Intracranial Electroencephalography Reveals Different Temporal Profiles for Dorsal-and...

Intracranial Electroencephalography Reveals Different Temporal Pro!les for Dorsal- andVentro-lateral Prefrontal Cortex in Preparing to Stop Action

Nicole C. Swann1, Nitin Tandon2,4, Thomas A. Pieters2 and Adam R. Aron1,3

1Neuroscience Graduate Program, University of California, San Diego, CA, USA, 2Department of Neurosurgery, University ofTexas Medical School at Houston, Houston, TX, USA, 3Department of Psychology, University of California, San Diego,9500 Gilman Drive, La Jolla, CA 92093, USA and 4Memorial Hermann Hospital, Texas Medical Center, Houston, TX, USA

Address correspondence to Adam R. Aron. Email: [email protected]

Preparing to stop an inappropriate action requires keeping in mindthe task goal and using this to in"uence the action control system.We tested the hypothesis that different subregions of prefrontalcortex show different temporal pro!les consistent with dissociablecontributions to preparing-to-stop, with dorsolateral prefrontalcortex (DLPFC) representing the task goal and ventrolateral prefron-tal cortex (VLPFC) implementing action control. Five human sub-jects were studied using electrocorticography recorded fromsubdural grids over right lateral frontal cortex. On each trial, a taskcue instructed the subject whether stopping might be needed ornot (Maybe Stop [MS] or No Stop [NS]), followed by a go cue, andon some MS trials, a subsequent stop signal. We focused on gotrials, comparing MS with NS. In the DLPFC, most subjects had anincrease in high gamma activity following the task cue and the gocue. In contrast, in the VLPFC, all subjects had activity after the gocue near the time of the motor response on MS trials, related tobehavioral slowing, and signi!cantly later than the DLPFC activity.These different temporal pro!les suggest that DLPFC and VLPFCcould have dissociable roles, with DLPFC representing task goalsand VLPFC implementing action control.

Keywords: cognitive control, electrocorticography, gamma frequency band,stop signal task

IntroductionMuch evidence shows that stopping an action outright (i.e.completely, and to an external signal) implicates a predomi-nantly right-hemisphere fronto-basal-ganglia network. Thisincludes the ventrolateral prefrontal cortex (VLPFC), the pre-supplementary motor area (preSMA) and the basal ganglia(reviewed by Aron et al. 2007b; Nachev et al. 2008; Chamberset al. 2009; Chikazoe 2010; Levy and Wagner 2011). However,everyday life not only requires us to stop outright but also toprepare to stop. Here we investigate the neural correlates ofpreparing to stop using the high spatio-temporal resolution ofelectrocorticography (ECoG).

Studies with functional magnetic resonance imaging (fMRI)have recently investigated this issue by designing tasks wherepreparing to stop can be separated from outright stopping.These studies show preparing to stop recruits some of thesame prefrontal regions as outright stopping, including thepreSMA and the right VLPFC (speci!cally the inferior frontalgyrus), and in addition, the dorsolateral prefrontal cortex(DLPFC) (Vink et al. 2005; Chikazoe et al. 2009b; Jahfari et al.2010; Zandbelt and Vink, 2010; Zandbelt et al. 2012). TheDLPFC !nding is consistent with a role for representing goalsin working memory (Goldman-Rakic, 1990; Petrides, 2000;

Olesen et al. 2004; Muller and Knight, 2006) and/or atten-tional monitoring (Corbetta and Shulman, 2002)—in this casefor the stopping task goal. This motivates the hypothesis thatthe DLPFC and VLPFC play dissociable roles in preparing tostop. Speci!cally, the DLPFC may represent the task goalwhile the VLPFC may implement action control—by whichwe mean an active, although partial, suppression of the motorresponse (i.e. braking). A key aspect of this prediction is thatthe DLPFC and VLPFC activity should be temporally dissoci-able, with the DLPFC activity occurring early, around the timeof a task goal cue (task cue) and the VLPFC activity occurringlater, around the time when stopping is anticipated orperhaps around the time of the motor response itself. To testthese predictions, we recorded ECoG in 5 subjects, each withelectrode coverage of both DLPFC and VLPFC, while they per-formed a preparing-to-stop task.

On each trial, a stopping task cue was presented (“MaybeStop” [MS] or “No Stop” [NS]), followed by a Go cue, andthen, on the MS trials alone, a stop signal occurred half thetime (Fig. 1). In a previous fMRI study with this task inhealthy controls, we reported that the contrast of MS Go trialsversus NS Go trials activated the DLPFC bilaterally and theVLPFC in the right hemisphere, as well as the preSMA (Swannet al. 2012). The preSMA is part of the stopping network,having functional and structural connectivity with the VLPFCand the subthalamic nucleus of the basal ganglia (Forstmannet al. 2010, 2012; Neubert et al. 2010; Swann et al. 2012), andall 3 of these regions show similar electrophysiologicalresponses for outright stopping (Swann et al. 2009, 2012; Le-venthal et al. 2012; Ray et al. 2012). However, in this study,we focused uniquely on the DLPFC and VLPFC (where wehad coverage in all subjects), to test the hypothesis abouttheir distinct functional roles in preparing to stop. Notably,we examined Go trials only, to evaluate the neural correlatesof preparing to stop, in a context in which there was always amotor response. Speci!cally, we analyzed MS Go trials, NSGo trials, and the difference between them.

