When the brain simulates stopping: Neural activity recorded during real and imagined stop-signal tasks

González-Villar, Alberto; Bonilla, Fidel Mauricio; Carrillo-de-la-Peña, M. T.

doi:10.3758/s13415-016-0434-3

Cited by 23 publications

(27 citation statements)

References 66 publications

(86 reference statements)

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“…To address this issue, the time-frequency analysis was employed in the present study to characterize event-related theta (4-8 Hz) and delta (1-4 Hz) oscillations that index two separable, but highly overlapping processes underlying the stop N2-P3 complex in response inhibition in patients with MwoA. With regard to theta and delta oscillations, the time-frequency analysis found signi cantly higher theta and delta activities in response to the stop signal than in response to Go stimuli in the time windows used to extract mean amplitudes of the stop-N2 and stop-P3 components, which is consistent with ndings from previous studies (32,34,45,46). Although both theta and delta activities in response to either the stop signal or Go stimuli in the time window used to extract mean amplitude of the stop-N2 component did not highlight any signi cant difference between patients with MwoA and healthy controls, patients with MwoA were associated with increased delta activity (not theta activity) relative to healthy controls in response to the stop signal rather than in response to Go stimuli in the time window used to extract mean amplitude of stop-P3 component.…”

Section: Discussionsupporting

confidence: 89%

“…Regarding statistical analysis on electrophysiological data, our data were analyzed according to the topographical distribution of grand averaged ERP activity as well as the methods of previous ERP studies (3,32,34,46). The ERP statistical analysis involved two ERP indices of response inhibition: the stop-N2 and stop-P3.…”

Section: Discussionmentioning

confidence: 99%

“…In order to examine effects of migraine on these ERP components, we conducted a mixed analysis of variance (ANOVA), with group as a betweenparticipants factor (patients with MwoA versus healthy controls), and trial type ( (32), the same statistical analyses were conducted on eventrelated delta and theta power. Based on visual inspection of time-frequency plots and the methods of previous studies (45)(46)(47), the same area and laterality factors were included in such time-frequency analyses and mean power values in delta (1-4 Hz) and theta (4-8 Hz) frequency bands were extracted in the time windows used to extract mean amplitudes of the stop-N2 and stop-P3 components in order to disentangle the multiple processes underlying the stop N2-P3 complex in response inhibition.…”

Section: Discussionmentioning

confidence: 99%

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Response inhibition alterations in migraine: An event-related potential study

Chen¹,

Y²,

Zhao³

et al. 2020

Preprint

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Background Migraine is characterized by a hypersensitivity to environmental stimulation which climaxes during headache attacks but persists during attack-free period. Despite ongoing debates about the nature of the mechanisms giving rise to this abnormality, the presence of deficient inhibitory cortical processes has been proposed to be one possible mechanism underlying its pathogenesis. Empirical evidence supporting this notion is mainly based on previous findings showing functional cortical hyperexcitability in the sensory domain. Considering that a general inhibitory control process can play an important role across early to later stage of information processing, this may in turn indicate the important role other dimensions of inhibitory control can play in migraine disability. To this end, the present study was designed to examine the pathophysiological basis of inhibitiory control that takes place during suppression of prepotent responses. Methods Twenty-two patients with migraine without aura (mean age = 30.86 ± 5.69 years; 19 females) during the interictal period and 25 healthy controls (mean age = 30.24 ± 3.52 years; 18 females) were recruited. We employed a stop signal task in combination with event-related potentials (ERPs) to examine participants’ neural activity supporting response inhibition. Results Behaviorally, migraineurs exhibited prolonged reaction times to the stop signal relative to healthy controls. At the neural level, the amplitude of the stop-N2, a component of the ERPs related to conflict monitoring during early, non-motoric stages of inhibition, was significantly increased in migraineurs. Meanwhile, the amplitude of the stop-P3, a component of the ERPs reflecting late-stage inhibition of the motor system itself and cognitive evaluation of motor inhibition, was also significantly increased in migraineurs. Moreover, our time-frequency analysis has further revealed increased delta activity in the time window used to extract the mean amplitude of the stop-P3 in migraineurs relative to healthy controls. Conclusions Consistent with the theory that cortical hyperexcitability is a key signature of migraine, these findings revealed a decrease in suppressing prepotent responses in migraineurs, which can be attributable to cortical hyperexcitability. These novel findings imply the existence of dysfunctional inhibitory control at later stage of information processing.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Response inhibition alterations in migraine: An event-related potential study

Chen¹,

Y²,

Zhao³

et al. 2020

Preprint

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“…Some argue that the P3 represents the manifestation of the inhibition process (for a review, see Huster, Enriquez-Geppert, Lavallee, Falkenstein, & Herrmann, 2013) and this is based largely on the finding that P3 latency tends to coincide with SSRT latency (Kok, Ramautar, De Ruiter, Band, & Ridderinkhof, 2004;Wessel & Aron, 2015). However, others have argued that P3 peaks too late to represent inhibition and is more likely to reflect postinhibition processing (González-Villar, Bonilla, & Carrillo-de-la-Peña, 2016;Huster et al, 2013;. Importantly, both arguments are based on conventional estimates of SSRT, which as discussed above, may be biased.…”

Section: Temporal Processes Of Response Inhibitionmentioning

confidence: 99%

“…Consistent with many earlier stop-signal studies, we used an analysis approach that maximized automated preprocessing of EEG data, while adhering to conventional processing guidelines (e.g., Dimoska & Johnstone, 2008;González-Villar et al, 2016;Lansbergen et al, 2007;Senderecka, Grabowska, Szewczyk, Gerc, & Chmylak, 2012), rather than more recently used independent component analyses (ICA) methods (e.g., Wessel & Aron, 2015). Huster et al (in press) showed no interpretable differences in the size of the correlations between SSRT and ERP components derived from ICA-based versus conventional artifact removal pipelines.…”

Section: Caveats and Future Workmentioning

confidence: 99%

Reconsidering electrophysiological markers of response inhibition in light of trigger failures in the stop‐signal task

et al. 2020

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This study investigates the neural correlates underpinning response inhibition using a parametric ex‐Gaussian model of stop‐signal task performance, fit with hierarchical Bayesian methods, in a large healthy sample (N = 156). The parametric model accounted for both stop‐signal reaction time (SSRT) and trigger failure (i.e., failures to initiate the inhibition process). The returned SSRT estimate (SSRTEXG3) was attenuated by ≈65 ms compared to traditional nonparametric SSRT estimates (SSRTint). The amplitude and latency of the N1 and P3 event‐related potential components were derived for both stop‐success and stop‐failure trials and compared to behavioral estimates derived from traditional (SSRTint) and parametric (SSRTEXG3, trigger failure) models. Both the fronto‐central N1 and P3 peaked earlier and were larger for stop‐success than stop‐failure trials. For stop‐failure trials only, N1 peak latency correlated with both SSRT estimates as well as trigger failure and temporally coincided with SSRTEXG3, but not SSRTint. In contrast, P3 peak and onset latency were not associated with any behavioral estimates of inhibition for either trial type. While the N1 peaked earlier for stop‐success than stop‐failure trials, this effect was not found in poor task performers (i.e., high trigger failure/slow SSRT). These findings are consistent with attentional modulation of both the speed and reliability of the inhibition process, but not for poor performers. Together with the absence of any P3 onset latency effect, our findings suggest that attentional mechanisms are important in supporting speeded and reliable inhibition processes required in the stop‐signal task.

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