Contrasting the roles of the supplementary and frontal eye fields in ocular decision making Shun-nan Yang and Stephen Heinen J Neurophysiol 111:2644-2655, 2014. First published 26 March 2014; doi:10.1152/jn.00543.2013

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Journal of Neurophysiology publishes original articles on the function of the nervous system. It is published 12 times a year (monthly) by the American Physiological Society, 9650 Rockville Pike, Bethesda MD 20814-3991. Copyright © 2014 by the American Physiological Society. ISSN: 0022-3077, ESSN: 1522-1598. Visit our website at http://www.the-aps.org/. J Neurophysiol 111: 2644–2655, 2014. First published March 26, 2014; doi:10.1152/jn.00543.2013.

Contrasting the roles of the supplementary and frontal eye fields in ocular decision making

Shun-nan Yang1,2 and Stephen Heinen2 1Vision Performance Institute, College of Optometry, Pacific University, Forest Grove, Oregon; and 2Smith-Kettlewell Eye Research Institute, San Francisco, California

Submitted 29 July 2013; accepted in final form 25 March 2014

Yang SN, Heinen S. Contrasting the roles of the supplementary and specified by the motion stimulus (Britten et al. 1996; Pu- frontal eye fields in ocular decision making. J Neurophysiol 111: 2644–2655, rushothaman and Bradley 2005). 2014. First published March 26, 2014; doi:10.1152/jn.00543.2013.—Single- LIP is also involved in perceptual decision making (Shadlen unit recording in monkeys and functional imaging of the human and Newsome 2001). Neurons here are normally directionally frontal lobe indicate that the supplementary eye field (SEF) and the selective (Andersen et al. 1992; Colby et al. 1996; Snyder et al. frontal eye field (FEF) are involved in ocular decision making. To test whether these structures have distinct roles in decision making, 2000), which should in theory limit the amount of flexibility single-neuron activity was recorded from each structure while mon- they have in deciding the direction of moving stimuli. How- keys executed an ocular go/nogo task. The task rule is to pursue a ever, there is evidence that LIP neurons can modify their moving target if it intersects a visible square or “go zone.” We found tuning to respond to motion directions that fall outside of their that most SEF neurons showed differential go/nogo activity during the inherent tuning range to reclassify those motions when the delay period, before the target intersected the go zone (delay period), decision boundary changes (Freedman and Assad 2006). In this Downloaded from whereas most FEF neurons did so after target intersection, during the work, the animals (and neurons) were trained to classify two period in which the movement was executed (movement period). sets of motions with respect to a single decision boundary. Choice probability (CP) for SEF neurons was high in the delay period Most cells that were tested classified the motions correctly. but decreased in the movement period, whereas for FEF neurons it Following a period of retraining with two new sets of motions was low in the delay period and increased in the movement period. and a new boundary, the neurons adjusted to the new boundary Directional selectivity of SEF neurons was low throughout the trial, whereas that of FEF neurons was highest in the delay period, decreas- and were able to encode the new motions. This indicates that on July 30, 2014 ing later in the trial. Increasing task difficulty led to later discrimina- the LIP has a more flexible role than MT in decision making. tion between go and nogo in both structures and lower CP in the SEF, Consistent with this, LIP neurons have higher CP than do MT but it did not affect CP in the FEF. The results suggest that the SEF neurons (Law and Gold 2008). interprets the task rule early but is less involved in executing the The frontal lobe could be involved in linking perceptual motor decision than is the FEF and that these two areas collaborate decisions performed in posterior cortical areas to movement dynamically to execute ocular decisions. decisions. Recent work has revealed neural activity related to sensorimotor decisions in various frontal substrates, including decision making; smooth pursuit; ocular baseball; go/nogo task; the prefrontal cortex (PFC; Muhammad et al. 2006; Wallis and choice probability; rule-based decision Miller 2003; White and Wise 1999) and the premotor cortex (PMC; Muhammad et al. 2006; Wallis and Miller 2003). These MUCH RESEARCH HAS BEEN DONE to determine the functional studies have shown that PFC neurons encode perceptual cate- pathways for decision making in the primate brain. Part of this gories, whereas those in the PMC display a mixture of activity work was initiated in visual cortex in studies that targeted the reflecting either perceptual categories or motor alternatives. medial temporal cortex (MT) and the lateral intraparietal sulcus The differential activity in these areas is consistent with the (LIP). MT is at the perceptual stage in the decision-making anatomic organization of the frontal lobe, as the PFC receives stream. It has been shown that MT neurons can indicate inputs from posterior sensory areas (Miller 1999; Passingham whether a stimulus moves in a direction with an angle clock- 1985, 1993), and the PMC receives inputs from the PFC wise or counterclockwise to a decision boundary [e.g., decide (Barbas and Pandya 1989, 1991; Pandya and Yeterian 1990). whether stimulus motion has a rightward or leftward compo- Therefore, there appears to be a cascade of neural events in the nent relative to up (Britten et al. 1996; Newsome et al. 1989)]. frontal lobe that transforms perceptual signals into motor However, when the decision boundary is changed, because of decisions. their strict selectivity for the direction of the moving stimulus, We have adopted a different type of task to study decision MT neurons lack the flexibility to signal motion directions that making in structures beyond perceptual areas that process fall outside of their tuning range (Britten et al. 1996). The motion for action. This task is a go/nogo eye movement task relatively hard-wired nature of sensory motion neurons also that we call “ocular baseball” (Heinen et al. 2006; Kim et al. limits their level of choice probability (CP), which character- 2005; Yang et al. 2010). In ocular baseball, a target approaches izes how well a neuron reflects the behavioral decision that is a visible go zone on a computer screen while the observer fixates a spot in the center of the screen. If the target intersects Address for reprint requests and other correspondence: S.-n. Yang, Vision the zone, the observer must follow it with a pursuit eye Performance Institute, College of Optometry, Pacific Univ., 2043 College movement; if not, the observer must maintain fixation. The Way, Forest Grove, OR 97116 (e-mail: shunnan.yang@pacificu.edu). delay period between when the target begins to move, and

