'response inhibition can be localized to a discrete region of the PFC,' namely, the rIFG. This finding has spawned a wider, highly influential theory of inhibitory control, which, in its most recent iteration, holds that 'rIFG implements a brake over response tendencies' (Aron et al., 2014). While some subsequent lesion studies have cast some doubt upon specific claims of this theory (e.g., by demonstrating that other regions outside of rIFG lead to comparable deficits, e.g., Picton et al., 2007; Swick et al., 2008; Yeung et al., 2021) or have reported conflicting findings (Floden and Stuss, 2006), the crucial role of rIFG in action-stopping, indicated by the increase in SSRT in rIFC lesion populations, is still widely prevalent in current-day neuroscientific theory. However, the SSRT method underlying this (and other) seminal work has recently undergone several substantial challenges (e.g., Bissett et al., 2021; Matzke et al., 2017b). One of the most prominent shortcomings of SSRT is that it does not account for trigger failures (Band et al., 2003) – trials with stop-signals in which inhibitory control process was never initiated to begin with. On such trials, erroneously executed responses do not result from an insufficiently fast stop-process losing the horse race, but from the mere fact that inhibitory control never 'entered the race.' Crucially, including such trials in the SSRT calculation leads to artificially inflated SSRT estimates and – in the worst case – can produce fictitious group differences in inhibitory control speed, which are instead more likely due to attentional lapses (e.g., Matzke et al., 2017a). In other words, in the abovementioned example of crossing a street, a trigger failure could be indicative of a deficit in noticing the approaching car, rather than in intercepting the walking movement fast enough. In light of this confound, it is necessary to reassess the causal link between stop-signal performance and rIFG damage. Therefore, we here repeated the original lesion study by Aron et al., 2003 with a stop-signal task that was optimized to implement a hierarchical Bayesian technique for the identification of trigger failures (Matzke et al., 2017b). We specifically tested whether rIFG lesion patients show increased trigger failure rates, especially in light of the fact that rIFG is a key region that is often implicated in stimulus-driven attention more generally (Corbetta and Shulman, 2002). Moreover, we extended the behavioral investigation by Aron and colleagues by measuring scalp-EEG. Specifically, we aimed to investigate the influence of rIFG lesions on stop-related β-bursts dynamics. β-Bursts are a recently discovered neurophysiological signature of inhibitory control (Diesburg et al., 2021; Wessel, 2020) and can provide additional insights into the distinction between the initiation and the implementation of inhibitory control. Specifically, β-burst rates over frontal cortex are increased on stop- compared to go-trials (Enz et al., 2021; Wessel, 2020; Jana et al., 2020), which has been proposed to reflect the initial stage of the inhibitory control cascade that ultimately results in action-stopping. In other words, frontal β-bursts purportedly reflect the initiation of inhibitory control. At the other end of the cascade, β-bursts over sensorimotor areas can be used to measure the successful implementation of inhibitory control. Sensorimotor β activity reflects an inhibited state of the motor system at baseline (Kilavik et al., 2013; Soh et al., 2021), and stop-related frontal β-bursts are followed by a rapid re-instantiation of these sensorimotor bursts (Wessel, 2020). This purportedly reflects the successful implementation of the inhibitory cascade and a return to an inhibited motor system (Diesburg et al., 2021). Hence, in line with our behavioral hypothesis, we predicted that if rIFG lesion patients showed increased trigger failure rates, they would also show reduced frontal β-burst rates compared to both non-rIFG patients and healthy adult comparisons, reflecting a deficit in triggering/initiating inhibitory control. However, we furthermore predicted that when a frontal β-burst does take place in rIFG legion patients (i.e., when the cascade is successfully triggered), sensorimotor β-burst rates would be appropriately upregulated, reflecting a retained ability to implement inhibitory control, despite damage to rIFG. Results Participants Participants included 16 rIFG lesion patients, 16 non-rIFG lesion patients, and 32 age- and sex-matched comparisons. Lesion overlap maps are provided in Figure 1, and demographic