Abstract

A previous study has shown that actively pursuing a moving target provides a predictive motor advantage when compared with passive observation of the moving target while keeping the eyes still [Burke, M. R., & Barnes, G. R. Anticipatory eye movements evoked after active following versus passive observation of a predictable motion stimulus. Brain Research, 15, 74–81, 2008b]. By using a novel paradigm based on combining a smooth pursuit stimulus with a go/no-go task, we have been able to reveal significant differences in brain activity for the inhibition of pursuit during the presentation of a smoothly moving target. Areas that show specific inhibitory and retinocentric velocity storage activity for the passive (no-go) condition include the dorsolateral pFC, the caudate, and the posterior cingulate. The FEFs, the supramarginal gyrus, the medial occipital gyrus, and the superior parietal lobe were found to be more involved in both the acquisition and response generation during no-go trials when compared with go trials. The go trials revealed higher activity than the no-go during the acquisition phase in the uncus and posterior cingulate. Furthermore, higher motor-related activity in the go task was found in the cerebellum. In summary, the areas involved in inhibiting smooth pursuit are consistent with the findings from the saccade literature, providing further evidence in support of overlapping cortical control networks.

INTRODUCTION

Inhibiting eye movements is a fundamental feature of motor control. Inhibition during saccadic eye movements has been extensively investigated; however, to date, there has been limited investigation into the processes involved during inhibition of an eye movement to a smoothly moving target. This study aimed to identify the brain areas involved in inhibiting a reflexive eye movement to a moving target by using a novel variation of a go/no-go task. Subjects were instructed via a colored cue to either maintain fixation or follow the target during motion. In a previous article, we found that maintaining fixation during the first presentation of the stimulus (no-go) instead of following it (go) increases the predictive latency and decreases the initial eye velocity to the second presentation of that same stimulus (Burke & Barnes, 2008b), thus reducing the ability to predict stimulus motion. The difference in these behavioral results could be attributed to either (i) additional supportive information about the target motion during active pursuit (e.g., efference copy or proprioceptive input) or (ii) inhibitory mechanisms that continue to affect the behavioral response to the second stimulus. Previous experiments using the antisaccade task have revealed a network of brain areas involved in the inhibition of saccadic eye movements including the frontal cortex, BG, and superior colliculus (SC; for a review, see Munoz & Everling, 2004). A common version of the task used in fMRI imaging is the delayed go/no-go task in which a cue informs the subject to either inhibit a reflex to look at the target (no-go) or look at the target after a delay (go; Hester, Fassbender, & Garavan, 2004). However, a confound may be observed in the motor response between go/no-go trials as the subjects is also required to inhibit a motor action in the go task until the delay has passed or, indeed, differences in the motor response may be observed (Snyder & Lawrence, 2004). In a recent experiment by Brown, Villis, and Everling (2008) a paradigm was used, which omitted the response element of the trial to avoid this confound. This study found greater activity for the no-go trials in supplementary eye fields (SEFs), anterior cingulate, inferior frontal gyrus (IFG), and right supramarginal gyrus (SMG; Brown et al., 2008).

Smooth pursuit and saccades share many common components in their neural pathways (for a review, see Krauzlis, 2004); however, it is also clear that subregional and regional differences do exist between the eye movements specific to the task demands and response required (Burke & Barnes, 2008a, 2008b; Petit, Clark, Ingeholm, & Haxby, 1997). Because of these similarities, it could be assumed that both eye movements share a common inhibitory pathway which has been found at the level of the brainstem (Missal & Keller, 2002). Despite the major advances in understanding the inhibitory network involved in saccades, still very little is known about the inhibitory network involved in suppressing pursuit eye movements to a moving target. The results shown here reveal a unique but overlapping network of brain areas involved in inhibition during smooth pursuit eye movements.

METHODS

Subject Population

This study was approved by both local and regional National Health Service ethical committees. Informed consent was obtained from each of the 11 healthy volunteers that took part in the study (5 men; mean age = 29.7 years; SD = 8.5 years). All subjects had an absence of any relevant neurological or visual defects with good visual acuity, which, in two subjects, was achieved by wearing contact lenses throughout the experiment.

Paradigms

Each subject was given the same information sheet and verbal instructions before performing the set of tasks listed below. In addition, all subjects performed the same task under laboratory conditions approximately 1 week before the scanning session. Subjects performed three blocks in a single experimental session consisting of 48 pairs of presentations (i.e., 144 pairs in total for each of the 11 subjects) taking around 40 min. The design for the presentation of the stimulus has been reported previously (Burke & Barnes, 2008b), in which each pair within each block was chosen at random from the four possible conditions outlined below (go, no-go, rnd, and control tasks). The velocity of the target was randomized between pairs for all three conditions. Each pair of step–ramp presentations was presented in either a predictable or randomized manner, in which either the first and second presentations were matched in time and velocity or the two presentations were randomized in both time and velocity, respectively. The fixation cue was a white square that subtended ∼1 degree of visual angle (dva) on the eye that either changed color or remained white to indicate which of the conditions the subjects would subsequently perform. The target again subtended ∼1 dva on the eye and was a colored disk that moved up, down, left, or right at either 15°/sec or 30°/sec. All experiments took place in a darkened scanning room to minimize any external light source unrelated to the task.