2644 0022-3077/14 Copyright © 2014 the American Physiological Society www.jn.org SEF AND FEF IN OCULAR DECISION 2645 when it intersects the go zone allows us to distinguish whether shima et al. 2011). Compared with the FEF, SEF activity is neural activity is more related to rule interpretation or move- more predictive of changes in saccade latency than saccade ment execution. generation in a countermanding task (Hanes et al. 1998; Stu- Recently, using a modified version of this task, we demon- phorn et al. 2010). These findings suggest a more upstream strated that neurons in the supplementary eye field (SEF) are position for the SEF than the FEF in eye movement generation relatively flexible in their interpretation of a changing decision during decision tasks. boundary (Heinen et al. 2012). These neurons interpret iden- The present study examined whether neurons in the FEF and tical motion trajectories as conforming to different rule states SEF are involved in different stages of the ocular decision. We (go vs. nogo) when the decision boundary is changed and can recorded the activity of neurons in each structure while mon- do it rapidly when it is changed from trial to trial. Because the keys performed the go/nogo ocular baseball task. Task diffi- interpretation is independent of motion direction, this places culty was manipulated to alter the timing and accuracy of the the SEF beyond motion perception regions in the decision- decision, and activity was analyzed separately for the delay and making stream. CP here is also high (Yang et al. 2010) relative movement periods of the task. CP as well as rule and direc- to that of sensory neurons (Britten et al. 1996; Shadlen and tional selectivity (RI and DI) of SEF and FEF neurons were Newsome 2001). However, the SEF may lie before movement computed throughout the trial to determine the temporal evo- control regions because SEF neurons have poor directional lution of task-related signals in each structure. tuning and their activity does not agree with the movement on error trials (Yang et al. 2010). Consistent with this, the SEF is involved more in the ocular countermanding decision than in METHODS pursuit and saccade generation (Shichinohe et al. 2009; Stu- Subjects and Surgical Procedures phorn et al. 2009). To complete the ocular decision process, signals from the Two juvenile, male macaque monkeys (ED and LE) were involved in the study. Surgeries were performed to implant SEF and FEF

SEF are likely transformed into a motor command. A candidate Downloaded from recording chambers, a head holder, and a search coil on one eye to structure should have movement neurons that are directionally measure eye movements. SEF chambers were centered 21.5 mm tuned, with activity that agrees with the movement decision. anterior and 18 mm to the right of centerline in Horsley-Clark The frontal eye field (FEF) potentially participates in this stereotaxic coordinates; FEF chambers were centered 23 mm anterior transformation. The FEF has extensive reciprocal connections and 18 mm to the left of centerline. The head holder was positioned with the SEF (Huerta et al. 1987; Luppino et al. 2003). Both on the midline. The search coil was constructed from Teflon-coated the SEF and FEF are active in many ocular decision tasks such stainless steel wire and was implanted under the conjunctiva of one as the antisaccade task (Amador et al. 2004; Schlag-Rey et al. eye (Judge et al. 1980). All surgical procedures were approved by the on July 30, 2014 1997) and the countermanding paradigm (Brown et al. 2008; Institutional Animal Care and Use Committee and were in compliance Hanes et al. 1998; Stuphorn and Schall 2006; Stuphorn et al. with the guidelines set forth in the National Institutes of Health (NIH) 2000). In a functional imaging study in humans designed to Guide for the Care and Use of Laboratory Animals. isolate cortical areas active during go/nogo ocular baseball, the SEF and FEF were active for the decision component of the Testing Procedure and the Ocular Go/Nogo Task task (Heinen et al. 2006). Recent research suggests differential SEF and FEF involve- At the beginning of each recording session, the monkey was seated in a primate chair with his head fixed by a post. The eyes were 50 cm ment in decision making for smooth pursuit and saccades from the screen, and the line of sight was perpendicular to it. Eye (Fukushima et al. 2011; Stuphorn et al. 2010). The SEF position was first calibrated with a single dot of 0.5° angular extent appears to be more involved in signaling and maintaining a that was shown 10° away from a central fixation point vertically or decision during memory-based pursuit, whereas the FEF seems horizontally. An 85- to 100-mm tungsten electrode that usually had to be more directly responsible for pursuit generation (Fuku- 1.0- to 2.0-M⍀ impedance was then lowered into the chamber via a A B 40°(easy no-go) delay movement period period 30°(difficult no-go) 20°(difficult go) fixation point 10°(easy go) Fig. 1. Experimental design. A: temporal sche- & plate matic for the go/nogo ocular decision task. B: target trajectories in relation to the visible 300ms 500ms 1200ms 8° square. The locations where target trajectories target intersect the vertical, dashed lines indicate square intersection time. Go trials (green lines) intersect the square, and nogo trials (red lines) target onset miss the square. square intersection

