New Results

Robust vestibular self-motion signals in macaque posterior cingulate region

Bingyu Liu, Qingyang Tian, Yong Gu
doi: https://doi.org/10.1101/2020.11.13.381780

Abstract

Self-motion signals, distributed ubiquitously across parietal-temporal lobes, propagate to limbic hippocampal system for vector-based navigation via hubs including posterior cingulate cortex (PCC) and retrosplenial cortex (RSC). Although numerous studies have indicated that posterior cingulate areas are involved in spatial tasks, it is unclear about how their neurons represent self-motion signals. By providing translation and rotation stimuli to macaques on a 6-degree-of-freedom motion platform, we discovered robust vestibular responses in PCC. A combined 3-dimensional spatiotemporal model captured data well and revealed multiple temporal components including velocity, acceleration, jerk, and position. The former three signals, but not position, conveyed consistent spatial information. Compared to PCC, RSC contained moderate vestibular temporal modulations and lacked significant spatial tuning. Visual self-motion signals were much weaker in both regions compared to the vestibular signals. We conclude that macaque posterior cingulate region carries vestibular-dominant self-motion signals with plentiful temporal components that could be useful for path integration.

Introduction

Navigation is a fundamental and indispensable ability for creatures to survive and live in the world, such as in foraging and exploration. In cognitive studies, landmark-based and vector-based navigation are two commonly used strategies for spatial tasks (Gallistel, 1990). Landmarks are typically strong visual cues for helping localize and calibrate self-positions, yet this strategy is easily affected by environments such as open fields of deserts and grasslands, or woods without clear and reliable markers. Vector-based navigation, or path integration on the other hand, involves momentary self-motion cues from multiple sensory channels such as optic flow and vestibular ones for updating and accumulating one’s heading direction and location (Etienne & Jeffery, 2004; Valerio & Taube, 2012). For example, linear and angular vestibular signals originating from peripheral otolith and semicircular canal organs are thought to provide critical inputs to head direction cells (HD cells), place cells or grid cells in the hippocampal-entorhinal navigation system (B J Clark & Taube, 2012; Hitier, Besnard, & Smith, 2014; Jacob, Poucet, Liberge, Save, & Sargolini, 2014; Knierim, Kudrimoti, & McNaughton, 1998; Stackman, Clark, & Taube, 2002; Winter, Clark, & Taube, 2015).

To date, robust vestibular and visual signals related with self-motion have been discovered in numerous cortical areas including the dorsal part of medial superior temporal sulcus (MSTd) (Duffy & Wurtz, 1997; Gu, Watkins, Angelaki, & DeAngelis, 2006; Page & Duffy, 2003), the ventral intraparietal area (VIP) (F Bremmer, Graf, Hamed, & Duhamel, 1999; Frank Bremmer, Klam, Duhamel, Ben Hamed, & Graf, 2002; Chen, DeAngelis, & Angelaki, 2011a, 2011c; Zhang & Britten, 2004), the visual posterior sylvian area (VPS) (Chen, DeAngelis, & Angelaki, 2011b), parietal insular vestibular cortex (PIVC) (Chen, DeAngelis, & Angelaki, 2010; Chen et al., 2011a; Grüsser, Pause, & Schreiter, 1990a, 1990b), the smooth eye movement of the frontal eye field (FEFsem) (Gu, Cheng, Yang, DeAngelis, & Angelaki, 2016), and 7a (Avila, Lakshminarasimhan, DeAngelis, & Angelaki, 2019), composing a network (Britten, 2008; Cheng & Gu, 2018; Gu, 2018; Guldin & Grüsser, 1998). However, most of these areas (except for 7a) have not shown direct connections with the hippocampal navigation system. What regions may bridge the cortical self-motion system and the limbic hippocampal navigation system? One hypothesis is that PCC and RSC may be one of these hubs (Kravitz, Saleem, Baker, & Mishkin, 2011; Rushworth, Behrens, & Johansen-Berg, 2006; Vincent, Kahn, Van Essen, & Buckner, 2010). On one hand, there is evidence that PCC and RSC directly project to entorhinal and parahippocampal cortices (Insausti, Amaral, & Cowan, 1987; Morecraft, Van Hoesen, & Maynard, 1989; Rosene & Van Hoesen, 1977; Vogt, Pandya, & Rosene, 1987; Vogt, Rosene, & Pandya, 1979). On the other hand, these areas are also heavily connected with cortical areas including area 7a (Cavada & Goldman – Rakic, 1989; Musil & Olson, 1988; Olson & Musil, 1992; Pandya, Van Hoesen, & Mesulam, 1981; Shinder & Taube, 2010; Vogt et al., 1987), MSTd (Akbarian, Grusser, & Guldin, 1994; De Castro et al., 2020), FEF(De Castro et al., 2020), PIVC, VPS and 3a (De Castro et al., 2020; Guldin & Grüsser, 1998). Thus, posterior cingulate region may be one important hub mediating the propagation of self-motion signals from parietal-temporal cortices to the hippocampal-entorhinal system.

Supporting this notion, numerous studies have provided evidence showing that posterior cingulate region is involved in spatial navigation. For example, navigation related neurons in PCC are reported when monkeys navigate in a virtual environment (Sato, Sakata, Tanaka, & Taira, 2006, 2010). Lesion studies confirmed that PCC in monkeys (Murray, Davidson, Gaffan, Olton, & Suomi, 1989; Sutherland, Whishaw, & Kolb, 1988) and the homologues area in rodents were actively involved in spatial memory tasks (Brenton G Cooper, Manka, & Mizumori, 2001; B G Cooper & Mizumori, 2001; Sutherland et al., 1988; Whishaw, Maaswinkel, Gonzalez, & Kolb, 2001). Using functional MRI with galvanic or caloric stimulation, vestibular signals were discovered in human PCC (Schindler & Bartels, 2018; Andrew T. Smith, Wall, & Thilo, 2012), and visual signals were reported in human and macaque PCC (Cottereau et al., 2017; Fischer, Bülthoff, Logothetis, & Bartels, 2012; Schindler & Bartels, 2018; Andrew T Smith, Greenlee, & Deangelis, 2017; Wall & Smith, 2008). Similarly, RSC has also been implicated to be involved in navigation context for a longtime, mainly from studies in rodents and human. For example, HD cells have been reported in rat RSC (Cullen & Taube, 2017), and inactivation of RSC disrupts HD cell and place cell activities in hippocampal circuit (Benjamin J. Clark, Bassett, Wang, & Taube, 2010; B G Cooper & Mizumori, 2001). In human, RSC is active in virtual navigational tasks (see reviews by Maguire, 2001; Spiers & Maguire, 2007; Vann, Aggleton, & Maguire, 2009). In addition, RSC is directly involved in spatial coordinate transformation (Epstein, 2008; Hashimoto & Nakano, 2014; Iaria, Chen, Guariglia, Ptito, & Petrides, 2007), which may be crucial for transforming egocentric self-motion signals to allocentric navigational signals in the hippocampal system (Byrne, Becker, & Burgess, 2007; Wang et al., 2018).

