“Shut up!” An electrophysiological study investigating the neural correlates of vocal inhibition

Exogenous verbal response inhibition in adults who do and do not stutter

INTRODUCTION

Behavioral and questionnaire-based studies suggest that children who stutter (CWS) exhibit poorer response inhibition than children who do not stutter (CWNS). However, the behavioral findings in adults who stutter (AWS) are less unequivocal and mainly based on manual response inhibition. Further study is therefore needed, especially given the lack of studies on verbal response inhibition among these groups.

METHODS

Thirteen AWS and 14 adults who do not stutter (AWNS) participated in a verbal stop signal task (SST) in which they were asked to read aloud six Chinese characters as fast as possible during the go-signal and ignore-signal trials and refrain from naming them during the stop-signal trials.

RESULTS

The two groups showed a comparable response reaction time in the go-signal and ignore-signal trial conditions. Furthermore, there were no significant differences in terms of the stop-signal reaction time (SSRT) and accuracy. However, a significant positive correlation was found between SSRT and the frequency of stuttering in conversation but not in reading.

CONCLUSION

Current findings seem to provide additional support that exogenously triggered response inhibition among AWS does not differ from AWNS. The association between stuttering frequency and SSRT seems to suggest that individuals with more severe stuttering in conversational speech have reduced exogenous response inhibition. However, this finding needs to be further explored in future studies using different measures of stuttering severity.

Electrophysiological correlates underlying interference control in motor tasks

Changing pre-existing, automatized motor skills often requires interference control. Prepotent response inhibition - one subdimension of inhibition - has been theorized to be particularly associated with successful interference control in motor skills. Recent evidence suggests that different inhibition subdimensions elicit distinct ERP patterns (with larger P3 components for response inhibition). Therefore, we examined whether a similar ERP pattern would arise in a task demanding participants to overcome interference emerging from strong motor automatisms. This was realized within a typing paradigm involving a letter switch manipulation which is able to produce strong, immediate interference effects. Most importantly, stimulus-locked ERP analyses revealed an enhanced P3 component at frontal, central and most pronouncedly parietal sites for interference trials, in line with previous reported patterns for response inhibition. Together, different analyses provide first insights into the electrophysiological correlates of motor skill change, corroborating the pivotal role of response inhibition for successful interference control.

Oscillatory brain mechanisms supporting response cancellation in selective stopping strategies

Although considerable progress has been made in understanding the neural substrates of simple or global stopping, the neural mechanisms supporting selective stopping remain less understood. The selectivity of the stop process is often required in our everyday life in situations where responses must be suppressed to certain signals but not others. Here, we examined the oscillatory brain mechanisms of response cancellation in selective stopping by controlling for the different strategies adopted by participants (n = 54) to accomplish a stimulus selective stop-signal task. We found that successfully cancelling an initiated response was specifically associated with increased oscillatory activity in the high-beta frequency range in the strategy characterized by stopping selectively (the so called dependent Discriminate then Stop, dDtS), but not in the strategy characterized by stopping non-selectively (Stop then Discriminate, StD). Beamforming source reconstruction suggests that this high-beta activity was mainly generated in the superior frontal gyrus (including the pre-supplementary motor area) and the middle frontal gyrus. Present findings provide neural support for the existence of different strategies for solving selective stopping tasks. Specifically, differences between strategies were observed in the oscillatory activity associated with the stop process and were restricted to the high-beta frequency range. Moreover, current results provide important evidence suggesting that high-beta oscillations in superior and middle frontal cortices play an essential role in cancelling an initiated motor response.