Subjects were given four tasks to perform:

  • (1) 

    Go task: This task consisted of a white fixation cue visible for 200 msec that subsequently changed color to green for a further 200 msec before the screen went blank (gap) for 400 msec. After the gap, the green cue and a green target (T1) appeared, with the target displaced toward the direction of motion (3° or 6°) and smoothly moving at either 15°/sec or 30°/sec for 800 msec before being extinguished. This combination of a blank gap and initial offset has been shown to facilitate the development of anticipatory pursuit in predictable conditions (Collins & Barnes, 2006). A delay of either 2, 4, or 6 sec was then included, during which a black screen was displayed before the same cue and target presentation was repeated as above. The subjects were informed that they must follow the green moving target when it appeared and fixate the centrally positioned cues.

  • (2) 

    No-go task: This task is similar to the task described above (see Figure 1); however, the white fixation cue changed to magenta instead of green in the first presentation, indicating to the subject must maintain fixation while a target would smoothly move in their peripheral field of view. Again a 2-, 4-, or 6-sec delay was used, which was then followed by the cue turning green, indicating the subject to follow the preceding target in the second presentation.

  • (3) 

    Random (rnd) task: This task was designed to mimic the go-go task design above and was cued with a green square with a black cross inside; however, this time the duration of the gap was randomized (200–600 msec) and also the direction and speed (i.e., velocity) of the target between each of the two presentations in the pair so that the first and second presentations were different. The subjects were instructed to simply follow the green target when it appeared.

  • (4) 

    Control (con) task: This task was designed to mimic the timing of the stimuli in the above tasks but did not involve the subject moving their eyes and required simple fixation of the central target. A white square target appeared in the center of the screen for 400 msec, after which the target disappeared during a randomized gap (200–600 msec). The target then reappeared in the center of the screen for 800 msec before a blank screen was again presented for 2, 4, or 6 sec. This presentation was then repeated. Like the previous tasks, all the various conditions were balanced.

Figure 1. 

A timeline diagram of the go/no-go task. The colored square presented during the cue phase indicated to the subjects what task was being presented: the green square with black cross was the random task, the red square was the no-go task, the green square was the go task, and the white square was the control condition. During the delay (2, 4, or 6 sec), a blank screen appeared in which they were instructed to try and maintain fixation (total duration of trials = 5.2 sec, 7.2 sec, or 9.2 sec).

Figure 1. 

A timeline diagram of the go/no-go task. The colored square presented during the cue phase indicated to the subjects what task was being presented: the green square with black cross was the random task, the red square was the no-go task, the green square was the go task, and the white square was the control condition. During the delay (2, 4, or 6 sec), a blank screen appeared in which they were instructed to try and maintain fixation (total duration of trials = 5.2 sec, 7.2 sec, or 9.2 sec).

Equipment Setup and Acquisition

Eye movements were monitored both in a laboratory setting using the Chronos eye tracker running at 200 Hz (Chronos Vision GmbH, Berlin, Germany) and inside the fMRI scanner using the ASL long range optics video eye tracker (Applied Science Laboratory, Bedford, MA) running at 60 Hz. When subjects lay supine in the scanner, an image of the right eye was reflected into the ASL video camera positioned near the head of the subject via a mirror positioned on the head coil. The visual stimuli were generated using Cogent software (www.vislab.ucl.ac.uk/cogent.php) running in a MatLab (Mathworks, Inc., Natick, MA) environment. This system was linked to a liquid crystal projector, which back-projected the target (image) onto a large white screen situated at the feet of the subject. The subject was able to see the stimulus via a mirror positioned on the headcoil. Head movements were minimized during the task by the use of foam padding either side of the head. The eye movements were analyzed off-line by capturing the pupil from the video image. Many of the resultant eye movement data files from the scanner proved noisy and difficult to interpret. However, additional visual inspection of a video image of the eye during the scanning provided evidence that subjects performed the task correctly. We used functional magnetic imaging at 3T (Philips 3.0T Achieva, Gurgaon, India) with an eight-channel Sense head coil (Achieva 3.0T Neuro Coil, Gurgaon, India) specially designed for greater signal-to-noise ratio. Foam pads were used with all subjects to minimize head motion during the trials.

Eye Movement Data Analysis

Details of the eye movement analysis for the laboratory-based experiment have been described in detail previously (Burke & Barnes, 2008b). We analyzed the eye movements from the fMRI experiment off-line by defining the pupil and tracking this to obtain horizontal and vertical displacement of the eyes during the tasks. Data were subjected to a low-pass filter with a zero-phase digital filter (30 Hz), and blinks were identified and removed using custom-made MatLab programs. Velocity and acceleration were generated using a two-point difference of the displacement and velocity profiles, respectively. Differences in level of prediction were calculated based on an established and reliable technique of measuring velocity 50 msec after target onset (V50). This measure is known to provide information about the storage of velocity information before any visual feedback informing the brain of the target motion. Latency was calculated by finding the point in the velocity profile in which eye velocity reached 10% of the peak velocity. A linear regression was performed from this point on the velocity profile back to the x axis (y = 0), and this time point was subtracted from target onset to obtain latency.

fMRI Data Acquisition and Analysis

We measured BOLD changes in cortical activity during the tasks. During each scan, a T2* sensitive EPI pulse sequence was used with a repetition time of 2000 msec, an echo time of 30 msec, and flip angle of 90°. Each volume comprised 30 slices of the full brain by using 3 × 3 × 3 mm3 voxel size and a field of view of 256 mm.

