Elsevier

Cortex

Volume 132, November 2020, Pages 404-422
Cortex

Review
Please, don't do it! Fifteen years of progress of non-invasive brain stimulation in action inhibition

https://doi.org/10.1016/j.cortex.2020.09.002Get rights and content

Abstract

The ability to inhibit prepotent responses is critical for survival. Action inhibition can be investigated using a stop-signal task (SST), designed to provide a reliable measure of the time taken by the brain to suppress motor responses. Here we review the major research advances using the combination of this paradigm with the use of non-invasive brain stimulation techniques in the last fifteen years. We highlight new methodological approaches to understanding and exploiting several processes underlying action control, which is critically impaired in several psychiatric disorders. In this review we present and discuss existing literature demonstrating i) the importance of the use of non-invasive brain stimulation in studying human action inhibition, unveiling the neural network involved ii) the critical role of prefrontal areas, including the pre-supplementary motor area (pre-SMA) and the inferior frontal gyrus (IFG), in inhibitory control iii) the neural and behavioral evidence of proactive and reactive action inhibition. As the main result of this review, the specific literature demonstrated the crucial role of pre-SMA and IFG as evidenced from the field of noninvasive brain stimulation studies. Finally, we discuss the critical questions that remain unanswered about how such non-invasive brain stimulation protocols can be translated to therapeutic treatments.

Introduction

The control of voluntary action involves a series of different processes that need to be harmoniously orchestrated, such as choosing from a range of possible actions as well as inhibiting responses as circumstances demand. The ability to inhibit prepotent ongoing actions is crucial to prevent potentially harmful behavioral outcomes. The ability to inhibit prepotent responses can be investigated experimentally using a stop-signal task (SST), designed to provide a sensitive measure of the time taken by the brain to inhibit or suppress inappropriate motor responses (Lappin & Eriksen, 1966; Logan, Cowan, & Davis, 1984; Vince, 1948). It requires participants to respond to a go stimulus and to abort the ongoing response when a stop signal is presented. To measure the participant's performance on the SST, the stop signal reaction time (SSRT), an index of inhibition, is computed based on Logan and Cowan's notion (Logan et al., 1984). Logan and Cowan proposed the existence of a race between a go process, triggered by the presentation of the go stimulus, and a stop process, triggered by the presentation of the stop signal.

When the stop process terminates before the go process, the response is inhibited, but when the go process terminates before the stop process, the response is activated. Thus, unpredictable stop signals require withholding the response when a go response has already been initiated. The stopping process needs to be estimated from a stochastic model, the so-called ‘race model’ (Logan et al., 1984), which estimates the SSRT from the distribution of ‘go’ reaction times and the observed probability of responding on ‘stop’ trials for a given stop-signal delay. This measure is an indicator of inhibitory control, with a lower value indicating a more rapid ability to respond to a stop signal (Cai et al., 2015). The time between presentation of the go stimulus and presentation of the stop signal is termed the ‘stop signal delay’ (SSD). Critically, the motor inhibition will be successful only when the SSD is short enough to allow inhibitory processes to stop the ongoing motor program, while an increased SSD will result in an increased likelihood of failure to inhibit the response to the go stimulus. SSD can be fixed or dynamically modified trial-by-trial based on the participant's performance (Verbruggen & Logan, 2009). However, estimated SSRT gives the measure of the duration of the inhibitory process by revealing the time necessary for successful motor inhibition (Logan et al., 1984).

