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1Centre for Vision Research, 2Canadian Institute of Health Research Group for Action and Perception, and 3Department of Biology, York University, Toronto, Ontario; 4Departments of Psychology and Kinesiology and Health Sciences, York University, Toronto, Ontario, Canada, M3J 1P3; and 5Department of Physiology, McGill University, Montreal, Quebec, Canada
Submitted 26 February 2007; accepted in final form 25 May 2007
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ABSTRACT |
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INTRODUCTION |
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; Guitton and Volle 1987). To accurately program these movements, the brain must transform the information from coordinate systems centered on different input sensory organs (the eyes, the skin, the ear) into the head and body-fixed frames preset to the muscles that control the eyes and head (Andersen et al. 1997; Cohen and Andersen 2002; Colby 1998; Graziano and Gross 1998; Soechting and Flanders 1992). An important question in gaze control is what are the intermediate frames of reference used by the cortex to represent gaze targets?
The main cortical centers involved in saccade control include the frontal eye fields (FEFs) (Astafiev et al. 2003; Hanes et al. 1998; Russo and Bruce 1996; Schall 2002; Sparks et al. 2001; Wurtz et al. 2001), the supplementary eye fields (SEFs) (Chen and Walton 2005; Martinez-Trujillo et al. 2003a,b, 2004), and the lateral intraparietal area (LIP) (Goldberg et al. 2002; Pare and Wurtz 1997, 2001; Thier and Andersen 1996, 1998). Neurophysiological studies suggest that FEF encodes information in an eye-fixed (retinotopic) frame of reference (Bruce et al. 1985; Russo and Bruce 1993; Tu and Keating 2000), whereas both unit recording and microstimulation studies suggest that SEF uses multiple reference frames (Dominey et al. 1997; Martinez-Trujillo et al. 2004; Park et al. 2006; Russo and Bruce 1996, 2000; Tehovnik et al. 2000).
In the present study, we focused on the question of what reference frame does LIP use to encode gaze. Previous single-unit recording studies have suggested that LIP might encode saccades using eye (Andersen and Buneo 2002; Boussaoud and Bremmer 1999; Colby and Duhamel 1996; O'dhaniel et al. 2005), head (O'dhaniel et al. 2005), body/space (Brotchie et al. 1995; Snyder et al. 1998), or a more complex reference frame (Platt and Glimcher 1997). A number of studies suggested that LIP neurons have a retinotopic (eye-fixed) motor field, but its activity is modulated by eye/head position (Andersen et al. 1985; Barash et al. 1991; Li and Andersen 2001; Linden et al. 1999; Mazzoni et al. 1996). This suggests that although these neurons may code target locations in eye-fixed coordinates, they also possess the information necessary to transform this into other frames (Andersen 1989; Andersen and Zipser 1988). Electrical microstimulation studies (Kurylo and Skavenski 1991; Shibutani et al. 1984; Thier and Andersen 1996) have reported stimulation-evoked saccades with goals that were best defined in an eye- or head-fixed reference frame, depending on the site.
All these previous studies had the limitation that they restricted the head movements of their subjects, either completely or by limiting the head to one axis of rotation (Brotchie et al. 1995; Thier and Andersen 1998). However, previous microstimulation studies of other brain areas (SC, SEF) involved in gaze motor control have showed that when the head is free to move naturally, the head contribution to the overall gaze shifts influences the amplitude of the stimulation-evoked gaze shifts (Freedman et al. 1996; Martinez-Trujillo et al. 2003a; Roucoux et al. 1980). Moreover, the eyes and the head move in a three-dimensional (3-D) space performing movements about the corresponding 3-D, i.e., vertical, horizontal, and torsional (line of sight) axes (Glenn and Vilis 1992; Tweed et al. 1995). Therefore it is important to measure these three components when we record the eye and head movements during head-unrestrained gaze shifts (Klier and Crawford 2003; Klier et al. 2003). Thus far, no electrophysiological study of macaque area LIP has used monkeys the heads of which were completely unrestrained (i.e., allowed to move freely both the eyes and the head) and measured eye and head movements in 3-D. These procedures—recording 3-D rotations in head-unrestrained monkeys—are essential for discriminating movement coding in different reference frames (Klier et al. 2002; Soechting and Flanders 1992).
The goal of this study was to analyze 3-D gaze saccades evoked during electrical stimulation of LIP in head-unrestrained macaques. One possibility is that stimulating the LIP will evoke fixed-vector like gaze shifts—gaze movements with constant amplitude and direction, which vary as a function of the stimulation location (Hanks et al. 2006; Kurylo and Skavenski 1991; Schiller and Tehovnik 2005; Thier and Andersen 1996). A second possibility is that stimulation of LIP will produce gaze shifts directions that are modulated by initial eye position, consistent with a retina-fixed (eye-fixed) pattern (Crawford and Guitton 1997a; Klier et al. 2001). Another possibility is that LIP stimulation will evoke gaze shifts toward a goal that is fixed with respect to the head or with respect to space (Cutrell and Marrocco 2002; Mushiake et al. 1999; Shibutani et al. 1984; Thier and Andersen 1998).
Based on a recent study of the superior colliculus (SC) (Klier et al. 2001), on the information that LIP is functionally situated upstream from the SC (Ferraina et al. 2002; Gaymard et al. 2003; Pare and Wurtz 1997; Wurtz et al. 2001), and on previous single-unit studies (Andersen et al. 1990a; Barash et al. 1991; Colby et al. 1995, 1996; Ipata et al. 2006; Mazzoni et al. 1996), we hypothesized that LIP uses an eye-centric reference frame to code for the eye movements while the head is free to move. However, based on the numerous studies that have shown eye-position "gain field" modulations in LIP (i.e., where location of the receptive field remains fixed in eye coordinates, but the sensitivity gain of the neuron is increased or decreased as a linear function of eye position) (Andersen et al. 1985, 1990b), we also analyzed the data to detect if the evoked movements were also modulated by gaze position in the same way. We found that stimulation of LIP in head-unrestrained monkeys did produce an eye-fixed pattern of gaze shifts but that the probability of evoking these gaze shifts depended on initial gaze position in a way that may be related to the existence of eye-position gain fields.