Although ECoG provides information about many differentfrequency bands of oscillatory activity, our present interest inthe temporal pro!les of DLPFC and VLPFC motivated a focuson high gamma activity (!90–130 Hz). This high-frequencyactivity is a putative marker of local neural processing withina region (Edwards et al. 2005; Canolty et al. 2007; Miller et al.2007a, 2007b; Ray et al. 2008; Flinker et al. 2010; Conneret al. 2011; Scheeringa et al. 2011), while lower-frequencyactivity (beta, alpha, and theta) is putatively important forlong-distance communication between brain regions (Kopellet al. 2000, 2010; Fries, 2005). Thus, given our current goal ofdifferentiating the temporal pro!les of DLPFC and VLPFC, we

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focused only on high gamma activity as an index of localneural processing within a region.

Behaviorally, we expected that action control would bemanifest as slower reaction time (RT) for MS Go versus NS Gotrials (the “response delay effect,” RDE, cf. Chikazoe et al.2009b; Jahfari et al. 2010; Zandbelt and Vink, 2010; Swannet al. 2012; Zandbelt et al. 2012). With ECoG we focused on 2main aspects. First we tested the relative timing of activity inDLPFC versus VLPFC. We expected increased gamma activityin the DLPFC around the time of the task cue and increasedgamma activity in the VLPFC following the go cue. Thiswould support dissociable roles for these regions. Second, wemore closely examined the VLPFC to better understand itsrole in preparing to stop. Whereas fMRI shows increasedactivity in VLPFC for MS Go compared with NS Go, it isunclear when this difference occurs in time. We expected thatECoG, with its higher temporal resolution, would clarify thisissue, and thus provide novel information about the func-tional role of right VLPFC.

Methods

ParticipantsECoG was collected in 7 subjects with pharmacologically resistant epi-lepsy who underwent surgery for resection of seizure foci. All subjectsprovided informed consent under the auspices of the Internal ReviewBoard at University of Texas at Houston. Data from 2 subjects are notincluded in this report because of poor task performance (>30% omis-sions on Go trials). Thus, the analysis focuses on 5 subjects (3 female,mean age 34 years, SD = 9.8). All had right lateral frontal coveragethat included the DLPFC and VLPFC and none of them had any evi-dence for right frontal abnormalities or underwent subsequent resec-tion of these regions. One of these subjects (s2) was included in aprevious report (Swann et al. 2012). However, here we focus on otheraspects of this subject’s data and also on the 4 additional subjects.

TaskWe used a MS/NS task, (Swann et al. 2012; Fig. 1). Each trial beganwith a task cue presented for 1 s. The task cue was either “MS” (redtext on a black background) or “NS ” (green text on a black back-ground). Each cue was equally likely to occur. The task cue was fol-lowed by a go cue (white arrow), pointing to either the right or theleft, to which subjects were instructed to respond with a button press.On half the MS trials an auditory beep followed the go cue,

instructing the subjects to attempt to stop the motor response. In 3subjects (s2, s4, and s5), the delay between the go cue and the stopsignal (stop signal delay, SSD) was dynamically adjusted (for fulldetails see Aron and Poldrack, 2006), with an initial delay set to 150ms, whereas for the other 2 subjects (s1 and s3), SSDs were randomlyselected from 3 individually determined !xed values spaced 100-msapart based on an initial training session. We think it is unlikely thatthis procedural difference in"uenced the current results since the be-havioral results for the go trials for subjects with the !xed SSD methoddid not differ systematically from the other subjects, and also becausethe !xed SSD method is a standard procedure (Verbruggen andLogan, 2009). After the go cue, there was a 1.5-s response window fol-lowed by a jittered intertrial interval (1–1.4 s). All subjects respondedwith their left hand (so that primary motor cortical responses could berecorded from the right lateral grids). The subjects performed either 6or 7 blocks of the experiment, with 96 trials per block.

Behavioral AnalysisFor each subject we calculated mean RT for MS and NS Go trials (withRTs less than 100 ms excluded), the difference between the 2 (theRDE), the average SSD, the percentage discrimination and omissionerrors on go trials, the percentage of successful stop trials, and the RTfor failed stop trials. We also estimated stop signal reaction time(SSRT) for each subject. For the subjects whose SSDs were deter-mined using the “tracking” method (s2, s4, and s5), SSRT was esti-mated with the “mean method” (Logan et al. 1997); for the other 2subjects (s1 and s3) SSRT was estimated with the “integrationmethod” (Verbruggen and Logan, 2009).

Electrode LocalizationElectrode localization procedures were the same as our previousreports (Swann et al. 2009, 2012). In brief, a computed tomographyscan was obtained after electrode implantation, and the scan was cor-egistered to a preimplantation MRI scan. These electrodes were thenprojected onto a cortical surface model generated in FreeSurfer (Daleet al. 1999). SUMA software was used to visualize the electrodes asspheres (Saad et al. 2003). Locations were veri!ed with intraoperativephotos.

ECoG Acquisition and AnalysisThis was similar to our previous reports (Swann et al. 2009, 2012).Data were acquired with an electroencephalography (EEG) 1100 Neu-rofax clinical Nihon Koden acquisition system sampled at 1000 Hz.Trial onsets were sent to the EEG acquisition computer with transis-tor–transistor logic pulses.