Recent theories (Aron, 2011; Braver, Gray, & Burgess, 2007) propose that inhibitory control can be divided into proactive mechanisms (response slowing in anticipation of a stop-signal) and reactive mechanisms (outright stopping in response to a stop-signal, measured by means of the SSRT). An example of these mechanisms includes the possibility to brake roughly if a person gets in your way while you are driving your car, as a reaction to the rapid and unexpected change in the context (reactive inhibition). However, one could also pre-emptively act so as not to drive too fast, as the probability of encountering a bypassing pedestrian in the street is often high, and therefore this approach would almost guarantee a successful outcome (proactive inhibition and working memory). Typically, proactive inhibitory control is measured by the increase in reaction times in the go trial when the probability of a stop-signal is high (Cai et al., 2015; Meyer & Bucci, 2016; Zandbelt, van Buuren, Kahn, & Vink, 2011) or by an index of the proactive inhibitory control, named ‘preparatory cost’ (PC) (Cai et al., 2015). Crucially, a low preparatory cost indicates a weaker anticipation of a stop-signal, while a higher preparatory cost indicates better proactive inhibitory control.

Several brain areas have been associated with the mechanisms underlying inhibitory control, with a network including left and right inferior frontal gyrus (IFG) (Aron, Fletcher, Bullmore, Sahakian, & Robbins, 2003; Chevrier, Noseworthy, & Schachar, 2007; Leung & Cai, 2007; Zhang, Geng, & Lee, 2017), dorsolateral prefrontal cortex (dlPFC) (Bari & Robbins, 2013; Leung & Cai, 2007), anterior cingulate (ACC) (Ito, Stuphorn, Brown, & Schall, 2003; Zhang et al., 2017), pre-supplementary motor area (pre-SMA) (Bari & Robbins, 2013; Leung & Cai, 2007), supplementary motor area (SMA) (Bari & Robbins, 2013; Li, Huang, Constable, & Sinha, 2006; Zhang et al., 2017), bilateral superior temporal gyri (Zhang et al., 2017), parietal cortex (Bari & Robbins, 2013; Leung & Cai, 2007; Zhang et al., 2017), insula (Bari & Robbins, 2013; Leung & Cai, 2007; Zhang et al., 2017), basal ganglia (Bari & Robbins, 2013; Chevrier et al., 2007; Zhang et al., 2017), cerebellum (Clark, King, & Turner, 2020), frontal eye fields (FEF) and supplementary eye field (SEF) (Dillon & Pizzagalli, 2007; Hanes & Schall, 1996; Leung & Cai, 2007; Stuphorn & Schall, 2006; Stuphorn, Taylor, & Schall, 2000), all implicated by a range of studies (see Zhang et al., 2017 for meta-analysis). The critical and specific role of each of these areas in action inhibition is still debated. Recent studies, for instance, have demonstrated that lesions to IFG cause a deficit of response inhibition, as measured using tasks that require the suppression of an initiated manual response (Aron et al., 2003) or the suppression of a reflexive saccade (Hodgson et al., 2007). In addition, human patient studies revealed that damage to another section of the prefrontal cortex, the right superior frontal regions (including pre-SMA and SMA), raised SSRT in the SST (Floden & Stuss, 2006 for a review see Mostofsky & Simmonds, 2008). In support of the critical role of pre-SMA in action control, several studies with macroelectrode stimulation in epilepsy patients show that pre-SMA stimulation leads to the arrest of ongoing vocal or manual movements (Lüders et al., 1988; Mikuni et al., 2006; Swann et al., 2012) and a single case report of a patient with a lesion of the pre-SMA extending to cingulate and superior frontal gyri also showed a behavioral stopping deficit for a stop-change task (Nachev, Wydell, O'Neill, Husain, & Kennard, 2007). From all this evidence, it seems clear that dorsomedial damage impairs stopping; however, lesions were not restricted to the pre-SMA, preventing the possibility to draw any conclusions. Indeed, macrostimulation of both the pre-SMA and the right IFG can induce motor arrest (Fried et al., 1991; Lüders et al., 1988), and stimulation of the pre-SMA evoked responses in the right IFG, corresponding well to a white-matter connection, and also the stopping-related task evoked responses (Swann et al., 2012). As suggested by the multiple domain hypothesis of response inhibition (Rubia et al., 2001), distinct parts of the frontal lobes may be responsible for different aspects of inhibitory control, for instance the recruitment of the dlPFC seems to be relevant under conditions in which greater cognitive demand (i.e., more working memory load) is necessary to guide response inhibition (Criaud & Boulinguez, 2013; Jahfari, Stinear, Claffey, Verbruggen, & Aron, 2010; Mostofsky et al., 2003). Indeed, although the modulation of behavior when expecting a stopping stimulus (proactive inhibition) is a proposed function linked to the pre-SMA (Aron, 2011; Boulinguez, Ballanger, Granjon, & Benraiss, 2009; Chikazoe et al., 2009; Forstmann et al., 2008; Jahfari et al., 2010; Zandbelt & Vink, 2010), some hypotheses posit the dlPFC as a candidate for proactive inhibition due to the working memory component in such behavior (Criaud & Boulinguez, 2013; Jahfari et al., 2010; Mostofsky et al., 2003). Importantly, monkey evidence recorded firing in the pre-SMA during both stop trials and non-stop trials (Stuphorn & Emeric, 2012). Firing peaks during non-stop trials suggested that the pre-SMA could induce proactive inhibition throughout phasic firing due to its reactive function. In addition, switching from repetitive to new movements became worse by disrupting pre-SMA activity (Rushworth, Hadland, Paus, & Sipila, 2002). Thus, the pre-SMA appears to be recruited during both response inhibition and action switching.