We applied standard preprocessing procedures to the resultant fMRI data using SPM2 (www.fil.ion.ucl.ac.uk/spm/) that included slice time correction, spatial realignment, normalization to Montreal Neurological Institute coordinates and a 9-mm FWHM Gaussian filter. The data were high-pass filtered (128 sec cutoff), and global drifts were removed with proportional scaling. We performed an event-related analysis on the data and presented data across the whole trial (Figure 3) and also for each segment of the trial (i.e., first presentation, delay, and second presentation) in Figure 4. For Figure 3, the contrast presented includes the whole trial and was collapsed across delay, direction, and speed for each condition, creating a design matrix for the three tasks of interest (task: go-go, no-go-go, and rnd), resulting in 48 repetitions of each condition per subject. This design was also used for Figure 4; however, in this analysis, we also split the three conditions of interest into three segments (first presentation, delay, and second presentation of the stimuli), which were each modeled independently (see Table 1). It should be noted that these conditions were created from a contrast with the control condition baseline so that activity related to fixating a stationary stimulus was removed. This design formed the basis for the first level “fixed effects” analysis performed in each subject in which t contrasts for the first presentation of the stimulus (go > rnd, no-go > rnd, and go > no-go) and likewise for the second presentation of the stimulus. A group level “random effects” (RFX) analysis (one-sample t test) was then performed on the resultant contrasts from each of the individual subjects. Brain areas were identified using SPM2 anatomy toolbox (v1.6; Eickhoff et al., 2007), the MNI coordinates were transformed to Talairach using MNI2TAL.m (imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach), and additional confirmation of locations was sorted using the Talairach Daemon client (Lancaster et al., 2000).

Table 1. 

Summary of the Three Main Conditions and Contrasts Used for Analysis of the Data


First Presentation
Delay
Second Presentation
Contrasts
Go-go trial Go (rnd) Fix: 2, 4, 6 sec Go (prd) (1) Go > rnd: prd vs. rnd with action 
No-go trial No-go (rnd) Fix: 2, 4, 6 sec Go (prd)  
rnd trial Go (rnd) Fix: 2, 4, 6 sec Go (rnd) (2) No-go > rnd: prd vs. rnd without action 
Contrasts Go > no-go: Inhibition and passive velocity storage  Go > no-go: Effect of motor rehearsal N.B. All analyses conducted on data collapsed across three delays, four directions, and two speeds. 

First Presentation
Delay
Second Presentation
Contrasts
Go-go trial Go (rnd) Fix: 2, 4, 6 sec Go (prd) (1) Go > rnd: prd vs. rnd with action 
No-go trial No-go (rnd) Fix: 2, 4, 6 sec Go (prd)  
rnd trial Go (rnd) Fix: 2, 4, 6 sec Go (rnd) (2) No-go > rnd: prd vs. rnd without action 
Contrasts Go > no-go: Inhibition and passive velocity storage  Go > no-go: Effect of motor rehearsal N.B. All analyses conducted on data collapsed across three delays, four directions, and two speeds. 

The conditions are outlined in the upper section of the table with all analyzed data collapsed across delay, direction, and speed (as with the behavioral analysis). The results of the contrasts go > rnd and no-go > rnd are displayed in Figure 3. The further analysis for each segment of the response (again collapsed data) is shown in the lower section of the table (go > no-go) and in Figure 4.

RESULTS

Eye Movement Results

A repeated measures ANOVA (Task [2] × Delay [3] × Direction [4] × Speed [2]) was performed on the mean eye movement results from all 11 subjects and revealed significant differences between the Go and No-go conditions in the laboratory setting. Previous studies have shown that taking a sample of eye velocity 50 msec after the target appears (V50) gives a reliable measure of prediction (Barnes, Barnes, & Chakraborti, 2000), because this occurs well before the minimum RT to a visual stimulus (80–100 msec). A small but significant higher eye velocity at V50 was observed in the go-go condition when compared with the no-go condition (F(1, 10) = 25.58, p = .004, η2 = 0.837). In addition, we found a shorter latency in the go task than the no-go task, revealing an earlier onset toward the target in the active condition (Burke & Barnes, 2008b). Together these results indicate that both the timing and velocity information stored in the passive (no-go task) condition is not equal to the active pursuit condition (go task) with the latter condition revealing a motor advantage. We found reactive latencies for this step–ramp paradigm to be around 93 msec and V50 values of <0.16°/sec to the randomized (rnd) target condition, which are comparable with previous studies.

Group fMRI Results

The group level activation with a threshold of T > 5 and a minimum cluster size of 15 is shown in Figures 3 and 4. The data were analyzed in two parts. First, a comparison was made between the predictive and randomized conditions; go and no-go conditions were analyzed separately, and in each case, analysis included both the first and second presentations. Second, go and no-go conditions were compared for the first and second presentations separately.

Predictive versus Random

An explicit cognitive cue was used to inform the subjects whether the trial was predictable or random, and whether they should follow the first target presentation (go) or maintain fixation (no-go). This cue provided the basis for the subsequent storage of velocity information in order that subjects could produce prediction to the second presentation of the target.