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METHODS |
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The surgical and experimental procedures used in this study were approved by the York University Animal Care Committee and were in compliance with the Canadian Council on Animal Care policy on the use of laboratory animals. Two female monkeys, Macaca mulata (rhesus), underwent surgery under general anesthesia (isoflurane, 1.5%; and ketamine, 10 mg/kg), during which a head post for immobilizing the head, a plastic recording chamber (Narishige), and sockets for cable connection (located dorsally) were implanted into each animal; all fixed to a dental acrylic cap attached to the skull by orthopedic screws (Synthes). The recording chamber of 20 mm diam was positioned on the skull at 5 mm posterior and 12 mm lateral in stereotaxic coordinates, over the right lateral intraparietal sulcus (IPS; Fig. 1A). The head post, which consisted of a stainless steel cylinder with a rapid release mechanism (for details, see Crawford et al. 1999), was located frontally. We also implanted two custom-built scleral search coils (copper wire), of 5 mm diam, fitted subconjunctivally in the nasal quadrants of one eye. This allowed 3-D measurements of eye orientation (for details, see Tweed and Vilis 1990; Tweed et al. 1990). After the surgery, the monkeys were allowed 2 wk for recovery before initiating the training and experimenting sessions.
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). The primate chair was placed near the center (±15 cm) of three orthogonal magnetic fields (Tweed et al. 1990), and coil signals were recorded at a sampling frequency of 1,000 Hz. During experimentation, we temporarily fitted the acrylic cap of the animal with two orthogonal coils, of 1-cm diam, for 3-D measurements of head orientation. Electrode penetrations were made using a small hydraulic microdrive (Narishige model MO-95S) customized to fit onto a stage with a radial placement system, positioned on the top of the recording chamber. The weight of this entire system was 48 g, which remained constant throughout experimentation. Training and experiments
Each animal was trained only with the head unrestrained (head-free); they were trained to perform head-free gaze shifts to light-emitting diodes (LEDs) in the dark. The LED screen was situated at a distance of 80 cm in front of the monkey. A total of 17 LEDs located at ±20 and ±40°, on vertical, horizontal, and oblique axes, were positioned on the screen. The animals were cued to orient their gaze toward a specific quadrant—upper left or right, lower left or right—(with the help of a short, high pitched sound) and were then required to fixate an LED illuminated in that quadrant. The LED was then turned off, but animals were trained to maintain the gaze on the target LED position for a period of 500–800 ms, within a fixation window of 5 to 10° centered on the LED, to receive a juice reward.
The electrophysiology experiments began after a training period of 3 wk, in both monkeys. Single-unit recording and microstimulation sessions were performed with the use of tungsten microelectrodes (0.15 
2.5 m
impedance at 1 kHz, FHC) directed through a stainless steel guide tube, which penetrated 2–3 mm beyond the dura. During each session, one to two electrode penetrations (tracks) were made. During each track, the electrode was slowly lowered while restraining the head of the animal for a short interval, and single-unit activity was monitored using an oscilloscope and an audio monitor and compared on-line to the visually guided gaze, eye, and head movements. When correlations between a neuron's activity and eye movements were detected, we stimulated the corresponding site using 10–20 cathodal trains (100–300 Hz, 80–100 µA, and 200 ms). If the electrical microstimulation evoked eye movements, we allowed the monkey's head to go free and commenced the experiments.
We found that higher stimulation currents were required to evoke movements with the head free (300 Hz, 100–150 µA, 200 ms). It is possible that these higher stimulation thresholds were required to overcome greater activation of the gaze fixation system, which animals might engage to overcome the greater inertia of the head-free system (Corneil et al. 2002). These current intensities were similar with those used in previous studies (Thier and Andersen 1996, 1998) that stimulated LIP using mostly currents of 100–200 µA. We delivered stimulation for each target position, in a random order. Stimuli were delivered 400–800 ms after the LED was extinguished while the animal fixated in complete darkness. Thus the animals received the juice reward immediately after stimulation offset. We applied the electrical stimulus for an average of 50 times per site with a range of 30–80 (depending on the probability of evoking a movement from a given site). The order of illumination of the LEDs was randomized; in most cases, this resulted in 
2 stimulations per target and maximum of 10. Afterward we advanced the electrode 0.5 mm vertically and repeated the process. The area was thoroughly investigated with tracks positioned 1 mm apart (plotted as gray, parallel lines in Fig. 1, B and C), until we had fully explored the areas that did evoke movements and reached areas that did not evoke any movements. Following these procedures, movements evoked from 141 sites (from 47 electrode penetrations) in monkey 1 (M1) and 214 sites (from 47 electrode penetrations) in monkey 2 (M2) were analyzed. The subsequent data analysis reduced the number of sites used in this study to 85 sites in M1 and 194 sites in M2.
Data analysis
Data analysis was conducted off-line using a personal computer (PC) and a custom-made program. We used the computer to convert the coil signals into position signals in quaternions, for the eyes in space (gaze) and head in space (head), and to calculate the orientation for the eyes relative to head (eye) (Crawford et al. 1999; Tweed et al. 1990). Quaternions represent positions as a fixed-axis rotation from a reference position, i.e., when the animal is looking straight ahead (Crawford et al. 1999; Tweed et al. 1990; Westheimer 1957). From these quaternions, we computed the gaze trajectories, angles of rotation, and angular velocities (Crawford et al. 1999; Tweed et al. 1990).