square intersection time

J Neurophysiol • doi:10.1152/jn.00543.2013 • www.jn.org 2646 SEF AND FEF IN OCULAR DECISION stainless steel tube to a predetermined site. The recording session 6.7° of angular distance to the target until the end of the trial. In nogo typically lasted 2 h. trials, the monkey was required to maintain fixation within the visible The two monkeys were first trained to perform successfully an easy square throughout the trial. Since the angle and direction of target version of the ocular go/nogo task as described in Kim et al. (2005). movement was randomized from trial to trial, it was helpful for the The same version was used to search for task-related neurons. Once a monkey to evaluate whether it was a go or nogo condition in the delay neuron was isolated, a modified version of the task was conducted period (before square intersection) so that he could make the decision where the difficulty of go/nogo decision was manipulated. Figure 1A within the narrow time window after square intersection. Liquid shows the temporal schematic of the task. Each trial began with the reward was given at the end of a trial only when the monkey appearance of a 0.5° white spot at the center of the screen surrounded successfully executed the task; the same amount of liquid was given by an 8-by-8° visible square. To trigger the appearance of a moving for trials of different difficulties. The intertrial interval was 300 ms. target, another 0.5° white spot, the monkey had to maintain his gaze SEF neurons were located by lowering the electrode to a predeter- within a 6.7-by-6.7° invisible electronic square window centered at mined track in the SEF chamber while the monkey was performing the fixation point for 500 ms. At the end of the fixation period, the the ocular go/nogo task (Kim et al. 2005). Once a neuron was isolated, target appeared 13.3° to the left or right and moved toward the visible online analysis of neural activity was conducted to determine whether square at a constant velocity (20°/s) and angle relative to horizontal the neuron responded differently in go and nogo trials. After a (easy: 10 and 40°; difficult: 20 and 30°) for 1,200 ms. The fixation task-related neuron was identified, the modified version of the ocular point and the square remained visible throughout the trial. Figure 1B go/nogo task was employed. FEF neurons were identified with the shows the possible trajectories in relation to the visible square and same procedure by lowering the electrode into the FEF chamber. To fixation point and the defined point of square intersection. Because of classify a neuron as located in the FEF, microstimulation Ͻ50 ␮A had the difference in the trajectory angle, the timing of square intersection to evoke saccades with Ͼ50% likelihood from the same track and and the difficulty of making the go/nogo decision varied. with a similar site depth. In the delay period, defined as the time between target onset and square intersection, the monkey was required to maintain fixation Data Analysis regardless of whether the target eventually intersected the square. Targets moving at 10 and 20° angles intersected the square (go trials), Eye movement detection. Vertical and horizontal eye position and those moving at 30 and 40° angles did not (nogo trials). In go signals were digitized (1 kHz) and stored for offline analysis. Eye Downloaded from trials, the monkey had to initiate a pursuit movement to acquire the velocity was obtained by digital differentiation of eye position, and target within 300 ms of square intersection and maintain gaze within movement initiation time was detected using two criteria modified

ABSEF go neuron SEF nogo neuron align time align time separation time separation time on July 30, 2014 15 sp/s 20 sp/s

6 6 3 3 0 0 # pursuits # pursuits

6 6 3 3 0 0 0 300 600 900 0 300 600 900 # pursuits 0 300 600 900 0 300 600 900 # pursuits time(ms) re: target onset time(ms) re: target onset

-600-900 -300 0 300 -600-900 -300 0 300 -600-900 -300 0 300 -600-900 -300 0 300 time(ms) re: eye movement onset time(ms) re: eye movement onset Fig. 2. Results from example supplementary eye field (SEF) go and nogo neurons. A: SEF go neuron. Traces are grouped by alignment time (top: aligned on target motion onset; bottom: aligned on movement onset). In each subpanel within an alignment, activity in go (green curves) and nogo (red curves) trials is plotted for the 4 trajectories shown in the inset, averaged over the 2 go and the 2 nogo trajectories, respectively. Vertical, dashed lines show alignment events. Vertical arrows show separation times for recorded activity. Traces without arrows did not separate before movement onset. The frequency distribution below the traces shows when eye movements were initiated during success trials. B: SEF nogo neuron. Note that activity in nogo trials is higher than on go trials for all directions. sp/s, Spikes per second; #, number of.

J Neurophysiol • doi:10.1152/jn.00543.2013 • www.jn.org SEF AND FEF IN OCULAR DECISION 2647 from a previous study (Badler and Heinen 2006): either the pursuit rates for all recording sessions were 93 and 76% for easy and difficult movement had a minimum velocity of 10°/s for at least 250 ms or it go success trials and 96 and 91% for nogo success trials. surpassed the saccade threshold of 40°/s for 80 ms and was followed Task-related activity. To characterize neural activity as a continu- by a pursuit movement. MATLAB and its Signal Processing and ous function, the spike rate recorded in each trial was convolved with Statistics Toolboxes (MathWorks 2006) were used to conduct signal a 30-ms Gaussian. To determine whether and when a neuron signaled processing and data analysis. a rule state (go vs. nogo) in the task, the difference in spike rate for go Behavioral responses. Only responses from successful go and nogo and nogo trials was statistically tested (paired 2-tail t-test). A sliding trials were included in the analyses. Go success trials had to satisfy 20-ms window moved at 5-ms steps, and the test was repeated for two criteria: the animal had to maintain fixation within a 6.7° fixation each step. Separation time was defined as the beginning of the 1st 20 window during the delay period, and the target had to be acquired and consecutive, significantly different steps in the delay period, based on continuously pursued within the 6.7° window in the movement period. the Bonferroni familywise comparison (␣ Յ 0.05). When a neuron Nogo success trials were defined as continuous fixation within the displayed a higher spiking rate in go trials than in nogo trials, it was 6.7° fixation window for the duration of the trial. The mean success classified as a go neuron; when activity was higher in nogo trials, it

ABFEF go neuron FEF nogo neuron

align time align time separation time separation time separation time(ms) 15 sp/s 20 sp/s

6 6 3 3 0 6 0 # pursuits # pursuits Downloaded from

6 6 3 3 0 0 # pursuits 0 300 600 900 0 300 600 900 0 300 600 900 0 300 600 900 # pursuits on July 30, 2014 time(ms) re: target onset time(ms) re: target onset

-600-900 -300 0 300 -600-900 -300 0 300 -600-900 -300 0 300 -600-900 -300 0 300 time(ms) re: eye movement onset time(ms) re: eye movement onset

C Evoked saccades 0

-5

-10 vertical position

re: stim start position (deg) -15 0 2.5 5 7.5 horizontal position re: stim start position (deg) Fig. 3. Results from example frontal eye field (FEF) go and nogo neurons. A: example FEF go neuron. Illustrated as in Fig. 2. In contrast with the SEF example neuron (Fig. 2), activity on go trials peaks slightly after movement onset and has relatively narrow tuning. B: example FEF nogo neuron. Higher activity during nogo than go trials is evident, but no consistent peak or directional selectivity is evident. C: evoked saccades from the site where the example go neuron was recorded. Saccades are plotted in Cartesian coordinates with 0,0 point corresponding to saccade origin and red squares depicting saccade endpoints. stim, Stimulation; deg, degrees.

