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1Division of Visual Science, Yerkes National Primate Research Center and 2Department of Neurology, Emory University, Atlanta, Georgia; and 3Beckman Vision Center, University of California, San Francisco, California
Submitted 8 June 2004; accepted in final form 13 August 2004
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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; Glickstein et al. 1994; May and Andersen 1986), which plays an essential role in smooth pursuit and gaze control (Mustari et al. 1988; Ono et al. 2004; Suzuki and Keller 1984; Suzuki et al. 2003; Thier et al. 1988). The DLPN receives visual inputs from the extrastriate cortex (Distler et al. 2002; Glickstein et al. 1980, 1994; May and Andersen 1986) and sends mossy fiber projections to the contralateral ventral paraflocculus and dorsal paraflocculus (Glickstein et al. 1994; Nagao et al. 1997) and vermal lobule VI and VII (Brodal 1979, 1982; Langer et al. 1985). The NRTP is known to receive inputs from the frontal eye fields (FEF) and supplementary eye fields (SFE) (Brodal 1980a; Giolli et al. 2001; Huerta et al. 1986; Kunzle and Akert 1977; Shook et al. 1990) and sends mossy fiber projection to the vermal lobules VI and VII (Brodal 1980b, 1982). Previous lesion and electrical stimulation studies established the DLPN and rostral region of the nucleus reticularis tegmenti pontis (rNRTP) as being primarily involved in smooth pursuit eye movements (May et al. 1988; Ono et al. 2003; Suzuki et al. 1999; Yamada et al. 1996). Further evidence for postulating an expanded role of DLPN and rNRTP in eye movements comes from single-unit studies (Mustari et al. 1988; Ono et al. 2004; Suzuki and Keller 1984; Suzuki et al. 2003; Thier et al. 1988). These single-unit recording studies show that different DLPN and rNRTP neurons could be classified as preferentially sensitive to smooth pursuit eye velocity, position, or acceleration.
Recently, using sinusoidal visual and vestibular stimuli, we have shown that a large proportion of DLPN and rNRTP smooth-pursuit neurons encode gaze movements with a small contribution from retinal error (Ono et al. 2004). However, the predictability and steady-state nature of sinusoidal stimuli tends to obscure relative contributions of motion parameters during the different stages of tracking (for example: smooth-pursuit initiation vs. smooth-pursuit maintenance). Two issues that remained unresolved due to the steady-state nature of sinusoidal testing was whether contribution of retinal error was masked and whether DLPN and rNRTP neurons evinced relatively different sensitivity to position, velocity, and acceleration components of the stimulus. Therefore in this study, we further examined the role of the DLPN and rNRTP during step-ramp smooth pursuit by applying a modeling procedure employing multiple linear-regression. The main aims of this study were to establish whether the smooth pursuit-related neurons in DLPN and rNRTP show significant sensitivity to eye and retinal-error motion parameters during step-ramp tracking and to identify relative contributions of parameters of motion (position, velocity, and acceleration) toward the unit response.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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A detailed description of our surgical procedures can be found in earlier publications (Mustari et al. 1988, 1997, 2001). Behavioral and single-unit data were collected from two normal juvenile rhesus monkeys (Macaca mulatta) weighing 4–5 kg. Surgical procedures, carried out under aseptic conditions using isoflurane anesthesia (1.25–2.5%), were used to stereotaxically implant a stainless steel head-stabilization post (Crist Instruments) and stainless steel recording chambers. In the same surgery, a scleral search coil for measuring eye movements (Fuchs and Robinson 1966) was implanted underneath the conjunctiva of one eye using the technique of Judge et al. (1980). All surgical procedures were performed in strict compliance with National Institutes of Health guidelines and the protocols were reviewed and approved by the Institutional Animal Care and Use Committee at Emory University.
Behavioral paradigms
During all experiments, monkeys were seated in a chair with the head stabilized in the horizontal stereotaxic plane. Neurons in the DLPN and rNRTP were first classified as visual or smooth-pursuit related. We tested neurons for visual sensitivity by requiring the monkey to fixate a stationary target while a large-field visual stimulus was moved in eight cardinal directions separated by 45°. Only neurons that responded during horizontal or vertical smooth pursuit of a small-diameter (0.2°) target spot moving at low frequency (0.1–0.75 Hz; ±10°) were included in this study. All neurons were tested as monkeys tracked a target that moved with a step-ramp trajectory with a constant velocity ramp (10–30°/s) over a dark background. The size of the step was adjusted so that smooth pursuit was initiated without initial saccades (Rashbass 1961). Usually the size of the step was between 2 and 4°. Data collected during step-ramp testing were used for the model fitting procedure described in the following text.