The beginning and the end of stimulation-evoked gaze, eye, and head movements were manually selected, i.e., by visually inspecting position and velocity components plotted as a function of time and then placing a vertical cursor at the appropriate onset/offset points. Peak velocities for the selected trajectories were then computed. We included movements with maximum gaze velocities >50°/s and latencies (time from stimulation onset to peak velocity) <200 ms (the duration of the electrical stimulus). In addition, we plotted the distribution of all movement latencies and excluded movements with values larger than the mean plus 2 SD. Additionally, colliding saccades (saccades resulting from microstimulation delivered during a visually evoked saccades) (Schlag and Schlag-Rey 1990) were removed from the data set. In general, stimulation did not evoke saccades on every trial, even at sites where it did sometimes evoke movements. We only quantified data from sites in which we evoked at least seven movements from at least three of the four visual quadrants (i.e., minimum 2 evoked movements from each quadrant), which resulted in 85 sites in M1 and 194 sites in M2.
During our experiments, we observed that the probability of evoking a saccade during stimulation depended on gaze position. Moreover the distribution of evoked movements as a function of initial gaze orientation varied from site to site. We quantified the probability of evoking saccades as a function of initial eye orientation by calculating a probability vector (PV) for each site. Details of our method are provided in the results section. For this PV analysis, we only selected the sites in which we applied minimum two stimulations in no less than three quadrants, with initial gaze orientation that covered an area of at least ±20° in each of the three quadrants. This resulted in 28 (of 85) sites for M1 and 180 (of 194) sites for M2; only these sites were used in the PV analysis.
We used two methods to assess the reference frame that best characterizes the goal of the stimulation-evoked movements. To characterize the typical results from the stimulation of each site, we used a method described in Klier et al. (2001) and Martinez-Trujillo et al. (2004). First we calculated a characteristic vector (CV) for stimulation-evoked gaze shifts from each site. This vector represents the trajectory that would be expected from stimulating the site when the eyes and head of the animal are pointed straight ahead at the beginning of the stimulation. Second, we used initial eye and head orientation quaternions to rotate gaze trajectories into different coordinate systems (reference frames)—from eye-in-space to head and to eye-in-head reference frames (Martinez-Trujillo et al. 2004).
To characterize how convergent the gaze trajectories are in different reference frames, we used different methods. First, we fitted an ellipse to the endpoints of the gaze movements using the least-squares method, such that 95% of the endpoints fall inside the ellipse (Martinez-Trujillo et al. 2004). Second, we computed the convergence of the gaze trajectories as a function of the initial gaze position resulting in the computation of the convergence index (CI) for the movement direction (on-axis component, i.e., for the component parallel to the characteristic gaze vector) and for the movement amplitude (off-axis component, i.e., for the component orthogonal to the characteristic gaze vector) (Klier et al. 2001; Martinez-Trujillo et al. 2003a, 2004). Finally, we compared such indices with the ones predicted by models of different reference frames coding by computing the normalized residuals for each model, which allowed us to compare the data plotted in the different reference frames (Martinez-Trujillo et al. 2004). The latter analysis accounted for the possibility that the data may not neatly fit into one reference frame or another, i.e., if the endpoints of the stimulation-evoked movements did not converge perfectly in any frame.
Anatomical reconstruction of the stimulated area
The first step in the reconstruction of the stimulation sites was made by assembling a 3-D map of the recording chamber with anterior-posterior, lateral-medial, and depth coordinates and superimposing the coordinates of the stimulation sites onto this map. Figure 1 shows the location of the 85 LIP sites (black circle) for M1 (Fig. 1, A and B) and 194 LIP sites for M2 (Fig. 1C). Each of these sites met the analysis criteria described in Data analysis.
The second step in the anatomical reconstruction consists in histological analysis of the stimulated brain areas. While M1 was anesthetized, LIP sites chosen as points of reference were marked using controlled lesions (1.5-mA currents were delivered for 15 s). Next, M1 was killed, and its brain was perfused with phosphate-buffered paraformaldehyde. High-resolution digital images of the brain were taken before sectioning (Fig. 1A). The tissues containing the stimulation sites were then sectioned on a freezing microtome at 100 µm. Visual inspection confirmed that the stimulation sites reported in this study were situated on the lateral bank of the intraparietal sulcus (IPS). M2 is still being actively used in other experiments but step 1 (stereotaxic mapping) showed similar results as in M1 (Fig. 1C).
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RESULTS |
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Putative LIP sites evoked leftward gaze shifts with a large range of vertical movement components and with amplitudes varying between 1.66 and 23.87° in M1 and between 1.9 and 22.77° in M2. Figure 2, A and C plots horizontal components as a function of time for one typical stimulation-evoked movement. Figure 2A shows the horizontal components of gaze (thick line), head (thin line) and eye (dotted line) position, and the corresponding velocity traces (Fig. 2C). In this example (as in most of our data), the gaze shift was accomplished almost completely by an eye movement, accompanied by an insignificant head component. Figure 2D shows the main sequence plots (peak velocity as a function of amplitude) for all evoked movements from M1 (black circle) and M2 (white circle), divided into bins and averaged. There was no statistical difference between the data from the two animals (P = 0.89, Student's t-test). The latency distribution for the stimulation-evoked gaze shifts for the two monkeys is plotted in Fig. 2B, showing a peak between 40 and 60 ms in both animals.
