C. Kennard & R.J. Leigh (Eds.) Progress in Brain Research, Vol. 171 ISSN 0079-6123 Copyright r 2008 Elsevier B.V. All rights reserved
CHAPTER 4.11
A reinterpretation of the purpose of the translational vestibulo-ocular reflex in human subjects Ke Liao2, Mark F. Walker1, Anand Joshi2, Millard Reschke3, Zhong Wang2 and R. John Leigh1,2, 1
Department of Neurology, Daroff-Dell’Osso Laboratory, Veterans Affairs Medical Center and University Hospitals, Case Western Reserve University, Cleveland, OH, USA 2 Department of Biomedical Engineering, Daroff-Dell’Osso Laboratory, Veterans Affairs Medical Center and University Hospitals, Case Western Reserve University, Cleveland, OH, USA 3 Neurosciences Laboratories, Johnson Space Center, Houston, TX, USA
Abstract: In a prior study we reported that the human translational vestibulo-ocular reflex (tVOR) in response to vertical (bob) 2 Hz oscillations generated eye rotations of only 60% of those required to keep the eyes pointed at a stationary visual target, whether located at near (B17 cm) or far (2 m). Best responses occurred in ambient illumination and we concluded that relative image motion between the target and background was an important determinant of tVOR behaviour. To investigate further how visual conditions influenced tVOR, we measured responses as subjects binocularly viewed the bridge of their own nose in a mirror at B8.5 cm, a visual condition that required similar convergence to viewing the near target, but cancellation of tVOR. Median tVOR cancellation gain [(near-viewing responsemirror viewing response)/ near-viewing response] was 0.81 (range 0.550.97), which was substantially greater than the gain of smooth visual tracking of a large visual display moving at 2 Hz (median gain 0.27, range 0.090.42). Thus, visual inputs other than smooth tracking must contribute to tVOR cancellation. We then compared tVOR response to 2 Hz bob as subjects fixed upon a visual target at 17 cm and viewed a large textured background at 1.5 m that was either stationary or moving at 2.1 Hz. Vertical eye rotations waxed and waned as a function of the difference between platform and background oscillations. These findings support our hypothesis that tVOR evolved not to stabilize the image of the target on the fovea, but rather to minimize retinal image motion between objects lying in different planes, in order to optimize motion parallax information. A geometrically based optimization function is proposed to account for tVOR responses at different target distances. Keywords: locomotion; vergence angle; moving platform; retinal slip translations in ambient light, and also in combination with horizontal (yaw) rotations (Liao et al., 2008). Our goal was to determine whether these test conditions, which approximated components of head perturbations during locomotion, would increase the overall tVOR response. We found that tVOR increased only slightly during translation– rotation in ambient light compared with reported
Introduction In a recent study, we measured translational vestibulo-ocular reflex (tVOR) during vertical (bob)
Corresponding author. Tel.: + 216-844-3190; Fax: + 216-231-3461; E-mail:
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DOI: 10.1016/S0079-6123(08)00643-2
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values (Gresty et al., 1987; Israe¨l and Berthoz, 1989; Paige, 1989; Gianna et al., 1997; Ramat and Zee, 2003; Ramat et al., 2005). Although the velocity of eye movements increased by almost a factor of 10 between far versus near viewing, compensation gain (eye rotational velocity/required eye rotational velocity to maintain foveal target fixation) remained at 0.550.60. Based on responses during binocular or monocular viewing, and ambient or reduced illumination, we concluded that relative image motion between the target and background was a critical determinant of tVOR behaviour. The goals of the present study were to investigate further the influence of vision on tVOR performance during two test paradigms: (1) to compare tVOR cancellation versus smooth visual tracking. (2) To determine whether motion of the visual background influenced tVOR during viewing of a near target.
Methods Subjects We studied 13 healthy subjects (6 females, median age 60 years, range 25–72), who were subjects in a prior study of tVOR (Liao et al., 2008). All gave informed, written consent, in accordance with the Declaration of Helsinki and the Institutional Review Board of the Cleveland Veterans Affairs Medical Center.