data for all participants are presented in Table 1. All participants performed a version of the stop-signal task (Figure 2A) that was optimized for the usage of BEESTS, a hierarchical Bayesian modeling technique that simultaneously accounts for the shape of Go-RT and SSRT distributions and the prevalence of trigger failures in the stop-signal task (Matzke et al., 2013; Matzke et al., 2017b, Figure 2B). This was a typical version of the stop-signal task (Verbruggen et al., 2019), which, however, included more trials than necessary for standard analyses of SSRT (see Task and Procedure). Figure 1 Download asset Open asset Overlap of lesions in patients with right inferior frontal gyrus (rIFG) lesions (rIFG lesion patients, top, A) and in patients with lesions outside of rIFG (non-rIFG lesion patients, bottom, B). (A) Top left: a lateral view of the lesion overlaps for rIFG lesion patients at the cortical level. Within the rIFG, three subregions are labeled: pars opercularis, pars triangularis, and pars orbitalis. The anterior insula is also highlighted. Top right (in order): sagittal, coronal, and axial views of the lesion overlap for rIFG lesion patients. The crosshair is centered on the right inferior frontal gyrus (IFG) on the Harvard-Oxford Atlas. All rIFG patients were included (N = 16). (B) Bottom: axial view of the lesion overlap for non-rIFG lesion patients. One lesion mask for a patient was missing and not included (N = 15). The color bar indicates the number of patients overlapped in lesion sites. R: right; L: left. Table 1 Demographic information all four groups. GroupSexHandednessAgeChronicityrIFG lesions10M/6F16R/0L54.25 (15.19)19.88 (21.54)rIFG comparison10M/6F15R/1L54.63 (15.28)n/aNon-rIFG lesions10M/6F13R/3L61.50 (14.62)10.97 (15.97)Non-rIFG comparison10M/6F15R/1L61.94 (14.40)n/a M, male; F, female; R, right-handed; L, left-handed; rIFG = right inferior frontal gyrus; Age: mean age at testing in years (standard deviation); Chronicity: median length of time between lesion onset and current experiment in years (inter-quartile range). Figure 2 Download asset Open asset Task diagram and BEESTS model. (A) Schematic design of the stop-signal task. (B) Graphical representation of the censored BEESTS model. The model assumes that the finishing times of the go (Go-RT distribution) and the stop runner (stop-signal reaction time [SSRT] distribution) follow an ex-Gaussian distribution with parameters µgo, σgo, and τgo, and µstop, σstop, and τstop, respectively. The finishing time distributions, and hence also the observed distribution of signal-respond RTs, are assumed to be censored above at 1000 ms to accommodate trials where the runners did not finish before the response window. The trigger failure parameter, P(TF), quantifies the probability that the stop runner was not initiated. Behavioral and modeling results BEESTS assumes that the finishing times of the go (Go-RT distribution) and the stop runners (SSRT distribution) follow an ex-Gaussian distribution with parameters µ, σ, and τ. The µ and σ parameters reflect the mean and the standard deviation of the Gaussian component and τ gives the mean of the exponential component and reflects the slow tail of the distribution. The mean and variance of the finishing time distributions can be obtained as µ + τ (i.e., mean Go-RT and SSRT) and σ2 + τ2, respectively. Using a mixture-likelihood approach, the model can be augmented with a parameter, P(TF), that quantifies the probability that participants fail to trigger the stop runner (Matzke et al., 2017b). Table 2 presents the posterior means and the corresponding 95% CIs of the population-level mean parameters in the four groups. The overlap of the posterior distributions of the go parameters indicated that μgo was lower, whereas τgo was higher in the rIFG lesion group relative to matched comparisons, suggesting that the Go-RT distribution of the lesion group had a faster leading edge but a larger skew. Resulting from the nearly perfect trade-off between μgo and τgo, mean Go-RT did not differ between the two groups. The overlap of the posterior distributions of the stop parameters indicated that μstop, σstop, and mean SSRT were higher in the rIFG lesion group than in matched comparisons, although the difference in σstop was small. Crucially, we found a more than fivefold increase in the P(TF) parameter of the lesion group (16% vs. 3% in healthy comparisons). These results suggest that poor stop-signal performance associated with rIFG lesions is mainly attributable to increased trigger failure rate and a slowing of SSRT as a result of a shift