These results revealed a clear difference in activation for the predictive versus the randomized task. The contrasts between the go and random tasks (Figure 3, left) revealed higher activity in the go task in the right superior parietal cortex (superior parietal lobe [SPL], BA 40), the left precuneus, bilaterally in V5+ (BA 19), and the posterior lobe of the cerebellum. Higher activity during the random condition was found in the left superior frontal gyrus (SFG; BA 10) and the left thalamus and bilaterally in the ventrolateral pFC (VLPFC).

Comparisons of the no-go and random task (Figure 3, right) revealed higher activity for no-go in a number of areas bilaterally including the SMG, the V5+, and the SPL and medial activation in the supplementary eye fields (SEFs), the right FEFs, and the right dorsolateral pFC (DLPFC). Higher activation for the random condition was found bilaterally in the FEFs, the right middle occipital gyrus (MOG), the right IFG, the left SFG, and the caudate.

There were some areas, notably V5 and SPL, that were common to both go and no-go conditions. These areas would, therefore, appear to be specifically associated with the comparison of predictable and randomized conditions whether or not there was any motor response to the first presentation. The remaining areas, notably the SMG, the SEF, and the VLPFC, were more closely associated with the differences between go and no-go tasks, that is, with inhibition of the motor response in the first presentation. In the second analysis, therefore, we specifically examined differences between go and no-go conditions in the first two presentations.

Go versus No-go

We performed a contrast between go and no-go tasks for the first presentation of the stimulus in which subjects maintained fixation in the no-go task but followed the target in the go task (Figure 4, left) and the second presentation of the stimulus in which subjects followed the stimulus in both the go and no-go conditions (Figure 4, right). In both the go and no-go tasks, the first presentation represented a randomized stimulus, whereas the second was predictable on the basis of information gleaned from the first presentation (Table 1). There was greater activity in both the first and second presentations of the stimulus for the no-go task, with only the left posterior cingulate cortex (PCC), the right inferior temporal cortex (BA 20), the culmen, and the right SFG (BA 8), revealing higher activity for the go task. In the first presentation, higher activity for the no-go task was found in the SFG (BA 10), bilaterally in the PCC (BA 23 and BA 31), the right caudate, the right superior temporal gyrus (STG), the left V5+, the left FEF/SEF, the right SPL (BA 7), and the thalamus. The areas revealed by this contrast represent ones that are involved in fixation and inhibition of pursuit and/or in the processing of motion information when the eye is stationary as opposed to moving. During the second presentation of the stimulus, some areas were activated in common with the first presentation, including the left FEF, the left V5+, the PCC, and the right STG. Further to this, additional activation for the second presentation of the stimulus was found in the right VLPFC, the left MOG, and the left parahippocampus (pHPP). The major behavioral difference in this second presentation was in the use of stored motion information, in particular, the absence in the no-go condition of any possible “motor memory” component. This behavioral difference may have resulted in the slightly reduced initial eye velocity (V50) for the no-go condition.

Finally, it should be noted that data from a single subject showed lateralization effects that were present regardless of direction of motion. Furthermore, equal numbers of eye movements to each direction were included in each condition during the task to counteract possible direction effects in the floccular lobe (Glasauer, Stephan, Kalla, Marti, & Straumann, 2009). However, we did not find significant directional effects in the present study.

DISCUSSION

The behavioral data revealed significantly shorter latencies and higher early eye velocity (V50: eye velocity before visual feedback) to the second presentation of the target in the go task (Figure 2). In a previous article, we hypothesized that actively following the first presentation instead of passively observing it results in a more enhanced motor preparation for the subsequent release of information (Burke & Barnes, 2008b).

Figure 2. 

The mean and standard error for V50 and latency in the second presentation of the go-go, no-go-go, and random conditions are shown on the left and right, respectively, for all 11 subjects. Behavioral data for nine subjects' data have been reported previously (Burke & Barnes, 2008b) and display similar results.

Figure 2. 

The mean and standard error for V50 and latency in the second presentation of the go-go, no-go-go, and random conditions are shown on the left and right, respectively, for all 11 subjects. Behavioral data for nine subjects' data have been reported previously (Burke & Barnes, 2008b) and display similar results.

Comparison of Predictive and Random Brain Activity

We found similar areas activated for both the go and no-go tasks when contrasted independently with the random task (see Figure 3). These areas were similar to those that have been highlighted in previous fMRI and primate neurophysiological studies (Shichinohe et al., 2009; Lencer & Trillenberg, 2008; Lencer et al., 2004; Missal & Keller, 2002; Schmid, Rees, Frith, & Barnes, 2001). Overall, more significantly activated voxels occurred between the no-go and rnd contrast than the go and rnd contrast, suggesting that the brain is recruiting more areas and/or revealing higher BOLD activity in the no-go condition.

Figure 3. 

The group level RFX contrasts for all 11 subjects for the contrasts: go > rnd (left) and no-go > rnd (right). The upper images (A) show the brain activity on a template brain. In red is the positive activation for the contrasts (go and no-go), and in blue is the negative activation for the contrast (i.e., rnd) contrasts. Beneath (B) is a table of the positive and negative activation for the image showing MNI coordinates (X, Y, and Z), t values, and a family-wise error significance value using small volume correction and a sphere of 10 mm3.

Figure 3. 