Although signals related with spatial perception have been previously indicated in posterior cingulate region, previous studies often include too complex cognitive factors or do not apply techniques with enough spatiotemporal resolution for pinning down properties of neuronal signals in these areas. Hence in our current study, we systematically examined self-motion related signals in macaque PCC and RSC on a virtual reality system with a 6-degree-of-freedom motion platform. We recorded single unit activities in both subregions in the posterior region of the cingulate cortex under linear translation and angular rotation conditions. We also compared their temporal and spatial neuronal activity properties with those in the extrastriate visual cortex to infer how self-motion signals may be reserved or varied when they are propagated from parietal-temporal areas to the hippocampal system via the hub of PCC and RSC.

Results

Self-motion cues including vestibular and optic flow were provided through a laboratory-built up virtual reality system (Figure 1A). To activate the vestibular channel, each trial contains a Gaussian velocity profile with a corresponding biphasic acceleration profile, lasting 1.5 second (Figure 1B shows the motion profile in translation condition). Animals were translated or rotated along 26 directions or axes that were equally distributed in 3-dimentional space (Figure 1C, see Materials and methods). Visual stimuli profile simply followed that in the vestibular condition. In the following, we first analyzed and presented data under the vestibular condition, then we compared these data to that in the visual condition in the last section of the Results.

Figure 1.
Figure 1.

Diagram of experimental set-up. (A) Monkeys were translated or rotated by a 6-degree-of-freedom motion platform (MOOG). A visual display is mounted on the platform, providing visual stimuli that simulate real motion. (B) Motion profiles. Acceleration (blue) signals are collected from an accelerometer mounted on the platform. Velocity quantity (red) is an integral from the acceleration. Stimulus duration is 1.5 second. (C) Illustration of 26 motion vectors in 3D space under translation (straight arrow) and rotation (curved arrow) conditions. Left panel indicates the eight equally distributed directions in the horizontal plane from the top view. Middle panel shows five equally distributed directions in the vertical plane from the side view.

A total of 499 single neurons were recorded in the posterior part of the cingulate sulcus above the corpus callosum, including the posterior cingulate cortex (PCC) and retrosplenial cortex (RSC) from five hemispheres of three macaques. Among these neurons, 381 units were identified in PCC (Monkey Q: n = 274, monkey P: n = 89, monkey W: n = 18), and 118 units were in RSC (Monkey Q: n = 84, monkey W: n = 34). Location of recording sites was identified and confirmed based on combination of MRI scans with simultaneous electrode penetration, atlas, and gray/white matter patterns (Figure 2).

Figure 2.
Figure 2.

Recording sites reconstructed on MRI images of three monkeys. (A) Sagittal and coronal planes in one animal (monkey Q), with green shaded areas indicating PCC and red shaded areas indicating RSC. Electrode penetration is indicated by the dark shaded lines. (B-C). Recording sites are reconstructed and superimposed onto one plane in each of the three animals. RSC, retrosplenial cortex; PCC, posterior cingulate cortex; cs, central sulcus; ips, intraparietal sulcus; ls, lateral sulcus; lv, lateral ventricle.

Temporal and spatial tuning properties

Many neurons in the posterior cingulate region particularly in PCC carried robust vestibular signals yet with heterogeneous temporal and spatial tuning. Figure 3A showed peristimulus time histograms (PSTHs) from two example PCC neurons under the translation condition. For the first example neuron (upper panels), there were clear activities in response to rightward and upward motion stimuli under the translation condition, with response envelope close to the Gaussian velocity profile, indicating a strong velocity signal. In addition, the response peak time was consistent across all directions, thus it was defined as a “single-peaked” neuron. For single-peaked neurons, directions with maximal firing were unvaried across stimulus duration (Figure 3B, upper panels).

Figure 3.
Figure 3.

PSTHs from example neurons in the vestibular condition. (A) PSTHs of two example PCC neurons across 26 directions under the translation condition. Upper panels: an example single-peaked neuron. Bottom panels: an example double-peaked neuron. The red dashed lines indicate the velocity peak time of velocity profile and the two blue dashed lines indicate the peak time of acceleration peak and trough. (B) Contour figures show firing rates as a function of azimuth and elevation at three different time points (velocity peak time, acceleration peak and trough, respectively). (C, D) PSTHs and contour figures of the same neurons as in (A) and (B) under the rotation condition. (E, F) Two example RSC neurons under translation and rotation conditions, respectively.

By contrast, the second example neuron as shown in the lower panel of Figure 3A exhibited strong peak-to-trough modulation under the translation condition, resembling the biphasic acceleration profile. This type of neurons contained strong acceleration-component signals. Their peak response times were different across motion directions, particularly for opposite directions (e.g. upward versus downward), so these units were defined as “double-peaked” neurons. Unlike the single-peaked neurons, the maximal firing directions of double-peaked neurons vary across stimulus duration. For example, the unit in the lower panel of Figure 3A fired strongest at the direction of downward and leftward during the early phase (0.58 s), and to upward and rightward during the late phase (0.95 s, Figure 3B, lower panels).

Note that the category of single-peaked and double-peaked neurons could be stimulus type dependent. For example, the first unit in Figure 3A exhibiting single-peaked response under translation condition also exhibited single-peaked modulation under the rotation condition (Figure 3C-D, upper panels). However, the second unit in Figure 3A exhibiting double-peaked response under translation condition exhibited a single-peaked, delayed velocity like signal under the rotation condition (Figure 3C-D, lower panels).

In RSC, we also found a population of neurons carried significant vestibular modulations, albeit less reliable compared to those in PCC. In Figure 3E, the example RSC neurons showed some single-peaked temporal modulations under both translation and rotation conditions, but the spatial tunings were not very clear (Figure 3F).

To quantify the strength of vestibular temporal and spatial modulations in each area, we conducted our analyses based on the averaged PSTHs across trials in each translation direction or rotation axis. For each neuron, we first identified temporal tuning by compared response in each PSTH bin (300 ms, other sizes of epochs produced qualitatively similar results) with the baseline (−100 ms before and 300 ms after stimulus onset). To avoid false positive results, temporal tuning of each neuron is considered as significant by two requirements. First, at least five consecutive bins need to pass significant test (p<0.01, Wilcoxon rank sum test). Second, more than two adjacent directions need to be significant. According to this criterion, in PCC, 68% (252/372) cells were significantly modulated under translation condition, whereas 59% (161/271) cells were significantly modulated under the rotation condition (Figure 4A, pie graph and Table 1). About half neurons (50%, 131/263) were significantly modulated by both translation and rotation stimuli, indicating that quite a large proportion of PCC neurons received inputs from otolith and semicircular canals at the same time. Compared to PCC, fewer RSC neurons were significantly modulated under translation (32%, 37/114) and rotation (29%, 25/87) conditions (Figure 4A, pie graph). Only 17% (14/83) RSC neurons responded to both translation and rotation stimuli. Thus, more PCC neurons (~2/3) than RSC neurons (~1/3) are temporally modulated by inertial motion stimuli.

Figure 4.
Figure 4.