Comparison of cancellation of tVOR and smooth tracking For the first set of experiments, for which all 13 subjects participated, there were three visual conditions, each employed for one experimental run. (1) Subjects binocularly viewed a laser spot projected on a wall at a distance of 2 m. (2) Subjects binocularly viewed an earth-stationary near target (reflective ball, diameter 1 cm) suspended at a distance of B17 cm in front of their left eye. (3) Subjects viewed a small mark on the bridge of their own nose through an earth-fixed mirror at a distance of B8.5 cm. The actual positions of the near target and the mirror, for each subject, were measured directly. During these experiments, room lights were turned on so that natural visual cues, such as motion parallax and relative size, were available. We compared the cancellation of tVOR during mirror viewing with smooth visual tracking of a moving visual stimulus (Amsler grid), subtending 25.61 horizontally and 18.61 vertically with a central dot, at a target distance of 110 cm. The stimulus moved sinusoidally, in the vertical plane: (A) through 75.61 at 2.0 Hz (peak velocity 701/s) or (B) through 72.81 at 2.0 Hz (peak velocity 351/s). The first moving visual stimulus imposed the same requirements on eye movements as those imposed by the translation stimuli, if there were no vestibulo-ocular responses; the second stimulus corresponded to the remaining visual motion if tVOR compensated for half of that required.
Investigation of background motion on tVOR Vestibular stimuli Subjects sat in a chair on a Moog 6DOF2000E electric motion platform (East Aurora, New York) that could move with 61 of rotational and translational freedom. Each experimental run started with three cycles of bob at 0.2 Hz (typical amplitude 75.6 cm). Responses to this 0.2 Hz stimulus held subjects’ eyes continuously on target and served as a calibration check. Then, we applied bob translations at 2 Hz (typical amplitude 71.5 cm) for 12 s.
For the second set of experiments, subjects viewed the near target at B17 cm with a background consisting of horizontal stripes or a photograph of a park, displayed on a large flat screen at 1.5 m, which subtended 501 horizontally and 301 vertically. Room lights were turned out, but the earthstationary near target could be easily seen by its reflected light, which emanated from the large screen; however, other background landmarks in the room could not be seen. The two visual conditions were then (1) near target against the
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background which was stationary; (2) near target against the background that moved sinusoidally at 2.1 Hz. Three subjects took part in these experiments (age range 25–60).
Measurement of eye and head movements and data processing Three-dimensional eye rotations were measured using the magnetic search coil technique; details of the system and processing of coils signals have been previously described (Liao et al., 2008). Linear and rotational movements of the chair frame and subject’s head were monitored by an infrared reflection system (Vicon Motion Systems, Los Angeles, CA), and its signals were used to calculate the movements of the subject’s head, the coil frame, and ‘‘ideal eye rotations’’ to hold gaze (corresponding to the line of sight) on the visual target (Liao et al., 2008). We carried out Fourier transforms of eye and head velocity, measuring the response at the frequency of the stimulus. We quantified the responses with two parameters, which we term the responsivity and the compensation gain. We define the responsivity (output/input) of tVOR as eye rotational velocity/head translational acceleration, which has units of degrees/second of eye rotation per meters/second2 of head translation (hereafter stated as deg s/m). We also calculated compensation gain as eye rotational velocity/required eye rotational velocity to maintain foveal fixation of the visual target (Ramat et al., 2005). In the case of mirror viewing, ideally, tVOR should be negated. Thus, as an index of tVOR cancellation, we compared tVOR responses during mirror viewing with responses during viewing of the near target. The measured viewing distance of the near target (16.970.9 cm) and virtual image in the mirror (17.771.0 cm) were similar but not identical and, accordingly, we made a geometric correction to the near-target responses. We then calculated tVOR cancellation ratio (CanR) from [(near-viewing responsemirror viewing response)/ near-viewing response]. The gain (eye velocity/ target velocity) and phase lag of smooth tracking with respect to visual target motion were calculated
by desaccading eye velocity data, and computing Fourier transforms. To test whether smooth tracking was the only factor that cancelled the tVOR response during mirror viewing, we calculated the expected response during mirror viewing (MR) by subtracting smooth pursuit response (SP) from tVOR response during near viewing MR ¼ tVOR SP The predicted cancellation gain or ratio (CanR) can then be calculated as: CanR ¼
AtVOR AMR AtVOR
where AtVOR is the measured amplitude of tVOR, and AMR the calculated amplitude of the MR. In this way we could compare values of predicted CanR with measured results.
