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1Departments of Neurology, 2Otolaryngology, and 3Anatomy, University of Mississippi Medical Center, Jackson, Mississippi; 4Department of Otolaryngology, University of Texas Medical Branch, Galveston, Texas; and 5Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan
Submitted 5 January 2006; accepted in final form 22 August 2006
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ABSTRACT |
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100 spikes · s–1 · g–1 at f = 1 Hz), and little or no change in discharge rate during static head tilt (0.32 spikes · s–1 · °–1). The firing rates of some neurons in both groups were modulated during rotation about an earth-vertical axis (42%), but the modulation was greater for neurons classified as high sensitivity units. Previous reports have described neurons similar to the high sensitivity group; however, the low sensitivity or tilt neurons have not previously been characterized. Significantly, recent theoretical models have predicted neurons with discharge patterns similar to those of low- and high-sensitivity neurons. |
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INTRODUCTION |
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). These movements generate angular and linear accelerations that are detected by the vestibular system, which must in turn modulate posture and stabilize gaze. Angular accelerations generated by head turns are detected unambiguously by the semicircular canals (Wilson and Melvill Jones 1979). Linear acceleration of the head is detected by the otolith organs (Fernandez and Goldberg 1976a; Fernandez et al. 1972). The otoliths experience physically equivalent forces caused by reorientation of the head with respect to gravity or by linear translation of the head through space. The vector difference of gravity and linear acceleration is called gravito-inertial force (GIF, see Fig. 1) and is sensed by the otolith organs (Angelaki et al. 1999; Fernandez and Goldberg 1976a; Green and Angelaki 2004; Merfeld 1995b).
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Two hypotheses, not mutually exclusive, have been proposed to explain how the central vestibular system interprets GIF. According to the frequency segregation hypothesis (Mayne 1974; Paige and Tomko 1991; Telford et al. 1997), low-frequency (<0.1 Hz) modulations of central otolith signals are selectively low-pass filtered and associated with changes in head position, whereas higher-frequency modulations are selectively high-pass filtered and associated with head translations in space. Frequency segregation of otolith signals has been associated with the type of vestibulocular response evoked. Head tilts are compensated by torsional eye movements (ocular counter-roll), but head translations are compensated by horizontal or vertical eye movements (Paige and Tomko 1991; Telford et al. 1997). However, this simple scheme fails to adequately account for how otolith signals might distinguish higher-frequency tilts from translation, a problem that can be solved by combining sensory inflows from multiple systems. For example, convergent otolith and canal sensations could be used to distinguish higher-frequency head tilts from translations because canal afferent firing rates would be modulated during the occurrence of tilts but not during translations (Angelaki et al. 1999; Guedry 1974; Young 1984). More recent work has demonstrated how canal and otolith signals could be combined over a wide range of dynamic frequencies to distinguish changes in head orientation from translational motion (Angelaki et al. 2001b, 2004; Dickman and Angelaki 2002; Green and Angelaki 2004; Hess and Angelaki 1999; Merfeld 1995b; Merfeld and Young 1995; Merfeld and Zupan 2002; Merfeld et al. 2001, 2005a,b; Zupan et al. 2000, 2002). Interestingly, the cues used to distinguish translation from tilt may differ for perception and eye movement control in humans (Merfeld et al. 2005a,b)
Previous studies have described the discharge properties of otolith afferents and central vestibular neurons that process signals related to gravity or linear translation in a variety of mammalian species (Angelaki and Dickman 2000; Angelaki et al. 1993, 2004; Chen-Huang and McCrea 1999; Dickman et al. 1991; Fernandez and Goldberg 1976a–c; Fernandez et al. 1972; Goldberg et al. 1990; Meng et al. 2005; Peterson 1967, 1970; Schor et al. 1985, 1998; Tomlinson et al. 1996). However, for technical reasons, there are little data describing such activity in non-human primates during head translations rather than head tilts (Angelaki et al. 1993, 2004; Chen-Huang and McCrea 1999; Dickman and Angelaki 2002; King et al. 2000; Zhou et al. 2000). This study describes central vestibular activity related to head translation or tilt, and is focused on neurons whose activity is unrelated to eye movement. Although most otolith afferents exhibit activity related to gravito-inertial force, whether it is due to inertial (translational) acceleration or gravity, a novel finding of this study is a description of two, possibly overlapping, populations of central vestibular neurons that are likely to play a role in distinguishing translational acceleration from gravity. One group strongly encodes head orientation with respect to gravity but only weakly, if at all, encodes head translation. The other group strongly encodes head translation but not static head orientation. These data suggest that neurons in the vestibular nuclei can distinguish gravitational acceleration from inertial acceleration due to head translation.
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METHODS |
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Surgical preparation and eye-movement recording
Animals were prepared for eye-movement and single-unit recording. Initially, a stainless steel receptacle was implanted on the animal's skull so that the head could be stabilized with respect to the vestibular stimulator and the field coils of the eye movement monitor. Binocular eye movements were measured using the magnetic search coil method (Robinson 1963). A stainless steel recording well, aimed at the vestibular nuclei, was implanted stereotaxically on the skull during a later surgery. The firing rates of the neurons reported herein were unresponsive to eye-movement: discharge rates were unmodulated during saccades, smooth pursuit, or fixation of near and far targets when the animal was stationary, and activity generated during head movement was uninfluenced by vergence.
Data collection and analysis
Neuronal activity was recorded extracellularly with tungsten microelectrodes. Action potentials were amplified, filtered (100–10,000 Hz) and discriminated electronically (Bak Electronics). The output of the discriminator, a digital pulse associated with each action potential, was detected by the data-acquisition system with a temporal resolution of 0.01 ms. Eye movement, target position, rotary or linear motion signals, and tilt position were sampled at 1 kHz and stored with the single-unit responses on disk for later analysis. Linear acceleration of the monkey's head was measured using a miniature tri-axial linear accelerometer (Entran EGAXT3) attached directly to the head. Linear accelerations in directions other than those imposed by the sled were negligible. Furthermore, careful inspection of the rotary position signal failed to reveal any unwanted rotation associated with linear motion of the sled.