Data collection
Eye movements were detected and calibrated using standard electromagnetic methods (Fuchs and Robinson 1966) using precision hardware (CNC Electronics, Seattle, WA). Motion of the laser spot was controlled by a two-axis mirror galvanometer (General Scanning, Watertown MA). Stimulus motion was controlled with custom Labview software and National Instruments hardware (Austin, TX). Eye- and target-position feedback signals were processed with anti-aliasing filters at 200 Hz using six-pole Bessel filters prior to digitization at 1 kHz with 16-bit precision. Velocity data were generated by digital differentiation of position data using a central difference algorithm in Matlab (Mathworks, Natick, MA). Unit activity was recorded using custom-made glass-coated tungsten electrodes or modified commercial epoxy-coated tungsten (Frederick-Haer, Brunswick, ME). The impedance of the electrodes was in the 1- to 3-M
range. Single-unit action potentials were detected with either a window discriminator (Bak Electronics, Mount Airy, MD) or template-matching algorithm (Alpha-Omega, Israel) and represented by a TTL pulse that was sampled at high precision as an event mark in our data-acquisition system (CED Power1401, Cambridge, UK). During analysis, neuronal response was represented as a spike-density function that was generated by convolving spike times with a 5-ms Gaussian function (Richmond et al. 1987). We used both functional and anatomical criteria for localizing our recording sites in the DLPN or rNRTP as previously described (Mustari et al. 1988; Ono et al. 2004). Briefly, at the end of a series of recording experiments, we placed micro-lesions (10 µA; 10 s) on representative electrode tracks to mark the relative positions of smooth pursuit-related neurons (Fig. 1). Subsequent histological processing and Nissl staining allowed us to reconstruct our recording sites and confirm that units were derived from the rNRTP and DLPN. This was accomplished by comparing the depth readings on the microdrive associated with related neurons and microlesions. For example, the photomicrograph in Fig. 1 shows a Nissl-stained section that includes an electrode track that passed along the medial edge of the rNRTP. Portions of neighboring tracks can be seen traveling toward the rNRTP and DLPN.
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We used a model estimation procedure to identify smooth pursuit-related signals in DLPN or rNRTP during step-ramp tracking. Briefly, we attempted to reconstruct the individual response profiles of smooth pursuit-related neurons by using combinations of position, velocity, and acceleration. Similar procedures have been used with success in other parts of the oculomotor system including the cerebellum, oculomotor nuclei, the pretectal nucleus of the optic tract (NOT), and MST cortex (Das et al. 2001; Inoue et al. 2000; Shidara et al. 1993; Sylvestre and Cullen 1999). Velocity data were filtered using an 80-point finite impulse response (FIR) digital filter with a passband of 50 Hz, and acceleration data were filtered using an 80-point FIR digital filter with a passband of 30 Hz. The spike density function was also filtered at 50 Hz to reduce the variability in the unit response. Saccades were marked with a cursor on eye-velocity traces and were removed. After desaccading, the missing eye data were replaced with a linear fit connecting the pre- and postsaccadic regions of data using custom Matlab routines (Mathworks). Averaged data, taken from 
10 trials in which the animal performed smooth pursuit, were then used to identify coefficients in the following model
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1" and "2" terms, respectively. 2 was calculated from the data as the latency between onset of target motion and the onset of unit response. While it appeared safe to assume that the initial part of the unit response was due to retinal error parameters, the contribution of eye parameters to unit response may not have started at the same time. This led to a certain ambiguity in calculating 1 directly from the experimental data. We therefore calculated a set of coefficients (A–G) and estimated coefficients of determination (CD) for a series of 1 latencies in steps of 5 or 10 ms (Fig. 2Bi). In our final model, we used coefficients that yielded a maximum CD for a specific eye latency value (Fig. 2Bi, · · · ).