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) point to the end of the movements. We plotted the trajectories for gaze (Fig. 3, A–C), head (D–F), and eye-in-head (G–I) movements evoked by stimulating three different LIP sites. As stated in METHODS, stimulation was applied 
10 times per target location. For example, for the central target, stimulation evoked either three (sites 1 and 3) or eight movements (site 2). The movements evoked from each target position had similar direction and amplitude, as in fact did movements from all of the different target positions.
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). Each stimulation site evoked a different size and direction of gaze shift. To document the typical results of each stimulation site and to compare the evoked gaze shifts across sites, we calculated the CV, using only the stimulation-evoked movements that met the criteria stipulated in METHODS. Each CV indicates the theoretical movement trajectory evoked by stimulating a given site when the gaze is pointing straight ahead.
Figure 4 (A and B) illustrates the distribution of the CVs for all sites, from M1 (Fig. 4A) and M2 (Fig. 4B); each arrow represents the CV for one particular site. The purpose of this plot and the following analyses is to consider the amplitudes and directions of the stimulus-evoked gaze shifts as a population. For M1 (Fig. 4A), the amplitude of the CVs varied between 2.65 and 14.59° with an average of 8.36 ± 2.90°. The CV's direction was mostly toward the upper-left quadrant of space. For M2 (Fig. 4B), the amplitude of the CVs varied between 2.1 and 14.12° with an average of 7.71 ± 2.25°. For both monkeys, the calculated CV's direction was always contralateral to the location of the stimulated area, i.e., stimulation of right LIP evoked leftward movements with a wide range of vertical directions, more evident in M2 where the CV's direction spread out evenly between the upper and lower left quadrant; in M1, the CV directions did not spread evenly with just two sites with downward movements. The embedded histograms show the distribution of the CV's amplitudes for each monkey; these distributions were relatively similar for both monkeys.
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10–14] such that the CVs have a larger vertical component when they are evoked from more posterior sites. Moreover CV's direction varies as a function of site location on the m-l axis [rvertical/m-l (194) = –0.231, with P = 0.001] such that more medial sites evoked movements with larger horizontal component, similar to M1. Furthermore, deeper sites evoke more downward movements and more superficial sites evoke more upward movements [rvertical/depth (194) = –0.289, with P 10–5].
FRAMES OF REFERENCE.
What frame of reference best describes the goal of the stimulation-evoked gaze shifts obtained in our experiments? Figure 5, left, shows simulated models of gaze trajectories, corresponding to three possibilities plotted in their correspondent coordinate systems. For instance, the goal could be 60° upward of where the eye is pointing—a coordinate system fixed with the eye and plotted in an eye-fixed reference frame (Fig. 5G); or it could be 60° upward of where the head is pointing, with a coordinate system fixed to the head and plotted in head-fixed reference frame (Fig. 5D); or 60° upward of where the body is pointing, with a coordinate frame fixed to the body and plotted in body-fixed reference frame (Fig. 5A). When data are plotted in the intrinsic frame of the model, the simulated trajectories converge to a common point (Martinez-Trujillo et al. 2004).
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; Klier et al. 2001). The different predictions of these two models will be addressed quantitatively in the following text. We do not yet know the correct intrinsic frame for our data, but by corollary with the simulations, the actual stimulus-evoked trajectories should converge best when plotted in this (intrinsic) frame such that if the LIP encodes a gaze motor command using one of the three reference frames (eye, head, or space), consequently the stimulation-evoked gaze shifts trajectories should converge when plotted in that reference frame.
To compare the stimulation data with the simulations, we selected real (stimulation-evoked) gaze shifts evoked from a variety of initial eye and head orientations (see METHODS), and we rotated them into three different coordinate systems (eye, head, and body) as we did with the simulated trajectories. Note that the data are plotted in three different frames, such that the frames used to plot the data correspond to the frames used for the simulated data. Figure 5 (middle and right) illustrates the gaze shifts trajectories evoked from stimulating two putative LIP sites—site 1 from M1 and site 2 from M2; the trajectories are plotted in three coordinate systems—space/body (1st row), head (2nd row), and eye (3rd row). For both monkeys, the trajectories appear to converge best when they are plotted in the eye-fixed coordinate system (Fig. 5, H and I). We repeated the same visual analysis for all the stimulation LIP sites and made similar observations for every single site: qualitatively, the evoked gaze shifts most resembled the predictions of an eye-fixed (or fixed-vector) model. These observations, and the distinction between the latter two models, are quantified in the next two sections.
ELLIPSE FITTING.
Following Martinez-Trujillo et al. (2004), we first utilized a simple method to quantify our data, focusing on the final positions of the stimulation-evoked gaze shifts plotted in different coordinate systems: space, head, and eye. Therefore we computed where the gaze was at the end of the stimulation-evoked movement and quantified the spread area for the endpoints gaze positions by fitting an ellipse to the cluster of points (see METHODS) in each reference frame and by comparing in which reference frame the ellipse's areas are the smallest.
Figure 6, left, an example of the ellipse fitting method. Each shows the end position of the stimulation-evoked gaze shifts, plotted in space (Fig. 6A), head (Fig. 6D), and eye (Fig. 6G) coordinate systems. The ellipse with the smallest area corresponds to data plotted in an eye reference frame (Fig. 6G). For an easier visual comparison, we realigned the fitted ellipses with the centers of the corresponding coordinate systems; the "new" normalized ellipse is plotted with a dotted line. The CVs in Fig. 3 show the approximate center of the gaze ellipses for all sites.
