Center for Neuroscience and Department of Neurobiology, Physiology, and Behavior, University of California, Davis, CA, USA
Kenneth H. Britten, Center for Neuroscience, 1544 Newton Ct, Davis, CA 95616, USA. Email: khbritten{at}ucdavis.edu.
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Abstract |
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Introduction |
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Electrophysiological experiments on the motion system of dorsal extrastriate cortex in monkeys have identified cortical areas that might be involved both in analyzing complex optic flow information and in compensating this information for ongoing pursuit eye movements. Although it is not the only area representing such information, MST is one promising candidate. Cells in this region have large receptive fields and many are highly selective for particular optic flow patterns, such as expansion, contraction, or rotation (Saito et al., 1986; Tanaka et al., 1986
; Duffy and Wurtz, 1991
, 1995
, 1997
). Such components are present in the optic flow patterns produced by self-motion through normal scenes (Koenderink, 1986
; Koenderink and van Doorn, 1987
). Indeed, MST cells are often selective for particular headings (Bradley et al., 1996
; Duffy and Wurtz, 1997
) and their responses are at least partially compensated for smooth pursuit eye movements (Bradley et al., 1996
). These features suggest that MST may be a substrate for the recovery of heading from optic flow information.
To test the hypothesis that macaque MST is involved in heading tasks, we trained two monkeys to discriminate their perceived heading from random dot patterns simulating trajectories toward three-dimensional clouds. Here we report that the perceptual performance of monkeys on this task appears to resemble that of human subjects on similar tasks. Furthermore, we tested the involvement of area MST by using electrical micro-stimulation to perturb its activity during task performance. The general approach is similar to that used in other experiments testing the role of MT and MST in the perception of translational dot motion (Salzman et al., 1992; Celebrini and Newsome, 1994b
). In our experiment, we applied microstimulation during smooth pursuit eye movements or during fixation. When applied to a region of MST preferring one heading alternative, micro-stimulation frequently induced biases, which correlated well with the heading preferences of neurons at the stimulation site. In addition, bias frequently depended on the pursuit condition. This pattern of results supports the hypothesis that MST is involved in recovering self-motion from optic flow and compensating heading perception for pursuit eye movements.
Parts of this work have appeared in short form (Britten and van Wezel, 1998). This paper additionally analyzes the normal psychophysics of our monkey subjects and relates stimulation site tuning to the effects of microstimulation.
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Materials and Methods |
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Two female rhesus macaques (Macaca mulatta) were used in this study. Each was implanted with a head restraint post and a scleral search coil following the previously described method (Judge et al., 1980). The hardware was implanted under surgical anesthesia using sterile techniques in a dedicated primate surgical suite (California Regional Primate Research Center, UC Davis). After several months of training on the psychophysical task, each monkey was additionally implanted with a chronic recording cylinder over occipital cortex. This cylinder (Crist Instruments Inc.) was oriented parasaggitally, 17 mm lateral to the mid-saggital plane and elevated 20° above the horizontal plane, allowing posterior access to extrastriate cortex in the superior temporal sulcus. All animal procedures were approved by the UC Davis Animal Care and Use Committee and fully conformed to ILAR and USDA guidelines for the treatment of experimental animals.
Task and Stimulus
The monkeys were initially trained to fixate small targets on a CRT screen and make saccades to eccentric targets, and then trained on the heading discrimination task. The task was a two-alternative forced-choice task in which the monkeys discriminated between headings to the left or right of dead ahead (Fig. 1). The stimulus simulated a linear virtual trajectory through a three-dimensional cubic cloud of points. Monkeys performed a difference limen task in which the heading angle could be very close to zero. A top view of a representative simulated path (Fig. 1A
) illustrates depth relationships and relative trajectory length. The simulations corresponded to a trajectory of 1 m toward a cube of points 10 m on a side, centered 5.5 m away, occurring over 1 s. (Other equivalent real-world situations differ by only a scale factor.) Thus, the stimulus contained a large range of simulated depths, which produced a relatively large range of local velocities in the subject's view of the stimulus. The linear trajectory was always left or right of center and the monkey's task was to report which was presented, by making a saccadic eye movement to a subsequently presented target on the same side of the screen. In an individual trial (Fig. 1B
), the fixation point appeared first and after the monkey fixated it the (stationary) stimulus dots appeared. The fixation point commenced motion 250 ms later if the trial included smooth pursuit; otherwise, it remained at its initial location. Another 250 ms later, the stimulus dots moved, simulating the specified trajectory for the trial. After 1 s of continuous motion, the stimulus dots and fixation point disappeared and two saccade targets appeared. The targets were on the line of possible headings, 8° left and right from dead ahead. Choices to the correct target were rewarded with a small drop of water or juice; incorrect choices resulted in a brief time-out period. Incomplete trials were discarded from analysis. This trajectory varied along the horizontal plane, specified by a heading angle (Fig. 1A
), the angle between the simulated trajectory and straight ahead. The center of this range of headings (objective dead ahead) was always at the center of the screen and was thus fixed with respect to the monkey's head. The heading angle was varied during a block of trials from 0.5 or 1° to 8° by factor of two increments, according to the method of constant stimuli. If an animal developed a strong bias toward one or the other alternative, correction trials were presented, with the stimulus fixed in the less-preferred direction. These correction trials were discarded from analysis.
