| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
1Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri; and 2Department of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, New York
Submitted 23 June 2008; accepted in final form 25 August 2008
 |
ABSTRACT |
|---|
|
|
|
INTRODUCTION |
|---|
|
, 1997). One possibility is that clustering facilitates local information processing by placing functionally related neurons near each other in the cortex, thus simplifying the wiring of the cortex and reducing the risk that related neural discharges will be out of phase temporally due to conduction delays. Thus the presence of functional clustering for a particular stimulus parameter might indicate an important role of a cortical area in processing that parameter (but see Horton and Adams 2005 for counter-examples).
Neurons in the dorsal subdivision of the medial superior temporal area (MSTd) are multimodal, with responses to both visual and vestibular stimuli (Bremmer et al. 1999; Duffy 1998; Froehler and Duffy 2002; Gu et al. 2006; Page and Duffy 2003). Visual inputs largely derive from area MT, which is well known for having a robust columnar organization for direction of motion (Albright et al. 1984; DeAngelis and Newsome 1999; Malonek et al. 1994; Zeki 1974), binocular disparity (DeAngelis and Newsome 1999), and other stimulus attributes (Born and Bradley 2005; Born and Tootell 1992). Whereas a few earlier studies commented on a potentially clustered organization for optic flow signals in MSTd (Duffy and Wurtz 1991; Lagae et al. 1994; Saito et al. 1986), Britten (1998) was the first to examine this issue quantitatively. Using a few different types of optic flow stimuli, Britten (1998) provided quantitative evidence that nearby neurons in MST have similar tuning for optic flow. This suggests that area MSTd may contain a topographic map of heading based on optic flow, which may facilitate decoding of self-motion from population activity.
More recent work has shown that MSTd neurons also represent the direction of self-motion based on vestibular signals (Duffy 1998; Gu et al. 2006; Page and Duffy 2003). Specifically, individual MSTd neurons are tuned in three dimensions for both the direction of translation and the axis of rotation when animals are moved in the absence of optic flow (Gu et al. 2006; Takahashi et al. 2007). Moreover, these responses are vestibular in origin because they are abolished following lesions to the vestibular labyrinth (Gu et al. 2007; Takahashi et al. 2007). The majority of neurons in MSTd show selectivity for both optic flow and vestibular signals and their directional preferences for the two cues may be either congruent or opposite (Gu et al. 2006; Takahashi et al. 2007). Thus MSTd could contain a topographic map for direction of self-motion as defined by both visual and vestibular cues. This raises the question of whether vestibular signals in MSTd show a clustered organization similar to that seen for responses to optic flow (Britten 1998).
Herein we examine clustering of vestibular response properties in area MSTd and we compare the strength of this clustering with that seen for optic flow stimuli. To address this issue, we compare the translation and/or rotation selectivity of isolated single units (SUs) with the selectivity of multiunit (MU) signals that represent the combined activity of several other neurons near the tip of the electrode. This allows assessment of local clustering of tuning for translation and rotation. In addition, we also investigate whether other basic visual response properties of MSTd neurons are clustered, including the local directional preference within the visual receptive field and the overall spatial profile of the receptive field. Our results indicate that directional selectivity for both vestibular and visual stimuli is clustered within area MSTd. These results reinforce the notion that MSTd plays important roles in visual/vestibular integration for self-motion perception.