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10–46 for M1 and P 10–88 for M2 (paired Student's t-test, after Bonferroni correction). The difference between the eye and space/body reference frames was also statistically significant with P 10–42 for M1 and P 10–80 for M2 (paired Student's t-test, after Bonferroni correction). Thus the ellipse areas are the smallest when they are fitted to data plotted in an eye reference frame for both monkeys. Because the large quantity of data in these plots tends to obscure many of the individual ellipses, we have supplied a graphical plot of the distributions of all the ellipse areas in each frame in supplementary Fig. 1.1 We found that the distributions of ellipse areas between sites was widest in space coordinates, intermediate in head coordinates, and far more narrow (i.e., consistent) in eye coordinates.
However, this method does not account for the possibility that the data may best fit an intermediate frame of reference. Moreover, it does not allow one to test between a truly fixed vector versus eye-fixed model, which was an important aspect of our previous analysis of gaze shifts evoked from the SC (Klier et al. 2001).
CI. This second, more sophisticated method of quantifying the convergence of the movements took into account the components of both the initial and final gaze position of the evoked movements. We computed convergence indices for direction and amplitude of the stimulation-evoked gaze shifts for each sites (see METHODS) and plotted them as a function of the CV direction and amplitude for that particular site. In the same plots, we generated predictions for the different models of reference frame coding and compared the data against such predictions. Thus the CI has a value of –1 for perfect convergence (movements are convergent to 1 point) and CI has a value of 0 if there is no convergence (the movements are parallel). For the eye-fixed model predictions, the convergence is also a function of the length of the movements (length of CV). For smaller movements, the CI is close to zero and decreases exponentially for larger movements until reaching –1 for movements of 90°.
Figure 7, A and B, shows the CI for the movement direction (CIOff axis) and movement amplitude (CIOn axis) calculated for gaze in space/body-fixed reference frame for each site and plotted as a function of corresponding CV amplitude, for all sites, in both monkeys (M1, and M2, 
). The space/body-fixed model predicts a flat slope of CI = –1, whereas the fixed-vector model predicts a flat slope of CI = 0. The - - - shows the predictions of an eye-fixed model for "off axis" data—data component orthogonal to the CV (Fig. 7A, 
), which is unfortunately very close to 0 (the fixed-vector model) in most of this range of movement (as calculated by Klier et al. 2001); too close to make a qualitative distinction between them based on visual inspection. However, these two predictions begin to diverge toward the right side of the panel (we will use this observation in the following text, to quantitatively test between these 2 models).
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10–90 for M1 and P 10–200 for M2; Student's t-test, after Bonferroni correction applied to both CIOff axis and CIOn axis) with average values for CIOff axis of –0.018 ± 0.054 for M1 and –0.045 ± 0.029 for M2 and average values for CIOn axis –0.038 ± 0.041 for M1 and –0.046 ± 0.093 for M2. The space/body-fixed model did not fit the population or even a single point.
However, for the CIs plotted in Fig. 7, top, the predictions of a head-fixed model cannot be represented by a single line because the predicted values depend on the relative contributions of the eyes and head to gaze, which can vary for different subjects or for different stimulation sites. In theory, the head- and eye-fixed models could make very similar predictions, depending on the animal's intrinsic patterns of eye-head coordination at the initial positions (Martinez-Trujillo et al. 2004). Therefore to test if data follow the predictions of the head model, we calculated the CI for gaze movements rotated into a head reference frame. Figure 7, C and D, plots the CI for the movement direction (CIOff axis) and movement amplitude (CIOn axis) calculated for gaze in head-fixed reference frame and plotted as a function of corresponding CV amplitude for all sites, in both monkeys (M1, and M2, ). In this case, the head model would predict values equal to –1. The eye model does not predict specific values just that the values would be below the line of perfect convergence. Figure 7, C and D, shows that in both animals, data were statistically different from the head-model predictions (P 10–70 for M1 and P 10–200 for M2; Student's t-test, after Bonferroni correction applied to both CIOff axis and CIOn axis) with average values for CIOff axis of –0.130 ± 0.122 for M1 and –0.096 ± 0.069 for M2 and with average values for CIOn axis of –0.132 ± 0.098 for M1 and –0.050 ± 0.068 for M2.
Thus in sharp contrast to similar data collected from the SEF (Martinez-Trujillo et al. 2004), we found no tendency here for the data to follow a distribution that fell between models and/or spanned across several models; the data simply followed an eye-fixed (or fixed-vector) curve. None of our LIP sites followed the predictions of either a space/body- or head-fixed model. However, for this range of gaze amplitudes (i.e., between 0 and 16°), there is no visible difference between the predictions of the eye-fixed and fixed-vector models in these plots. In fact, this difference only become clear for movements >30° and then becomes progressively larger for really big gaze shifts (Klier et al. 2001).
To summarize these results and test quantitatively between the fixed-vector and eye-centered models, we calculated the normalized squared residuals of the fits of the CI data to predictions of each model (between the data CIs and the CIs predicted by the 3 different models), following a method proposed by Martinez-Trujillo et al. (2004). The resulting values were plotted in Fig. 8, for M1 (top) and M2 (bottom), with residuals calculated for off and on axis CIs. The line graphs show that the residual values are smaller for the eye model predictions (data fits the eye model predictions) compared with the head or space model predictions.