Data were analyzed off-line using a suite of computer programs (for more details, see Snyder and King 1992; Zhou and King 1998; Zhou et al. 2003). To assess the regularity of a neuron's discharge (Goldberg and Fernandez 1971), the coefficient of variation (CV) of instantaneous firing rate was computed when the monkey was alert and stationary. The mean interspike interval (t), the SD of the intervals (s) and the coefficient of variation were calculated for each data sample (CV = s/t). CVs were computed for different mean firing rates resulting from random variations or different head orientations. A normalized coefficient of variation (CV*) was computed at a mean interval of 20 ms from linear interpolation of CVs at different firing rates. To analyze vestibular responses, signals related to angular or linear motion, eye movement, and instantaneous firing rate (reciprocal interspike interval) were averaged over several sinusoidal cycles. Individual cycles were subject to review prior to their inclusion in an average. Occasionally, cycles were excluded from analysis when unit isolation was poor or when the monkey failed to perform the behavior required by the task. The eye-movement data were used to confirm that the firing rates of neurons included in this report were unmodulated by eye movement or eye position (e.g., responses to head movement were identical whether or not compensatory eye movements were suppressed). Because firing rates were unresponsive to eye movement, it wasn't necessary to de-saccade or otherwise manipulate the data to remove eye movement effects. Steady-state responses to sinusoidal motion were computed from averaged data using MatLab to compute an FFT so as to extract the fundamental response. Gain and phase shift were calculated at each stimulus frequency. For linear motion, phase shifts are reported relative to head acceleration; for angular motion, phase shifts are reported relative to head velocity.
During experiments, recording sites in the vestibular nuclei were identified in relation to well-known landmarks such as the abducens nucleus, the fourth ventricle and by the characteristic discharge patterns of neurons located in the vestibular nuclei (Chubb et al. 1984; Fuchs and Kimm 1975; McCrea et al. 1987a,b; McFarland and Fuchs 1992; Scudder and Fuchs 1992). Based on chamber coordinates, vestibular-only neurons were located 1–3 mm caudal to the abducens nucleus and within 4 mm of the midline at depths similar to those of abducens neurons (Fig. 2). Electrolytic lesions were made at the recording sites of a small number of neurons prior to perfusion. The locations of these sites were confirmed histologically to be within the medial and lateral vestibular nuclei.
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During experiments, the monkey's head was aligned with the horizontal stereotaxic plane. As a vestibular search stimulus, monkeys were rotated about an earth-vertical axis sinusoidally at 0.5 Hz and oscillated linearly along the interaural axis at 1 Hz. Many units were encountered that exhibited regular spontaneous discharge rates but that appeared to be unresponsive to the search stimuli. As a further check, these neurons were tested during static tilts and manual oscillation about the earth horizontal axis to detect possible vertical canal inputs or static tilt sensitivity. In addition, neurons that discharged spontaneously and/or responded to the search stimuli were further tested for sensitivity to eye movements. Eye-movement sensitivity was assessed using horizontal and vertical smooth pursuit and saccade paradigms. In addition, fixations of both near and far targets were employed to evaluate possible vergence responses. During vestibular stimulation, paradigms were designed to show that firing rate modulation was related to head movement regardless of eye movement (e.g., VOR suppression paradigm) or to show that viewing distance did not influence head-movement-related activity (e.g., far and near target fixation paradigms during translational motion).
Monkeys were trained to fixate small targets projected by lasers onto a far screen (located 1.5 m from the monkey's eyes) or onto a near screen in a horizontal plane extending 8–25 cm from the animal's nose. During linear motion, the amplitude of compensatory eye movements (translational vestibuloocular reflex or TVOR) varied with target distance and was minimal if the animals viewed targets on the far screen or was scaled with viewing distance if the animals viewed targets on the near screen. This behavior and VOR suppression paradigms were used to help dissociate neural activity related to eye movement from neural activity related to the vestibular stimulus. Laser target positions on either screen were controlled by the computer and servocontrolled mirror galvanometers (General Scanning).
Linear motion sensitivity was assessed using interaural (IA, ipsilateral direction positive) and nasooccipital (NO, backward direction positive) sinusoidal linear motion. Units were tested at 0.2, 0.35, 0.5, 0.8, 1, 2, and 4 Hz. For frequencies >0.2 Hz, the amplitude of the linear stimulus was adjusted to maintain a constant 0.2 g peak acceleration. At 0.2 Hz, the peak acceleration was limited to 0.07 g. The smaller acceleration was necessary because linear travel was limited to ±100 cm. Several studies have demonstrated spatial-temporal convergence of otolith signals in central vestibular neurons (Angelaki and Dickman 2000; Dickman and Angelaki 2002; Schor and Angelaki 1992). The collection of a data set sufficient to fully characterize these properties was neither possible nor the key focus of this study. We did, however, collect limited data over a range of horizontal directions of linear motion (2D). For most neurons, spatial-temporal convergence was assessed using two directions of motion (IA and NO). This analysis demonstrated that spatial-temporal convergence of otolith signals was absent or minimal for the majority of neurons included in this report. Another set of paradigms was employed to examine angular sensitivity of vestibular neurons with angular velocity maintained at 30°/s peak across the tested frequency range of 0.2–4 Hz. Although limited by our stimulator to earth-vertical axis rotation, we attempted to assess the relative contribution of horizontal and vertical canal afferents to unit activity by testing angular sensitivity with the head tilted 20° forward (to maximize the stimulus to the horizontal canals) or 20° backward and oriented ±45° to the left or the right (to maximize the stimulus to the vertical canals).