Population summary of temporal and spatial tuning properties of vestibular signals in PCC and RSC. (A). Proportion of neurons in each category. (B) DDI distribution of neurons with significant temporal tuning. Filled bars indicate neurons with significant spatial tuning. (C) Distribution of preferred directions for neurons with significant spatial tuning. Preferred direction is defined by responses around the peak time of single-peaked neurons and the early peak time of double-peaked neurons. Dark blue symbol: translation condition; Light blue symbol: rotation condition.

View this table:
Table 1.

Temporal and spatial tuning properties across subregions of posterior cingulate area under different conditions.

We considered these neurons with significant temporal modulations as vestibular tuned cells, and further assessed their spatial tuning by statistics of one-way ANOVA. Specifically, we examined whether responses in a 300 ms epoch were significantly different across all 26 motion vectors. The epoch was moved across the entire stimulus duration so that there were cases of one main peak time (single-peak neuron) and two peak times (double-peak neurons). Cases with other situations were rare and neglected. Again in PCC, a large proportion of neurons showed significant direction tuning (p<0.01, one-way ANOVA) under translational condition (75%, 189/252) and under rotation condition (77%, 124/161, Figure 4A, bar graph). Around 71% (93/131) neurons were spatially tuned under both translation and rotation conditions. However, in RSC, only 14% (5/37) and 36% (9/25) neurons were spatially tuned to translation and rotation stimuli, respectively (Figure 4A, bar graph). And only 2% (3/14) neurons had significant spatial tuning in both translation and rotation conditions.

In addition to the ANOVA analysis, we also used a direction discrimination index (DDI, see Materials and methods) to assess the strength of spatial tuning for the vestibular-tuned cells in each region (Figure 4B). DDI ranges between 0 and 1, with values close to 1 indicating strong spatial preference. The average DDI of all PCC neurons was 0.57 ± 0.007 (mean ± S.E.M.) under the translation condition, and 0.58 ± 0.009 under the rotation condition. For RSC neurons, the average DDI was 0.49 ± 0.013 and 0.50 ± 0.017 under the translation and rotation condition, respectively, which was significantly weaker compared to that in PCC (p = 0.00017 and p = 0.0011 for translation and rotation, respectively, t-test). Thus, neurons in RSC carry much weaker vestibular spatial tuning than that in PCC.

For neurons with significant spatial tuning, we investigated their preferred directions in 3D space. Specifically, spatial preference of each unit was calculated from vector sum of responses in all directions at the single-peak time and the early phase of double-peaked neurons, with azimuth and elevation as spherical coordinates. Under translation condition (Figure 4C, dark blue circles), the preferred directions of PCC neurons were distributed almost everywhere in the 3D space, yet there was a tendency that more neurons preferred leftward and rightward motion in the horizontal plane (p = 0.01, Uniform test, see Materials and methods). Such a pattern suggests that PCC neurons may be suitable for fine heading discrimination around straight forward during spatial navigation (Gu, Fetsch, Adeyemo, DeAngelis, & Angelaki, 2010). Interestingly under rotation condition (Figure 4C, light blue triangles), the preferred rotary axis also concentrated around the horizontal plane (p = 0.0225, Uniform test), corresponding to pitch (i.e., forward or backward tilt). The functional implications of such a preference was further discussed in the section of Discussion. Finally for RSC, because there were not enough faction of neurons exhibiting significant spatial tuning, it was not possible to obtain true distribution of their direction preference in the 3D space.

Dark and sound control

To further confirm that the observed vestibular responses in the posterior cingulate region indeed originated from otolith and canals instead from other cues, we conducted two control experiments.

The first control experiment was to exclude the possibility that the vestibular responses arose from retinal slip due to incomplete vestibular ocular reflex (VOR) suppression under condition when there was residual light in the experimental room. The visual display was thus turned off to remove any possible light in the environment, generating a total darkness condition. Figure 5A shows the PSTHs of an example neuron during translational fixation and dark conditions. The PSTHs in dark condition showed consistent spatial and temporal modulation patterns with those in the fixation condition (r = 0.76, p << 0.001, Pearson’s correlation).

Figure 5.
Figure 5.

Neural activity in total darkness and sound conditions. (A) Responses from an example cell in the regular vestibular translation condition (top panel), total darkness condition (middle panel), and sound control condition (bottom panel). (B-D) Pearson correlation coefficient of PSTHs (B), DDI (C), maximum firing rate (D) between regular vestibular and total darkness conditions. Filled symbols indicate neurons with significant spatial tuning, whereas open symbols indicate neurons without significant spatial tuning in either stimuli condition. Dark blue symbol: translation condition; Light blue symbol: rotation condition. (E-G) Pearson correlation coefficient of PSTHs (E), DDI (F), maximum firing rate (G) between regular vestibular and sound control condition.

We analyzed data based on a population of neurons that were tested under both fixation and darkness conditions. Majority of the neurons showed fairly consistent modulations under the two conditions as indicated by the Pearson’s correlation coefficient between the PSTHs in the two conditions (Figure 5B, and see Table 2 for tuning property details). Moreover, DDIs in the two tasks were highly correlated (r = 0.79, p << 0.001, Pearson’s correlation), and their mean values were basically the same (p = 0.36, paired t-test, Figure 5C). Response strength as indicated by the maximal firing rate also had a strong correlation and did not show significant difference between the two tasks (r = 0.80, p << 0.001, Pearson’s correlation; p = 0.74, paired t-test, Figure 5D).

View this table:
Table 2.

Temporal and spatial tuning properties under total darkness and sound control experiments. Note in the sound control conditions, there is no neuron with significant temporal tuning, so we did not calculate the spatial tuning properties.

The second control experiment was a sound condition, aiming to exclude the possibility that the observed responses under the vestibular condition was from sound generated from motion platforms during mechanical motion. To do this, we recorded the sounds when the motion platform moved in the vestibular condition, and delivered them to the animals when motion platform was stationary in the sound control experiment. As shown by the example neuron in Figure 5A, PSTHs in sound condition were dramatically changed, generating a null-modulation pattern that was totally unlike that in the fixation condition (r = −0.07, p = 0.0002, Pearson’s correlation).

We tested 11 neurons in total. None of these neurons exhibited significant temporal tuning in the sound control experiment (p>0.01, one-way ANOVA, and see Table 2 for details), and all neurons showed quite weak correlation with the PSTHs in the vestibular condition (Figure 5E). Moreover, neither DDI nor Maximal firing rate were close in the fixation and sound tasks (Figure 5F, G).

Thus, we exclude the possibility that the vestibular responses observed in the posterior cingulate region are visual responses due to residual retinal slip, or auditory responses from mechanical motions.

Fitting neural dynamics by a 3D spatiotemporal model

The aforementioned traditional analyses revealed that there were robust vestibular signals in the posterior cingulate sulcus particularly in PCC. These vestibular-tuned cells typically carried complex temporal dynamics that contained both velocity and acceleration components, as reflected qualitatively by the so defined single- and double-peaked neurons. To further characterize the temporal and spatial tuning properties for different signal components, we fitted responses (PSTHs) of each neuron with a 3D spatiotemporal dynamic model. Note that we only considered neurons with significant temporal tuning, thus for PCC, 252 neurons under translational motion and 161 neurons under rotational motion were included. For RSC, only 37 neurons for translation and 25 neurons for rotation were included in this analysis.