Results Comparison of cancellation of tVOR and smooth visual tracking Representative records from one subject during the three visual test conditions are shown in Fig. 1A; note that, apart from vergence, individual traces have been offset to aid clarity of display. The magnitude of tVOR (responsivity) increases from far to near viewing, but is largely negated during mirror viewing, although the vergence angle is similar to during near viewing. Results from all 13 subjects are summarized in Fig. 1B. For the group of subjects during mirror viewing, mean vergence angle was 20.01 and median tVOR cancellation gain was 0.81 (range 0.550.97). The median gain (range) for smooth tracking for the full amplitude stimulus (located at 110 cm) was 0.27 (0.090.42) with mean phase lag of 58.61715.6 for measured eye velocity with respect to ideal eye velocity required to follow the target; for the half amplitude stimulus, median gain was 0.18 (0.060.38), with a phase lag of 56.41 (713.7). As shown in Fig. 1B, the cancellation gain (CanR) calculated from either full or half amplitude smooth
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Fig. 1. (A) Representative records from the left eye of one subject illustrating typical responses during viewing of the far target, near target, and mirror viewing. Note that, except for vergence, individual traces have been offset in position to aid clarity of display. Note how tVOR (vertical eye rotation) increases during near viewing compared with far, but is largely cancelled during mirror viewing (visual target moves with subject). Required eye rotations were computed from measured head movements (Liao et al., 2008). (B) Comparison of measured CanR during mirror viewing and estimated CanR based on smooth-tracking performance (see text for details); measured CanR values are significantly greater than values calculated from either ‘‘full amplitude’’ or ‘‘half amplitude’’ smooth tracking responses.
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pursuit response is significantly smaller than the measured CanR ( po0.001). Furthermore, a paired comparison for each subject (Wilcoxon rank-sum test) showed a significantly lower value of calculated CanR than measured CanR ( po0.001). Thus, smooth visual tracking could not account for cancellation of tVOR responses during near mirror viewing. Effect of moving visual background on tVOR Representative responses from one subject are shown in Fig. 2. As the difference between platform and background motion decreased, so did the tVOR response compared with responses with a stationary background (for whom median peak eye velocity is indicated by horizontal dotted lines). Subsequently, as the difference between platform and background increases, tVOR started to increase. It is evident that the effect on tVOR lags
the changing stimulus by 2 s. This behaviour is addressed further in the Discussion.
Discussion We found that visual cancellation of tVOR, as each subject viewed the bridge of their nose in a near mirror, was achieved much more successfully than could be accounted for by visual tracking mechanisms such as smooth pursuit. These results are consistent with a prior study of visual cancellation of tVOR in bob in two human subjects (Paige, 1989). Thus, similar to visual cancellation of aVOR (Leigh et al., 1989; Huebner et al., 1992; Das et al., 1998), mechanisms other than superposition of visual tracking appear to contribute. During smooth visual tracking at 2 Hz, large phase lags of the eye occur with respect to the target. In contrast, the phase lag of tVOR at 2 Hz
Fig. 2. Effects of tVOR as a subject fixed upon a small earth-stationary target at 17 cm against a moving background (horizontal gratings) at a viewing distance of 1.5 m. The subject bobbed sinusoidally at 2.0 Hz and the background moved at 2.1 Hz. The subject’s vertical eye velocity and the difference between platform and background, which was constantly changing, are plotted. Horizontal dotted lines correspond to median values of peak eye velocity while this subject viewed the same near target against a stationary background.