Responses to head orientation in space ("tilt") were evaluated by recording unit activity with the monkey's head statically tilted or manually rocked about an earth-horizontal axis. For most neurons, dynamic tilts were attempted near the natural frequency of the device (circa 1 Hz). If a neuron remained isolated after completion of other tests, we attempted to collect dynamic tilt data at other frequencies (e.g., 0.5 and 2 Hz), but such data were collected on only small number of cells. In initial experiments, the monkey could be tilted only in the pitch plane. In later experiments, the apparatus was modified to allow tilts in pitch, roll and intermediate directions. In a few cases, data were obtained over a range of head orientations from pure roll to pure pitch on order to determine the optimal direction for a unit's response to head tilt. It is unlikely that any neurons not responsive to pitch prior to completion of the tilt apparatus were responsive to roll (see RESULTS) because all tilt-sensitive neurons had other discharge characteristics that distinguished them from translation sensitive neurons (see RESULTS for details).
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RESULTS |
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; Iwamoto et al. 1996; Kasper et al. 1988), rat vestibular nucleus (Bush et al. 1993), monkey vestibular nucleus (Chen-Huang and McCrea 1999; Dickman and Angelaki 2002; Tomlinson et al. 1996), and monkey fastigial nucleus (Büttner et al. 1999; Shaikh et al. 2005a; Siebold et al. 1997, 1999; Zhou et al. 2001). Responses to horizontal translation
Vestibular-only neurons responded to linear translation with a wide range of modulation depths ranging from a fraction of a spike · s–1 · g–1 to several hundred spikes · s–1 · g–1. Figure 3 shows the responses of two representative vestibular-only neurons to IA linear translation. These two cells illustrate the range of modulation depths present in our sample of neurons. To emphasize the difference in their responses to translation, firing rates of both cells are scaled identically. Although difficult to discern, the discharge of the unit illustrated in the middle traces is modulated by linear translation at 0.2 Hz (±1.2 spikes/s for the 0.07 g stimulus amplitude, left). This modulation corresponds to a sensitivity of 17.1 spikes · s–1 · g–1 in the IA direction. The neuron's sensitivity to NO motion (not shown) was similar (16.6 spikes · s–1 · g–1). Because this neuron discharged at a regular rate, even small modulations of firing rate could be accurately measured. However, during 1-Hz linear translation at 0.2 g (far right traces), the modulation of the neuron's firing rate was very small (±0.6 spikes/s), corresponding to a sensitivity of only 3 spikes · s–1 · g–1 in the IA direction (in the NO direction, sensitivity was 2 spikes · s–1 · g–1). In contrast, the firing rate modulation of the unit illustrated in the bottom traces was large at 0.2 Hz (±12.7 spikes/s, left), corresponding to a sensitivity of 181.2 spikes · s–1 · g–1 in the IA direction. In the NO direction, the unit's sensitivity was also large (60 spikes · s–1 · g–1). The traces on the bottom right show that this unit remained highly sensitive to linear translation at 1 Hz (168 spikes · s–1 · g–1 IA and 137 spikes · s–1 · g–1 NO).
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Low- and high-sensitivity units were further characterized by their frequency responses during linear translation. Figures 5 and 6 illustrate the frequency responses (gray lines) of every neuron for which data were obtained at two or more frequencies between 0.2 and 4 Hz and in at least one linear direction (IA or NO). The mean gain or phase shift of the population response at each frequency is plotted as open circles connected with heavy black lines in each panel. For most low sensitivity units (Fig. 5), the gain (firing rate modulation divided by acceleration amplitude) declined rapidly with frequency, from a mean of 22.0 ± 3.2 (SE) spikes · s–1 · g–1 at 0.2 Hz (n = 42) to 2.4 ± 0.7 spikes · s–1 · g–1 at 4 Hz in the IA direction (Fig. 5A) and from 42.9 ± 7.6 spikes · s–1 · g–1 to 3.3 ± 0.5 spikes · s–1 · g–1 in the NO direction (Fig. 5B). Over the same range of frequencies, the phase shifts of the responses with respect to linear translation changed less dramatically, from a mean lag of –165.0 ± 20.8° at 0.2 Hz to a lag of –171.3 ± 25.8° for IA motion (Fig. 5C) and from a mean lag of –211.8 ± 9.0° to a lag of –251.6 ± 27.5° for NO motion (Fig. 5D). At 0.2 Hz, some neurons had phase lags <180° and others had lags >180°. To emphasize the dynamics of the responses (because phase lags of 180° imply changes in direction), 180° was added to the phase of any neuron with a phase lag <180° at 0.2 Hz. After this transformation, the mean phase shift at 0.2 Hz was –46.0 ± 8.7° (IA) and –58.6 ± 12.7° (NO). At 4 Hz, the corrected phase shifts declined to –90.5 ± 21.7° (IA) and –106.7 ± 23.7° (NO). The mean gain and phase characteristics (after correcting for direction, see preceding text) suggest a low-pass filter process with a corner frequency near 0.2 Hz. However, phase shifts of individual neurons were distributed over the entire range from 0 to –180°, demonstrating a wide range of dynamic responses to head movement.
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reported finding three populations of vestibular neurons that were highly responsive to linear translation. Their population was divided into three groups with different dynamic characteristics. If the frequency range is limited to 
4 Hz, our data (Fig. 6) most closely resemble their group with relatively flat gain and phase characteristics. We did not identify a subpopulation of neurons with flat gain but rising phase characteristics as reported in their study. Responses to head tilt
Low- and high-sensitivity units also differed in their responses to changes in GIF generated by dynamic rocking and persistent head tilt. Figure 7A illustrates the response of a low-sensitivity neuron during dynamic "head tilt." The top traces were obtained with the monkey's head turned 45° to the left with respect to the axis of the linear track. In this position, the tilt stimulus was comprised of leftward roll and forward pitch components. The top left shows that the neuron's instantaneous firing rate (heavy black dots) is clearly related to head position (gray trace) during dynamic tilts. During tilt, the neuron's firing rate overshoots and then decays slowly to a stable firing rate with a time constant of several seconds. If the tilted position is maintained for 30 s (not shown), firing rate stabilizes with an overall sensitivity to tilt of 0.54 spikes · s–1 · °–1 of head tilt. The neuron's firing rate also modulates during sinusoidal rocking (middle and right). At 0.5 Hz (middle), firing rate is closely related to head position with a sensitivity of 1.2 spikes · s–1 · °–1 (determined by linear regression using a sinusoidal model). At 1 Hz, the firing rate is still deeply modulated (1.42 spikes · s–1 · °–1) and remains in phase with head position. If the monkey is turned 90° from this position, the tilt stimulus is comprised of rightward roll and backward pitch components (bottom). In the new direction, the neuron's response is virtually nil (dynamic gains during sinusoidal rocking were 0.09 and 0.16 spikes · s–1 · °–1 at 0.5 and 1 Hz, respectively).