The spatiotemporal model was a linear weighted sum of temporal functions with different signal components, with each component multiplied by a spatial function (See Materials and methods and Supplementary figure 1 for details). We first considered a simpler model by including two most salient components of the velocity and acceleration (“VA” model) that were frequently observed in many neurons. A more complex model including more terms would be further illustrated in the next section. Specifically in the VA model, the velocity temporal kernel was defined by a Gaussian function and the acceleration temporal kernel was the derivative of the velocity function. Note that the peak velocity time was set to lag the acceleration peak time because velocity signal is supposed to be an integral from the acceleration quantity. For the spatial term, it was modelled as a cosine function fed through a nonlinear function that was depended on parameters of preferred azimuth and preferred elevation (see Materials and methods).

Figure 6A showed PSTHs from two example neurons fitted with VA model. The first neuron in the upper panel was the same single-peaked unit as in Figure 3A. This neuron was fitted well by the VA model (R = 0.95), as evident by the large overlap between the fitted curve and the responses. Moreover, the fitted weight of the velocity term was much larger than that of the acceleration (wV = 0.77 versus wA = 0.23), indicating a velocity domination signal. As a comparison, the second example neuron in the lower panel which was the same double-peaked unit as in Figure 3A, was also fitted well by the VA model (R = 0.76), yet the weight of acceleration component was much larger than that of the velocity (wV = 0.33 versus wA = 0.67), indicating a greater contribution of the acceleration signal.

Figure 6.
Figure 6.

Fitting results with VA model. (A) PSTHs of two example cells were fitted by VA model. Gray areas are PSTHs and superimposed black lines are fitted data. Red dashed lines: peak time of velocity; Blue dashed lines: peak and trough time of acceleration. The fitted weight of velocity and acceleration are shown in the gray box. These two examples are the same neurons shown in Figure 3A. (B) Contour figures of spatial kernels of the velocity and acceleration components. (C) Goodness of fit for PCC and RSC. Triangles are median value of each distribution. (D) Log ratio of the velocity weight to the acceleration weight. Triangles show the median value. (E) Congruency of spatial tuning between velocity and acceleration components as assessed by Pearson correlation coefficient. Filled bars indicate significant correlation. Only neurons with signficant temporal-modulation and goodness of fit (R2) > 0.5 are included in the analysis.

Across population, we first assessed the goodness of fit by VA model through calculating coefficient of determination between the fitted and measured response (R2). The distributions of R in PCC and RSC under translational and rotational conditions were shown in Figure 6C. Overall, PCC responses were well fitted by VA model in both conditions, with median R value of 0.56 for both stimulus conditions. However, fitting in RSC was worse (p<<0.001 in both translation and rotation, t-test), with only 14 neurons and 9 showing R value greater than 0.5 under translational and rotation condition, respectively. This result was roughly matched with a lack of clear velocity or acceleration signals inspected by eyeballing from the PSTHs of RSC neurons. Thus in the following analysis, we only included PCC data.

We then characterized contributions of the velocity and acceleration quantity for the posterior cingulate neurons by calculating the log ratio of the velocity weight to the acceleration weight (V/A, Figure 6D). To guarantee reliable results, we only selected those neurons with model’s goodness of fit (R2) larger than 0.5. In this area, the log V/A ratio distribution tended to be smaller than 0 under the translational condition (median = −0.27, p << 0.001, Wilcoxon rank sum test), while the distribution tended to be larger than 0 under the rotation condition (median = 0.10, p = 0.001, Wilcoxon rank sum test). Thus, PCC neurons in general carry more acceleration signals in translation condition and more velocity signals in rotation condition.

We further analyzed spatial tuning for each temporal component. In particular, preferred direction for the velocity component was defined straightforwardly based on the spatial kernel (Supplementary figure 1). For example, both units as shown in Figure 6B exhibited similar preferred directions. In particular, the single-peaked neuron preferred rightward motion for both velocity and acceleration components (Figure 6B, upper panels), and the double-peaked neuron preferred downward and a bit leftward motion for both of the temporal components (Figure 6B, lower panels). Across population, we calculated Pearson’s correlation coefficient of the spatial tuning between the two temporal components for those neurons with model’s goodness of fit value (R2) larger than 0.5. We found that in general the two components had strong correlations in both PCC (Figure 6E) under both translation and rotation conditions, indicating that the velocity and acceleration signals in each neuron tend to carry consistent spatial information, e.g., self-motion direction.

Jerk and position signals

In addition to velocity and acceleration components, we also noticed some other temporal signals that were missed in the VA model. Thus, in a more complicated spatiotemporal model, we included two additional terms of a jerk and a position signal. The jerk signal is the derivative of acceleration and defines smoothness of the accelerated motion of the head or whole body in the environment. Previous psychophysical and neurophysiological studies have indicated that such a signal was present in the vestibular afferents (Fernandez & Goldberg, 1976) and used by subjects to help detect self-motion (Benson, Spencer, & Stott, 1986; Grant & Haycock, 2008). The position signal is the integral of the velocity signal and defines the travelled distance, which is critical for path integration (Etienne & Jeffery, 2004). The new full model contained 4 signal components in total (i.e., position, velocity, acceleration, jerk, so called “PVAJ” model), with each component having its own temporal and spatial kernel (Supplementary figure 1). For example, Figure 7A showed PSTHs from two example neurons fitted by the full PVAJ model. The fitted weight of the first unit (Figure 7A, upper panels) was wV = 0.18, wA = 0.29, wJ = 0.35 and wP = 0.17, among which the weight of jerk was largest. The second unit (Figure 7A, lower panels) instead presented a larger weight of position signal: wV = 0.2, wA = 0.23, wJ = 0.22 and wP = 0.35.

Figure 7.
Figure 7.

Fitting results with PVAJ model. (A) PSTHs of two example neurons (one in top panels and the other in bottom panels) fitted by PVAJ model. The fitted weight of velocity, acceleration, jerk and position components are shown in the gray box. (B) Contour figures of spatial kernels of the velocity, acceleration, jerk and position components, respectively. (C) Goodness of fit (R2) from the PVAJ model assessed by Pearson correlation between the experimental and fitted data. Triangles: median value. (D) Comparison of BCI between VA model and PVAJ model. The large blue circles indicate mean value that are superimposed on individual values (small circles). (E) Cumulative distributions of the weight of different signal components. Only data with R2 > 0.5 were included. (F). Spatial congruency assessed by Pearson’s correlation between different components of PVAJ model. Only data with R2 > 0.5 are included. The filled bars indicate p < 0.05.