The group level RFX contrasts for all 11 subjects for the contrasts: go > rnd (left) and no-go > rnd (right). The upper images (A) show the brain activity on a template brain. In red is the positive activation for the contrasts (go and no-go), and in blue is the negative activation for the contrast (i.e., rnd) contrasts. Beneath (B) is a table of the positive and negative activation for the image showing MNI coordinates (X, Y, and Z), t values, and a family-wise error significance value using small volume correction and a sphere of 10 mm3.

On contrasting the go with the rnd condition, we found bilateral activation in the VLPFC (BA 47) and SFG (BA 10) and also higher activity in the thalamus for the random condition. Although VLPFC and SFG are not classically associated with generating smooth pursuit, they have been implicated in decision-making about whether the visual stimulus is familiar or novel (Petrides, Alivisatos, & Frey, 2002). We suggest a role for these areas in identifying reduced automaticity during unpredictable tasks. The central thalamus has an established role in the regulation and monitoring of smooth pursuit (Tanaka, 2005) and, thus, will provide more direct input to the pursuit system during the random task, whereas in predictable conditions, a more comparative role may be utilized (Barnes & Asselman, 1991).

The contrast between the no-go and rnd condition indicated higher activity for the random condition in the caudate, the medial frontal gyrus (MFG; BA 4/BA 6), and the MOG (BA 18). The activity in BA 4/BA 6 (equivalent to the FEF) for the randomized tasks is consistent with evidence that FEF is more involved in visually driven pursuit and SEF in memory-driven pursuit (Shichinohe et al., 2009; Burke & Barnes, 2008b). It is also possible that the foveofugal step–ramp used in this task may have encouraged more saccades in the rnd task, also contributing to the higher activity observed in FEF. The early visual area BA 18 was also more active in the randomized task, consistent with the suggestion that the predominantly visually dependent task relies more on early visual information than the predominantly memory-driven predictive task (Burke & Barnes, 2008a). Overall, predictive responses (go and no-go tasks) revealed higher activity in the V1, the V2, the V5+, the SPL, and the cerebellum as found and discussed previously (Burke & Barnes, 2008a). We also found additional significant activity for the no-go > rnd contrast in the SEF, the SMG, the IFG, and the DLPFC, which are discussed later.

Inhibition Network in Smooth Pursuit (Go > No-go)

We segregated activity related to the first presentation of the stimulus and activity related to the second presentation of the stimulus (see Figure 4). The delay between these presentations allows the bold signal to approach baseline levels, providing insight into the differences in (i) the inhibition (first presentation) and motion perception and (ii) the resultant motor response (second presentation).

Figure 4. 

The group level RFX contrasts for all 11 subjects for the contrasts: go > no-go for the first presentation of the stimuli (left) and the second presentation of the stimuli (right). In the upper images (A), brain areas in red show the positive activation for the go task, and the blue shows positive activation for the no-go task. Beneath (B) are tables of the positive and negative activation for the image showing MNI coordinates (X, Y, and Z), Z values, and a family-wise error significance value using small volume correction and a sphere of 10 mm3. CBM, cerebellum.

Figure 4. 

The group level RFX contrasts for all 11 subjects for the contrasts: go > no-go for the first presentation of the stimuli (left) and the second presentation of the stimuli (right). In the upper images (A), brain areas in red show the positive activation for the go task, and the blue shows positive activation for the no-go task. Beneath (B) are tables of the positive and negative activation for the image showing MNI coordinates (X, Y, and Z), Z values, and a family-wise error significance value using small volume correction and a sphere of 10 mm3. CBM, cerebellum.

First Presentation of the Stimulus

The contrast between go and no-go (Figure 4, left) revealed higher activation for the go condition in the dorsal PCC and the inferior temporal cortex (ITC). The PCC has been implicated previously in anticipatory allocation of spatial attention (Small et al., 2003). The results provided here support this finding, and we speculate that this area may be important for the monitoring of motor activity required in a task but further investigation is needed. Activity in the ITC has a role in high-level visual processing and recognition memory (Ranganath & D'Esposito, 2005; Sigala & Logothetis, 2002). Our experiment reveals a specific role for this area in the active acquisition of the moving stimulus and the subsequent response.

Higher activity for the no-go task (when contrasted with the go condition; Figure 4, left) was principally found in the left medial frontal cortex (BA 10), the left V5+, the left FEF, the left SEF, the right caudate, the right precuneus, the right SMG, the right PCC, the right STG, and the thalamus. The increased activity in these areas was initially surprising given their established role in pursuit; however, we suggest an additional role of these areas in either the inhibition of smooth eye movements and/or the passive (retinocentric) acquisition of target velocity information. Previous studies investigating saccadic eye movements have found several areas to play a prominent role in the inhibition of a saccade, including the DLPFC, the FEF, and the BG (Munoz & Everling, 2004). In addition, FEF has extensive reciprocal connections to SC, and it has been established that interneurons within these structures are responsible for the inhibitory control of saccadic eye movements (Munoz & Everling, 2004; Munoz & Istvan, 1998). Our results support these findings and suggest an equivalent network within the right FEF is also used to inhibit smooth pursuit eye movements. Furthermore, we suspect the activity in FEF is not associated with fixation, as this the baseline/control condition was removed before the go < no-go contrast. A possible source of inhibitory input into FEF and SC may include pFC (BA 10) as it has direct projections to this area. It is feasible that pFC may also alter the excitability of the neurons within right FEF and SC. pFC has previously been implicated in predictive smooth pursuit eye movements (Burke & Barnes, 2008a; Nagel et al., 2006; Lencer et al., 2004; Schmid et al., 2001). Other fMRI studies of go/no-go tasks in saccades concluded that pFC plays a role in decision-making and executive function. Further support from Zheng, Oka, Bokura, and Yamaguchi (2008) found that this area is specific for response inhibition during a go/no-go button press task. Notably, we found a small increase in activity in SEF in the no-go condition (that was also present in the no-go/random contrast). As both the go and no-go tasks are predictive, we would expect SEF to be equally active in both tasks. However, we observed a dichotomy in FEF and SEF that suggests the SEF may hold more influence during passive motion acquisition (when no motor plan is used) and FEF may be more important when a motor plan is created and used. The ventral PCC (BA 23/BA 31) has been implicated in error-related processing (Menon, Adleman, White, Glover, & Reiss, 2001); however, our results suggest a more general role for this area in inhibition and error monitoring.