Stop signals delay synchrony more for finger tapping than vocalization: a dual modality study of rhythmic synchronization in the stop signal task

BACKGROUND

A robust feature of sensorimotor synchronization (SMS) performance in finger tapping to an auditory pacing signal is the negative asynchrony of the tap with respect to the pacing signal. The Paillard-Fraisse hypothesis suggests that negative asynchrony is a result of inter-modal integration, in which the brain compares sensory information across two modalities (auditory and tactile). The current study compared the asynchronies of vocalizations and finger tapping in time to an auditory pacing signal. Our first hypothesis was that vocalizations have less negative asynchrony compared to finger tapping due to the requirement for sensory integration within only a single (auditory) modality (intra-modal integration). However, due to the different measurements for vocalizations and finger responses, interpreting the comparison between these two response modalities is problematic. To address this problem, we included stop signals in the synchronization task. The rationale for this manipulation was that stop signals would perturb synchronization more in the inter-modal compared to the intra-modal task. We hypothesized that the inclusion of stop signals induce proactive inhibition, which reduces negative asynchrony. We further hypothesized that any reduction in negative asynchrony occurs to a lesser degree for vocalization than for finger tapping.

METHOD

A total of 30 participants took part in this study. We compared SMS in a single sensory modality (vocalizations (or auditory) to auditory pacing signal) to a dual sensory modality (fingers (or tactile) to auditory pacing signal). The task was combined with a stop signal task in which stop signals were relevant in some blocks and irrelevant in others. Response-to-pacing signal asynchronies and stop signal reaction times were compared across modalities and across the two types of stop signal blocks.

RESULTS

In the blocks where stopping was irrelevant, we found that vocalization (-61.47 ms) was more synchronous with the auditory pacing signal compared to finger tapping (-128.29 ms). In the blocks where stopping was relevant, stop signals induced proactive inhibition, shifting the response times later. However, proactive inhibition (26.11 ms) was less evident for vocalizations compared to finger tapping (58.06 ms).

DISCUSSION

These results support the interpretation that relatively large negative asynchrony in finger tapping is a consequence of inter-modal integration, whereas smaller asynchrony is associated with intra-modal integration. This study also supports the interpretation that intra-modal integration is more sensitive to synchronization discrepancies compared to inter-modal integration.

The interplay between selective and nonselective inhibition during single word production

The present study investigated the interplay between selective inhibition (the ability to suppress specific competing responses) and nonselective inhibition (the ability to suppress any inappropriate response) during single word production. To this end, we combined two well-established research paradigms: the picture-word interference task and the stop-signal task. Selective inhibition was assessed by instructing participants to name target pictures (e.g., dog) in the presence of semantically related (e.g., cat) or unrelated (e.g., window) distractor words. Nonselective inhibition was tested by occasionally presenting a visual stop-signal, indicating that participants should withhold their verbal response. The stop-signal was presented early (250 ms) aimed at interrupting the lexical selection stage, and late (325 ms) to influence the word-encoding stage of the speech production process. We found longer naming latencies for pictures with semantically related distractors than with unrelated distractors (semantic interference effect). The results further showed that, at both delays, stopping latencies (i.e., stop-signal RTs) were prolonged for naming pictures with semantically related distractors compared to pictures with unrelated distractors. Taken together, our findings suggest that selective and nonselective inhibition, at least partly, share a common inhibitory mechanism during different stages of the speech production process.

Impulsive aggression and response inhibition in attention-deficit/hyperactivity disorder and disruptive behavioral disorders: Findings from a systematic review

BACKGROUND

Although impulsive aggression (IA) and dysfunctional response inhibition (RI) are hallmarks of attention-deficit/hyperactivity disorder (ADHD) and disrupted behavioral disorders (DBDs), little is known about their shared and distinct deviant neural mechanisms.

AIMS AND METHODS

Here, we selectively reviewed s/fMRI ADHD and DBD studies to identify disorder-specific and shared IA and RI aberrant neural mechanisms.

RESULTS

In ADHD, deviant prefrontal and cingulate functional activity was associated with increased IA. Structural alterations were most pronounced in the cingulate cortex. Subjects with DBDs showed marked cortico-subcortical dysfunctions. ADHD and DBDs share similar cortico-limbic structural and functional alterations. RI deficits in ADHD highlighted hypoactivity in the dorso/ventro-lateral PFC, insula, and striatum, while the paralimbic system was primarily dysfunctional in DBDs. Across disorders, extensively altered cortico-limbic dysfunctions underlie IA, while RI was mostly associated with aberrant prefrontal activity.