These two example units presented better goodness of fit (R2) of the full PVAJ model compared to the fit of the VA model (R2 = 0.79 & 0.90 versus R2 = 0.45 & 0.76, respectively). This was also the case across the population (p<<0.001 for both translation and rotation in PCC, paired t-test, Figure 6C & Figure 7C). However, the full model contained more free parameters that would surely lead to better fit compared to models with fewer parameters, so we further applied Bayesian Information Criterion (BIC) analysis to calibrate this parameter-size effect. As shown in Figure 7D, after removing the effect of the number of free parameters, BIC value of PVAJ model was still significantly smaller than that of VA model, confirming that including the jerk and position terms did capture more signals embedded in PCC neurons (p<<0.001, Paired t-test). Like the VA model, fitting RSC data with the PVAJ model was much worse compared to fitting in PCC (p<<0.001, t-test), suggesting that vestibular signals in RSC neurons may not simply follow dynamics defined by the classic temporal components, or may not be as reliable as that in PCC.

Among all examined temporal components, the jerk and position signals were less dominant compared to the velocity and acceleration, as revealed by the weight distribution for the PCC population as shown in Figure 7E. Note that consistent with the VA model, acceleration was still more dominant compared to the velocity component in this area in translation and velocity was still more dominant in rotation. For spatial tuning, like that in VA model, velocity and acceleration components had a strong correlation under both translation and rotation condition (Figure 7F, the first column). Moreover, spatial tuning of the velocity, acceleration and jerk signals were overall consistent among each other, indicating that these three temporal signals tended to convey similar spatial information, e.g., self-motion direction in each neuron. On the contrary, spatial tuning of the position signal was not consistent with that of the other three signals, suggesting that the position signal may not have to originate from the same source as the other signals.

Comparison with extrastriate visual cortex

Previous studies have discovered robust vestibular self-motion signals in other cortical areas (Cheng & Gu, 2018; Gu, 2018). One of these areas that has received heavy studies is the dorsal portion of the medial superior temporal sulcus (MSTd) in the dorsal visual pathway (F Bremmer et al., 1999; Duffy & Wurtz, 1997; Gu et al., 2006; Page & Duffy, 2003). Population analysis has indicated that MSTd neurons contain robust vestibular signals with velocity dominant component under translation conditions (Gu et al., 2006; Laurens et al., 2017). To quantitatively compare temporal dynamics in MSTd with that in the posterior cingulate region, here we used the same spatiotemporal model, including both VA and PVAJ models to fit MSTd responses.

Neural data was fitted quite well by the two spatiotemporal models in MSTd (Figure 8A). Consistent with previous findings, Velocity signals tended to be more prevalent in MSTd (VA model: p = 0.086; PVAJ model: p <<0.001, Wilcoxon rank sum test, Figure 8C), a pattern of which was different from that in PCC. Position and jerk signals were also revealed by the PVAJ model in MSTd. However, compared to the VA model, fitting MSTd data with the full model did not boost fit quality as much as that in PCC. To quantify this, we computed the difference in BIC between the two models (BIC difference index, BDI). BDI is expected to be larger if including additional terms produces better fit. The median value of the BDI distribution was 0.21 in the posterior cingulate region (only PCC was considered here), and was 0.10 in MSTd (Figure 8B, left column). Compared between the two areas, BDI analysis revealed that the full model successfully captured more types of signals in PCC than in MSTd (p<<0.001, t-test, Figure 8B, right column). A closer examination indicated that it was mainly the position signal that was more prevalent in PCC compared to that in MSTd (p = 0.018, t-test,

Figure 8.
Figure 8.

Comparison between PCC and MSTd fitted with VA and PVAJ models. (A) Distribution of goodness of fit (R2). Triangles: medians. (B) Left column: difference in the BCI between the two models (BDI). Three cases in PCC and one case in MSTd with BDI larger than 2 were cut off at 2 for sake of illustration. Right column: Comparison of the BDI between PCC and MSTd. Bars are the mean value and the errorbars are s.e.m. ***: p<<0.001. (C) Cumulative distributions of the weight of signal component.

Figure 8C, right columns, green curves). Thus, this finding suggests that posterior cingulate neurons reserve self-motion related signals from upstream areas including the extrastriate visual cortex, but the position signal is further developed here, which may be useful for path integration.

Visual signals in posterior cingulate region

Self-motion in the environment evokes visual stimuli such as optic flow or array which contains rich information about heading direction or travel distance during spatial navigation (Britten, 2008; Frenz & Lappe, 2005; Frenz, Lappe, Kolesnik, & Bührmann, 2007; Gibson, 1950). In addition to inertial motion, here we also measured neural activities in response to optic flow under the translation and rotation conditions on a large proportion of posterior cingulate cortical neurons (PCC: translation: n = 367; rotation: n = 260; RSC: translation: n = 114; rotation: n = 87). In particular, to simulate real motion, optic flow stimuli containing identical motion profiles as used in the vestibular-only condition were provided to the animals (Gu et al., 2006; Takahashi et al., 2007). Note that the motion platform was always stationary under this condition so that all the motion information could only be acquired from the visual signals.

Surprisingly, we found that visual self-motion signals were much weaker compared to the vestibular signals in either region (Figure 9A and Table 1). In PCC, only one third showed significant temporal modulations to optic flow (Translation: 31%, 114/367; Rotation: 32%, 84/260). Furthermore, among those cells with significant temporal modulations, only one third units showed significant (p<0.01, one-way ANOVA) spatial modulations (Translation: 36%, 41/114; Rotation: 24%, 20/84). In RSC, visual responses were even negligible. Only about one tenth neurons exhibited significant temporal modulations (Translation: 11%, 13/114; Rotation: 14%, 12/87), among which almost no units presented significant (p<0.01, one-way ANOVA) spatial modulations (Translation: n = 0; Rotation: n = 1). Thus, across all the cells recorded with an unbiased sampling (see Materials and methods), the proportion of neurons showing significant spatiotemporal modulations in response to visual heading stimuli was fairly small (~10% in PCC, and ~0% in RSC).

Figure 9.
Figure 9.

Visual self-motion signals (optic flow) in the posterior cingulate region. (A) Proportion of temporal and spatial tuning in PCC and RSC. (B) Distributions of DDI. (C) Preferred direction. Symbol representations are same as in Figure 4.

We also calculated DDI for the posterior cingulate cortical neurons to assess their visual tuning strength (Figure 9B). It turned out that the average DDI was much weaker compared to the vestibular DDI in PCC (p<<0.001, t-test), as well as compared to the visual tuning in the extrastriate visual cortex (e.g., MSTd, Gu et al., 2006; Takahashi et al., 2007). Hence, visual self-motion signals are overall weak in the posterior cingulate region including both PCC and RSC subregions.

Discussion

Numerous studies have indicated that the posterior part of cingulate cortex is involved in many complex behavioral contexts, yet in these studies top-down signals such as attention, anticipation, memory, reward are often mixed with bottom-up sensory driven signals which are difficult to be disentangled from each other. In our current study, by recording spiking activities from these areas in macaques during passive physical motions, we provided solid evidence showing that majority of PCC neurons (~2/3), and a modest proportion (~1/3) of RSC neurons carry robust vestibular linear translation and rotation signals originated from otolith organs and semicircular canals, respectively. A combined 3D spatiotemporal model captured PCC data well, and revealed multiple temporal components that could be useful for estimation of heading direction or travel distance. These properties are consistent with the view that posterior cingulate region may serve as an important hub mediating self-motion related signals propagated from parietal-temporal lobes to hippocampal system for path integration during vector-based spatial navigation (Kravitz et al., 2011; Rushworth et al., 2006; Vincent et al., 2010). Surprisingly though, the same region does not appear to carry robust visual self-motion signals such as optic flow, indicating that posterior cingulate cortex is a vestibular dominant region.