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was classified as a nogo neuron. Note that the Gaussian filter is RESULTS noncausal, and there is debate about whether a causal filter is more appropriate for specifying the timing of neuronal activity. Previously, Task-Related SEF and FEF Neurons we performed an ␣-function analysis alongside the Gaussian analysis on separation times for SEF neurons recorded during ocular baseball We recorded 31 go and 5 nogo SEF neurons as well as 29 go with the same trajectories as in the current study (Yang et al. 2010). and 13 nogo FEF neurons from the 2 monkeys. Figure 2, A and The difference between the ␣- and Gaussian-determined separation B, shows activity of example SEF go and nogo neurons, times was not significant (paired t-test, P ϭ 0.55). respectively. As a first step in evaluating whether the activity Directional- and rule-tuning indices. To determine whether SEF of the neurons was more related to the moving target or the eye and FEF activity reflected the direction of a target trajectory or pursuit movement, neural activity in successful trials was aligned to movement, a directionality index (DI) was calculated for each neuron as below: either target motion onset (upper panels) or eye movement onset (lower panels). Subpanels show neural activity in go ␮ Ϫ ␮ pref opp (green curves) and nogo (red curves) trials for each quadrant of DI ϭ ͯ ͯ. ␮ ϩ ␮ pref opp trajectory motion. At top left, the activity of the SEF go neuron increased ␮ pref was defined as the mean spike rate for the preferred direction shortly after the target began to move in go trials but not in and was derived from the pair of adjacent trajectories that specified nogo trials. This occurred well before the time the target the same rule and had the highest mean spike rate compared with the intersected the square (ϳ500 ms after target motion onset; data other seven pairs of trajectories; the mean spike rate for the two not shown). A frequency distribution of eye movement onset opposite trajectories was noted as ␮ . The formula yields DI values opp times is also shown below the cell activity. Most eye move- that range between 0 and 1 with a value of 1 corresponding to maximum directional tuning. We consider a neuron to be directionally ments were initiated around the time of square intersection, tuned if its DI value is significantly different from 0. Note that since indicating that the monkey attempted to predict when the target the preferred and opposite trajectory pairs are within the same rule entered the zone. Note that activity for go trials in all four state, the DI computation controls for rule-related influence on the quadrants is higher than for nogo trials during the delay period Downloaded from spike rate. DI values were calculated for the delay and movement regardless of the direction of target motion. In the lower panels, periods, respectively, for each neuron. activity from the same neuron is shown aligned with eye To characterize the degree to which a neuron was involved in movement onset. Here, it can be seen that the activity of the go interpreting the rule, a rule index (RI) was also calculated as below: neuron peaked before the movement began for all pursuit ␮ Ϫ ␮ directions. Note that only results of go trials are shown, as pref rule.diff RI ϭ ͯ ͯ. these are the trials in which pursuit occurred. ␮ ϩ ␮ on July 30, 2014 pref rule.diff Figure 2B shows results from an example SEF nogo neuron. Conceptually, RI compares the pair of adjacent trajectories with the At the top, activity for nogo trials was greater than for go trials highest activity in one rule state with the closest pair in the opposite rule early in the delay period for most quadrants. When aligned on state. RI should be maximal if a neuron is active for only one rule state, eye movement onset (lower panels), no consistent change was analogous to how DI is maximal if a neuron is active for one direction of observed in the pattern of neural activity. Note that 100-␮A ␮ motion. Specifically, pref was unchanged from the DI calculation but microstimulation of the sites where example SEF go and nogo ␮ was now compared with rule.diff, the mean spike rate for the nearest neurons were recorded did not evoke pursuit or saccadic eye pair of trajectories in the opposite rule state. The formula yields RI movements. values that range between 0 and 1 with a value of 1 corresponding to maximum rule tuning. We consider a neuron to be rule-tuned if its RI 100 value is significantly different from 0. Note that since the preferred SEF easy SEF difficult and opposite-rule trajectory pairs are adjacent, the RI computation FEF easy generally controls for directional tuning except for that which is 80 FEF difficult extremely narrow. RI values were also calculated for the delay and easy latency difficult latency movement periods, respectively, for each neuron. CP. To determine the extent to which the activity of a task-related 60 SEF or FEF neuron predicted the ocular decision (go or nogo), we calculated the CP of its activity at different times. CP is adopted from the signal detection theory (Green and Swets 1966) and is used to 40 specify the degree to which single neurons reflect decisions (Britten et al. 1996). For our experiment, CP is an estimate of the probability that an observer would correctly predict whether the monkey pursued or 20 cumulative percentile (%) fixated on a given trial from the firing rate of the neuron. CP was derived here by first compiling a distribution of neuronal activity for 0 each of the two choices that the animal made. A receiver operating 0 200 400 600 800 1000 characteristic (ROC) curve was then computed from the distributions, separation time re:target onset(ms) and the area under the curve was integrated to yield CP. Note that CP is in theory best conducted on a trajectory with no Fig. 4. Cumulative distributions for the separation time between spike density signal, where two alternatives are equally likely. The resultant CP curves in go and nogo trials as well as average pursuit latency for go trials. would be uncontaminated by the signal in this situation and can be Results from easy (solid curves) and difficult (dashed curves) trials are plotted separately for all SEF and FEF go and nogo neurons. The arrows above the linked purely to the decision. However, in our study, CP is calculated horizontal axis indicate the median separation time and pursuit latency (50% of using the same trajectories in different structures and compared neurons) for the corresponding curves. Vertical lines indicate square intersec- between the structures, which is reasonable since the same amount of tion times for easy (solid) and difficult (dashed) and go (green) and nogo (red) visual signal contamination should be present in both CP numbers. trials, respectively.