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50% larger than the size of the average residuals for the eye-fixed model 0.0023 ± 0.0061. Pooling all data, there was a highly significant difference between these two fits P 10–17 (Student's t-test). Thus as was the case previously for the SC (but not for the SEFs), the eye-fixed model won out over the fixed-vector, head-fixed, and space/body-fixed models in our LIP data. Although this difference may not be important for the range of movements that we were able to LIP, it is perhaps useful to note that with this analysis, we have now eliminated the fixed-vector model from explaining the vast majority of our SC, LIP, FEF, and SEF data (Ascensio-Monteon et al. 2006; Constantin et al. 2004b; Klier et al. 2001; Martinez-Trujillo et al. 2004). Probability of evoking saccades as a function of gaze position
For all of our stimulation sites, we observed qualitatively that stimulation did not evoke gaze movements on every trial. We further observed that on many stimulation sites, it seemed that the probability of evoking a movement when stimulated depended on where the monkey was looking (fixating a target) before we stimulated. The probability of evoking a movement for a given stimulation trial was not equal for all stimulation sites or for all initial target positions within a given stimulation site. In other words, the probability of evoking gaze shifts from different initial gaze position changed for each site of stimulation and for each target position within the stimulation site (Fig. 9, A and B). The following section documents this effect quantitatively.
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To quantify the directional vector for these gaze orientation effects, we fitted a plane (probability plane) to the resulting probability columns (Fig. 9, E and F). These are planes fitted to the probability columns shown in C and D (see METHODS for details). We also tried higher-order fits, which of course made better fits, but it is unclear at this point whether these higher-order fits are justified without a much larger data set of stimulation trials from smaller position increments, needed to clearly distinguish between subtle signal and noise effects. Therefore the first-order plane fit used, allowed focus on the directional tuning of this response at the expense of possibly missing higher-order characteristics, such as localized hills and troughs.
Figure 9, E and F, graphically illustrates our complete analysis for the two representative stimulation sites used in the columns above. The slope (
) and orientation (β) of the probability plane was expressed using a PV (outlined arrow in E and F). The magnitude of PV was proportional with the tilt of the plane (the amplitude of ) and the orientation of the PV was proportional with the angle between the fitted probability plane and the horizontal-vertical plane (β). Applying the same analysis across the sites of stimulation, we obtained 28 PVs for M1 and 180 PVs for M2 with an orientation that varied widely for both monkeys, covering the entire 360° of the 2-D space.
The PVs for all sites of stimulation from animals M1 and M2 are illustrated in Fig. 9, G and H, respectively. As one can see, the distribution of magnitude for these vectors (i.e., the tilt of the probability plane as a function of gaze position) was similar for both monkeys and quite prominent for most sites of stimulation—prominent enough that we could notice it qualitatively during experiments. However, there was no directional preference in these vectors (they covered the full range of directions). In other words, some stimulation sites could be more likely to evoke gaze shifts with the eyes looking up, others down, others lefts, others right, and anything in between. Moreover, we found no topographical correlation between the direction of these PVs and the anatomic location of the stimulation site—this organization appeared to be random.
We also investigated the possibility of any correlation between the PV and CV directions. For example, we wondered if the PV might align with the CV. For stimulation site 1 (Fig. 9A), the CV (gray thick, arrow) and the PV (white, thick arrow) are almost perpendicular to each other; for stimulation site 2 (Fig. 9B), the CV and PV have almost opposite directions, which seems to indicate that there is no correlation between the direction of the PV and the direction of the corresponding CV for these two particular sites of stimulation. Furthermore, across stimulation sites, there is no correlation between the probability of electrically eliciting saccadic eye movements and the average direction of the corresponding evoked gaze shifts (R2 = 0.031 for M1 and R2 = 0.0004 for M2). In other words, for a given stimulation site with a given CV, the PV could be in any direction.
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DISCUSSION |
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What is the frame of reference for LIP?
Most previous single-unit recording studies of visual receptive fields in LIP have suggested an eye-fixed organization (Andersen et al. 1992; Boussaoud and Bremmer 1999; Colby et al. 1995; Culham et al. 2006). Many of these studies have also pointed out that these same neurons carry position information—in the form of gain fields—that would allow these neurons to implicitly encode higher-order frames of reference (Andersen et al. 1990b; Barash et al. 1991; Brotchie et al. 1995; Snyder et al. 1998). However, this is a topic that we will take up in a later section in the following text. In addition, the amplitude and direction of a given LIP unit's preferred saccade motor tuning usually overlaps with the location of the unit's eye-fixed visual receptive field (Andersen et al. 1987; Barash et al. 1991; Colby 1998; Colby et al. 1996; Wurtz et al. 2001). An exception to the general rule is a recent study, which suggests that LIP units may have eye-fixed, head-fixed, or perhaps body-fixed visual receptive fields (O'dhaniel et al. 2005).
Our stimulation data are generally consistent with the idea of an eye-fixed coding scheme in LIP. Our data clearly show that head- and body-fixed codes do not play as prominent a role in the output of the LIP, for example, as compared with that of the SEF (Martinez-Trujillo et al. 2004). It is possible that these signals play a more subtle role at the level of individual units but are simply swamped in the population vote cast during electrical stimulation. Moreover, our data also tend to rule out the fixed-vector model for LIP at least at a quantitative level. The difference between this model and the eye-fixed model is admittedly very small within the range that we tested here, but it is notable that the fixed-vector model has failed to provide a best fit for all but a very few sites in every area that we have tested to date. Moreover, an eye-centered code provides a common language for the LIP to communicate with other brain areas, such as the SC, where the difference between the two models is much more clear (Klier et al. 2001).
However, before drawing any conclusions, it is important to note that unit recording and stimulation are different techniques that show different things. This is not just a matter of unit recording showing the physiological activity of single neurons, as opposed the ensemble activation of a region of neurons and fibers (and their various orthodromic and antidromic projections) through stimulation. Receptive field mapping reveals correlations to the unit's inputs, whereas stimulation reveals the influence of an area's output on other regions of the brain (Pare and Wurtz 1997; Tehovnik et al. 2003). For example, when the equivalent experiments are simulated in artificial neural networks that implement a reference frame transformation, the receptive field mapping correlates with the reference frame of the input structures, whereas stimulation reveals the reference frame code of the output targets (Smith and Crawford 2005). Stimulation results may correlate better with the motor tuning of neurons yet even these two measures do not necessarily reveal the same thing in complex population coding schemes (Blohm et al. 2006). Thus it may be an oversimplification to think of LIP, or even single units within LIP, as using a single frame of reference to perform its computational role in the gaze control system.