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The monkeys were initially trained on easy (for human subjects) versions of the task, with central fixation and large heading angles. They learned the basic task rapidly, typically within two sessions. Next, smaller heading angles were included, until the animals' psychometric functions allowed us to estimate their heading thresholds. Next, the monkeys were trained to generalize across a range of fixation locations and to report heading during pursuit eye movements. Lastly, the monkeys were presented different conditions in rapid alternation, emulating experimental conditions. When thresholds were asymptotically low, monkeys were deemed ready for the stimulation experiment.
Recording and Stimulation
Monkeys were seated in a primate chair with their heads restrained. Eye movements were measured with a scleral search coil system (David Northmore Inc.) and sent to a PC running the public domain experimental control software REX (Hays et al., 1982). On recording days, the cap covering the cylinder was removed and an electrode (glass-insulated PtIr, 0.51.0 M
, FHC Inc.) was introduced into occipital cortex via a transdural guide tube. Initial mapping penetrations located the superior temporal sulcus (STS) and identified approximate boundaries of the motion-sensitive areas in its depths. MST was identified according to previously published methods (Celebrini and Newsome, 1994a
). To identify the STS, we used a combination of anatomical and physiological landmarks, including the depth from the brain surface, grey matter/white matter transitions, sulcus crossings and response properties. Within the STS, we located and mapped MT on the posterior bank, using its well-understood and consistent retinotopy and responses for physiological confirmation. MST was encountered after crossing the STS to its anterior bank and was identified by large RFs that often included the fovea or extended substantially into the ipsilateral hemifield. In addition, cells on the anterior bank often showed MST-like stimulus selectivities, preferring rapidly moving stimuli and complex optic flow stimuli. All experiments reported here came from penetrations in which the lumen of the STS was crossed and thus most likely from the dorsal subdivision (MSTd). Furthermore, the neurons we recorded typically preferred large stimuli over smaller ones, a hallmark of MSTd. Histological verification of recording sites has not yet been obtained, as both monkeys are still being used in related experiments.
Heading selectivity was measured for multiunit sites at ~100 µm intervals. When a region of clear and consistent heading selectivity was found, quantitative heading-tuning measurements were made at more frequent intervals. Sites were deemed acceptable if they maintained clear and consistent selectivity for a distance of 250 µm or more. The electrode was then positioned in the middle of this region and the microstimulation experiment initiated.
The receptive field of each site was established using a mixture of hand-and computer-presented stimuli. The fixation point was adjusted to bring the center of the range of headings into the receptive field and then to maximize heading selectivity. The site's tuning to heading stimuli was then measured under the pursuit conditions used in the stimulation experiment. A range of headings was chosen to span the expected threshold at that location. During the experiment, a block of trials containing 15 or 20 trials for each condition, was presented. Typically, there were eight or 10 heading angles, three pursuit conditions and two microstimulation conditions, presented interleaved in a fully crossed, block-random design.
The microstimulation consisted of 200 Hz pulse trains delivered through the recording electrode from a multichannel pulse generator (AMPI) and linear stimulus isolator (FHC Inc.). Pulses were biphasic, 20 µA in amplitude, cathodal leading. Each phase was 200 µs in duration, and 100 µs intervened between phases. The pulse train was 1 s in duration, exactly simultaneous with the visual stimulus motion upon which the monkey's decision was based.