Comparison with previous studies

Vestibular signals in the posterior region of cingulate cortex have been indicated in previous studies using functional magnetic resonance imaging (fMRI) techniques. For example, Smith et al. reported clear vestibular signals in a homologues area (cingulate sulcus visual area, CsV) in the human brain (Andrew T. Smith et al., 2012). Other researchers using similar techniques also observed similar activity in similar areas (Bense, Stephan, Yousry, Brandt, & Dieterich, 2001; Shinder & Taube, 2010). In these imaging studies, vestibular stimuli were typically provided through caloric or galvanic stimulations due to restraint of head positions during scanning. To achieve more real inertial motion stimuli, Schindler and Bartels adopted a clever method by physically rotating the head of human subjects in the scanner, and measured the blood oxygen level dependent (BOLD) signal immediately after termination of head motion (Schindler & Bartels, 2018). While these imaging studies provide valuable information of identified vestibular-related signals across the whole brain, there are limitations. In particular, caloric or galvanic stimulation techniques can only activate the vestibular system in a general way (Fitzpatrick & Day, 2004; Suzuki et al., 2001; Utz, Dimova, Oppenländer, & Kerkhoff, 2010), instead of being able to simulate real and accurate self-motion including translation along certain directions or rotation around specific axis in 3D environment. Moreover, vestibular signals typically carry complex temporal dynamics (i.e., in milliseconds) that cannot be easily captured by BOLD signals with a relatively slow temporal resolution (i.e., in seconds). Thus, it is important to further conduct studies using single-unit recording techniques to acquire neurophysiological data with much higher spatial and temporal resolutions. In general, we found robust vestibular signals in similar regions as indicated in previous imaging studies (yet with more spatiotemporal information as discussed in the following sections), confirming a rough consistency between spiking activity from single neurons with BOLD signals (Berens, Logothetis, & Tolias, 2010).

However, Smith and colleagues also reported robust visual signals in human cingulate sulcus (Fischer et al., 2012; Wall & Smith, 2008), as well as in macaque PCC (Cottereau et al., 2017). By contrast we discovered much weaker visual responses using global optic flow or grating. Our results seem to be more consistent with that in another study, in which BOLD signals were suppressed in response to random motion and static visual stimuli (Pitzalis et al., 2013). The inconsistency of the visual responses may be due to the different stimuli used in different studies. For example, researchers observed visual activity in the posterior region of cingulate cortex when complex optic flow patterns were dynamically changing over time (e.g., switching back and forth between expansion and rotation) (Cottereau et al., 2017; Wall & Smith, 2008). These activities were basically gone when patterns of optic flow became consistent and stable (Andrew T Smith et al., 2017). It is possible that the posterior cingulate region is more involved in more complex environment when visual stimuli are ever-changing. More types of stimuli could be used to test this hypothesis in future experiments.

Functional implications of vestibular signals in PCC

Direct inputs of vestibular signals to PCC originate from a number of sources, including the thalamus (Shinder & Taube, 2010), the parietal insular vestibular cortex (PIVC) (Guldin & Grüsser, 1998), and the parietal-temporal cortices (Akbarian et al., 1994; Cavada & Goldman – Rakic, 1989; Musil & Olson, 1988; Olson & Musil, 1992; Pandya et al., 1981; Shinder & Taube, 2010; Vogt et al., 1987). Previous studies have demonstrated that vestibular signals with diverse temporal and spatial modulations are ubiquitously distributed across many areas, pointing out a vestibular network in sensory cortices (Cheng & Gu, 2018; Gu, 2018; Guldin & Grüsser, 1998; Laurens et al., 2017).

In the current study, we also found clear temporal and spatial modulations in a large proportion of neurons in PCC, with many properties qualitatively similar to those in the insular and parietal-temporal areas, indicating PCC is one of the nodes within the cortical vestibular network. For example, preferred directions of PCC neurons in response to linear translation overall have a bias towards leftward and rightward motion in the horizontal plane, a pattern of which has been seen in several parietal-temporal areas such as MSTd (Gu et al., 2006). Information theory predicts that such a pattern of distributed preferred directions is useful for detection of fine-varied headings around straight ahead (Gu et al., 2010), suggesting that PCC could be involved in heading perception. As to rotation signals, the distributed preferred axis tends to bias around pitch and roll, but not yaw. A previous study has reported that in mice, azimuth-tuning of head direction cells is affixed to the head-horizontal plane when the animals’ head positions were changed relative to gravity (Dora E. Angelaki et al., 2020). Therefore, these rotation signals could be essential for head direction cells to function properly in 3D environments.

Our 3D spatiotemporal model reveals strong temporal components of velocity, acceleration and jerk that carry consistent spatial information about self-motion direction (i.e., heading) in each individual neuron. This result suggests that the three types of signals may arise from a same source, either from upstream neurons or from the same neuron within PCC. Alternatively, they may receive inputs from multiple upstream areas yet with consistent spatial tuning functions. Note that such a result was not found in previous studies (Chen et al., 2011c; Laurens et al., 2017). One possibility is that previous models have used identical delays among different temporal components for each unit, whereas we allowed varied delay between different components (Supplementary figure 1). Indeed, if we removed this variability, spatial tuning became uncorrelated among most components, which was similar to that seen in previous studies. At this stage we cannot identify which case is true in the brain, but we would like to argue that in the varied-delay case, it is a convenient way for downstream neurons to compute a consistent signal of self-motion directions from either temporal component.

Besides temporal components of velocity, acceleration and jerk that are all related with momentary self-motion directions, our model also captured a position signal in some posterior cingulate neurons. This component is less dominant compared to the others, but is more prevalent than that in the extrastriate visual cortex, suggesting that a process of integrating the velocity quantity to position may happen in PCC when self-motion related information is propagated closer to the hippocampus system. Unlike the spatial consistency among velocity, acceleration and jerk, spatial tuning of the position signals is uncorrelated with the other three components of signal. Therefore, position and direction signals in each individual neuron do not have to be registered in a congruent way.

As to the outputs of PCC, it also directly sends signals back to the vestibular nucleus (VN) in brainstem (Akbarian et al., 1994). This pathway may be involved in gaze control or other reflex behaviors during coordinated eye, head, and body movements during natural navigation (Dean & Platt, 2006; Shinder & Taube, 2010). Thus as postulated by previous researchers, PCC “… do not serve a simple or direct role either in sensory information processing or in motor control… “ (Vogt & Gabriel, 1993). Instead, vestibular signals with rich temporal and spatial information in PCC may compose a high and mixed dimensional neural representations that could be used by downstream neurons through a simple, linear readout algorithm to compute an efficient and flexible variable according to ongoing task’s need.