The ECoG data were !rst digitally re-referenced to the commonaverage, excluding channels with frequent artifacts or ictal contami-nation. Manual inspection was used to identify and exclude trials witheither ictal activity that spread across channels or other sources ofnoise. Following artifact rejection and rejection of trials with incorrectbehavior, there was on average 132 MS Go trials remaining persubject (SD = 12, range 116–148), and roughly twice as many NS Gotrials. Channels identi!ed as seizure onset channels were also ex-cluded from the entire analysis.

Analysis proceeded as follows. First, the data were !ltered withGabor wavelets into separate analytic amplitudes at 128 differentcenter frequencies (ranging from 2.5 to 250 Hz; see Canolty et al.2007). Second, analytic amplitudes for each frequency were alignedto the task cue (for the main analysis), averaged across trials, andcompared with an average baseline of 500 ms in the center of the in-tertrial interval ("750 to "250 ms prior to trial start). Third, theaverage analytic amplitude values were converted to z-scores using apermutation technique where a standard deviation of the data wasderived for each frequency from a distribution generated by repeatingthe above procedure 10 000 times, time locking to random points intime (see Canolty et al. 2007). For calculations of between-conditiondifferences (MS Go vs. NS Go) average analytic amplitude values fordifferences were directly subtracted (without a baseline correctionapplied), and z-scores were derived from an analogous permutation

Figure 1. Task: each trial begins with a task cue, either NS or MS, followed by anarrow pointing left or right. Subjects were instructed to respond with a button pressto the arrows as quickly as possible. On half of the MS trials an auditory beepfollowed the arrow and indicated that subjects should attempt to stop the initiatedresponse.

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method as above, but now using 5000 iterations of a label-swappingprocedure. Fourth, multiple comparisons were corrected for using thefalse discovery rate (FDR) method. Fifth, while some results show thefull time–frequency spectrum, others are speci!cally focused on highgamma activity (!90–130 Hz). This is the frequency band that hasbeen most closely linked to local neuronal activity, and to the bloodoxygen level dependent signal (Ray et al. 2008; Conner et al. 2011;Scheeringa et al. 2011). For these analyses data were extracted fromthis frequency band (center frequency: 110 Hz) and statistics wereperformed for this band separately.

Results

BehaviorAll 5 subjects performed well, having less than 3% discrimi-nation errors on Go trials (i.e. errors where the incorrectbutton was pressed) and 10% or less omission errors on Gotrials (see Table 1). The speed of stopping (SSRT) was in atypical range for ECoG subjects (Swann et al. 2009). Three ofthe 5 subjects (s1, s2, and s3) showed a signi!cant RDE, withMS RT being signi!cantly longer than NS RT, as in healthyparticipants (Chikazoe et al. 2009b; Jahfari et al. 2010; Swannet al. 2012). However, the other 2 subjects (s4 and s5)showed no signi!cant RDE, perhaps because they had dif!-culty keeping this additional rule in mind. Instead, they mighthave treated the task like a standard stopping task and thusanticipated stopping on all trials regardless whether theywere in an MS or NS condition (such that a task goal andaction control strategies were maintained for all trials, insteadof for MS Go trials only). As these 2 subjects did perform thetask well otherwise (i.e. their stopping behavior was typical),they are included in the ECoG analysis, and in fact theabsence of the RDE is an informative control condition whenexamining their activity compared with the other subjects.

ECoG

Prefrontal Activity When Preparing to StopOur hypothesis was that DLPFC represents the task goal (i.e.“this is [or is not] a trial on which stopping may be needed”)and that VLPFC implements action control, by which wemean an active, although partial, suppression of the motorresponse (i.e. braking). This predicts that DLPFC activityshould occur in the task cue period, while VLPFC activityshould occur later, perhaps following the go cue or aroundthe time of the motor response.

First, to give a descriptive example of the neural time-course for the whole brain we examined high gamma

amplitudes for the full time course for 1 subject, s2 (Fig. 2).Here the amplitude was averaged over 100 ms for eachtime-bin, and converted to a z-score (P < 0.05, FDR correctedfor all electrodes and time-bins). We focus on s2 because thissubject was unique in having extensive brain coverage, in-cluding of primary visual cortex and primary motor cortex,and also a typical pattern of activity for the task. Note that theDLPFC shows bursts of activity approximately 300 after theMS cue and approximately 200 ms after the go cue, whereasthe VLPFC shows activity mainly after the go cue, peakingaround the time of the motor response. There is also activityin occipital cortex around the time of the visual stimulus andin the precentral gyrus around the time of the motorresponse, providing validation of the ECoG processingstream, and of the electrode localization method.

Next, to formally test this hypothesized temporal differencefor DLPFC and VLPFC, we proceeded as follows. We identi!edelectrodes with signi!cant high gamma amplitude increasesfor either MS Go trials versus baseline or for NS Go versusbaseline for at least 50 ms, to exclude spurious events (P <0.05, FDR corrected, for all electrodes and all time points).Then, for each of these electrodes, we examined if MS Goactivity was greater than NS Go, for at least 50 ms (P < 0.05,FDR corrected for all electrodes and time-points examined).The above steps were done separately for the 2 trial periods:1) the task cue period (de!ned as 1 s following the task cue)and 2) the go cue period (de!ned as 1 s following the go cue).