CONCLUSION

Control network deficits were evidenced across clinical phenotypes in IA and RI. Dysfunctions at any level within these cortico-subcortical projections lead to deficient cognitive-affective control by ascribing emotional salience to otherwise irrelevant stimuli. The clinical implications of these findings are discussed.

Neural and behavioral correlates of selective stopping: Evidence for a different strategy adoption

The present study examined the neural and behavioral correlates of selective stopping, a form of inhibition that has scarcely been investigated. The selectivity of the inhibitory process is needed when individuals have to deal with an environment filled with multiple stimuli, some of which require inhibition and some of which do not. The stimulus-selective stop-signal task has been used to explore this issue assuming that all participants interrupt their ongoing responses selectively to stop but not to ignore signals. However, recent behavioral evidence suggests that some individuals do not carry out the task as experimenters expect, since they seemed to interrupt their response non-selectively to both signals. In the present study, we detected and controlled the cognitive strategy adopted by participants (n=57) when they performed a stimulus-selective stop-signal task before comparing brain activation between conditions. In order to determine both the onset and the end of the response cancellation process underlying each strategy and to fully take advantage of the precise temporal resolution of event-related potentials, we used a mass univariate approach. Source localization techniques were also employed to estimate the neural underpinnings of the effects observed at the scalp level. Our results from scalp and source level analysis support the behavioral-based strategy classification. Specific effects were observed depending on the strategy adopted by participants. Thus, when contrasting successful stop versus ignore conditions, increased activation was only evident for subjects who were classified as using a strategy whereby the response interruption process was selective to stop trials. This increased activity was observed during the P3 time window in several left-lateralized brain regions, including middle and inferior frontal gyri, as well as parietal and insular cortices. By contrast, in those participants who used a strategy characterized by stopping non-selectively, no activation differences between successful stop and ignore conditions were observed at the estimated time at which response interruption process occurs. Overall, results from the current study highlight the importance of controlling for the different strategies adopted by participants to perform selective stopping tasks before analyzing brain activation patterns.

Event-related fields evoked by vocal response inhibition: a comparison of younger and older adults

The current study examined event-related fields (ERFs) evoked by vocal response inhibition in a stimulus-selective stop-signal task. We compared inhibition-related ERFs across a younger and an older group of adults. Behavioural results revealed that stop-signal reaction times (RTs), go-RTs, ignore-stop RTs and failed stop RTs were longer in the older, relative to the younger group by 38, 123, 149 and 116 ms, respectively. The amplitude of the ERF M2 peak (approximately 200 ms after the stop signal) evoked on successful stop trials was larger compared to that evoked on both failed stop and ignore-stop trials. The M4 peak (approximately 450 ms after stop signal) was of larger amplitude in both successful and failed stops compared to ignore-stop trials. In the older group, the M2, M3 and M4 peaks were smaller in amplitude and peaked later in time (by 24, 50 and 76 ms, respectively). We demonstrate that vocal response inhibition-related ERFs exhibit a similar temporal evolution to those previously described for manual response inhibition: an early peak at 200 ms (i.e. M2) that differentiates successful from failed stopping, and a later peak (i.e. M4) that is consistent with a neural marker of response checking and error processing. Across groups, our data support a more general decline of stimulus processing speed with age.

Vocal response inhibition is enhanced by anodal tDCS over the right prefrontal cortex