Among the brain limbic system, PCC is the one that has been less studied and its exact functions to date remain unclear and debatable compared to the other parts, for example, its counterpart at the anterior cingulate cortex (ACC), an area of which is identified to be involved in emotion processing (Vogt & Gabriel, 1993). Both anterior and posterior parts of cingulate cortex are actually heavily connected with each other. PCC is also reciprocally connected with PIVC that in turn receives projections from the viscera system through the solitary tract and the parabrachial complex (Vogt, 2019). Electrical stimulation in PIVC evokes unpleasant emotions including vomiting, nausea, dizziness, and pain (Mazzola, Isnard, Peyron, & Mauguière, 2012; Wilder Penfield, 1957; W. Penfield & Faulk, 1955). Thus vestibular signals in PCC may also be related to emotion, which requires further investigations in the future. In addition, PCC has been indicated to play roles in many other cognitive functions including default mode network (DMN), visual spatial map, action, learning, spatial memory, decisions (See reviews and books by Fransson & Marrelec, 2008; Leech & Sharp, 2014; Vogt, Finch, & Olson, 1992; Vogt & Gabriel, 1993; Vogt & Laureys, 2005). For example, in a recent study, Gold and his colleagues reported that PCC neurons contain spatial choice and reward-target signals in a complex, adaptive task (Li, Nassar, Kable, & Gold, 2019). Future studies need to be conducted for investigation of how vestibular signals in PCC are involved in these functions.

Self-motion signals in macaque RSC

Similar to PCC, RSC has also been indicated to be involved in spatial navigation tasks albeit mainly in human and rodent studies (Benjamin J. Clark et al., 2010; Brenton G Cooper et al., 2001; Cullen & Taube, 2017; Maguire, 2001; Spiers & Maguire, 2007; Vann et al., 2009). Vestibular yaw-rotation signals have been observed in mice primary visual cortex (V1), and they are inferred to originate directly from RSC as revealed by tracing and functional mapping (Vélez-Fort et al., 2018). At the same time, mice RSC also receives heavy inputs from V1 as well as other sensory information related with spatial perception, suggesting RSC is a multisensory area albeit biased toward visual (Zingg et al., 2014). Recently Powell and colleagues also reported visual responses modulated by grating stimuli in mice RSC (Powell et al., 2020). By contrast, although we discovered that about one third RSC neurons in macaques exhibited significant temporal modulations to physical translation or rotation stimuli, visual modulations overall by optic flow appeared to be fairly weak in this area (Figure 9B). To verify that we have not missed other visual motion signals, we also introduced the visual gratings stimuli. Similar to optic flow, the responses to grating stimuli is also quite weak in this region (data not shown). A number of factors may lead to heterogeneous results across studies.

First, previous studies on human found strong involvement of RSC in virtual navigation task or when animals were imagining or planning route, which required many high-level top-down signals such as attention, anticipation, or action (see reviews by Maguire, 2001; Spiers & Maguire, 2007; Vann et al., 2009). By contrast, in our study, the monkeys performed a simple fixation task while they were passively experienced self-motion stimuli, a process of which mainly involved sensory-driven signals. Second, previous studies suggest that RSC may be more involved in landmark-based navigation instead of vector-based navigation (i.e., path integration). For example, lesion of RSC significantly impaired mice’s performance during spatial navigation when they relied on visual landmarks but not much when animals were required to rely on self-motion cues (Benjamin J. Clark et al., 2010; Mitchell, Czajkowski, Zhang, Jeffery, & Nelson, 2018). Thus RSC may not contribute too much to optic flow that indicates about update of self-motion directions or travel distance for path integration. Third, the exact area of RSC has been defined differently across species. In rodents, RSC (usually defined as RSP) is a relatively large region and PCC is small, whereas in monkeys the region of PCC is very large and RSC is small. In human, both areas are fairly small. Thus, homologous areas across species could be somehow different and execute heterogeneous functions to adapt to environments during evolution.

Compared to PCC, we discovered that vestibular signals overall are less clear in macaque RSC. Close to one third proportion of RSC neurons, which is about a half of that of PCC neurons, exhibit significant temporal modulations. Moreover, the same 3D spatiotemporal model does not fit data in RSC as well as that in PCC, suggesting that temporal component signals in RSC may either be less reliable, or more complex that need other models to be further developed to better capture the data. Also unlike PCC, spatial tuning in RSC neurons is basically lacking. All these results suggest that macaque PCC and RSC may contribute to spatial navigation in different ways. Future studies could be conducted to investigate spatial reference frames of self-motion signals in these regions, and how these signals are functionally linked to the animals’ perceptual decisions during spatial-related tasks.

Materials and methods

Subjects and system set-up

We performed the experiments on three male monkeys (Macaca mulatta), weighted 8~12 kg (monkey Q, P and W). The monkeys were chronically implanted with a circular molded lightweight plastic ring above cranium for head fixation and recording, and scleral coils for monitoring eye movements in real time inside a magnetic field with phase detector (Riverbend instrument).

During behavior training and neural recording, monkeys were comfortably seated in a chair, which was secured to a six-degree of freedom (heave, surge and sway for translation; pitch, roll and yaw for rotation) motion platform (MOOG, 6DOF2000E) with an attached LCD screen in front of it (Figure 1A). With such set-up, we could flexibly switch different stimuli types, vestibular stimuli from the motion of platform and visual stimuli from the screen given by an OpenGL accelerator board, depending on different trial condition. To simulate the visual pattern of real motion projected onto the retina, visual stimuli are depicted as a three-dimensional cuboid space full of 0.3 × 0.26 cm yellow triangles (just like stars in the star sky), with 100cm wide, 150cm high, 160cm deep and the density of 0.002 /cm. The optic flow field is a simulated 3D space with motion parallax and size information that is dependent on depth. The monkeys were head-fixed and sat at a distance of about 30 cm from the screen, with a visual angle of approximately 90° × 90°.

After monkeys were well-trained to the tasks, a recording grid was tightly settled inside the plastic ring horizontally. The grid contains small holes arranged in array with a diameter of 0.5 mm and they were apart from each other of 0.3 mm. With the help of the grid and figures from magnetic resonance imaging (MRI), we can map the position of each site which we performed recording on. All animal procedures were approved by the Animal Care Committee of Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and have been described previously in detail (Gu et al., 2006).

Experimental protocol

During each task, there was a small fixation point (0.2°×0.2°) appeared at the center of the screen from the beginning and disappeared till the stimulus was vanished. The monkeys needed to saccade to the fixation point and maintain fixation for 200 ms to initiate the self-motion stimuli. The animals maintained fixation for the following 1.5 s within an electronic window (3°×3°) before the stimulus was removed. Monkeys were rewarded with juice at the end of trial if they completed the trial successfully, otherwise the trial would stop and the data were aborted.

To characterize basic tuning properties in posterior cingulate region, two sensory modality conditions including vestibular and visual stimuli were conducted in the current study. In the vestibular condition, motion platform was moving along a straight line (i.e., translation condition) or rotating around the center of the interaural axis (i.e., rotation condition) in 26 directions that were equally distributed in 3D space (Figure 1C). Only a fixation point was presented on the visual display for the animal to maintain fixation at a head-fixed target (i.e., suppression of vestibular ocular reflex, VOR). In the visual condition, the motion platform was stationary while visual stimuli (optic flow) were presented on the screen, simulating real motions that were delivered in the vestibular condition. Visual and vestibular stimuli conditions were interleaved in one experimental block.