The SPL has an established role in generating internal representations of visual space for both saccades (Colby, Duhamel, & Goldberg, 1995) and smooth pursuit (Heide, Kursidim, & Kömpf, 1996). In addition, a region of the posterior parietal cortex (PPC; LIP/7a) has directionally selective pursuit-related activity that is modulated by eye position (Bremmer, Distler, & Hoffman, 1997). This suggests a role for this area in the transformation between retinocentric coordinates in the absence of eye movement information (no-go task) into a motor plan for the eye in oculocentric coordinates, which would be a greater requirement in the no-go task. In line with previous studies (Burke & Barnes, 2008a, 2008b), we found that the STG and the precuneus play a more substantial role in visuomotor transformations of predictive responses when information is acquired passively. Finally, higher activity for the no-go condition was found in V5+ for both the first and second presentations of the stimuli. V5+ has a well-established role in motion perception (for a review, see Maunsell & Newsome, 1987) and eye movements (for a review, see Krauzlis, 2004). One principal difference between the no-go and go task is the extent of target motion on the retina, which is greater for the no-go task than the go task. We postulated that higher activity in V5+ may also be related to this more sustained retinal motion in the no-go condition. Furthermore, based on the evidence presented above, we postulate that V5, FEF, STG, and SPL form a circuit that is important for prediction in smooth pursuit. Further to this, we suggest this circuit is modulated as a unit depending on the demands of the task, which are higher in the case of the no-go condition because of the initial inhibition. Further to this, the combination of the caudate and the thalamus is thought to provide inhibitory motor control during priming (Aron et al., 2003). Isoda and Hikosaka (2008) found the importance of the BG in inhibiting predictive saccades, suggesting a circuit of activity that also involves the thalamus. Our findings provide evidence for a previously unknown role of these areas in the inhibition of smooth pursuit eye movements.

Second Presentation of Stimulus

The response from the subject to the second presentation of the stimulus was the same for both the go and no-go conditions. Thus, any difference observed between this second presentation of the stimuli can be considered to arise from storage or motor execution differences. Three areas revealed higher bold activity to the go condition (when contrasted with the no-go condition) and included the right uncus (BA 20), the left culmen in the cerebellum, and the MFG (BA 6). Higher activity in the cerebellum for the go task may stem from additional motor learning and timing differences found in the behavioral response. MFG corresponds to one area of the FEF (Petit et al., 1997) that revealed higher activity in the no-go task to the second stimulus presentation when compared with the go condition. The go and no-go contrast to the second presentation revealed several similar areas of activation to the first presentation including the V5+, the left FEF, and the left precuneus. In addition, several distinct areas for this contrast were also identified, including the right VLPFC (BA 47), the right STG, and the left pHPP (Figure 4, right). The VLPFC has been associated with both motor inhibition (Del-Ben et al., 2005) and working memory (Ranganath, Johnson, & D'Esposito, 2003). In our task, it seems the medial pFC (area BA 10) is more related to the inhibition of the eye movements and VLPFC is more related in generating a motor response after this inhibition during prediction. It is well established that the pHPP is important for spatial memory (Ploner et al., 2000). In line with this finding, we provide evidence that the left pHPP is more important for generating predominantly memory-driven smooth pursuit eye movements.

Conclusions

This is the first fMRI experiment in which a go/no-go task has been integrated with predictive and randomized smooth pursuit. This novel design has provided a unique opportunity to investigate differences between active and passive acquisition of visual information that is subsequently used in a predictive smooth pursuit response, and the inhibitory process needed to inhibit a reflexive pursuit eye movement. The contrast between these paradigms has revealed significant differences between the behavioral responses in the go and no-go tasks (see Figure 2), which have now been realized in the activity of the brain (see Figure 4). It is important to note that both the go and no-go tasks were spatially and temporally equivalent and the only difference was a colored cue that instructed the subjects to either maintain fixation during the first presentation of the stimulus in the no-go condition or follow the target. The results show a specific network for the inhibition of smooth eye movements that includes the medial pFC (BA 10), the caudate, and the thalamus. Passively acquired target information recruited higher activity in the VLPFC and the pHPP. We also found the network of brain areas involved in predictive pursuit (V5+, FEF, SPL, and STG) to be modulated by the inhibitory process (Figure 5).

Figure 5. 

A diagram of the brain areas involved in modulating (gray) the predictive pursuit signal, inhibiting the pursuit eye movement (red), and generating the motor response after inhibition (green) during a go/no-go smooth pursuit task.