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30 s are shown in Fig. 7B. The polar plot (left) shows the unit's sensitivity to persistent tilt as a function of the direction of tilt. In the figure, an orientation of 0° corresponds to ipsilateral roll; an orientation of 90° corresponds to forward pitch. The neuron responded to tilt with higher firing rates for ipsilateral roll and backward pitch. Figure 7B, right, re-plots these data to show that the modulation of firing rate followed a cosine rule as a function of tilt direction (r2 = 0.97). The modulation amplitude was 10.9 spikes/s at the optimal tilt direction of 328°. Because the tilt amplitude was ±20°, the neuron's tilt sensitivity was 0.54 spikes · s–1 · °–1 of head tilt or equivalently 11.6 spikes · s–1 · g–1.
Near the optimum direction, the dynamic sensitivity of the neuron during a rocking stimulus is about three times the neuron's sensitivity to maintained tilt. The dynamic tilt stimulus excites otoliths and vertical canals simultaneously. With the head oriented 45° to the left (the neuron's preferred direction), angular acceleration during head tilt was approximately aligned with the plane of the right anterior and left posterior canals (RALP plane). The neuron's enhanced response to dynamic, as compared with static tilt, might be the result of an interaction between vertical canal and otolith inputs. However, because of equipment limitations, we could not stimulate the vertical canals in the RALP plane without also stimulating the otolith organs; thus we could not directly test this hypothesis. However, we could show that the neuron's firing rate was modulated by angular acceleration. During angular acceleration at 0.5 Hz about the earth-vertical axis, the neuron's firing rate was modulated with a gain of 0.03 spikes/s per °/s and a phase lag of 9.1° with respect to angular head velocity. The firing rate modulation was greater when the animal was tilted backward 20° to optimize the angular acceleration stimulus to the vertical canals. Nevertheless, the canal response was unexpectedly weak compared with the neuron's strong modulation during dynamic tilt suggesting the possibility of nonlinear canal-otolith interactions (Angelaki et al. 1999; Green and Angelaki 2004; Merfeld 1995b; Merfeld et al. 1993, 1999) or a contribution of unidentified inputs that were present during dynamic tilt. The response pattern of this neuron was typical of low-sensitivity units. Data during dynamic tilts (1 Hz) were obtained from 11 low-sensitivity units during roll and 28 units during pitch. The mean gain during roll was 1.1 spikes · s–1 · °–1 and during pitch, 1.3 spikes · s–1 · °–1. Mean phase leads were near 45° with respect to head position, but the distributions were bimodal. Seven of the 11 units tested in roll discharged nearer in phase with head position, and 4 discharged nearer in phase with head velocity; in pitch the distribution was more evenly divided, 12 units discharged in phase with head position, and 16 in phase with head velocity.
Figure 8A illustrates the head tilt responses of a high-sensitivity neuron. The top traces show head tilt responses with the monkey's head turned 45° to the right with respect to the linear track axis (LARP plane, thereby generating tilts with rightward roll and backward pitch components). The top left shows that the neuron's instantaneous firing rate (heavy black dots) is related to head position (gray trace) during dynamic tilts. In comparison to the low-sensitivity unit shown in Fig. 7A, this neuron's firing rate is less regular, responds more rapidly to tilt with a large overshoot that declines to a smaller stable firing rate. If the tilted position is maintained for 30 s (not shown), firing rate stabilizes with an overall sensitivity to tilt of 1.4 spikes · s–1 · °–1 of head tilt. The neuron's firing rate is modulated during sinusoidal rocking (middle and right). At 0.5 Hz (middle), firing rate is related to head velocity with a sensitivity of 3.1 spikes · s–1 · °–1 (determined by linear regression using a sinusoidal model). At 1 Hz, the firing rate is still deeply modulated (3.7 spikes · s–1 · °–1) and remains in phase with head velocity. If the monkey is turned 90°, the tilt stimulus is comprised of leftward roll and forward pitch components. In this position, the neuron's response is reduced by 60% (dynamic gains during sinusoidal rocking were 1.3 and 1.6 spikes · s–1 · °–1g at 0.5 and 1 Hz, respectively; bottom).
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Fig. 10). Details of the neuron's response to sustained head tilts of 30 s (after adaptation was complete) are shown in Fig. 8B. The cell's firing rate is greater during contralateral roll and backward tilts of 20° (left hand panel). Figure 8B, right, shows that firing rate followed a cosine rule as a function of tilt direction (r2 = 0.98). The modulation amplitude was 27.5 spikes/s at the optimal tilt direction of 228°, corresponding to a tilt sensitivity of 1.38 spikes · s–1 · °–1 of head tilt or 28.6 spikes · s–1 · g–1. Most high-sensitivity units were relatively unresponsive to sustained changes of head orientation with respect to gravity (tilt sensitivity <0.5 spikes · s–1 · °–1), suggesting that these signals have been low-pass filtered to remove the static component of acceleration.