Data Analysis
Psychophysical performance was measured both with and without microstimulation, and all resulting psychometric functions were similarly treated. Data consisted of the proportion of rightward choices as a function of heading angle (distance of the simulated heading from straight ahead). Such functions were fit with probit functions (cumulative Gaussians) expressed as follows:
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In this expression, P(r) is the proportion of rightward choices, h is the heading angle, µ is the mean of the Gaussian and is the standard deviation or width. The data were fit with an iterative method called Stepit (Chandler, 1965
) using maximum-likelihood fitting, assuming binomially distributed choices. In this application, the mean (µ) estimates the bias of the monkey and is zero if the monkey's subjective dead ahead point is veridically in the center of the screen. The width parameter (
) captures the monkey's sensitivity to heading and is the heading angle required to support 84% correct performance. For the psychophysical data (without microstimulation), each individual function (from the three interleaved pursuit conditions) was fit separately. This approach does not allow statistical testing of individual effects, but provides a simple, unbiased estimate of the fit parameters across the range of conditions tested, which was all we desired to extract from these data.
For the microstimulation and pursuit data, the probit model was elaborated to include additional terms, so that we could test the significance of effects on an experiment-by-experiment basis. If we simplify the above expression (1) as probit(µ,), then for any micro-stimulation condition, i(0,1) and pursuit condition j(0,1,2), we get:
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Results |
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Heading performance has been extensively studied in humans, but little described in monkeys. We measured heading discrimination in two adult female monkeys during their last month of training. During this period, the monkeys' measured thresholds were asymptotically low, with no trend toward improved performance. Thresholds were measured in half-hour blocks of trials, consisting of 2430 conditions and 15 trials per condition. In each block, heading eccentricity (distance in degrees from fixation point to the center of the range of headings) was held constant and three different pursuit conditions were usedleft, right and no pursuit. All conditions were pseudorandomly interleaved.
In humans, sensitivity is largely invariant to heading eccentricity across a range from 0 to ~30° (Crowell and Banks, 1993). We found a similar result in monkeys (Fig. 2A
). Monkey C (open circles) showed reliably higher average thresholds (
from probit fits) than monkey B (t = 4.4, P < 0.0001), but in neither animal was the relationship with eccentricity statistically significant (linear regression; monkey C, P > 0.17; monkey B, P > 0.45). Heading thresholds, therefore, were consistent across our range of eccentricities.
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Horizontal retinal image flow is also influenced by horizontal smooth pursuit eye movements, so we measured their effects on bias and sensitivity. As did the variation of heading eccentricity, including pursuit encouraged our monkeys to attend to more global cues and not make a local direction judgement. Because we wished to investigate how microstimulation interacted with pursuit (see below), we needed baseline information on responses during pursuit. Smooth pursuit produced subtle influences on performance, with substantial day-to-day variation. Both monkeys showed a slight decline in sensitivity under pursuit, with thresholds rising ~10% during pursuit at 10°/s (Fig. 3). This decline was significant by ANOVA [F(338,2) = 2.803, P < 0.05], as was the difference between monkeys [F(338,1) = 125.6, P < 0.001]. In both monkeys, threshold declined equivalently for either direction of pursuit.
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Microstimulation Effects
The primary goal of these experiments was to determine the role of area MST in heading perception by perturbing its activity with electrical microstimulation. We first mapped the heading selectivity of multiunit recording sites along oblique penetrations through area MST to find regions of consistent heading selectivity, as MST is organized in a clustered or columnar manner by optic flow preference (Tanaka et al., 1986; Geesaman et al., 1997
; Britten, 1998
). When a region with consistent heading tuning was identified, we positioned the electrode tip in its center, measured the heading tuning of neurons there and commenced a microstimulation experiment. Figure 6
shows schematically the distance traversed by the electrode with respect to the landmark of the entry into grey matter. The tuning functions below each site show the multiunit tuning for horizontal heading. The hatched region on the penetration denotes the boundaries of the region that showed consistent preference for left headings. This site was ~300 µm in extent, a typical value. The results reported here derive from 67 such experiments, conducted in the same two monkeys whose psychophysical data appear in the previous section. In 16 of these experiments, we did not include multiple pursuit conditions due to lack of time during the session.
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Effects in the Presence of Pursuit
Because of evidence that area MST contains extraretinal signals affecting responses to flow-field stimuli, we were particularly interested in how microstimulation interacted with pursuit (Fig. 9). Each panel shows performance under a different pursuit condition (static, left, or right at 10°/s; microstimulation trials, filled symbols and bold lines). The effect of microstimulation systematically differs, depending on the presence and direction of pursuit: it is modest for the static condition (Fig. 9B
), largest for left pursuit (Fig. 9A
) and disappears for right pursuit (Fig. 9C
).