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We calculated the relative decrease in variance of the six-component model (model 3) compared with the three-component model for eye (model 1) or retinal error (model 2) using the following indices
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We also calculated partial r2 values for each component (eye and retinal-error position, velocity, and acceleration) to estimate the relative contribution of eye and retinal-error position, velocity, and acceleration to the firing rate of the neuron in DLPN and rNRTP. All statistical tests were executed with a significance value of 0.05 unless otherwise specified.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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We recorded 20 smooth pursuit-related neurons in DLPN during step-ramp tracking. As in previous studies, we found individual DLPN neurons had preferred directions that could be horizontal, vertical, or oblique (Mustari et al. 1988; Suzuki et al. 1990; Thier et al. 1988). Figure 2 illustrates the response of a representative neuron in DLPN. Unit response lags the onset of target motion by 74 ms and leads onset of eye motion by an average of 60 ms. The figure shows that the neuron is most likely a velocity sensitive and is well modulated during step-ramp tracking with a rightward (contralateral) preference. The model estimation procedure for the unit of Fig. 2A is shown in Fig. 2B, a–h. Figure 2B, a–f, illustrates the components that were used to make up the model, g illustrates the contribution of each term of the model toward the total fit, and h illustrates the experimentally derived unit spike-density function and the corresponding model estimated fit. The fit obtained using this six-component model had a coefficient of determination of 0.88. Examination of each component of this model (Fig. 2Bg) indicates that velocity contributes most to the unit response during step-ramp tracking, whereas contributions of position and acceleration are relatively small. Figure 2Bi shows how we selected the value to use for 1. For example, if 1 was derived from the observed response at –60 ms (i.e., latency between onset of unit response and onset of eye motion), the CD was less than that obtained when using a 1 value of –50 ms where the CD reached a maximum (Fig. 2Bi, · · · ). We always used the 1 associated with the highest CD to conduct our modeling of eye motion. Therefore for the example neuron in Fig. 2 the first 10 ms of the unit response was due to retinal-error motion; thereafter unit response was due to a combination of retinal-error and eye motion.
Response properties of NRTP neurons during step-ramp tracking
We recorded 19 smooth pursuit-related neurons in rNRTP during step-ramp tracking. The preferred smooth pursuit directions for rNRTP neurons were distributed around the clock as reported in previous studies (Suzuki et al. 2003) Fig. 3A illustrates the response of a representative rNRTP neuron. The figure shows that the neuron is well modulated during step-ramp tracking with a rightward (ipsilateral) preference. Unit response lags the onset of target motion by 73 ms and leads onset of eye motion by 24 ms. The model estimation procedure for the unit illustrated in Fig. 3A is shown in B. Figure 3B, a–f, illustrates the contribution of components that were used to make up the models, g illustrates the contribution of each term of the model toward the total unit response, and h illustrates the experimentally derived unit spike density function and the corresponding model estimated fit. The six-component model provided a good fit to the experimental derived data (CD = 0.87). Examination of each component of this model (Fig. 3Bg) indicates that eye-acceleration sensitivity contributes strongly to the initial part of the step-ramp tracking and eye-position sensitivity contributes more to the steady-state part of the step-ramp tracking. Figure 3Bi shows the effect of changing the value of 1. If 1 was calculated from the actual observed response of –24 ms, the CD obtained was less than that obtained using –14 ms where the CD reached a maximum value (Fig. 3Bi, · · · ).
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1. We used coefficients that yielded a maximum CD (1 = –27 ms; · · · ).