Critically, all the aforementioned studies employed functional magnetic resonance (Aron et al., 2003; Bari & Robbins, 2013; Chevrier et al., 2007; Clark et al., 2020; Dillon & Pizzagalli, 2007; Hanes & Schall, 1996; Ito et al., 2003; Leung & Cai, 2007; Li, Huang, et al., 2006; Stuphorn & Schall, 2006; Stuphorn et al., 2000; Zhang et al., 2017) or patients with brain lesions (Aron et al., 2003; Floden & Stuss, 2006; Fried et al., 1991; Hodgson et al., 2007; Lüders et al., 1988; Mikuni et al., 2006; Nachev et al., 2007; Swann et al., 2012). Although neuroimaging studies provide evidence that links areas in the brain to task performance, this approach is unable to provide a causal link showing which areas play an essential role. Similarly, human patient studies cannot avoid associated limitations as a result of neural plasticity or reorganization. A way to solve this issue, is to use non-invasive brain stimulation (NIBS) techniques to selectively manipulate in healthy participants single cortical components of the action inhibition network (AIN) to investigate their – specific – contribution in the several processes underlying action control (i.e., inhibition, selection, competition and switching of actions). Recently, there has been a growing interest in the application of different non-invasive brain stimulation techniques to induce neuroplasticity and to modulate cognition and behavior (Huang et al., 2017). In this review, we report and compare, whenever possible, the main results of action inhibition studies that undertook to investigate the critical role of the key nodes of the AIN by means of NIBS using an SST paradigm. In particular, only peer-reviewed published studies in English language were included. Studies were required to report experimental designs on healthy volunteers, using TMS (we excluded studies testing motor evoked potentials) or tDCS in human participants with a control condition (sham stimulation, active control stimulation or baseline performance). Only studies implementing the stop-signal task and reporting stop-signal reaction time (SSRT) as outcome were included. Our aim is to understand the crucial role of cortical brain regions of the AIN in motor action suppression and to disclose which are the most efficient NIBS protocols.