Stopping outright (reactive inhibition) and slowing down (proactive inhibition) are types of response inhibition which have mainly been investigated in the manual effector system. This study compared reactive inhibition across manual and vocal effector systems, examined the effects of excitatory anodal transcranial direct current stimulation (anodal tDCS) over the right prefrontal cortex (right-PFC) and looked at the relationship between reactive and proactive inhibition. We hypothesised (1) that vocal reactive inhibition would be less effective than manual reactive inhibition as evidenced by longer stop signal reaction times; (2) that anodal tDCS would enhance both vocal and manual reactive inhibitions and (3) that proactive and reactive inhibitions would be positively related. We tested 14 participants over two sessions (one session with anodal tDCS and one session with sham stimulation) and applied stimulation protocol in the middle of the session, i.e. only during the second of three phases. We used a stop signal task across two stop conditions: relevant and irrelevant stop conditions in which stopping was required or ignored, respectively. We found that reactive inhibition was faster during and immediately after anodal tDCS relative to sham. We also found that greater level of proactive inhibition enhanced reactive inhibition (indexed by shorter stop signal reaction times). These results support the hypothesis that the right-PFC is part of a core network for reactive inhibition and supports previous contention that proactive inhibition is possibly modulated via preactivating the reactive inhibition network.

It's not too late: the onset of the frontocentral P3 indexes successful response inhibition in the stop-signal paradigm

The frontocentral P3 event-related potential has been proposed as a neural marker of response inhibition. However, this association is disputed: some argue that P3 latency is too late relative to the timing of action stopping (stop-signal reaction time; SSRT) to index response inhibition. We tested whether P3 onset latency is a marker of response inhibition, and whether it coincides with the timing predicted by neurocomputational models. We measured EEG in 62 participants during the stop-signal task, and used independent component analysis and permutation statistics to measure the P3 onset in each participant. We show that P3 onset latency is shorter when stopping is successful, that it is highly correlated with SSRT, and that it coincides with the purported timing of the inhibition process (towards the end of SSRT). These results demonstrate the utility of P3 onset latency as a noninvasive, temporally precise neural marker of the response inhibition process.

Inhibitory motor control based on complex stopping goals relies on the same brain network as simple stopping

Much research has modeled action-stopping using the stop-signal task (SST), in which an impending response has to be stopped when an explicit stop-signal occurs. A limitation of the SST is that real-world action-stopping rarely involves explicit stop-signals. Instead, the stopping-system engages when environmental features match more complex stopping goals. For example, when stepping into the street, one monitors path, velocity, size, and types of objects and only stops if there is a vehicle approaching. Here, we developed a task in which participants compared the visual features of a multidimensional go-stimulus to a complex stopping-template, and stopped their go-response if all features matched the template. We used independent component analysis of EEG data to show that the same motor inhibition brain network that explains action-stopping in the SST also implements motor inhibition in the complex-stopping task. Furthermore, we found that partial feature overlap between go-stimulus and stopping-template led to motor slowing, which also corresponded with greater stopping-network activity. This shows that the same brain system for action-stopping to explicit stop-signals is recruited to slow or stop behavior when stimuli match a complex stopping goal. The results imply a generalizability of the brain's network for simple action-stopping to more ecologically valid scenarios.

Reduced activation of left orbitofrontal cortex precedes blocked vocalization: a magnetoencephalographic study

While stuttering is known to be characterized by anomalous brain activations during speech, very little data is available describing brain activations during stuttering. To our knowledge there are no reports describing brain activations that precede blocking. In this case report we present magnetoencephalographic data from a person who stutters who had significant instances of blocking whilst performing a vowel production task. This unique data set has allowed us to compare the brain activations leading up to a block with those leading up to successful production. Surprisingly, the results are very consistent with data comparing fluent production in stutterers to controls. We show here that preceding a block there is significantly less activation of the left orbitofrontal and inferiorfrontal cortices. Furthermore, there is significant extra activation in the right orbitofrontal and inferiorfrontal cortices, and the sensorimotor and auditory areas bilaterally. This data adds weight to the argument forwarded by Kell et al. (2009) that the best functional sign of optimal repair in stutterering is activation of the left BA 47/12 in the orbitofrontal cortex.

EDUCATIONAL OBJECTIVES

At the end of this activity the reader will be able to (a) identify brain regions associated with blocked vocalization, (b) discuss the functions of the orbitofrontal and inferior frontal cortices in regard to speech production and (c) describe the usefulness and limitations of magnetoencephalography (MEG) in stuttering research.