J Neurophysiol • doi:10.1152/jn.00543.2013 • www.jn.org SEF AND FEF IN OCULAR DECISION 2649

Figure 3A shows activity of an example FEF go neuron. The statistically differentiated between go and nogo trajectories in peak activity of this neuron in go trials occurs much later than both easy (10 and 40° from horizontal; see Fig. 1) and difficult that of the SEF neuron and is more prominent when aligned on (20 and 30°) trials. We called this time the separation time (see movement onset (lower panels) than on target onset (upper METHODS for details on separation time derivation). Figure 4 panels). Furthermore, the cell is selective for the leftward/ shows cumulative percentiles of separation times in easy and downward trajectories, displaying narrower tuning than that of difficult trials for all neurons in SEF and FEF. The median the SEF neuron in Fig. 2. Microstimulation applied to the site separation time (50th percentile) for SEF neurons occurred evoked saccades with a direction consistent with the target well before the square intersection time (easy: 262 ms after trajectory that had the highest go activity (Fig. 3C). Figure 3B target onset; difficult: 405 ms). The median separation time for shows results from an example FEF nogo neuron. Activity was FEF neurons occurred later, close to square intersection time generally higher for nogo than go trajectories but without clear (easy: 406 ms; difficult: 591 ms). To compare neural activity consistent tuning or timing of peak activity. with behavior, cumulative percentiles of pursuit latencies in To compare when the activity for SEF and FEF neurons easy and difficult go trials are also shown. Median pursuit provided information about the different trajectory angles, we latencies occur later than the separation times of both structures determined the earliest time that the activity of each structure (easy: 627 ms; difficult: 825 ms).

ABSEF go neuron SEF no-go neuron preferred direction pair DI=0.13 DI=0.038 opposite direction pair RI=0.40 90 RI=0.32 opposite rule state pair 120 1.0 60 Downloaded from

150 30 0.5

180 0.0 0° on July 30, 2014 delay period

210 330

240 300 270 DI=0.090 DI=0.091 RI=0.27 RI=0.26 movement period

Fig. 5. Directional and rule tuning of example SEF neurons in the delay and movement periods. A: results from an example SEF go neuron (top: delay period; bottom: movement period). The length of the radiating lines indicates the normalized mean spike rate for corresponding trajectories in relation to the 1 with the highest spike rate. The arrows indicate the direction of motion (rightward: 0°; leftward: 180°). The green shaded area encompasses the go rule state; gray shading demarcates the tuning function. Thick circles indicate the normalized mean spike rate (center: 0; border: 1) for all trajectories with the same decision criteria within a rule state (go rule state: green; nogo rule state: red), and the 2 corresponding thin circles represent the 95% confidence interval of spike rate. The preferred direction of the neuron was determined by the pair of trajectories with the highest mean spike rate with the additional criterion that both trajectories lie within the same rule state (solid black arrows). The activity for the pair of trajectories with the opposite direction (dotted black arrows) was compared with the preferred direction to calculate directionality index (DI). For rule index (RI), the preferred direction pair was compared with the adjacent pair of trajectories that lie in the other rule state (purple arrows). These were used to compute directional and rule indices (see METHODS) to reflect the degree of neural activity in encoding target direction and decision rule. B: results from an example SEF nogo neuron.

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Therefore, SEF neurons appear to discriminate the rule state was higher than that for the nogo trials (red circle). The earlier than FEF neurons. We wished to gain more insight into directional tuning of this neuron was weak in both periods, whether neurons in these structures were involved in interpret- with similar mean activity for its preferred and opposite motion ing trajectories in the context of the rule or simply dealt with directions (computed from adjacent trajectory pairs; see METHODS). sensory or motor processing. To evaluate this, we determined However, its rule tuning was strong, given the high activity for how well their activity was tuned to a single direction (direc- all trajectory direction within the go rule state and much lower tional tuning) and to a specific rule state (go/nogo). A neuron activity for nogo trajectories. Therefore, this neuron had a was considered to have strong directional tuning if its activity much higher rule tuning index than directional tuning index in for a preferred direction was significantly higher than activity both the delay (RI ϭ 0.40; DI ϭ 0.13) and movement (RI ϭ for the opposite direction when both directions were in the 0.26; DI ϭ 0.09) periods. same rule state. To quantify this, we computed a DI, defined as Figure 5B shows the mean activity of an SEF nogo neuron. the normalized difference in neural activity between the pair of Here, overall mean activity for nogo trajectories (red circle) trajectories with the highest mean spike rate within a rule state, was higher for nogo than for go trajectories (green circle). and the opposite pair (see METHODS). We considered a neuron to have strong rule tuning if the activity for the preferred direction The directional tuning of this neuron was weak in both within one rule state was higher than for the nearest direction periods, as it had similar mean activity for its preferred and in the opposite rule state. To quantify this, we computed an RI, opposite motion directions in both the delay and movement the normalized difference in neural activity between the pre- periods, and activity for the go trajectories was much lower ferred pair of trajectories, and the nearest pair in the opposite than for the nogo ones. Rule tuning was strong, given the rule state (see METHODS). elevated activity for most nogo directions and significantly Figure 5A shows polar plots of the activity of an example lower activity for go trajectories. Rule tuning for this cell SEF go neuron for each of the 16 target trajectories in the delay was much higher than directional tuning in both the delay (RI ϭ 0.32; DI ϭ 0.04) and movement (RI ϭ 0.27; DI ϭ

(top) and movement (bottom) periods. This was considered a Downloaded from go neuron because mean activity for the go trials (green circle) 0.09) periods.