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2.5 times the neuron's response to maintained tilt (Fig. 8B). With the head oriented 45° to the right (the neuron's preferred direction), angular acceleration during head tilt was aligned with the plane of the left anterior and right posterior canals (LARP plane). The neuron also responded to angular acceleration about an earth-vertical axis at 0.5 Hz with a gain of 0.21 spikes/s per °/s and a phase lag of 21.8° with respect to angular head velocity. The angular rotation response was greater when the animal was tilted backward 20°, suggesting a contribution of the vertical canals to the observed response.
Figure 9A shows that units classified as having high sensitivity to linear translation were also more sensitive to angular rotation (t-test P < 0.05, high sensitivity, 0.33 ± 0.11 spikes/s per °/s; low sensitivity, 0.08 ± 0.01 spikes/s per °/s). Figure 9B shows that high-sensitivity units also tended to lead angular velocity (phase 18.6 ± 5.9°), whereas low-sensitivity units slightly lagged head velocity (–1.8 ± 7.8°). The difference in phase shifts was also significant (P < 0.05). Because we could not rotate the monkey in the optimum plane for canal stimulation, these values may underestimate actual sensitivities. Data during dynamic tilts (1 Hz) were obtained from 10 high-sensitivity units during roll and 8 units during pitch. The mean roll gain was 1.8 spikes · s–1 · °–1 and the mean pitch gain was 1.2 spikes · s–1 · °–1. Seven of the 10 units tested in roll discharged in phase with head position, and 3 discharged in phase with head velocity; in pitch, 3 units discharged in phase with head position and 5 in phase with head velocity.
Discharge regularity and responses to translation
There was a strong tendency for neurons with greater sustained tilt responses to exhibit higher discharge regularity than neurons with poor tilt sensitivity. Figure 10A shows a scatter plot of translation sensitivity versus CV* for our sample population. In accord with previous studies, irregularly discharging units (
) tended to have larger responses to vestibular stimulation than regularly discharging units ( 
) (Chen-Huang et al. 1997; Iwamoto et al. 1990). Every unit classified as low sensitivity (<25 spikes · s–1 · g–1 at 1 Hz) was also characterized by an extremely regular discharge (CV* <0.2). At frequencies <1 Hz, low-sensitivity units exhibited modulation but were, on the whole, less responsive to linear translation than units classified as high sensitivity units (compare Figs. 5 and 6). Figure 10B supports the segregated character of these responses because low-sensitivity units ( ) also tended to respond to static tilt more strongly than high-sensitivity units (). Although these neurons may be related by a continuum of properties, we prefer to refer to them for convenience as two groups. With two exceptions, low-sensitivity neurons have CV* values <0.2 (mean value, 0.095 ± 0.02) and high-sensitivity units have CV* values >0.2 (mean value, 0.381 ± 0.04). The difference in mean CV* was highly significant (t-test, P < 0.001) between the two groups. The exceptions to this classification scheme are two cells (
, Fig. 10A), one with a CV* of 0.03 and a large static tilt gain (1.16 spikes · s–1 · °–1) but a translation gain of 62.5 spikes · s–1 · g–1 (classified as high sensitivity) and a second with a CV* of 0.25 and zero static tilt gain, but a translation gain of only 16 spikes · s–1 · g–1 (classified as low sensitivity). Despite differences in CV*, the mean resting rates of neurons classified as low or high sensitivity were similar (52.9 ± 3.6 or 58.4 ± 5.0 spikes/s respectively; P > 0.3). At 1 Hz, none of the low-sensitivity neurons exhibited responses to linear translation in their optimal directions >25.0 spikes · s–1 · g–1. At 1 Hz, the mean gain of low-sensitivity units was 4.8 spikes · s–1 · g–1), much less than the gain of high-sensitivity units at the same frequency (125.5 spikes · s–1 · g–1; t-test, P < 0.0001).
All of the low-sensitivity neurons responded to static head tilt (mean: 0.68 ± 0.06 spikes · s–1 · °–1 of head tilt), but only about half of the high-sensitivity neurons responded to head tilt (for tilt responsive units, the mean was 0.32 ± 0.08 spikes · s–1 · °–1 of head tilt). The difference in head tilt sensitivity between the two groups of neurons was highly significant even when the nonresponding high-sensitivity units were excluded (P < 0.0007, t-test).
Figure 11 shows that the directions for optimal sensitivity to linear translation were intermediate between IA and NO for most neurons. In general, for high-sensitivity neurons, optimal directions were near 45 or 135° (top left), suggesting a possible alignment with vertical canal planes. Because of their weak responses to linear translation, the data are less clear for low-sensitivity neurons (top right). The optimal directions for static head tilt are illustrated in Fig. 11, bottom. For most low-sensitivity neurons (right), the optimal directions were ipsilateral roll combined with forward or backward tilt. High-sensitivity neurons responded either to ipsilateral and forward tilt or to contralateral and backward tilt (left).
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DISCUSSION |
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; Dickman and Angelaki 2002; Tomlinson et al. 1996); however, our study is the first to characterize a population of cells we call low sensitivity or tilt neurons. These neurons are distinguished by the regularity of their spontaneous discharge rates and in their responsiveness to changes in GIF produced by reorientation of the head with respect to gravity. Comparison of findings with previous studies
Neurons were recorded if they responded to translation in the horizontal plane. In our sample, 58% of the neurons responded to translation but not to angular rotation about an earth-vertical axis. The remaining 42% of the sample responded to translation and angular rotation. These proportions are different from those reported by Dickman and Angelaki (2002) in their study of central neurons responding to vestibular stimuli. Only 36% of their sample consisted of otolith only neurons; the remaining translation sensitive cells (64%) also responded to earth-vertical axis rotation. Dickman and Angelaki referred to the latter cells as "convergent" otolith plus canal neurons. The minimum gain, measured at 0.5 Hz, of their convergent and nonconvergent (otolith only) neurons was 20 spikes · s–1 · g–1. It is likely that all of those neurons would have been classified as high-sensitivity neurons using the criteria employed in this study (gain >25 spikes · s–1 · g–1 measured at 1 Hz). However, the frequency responses of our sample of high-sensitivity neurons appear to be quantitatively different: mean gain was 124.0 ± 17.6 spikes · s–1 · g–1 (IA translation at 0.2 Hz) compared with the 219 ± 155 spikes · s–1 · g–1 at 0.5 Hz reported by Dickman and Angelaki. Mean phase shifts also appear to be different. One possible explanation for this apparent difference in frequency response may be that Dickman and Angelaki computed maximum sensitivity vectors. We report gains and phases along either the IA or NO axes, neither of which may correspond to the maximal sensitivity of the neuron's response. Other characteristics, such as dynamic tilt responses, appear to be similar for high-sensitivity neurons and the convergent neurons of Dickman and Angelaki. More recent studies from Angelaki's lab have also described translation sensitive neurons within the vestibular nuclei with discharge patterns qualitatively similar to our sample of high sensitivity neurons (Angelaki and Dickman 2003; Angelaki et al. 2004).
