Towards real-world generalizability of a circuit for action-stopping

Abstract

Two decades of cross-species neuroscience research on rapid action-stopping in the laboratory has provided motivation for an underlying prefrontal-basal ganglia circuit. Here we provide an update of key studies from the past few years. We conclude that this basic neural circuit is on increasingly firm ground, and we move on to consider whether the action-stopping function implemented by this circuit applies beyond the simple laboratory stop signal task. We advance through a series of studies of increasing 'real-worldness', starting with laboratory tests of stopping of speech, gait and bodily functions, and then going beyond the laboratory to consider neural recordings and stimulation during moments of control presumably required in everyday activities such as walking and driving. We end by asking whether stopping research has clinical relevance, focusing on movement disorders such as stuttering, tics and freezing of gait. Overall, we conclude there are hints that the prefrontal-basal ganglia action-stopping circuit that is engaged by the basic stop signal task is recruited in myriad scenarios; however, truly proving this for real-world scenarios requires a new generation of studies that will need to overcome substantial technical and inferential challenges.

Fig. 1 ∣. Cortico-basal ganglia-thalamocortical networks for action control in the stop signal task.

a ∣ In the stop signal task, a ‘go’ signal (here, an arrow) requiring a response (a key press indicated by the direction of the arrow) is presented on every trial. In about 25% of trials, the go signal is followed shortly by a ‘stop’ signal (here, the arrow turning red), and on those trials, participants must attempt to stop the impending response. The delay between the go and stop cues varies from trial to trial and, if the delay between them is short, the participant is more likely to stop. By titrating the probability of successfully stopping at various delays and examining the response-time distribution, it is possible to estimate the latency of action-stopping, the stop signal reaction time (SSRT) ^,. b ∣ The basal ganglia (BG) output nuclei (the globus pallidus interna (GPi) and substantia nigra pars reticulata (SNr)) provide tonic inhibition of the thalamic outputs that facilitate motor cortical areas. A classic view is that the direct pathway, which involves striatal projections to the GPi/SNr, supports actions by inhibiting the BG output, thus disinhibiting thalamic output to the cortex. Meanwhile, the indirect pathway, which involves projections from the striatum to the GPi/SNr via the globus pallidus externa (GPe), may help to suppress actions by increasing inhibitory control over BG output (for a more nuanced discussion, see ref. ). The hyperdirect pathway is also thought to be involved in inhibiting BG output, offering a fast route for stopping because of the short-latency, monosynaptic connections between the cortex and subthalamic nucleus (STN), and the direct access of the STN to the GPi/SNr. c ∣ The prefrontal-BG-thalamocortical model of action-stopping^, proposes that, on detection of a stop signal, sensory information about the cue is fed forward to the prefrontal cortex, where the stop command is produced. Two prefrontal areas, the right inferior frontal cortex (rIFC) and dorsomedial prefrontal cortex (particularly the pre-supplementary motor area (preSMA)) are thought send the stop command via the STN. Output from the STN excites the GPi (or SNr, for example with eye movements), which in turn inhibits thalamic excitatory drive to M1 and thus reduces the likelihood of movement. The striatum, acting via the GPe, has also been implicated in action-stopping, although its precise role is currently debated. Both the GPe and striatum have been left unshaded in the figure to illustrate the questions surrounding their inclusion in the network.

Fig. 2 ∣. Timing of events in the action-stopping network.

Top panel illustrates the approximate timing of events, and bottom panel shows a schematized version of changes in neural activity, with arrows representing the same key time points as in the main text. This example is for stopping a manual response in relation to a salient stop signal. Following a go signal, a putative go process (green) is initiated and proceeds over time. After some delay, typically about 200 ms, a stop signal is presented, prompting the initiation of a stop process (red) that races to completion with the go process. Physiologically, the generation of the stop command is reflected by activity in the right prefrontal cortex and frontal cortex (rPFC/rFC; particularly the right inferior frontal cortex and pre-supplementary area) within approximately 120 ms after the stop signal^,. This is followed by activity in the basal ganglia (BG), starting with the subthalamic nucleus (STN), which receives hyperdirect input from the rPFC shortly after^,. We presume activity in the BG precedes a global suppression of the primary motor cortex (M1) that occurs from about 140 ms after the stop signal^,. In the meantime, muscle activity related to the go process may already have been initiated, but has not developed sufficiently to produce an overt response. A stop-related suppression of any ongoing muscle activity is evident in the electromyogram (EMG) within approximately 160 ms^,,,. Assuming that this occurs soon enough — that is, that the stop process reaches completion before the go process — behavioural stopping as indicated by the stop signal reaction time (SSRT) occurs within about 220 ms. These timings impose new constraints on when activity in a brain region must occur by to be included in the network model. For example, recent work suggested that parietal cortex might functionally contribute to stopping. However, the timing of its supposed role, which comes after the cancellation of muscle activity, argues against its inclusion in the action-stopping network and instead hints at a role in action-execution (for example, ensuring the timely release of actions). Note, however, that there remains a degree of uncertainty in the timing of the neurophysiological events described here. For example, some brain processes may take time to develop following the initial receipt of a signal from another brain region and may not be immediately detectable in currently available neurophysiological measures. There is also variability in how timings are reported, wherein, for example, beta-band burst timings are measured from the peak of activity, whereas EMG suppression is taken from its onset. Figure adapted from ref..

Fig. 3 ∣. From the laboratory to the real-world: gait.

a ∣ A research programme might begin by studying stopping in the laboratory with the stop signal task (level 1 in Table 1), using the primary effectors involved in the high-level behaviour of interes — in this case, the legs. Neurophysiological techniques can be used to look for the neural correlates of stopping, such as beta-band activity in the right prefrontal cortex measured usingelectroencephalography (EEG) and global motor system suppression measured using transcranial magnetic stimulation (TMS) and electromyography (EMG; here, on the task-unrelated hand muscle), b ∣ One could then progress to more complex and naturalistic actions, but still within a highly constrained laboratory setting (level 2 in Table 1). In this example, the participant is instructed to perform simple stepping actions. On some trials, an obstacle suddenly appears, requiring the participant to prevent the step. Here, EEG and TMS could be used alongside EMG, force plates and motion-capture imaging that provide behavioural readouts of action-stopping, c ∣ The next step might involve free-moving and naturalistic gait while individuals explore virtual environments. Wearable EEG, in this case an intracranial device implanted in a patient for recording and stimulating the brain, could be combined with motion tracking. (level 3 in Table 1). Numerous situations could conceivably involve action-stopping: an open manhole, another person crossing one’s path or a door closing. Sudden body or limb decelerations time-locked to the events in the visual scene would provide indices of behavioural stopping, such as the latency of stopping, and would provide a time window in which to look for neural correlates of stopping. d ∣ Finally, one might approach real-world studies, as people explore their normal environments. Here, wearable EEG in patients could be combined with gaze-tracking and smartphone-based accelerometry to examine similar instances in which the stopping network might be recruited (level 4 in Table 1).