ABFEF go neuron FEF no-go neuron preferred direction pair DI=0.082 DI=0.18 opposite direction pair RI=0.030 RI=0.0062 90 opposite rule state pair on July 30, 2014 120 1.0 60

150 30 0.5

180 0.0 0° delay period

210 330

240 300 270 DI=0.21 DI=0.25 RI=0.29 RI=0.38 movement period

Fig. 6. Directional and rule tuning of example FEF neurons in the delay and movement periods. A: results from an example FEF go neuron. B: example FEF nogo neuron. Details as in Fig. 5.

J Neurophysiol • doi:10.1152/jn.00543.2013 • www.jn.org SEF AND FEF IN OCULAR DECISION 2651

A SEF neurons increased in the movement period, and rule tuning surpassed DI directional tuning (RI ϭ 0.38; DI ϭ 0.25). Figure 6B shows the 1.0 RI activity of an FEF nogo neuron, which displayed a hint of both 0.9 directional and rule tuning during the delay period (RI ϭ 0.01; 0.8 DI ϭ 0.18). The rule tuning significantly increased during the 0.7 movement period, but the directional tuning did not (RI ϭ ϭ 0.6 0.29; DI 0.21). We also evaluated how directional and rule tuning evolved 0.5 in SEF and FEF neurons over the course of the trial. To this 0.4 end, the DI and RI were computed for each neuron in 50-ms 0.3 bins (see METHODS). Higher DI and RI reflect greater directional 0.2 and rule tuning, respectively. Figure 7A shows that for SEF

directionality and rule tuning 0.1 neurons, average DI (black) values were consistently low 0 throughout the trial; however, average RI (red) values began to 0 200 400 600 800 1000 1200 increase around 200 ms after target onset and asymptoted time re: target onset (ms) around the time of square intersection. In contrast, FEF neu- rons had consistently higher DI values than did SEF neurons, B FEF neurons 1.0 A DI SEF: delay SEF: movement 0.9 RI mean mean 15 (n. sig.) mean(sig.) (n. sig.) mean(sig.) 0.8 sig. DI

n. sig. DI Downloaded from 0.7 10 0.6 5

0.5 0 0.4 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 directionality index directionality index 0.3 mean

# of neurons mean 0.2 15 mean(sig.) (n. sig.) mean(sig.) on July 30, 2014 (n. sig.) sig. RI

directionality and rule tuning 0.1 10 n. sig. RI 0 0 200 400 600 800 1000 1200 5 time re: target onset (ms) 0 C 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Delay period Movement period rule index rule index 0.3 0.3 DI B FEF: delay FEF: movement RI mean mean 15 (n. sig.) mean(sig.) (n. sig.) mean(sig.) 0.2 0.2 sig. DI 10 n. sig. DI

0.1 0.1 5

0 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 mean directionality/rule index SEF FEF SEF FEF directionality index directionality index

# of neurons mean Fig. 7. Mean values of DI and RI across the duration of the ocular go/nogo mean (n.sig.) trial. A: results for all SEF neurons (go and nogo neurons pooled). Each dot 15 mean(sig.) (n. sig.) mean(sig.) sig. RI indicates the index value based on 50-ms bins for a specific neuron (black: DI; n. sig. RI red: RI). Curves indicate the mean DI and RI for all neurons. Shaded area 10 represents the 95% confidence interval for the curves with corresponding color. 5 Vertical lines indicate square intersection time for go/nogo trials. B: results for all FEF neurons. C: mean values of DI and RI for the delay and movement 0 periods for SEF and FEF neurons. Error bars indicate 95% confidence 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 intervals. rule index rule index Fig. 8. Histograms of DI and RI for all SEF and FEF neurons in the delay and Figure 6A shows a polar plot of the mean activity for an movement periods of the ocular go/nogo trial. A: DI (upper panels) and RI example FEF go neuron. The activity was highest for targets (lower panels) values for SEF neurons in the delay (left panels) and movement moving leftward and downward and lowest for the opposite (right panels) periods. Here, easy and difficulty trials were combined to calculate DI and RI. Dark bars indicate significant DI and RI (different from trajectories in both delay (top) and movement (bottom) periods. 0) and light bars insignificant (n. sig.). The vertical lines indicate the mean DI This neuron had moderately higher directional tuning than rule and RI values for all significant and nonsignificant neurons. B: results for FEF tuning in the delay period (RI ϭ 0.03; DI ϭ 0.08). Both indices neurons.