|
|
METHODS |
|---|
|
; Gu et al. 2006; Takahashi et al. 2007). This study involves new analyses of data collected as part of these previous efforts. Accordingly, the methods will be described only briefly here. Each animal was chronically implanted with a circular molded, lightweight plastic ring for head restraint and a scleral search coil for monitoring eye movements inside a magnetic field (CNC Engineering, Seattle, WA). Behavioral training was accomplished using standard operant conditioning procedures. All animal care and experimental procedures conformed to guidelines established by the National Institutes of Health and were approved by the Animal Studies Committee at Washington University. Vestibular and visual stimuli
During experiments, the monkey was seated comfortably in a primate chair, which was secured to a 6-degree-of-freedom motion platform (MOOG 6DOF2000E; East Aurora, NY). Three-dimensional (3D) movements along or around any arbitrary axis were delivered by this platform. Computer-generated visual stimuli were rear projected (Mirage 2000; Christie Digital Systems, Cyrus, CA) onto a tangent screen placed 30 cm in front of the monkey (subtending 90 x 90° of visual angle), simulating self-motion through a 3D random-dot field (100 cm wide, 100 cm tall, and 40 cm deep). Visual stimuli were programmed using the OpenGL graphics library and generated using an OpenGL accelerator board (Quadro FX 3000G; PNY Technologies, Parsippany, NJ; see Gu et al. 2006 for details). The projector, screen, and magnetic field coil frame were mounted on the platform and moved together with the animal. Image resolution was 1,280 x 1,024 pixels and refresh rate was 60 Hz. Dot density was 0.01/cm3, with each dot rendered as a 0.15 x 0.15-cm triangle. Dot sizes were fixed in the virtual environment, such that dots grew larger on the display screen as they became closer to the eyes. Stimuli were presented stereoscopically as red/green anaglyphs, viewed through Kodak Wratten filters (red #29, green #61). The visual stimulus therefore contained a variety of depth cues, including horizontal disparity, motion parallax, and size information.
Electrophysiological recordings
Tungsten microelectrodes (FHC, Bowdoinham, ME; tip diameter 3 µm, impedance 1–2 M
at 1 kHz) were inserted into the cortex through a transdural guide tube, using a hydraulic microdrive (FHC). Behavioral control and data acquisition were accomplished using a commercially available software package (TEMPO; Reflective Computing, Olympia, WA). Neural voltage signals were amplified, filtered (400 to 5,000 Hz), discriminated (Bak Electronics, Mount Airy, MD), and displayed on an oscilloscope. The times of occurrence of action potentials and all behavioral events were recorded with 1-ms resolution. Eye-movement traces were sampled at a rate of 200 Hz. At the same time, raw neural signals were digitized at a rate of 25 kHz using a CED Power 1041 data acquisition system (Cambridge Electronic Design, Cambridge, UK) along with Spike2 software. These raw data were stored to disk for off-line spike sorting and additional analyses, including extraction of MU activity (see following text). Due to electrical noise from the MOOG motion platform, it was necessary to high-pass filter the neural signals sharply at 400 Hz, which precluded analysis of local field potentials.
Area MSTd was identified based on the patterns of gray and white matter transitions along electrode penetrations with respect to MRI scans, the response properties of single units and multiunits (direction selectivity, large receptive fields that often contained the fovea and portions of the ipsilateral visual field), and the eccentricity of receptive fields in underlying area MT (for details, see Gu et al. 2006).
Experimental protocol
We examined the 3D tuning of MSTd neurons for both translation and rotation by recording neural responses to stimuli defined by optic flow or physical motion (Gu et al. 2006; Takahashi et al. 2007). In each real (vestibular) or simulated (visual) motion stimulus, the animal was translated along or rotated around one of 26 directions sampled evenly from a sphere (Fig. 1A). Each movement trajectory (either real or visually simulated) had a duration of 2 s and consisted of a Gaussian velocity profile. For the translation protocol, the amplitude was 13 cm (total displacement), with a peak acceleration of about 0.1 g (
0.98 m/s2) and a peak velocity of 30 cm/s. For the rotation protocol, the amplitude was 9°, with a peak angular velocity of about 20°/s (Gu et al. 2006; Takahashi et al. 2007). For both translation and rotation protocols, visual and vestibular stimuli were randomly interleaved within a single block of trials.