Thus the best interpretation of our main result is that LIP projects to (and influences gaze behavior through) structures that in turn use an eye-fixed frame to code gaze. This is consistent with the known projections of the LIP to the FEFs and the SC, which are thought to code gaze in eye-centered coordinates (Klier et al. 2001; Russo and Bruce 1996; Stanton et al. 1995; Wurtz et al. 2001). Because most LIP units appear to have eye-fixed visual receptive fields (Ben Hamed and Duhamel 2002; Ben Hamed et al. 2001; Colby et al. 1996), this suggests that LIP is not primarily involved in transforming eye-fixed frame signals into a different frame (Smith and Crawford 2005). However, it does not rule out the existence of nonretinal receptive fields in LIP units (O'dhaniel et al. 2005). In our scheme, such neurons would be involved in a transformation from nonretinal frames (perhaps influenced by nonvisual projections to LIP such as auditory and somatosensory inputs) into an eye-centered frame.
Previous stimulation studies of LIP have not always shown such consistently eye-fixed coding. For example Thier and Andersen (1996, 1998) explored sites in which the movement vectors were either slightly or strongly modulated by eye position. The majority of sites with slight modulations are likely consistent with our eye-fixed results because this is what an eye-fixed scheme looks like when projected into space coordinates (Crawford and Guitton 1997b; Klier et al. 2001; Martinez-Trujillo et al. 2004). However, Thier and Andersen also described a group of sites, primarily in an "intercalated zone" located on the floor of the IPS sulcus that gave stronger position-dependent convergent movements and larger head movements than we observed in our study. We looked for a region like this but simply did not find it.
There could be several explanations for the difference between our results. First, it could be a difference in stimulation parameters. Our trains were longer, compared with those of Thier and Andersen (1996, 1998), but because the movements occurred early in the train, this does not seem to be an important difference. Second, Thier and Andersen occasionally used higher stimulus amplitudes (400 µA), but this cannot account for the difference in the majority of our data, which used similar stimulus amplitudes. The most likely difference is that they used 500 Hz, whereas we used a lower frequency (300 Hz). It is also possible that the behavioral set in our monkeys was different due to either previous training or experimental conditions during the stimulation experiment. For example, Thier and Andersen's monkeys' heads were partially restrained, whereas our monkeys' heads were completely unrestrained. Previous studies have shown that restraining the head produces much more convergent looking movements, especially in areas that produce larger eye + head gaze shifts when the head is not restrained (Freedman et al. 1996; Martinez-Trujillo et al. 2003a; Pare and Guitton 1990). Moreover, "freeing" the head completely may have further raised the stimulation threshold, causing us to miss this area at our stimulation parameters. Finally, although we tried to fully explore the area within and surrounding LIP, it is possible that we somehow missed the more convergent area described by Thier and Andersen (1996, 1998). However—assuming that we did not outright miss this area—it would appear that for one reason or another it may have a higher threshold for stimulation-evoked than LIP proper, perhaps placing it even further from the motor output than the main portion of LIP.
Gaze position modulations in LIP
Numerous studies have shown that activity in LIP neurons is modulated by eye position (as well as head position and vestibular information) in a way commonly referred to as a gain field (i.e., where location of the receptive field remains fixed in eye coordinates, but the sensitivity gain of the neuron is increased or decreased as a linear function of eye position) (Andersen et al. 1985, 1990b). It has been shown using neural network simulations that such gain fields can be used to rotate the preferred movement vector into different coordinate systems (Zipser and Andersen 1988). Normally one thinks in terms of using these signals to rotate eye-fixed information into other frames of reference for downstream motor structures; however, by the same token, this information could be used to rotate information from other sensory frames (such as auditory information in head coordinates) into an eye-fixed frame within LIP—consistent with a suggestion that we made in the preceding text—or to perform both functions. For example, although the primary outputs of LIP may be to structures such as the FEFs and the SC that seem to primarily use an eye-fixed code, LIP should also influence activity in the SEFs, which appears to employ a more complex coding scheme involving multiple reference frames at both the input and output levels (Martinez-Trujillo et al. 2004; Olson and Gettner 1999; Olson and Tremblay 2000; Park et al. 2006). It has also been shown that position information may also be necessary for other computations that involve LIP, such as the updating of targets in 3-D space during self-motion (Medendorp et al. 2002, 2003; Smith and Crawford 2001). For these reasons, gain fields are likely to be an important and useful feature within LIP.
It is not possible to test the gain fields of individual units using the microstimulation technique. It is not likely that the mass activation of many units through stimulation would emulate the normal function of gain fields, as gain fields can only perform reference frame transformations through the differential activation of different units with various motor tuning directions as a function of position (Smith and Crawford 2005). Although our stimulation-evoked movements followed an eye-fixed pattern that was not systematically influenced by initial gaze position (at least when the data are plotted in eye coordinates), they were strongly influenced in another way: the probability of evoking an eye movement from a given site depended on initial gaze position. Similar observations have been made for SC (Freedman et al. 1996) and SEF (Martinez-Trujillo et al. 2003b).