During the task cue period, for MS Go versus baseline, 3out of 5 subjects (s1, s3, and s5), exhibited signi!cantly in-creased high gamma band activity in the prefrontal cortex,mainly in the DLPFC, that is, in the middle frontal gyrus, or inand around the inferior frontal sulcus (Fig. 3A). (Note, s2 alsohad DLPFC activity but it did not pass the correction for mul-tiple comparisons for this particular analysis, when correctingfor all time points, but the subject did show signi!cant activitywhen correcting for time-bins, see Fig. 2.) However, for MSGo versus NS Go, there were no signi!cant effects in prefron-tal regions for any subject. Only one subject showed anyVLPFC activity during this period (s1), and this was for 2 elec-trodes slightly more rostral than the VLPFC electrodes thatwere active in the other subjects during either time period.

During the go cue period, for MS Go versus baseline, 4 ofthe 5 subjects (all except for s4) again had activity in theDLPFC. Two of the 5 subjects (s2 and s3) also showed differ-ences for the MS Go versus NS Go comparison during thisperiod (Fig. 3B). For the VLPFC, each of the 5 subjects hadsigni!cant activity in the caudal VLPFC, that is, in the ventralinferior frontal gyrus or in and around the precentral sulcus

Table 1Behavioral results

Subjects Mean MS RT (SD), ms Mean NS RT (SD), ms RDE, ms Proactive slowing Disc errors (%) Omission errors (%) Failed stop RT (SD), ms SSRT Percent inhibit (%)

S1a 700 (168) 552 (125) 148 Yes 1.0 10 603 (114) 271b 56S2a 618 (225) 491 (138) 127 Yes 1.7 3.5 540 (172) 239 49S3a 735 (168) 535 (136) 200 Yes 2.2 2.4 653 (121) 124b 71S4 861 (325) 831 (313) 30 No 1.6 0 645 (160) 219 49S5 598 (140) 596 (139) 2 No 0.2 0.8 524 (163) 469 39

MS, Maybe Stop Go RT; NS, No Stop Go RT; RDE, response delay effect (mean MS RT—mean NS RT); Proactive slowing (whether or not there was a signi!cant RDE); Disc. errors, percentagediscrimination errors; omissions errors, percentage omission errors; failed stop RT, mean RT on failed stop trials; SSRT, stop signal reaction time; percent stop, percentage of stop trials with successfulstopping.aSigni!cant response delay effect (MS RT sig greater than NS RT).bSubjects who were tested with a !xed SSD method.

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(Fig. 3B). Additionally, for MS Go versus NS Go there wasactivity in the VLPFC in 3 out of 5 subjects (s1, s2, and s3),and notably all of these subjects had signi!cant slowing (theRDE), whereas the 2 subjects who did not have a signi!cantdifference in activity (s4 and s5) had an RDE close to 0. Thissupports the notion that the MS versus NS activity differencein the VLPFC could relate to the behavioral slowing effect. Wespeculate that subjects without an RDE were perhaps exercis-ing proactive control on all trials, and thus showed no differ-ence between the 2 trial types in VLPFC, but still exhibitedabove-baseline activity in these regions for both trial types,and see below for more on this.

In summary, across subjects and electrodes, for the DLPFCthere were high gamma responses after the task cue, the gocue, or both. For the VLPFC, there were high gammaresponses mainly only after the go cue, and more strongly onMS Go than NS Go trials (for the subjects who showed anRDE). For detailed information on each subject, see Sup-plementary Figures 1–5 for spectral plots.

Activity in the DLPFC Precedes the VLPFCThe comparison of task cue and go cue periods (Fig. 3A vs. B)suggests a timing difference in DLPFC and VLPFC. We for-mally tested this as follows. 1) For each subject, for the periodfrom 500 ms before the task cue until 1 s after the go cue(i.e. a 2500 ms window), electrodes were identi!ed in DLPFCand VLPFC which showed signi!cant activity for MS Goversus baseline (P < 0.05, FDR corrected for all electrodeswithin each region of interest (ROI) and all time points).(Note that for s4 there was no DLPFC electrode in either timewindow that surpassed the FDR corrected threshold, so forthis analysis, for s4 alone, the threshold was lowered to P <0.01, uncorrected to select a DLPFC electrode.) 2) For all ofthese electrodes, we found the time point (relative to the MScue) where the high gamma amplitude !rst became signi!-cantly greater than baseline (for subjects with more than onesigni!cant DLPFC or VLPFC electrode the onsets for all elec-trodes in each ROI were averaged). 3) We performed a pairedt-test across the 5 subjects on the latency values for DLPFC

Figure 2. The full dynamic picture of activity for a single subject (s2) during a preparing to stop trial. DLPFC was active after both the MS cue and the go cue, and VLPFC wasactive later, most strongly around the time of the motor response. The colored electrodes denote those with signi!cant high gamma activity (z-scores) for MS Go versus baselinefor each time bin (P< 0.05, FDR corrected). The MS cue occurred at 0 ms, the go cue occurred at 1000 ms, and for this subject average RT was 618 ms (1618 ms relative tothe MS cue).





