Establishing a Right Frontal Beta Signature for Stopping Action in Scalp EEG: Implications for Testing Inhibitory Control in Other Task Contexts

Abstract

Many studies have examined the rapid stopping of action as a proxy of human self-control. Several methods have shown that a critical focus for stopping is the right inferior frontal cortex (rIFC). Moreover, electrocorticography (ECoG) studies have shown beta band power increases in the rIFC and in the basal ganglia for successful vs. failed stop trials, before the time of stopping elapses, perhaps underpinning a prefrontal-basal-ganglia network for inhibitory control. Here we tested whether the same signature might be visible in scalp EEG – which would open important avenues for using this signature in studies of the recruitment and timing of prefrontal inhibitory control. We used Independent Component Analysis and time-frequency approaches to analyze EEG from three different cohorts of healthy young volunteers (48 participants total) performing versions of the standard stop signal task. We identified a spectral power increase in the band 13–20Hz that occurs after the stop signal, but before the time of stopping elapses, with a right frontal topography in the EEG. This right frontal beta band increase was significantly larger for successful compared to failed stops in two of the three studies. We also tested the hypothesis that unexpected events recruit the same frontal system for stopping. Indeed, we show that the stopping-related right lateralized frontal beta signature was also active after unexpected events (and we accordingly provide data and scripts for the method). These results validate a right frontal beta signature in the EEG as a temporally precise and functionally significant neural marker of the response inhibition process.

β-bursts reveal the trial-to-trial dynamics of movement initiation and cancellation

“…First, the topographical distribution and temporal evolution of the fronto-central β-burst reported here parallels the properties of the Stop-signal P3, an event-related potential that has been proposed to reflect motor inhibition in trial-averaging studies of phase-locked event-related EEG activity [30][31][32]. Second, trial-averaged power in the β-band has been repeatedly implicated in movement cancellation in studies of trial-averaged time-frequency activity [7][8][9][10]13]. This includes intracranial recordings from sites that could well underlie the fronto-centrally distributed pattern of β-bursting observed in our study, including the pre-supplementary motor area [33].…”

“…In particular, during movement initiation, a prominent desynchronization of β-band activity is clearly observable over sensorimotor areas [5,6]. In contrast, the rapid cancellation of movement is accompanied by β-power increases over (pre-)frontal cortical areas generally implicated in cognitive control [7][8][9][10][11]. Moreover, movement-related changes in β-power can also be observed in extrapyramidal parts of the motor system, including the basal ganglia [12][13][14][15], where abnormal β-rhythms are prominently observed in movement disorders such as Parkinson's Disease [16][17][18][19].…”

Temporally-precise disruption of prefrontal cortex informed by the timing of beta bursts impairs human action-stopping

“…For example, electrocorticographic activity from rIFC shows an increase in beta band oscillatory power in successful versus failed stop trials, and moreover in the time-period between the stop signal and the end of the inferred stop process (Swann et al, 2009;Wessel et al, 2013a). A similar pattern of beta band power is also seen in scalp electroencephalography (EEG) studies, putatively related to the same rIFC 'generator' (Wagner et al, 2018;Castiglione et al, 2019). However, there is a lack of causal evidence showing when in time activation of rIFC is critical for stopping.…”

“…Here we present data based on a new set of analyses. Note that two participants were excluded: one due to misaligned EEG markers resulting from a technical issue and another because we could not identify a right frontal spatial filter based on our standard method (Wagner et al, 2018;Castiglione et al, 2019).…”

“…In Experiment 1, we identified a right frontal spatial filter or, in a few cases where a right frontal filter was not evident, a frontal spatial filter, using a similar Independent Components Analysis approach as in our earlier reports (Wagner et al, 2018;Castiglione et al, 2019). The spatial filter was first identified using the scalp maps.…”