TMS is a non-invasive neural modulation technique with valuable potential in both neuroscience (Bergmann, Karabanov, Hartwigsen, Thielscher, & Siebner, 2016) and clinical studies (Lefaucheur et al., 2014). In particular, it has been shown that repetitive TMS (rTMS) can temporarily modify brain function for minutes to hours (Hamada et al., 2007; Huang, Edwards, Rounis, Bhatia, & Rothwell, 2005; Iyer, Schleper, & Wassermann, 2003; Jung, Shin, Jeong, & Shin, 2008). Stimulations at low (≤1 Hz) and high (≥5 Hz) frequency can decrease or increase neuronal excitability, respectively. Among rTMS protocols, theta-burst stimulation (TBS) can produce long after-effects (>20 min) using relatively short-term stimulation (typically, 40–190 sec) at a higher frequency (50 Hz) (Huang et al., 2005). In particular, continuous TBS (cTBS; 40 sec train of uninterrupted TBS is given for a total 600 pulses) is supposed to suppress cortical excitability, while intermittent TBS (iTBS; 2 sec train of TBS is repeated every 10 sec for a total of 190 sec and 600 pulses) is thought to facilitate it (Huang et al., 2005). While using tDCS, direct electric current, passing through two saline soaked sponge electrodes placed over the participant's skull, is applied to modulate human brain excitability (George & Aston-Jones, 2010). Through this type of stimulation, feeble electric currents (1–2 mA) are conducted through two electrodes (anode and cathode), which increase or decrease neuron activity by changing the membrane potential (Nitsche & Paulus, 2000). Although the exact functioning of tDCS is not entirely clear, it is well established that several minutes of stimulation with the anode placed over a target area increases its cortical activity, whereas placing the cathode sustainably reduces it. Thus, tDCS could increase or decrease neuron activity and also modulate behavior, and the effects depend on the duration and the current density of the stimulation (Nitsche et al., 2008).

The endeavor to understand the neural circuits of action inhibition in humans originated approximately 15 years ago; here we review the major advances, highlighting new methodological approaches to understanding and exploiting several processes underlying action control. In particular, we critically examine different non-invasive brain stimulation protocols in SST and the specific results obtained, aiming at trying to delineate and suggest important factors that can contribute to modulate inhibition performance. Hence, our goal is to provide fundamental implications of clinical relevance for the recent enhancing in the understanding of psychiatric disorders and thus improving relative treatments in this area of research. A summary of the experimental results, methods and stimulation protocols adopted in each NIBS study is reported in Table 1, Table 2, while Fig. 1, Fig. 2 show the stimulation site in each NIBS-SST experiment.

Section snippets

Transcranial magnetic stimulation and action inhibition

Although the cognitive mechanisms underlying response inhibition have been studied for several years (Logan, 1981, 1994), the neural mechanism subtending this process is still a matter of debate. The most accredited theory proposes that the human prefrontal cortex is responsible for executive control, but it is contentious whether discrete prefrontal regions are specialized to carry out domain-specific functions (Aron, Robbins, & Poldrack, 2004; Duncan & Owen, 2000; Goldman-Rakic, 1987, pp.

Transcranial direct current stimulation and action inhibition

A way to gain further evidence about the neural network implied in the action inhibition is the use of tDCS. Thus, the application of tDCS to the AIN would be a useful adjuvant intervention modality for modulation of response inhibition and its related dynamic behavioral changes. One of the first hypotheses of using this protocol in the SST framework was that excitatory tDCS, with the anode placed over the pre-SMA, would facilitate inhibitory control and that inhibitory tDCS, with the cathode

Conclusions

Action control represents a complex mechanism that subserves several different processes such as inhibition, selection, competition and switching of actions with a composite network of prefrontal areas (AIN) that have a role in orchestrating such processes. Indeed, deficits in performance in action control have been seen in various psychiatric disorders, including attention-deficit hyperactivity disorder and conduct disorder (Schachar & Logan, 1990; Schachar, Tannock, & Logan, 1993; Schachar,

Author contributions

Sara Borgomaneri: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Project administration, Funding acquisition. Gianluigi Serio: Methodology, Writing - Original Draft. Simone Battaglia: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing, Visualization, Project administration.

Declaration of competing interest

The authors declare no competing interests.

Acknowledgments

This work was supported by grants from Ministero della Salute, Italy [GR-2018-12365733] awarded to Sa.B.

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