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encountered in our prior experiments was about 181, irrespective of the viewing condition (Liao et al., 2008), which could be largely accounted for by the reported tVOR latency of about 25 ms (Gresty et al., 1987; Ramat and Zee, 2003). Thus, one conclusion is that although visual stimuli are important for setting tVOR responsivity to an appropriate level, smooth visual tracking (such as smooth pursuit) appears to play little role if any. Our second experiment addresses the issue: Why should the compensation ratio of tVOR be systematically smaller than the amount of eye rotation that is required to keep the fovea pointed at the near target? During translation of the observer, relative motion of objects located at different distance is inevitable, for which eye movements cannot compensate. This relative motion of retinal images provides a cue to the distance of objects in the environment. Motion detection follows Weber’s law, such that discrimination of relative motion is better at lower velocities of retinal image motion (Nakayama, 1985). We have postulated that tVOR responses are set to minimize retinal image speed (RIS) for both the target and the visual background (Liao et al., 2008). In this study, we found that motion of the visual background may influence tVOR performance compared with viewing a stationary background. Thus, when the platform and target were moving in opposite directions (1801 phase shift), assuming the subject’s gaze remained on the near target, then retinal image due to background motion should be minimized and we postulate that tVOR responsivity should be maximized. Conversely, when the platform and target were moving in the same direction (01 phase shift), then assuming the subject’s gaze remained on the near target, retinal image should be maximized, and we postulate that tVOR responsivity should be minimized. The responses shown in Fig. 2 generally conform to these predictions, but more studies are needed to confirm them. In our prior study, based on measured tVOR responses during viewing of targets at three viewing distances (2 m, 40 cm, 17 cm), we were able to calculate geometric predictions of retinal image velocity (Schwarz and Miles, 1991), assuming a compensation gain of 0.6. The predictions of this simple geometric model generally fitted the
measured values of RIS well (Fig. 3 — curve and squares). If tVOR has a compensation gain of 0.6, it will hold peak RIS below 51/s (a threshold for clear vision) for target distances greater than 90 cm. Note that measured peak RIS of the background lying at 200 cm (diamonds in Fig. 3), which might provide motion parallax information, is similar to that of the image of the fixation target, although opposite in direction. Our present finding that background motion may influence tVOR led us to develop a more general optimization function that minimizes retinal slip due to both the visual target and the background. RIS, when the eye is stationary, depends on the distance (D) between target and eye, the amplitude of the head movement (A), and the frequency of oscillations ( f ) in hertz RIS ¼
A f 360 D
(1)
RIS calculated with the eye stationary also corresponds to the speed of the required eye movement to keep the fovea pointed at the target. To calculate _ then if RIS when the eye is moving with velocity y, C=A f 360 C RIS ¼ y_ D
(2)
We postulate that tVOR is set to minimize the sum of squares of RIS of both the background (RISbk) and the foreground target (RIStar). Then, the optimization function (Fopt) is: F opt ¼ RIS2bk þ RIS2tar
(3)
Substituting F opt ¼ ðy_ C=Dbk Þ2 þ ðy_ C=Dtar Þ2 and its minimum occurs when C 1 1 y_ ¼ þ (4) 2 Dbk Dtar Taking into account the different effect of background RIS and target RIS, we assign a weight coefficient to each. Then the optimized eye velocity becomes: K 1 C=Dbk þ K 2 C=Dtar y_ ¼ K1 þ K2
(5)
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Fig. 3. Geometry of peak retinal image speed (RIS) as a function of target distance for three subjects versus their measured peak retinal image speeds. Squares indicate measured values of RIS of the fixation target at each of the target distances for each subject. Diamonds indicate measured values of RIS of the background at 200 cm, which is opposite to that of the target image motion, but is similar in magnitude The black curve defines responses based on a simple geometric model (Schwarz and Miles, 1991) with a mean compensation gain of 0.6. The dotted curves summarize the fit of the optimization model (see text) for RIS of target and background, which generally fit the data better than the simple geometric model. The dashed horizontal line corresponds to a retinal image speed of 51, above which visual acuity for high spatial frequencies will decline.
where K1 is the weight for the background, and K2 the weight for the target. To determine the value of K1 and K2, we fit the model to measured responses from three subjects for target distances of 40 and 17 cm, and a background distance of 2 m by minimizing the error of predicted eye velocity to the measured eye velocity with a least-squares method. Thus we obtain K 1 ¼ 0:43; K 2 ¼ 0:57 We then use this optimization model to fit the RIS of targets (black dotted line) and RIS of the background (grey dotted line) in Fig. 3. The optimization model generally gave better fits of the measured RIS of subjects for target or background than the curve based on the simple geometric model with a compensation gain of 0.6. Experiments are required to test the predictions of this model, such
as varying the background distance. This model proposes a new interpretation of the purpose of tVOR: to control retinal image motion of objects at different distances so that motion parallax information is optimized. Acknowledgements This study was supported by NASA/NSBRI NA00208, Office of Research and Development, Medical Research Service, Department of Veterans Affairs, NIH grant EY06717, and the Evenor Armington Fund. We are grateful to Drs. Harold Bedell, David Zee, and Gary Paige, for their helpful advice, and to Ulrich Bu¨ttner for critical review of the manuscript. The work reported in this paper constitutes research performed by Ke Liao as part of requirements for his Doctoral Dissertation.
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