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We used this approach to test all possible effects of microstimulation (Fig. 10). We tested for changes in bias and in threshold, and for the interaction of each of these effects with pursuit conditions. The principle of this hierarchical approach is very similar to that used in stepwise regression; the main difference is that we used a likelihood ratio test to evaluate the significance of each parameter in the model, instead of an F ratio. When fitting data that included pursuit, we always incorporated free parameters for pursuit alone; these account for the variable effects that pursuit often had on performance, with or without microstimulation.
The results of this analysis for the entire sample of micro-stimulation sites (Table 1) indicate that significant effects of microstimulation were frequent, occurring in 60% of the experiments. When significant effects were observed, these nearly always resulted in a significant shift of the function, or a change in bias. Effects of microstimulation on the sensitivity to heading (slope) were less frequent and only very rarely occurred in isolation, without bias effects. A similar pattern emerged from the analysis of pursuit interactions. Such interactions were frequent, occurring in over half of the sample. Again, effects on bias were more frequent than were effects on slope, consistent with the fact that pursuit itself had a larger effect on bias than on sensitivity (Figs 3 and 6
).
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We next examined whether the tuning of the neurons at the stimulation site predicted the effects of stimulation. To quantify heading tuning, we derived a contrast index comparing the average response across all left headings to the average response across all right headings:
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This index is bounded in a range from 1.0 to 1.0; the former indicates strong left heading tuning, whereas the latter indicates equally strong right heading tuning. Values near zero imply no tuning. This tuning index was significantly related (r = 0.25, P < 0.05) to the bias induced by microstimulation, captured by the average shift of the psychometric function under micro-stimulation (Fig. 12). Recall that the shift of the function is to the left if right choices are increased and to the left if right choices are increased. Therefore, the negative slope indicates that the relationship agrees with intuitionbiases tended to be stronger in a given direction when the neurons at the stimulation site were more strongly tuned in the same direction. Furthermore, this plot shows that effects that were opposite to the tuning were observed when we stimulated sites where the tuning was relatively weak.
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Discussion |
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Technical Issues
The first important question to consider is the task itself. It is important to the interpretation of these results to consider whether the monkeys were perceiving the global pattern of motion, or simply performing a linear (even local) direction or speed discrimination. In a complex, cue-rich stimulus such as ours, it is difficult to know unequivocally what cue was being used. However, the overall similarity of the monkey psychophysics with published human psychophysical work provides one strong indication. Human observers trained on heading tasks attempt to use global cues rather than local ones; this maximizes performance. If our monkeys were using local cues instead, their performance would probably have been much worse. In addition, local cues change substantially and systematically with heading eccentricity and with pursuit, yet our monkeys showed little systematic effect of these manipulations on their performance. Therefore, we believe it likely that our monkeys and human observers use similar global cues. Of course, the cues in use are impossible to know for certain, especially in nonhuman primates.
Microstimulation is an artificial perturbation of the complex local circuits of the cortex and it is important to think carefully about potential pitfalls in interpretation. The most obvious concern is the extent of current spread, which we cannot directly measure. Based on similar experiments in the better-understood architecture in MT, Newsome and colleagues (Salzman et al., 1992; Murasugi et al., 1993
) have estimated that currents such as ours should spread ~150 µm from the electrode tip. This is also consistent with 2-deoxyglucose labeling measurements made in the smooth cortex of the owl monkey (Tootell and Born, 1991
) and with dual electrode experiments in motor cortex (Asanuma, 1981
). This dimension is below the typical dimensions of clusters of similarly tuned neurons in MST (Britten, 1998
), suggesting that direct current spread is largely within a column. We were also concerned about whether current would spread to underlying white matter and that this might produce unpredictable effects. To address this, we performed a small number of experiments with the electrode intentionally lowered into the white matter underlying MST, ~250 µm from the exit from grey matter. In none of these four experiments were any effects of microstimulation observed. Therefore, the effects reported here are likely due to the direct activation of the local circuit surrounding the electrode.
Another concern for the experiments involving pursuit is whether microstimulation influenced the pattern of eye movements, which might spuriously introduce interactions. Indeed, Komatsu and Wurtz showed pursuit gain changes after stimulating MST, but their stimulation currents were much larger than ours (Komatsu and Wurtz, 1989). Nonetheless, to test this possibility, we measured eye movements in a subset of four experiments in which significant microstimulation effects were seen. We removed saccades from eye-movement records using a velocity criterion, and compared eye velocities on stimulation trials to those on control trials. In no case did we see the slightest trend toward a change in velocity during microstimulation. This analysis was sufficiently sensitive that 1% changes in pursuit gain would have been detected. Therefore, we believe that direct effects of microstimulation on eye movements did not substantially influence our results.