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versus VLPFC. The main result was that the DLPFC was activebefore the VLPFC (P = 0.01, t = 4.2, df = 4), and this was truein each subject (Fig. 3C). (Note that because there were nodifferences in average amplitude or standard deviationbetween the DLPFC and VLPFC [P!0.9 for each], it is unlikelythat the difference in onset times is an artifact of differentsignal-to-noise properties of the 2 regions.)

We also examined the DLPFC/VLPFC timing differenceusing a second method. We performed a single trial analysisfor MS Go trials for all the DLPFC and VLPFC channels pooledacross subjects that had signi!cantly above baseline highgamma activity (on average 132 trials per subject, SD = 12).The dependent variable for each trial, in DLPFC or VLPFC, wasthe time of maximum high gamma amplitude. DLPFC showeda relatively narrow peak after the MS cue and another broaderand slightly smaller one after the go cue (Fig. 3C). Thus, theDLPFC exhibited more variability, responding similarly to boththe task cue and the go cue. The VLPFC, in contrast, whileshowing some responses to the task cue, had its largest peakafter the go cue around the time of the response. This singletrial analysis supports the above impressions (Fig. 3A,B) thatthe DLPFC is active after both the task cue and the go cuewhile the VLPFC is active primarily after the go cue.

To help characterize the data summarized in Figure 3, spec-tral plots are shown from an example DLPFC and VLPFC elec-trode from 1 subject (s3; Fig. 4A). Here, the DLPFC electrode

had signi!cant high gamma activity shortly after the task cue,and did not differentiate between MS Go versus NS Go. Incontrast, the VLPFC was active only after the go cue, and sig-ni!cantly more for MS Go compared with NS Go (P < 0.05,FDR corrected for all frequencies, time points, and con-ditions). For comparison, we also show VLPFC activity froms4, who failed to show an RDE. Note that the MS Go activitypattern for this subject was very similar to s3. However, s4,did not show a signi!cant difference between MS Go and NSGo, and likewise had no behavioral difference either (noRDE). See Supplementary Figures 1–5 for spectral plots of allDLPFC and VLPFC with signi!cant MS Go activity relative tobaseline (indicated in Fig. 3A,B) from all subjects.

The Role of the VLPFC in Preparing to StopThese different temporal activation patterns for DLPFC versusVLPFC are consistent with different roles in preparing to stopaction. The DLPFC was active in the task cue and go cueperiods, consistent with a role in representing task goals,which could include any of encode, maintain, rehearse,attend, monitor, or retrieve. The VLPFC, in contrast, was con-sistently active later in the go phase around the time of theresponse, consistent with a role in either action control (Aronet al. 2003; Chambers et al. 2006, 2007; Aron et al. 2007b;Jahfari et al. 2010; Neubert et al. 2010), attentional monitoringfor the stop signal (Chao et al. 2009; Duann et al. 2009;

Figure 3. (A and B). DLPFC and VLPFC activity in the task cue and go cue periods. (A) The task cue period (i.e. 1 s following the MS cue). For most subjects, there is activity inand above the inferior frontal sulcus (i.e. DLPFC) for MS Go versus baseline, indicated with black arrows, but not for the comparison of MS Go versus NS Go. Note only 1 subject(s1) shows VLPFC during this period in 2 more rostral VLPFC electrodes. (B) The go cue period (i.e. 1 s following the go signal). In all subjects, there is activity in and around theprecentral suclus (i.e. VLPFC) and there are also differences between MS Go and NS Go for the 3 subjects who had signi!cant behavioral slowing (RDE), indicated with blackarrows. Note that there is also DLFPC activity in most subjects during this period and differences between MS Go versus NS Go in 2 subjects (s2 and s3). Red electrodesdenote signi!cantly greater high gamma amplitude (110 Hz) for MS Go versus baseline and black circles denote signi!cant MS Go versus NS Go trials (P<0.05, FDRcorrected). The dotted rectangles denote the 2 subjects who did not slow down in preparation for stopping. (C) DLPFC precedes VLPFC. Left panel: for all subjects the time ofsigni!cant gamma in DLPFC occurred earlier than VLPFC. Zero millisecond is the time of the “MS” cue and 1000 ms is the time of the go signal. Right panel: Pooling across thechannels and subjects in the left panel, a single trial analysis reveals that DLPFC activity increased after both the task cue and the go cue, while VLPFC had a tendency toincrease mainly after the go cue and after DLPFC. Zero millisecond is the time of the MS cue and 1000 ms is the time of the go signal.

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Hampshire et al. 2010; Sharp et al. 2010; Boehler et al. 2011;Chatham et al. 2012), both of these, although perhaps in dis-sociable subregions (Chikazoe et al. 2009a; Chikazoe, 2010;Verbruggen et al. 2010; Levy and Wagner, 2011), as well asother possible accounts such as a violation of expectanciesabout stopping (Zandbelt et al. 2012). We speculated that ifthe VLPFC is important for action control, that is, as a brake,than its activity might relate to the time of the motor response(especially in the “executive” setting of MS Go trials).