Locations of low-sensitivity neurons
The failure of other studies to find and report low sensitivity units is puzzling because our histological and recording track data suggest that the sites of both types of units overlap. Figure 2 (METHODS) shows the recording sites of the low ( 
)- and high (
)-sensitivity units recorded from two of the monkeys in our sample for which we have histological data. Because only a few recording sites were confirmed using electrolytic lesions (details in METHODS), recording sites were determined by transforming the polar coordinates of our electrode tracks and unit depths to a Cartesian frame centered on the abducens nucleus. Figure 2 shows that most of the neurons were located at about the same depth as abducens neurons, but were slightly lateral and within 6 mm caudal of the VIth nucleus. The figure shows clearly that the recording sites of low- and-high sensitivity units overlap extensively and that either cell type could be recorded along the same electrode track.
If the recording sites overlap, why have other laboratories failed to identify these neurons? We believe that a difference in search stimuli accounts for our ability to find and identify low-sensitivity neurons. The modulation of low-sensitivity neurons during linear translation at 0.5 Hz or during rotation at 0.5 Hz about the earth-vertical axis (common search stimuli) is nearly undetectable visually (e.g., on an oscilloscope or computer display, see Fig. 3) or by ear using an audio monitor. However, at the beginning of our experiments, we noticed neurons with regular discharge rates that appeared to be unresponsive to our search stimuli. After encountering several such units, we hypothesized they might be vertical canal neurons and modified our apparatus to tilt the animals in the pitch plane. After this modification, we discovered that many of these regularly firing neurons could be activated by static tilt and by rocking about an earth horizontal axis. These neurons were later identified as the "low-sensitivity" neurons in our sample and were identified subsequently by the regularity of their resting discharge and their responses to static or dynamic tilt.
Do low- and high-sensitivity units distinguish tilt from translation?
If we include the units reported in a recent Dickman and Angelaki (2002) study, there could be three, possibly overlapping, populations of vestibular-only neurons with distinctive properties: canal-only, canal plus otolith convergent (high- or low-sensitivity units), and otolith-only neurons. Low-sensitivity neurons encode static tilt and dynamic head tilt over a mid range of frequencies from 0.2 Hz. However, their activity could be ambiguous for low-frequency stimuli <0.2 Hz where they respond to linear translation (Fig. 5) as well as to tilt. High-sensitivity neurons signal linear translation across the tested frequency band (Fig. 6), but they were also highly sensitive to changes in GIF resulting from dynamic tilt (Fig. 8). Most high-sensitivity neurons in our sample, however, were not responsive to static tilt.
The relationship between low- and high-sensitivity neuronal firing rates and GIF is more easily seen if the responses of the neurons are expressed in terms of an equivalent GIF produced by head tilt. The mean low- and high-sensitivity responses to linear translation (Figs. 5 and 6) can be transformed to equivalent tilt responses by dividing them by the angle of the GIF vector (Fig. 1). Figure 12 illustrates the results of this transformation as a plot of mean high-sensitivity () and low-sensitivity (
) responses to equivalent GIF as a function of frequency. For example, at 0.2 Hz, the mean modulation of the low-sensitivity units to translation was 2.3 spikes/s and the mean modulation of high-sensitivity units was 7.8 spikes/s. These modulations are equivalent to tilt sensitivities of 0.56 spikes · s–1 · °–1 (2.3 spikes/s divided by 4.0°) for low-sensitivity units and 1.96 spikes · s–1 · °–1 for high-sensitivity units. On the same graph, the low- and high-sensitivity responses to static (0 Hz) and dynamic tilt (because the tilt axis was not motorized, dynamic tilt data were obtained near the natural frequency of the device, nominally at 1 Hz) can also be expressed in the same units and are plotted as filled symbols (low sensitivity, ; high sensitivity, 
).
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If low-sensitivity units' responses to pure translation and head tilt are considered together, the data in Fig. 12 suggest that low-sensitivity units reliably signal head tilt but not linear translation. First, low-sensitivity units exhibited a decreasing gain characteristic to linear translation. Thus at frequencies >0.5 Hz, despite modulation of GIF, low sensitivity were essentially unresponsive. Second, all but one of the units classified as low sensitivity exhibited a statistically significant response to static head tilt, which is plotted in Fig. 12 at 0 Hz. ( ). Third, low-sensitivity units' activity was modulated in relation to the angular position of the head during dynamic tilt stimuli that stimulated concurrent canal and otolith inputs (Figs. 7 and 12). For example, during dynamic rocking at 1 Hz, low-sensitivity units discharged vigorously ( ) at a frequency where head translation alone elicited a negligible response. We conclude that low-sensitivity units' responses to otolith inputs could by themselves signal head orientation during static and low-frequency head tilt (f < 0.2 Hz). However, at higher frequencies, concurrent canal and otolith inputs must interact to signal changes in head orientation ( ) at 1 Hz). Thus otolith input alone is sufficient to signal head orientation at frequencies near 0 Hz, but convergent canal and otolith inputs play a significant role at frequencies >0.5 Hz. However, the relative importance of these two vestibular inputs in the low-frequency range from 0 to 0.5 Hz cannot be determined from our limited data. Moreover, it is important to note that low-sensitivity units responded weakly to angular acceleration (Fig. 9) even when the animal's head was tilted backward to enhance vertical canal stimulation as would occur during dynamic tilt stimulation ( , Fig. 12). This observation suggests that the vigorous response to dynamic tilt observed at 1 Hz is not due to simple addition of semicircular canal and otolith inputs, but instead must reflect a nonlinear interaction of these signals.
