Spatiotemporal characterization of response inhibition
Introduction
Response inhibition, defined as the ability to suppress unwanted thoughts and actions, is crucial for successful adaptive behavior. Indeed, impaired response inhibition is thought to be a central feature of several neurological diseases and psychiatric disorders, including Hungtington's disease (Beste et al., 2008), obsessive–compulsive disorder (Bannon et al., 2002), borderline personality disorder (Ruchsow et al., 2008) and attention-deficit/hyperactivity disorder (Tamm et al., 2004). The Go/Nogo task is perhaps the most commonly used paradigm to study response inhibition in the laboratory setting. This paradigm involves the execution and inhibition of a motor response, triggered by a go and nogo stimulus, respectively. Many more go than nogo stimuli are generally presented in order to set up a pre-potent response tendency, thereby increasing the mobilization of inhibitory resources to withhold the response to nogo stimuli. However, using this traditional design, inhibitory processing is difficult to disentangle from and may be confound with processes related to detection of novelty such as stimulus-driven attention, since nogo stimuli elicit both types of cognitive processing. A modified version of the Go/Nogo task controlling for the oddball effect of infrequent nogo stimuli is therefore necessary to define more precisely the behavioral and neural mechanisms supporting response inhibition (Chikazoe et al., 2009a, Smith et al., 2008, Tamm et al., 2004).
Given their precise temporal resolution that allows neural processes to be tracked in milliseconds, the timing of brain mechanisms underlying response inhibition has been extensively examined using scalp-recorded event-related potentials (ERPs). Indeed, the successful suppression of a pre-potent response is known to be characterized by involving rapid (early latency onset) and brief (short duration) neural processes, some of the most important occurring within the first second after nogo stimulus presentation (Bokura et al., 2001, Falkenstein et al., 1999, Kiefer et al., 1998, Kok et al., 2004). Specifically, two frontocentral ERP components have been consistently associated with response inhibition: N2 (200–400 ms) and P3 (300–600 ms). This conclusion has been reached based on the fact that both components have shown increased amplitude to nogo compared to go stimuli during different inhibitory paradigms, being the stop-signal and Go/Nogo tasks the most prominent. Nevertheless, the precise functional significance of these two components remains unclear. Either or both components could reflect the inhibition process per se, but also processes that occur just prior or even subsequent to inhibition itself, such as stimulus-driven attention, detection of response conflict or evaluation of the outcome of inhibition. Importantly, it should be noted that both frontocentral N2 and P3 have shown to be very sensitive to stimulus frequency and novelty (Bruin and Wijers, 2002, Daffner et al., 2000, Duncan-Johnson and Donchin, 1977, Folstein and Van Petten, 2008, Friedman et al., 2001, Tarbi et al., 2011). Thus, frontocentral N2 and P3 amplitudes elicited by nogo stimuli during traditional Go/Nogo tasks might be reflecting, at least in part, cognitive processing associated with the presentation of rare infrequent stimuli (such as mismatch detection and stimulus-driven attention). Disentangling the contribution of this oddball effect in the generation of frontocentral Nogo-N2 and Nogo-P3 is thus needed to examine the specific association of each component with response inhibition.
The findings on the timing of response inhibition (when inhibition occurs) have been complemented by data on its anatomical substrates (where inhibition occurs). ERP source localization and especially functional magnetic resonance imaging (fMRI) studies indicate that response inhibition is subserved by a brain network distributed across multiple cortical and subcortical regions, including dorsolateral prefrontal cortex (dlPFC), inferior frontal cortex (IFC), dorsal anterior cingulate cortex (dACC), presupplementary motor area (preSMA), inferior parietal cortex and basal ganglia (Aaron and Poldrack, 2006, Chikazoe et al., 2007, Horn et al., 2003, Li et al., 2006, Li et al., 2008a, Liddle et al., 2001, Mostofsky et al., 2003, Rubia et al., 2001, Simmonds et al., 2008, Swick et al., 2011). Such widespread activation pattern raises the question of whether these regions support other processes not strictly circumscribed to inhibition per se that are also involved in inhibitory paradigms such as the go/nogo or the stop-signal tasks. Indeed, some of these structures, including the inferior parietal lobe and IFC, are known to play a significant role in the rapid processing of novel, rare or significant events (Kiehl et al., 2001, Kiehl et al., 2005, Strobel et al., 2008; see also Corbetta et al., 2008). Interestingly, some recent fMRI studies have examined the neural basis of response inhibition using inhibitory paradigms that controls for this novelty processing (Chikazoe et al., 2009a, Sharp et al., 2010). Chikazoe et al. (2009a) observed a functional dissociation within the IFC, which distinguishes a posterior region preferentially related to inhibition and an inferior frontal junction region primarily associated with novelty processing. By contrast, Sharp et al. (2010) found that preSMA, but not the IFC, was specifically linked to response inhibition. Further research employing other techniques that complement the hemodynamic data obtained by these fMRI studies may be useful to clarify which regions of the brain are specifically involved in response inhibition.