J Neurophysiol • doi:10.1152/jn.00543.2013 • www.jn.org 2652 SEF AND FEF IN OCULAR DECISION and RI values were initially low and continued to increase tual decision making (Purushothaman and Bradley 2005; throughout the trial (Fig. 7B). Heinen et al. 2012; see METHODS). A neuron with a CP value Figure 7C summarizes the population DI and RI of each of 1.0 perfectly predicts the animal’s behavior (go vs. nogo). structure for delay and movement periods. In the SEF, RI was Figure 9, A and B, shows the ROC curves used to compute CP higher than DI during the delay period [t(34) ϭϪ3.53, P Ͻ for easy trials for representative SEF and FEF neurons, respec- 0.05] and the movement period [t(34) ϭϪ6.46, P Ͻ 0.05]. In tively. Briefly, each curve was derived by plotting the propor- the FEF, DI was higher than RI during the delay period tion of instances in which the neuron correctly signaled that the [t(41) ϭ 4.36, P Ͻ 0.05], but although DI dropped and RI rose monkey would pursue against the proportion of instances in in the movement period, they were not significantly different at which the neuron incorrectly signaled that the monkey would this time [t(41) ϭϪ1.81, P ϭ 0.077]. Between structures, in fixate for each firing rate. The area under each curve yields the the delay period, DI was lower [t(75) ϭϪ2.45, P Ͻ 0.05] and CP. For the SEF neuron in easy trials, CP was higher in the RI was higher [t(75) ϭ 3.32, P Ͻ 0.05] in the SEF than in the delay period than in the movement period (0.73 vs. 0.65, FEF. In the movement period, DI was lower in the SEF than in respectively). In difficult trials, the reverse occurred: CP was the FEF [t(75) ϭϪ3.05, P Ͻ 0.05], but RI was not different slightly higher in the movement period than in the delay period between the structures [t(75) ϭ 0.92, P ϭ 0.36]. (0.67 vs. 0.63, respectively). For the FEF neuron, CP during Figure 8 summarizes DI and RI values for all neurons. both easy and difficult trials was higher in the movement About half of the SEF neurons (17/36) were directionally tuned period than in the delay period (0.66 vs. 0.58, respectively). in the delay period, and fewer were in the movement period Figure 10A plots the individual and mean CP values as a (9/36); most SEF neurons were tuned to the rule in both the function of time in a trial for all SEF and FEF neurons, binned delay (24/36) and movement (27/36) periods. In contrast, more in 50-ms intervals. Here, easy and difficult trials were com- FEF neurons were directionally tuned in both the delay (33/42) bined. Notice that CP values for SEF neurons rose during the and movement (28/42) periods, and fewer FEF neurons dis- delay period, reaching a peak just before square intersection.

played rule-tuning activity in the delay period (24/42) than in The CP of FEF neurons, on the other hand, continued to Downloaded from the movement period (38/42). increase until late in the trial. Figure 10B summarizes this To assess the extent to which the activity of task-related result. During the delay period, CP is higher in the SEF than SEF and FEF neurons predicted the ocular decision (fixation the FEF [t(75) ϭ 4.55, P Ͻ 0.05], but during the movement or pursuit), we computed the CP of individual neurons, as period CP is the same in both structures [t(75) ϭ 0.1209, has been done previously for neurons implicated in percep- P Ͻϭ0.90]. These results suggest that the FEF becomes more

ABSEF: easy FEF: easy on July 30, 2014 1 1

0.73 0.66 .8 .8

.6 0.65 .6 0.58

.4 .4

Fig. 9. Receiver operating characteristic (ROC) curves for .2 .2 example SEF and FEF neurons based on their mean spike delay movement rate in relation to the ocular decision. A: ROC curves for hit rate (% correct pursuit signals) an SEF neuron in easy trials. The x-axis represents the rate 0 of false alarm (target pursuit in nogo trials) and the y-axis 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 the rate of correct hit (target pursuit in go trials). The dark curve indicates the ROC for neural activity in the delay CDSEF: difficult FEF: difficult period and the light curve the movement period. Numbers shown in the figure indicate the area before the curve (e.g., 1 1 choice probability) for this neuron for the delay and movement periods, respectively. B: ROC curves for an FEF neuron in easy trials. C: ROC curves for the same .8 0.67 .8 0.66 SEF neuron in difficult trials. D: ROC curves for the same FEF neuron in difficult trials. .6 .6 0.63 0.58

.4 .4

.2 .2 hit rate (% correct pursuit signals) 0 0 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 false alarm rate false alarm rate (% erroneous pursuit signals) (% erroneous pursuit signals)

J Neurophysiol • doi:10.1152/jn.00543.2013 • www.jn.org SEF AND FEF IN OCULAR DECISION 2653

period their activity predicted the direction of target motion A Choice probability 1.0 SEF better than it predicted the rule state of the trajectory. FEF FEF neurons reflected the rule later in difficult trials than in easy 0.9 trials, but their ability to predict the decision was not affected by task difficulty, as demonstrated by no difference between 0.8 CP values in easy and difficult trials. Together, these findings 0.7 suggest that SEF and FEF neurons are differentially involved in our ocular decision task, with SEF neurons more involved in 0.6 interpreting the rule and FEF neurons in executing the ocular decision. These results are consistent with findings of more 0.5 direct FEF involvement in memory-based delayed pursuit and in saccade initiation in countermanding tasks (Brown et al. 0.4 2008; Fukushima et al. 2011; Hanes et al. 1998; Stuphorn et al.

choice probability 2010). 0.3 The different roles of the SEF and FEF in ocular decision 0.2 making are consistent with a hypothesis of how information is transformed between these structures, proposed previously by 0.1 Schall et al. (1993). In this scheme, SEF neurons encode goal-centric coordinates, whereas FEF neurons encode body- 0.0 centric ones. This idea is supported by previous findings that 0 200 400 600 800 1000 1200 SEF stimulation at some sites evokes goal-centric saccades, time re: target onset (ms) whereas FEF stimulation mostly evokes fixed-vector saccades

B (Huerta et al. 1987; Park et al. 2006; Robinson 1972; Russo Downloaded from 0.7 SEF and Bruce 1993, 1996). The goal-centric nature of the SEF is FEF consistent with the nondirectional rule tuning we find there. This type of rule tuning would render the SEF inadequate in signaling the direction in which a pursuit movement should be 0.6 generated. For the FEF to be involved in enforcing the ocular decision, the nondirectionally tuned, rule-based signal in the