in the leading edge of the distribution. Following Matzke et al., 2017b, we interpret this pattern of SSRT differences (increased μstop and constant τstop) to reflect differences in the speed of encoding the stop-signal and not differences in the decisional or inhibitory component of SSRT, suggesting an attentional deficit in rIFG lesion patients. In contrast, with the exception of σstop, our analyses did not indicate the presence of differences in go or stop parameters between non-rIFG lesion patients and their matched comparisons, neither did we find evidence for a difference in the probability of trigger failures. We also directly compared the two lesion groups. Table 3 presents the posterior mean and the corresponding 95% CIs of the population-level mean difference between the two lesion groups. The overlap of the posterior distributions of the go parameters indicated that μgo and the σgo were lower in the rIFG lesion group compared to the non-rIFG lesion group, whereas τgo was higher. Crucially, the difference in the P(TF) parameter was large and reliable as the rIFG lesion group showed high trigger failure rate while the non-rIFG lesion group did not (16% vs. 2% in the non-rIFG lesion group). These patterns were similar to the comparison between the rIFG lesion group and their matched healthy comparison group. Taken together, our results are mostly consistent with an attentional rather than inhibitory account of stop-signal deficits associated with rIFG lesions. Table 2 Posterior means and 95% credible intervals (CIs) of the population-level mean parameters in the four groups. Lesion patientsMatched comparisonsDifferencePosterior mean95% CIPosterior mean95% CIPosterior mean95% CIBayesian prIFGμgo479[435,521]578[505,650]–99[-181,–15]0.01σgo75[60,94]90[73,107]–15[–39,10]0.12τgo154[111,216]76[54,109]78[25,145]0Mean Go-RT633[572,708]654[576,730]–21[–124,80]0.34μstop223[197,249]198[182,215]25[–6,55]0.06σstop40[26,58]24[16,37]16[–3,37]0.05τstop45[30,65]34[22,50]11[–12,35]0.17Mean SSRT268[237,298]232[211,254]35[–2,73]0.03P(TF)0.16[0.08,0.29]0.03[0,0.12]0.13[0.01,0.27]0.02Non-rIFGμgo547[504,589]575[506,646]–28[–109,52]0.24σgo98[84,111]104[85,122]-6[–29,17]0.30τgo106[79,139]85[61,116]21[–19,62]0.15Mean Go-RT653[602,706]660[586,735]-7[–97,83]0.43μstop215[191,239]199[184,215]16[–11,43]0.12σstop54[24,104]24[14,36]31[0.11,80]0.02τstop29[18,44]26[13,44]3[–18,22]0.36Mean SSRT244[218,271]225[204,249]19[–12,49]0.11P(TF)0.02[0,0.05]0.04[0.02,0.08]–0.02[0.07,0.02]0.09 The parameters of the Go-RT and SSRT distributions are presented on the ms scale. The P(TF) parameter is presented on the probability scale. The posterior distribution of 'Difference' is computed by subtracting the posterior samples of the matched comparison group from the corresponding samples of the lesion group, i.e., positive values indicate that the parameter is higher in the lesion group than in the matched comparison group. 'Bayesian p' is computed as the proportion of posterior samples in the posterior distribution of the lesion group that is larger than in the matched comparisons group. Bayesian p values are presented as P = min(p, 1p), i.e., as non-directional tests, with results at a two-sided p of 0.05 (i.e., a one-sided critical p of 0.025) highlighted in bold. rIFG = right inferior frontal gyrus; SSRT = stop-signal reaction time. Table 3 Posterior mean and 95% credible interval (CI) of the population-level mean difference between the two lesion groups (rIFG lesion vs. non-rIFG lesion). Posterior mean of difference95% CI of differenceBayesian pμgo–68[-126,–10]0.02σgo–22[–44,1]0.02τgo49[–6,116]0.04Mean Go-RT–19[–103,71]0.32μstop8[–26,44]0.32σstop–15[–67,24]0.33τstop16[–5,39]0.07Mean SSRT24[–18,63]0.12P(TF)0.15[0.06,0.28]0 The parameters of the Go-RT and SSRT distributions are presented on the ms scale. The P(TF) parameter is presented on the probability scale. The posterior distribution of 'Difference' was computed by subtracting the posterior samples of the non-rIFG group from the corresponding samples of the rIFG lesion group; i.e., positive values indicate that the parameter is higher in the rIFG lesion group than in the non-rIFG lesion group. 