Motion profiles in either stimulus condition followed a Gaussian velocity, with a corresponding biphasic acceleration profile. Actual velocity and acceleration parameters were acquired from an accelerator and a gyroscope mounted on the motion platform (Figure 1B, blue line indicates the measured acceleration). By estimation, the peak velocity was ~0.3 m/s and the peak acceleration was 0.1 G/s in the translation condition. In the rotation condition, the peak angular velocity was ~20 °/s and the peak acceleration was 60 °/s.

To make sure that responses under the vestibular condition were not evoked from other cues, we also conducted two additional control experiments. In the total darkness condition, we turned off the visual display to eliminate any residual visual cues in the vestibular condition that might arise from incomplete suppression of VOR. The monkeys were free to make any eye movements in this condition. In the sound control experiment, we tried to test the possibility that the vestibular responses might arise from the sound cue generated from physical motions of the mechanical actuators (peak magnitude: ~90 dBs). To do this, we first put a recorder beside the animal’s ears and recorded the sounds when the motion platform was moving with the motion profiles used in the vestibular condition. We then played these sounds to the animals when the motion platform was stationary.

Under the visual condition, for a subpopulation of neurons, we ran an additional block using drifting gratings. Specifically, gratings with a full black-white contrast were moving along 8 directions orthogonal to their orientations in the fronto-parallel plane. The temporal frequency is 5 Hz and the spatial frequency is 0.2 and 0.4 cycles/degree in the experiment.

While the animals were experienced self-motion stimuli, single-unit activity was recorded from PCC and RSC in the posterior cingulate region. To validate the position of PCC and RSC, we first used MRI to help identify locations of electrode penetrations. Electrodes were penetrated in the earth-vertical plane. PCC were located roughly around anterior-posterior (−1~+12) and medial-lateral (0~10). RSC were located roughly around anterior-posterior (+1~+7) and medial-lateral (0~6).

After entering the targeted areas, single-unit extracellular recording was performed. Tungsten microelectrodes with impedance around 1 MΩ (FHC) were used. Data were recorded and single units were isolated using a multichannel recording system from Alpha Omega (Israel) and CED (Spike2).

Data Analysis

Characterizing tuning properties

Peristimulus time histograms (PSTHs) in each stimulus condition was constructed with a 25 ms bin and was smoothed with a 300 ms Gaussian filter. To quantify whether a neuron was significantly modulated by the stimulus, a two-sided Wilcoxon rank sum test was used. Briefly, we compared response in each time bin with the baseline response which was based on the time window of 100 ms before and 300 ms after stimulus onset (p < 0.01, Wilcoxon rank sum test). To avoid false positive results, a neuron was considered to have significant temporal tuning only when satisfying two criteria at the same time. First, at least five consecutive bins needed to show significant modulation. Secondly, the first requirement needed to happen in at least two adjacent spatial directions (Chen et al., 2010).

To quantify whether neurons were spatially tuned, we first calculated response in each 300 ms-bin and performed ANOVA analysis across 26 vectors (i.e., translation directions or rotation axes). Responses at a certain bin was considered to be significant when p value was smaller than 0.01. To identify the number of response peak across all bins, we performed an analysis as used in previous studies (Chen et al., 2010). Briefly, for all those bins with significant spatial tuning (p < 0.01, one-way ANOVA), a local peak was temporarily assigned when it was higher than its neighboring bins. Across all these local peaks, a next step was performed to identify which of them were within one group, meaning one real peak, or were separated from each other, meaning multiple peaks (actually only two-peak cases were considered whereas tri-peak or higher were not considered because those peaks were usually small and unreliable according to experience). In particular for each pair of neighboring local peaks, Pearson correlation coefficient was measured between the two vectors composed of responses in the 26 directions at the local peak bins. Positive correlation led to conclusion that the compared local peaks belonged to the same one peak and negative correlation suggested that they belonged to different peaks. If there was only one real peak in the end, a neuron was defined as “single-peak” cell, and if there were two peaks, it was defined as “double-peak” neuron.

To quantify the strength of direction tuning, we used a direction discrimination index (DDI) given by (Takahashi et al., 2007), as follows: Embedded Image where Rmax and Rmin are the maximal and minimal responses respectively, SSE is the sum squared error around mean responses, N is the total number of observations (trials), and M is the number of stimulus directions (here, M = 26). Neurons with strong response modulations will have DDI values closer to 1, while neurons with weak response modulations will have DDI values closer to 0.

The 3D spatiotemporal model

A combined spatiotemporal model was constructed to fit PSTH data in 26 directions at the same time. The simplest form of this model is a linear combination of different temporal components. Each term is a multiplication of a spatial tuning kernel, y(g(θ, φ)) and a temporal response profile f(t – τm): Embedded Image where A is the amplitude, FR0 is spontaneous firing rate (baseline) and wm is the corresponding weight of each component. The [] + means that any fitted firing rate less than 0 would be set to 0. For models with different components, f(t – τm) can be defined as following with τm representing the delay time for different components: Embedded Image and in the spatial tuning component, gm(θ, φ) is the cosine of the difference between each direction and the preferred direction, with θ and φ indicating the preferred azimuth and elevation. y(x) is a nonlinear function that is typically used to describe non-cosine spatial tuning frequently seen in central vestibular neurons instead of the peripheral afferents (D. E. Angelaki & Dickman, 2000). It is given by: Embedded Image where n is the nonlinearity modulator, allowing the spatial tuning to vary in both amplitude and width. As n gets larger, this function amplifies and narrows the peak, while suppressing and widening the trough. And DCm is the linear modulator for the spatial kernel, allowing asymmetric amplitude modulation.

To evaluate model fit quality, we use the Bayesian Information Criterion (BIC) (Schwarz, 1978) that could calibrate extra fitting benefit from additional free parameters included in the model. BIC is given by: Embedded Image where RSS is the residual sum of squares, n is the number of data points and p is the number of free parameters.

Supplementary figures

SF1.
SF1.

Illustration of the 3-dimensional temporal-spatial model. The full PVAJ model contains four different components, velocity, acceleration, jerk and position, which are summed linearly by their corresponding weights, with their own temporal kernel and spatial kernel. The delayed time could be varied for each component in the temporal kernel. The spatial kernel is cosine function fed through a nonlinear function that was depended on parameters of preferred azimuth and preferred elevation.

Acknowledgements

We thank Wenyao Chen for monkey care and training, and Ying Liu for C++ software programming. This work was supported by grants from the National Natural Science Foundation of China Project (31761133014), the Strategic Priority Research Program of CAS (XDB32070000), and the Shanghai Municipal Science and Technology Major Project (2018SHZDZX05) to Y.G.

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Posted November 15, 2020.
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Robust vestibular self-motion signals in macaque posterior cingulate region
Bingyu Liu, Qingyang Tian, Yong Gu
bioRxiv 2020.11.13.381780; doi: https://doi.org/10.1101/2020.11.13.381780
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