Full-length ArticleRoles for the pre-supplementary motor area and the right inferior frontal gyrus in stopping action: Electrophysiological responses and functional and structural connectivity
Highlights
► We used fMRI in healthy adults and multiple methods in a single epilepsy patient. ► We studied the presupplementary motor area and right inferior frontal gyrus. ► The preSMA and rIFG are structurally and functionally connected. ► Gamma activity in preSMA occurs before rIFG in anticipation of action control. ► Beta-band coherence between preSMA and rIFG occurs during action control.
Introduction
Lesion studies and Transcranial Magnetic Stimulation show that stopping an initiated response depends on the integrity of the right inferior frontal gyrus (rIFG) as well as the presupplementary motor area (preSMA) (reviewed by Aron, 2010, Chambers et al., 2009, Chikazoe, 2010, Levy and Wagner, 2011, Nachev et al., 2008). Additionally, diffusion tensor imaging (DTI) tractography shows that the IFG and the preSMA are structurally connected to one another, and to the basal ganglia, comprising a putative network for action control (Aron et al., 2007, Ford et al., 2010, Forstmann et al., 2010, Johansen-Berg et al., 2004). Electrophysiological studies have begun to characterize the neural communication within this putative network, pointing to increased activity in the beta frequency band during successful stopping (Kühn et al., 2004, Marco-Pallares et al., 2008, Swann et al., 2011, Swann et al., 2009).
Yet, while the preSMA and the rIFG are key nodes of this putative network, their relative functional roles in stopping are still unclear. For instance, it has been suggested that they could both be important for inhibitory control, or one could be important in monitoring for the stop signal and/or detecting conflict and another for implementing inhibitory control (Duann et al., 2009, Hampshire et al., 2010, Mostofsky and Simmonds, 2008, Sharp et al., 2010, Verbruggen et al., 2010). Notably, recent fMRI studies show that both regions are active when preparing to stop, as well as during outright stopping (Chikazoe et al., 2009b, Jahfari et al., 2009, Vink et al., 2005, Zandbelt and Vink, 2010). This raises the additional possibility that one or both regions are involved in setting up the stopping network in advance so that inhibitory control can subsequently be triggered when the stop signal is detected. While we refer to this as ‘preparing to stop’ we note that it could also be described as increased response caution, favoring accuracy over speed, or proactive inhibitory control. If the preSMA is important for setting up the stopping network, i.e. a task configuration role (Rushworth et al., 2004) then it should be active soon after a cue that instructs the participant that stopping is likely, and before the rIFG; whereas if the rIFG is important for this function then the reverse timing relation should be observed. Testing this with fMRI is difficult because of the poor temporal resolution. Instead, we studied a single, rare, patient who had electrocorticography (ECoG) electrodes implanted over both the preSMA and the rIFG.
We designed a new task to engage both preparing to stop and outright stopping (Fig. 1). Each trial began with a cue ‘Maybe Stop’ or ‘No Stop’ which was followed by a Go stimulus and then, on Maybe Stop trials alone, this was sometimes followed by a stop signal. By comparing Maybe Stop and No Stop trials we could examine the preparing-to-stop process; while by comparing Maybe Stop successful stop vs. unsuccessful stop trials (or Maybe Stop successful stop compared to Maybe Stop go trials or baseline) we could examine outright stopping. We used fMRI in healthy young participants to confirm that this task activated the preSMA and rIFG as expected.