To investigate this, we plotted spectral data for MS Go trialsfrom a VLPFC electrode for each subject time-locked to theMS cue (Fig. 4B, left column) and to the reaction time(Fig. 4B, middle column). For s1–s3, the electrode is the onethat showed the MS Go greater than NS Go difference (seeFig. 3B). (Note that s2 and s3 had 2 neighboring electrodeswith this difference. Although all showed a similar pattern,we show results from the one that was more clearly withinthe VLPFC for each subject, see Supplementary Figures 1–3for plots of data from the other electrodes). For subjects s4and s5, no electrodes had an MS Go versus NS Go difference,therefore we show the VLPFC electrode with an MS Go versusbaseline difference (shown in Fig. 3B). For all subjects theVLPFC activity was roughly centered around the time of themotor response, usually starting well before, and continuingwell after. These results point to a relationship between theVLPFC and the motor response (especially on MS Go trials).Although this is consistent with a role for the VLPFC in actioncontrol, the fact that the activity was spread across time couldalso be consistent with other accounts, such as attentionalmonitoring for the stop signal. If this is the case, then its en-gagement should be most prominent around the time of theexpected stop signal (i.e. the SSD) rather than the time of themotor response. To test this, we conducted a single trialanalysis sorted by either RT, or a proxy for expected SSD(based on the SSD from the last stop trial). For s2 and s5, theVLPFC electrode shown in Figure 4B had high gamma activitythat corresponded more strongly with RT than with the timeof expected SSD (Fig. 4C). For the other 3 subjects the singletrial analysis did not reveal any clear patterns in VLPFC whensorted by either variable. (See Supplementary Figures 1–5 foradditional examples of the single trial analysis sorted by RTfor other VLPFC and DLPFC electrodes.)

DiscussionWe recorded ECoG from right lateral subdural grids in 5 sub-jects while they performed the MS/NS task. The analysismainly focused on high gamma activity in the task cue periodand following the go cue. In most subjects, there was DLPFC

activity around the task cue and/or around the go cue withsome electrodes responding to both, while others respondedto one or the other. By contrast, in all subjects, there wasactivity in the VLPFC more consistently after the go cue,especially around the time of the motor response on MS trials,and signi!cantly later than the DLPFC activity. For the VLPFC,the activity on MS trials was more closely time-locked to themotor response than it was to the anticipated time of the stopsignal, and it was greater, relative to NS trials, in subjects whoslowed down in anticipation of stopping compared with thosewho did not. These results suggest that the DLPFC and VLPFCcould play dissociable roles. One interpretation is that theDLPFC represents the task goal and that the VLPFC implementsaction control, perhaps speci!cally by “braking” the initiatedmotor response when stopping is anticipated. This speaks tocurrent theories of action control as well as the debate about thefunctional specialization of the prefrontal cortex.

Functional Role of the DLPFCPreparing to stop a response requires having a stopping taskgoal and also using this information to control the responsetendency. This “task goal” could encompass functions relatedto encoding the rules of the task, retrieving these rules,holding the rules in working memory, and/or attending to ormonitoring for task-relevant stimuli. A strong candidateregion for representing this task goal is the DLPFC, based onmuch evidence implicating it in working memory (Goldman-Rakic, 1990; Fuster, 1997; Petrides, 2000; Olesen et al. 2004;Muller and Knight, 2006) and attention (Corbetta andShulman, 2002). Consistent with this, functional MRI showsgreater activity in DLPFC for preparing to stop (Chikazoeet al. 2009b; Jahfari et al. 2010; Zandbelt and Vink, 2010;Swann et al. 2012; Zandbelt et al. 2012), although in moststudies it was not possible to establish when this differenceoccurred, that is, during the task cue or go cue phase (but seeZandbelt et al. 2012). Here, we used ECoG to show thatDLPFC was active for MS Go versus baseline, and moreover,we could elucidate the timing. We found that DLPFC wasactive following the task cue, the go cue, or both. Oneinterpretation of this pattern could be that the DLPFC encodesthe task goal following the task cue and then shows a reacti-vation following the go cue, perhaps re"ecting attention tothe cue or task goal retrieval, although other accounts arepossible. Future studies using tasks that modulate differentcognitive processes (e.g. by adjusting working memory load)would be useful to more clearly de!ne the functional role ofDLPFC in preparing to stop. At present, with the current task,we can only suggest that the pattern of activity that we

Figure 4. (A) Example spectral plots for DLPFC and VLPFC for preparing to stop. For s3 high gamma amplitude increases are visible for both DLPFC and VLPFC, with DLPFCpreceding VLPFC. Note that only VLPFC shows a signi!cant difference for MS Go compared with NS Go (P<0.05, FDR corrected). For a subject that did not show behavioralslowing (s4), the high gamma increases relative to baseline are still present (and are similar in time to s3), but there is no difference for MS Go versus NS Go. Zero millisecondis the time of the task cue, 1000 ms is the time of the Go signal. Amplitude is expressed in color as a z-score. Signi!cant values (P< 0.05, FDR corrected) are outlined (blackindicates positive differences, red indicates negative differences). Electrode locations are indicated in blue for each subject and region. For similar plots from all the othersigni!cant DLPFC and VLPFC electrodes, for all subjects, see Supplementary Figures 1–5. (B) VLPFC shows consistent activity across subjects when preparing to stop. Spectralplots for MS Go versus baseline are shown for a VLPFC electrode from each subject time-locked to both the MS cue (left column), and the RT (middle column). For each subjectthe high gamma activity centers around the motor response. Zero millisecond is the time of the task cue and 1000 ms is the time of the go signal for the left column. Zeromillisecond is the time of the RT for the middle column. (C) VLPFC activity is time-locked to the motor response on a single trial level. In 2 subjects (s2 and s5), the VLPFCresponse on MS Go versus baseline relates to the motor response more than the expected stop signal. The top 2 plots show single trial high gamma amplitude (110 Hz) plottedfor all MS Go trials aligned to go cue (0 ms), and sorted by RT. The bottom 2 plots show the same data sorted by the expected SSD. The dark line indicates RT (top plots) orexpected SSD (bottom plots) for each trial. Raw amplitude relative to baseline is shown in color. For similar plots, showing the single trial analysis sorted by RT, from otherDLPFC, and VLPFC electrodes, for all subjects, see Supplementary Figures 1–5.