'Bayesian p' is computed as the proportion of posterior samples in the posterior distribution of the rIFG lesion group that was larger than in the non-rIFG lesion group. Bayesian p values are presented as P = min(p, 1p), i.e., as non-directional tests, with results at a two-sided p of 0.05 (i.e., a one-sided critical p of 0.025) highlighted in bold. rIFG = right inferior frontal gyrus; SSRT = stop-signal reaction time. Observed behavioral metrics of stop-signal performance, including non-parametric SSRT estimates computed with the integration method with replacement of go omissions (Verbruggen et al., 2019), can be found in Table 4. Compared to BEESTS, the integration approach estimated elongated SSRT in three groups except the healthy comparison for the rIFG lesion group; rIFG lesion: –39 ms, non-rIFG lesion: –11 ms, healthy comparison for rIFG lesion: 1 ms, and healthy comparison for non-rIFG lesion: –13 ms. Among them, the rIFG lesion group showed the largest attenuation. This suggests that the high trigger failure rate in the rIFG lesion group indeed inflated non-parametric SSRT (e.g., Doekemeijer et al., 2021; Matzke et al., 2017a; Skippen et al., 2019). Statistical comparisons for non-parametric SSRT, P(Go error), and P(Go miss) between groups can be found in Appendix 1. Table 4 Non-parametric integration estimates and behavioral metrics of stop-signal performance in the four groups. Lesion patientsMatched comparisonsMean (SD)Mean (SD)rIFGMean SSD286 (120)397 (131) Go RT589 (88)634 (108) SSRT307 (124)231 (43) SR-RT526 (76)569 (109) P(Go miss)0.058 (0.083)0.029 (0.04) P(Go error)0.016 (0.021)0.001 (0.003) P(Inhibit)0.5 (0.078)0.533 (0.019)Non-rIFGMean SSD371 (81)399 (119) Go RT634 (61)644 (87) SSRT255 (47)238 (46) SR-RT552 (66)564 (93) P(Go miss)0.031 (0.033)0.04 (0.092) P(Go error)0.005 (0.007)0.004 (0.007) P(Inhibit)0.532 (0.013)0.536 (0.024) Mean SSD, Go RT, SSRT, and signal-respond RT (SR-RT) are presented on the ms scale. Go misses, Go errors, and Inhibition are given as probabilities (0 ≤ p ≤ 1). With the exception of P(Go error), all measures are computed after removing incorrect RTs and RTs faster than 200 ms, treating RTs slower than 1000 ms as censored observations. rIFG = inferior frontal gyrus; SSRT = stop-signal reaction time; SSD = stop-signal delay. EEG results: Frontal β-bursts Figure 3 shows the results of the analysis of frontal β-bursts. The 2 × 3 ANOVA of frontal β-burst rates for the rIFG group and their matched comparisons revealed a main effect of TRIAL TYPE (F(2, 60) = 3.749, p=0.029, η2P = 0.111) and, crucially, a significant interaction between GROUP and TRIAL TYPE (F(2, 60) = 4.215, p=0.019, η2P = 0.123). The main effect of GROUP was not significant (F(1, 30) = 0.046, p=0.832, η2p = 0.002). Figure 3 Download asset Open asset Normalized β-burst rate (per second) for group comparisons. (A) Frontal β-burst rate before stop-signal reaction time (SSRT) was shown for TRIAL TYPE (Successful-stop, Failed-stop, and Go) for two groups, respectively. Top: right inferior frontal gyrus (rIFG) lesion group vs. matched healthy comparison group. Bottom: non-rIFG lesion group vs. matched healthy comparison group. Dots represent individual participant means. Error bar indicates ± SEM. (B) Stop vs. Go difference (Successful-stop – Go and Failed-stop – Go). Top: rIFG lesion group vs. matched healthy comparison group. Bottom: non-rIFG lesion group vs. matched healthy comparison group. Dots represent individual participant means. Error bar indicates ± SEM. Planned contrasts via paired-samples t-tests revealed that the significant interaction was specifically due to a significant reduction of the Successful-stop vs. Go difference in the rIFG lesion group (M = –0.005, = compared to the matched healthy comparison group (M = = = p = = with a large effect The Failed-stop vs. Go difference were also reduced in the rIFG lesion group (M = = compared to matched healthy comparisons (M = = that difference was not = = The 2 × 3 ANOVA for the non-rIFG lesion group and their healthy matched comparisons revealed the main effect of TRIAL TYPE (F(2, 60) = with a comparable effect to the rIFG group. However, for the rIFG group, the interaction between GROUP and TRIAL TYPE and the main effect of GROUP were not significant 60) = 30) = η2P = Furthermore, the contrasts for the Successful-stop vs. Go difference revealed difference in β-burst rate between non-rIFG lesion group (M = = and matched healthy comparison (M = = = = was significant difference in Failed-stop vs. Go difference between non-rIFG lesion group (M = = and their healthy comparisons (M = = = = these results show that rIFG lesion patients show a significant reduction of stop-related frontal β-bursts compared to healthy comparisons, which was not the case for non-rIFG lesion patients. We also directly compared the Stop vs. Go differences in frontal β between the two lesion groups This comparison showed a reduced β-burst rate difference between Successful-stop and Go trials in the rIFG lesion group (M = –0.005, = compared to