Comparison of low-sensitivity neuronal discharge patterns with theoretical model predictions
In a recent paper, Green and Angelaki (2004) proposed a model for tilt-translation discrimination based on a mathematical relationship that expresses the stimulus detected by the otoliths as a vector difference between the inertial and gravitational components of GIF (Angelaki et al. 1999; Merfeld and Young 1995). The model implements a network of vestibular-only "neurons" to explicitly demonstrate how nonlinear interactions between canal and otolith signals can disambiguate tilt from translation. Model simulations predict that there should be a population of "tilt" neurons (the model's VO5 unit) with discharge patterns similar to those of low-sensitivity cells. According to simulations of the Green and Angelaki model, the tilt cell, VO5, does not encode linear translation but does encode static or dynamic head position if the head is reoriented with respect to gravity. Low-sensitivity cells exhibit each of these characteristics (see Figs. 5, 7, and 12). The VO5 cell is not predicted to respond during rotation about an earth vertical axis; low-sensitivity units did respond to this stimulus but with significantly weaker modulation of activity than high-sensitivity neurons (Fig. 9). Because we attempted to enhance vertical canal stimulation during tilt, the weak modulation we recorded can only partially be ascribed to horizontal canal stimulation. Furthermore, the relatively weak response of these cells to earth-vertical axis rotation, even with the head tilted with respect to gravity, is consistent with the predictions of the model because the canal signal is presumed to be transformed into a spatially referenced estimate of the earth-horizontal component of rotation that is zero during earth-vertical axis rotation. During earth-vertical axis rotation, modulation of cell activity was weak; however, during earth-horizontal axis rotation (dynamic tilt), cell activity was strongly modulated as predicted by the model. Although our data are strikingly consistent with the predictions of this model, a critical experiment would be to record from low-sensitivity cells during a tilt-translation paradigm (Angelaki et al. 1999); this would unmask the spatially transformed ("hidden") canal signals from otolith signals when both sensors are stimulated (e.g., during dynamic tilt) (Green and Angelaki 2003, 2004) and to obtain such data over a wider range of frequencies and head orientations than we were able to do.
Low-frequency limitations of tilt discrimination
Low-sensitivity units signal head tilt, but their responses are potentially ambiguous for very low-frequency stimuli. For example, the mean sensitivity of low-sensitivity units at 0 Hz (e.g., to static changes in head position) is 0.68 spikes · s–1 · °–1 of head tilt (averaged over all directions). This value, plotted in Fig. 12 as a at 0 Hz, and the low-sensitivity unit response to translational motion at 0.2 Hz are similar in magnitude, implying that, in the absence of canal stimulation (or an extra-vestibular sensory inflow), low-sensitivity units cannot distinguish very-low-frequency translation from head tilt during passive motion or whole body tilt in the dark. Interestingly, simulations of the Green and Angelaki model do not resolve this problem because their small signal approximation limited their simulations to frequencies >0.1 Hz. Indeed, any model based on canal-otolith interaction is limited at very low frequencies (<0.1 Hz) because the canal estimate of head velocity deteriorates and cannot be used. Consistent with these observations, psychophysical studies have documented subjective tilt illusions during low-frequency head translation in the dark (Glasauer 1995; Merfeld et al. 2005a; Seidman et al. 1998).
High-sensitivity cells encode linear translation?
In comparison to low-sensitivity cells, the responses of high-sensitivity units are more diverse and complicated (see individual cell characteristics, Fig. 6), suggesting that this population could be heterogeneous. Thus subsets of high-sensitivity cells might play diverse functional roles. Most high-sensitivity units fail to encode GIF. Only 50% of these units are even modestly responsive to static tilt with a mean sensitivity of 0.32 spikes · s–1 · °–1 of head tilt (plotted in Fig. 12 as the at 0 Hz). This value is much less than high-sensitivity unit responses to translational motion at any tested frequency (). Based on this comparison, one might hypothesize that high-sensitivity units encode translation with greater sensitivity than tilt at any frequency. However, their mean response to dynamic tilt at 1 Hz (Fig. 12, ) is similar to their mean response to translation at 1 Hz (), implying that as a population, high-sensitivity units do not distinguish tilt from translation at this frequency and presumably at other frequencies as well. Some aspects of the discharge patterns of high sensitivity cells are consistent with predicted discharge patterns of model neurons in the Green and Angelaki model (2004). The model predicts two cell types that selectively encode either high-frequency linear translation (cell VO3) or a combination of translation and responses to tilt (cell VO4). High-sensitivity neurons most closely resemble the VO4 model cell, but there are important differences in their comparative behavior. First, during simulations, the VO4 neuron has an increasing gain characteristic with frequency. Although a few high-sensitivity neurons exhibited increasing gain characteristics, it was not a common finding. VO4 neurons are not predicted to respond to angular rotation about a vertical axis, but most high-sensitivity neurons were highly responsive to that stimulus (Fig. 9, with the caveat that at least some of this modulation is vertical canal related). Unfortunately, these data were recorded before the model was formulated, so we did not attempt to compare sensitivity to horizontal versus vertical canal stimulation, nor could we stimulate the vertical canals without concurrent otolith stimulation. It should be noted, however, that Dickman and Angelaki (2002) also have recorded vestibular-only neurons sensitive to linear translation that do have properties more in accord with the predictions of the Green and Angelaki model (e.g., neurons with an increasing gain characteristic). More recently, Angelaki and colleagues have recorded additional neurons in the vestibular nuclei and shown that they respond in accord with model predictions during roll-tilt paradigms (Angelaki et al. 2004) and after inactivation of the semicircular canals (Shaikh et al. 2005b) in ways predicted by the Green and Angelaki model (2004). We agree with Green and Angelaki (2004), who explicitly state that their model predictions are not meant to describe the properties of a uniform population of cells; rather, their model cells are meant to characterize the average response of cell populations wherein individual cells exhibit variable properties.