Figure 5. 

A diagram of the brain areas involved in modulating (gray) the predictive pursuit signal, inhibiting the pursuit eye movement (red), and generating the motor response after inhibition (green) during a go/no-go smooth pursuit task.

Acknowledgments

We would like to acknowledge the MRC and Translational Imaging Group for funding this project and the staff at the Magnetic Resonance Imaging Facility (MRIF) for support in running the fMRI experiments.

Reprint requests should be sent to Dr. Melanie Rose Burke, Institute of Psychological Sciences, Faculty of Medicine and Health, The University of Leeds, Leeds, LS2 9JT, U.K., or via e-mail: m.r.burke@leeds.ac.uk.

REFERENCES

REFERENCES
Aron
,
A. R.
,
Schlaghecken
,
F.
,
Fletcher
,
P. C.
,
Bullmore
,
E. T.
,
Eimer
,
M.
,
Barker
,
R.
,
et al
(
2003
).
Inhibition of subliminally primed responses is mediated by the caudate and thalamus: Evidence from functional MRI and Huntington's disease.
Brain
,
126
,
713
723
.
Barnes
,
G. R.
, &
Asselman
,
P. T.
(
1991
).
The mechanism of prediction in human smooth pursuit eye movements.
Journal of Physiology
,
439
,
439
461
.
Barnes
,
G. R.
,
Barnes
,
D. M.
, &
Chakraborti
,
S. R.
(
2000
).
Ocular pursuit responses to repeated, single-cycle sinusoids reveal behaviour compatible with predictive pursuit.
Journal of Neurophysiology
,
84
,
2340
2355
.
Bremmer
,
F.
,
Distler
,
C.
, &
Hoffman
,
K. P.
(
1997
).
Eye position effects in monkey cortex: II. Pursuit- and fixation-related activity in posterior parietal areas LIP and 7A.
Journal of Neurophysiology
,
77
,
962
977
.
Brown
,
M. R.
,
Villis
,
T.
, &
Everling
,
S.
(
2008
).
Isolation of the saccade inhibition processes: Rapid event-related fMRI of saccades and NoGo trials.
Neuroimage
,
39
,
793
804
.
Burke
,
M. R.
, &
Barnes
,
G. R.
(
2008a
).
Brain and behaviour: A task-dependent eye movement study.
Cerebral Cortex
,
18
,
126
135
.
Burke
,
M. R.
, &
Barnes
,
G. R.
(
2008b
).
Anticipatory eye movements evoked after active following versus passive observation of a predictable motion stimulus.
Brain Research
,
15
,
74
81
.
Colby
,
C. L.
,
Duhamel
,
J. R.
, &
Goldberg
,
M. E.
(
1995
).
Oculocentric spatial representation in parietal cortex.
Cerebral Cortex
,
5
,
470
481
.
Collins
,
C. J. S.
, &
Barnes
,
G. R.
(
2006
).
The occluded onset pursuit paradigm: Prolonging anticipatory smooth pursuit in the absence of visual feedback.
Experimental Brain Research
,
175
,
11
20
.
Del-Ben
,
C. M.
,
Deakin
,
J. F.
,
McKie
,
S.
,
Delvai
,
N. A.
,
Williams
,
S. R.
,
Elliott
,
R.
,
et al
(
2005
).
The effect of citalopram pretreatment on neuronal responses to neuropsychological tasks in normal volunteers: An fMRI study.
Neuropsychopharmocology
,
30
,
1724
1734
.
Eickhoff
,
S. B.
,
Paus
,
T.
,
Caspers
,
S.
,
Grosbras
,
M. H.
,
Evans
,
A.
,
Zilles
,
K.
,
et al
(
2007
).
Assignment of functional activations to probabilistic cytoarchitectonic areas revisited.
Neuroimage
,
36
,
511
521
.
Glasauer
,
S.
,
Stephan
,
T.
,
Kalla
,
R.
,
Marti
,
S.
, &
Straumann
,
D.
(
2009
).
Up-down asymmetry if cerebellar activation during vertical pursuit eye movements.
Cerebellum
,
8
,
385
388
.
Heide
,
W.
,
Kursidim
,
K.
, &
Kömpf
,
D.
(
1996
).
Deficits of smooth pursuit eye movements after frontal and parietal lesions.
Brain
,
119
,
1951
1969
.
Hester
,
R.
,
Fassbender
,
C.
, &
Garavan
,
H.
(
2004
).
Individual differences in error processing: A review and reanalysis of three event-related fMRI studies using the GO/NOGO task.
Cerebral Cortex
,
14
,
986
994
.
Isoda
,
M.
, &
Hikosaka
,
O.
(
2008
).
Role of the subthalamic nucleus neurons in switching from automatic to controlled eye movements.
Journal of Neuroscience
,
28
,
7209
7218
.
Krauzlis
,
R. J.
(
2004
).
Recasting the smooth pursuit eye movement system.
Journal of Neurophysiology
,
91
,
591
603
.
Lancaster
,
J. L.
,
Woldorff
,
M. G.
,
Parsons
,
L. M.
,
Liotti
,
M.
,
Freitas
,
C. S.
,
Rainey
,
L.
,
et al
(
2000