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observe is consistent with a role for the DLPFC in “represent-ing the task goal,” which could relate to working memory, at-tention, other executive functions, or a combination of these.

The fact that signi!cant DLPFC activity was observedaround the go cue and not the task cue for some subjectscould re"ect a statistical thresholding issue and/or stronger re-activation or retrieval of the stopping rule as the responseperiod draws near (i.e. when that information is most tem-porally pertinent). Notably, activity in the DLPFC precededVLPFC in all subjects, which is consistent with different func-tional roles for these regions.

Functional Role of the VLPFCActivity in the VLPFC had several features: 1) it occurredmainly after the go cue and more speci!cally around the timeof the response; 2) it was later than activity in the DLPFC forall subjects; 3) it was greater for MS Go than NS Go for the 3subjects who showed a behavioral RDE and not for the 2 whodid not; 4) in 2 subjects, activity was more tightly related toRT than the expected SSD on a single-trial level. This patternof data provides novel information about the temporal acti-vations of the VLPFC in preparing to stop, and speaks to adebate about the functional role of the VLPFC in cognitivecontrol. Whereas the right VLPFC is critical for the outrightstopping of action (Aron et al. 2003; Chambers et al. 2006,2007; Verbruggen et al. 2010), its underlying functional rolecould re"ect the active suppression of a response (entirely orpartially), that is, “action control” (Aron et al. 2007a; Jahfariet al. 2010), attentional monitoring (Hampshire et al. 2010;Sharp et al. 2010; Chatham et al. 2012), violation of a stop-ping expectancy (Zandbelt et al. 2012), or some combinationof these (Chambers et al. 2009; Boehler et al. 2010; Chikazoe,2010; Aron, 2011; Levy and Wagner, 2011).

The action control account proposes that there is slowingdown on MS Go trials because of partial suppression of theemitted response, and that VLPFC implements this. This isanalogous to a brake that is applied as movement occurs. Thispredicts VLPFC activity around the time of the motorresponse. In contrast, the attentional monitoring account pro-poses that the VLPFC is monitoring for the stop signal, whichpredicts earlier activity, around the time of SSD. Yet, we showthat the VLPFC activity in all subjects is centered around themotor response, and in two subjects, the single trial analysisclearly showed that this activity was much more tightly lockedto RT than the estimated SSD. Thus, the results are more com-patible with the action control account.

Thus, while attentional monitoring is implicit in the stopsignal paradigm, since one could not stop unless one detecteda signal to stop, and while it is possible that VLPFC plays arole in such attentional monitoring (Corbetta and Shulman,2002), the current data argue that VLPFC’s role is not merelyone of attentional monitoring. Instead, there is likely a sector(perhaps the more ventral and caudal aspect) that implementsaction control (Chikazoe et al. 2009a; Verbruggen et al. 2010;Levy and Wagner, 2011).

It is unclear as to how these results speak to the violationof stopping expectancy account. Based on fMRI, Zandbeltet al. (2012) proposed that VLPFC activity on go trials, inwhich stopping was not needed, although it was expected, re-"ected a violation of stopping expectancy. Such an interpret-ation could be consistent with our results if it is the motor

response that instantaneously triggers the realization that thestop signal did not occur.

In any event, other data argue for a role for VLPFC inaction control. For example, 2 studies using paired-pulse tran-scranial magnetic stimulation, with 1 coil on right VLPFC andthe other on M1, showed that VLPFC had a suppressive effecton M1 for response control tasks (Buch et al. 2010; Neubertet al. 2010).

Thus, the VLPFC activity may re"ect action control that isapplied as the response is being made (i.e. braking). Morespeci!cally, the VLPFC, which is critical for stopping outrightat the behavioral level, and putatively plays a role in imple-menting inhibitory control by projecting to the preSMA andthe basal ganglia to cancel motor output (Aron et al. 2007b),may also be recruited proactively, when stopping is antici-pated. Accordingly, the proactive recruitment of the VLPFCmight have the consequence that if a response is initiated it isalso dampened/slowed. Future studies could bolster thisinterpretation by, for example, using paired-pulse TMS toreveal if right VLPFC has inhibitory effects over the motorsystem when preparing to stop, or using electrical stimulationduring task performance (Filevich et al. 2012).