In the patient we performed macrostimulation and DTI, and we collected cortico-cortical evoked potentials (CCEP) and task-related ECoG. First, macrostimulation was applied systematically to different electrode contacts in dorso–medial frontal cortex while the patient performed vocal or manual movements. We aimed to confirm prior reports that macrostimulation of anterior SMA (preSMA) induces motor arrest (Fried et al., 1991, Luders et al., 1988). Second, DTI was used to verify the connection between the preSMA and the rIFG that has been established in normative populations (Aron et al., 2007, Ford et al., 2010, Forstmann et al., 2010, Johansen-Berg et al., 2004). Third, we evaluated task-independent functional connectivity between the preSMA and the rIFG using CCEPs (Matsumoto et al., 2007, Matsumoto et al., 2004). By stimulating different dorsomedial contacts (in a pair-wise fashion) we could ‘map’ the spatial and temporal responses in the right lateral frontal cortex. We were interested in determining whether CCEPs from specific preSMA electrodes elicited short-latency responses within specific rIFG electrodes. Finally, we recorded ECoG while the patient performed the Maybe Stop/No Stop task.
For preparing to stop (time-locked to the cue) we anticipated high gamma amplitude changes in both rIFG and preSMA since fMRI studies of preparing-to-stop have shown BOLD signal increases in both these regions (Chikazoe et al., 2009b, Jahfari et al., 2009, Vink et al., 2005, Zandbelt and Vink, 2010), and high gamma increases are associated with the BOLD signal (Conner et al., 2011, Logothetis et al., 2001, Scheeringa et al., 2011). Our observations go beyond previous reports by identifying when these regions are involved in preparing to stop, which could help differentiate their roles.
For outright stopping (time-locked to the stop signal) we aimed to examine both high gamma and beta band activity. High gamma activity was anticipated in both regions since they show BOLD signal increases for stopping, and, as described earlier, high gamma increases are associated with the BOLD signal (Conner et al., 2011, Logothetis et al., 2001, Scheeringa et al., 2011). We also expected an increase in beta band amplitude over rIFG for successful vs. unsuccessful stop trials based on our earlier report (Swann et al., 2009). We also predicted that there would be beta band increases in the preSMA, given the evidence for involvement of this region in response control and its structural connectivity to rIFG. Further, we planned to examine beta band coherence between the preSMA and the rIFG. Coherence is a measure of functional connectivity thought to reflect communication between brain areas (Fries, 2005). If beta band coherence between the preSMA and the rIFG is important for successful stopping then it should be stronger on successful than unsuccessful stop trials.
Section snippets
Participants
Sixteen young adults (8 female, 18–28 years old) were recruited from the local community. All participants provided written consent in accordance with the Internal Review Board of the University of California at San Diego. They received monetary compensation.
Task
An event-related Maybe Stop (MS)/No Stop (NS) task was used (see Fig. 1A). On each trial, a cue was presented in the center of the screen for 1 s. The cue was either the words ‘Maybe Stop’ (red on a black background) or ‘No Stop’ (green on a
Behavioral data
RT for MS_Go trials was 533 ms and for NS_Go trials was 404 ms and this was a significant difference, t(15) = 5.86, p < 0.001, Table 1. This shows that participants were using the MS cue to prepare to stop (c.f.Chikazoe et al., 2009b, Jahfari et al., 2009). SSRT was estimated at 182 ± 50 ms. However, because of limited stop trials for some subjects (who only performed one block of the task), SSD values did not always have time to dynamically adjust to a 50% probability of stopping. Thus SSRT may not
Discussion
We aimed to clarify the roles of the preSMA and the rIFG in action control. We designed a novel prepare-to-stop and outright-stop task, validated it with fMRI in healthy controls, and then examined ECoG during this task for a single rare patient with subdural coverage of the preSMA and rIFG. In healthy young controls fMRI showed that both the preSMA and the rIFG were more active for MS_Go compared to NS_Go trials—i.e. in preparation for stopping. By contrast, the rIFG alone was significantly
Acknowledgments
Funding is gratefully received from NIH grant DA026452 to A.R.A (PI) and N.T. (co-PI). N.S is supported by an NSF Graduate Student Fellowship and a NIH training grant via the Institute for Neural Computation at UCSD. NT is also supported by a Clinical and Translational Award KL2 RR0224149 from the National Center for Research Resources.
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