).
Automated Talairach Atlas labels for functional brain mapping.
Human Brain Mapping
,
10
,
120
131
.
Lencer
,
R.
,
Nagel
,
M.
,
Sprenger
,
A.
,
Zapf
,
S.
,
Erdmann
,
C.
,
Heide
,
W.
,
et al
(
2004
).
Cortical mechanisms of smooth pursuit eye movements with target blanking.
European Journal of Neuroscience
,
19
,
1430
1436
.
Lencer
,
R.
, &
Trillenberg
,
P.
(
2008
).
Neurophysiology and neuroanatomy of smooth pursuit in humans.
Brain and Cognition
,
68
,
219
228
.
Maunsell
,
J. H. R.
, &
Newsome
,
W. T.
(
1987
).
Visual processing in monkey extrastraite cortex.
Annual Review of Neuroscience
,
10
,
363
401
.
Menon
,
V.
,
Adleman
,
N. E.
,
White
,
C. D.
,
Glover
,
G. H.
, &
Reiss
,
A. L.
(
2001
).
Error-related brain activation during a Go/NoGo response inhibition task.
Human Brain Mapping
,
12
,
131
143
.
Missal
,
M.
, &
Keller
,
E. L.
(
2002
).
Common inhibitory mechanism for saccades and smooth-pursuit eye movements.
Journal of Neurophysiology
,
88
,
1880
1892
.
Munoz
,
D. P.
, &
Everling
,
S.
(
2004
).
Look away: The anti-saccade task and the voluntary control of eye movements.
Nature Reviews
,
5
,
218
228
.
Munoz
,
D. P.
, &
Istvan
,
P. J.
(
1998
).
Lateral inhibitory interactions in the intermediate layers of the monkey superior colliculus.
Journal of Neurophysiology
,
79
,
1193
1209
.
Nagel
,
M.
,
Sprenger
,
A.
,
Zapf
,
S.
,
Erdmann
,
C.
,
Kömpf
,
D.
,
Heide
,
W.
,
et al
(
2006
).
Parametric modulation of cortical activation during smooth pursuit with and without target blanking. An fMRI study.
Neuroimage
,
9
,
1319
1325
.
Petit
,
L.
,
Clark
,
V. P.
,
Ingeholm
,
J.
, &
Haxby
,
J. V.
(
1997
).
Dissociation of saccade-related and pursuit-related activity in human frontal eye fields as revealed by fMRI.
Journal of Neurophysiology
,
77
,
3386
3390
.
Petrides
,
M.
,
Alivisatos
,
B.
, &
Frey
,
S.
(
2002
).
Differential activation of the human orbital, mid-ventrolateral, and mid-dorsolateral prefrontal cortex during the processing of visual stimuli.
Proceedings of the National Academy of Sciences, U.S.A.
,
99
,
5649
5654
.
Ploner
,
C. J.
,
Gaymard
,
B. M.
,
Rivaud-Péchoux
,
S.
,
Baulax
,
M.
,
Clémenceau
,
S.
,
Samson
,
S.
,
et al
(
2000
).
Lesions affecting the parahippocampal cortex yield spatial memory deficits in humans.
Cerebral Cortex
,
10
,
1211
1216
.
Ranganath
,
C.
, &
D'Esposito
,
M.
(
2005
).
Directing the mind's eye: Prefrontal, inferior and medial-temporal mechanisms for visual working memory.
Current Opinion in Neurobiology
,
15
,
175
182
.
Ranganath
,
C.
,
Johnson
,
M. K.
, &
D'Esposito
,
M.
(
2003
).
Prefrontal cortex associated with working memory and episodic long-term memory.
Neuropsychologia
,
41
,
378
389
.
Schmid
,
A.
,
Rees
,
G.
,
Frith
,
C.
, &
Barnes
,
G.
(
2001
).
An fMRI study of anticipation and learning in smooth pursuit eye movements in humans.
NeuroReport
,
12
,
1409
1414
.
Shichinohe
,
N.
,
Akao
,
T.
,
Kurkin
,
S.
,
Fukushima
,
J.
,
Kaneko
,
C. R. S.
, &
Fukushima
,
K.
(
2009
).
Memory and decision-making in the frontal cortex during visual motion processing for smooth pursuit eye movements.
Neuron
,
62
,
717
732
.
Sigala
,
N.
, &
Logothetis
,
N. K.
(
2002
).
Visual categorization shapes feature selectivity in the primate temporal cortex.
Nature
,
415
,
318
320
.
Small
,
D. M.
,
Gitelmann
,
D. R.
,
Gregory
,
M. D.
,
Nobre
,
A. C.
,
Parrish
,
T. B.
, &
Mesulam
,
M.-M.
(
2003
).
The posterior cingulated and medial prefrontal cortex mediate the anticipatory allocation of spatial attention.
Neuroimage
,
18
,
633
641
.
Snyder
,
L. H.
, &
Lawrence
,
B. M.
(
2004
).
Don't go there.
Neuron
,
43
,
297
299
.
Tanaka
,
M.
(
2005
).
Involvement of the central thalamus in the control of smooth pursuit eye movements.
Journal of Neuroscience
,
25
,
5866
5876
.
Zheng
,
D.
,
Oka
,
T.
,
Bokura
,
H.
, &
Yamaguchi
,
S.
(
2008
).
The key locus of common response inhibition network for no-go and stop signals.
Journal of Cognitive Neuroscience
,
20
,
1434
1442
.