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The six-component model provided a good fit to the experimental-derived data in DLPN (CD = 0.86 ± 0.11; n = 20) and rNRTP (CD = 0.83 ± 0.08; n = 19). We also calculated the relative decrease in variance of the six-component model (model 3) compared with the three-component model for eye (model 1) or retinal error (model 2). This served as a method to estimate the relative contribution of eye- and retinal-error parameters to the neurons. Figure 5 plots this data for DLPN and rNRTP. The comparison of this values between eye and retinal error parameters (Fig. 5A) indicates that a large proportion of DLPN neurons (14/20) have larger contributions from eye motion (0.65 ± 0.24; n = 14) compared with retinal error motion (0.25 ± 0.21; n = 14) parameters (P < 0.001, paired t-test). The same type of comparison between eye and retinal error for rNRTP neurons is shown in Fig. 5B. We found that most of rNRTP neurons (17/19) in our sample had larger contributions from eye motion (0.48 ± 0.16; n = 17) compared with retinal error motion (0.23 ± 0.14; n = 17) parameter (P < 0.001, paired t-test). However, even though eye-motion parameters provide the strongest contributions in a given model, a significant contribution from retinal-error motion is shown in Fig. 5, A and B (dotted lines; >20% reduction in variance in 6-component model compared with 3-component models).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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Sensitivity to eye motion and retinal-error motion
Multiple lines of evidence suggested to us that there might be differences in smooth pursuit-related signal content of individual rNRTP and DLPN neurons and therefore net differences in the information delivered to the cerebellum from these pontine centers. First, previous single-unit recording studies described different balances in visual-, smooth-pursuit-, and eye-position-related neuronal types in NRTP and DLPN (Mustari et al. 1988; Ono et al. 2004; Suzuki and Keller 1984, 2003; Their et al. 1988). The distributions of directional preference and speed tuning in NRTP and DLPN neurons show considerable overlap. However, the NRTP appears to have a bias in the proportion of neurons with acceleration sensitivity (Suzuki et al. 2003). Second, recent anatomical studies have shown that different regions of cerebral cortex project to the NRTP and DLPN (Brodal 1980a; Distler et al. 2002; Giolli et al. 2001; Glickstein et al. 1994; Huerta et al. 1986; Kunzle and Akert 1977; Shook et al. 1990). NRTP receives stronger input from the FEFs than from MT or MST cortex. In contrast, DLPN received stronger inputs from MST than MT cortex (e.g., Distler et al. 2002). Each of these cortical areas is known to contain neurons that are modulated during smooth pursuit. However, MT, MST, and FEF smooth-pursuit-related neurons appear to support different aspects of smooth pursuit (Fukushima 2003; Wurtz et al. 1990). For example, MT lesions produce retinotopic pursuit deficits while MST and FEF lesions produce directional pursuit deficits.
Differential effects of cortical lesions on smooth pursuit may relate to the progression from sensation to action thought to occur at least in part in MT, MST, and FEF cortical areas. Specifically, MT may function primarily as a necessary visual sensory area working with MST and FEF to produce a partially formed smooth-pursuit command. Previous studies (Newsome et al. 1988) established that the pursuit response of MT neurons depends on visual motion on the retina by the pursuit target, whereas the pursuit response of the dorsal-medial region of MST (MSTd) neurons included extraretinal component of unknown origin. Recent studies have shown that smooth-pursuit-related neurons in FEF, like those in MSTd, maintain their firing in the absence of a visual target during smooth pursuit (Tanaka and Fukushima 1998). The source of this extraretinal signal is a topic of considerable interest. Extraretinal (nonvisual) signals could reach cortex by corollary discharge traveling from brain stem centers through oculomotor thalamus. Alternatively, some cortical areas may construct a nonvisual signal to support smooth pursuit for brief time periods, as would be required to track a target that is briefly occluded in a complex visual environment (e.g., Churchland et al. 2003). It is likely, that different cortical areas have different balances of nonvisual signals and that both visual and smooth-pursuit-related signals travel from cortex to different regions of the cerebellum by way of different neurons in NRTP and DLPN.
Sensitivity to position, velocity, and acceleration
The earlier NRTP and DLPN smooth pursuit studies mentioned in the preceding text did not include statistical modeling. Because pontine neurons often evince multiple sensitivities, we wanted to specifically examine the potential contributions of position, velocity, and acceleration due to eye or retinal image motion while tracking a target spot. We found that DLPN and rNRTP neurons appear to receive different contributions of position, velocity, and acceleration signals. Therefore we calculated the coefficients of partial determination (partial r2 values) to determine the relative importance of each term. The partial r2 values for DLPN neurons show that velocity makes a larger contribution than position or acceleration. In contrast, partial r2 values for rNRTP neurons show that acceleration makes a larger contribution to unit response than position or velocity. However, the other terms in our models for both DLPN and rNRTP neurons suggest that position and velocity make small but significant contributions to overall response during smooth pursuit. We suggest that different biases of acceleration and velocity sensitivity could be associated with different roles in initiation (rNRTP) and maintenance (DLPN) of smooth pursuit. It is important to note that we specifically chose to model smooth-pursuit-related neurons and did not model neurons that were sensitive to visual motion alone. Kawano and colleagues (1992) have conducted modeling studies of DLPN neurons that respond to large-field visual motion that produces short-latency ocular following and found them to be strongly related to retinal image motion. Similarly, neurons in the pretectal nucleus of the optic tract (NOT) are best modeled using retinal-error parameters during either ocular following (Inoue et al. 2000) or smooth pursuit (Das et al. 2001). Our results indicate that neurons in both rNRTP and DLPN carry, at least, a partially formed smooth-pursuit command to the cerebellum.