SEF would likely need to be transformed into a directionally on July 30, 2014 0.5 tuned signal in the FEF. The nonspatial, flexible interpretation of decision rules by SEF neurons has been demonstrated in earlier research. Chen mean choice probability and Wise (1996), for example, showed that SEF neurons 0.4 signaled one of several likely saccade target locations based on delay period movement period a nonspatial cue (e.g., a color cue that specified the location of Fig. 10. Choice probability for SEF (green) and FEF (blue) neurons across the a target among distractors), even when the cue appeared at a duration of the ocular go/nogo trial. A: choice probability for all SEF and FEF location inconsistent with the color-indicated target location. In neurons at different times in ocular go/nogo trials. Each dot represents the addition, Tremblay et al. (2002) found that SEF activity was choice probability value of an individual neuron for combined easy and not tuned to the absolute location of a selected target; rather, it difficult trials. The curves represent the mean choice probability over time- based 50-ms intervals and the corresponding shaded area the 95% confidence signaled the position of that target relative to a distractor. interval. Vertical lines indicate square intersection time for go/nogo trials. These findings, and our results, suggest a general role of the B: mean choice probability in the delay and movement periods. Error bars SEF in signaling the desired target specified by a decision rule, indicate 95% confidence intervals. independent of the spatial location of that target (White and Wise 1999). To implement the required movement, the loca- involved in the ocular decision during the course of the trial, tion and/or motion direction of the selected target must be possibly because the SEF signals to it about the go/nogo state transformed into fixed-vector coordinates in a region such as of the trajectory. the FEF. In our study, although rule encoding in the FEF is low DISCUSSION during the delay period, it increases at square intersection and The results of the current study demonstrate that SEF activ- beyond. This may seem inconsistent with our proposed roles of ity began to reflect the rule early in the delay period (Fig. 4), the SEF and FEF in ocular decision making. However, encod- when the trajectory of a moving target was being interpreted in ing of decision rules by FEF neurons has been observed relation to a visible square, and continued to predict the rule previously. Ferrera and colleagues reported that FEF activity better than target motion in both delay and movement periods predicted the perceptual categories of moving stimuli rather (Fig. 8). Increasing task difficulty delayed the time at which than the motor (saccadic) responses because the neurons had SEF neurons discriminated between go and nogo rule states low directional tuning during their task (Ferrera and Barborica (Fig. 4) and reduced their accuracy in predicting the ocular 2010; Ferrera et al. 2009). Note that their task did not have a decision according to calculated CP values (Fig. 9). In contrast, delay period and early directional activity may have oc- FEF neurons began to reflect the rule at a later time, closer to curred but could have been obscured because it was con- when the behavioral decision was made, and in the delay founded with rule-encoding activity. This explanation is

J Neurophysiol • doi:10.1152/jn.00543.2013 • www.jn.org 2654 SEF AND FEF IN OCULAR DECISION consistent with early FEF activity being dominated by GRANTS directional information and later FEF activity reflecting rule The study described here was supported by NIH Grant EY-117720 to S. information relayed there from the SEF. In our study as well Heinen and by R. C. Atkinson Fellowship at the Smith-Kettlewell Eye as in those of Ferrera and colleagues (Ferrera and Barborica Research Institute to S.-n. Yang. 2010; Ferrera et al. 2009), FEF activity likely reflects the nonspatial, perceptual category encoding propagated down- DISCLOSURES stream from the SEF. Because of the very different types of No conflicts of interest, financial or otherwise, are declared by the author(s). categorization in our study and theirs, for this transforma- tion to occur, the association between nonspatial encoding in the SEF and the activation of FEF neurons for specific AUTHOR CONTRIBUTIONS motor responses would have to be learned. S.-n.Y. and S.H. conception and design of research; S.-n.Y. performed experiments; S.-n.Y. analyzed data; S.-n.Y. and S.H. interpreted results of Because of the way go and nogo trajectories were specified, experiments; S.-n.Y. and S.H. prepared figures; S.-n.Y. and S.H. drafted rule- and direction-tuned activity may have been confounded. manuscript; S.-n.Y. and S.H. edited and revised manuscript; S.-n.Y. and S.H. If rule tuning were computed simply by comparing mean approved final version of manuscript. neural activity for go trajectories with mean nogo activity, a neuron could be considered to have high rule tuning merely by REFERENCES being strongly tuned to a single trajectory direction. To address Amador N, Schlag-Rey M, Schlag J. Primate antisaccade. II. Supplementary this, we developed the RI as a complement to standard DI eye field neuronal activity predicts correct performance. J Neurophysiol 91: computation since a directionally tuned neuron would not be 1672–1689, 2004. expected to show low activity for a trajectory adjacent to its Andersen RB, Brotchie P, Mazzoni P. Evidence for the lateral intrapa-rietal preferred trajectory. However, the RI value might still reflect area as the parietal eye field. Curr Opin Neurobiol 2: 840–846, 1992. Badler JB, Heinen SJ. Anticipatory movement timing using prediction and the motion trajectory rather than the rule state if a neuron has external cues. J Neurosci 26: 4519–4525, 2006. Downloaded from very fine directional tuning. In this case, even trajectories Barbas H, Pandya D. Patterns of connections of the prefrontal cortex in the adjacent to the preferred trajectory might generate less activity rhesus monkey associated with cortical architecture. In: Frontal Lobe and appear to signal a different rule. If high RI were merely Function and Dysfunction, edited by Levin HS, Eisenberg HM, Benton AL. New York: Oxford Univ. Press, 1991, p. 35–58. due to fine directional tuning, a neuron that has a high RI Barbas H, Pandya DN. Architecture and intrinsic connections of the prefron- would also have a high DI since by definition directionally tal cortex in the rhesus monkey. J Comp Neurol 286: 353–375, 1989. tuned neurons have high activity for the preferred trajectory Britten KH, Newsome WT, Shadlen MN, Celebrini S, Movshon JA. A

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