the non-rIFG lesion group (M = = = = The Failed-stop vs. Go difference showed the with the rIFG lesion group (M = = showing a reduced β-burst rate compared to the non-rIFG lesion group (M = = = = All other group comparisons of frontal β-burst rate differences were in Appendix 1 these results our hypothesis that rIFG lesion patients show reduced β-burst rates on compared to healthy comparisons, given their increase in stop-signal trigger failures. Moreover, this was specific to the rIFG lesion group as group difference was found between the non-rIFG lesion group and matched comparisons. the rIFG group also showed a significant reduction in frontal rates when directly compared to the non-rIFG lesion group. EEG results: Sensorimotor β-bursts after stop-related frontal bursts We the development of sensorimotor β-bursts after frontal β-bursts that the critical on Successful-stop trials (Figure we that the sensorimotor β-bursts were increased frontal β-bursts to trials frontal This was by the sensorimotor β-burst rates on Successful-stop trials that frontal β-bursts in the to that of trials frontal β-bursts (in which sensorimotor β-bursts were to a time in the with paired-samples at each time between and + the frontal β-bursts. significant difference between trials with frontal β-bursts and frontal β-bursts is as on the of the This analysis showed that sensorimotor β-burst rates were increased frontal β-bursts (Figure Wessel, for group differences in this we compared the sensorimotor rate between the lesion groups and their healthy comparisons (Figure time showed significant differences at for comparisons. this shows that the pattern of increased sensorimotor β-bursts stop-related frontal β-burst was in the rIFG group. Figure 4 Download asset Open asset Normalized β-burst rate at sensorimotor the frontal β-burst in individual Successful-stop trials the critical time (stop-signal to stop-signal reaction time (A) comparison between trials with frontal β-bursts to trials frontal β-bursts the sensorimotor β-bursts were to a time a critical time The sensorimotor rates frontal β-bursts were increased at the of the The shows the ± at each time (B) differences vs. matched healthy in sensorimotor β-burst significant difference was The shows the ± at each time for these between and and did not differ between groups. Discussion In this we the role that rIFG in and inhibitory control. rIFG has been at the of is the most influential current neural theory of inhibitory control. The proposal that 'response inhibition can be localized to a discrete region of the (Aron et al., 2003) has been highly influential (Aron et al., In its most recent iteration, the theory that rIFG a brake over response tendencies' (Aron et al., 2014). work shows that this theory rIFG does not to be for the implementation of inhibitory control. its role to be the or initiation of the cascade that ultimately leads to the stopping of in this rIFG patients showed more than fivefold increase in stop-signal trigger failures compared to matched healthy comparisons, as as a significant reduction in stop-related β-bursts over frontal cortex (in did not to be increase in frontal β-bursts in the rIFG group on stop compared to suggesting a causal role for rIFG in their However, the implementation of inhibitory control to take place in other regions – and to in the rIFG group. In this this was by the fact that in all groups including rIFG sensorimotor β-bursts were when a β-burst over frontal cortex did (i.e., when a initiation from frontal cortex was successfully are at two for the finding that lesions to the rIFG such a substantial increase in stop-signal trigger failures. for rIFG in cognitive control, and more The is that the of rIFG is to specifically trigger inhibitory control or (e.g., in the of that are in the stop-signal In this rIFG would still have an role – not in the implementation of the underlying process, but in its is that the of rIFG is to detect and that it role that is specific to inhibitory control. In the stop-signal rIFG would be of the of the stop-signal, but not specifically of the associated inhibitory In other words, it is for whether the rIFG a role that is specific to inhibitory control or or one that is more the of the rIFG is at the of influential which that rIFG is of a attention that functions as a In this role is to attention – which would but not be to – stop-signals (Corbetta et al., 2008; and Shulman, 2002). In line with this work has shown that similar regions of rIFG are not after but after of et al., et al., it is that the role of rIFG in stop-signal performance is