Frequency segregation versus multisensory convergence
Two hypotheses, frequency segregation and multisensory convergence ("sensory fusion") have enjoyed popularity in accounts of the vestibular system's ability to interpret GIF correctly. These hypotheses are not mutually exclusive, and there is behavioral evidence supporting both of them (Angelaki 2004; Angelaki and Dickman 2003; Angelaki et al. 1999, 2001a; Glasauer and Merfeld 1997; Green and Angelaki 2003, 2004; Merfeld and Young 1995; Merfeld and Zupan 2002; Merfeld et al. 2005a,b; Paige and Tomko 1991; Shaikh et al. 2005b; Telford et al. 1996, 1997; Zupan et al. 2002). Multisensory convergence can denote canal and otolith signal convergence as in the models of Green and Angelaki (2004) or those by Merfeld and Zupan (Merfeld 1995a; Merfeld and Zupan 2002; Merfeld et al. 1999; Zupan et al. 2002). As originally formulated, the frequency segregation hypothesis postulated that otolith afferent signals were low-pass filtered to extract tilt information and high-pass filtered to extract linear translation information. Our findings are not supportive of this hypothesis. Although low-sensitivity neurons appear to exhibit features of a low-pass filtering process (see Fig. 5, decreasing gain and phase characteristics), they distinguish tilt at unexpectedly high frequencies (e.g., 1 Hz, Fig. 12) when there is concurrent canal and otolith stimulation. High-sensitivity neurons encode linear translation across the tested frequency range but do not distinguish dynamic tilt from translation (Fig. 12). These findings are more supportive of the multisensory convergence hypothesis, specifically involving canal and otolith convergence because both groups of cells receive canal signals and both exhibit interactions of these signals during dynamic tilt. In further support of the multisensory convergence hypothesis, Angelaki et al. (1999) showed that horizontal eye movements of rhesus monkeys were compensatory to the translational component of motion during sinusoidal roll-tilt or pure translation in darkness (over a frequency range of 0.1–1 Hz). However, when Angelaki et al. (1999) inactivated (by surgical plugging), the semi-circular canals, sinusoidal roll-tilt produced robust noncompensatory eye movements, suggesting the animal could not disentangle translation from tilt in the absence of canal signals.
In addition to the vestibular inflows, visual, proprioceptive, and somatosensory afferents impinge on the vestibular nuclei and could contribute to the ability of the brain to distinguish tilt from translation (Dichgans et al. 1972; Henn et al. 1974; Mergner et al. 1997). Extra vestibular inputs may be especially important for resolving tilt-translation ambiguities in the very-low-frequency range where canal signals imperfectly encode angular head velocity. Similarly, Merfeld and colleagues (Merfeld and Zupan 2002; Merfeld et al. 1999, 2005b) demonstrated that humans may use canal cues to improve their distinction of tilt and translation perceptually but that the oculomotor system appears to use high pass filtering of otolith cues to produce the translational VOR (Merfeld et al. 2005a,b). The latter may be a species difference because Angelaki and co-workers' data support a multisensory convergence model for controlling eye movements in monkeys (e.g., Angelaki et al. 1999, 2004; Green and Angelaki 2004). The dynamic tilt data (Figs. 7 and 12) show that low-sensitivity neurons encode head tilt up to at least 2 Hz when there are concomitant canal and otolith signals. These data suggest that low-sensitivity units could contribute to perceptual judgments of head orientation and could play a role in generating compensatory eye movements during roll-tilt as suggested by the Green and Angelaki model (2004). Signals encoded by high-sensitivity units appear to reflect high-pass filtering to remove the static component of tilt. Thus although they appear to encode linear translation over the mid to higher frequency range, their responses to dynamic tilt reflect convergence of canal and otolith signals. Thus even the responses of these neurons do not unambiguously distinguish tilt from translation. Obviously, other sensory inputs must also play a role in resolving tilt-translation ambiguity. At the lower frequencies employed in the Angelaki study (<1 Hz) (Angelaki et al. 1999), ambiguity in low-sensitivity unit activity could be resolved by visual or other inputs that are normally present during natural behavior. Natural head tilts would also be associated with neck proprioceptive and motor intention cues.
Conclusion
The most significant and novel finding of this study is the population of low-sensitivity neurons. These cells exhibit a constellation of properties (e.g., tilt sensitivity, hence the term "tilt" neuron) that are largely concordant with those predicted by simulations of a model designed to demonstrate how canal and otolith signals could be used together to disambiguate tilt from linear translation (Green and Angelaki 2004). This model is an example of a broader class of models (e.g., Merfeld et al. 1999; Zupan et al. 2002) that have employed multisensory convergence of canal and otolith signals to resolve the inherent ambiguity in the otolith signal. Although these new data provide strong support for canal plus otolith sensory convergence in distinguishing tilt from translation, it is likely that the vestibular system also uses extra vestibular signals to discriminate the gravitational and translational components of GIF, especially at very low frequencies of linear motion. Future studies will be required to elucidate the role of these signals and to more precisely determine the roles of low- and high-translation-sensitive neurons in neural processing of otolith signals.
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GRANTS |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Address for reprint requests and other correspondence: W. M. King, Dept. of Otolaryngology, University of Michigan Medical Center, 1500 E. Medical Center Dr., Ann Arbor, MI 48105, (E-mail: wmking{at}umich.edu)
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ABSTRACT
INTRODUCTION
). Further details may be found in the text.