It is possible that this result provides a causal signature for gain fields at the population level. In other words, if enough units in an area have gain fields that roughly align in their position-dependent tuning, this would increase the background firing rate for these units for gaze positions in this direction (Andersen et al. 1990b; Barash et al. 1991; Brotchie et al. 1995; Snyder et al. 1998), perhaps making it easier to evoke eye movements from these positions. This is not contradicted by our observation that our PVs did not correlate to our CVs (hypothetically corresponding to the position tuning of gain fields vs. the directional motor tuning of cells, respectively) across stimulation sites. We found the same random relationship between gain field tuning, visual tuning and motor tuning when an artificial neural network was trained to perform the 3-D reference frame transformation for saccades (Smith and Crawford 2005). In fact, the presence of nonorthogonalities between these elements is necessary to implement the nonlinear aspects of these transformations.
If these arguments are correct, then the PV provides a direct measure for mass coding, or perhaps even topography, of gain fields. However, this cannot be proven without a more direct comparison between these two phenomena; there could be many other reasons why the probability of a stimulation-evoked eye movement from LIP is influenced by initial gaze position that have nothing to do with reference frame transformations, and/or these effects could have been mediated by downstream structures rather than within LIP.
Role of LIP in gaze control
Despite the fact that we were able to evoke gaze shifts from stimulation of a great many sites in the LIP region of each of our animals and despite the consistency of our results, two important caveats must be noted. First, higher current strengths were required to evoke gaze shifts from this area compared with other areas (FEFs, SEFs, and SC) where we otherwise used exactly the same stimulus parameters (Ascensio-Monteon et al. 2006; Klier et al. 2001; Martinez-Trujillo et al. 2003a,b ). Second, we did not evoke near the range and size of gaze shifts from LIP that we evoked from these other areas. The latter may arise from the lack of topography in LIP compared with areas like the SC, i.e., we may have been co-activating units that coded both large and small gaze shifts, leading to a vector-averaging effect (Gottlieb et al. 2005; Groh 2001; Thier and Andersen 1996, 1998). Nevertheless, these observations are consistent with the idea that LIP is not directly connected to the motor output of the gaze control system but rather is involved in more "cognitive" functions at the bridge between the sensory and motor stages of this system (Colby et al. 1996; Goldberg et al. 2002). What then is the place of the LIP within the reference frame transformations of this system?
LIP has strong and direct connections with the FEFs and the SC (Andersen et al. 1990a; Ferraina et al. 2002; Hanes and Wurtz 2001; Matelli and Luppino 2001; Pare and Wurtz 1997, 2001; Sparks et al. 2001; Stanton et al. 1988). This is supported by retrograde tracing studies confirming the anatomical connections from the LIP to the FEFs and SC and by electrical micro-stimulation studies confirming functional connections from the LIP to the FEFs and SC. According to these studies (Andersen et al. 1990a; Pare and Wurtz 1997; Schall et al. 1995), LIP has direct motor output to the SC—with strong connections to the intermediate layers of the SC. For this reason, it is convenient that LIP and the SC (and most sites in the FEF) appear to use a common eye-centered motor code. Our single-unit recording studies (DeSouza et al. 2006) and our microstimulation studies (Constantin et al. 2004a; Klier et al. 2001) in the head-unrestrained monkey suggest that the SC integrates its inputs into a primarily eye-fixed motor code for gaze commands. This in turn suggests that the nonlinear transformation of this signal into a motor code for eye and head muscles, which is highly significant in the head-free range, is performed at stages even later than the SC, perhaps at the level of the last few premotor synapses of the serial projection (Klier et al. 2001).
However, LIP is also thought to have some projections to the SEFs (Huerta and Kaas 1990; Luppino and Rizzolatti 2000; Matelli and Luppino 2001). As mentioned in the preceding text, this area appears to use a much more complex code that shows both ego- and object-centered signals at the level of individual units (Olson and Tremblay 2000) and multiple egocentric reference frames (eye, head, and space/body) at the level of stimulus-evoked gaze shifts (Martinez-Trujillo et al. 2004; Park et al. 2006; Schall et al. 1995). The SEFs in turn project to the FEFs (Bruce et al. 1985; Luppino and Rizzolatti 2000; Schall et al. 1995; Stanton et al. 1988). In our recent head-free stimulation study of the FEF (where we used the same paradigm as in the current study), we found that most sites used an eye-fixed code, but some (perhaps targets of the SEF) used a more head-fixed code (Ascensio-Monteon et al. 2006). In this regard, this placed the FEF in a position intermediate between the SEF and SC.
These observations and the data within our current study are consistent with our recent suggestion that there are two cortical streams within the higher levels of the gaze control system (Martinez-Trujillo et al. 2004). One pathway—running directly from visual cortex through LIP to the SC, and indirectly through LIP to the SC via FEF—that uses a primarily eye-centered code to affect geometrically simple aspects of the visuomotor aspects of gaze. This pathway would include and be an elaboration of the phylogenetically older subcortical paths directly from the eye to the optic tectum (Kostyk and Grobstein 1987a,b; Masino and Grobstein 1989), where now LIP interposes between the visual and motor layers of the SC (Albano et al. 1982; Wurtz and Albano 1980). A second pathway—running through LIP to the SEF to FEF and perhaps other more high level structures—is used for more complex spatial aspects of gaze control. This series of nested loops could provide the primate—compared with the amphibian, for example—with the capacity for reflexive gaze shifts, spatially simple gaze shifts with cognitive transformations within an eye-centered frame, and gaze shifts that involve more complex object-centered and body-centered plans. This scheme places LIP with an important early role within both of the latter two cognitive streams of computation.
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FOOTNOTES |
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1 The online version of this article contains supplemental data. 
Address for reprint requests and other correspondence: J. D. Crawford, Center for Vision Research, York University, 4700 Keele St., Toronto, Ontario M3J 1P3, Canada (E-mail: jdc{at}yorku.ca)
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