Heterogeneity of Results
Approximately a third of our experiments revealed effects of microstimulation opposite to those that were expected based on the preferences of neurons at the stimulation site. Although such a pattern of results is still consistent with MST signals being used in the judgement of heading, interpretation becomes more difficult. Such mixed results were almost never seen by Celebrini and Newsome in their microstimulation experiment exploring a discrimination between opposite directions of uniform, linear motion (Celebrini and Newsome, 1994b). Several possibilities remain open to explain this apparent difference.
One possible explanation lies in the taskours is a just noticeable difference task, where the alternatives are much closer to each other along the relevant stimulus dimension. This task design should, in principle, change the readout from MST, perhaps making the results of microstimulation less predictable from neuronal preferences.
Another, more likely possibility lies in the nature of the architecture in MST. Tuning for complex optic flow patterns is not as common in MST as tuning for direction of linear motion (Duffy and Wurtz, 1991). Our data are consistent with this: most of the sites that reached criterion length preferred linear motion with a horizontal flow component (making them tuned for extreme headings). Therefore, the local heterogeneity of single cell signals might be greater along the dimension of heading than along the dimension of linear motion direction. This in turn could cause the net effect of stimulating a column to be less predictable from the multiunit measurements of tuning. This possibility is supported by the relationship shown in Figure 13
. The more weakly tuned the site, the more likely we were to get a backward result. This could result either from activation of a subset of signals within the column being activated, or because activation of regions outside the column overwhelmed the within-column effects. At the very least, better neuronal tuning to linear motion might alone have produced the more consistent results in earlier experiments (Celebrini and Newsome, 1994b
).
Lastly, it is likely that some columns of MST neurons are involved in other perceptual roles besides heading. For example, the percept of structure from motion involves many of the same signals as does heading; both require the extraction of three-dimensional depth structure from the optic flow pattern. If, for example, a column of neurons more affiliated with structure-from-motion were activated, the resulting percept might have led to unpredictable reports in our heading task. The possibilities we have discussed are not exclusive; many factors may contribute to the heterogeneity of our results.
The Role of MST in Heading Perception
These experiments have provided positive evidence for the use of MST signals in heading tasks and in the compensation of performance on such tasks for the effects of eye movements. The sign of the interaction effects in our experiments was such that, on average, the effect of microstimulation produced larger biases in the direction of the pursuit movement. This is the sign of bias expected if eye movements were incompletely compensated, as retinal image flow opposite the pursuit direction would indicate headings to that side. From the psychophysical measurements, we have seen that monkeys are able to compensate for pursuit and one of ours even overcompensated. The finding that activation of MST signals led to less compensation for pursuit suggests that MST signals are signaling the direction of retinal image flow, rather than being transformed into head-or body-centered coordinates. This is consistent with results from Andersen and colleagues (Bradley et al., 1996; Shenoy et al., 1999
), which indicate that compensation of MST tuning for ongoing head or eye movements is incomplete.
The effects of microstimulation were larger and more consistent under pursuit than when the monkey was merely fixating. This suggests that the representation in MST is more engaged in heading perception when the animal is actively compensating for ongoing eye movements. MST contains extraretinal signals of pursuit eye movements (Newsome et al., 1988) and the tuning of MST cells is at least partially compensated for the distortions produced by such movements (Bradley et al., 1996
; Shenoy et al., 1999
). Therefore, if heading perception is guided by signals in multiple areas, for instance both MT and MST, the signals in MST might be more influential while pursuit is underway. In turn, this suggests fairly sophisticated gating of the outputs of such high-order sensory areas. Evidence for such gating has recently been uncovered in area MT (Seidemann et al., 1998
). In general, this notion is consistent with the idea that vision for action is an important function of the dorsal extrastriate area (Goodale, 1998
). Although the strongest versions of this hypothesis are clearly challenged by the substantial body of work connecting signals in dorsal extrastriate areas to perception, hybrid versions of the idea are attractive.
Although this work strongly supports the use of MST in heading tasks, microstimulation experiments do not provide much information as to the mechanism of heading perception. The results of the present experiment are consistent with a variety of models relating MST physiology to perception and more traditional methods of quantitative physiology will be required to understand further the cortical mechanisms of self-motion perception.
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Footnotes |
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Acknowledgments |
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References |
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