The present study attempted to better characterize the neural bases and dynamics of response inhibition by exploiting the high temporal resolution of the ERPs and recent advances in source localization. Concretely, a two-step approach analysis was devised. First, temporo-spatial principal component analysis (PCA) was employed to detect and quantify those ERP components related to response inhibition (i.e., frontocentral N2 and frontocentral P3). PCA is a data-driven method which has shown to be a powerful approach to isolate ERP components across time course (temporal PCA) and scalp recording sites (spatial PCA). The main advantage of PCA over traditional methods of analyzing ERP data is that presents each component free of the influences of adjacent or latent components, thus disentangling the overlapping of different electrical potentials that represent functionally distinct processes. For instance, PCA has been previously used to separate the late positive components elicited in oddball attention tasks, including the anteriorly distributed novelty-P3 and the posterior target-P3 (Simons et al., 2001, Spencer et al., 2001). In the second step, exact low resolution brain electromagnetic tomography (eLORETA; Pascual-Marqui, 2007, Pascual-Marqui et al., 2011) was performed on N2 and P3, as defined by temporal PCA, to identify which brain regions are specifically involved in each process. The use of PCA factors instead of raw voltages enabled us to improve the accuracy of these source localization analyses (Carretié et al., 2004, Dien, 2010, Dien et al., 2003). Furthermore, a modified Go/Nogo task that controls for the confounding effects of the low frequency appearance of nogo stimuli was employed, as previously recommended (Chikazoe et al., 2009a). Thus, by using this task design in conjunction with temporo-spatial PCA, we were able to examine more precisely whether frontocentral N2 and/or frontocentral P3 are related to response inhibition, as well as to elucidate the cortical regions that specifically mediate this process.
Section snippets
Participants
Forty healthy right-handed subjects (25 females), with an age range of 20–35 years (mean = 21.72; S.D. = 2.62), took part in this experiment. All participants reported normal or corrected-to-normal visual acuity and had no history of neurological or psychiatric disorders. The study was approved by the Research Ethics Committee of the Universidad Autónoma de Madrid, and all subjects provided informed consent.
Stimuli and procedure
Stimuli consisted of three capital letters (“N”, “M” and “W”) presented in Arial font. These
Behavioral data
Percentage error rates and mean RTs for each type of trial are shown in Table 1. Univariate repeated-measures ANOVAs on both behavioral measures were performed, as previously described. With respect to percentage error rates, a significant main effect of Trial type was observed (F(2,78) = 176.7, p < 0.001, ƞ2p = 0.82). Post hoc tests showed that percentage error rates were higher for infrequent-Nogo trials (i.e., commission errors) than for frequent- and infrequent-Go trials (omission errors). In
Discussion
The current study attempted to determine the timing and location of neural activity supporting response inhibition using scalp-recorded ERPs in conjunction with recent source localization techniques. To this end, we used a modified Go/Nogo task composed of three kinds of stimuli (frequent-Go, infrequent-Go, and infrequent-Nogo), which allowed us to dissociate brain electrical activity related to response inhibition from that related to processing of infrequent stimuli by directly contrasting
Acknowledgments
This work was supported by the grants PSI2011-26314 and PSI2012-37535 from the Ministerio de Economía y Competitividad (MINECO) of Spain. MINECO also supports Jacobo Albert through a Juan de la Cierva grant (JCI-2010-07766).
Conflict of interests
The authors declare no conflict of interest.
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