Anatomical studies show that the NRTP projects most strongly to the vermis (lobules VI and VII) and DLPN to the ventral paraflocculus and flocculus (Brodal 1982; Glickstein et al. 1994; Langer et al. 1985). Lesion studies have demonstrated differential effects of lesions in different regions of oculomotor cerebellum that receive input from rNRTP and DLPN. For example, recent study (Takagi et al. 2000) has demonstrated the lesions of oculomotor vermis (lobules VI and VII) produce the most significant deficits in the open-loop rather than closed-loop portions of smooth pursuit. In contrast, Stone and Lisberger (1990) found that lesions of the flocculus had a profound effect on the closed-loop portion of smooth pursuit. When both flocculus and ventral paraflocculus were damaged, deficits in initiation and maintenance of smooth pursuit were reported (Rambold et al. 2002). Our modeling studies and single-unit studies of Suzuki and colleagues (2003) strongly suggest that the rNRTP is a likely source of acceleration-related signals for the cerebellar vermis. These signals would be expected to have the largest impact on the initiation of smooth pursuit, where eye acceleration is required. In contrast, the DLPN is dominated by eye-velocity-related neurons that could provide the flocculus and ventral paraflocculus with signals appropriate for maintaining the closed-loop portion of smooth pursuit.
Conclusion and future studies
In conclusion, we have shown that the neurons in the rNRTP and DLPN with smooth-pursuit-related activity during step-ramp tracking are primarily encoding aspects of eye motion with secondary contributions from retinal-error motion, unlike the neurons in the NOT. These different functional roles may in part reflect different balances of cortical input to NRTP and DLPN neurons, thus allowing rNRTP and DLPN to play crucially different functional roles in the initiation, maintenance, and control of smooth eye movements. Our results support the suggestion that the rNRTP may play a larger role in the initiation, whereas DLPN contributes to maintaining steady-state eye velocity during smooth pursuit. One of the most important unresolved questions regarding NRTP, DLPN, and basilar pontine function in general is whether these areas simply relay signals to the cerebellum or whether significant processing occurs in the basilar pontine nuclei per se (see Schwarz and Thier 1999 for review). Given the multiple cortical inputs to each area, it seems likely that signal integration in the basilar pons is possible. By performing modeling studies like those reported here, we should be able to compare the properties of neurons in cortex, basilar pons, and cerebellum for evidence of signal transformation. Further studies will be required to consider the role of basilar pontine neurons not only in relatively simple position, velocity, and acceleration coding for smooth pursuit but also in more complex processing associated with, e.g., decision making and reward, that could be modulated, at least in part, in cortico-ponto-cerebellar circuits.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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FOOTNOTES |
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Address for reprint requests and other correspondence: M. J. Mustari, Yerkes National Primate Research Center, Division of Visual Science, Emory University, 954 Gatewood Road N.E., Atlanta GA 30322 (E-mail: mjmustar{at}rmy.emory.edu)
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
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) placed deep to the rostral nucleus reticularis tegmenti pontis (rNRTP) can be seen. Portions of neighboring tracks destined for rNRTP and dorsolateral pontine nucleus (DLPN) can also been seen. - - -, approximate borders of the rNRTP and DLPN. PN, pontine nucleus; III, oculomotor nucleus, DSCP, decussation of the superior cerebellar peduncle. Scale bar = 1 mm.
(t – 50) –0.07Ë(t – 50) + 8.20R(t + 75) + 2.79
(t + 75) + 0.03
(t + 75). i: the procedure for estimating unit latency related to eye motion (