LimitationsFirst, the data are from epilepsy subjects and may not general-ize well to the healthy population. However, some of the !nd-ings clearly were consistent across all or most of the 5subjects. Further, the !ndings corresponded well with extantfMRI studies of preparing to stop (Chikazoe et al. 2009b;Jahfari et al. 2010; Zandbelt and Vink, 2010; Swann et al.2012; Zandbelt et al. 2012). Further, all subjects had epilepticfoci outside of the reported regions. Second, the sample size,while acceptable by the standard of ECoG studies, is small,particularly for examining differences between individuals (i.e.whether or not behavioral slowing, the RDE, was present).Further, an important result, that single-trial gamma activityin the VLPFC tracked RT more tightly than expected SSD,could only be established in 2 subjects (probably becausesingle-trial analyses are noisy, particularly in prefrontalregions due to increased variability in the timing and consist-ency of the neural response in this region compared withprimary motor/sensory regions, for instance). This limits thestrength of the conclusion that can be drawn. Third, for 2 ofthe subjects there was no slowing down (RDE) for MS versusNS Go trials, running against a common observation in nor-mative populations (Chikazoe et al. 2009b; Jahfari et al. 2010;Zandbelt and Vink, 2010; Swann et al. 2012; Zandbelt et al.2012). We suggest they were preparing to stop on every trial,and so were not differentially sensitive to the MS versus NScues. However, their stopping and other task performancewas otherwise satisfactory, and the absence of an RDE alongwith a null ECoG difference for MS Go versus NS Go, pro-vided a nice control. Fourth, given the simplicity of our taskdesign we could not specify the role of the DLPFC with anyfunctional speci!city beyond representing task goals. Fifth,some of the electrodes with similar functional properties werein different locations across subjects with reference to gyral/sulcal landmarks. Yet, it well established that there is highvariability in the gyri!cation and cytoarchitecture of prefron-tal regions (Amunts et al. 1999). Sixth, our current focus wason Go trials only, and not on stop trials. While it is





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theoretically interesting to examine DLPFC and VLPFC on out-right stop trials, our previous report showed activity in bothof these regions prior to the stop signal (see Fig. 6 Swannet al. 2012). This, combined with the fact that the go and stopsignals occur in close temporal succession, and with a varyingdelay on different trials, makes it dif!cult to delineate differ-ent functional roles for these brain regions on stop trials.Hence, the focus on Go trials can better delineate the tem-poral pro!les of these regions, as well as clarifying how theycontribute to preparing-to-stop.

Conclusions and ImplicationsOur results speak to a long-standing debate about the func-tional specialization of the prefrontal cortex. Whereas somehave argued that the primary functional role of the prefrontalcortex is working memory (with different sectors processingdifferent kinds of information) (Goldman-Rakic, 1996), othershave argued that different sectors perform different functionssuch as working memory, attention, and inhibitory control(Fuster, 1997). The current results argue for functionalspecialization, at least to a degree, with DLPFC representingthe task goal, and VLPFC implementing action control.

Within VLPFC, we more speci!cally observed that theactivity was around the time of the motor response, ratherthan around the time of the estimated stop signal. This, alongwith other results (Buch et al. 2010; Jahfari et al. 2010;Neubert et al. 2010) speaks in favor of a role for VLPFC inactively braking response tendencies. This action controlmechanism over response tendencies, implemented via theVLPFC, could operate in concert with the basal ganglia and/or the primary motor cortex (Vink et al. 2005; Jahfari et al.2010; Zandbelt and Vink, 2010; Zandbelt et al. 2012).

If our interpretation is correct it would suggest that task goalprocessing (represented in DLPFC) is used to implement actioncontrol (in the VLPFC). This predicts a causal in"uence ofDLPFC over VLPFC, which could be tested in a future study ofinter-regional communication. However, communicationbetween these regions may not be direct. One possibility is thatthe preSMA may mediate communication between the 2. Thisaccount is supported by ECoG data from our previous reportwhere the preSMA was active between the early DLPFC activityand the later VLPFC activity in one unique subject with subduralcoverage in all these areas (Swann et al. 2012). This observedtiming pattern is consistent with a paired-pulse TMS study usinga response control task which showed preSMA’s in"uence onprimary motor cortex preceded VLPFC, and that VLPFC’s in"u-ence on primary motor cortex depended on preSMA (Neubertet al. 2010). It is possible that the preSMA plays a role in taskcon!guration (Rushworth et al. 2004), one aspect of which istranslating the stopping rule into the action system. Thus, thepreSMA may mediate the putative transfer of information fromDLPFC to VLPFC when preparing to stop. Clearly, future re-search is required to develop a richer understanding of the func-tional roles and neural communication of different regionswithin this overall putative response control network.

In conclusion, the current data provide a more mechanisticunderstanding of human self-control. They point to a dis-sociation between the task goal and the implementation ofaction control in different sectors of prefrontal cortex, andthey provide insight into how people respond more cau-tiously when they might have to stop.

Supplementary MaterialSupplementary material can be found at: http://www.cercor.oxfordjournals.org/

FundingFunding is gratefully received from NIH grant DA026452 to A.R.A. N.S. was supported by an NSF Graduate Student Fellow-ship, an NIMH training grant via the Institute for Neural Com-putation at UCSD, and an NSF Gk-12 fellowship. N.T. wassupported by a Clinical and Translational Award KL2RR0224149 from the National Center for Research Resourcesand by the Vivian Smith Foundation.

NotesThe authors thank John Serences for his helpful comments on themanuscript. We also thank our patients for their co-operation with thestudy, the nurses and the EEG technicians at the epilepsy monitoringunit at Memorial Hermann Hospital for facilitating patient data collec-tion, and Drs. Jeremy Slater, Giridhar Kalamangalam, and OmotolaHope for contributing patients to this study.

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