|
; Duffy and Wurtz 1995). Motion occurred simultaneously in each subfield and the direction of motion of each patch changed randomly every 100 ms (six video frames). The direction of motion in each subfield was chosen randomly from a uniform distribution across the eight possible directions and each subfield was updated independently of the others. Each 2-s trial thus contained a temporal sequence of 20 directions of motion within each subfield, and a new random sequence of directions was presented each trial. Typically, about 60–100 trials (mean = 80) were necessary to obtain a reasonably smooth receptive field map. This protocol was not run for every neuron because good isolation was sometimes lost during the 3D translation/rotation runs. For all stimulus conditions, the animal was required to fixate a central target (0.2 x 0.2°) for 200 ms before stimulus onset. The animals were rewarded with a drop of juice at the end of each trial for maintaining fixation throughout stimulus presentation. Trials were aborted and data discarded when the monkey's gaze deviated by >1° from the fixation target.
Data analysis
SINGLE- AND MULTIUNIT TUNING. Action potentials from a single unit (SU) were isolated with a dual voltage–time window discriminator on-line (Bak Electronics, Mount Airy, MD). Multiunit activity was obtained by off-line processing of the digitized raw neural signal. A multiunit (MU) event was defined as any deflection of the analog voltage signal that exceeded a threshold level. The absolute frequency of the MU response is somewhat arbitrary, depending on the level of the event threshold. To standardize our measurements across recording sites, we adjusted the event threshold to obtain a spontaneous activity level that was 50 spikes/s greater than the spontaneous activity level of the SU. The resulting MU signal reflected the combined activities of several neurons near the electrode tip, including the isolated SU. To make our SU and MU measurements independent, we subtracted one count from time bins of the MU signal that occurred within ±1 ms of each SU spike. This prevented SU spikes from leaking into the MU signal. Counts were subtracted from the MU bins immediately neighboring each SU spike to account for possible jitter in the time binning of events in the SU and MU signals (which were collected using two different hardware systems). This ensures removal of the SU spikes from the MU signal but also removes some additional events from the MU signal as well. To examine the efficacy of this procedure, we computed the cross-correlation function between simultaneous SU and MU recordings (e.g., Fig. 2B). This analysis was carried out for each pair of SU and MU recordings and showed that our procedure was quite effective at removing correlations between SU and MU responses (Fig. 4).
|
|
; Takahashi et al. 2007). To plot these spherical data on Cartesian axes (e.g., Fig. 2), the data were transformed using the Lambert cylindrical equal-area projection (Snyder 1987). In these plots, the abscissa represents azimuth and the ordinate represents a cosine-transformed version of elevation. We determined whether a neuron was spatially tuned for rotation and/or translation using a one-way ANOVA with a significance criterion of P < 0.05.
To quantify the strength of spatial tuning for translation and rotation, a direction discrimination index (DDI) was calculated for each data set (SU or MU) according to the following formula (DeAngelis and Uka 2003; Prince et al. 2002; Takahashi et al. 2007)
 | (1) |
RECEPTIVE FIELD ANALYSIS.
To obtain a receptive field (RF) map for each MSTd neuron, the spike train was cross-correlated with the temporal sequence of motion directions for each subfield in the stimulus grid. This approach is similar to that of Borguis et al. (2003) and is analogous to reverse-correlation techniques used previously in other stimulus domains (DeAngelis et al. 1993; Eckhorn et al. 1993; Jones and Palmer 1987; Ringach et al. 1997). This method produces a direction–time map for each individual subfield in the stimulus grid. If there is coupling between the stimulus and response over a range of correlation delays (T), then a pattern will emerge in the direction–time profiles (Fig. 9A); otherwise, the profiles will show no structure. These maps therefore reveal the direction tuning for each subfield that is contained in the MSTd neuron's RF. If a stimulus subfield is outside the RF, then its direction–time map will not show any structure. Note, however, that this particular method will reveal only portions of the RF that have directional selectivity. If there are also portions of an MSTd RF that are not direction selective, these maps will not identify those regions. To summarize the directional tuning and response modulation for each subfield, we identified the peak correlation delay (Tpeak) as the value of T at which the direction tuning curves have maximal variance. A horizontal cross section through the maps at Tpeak (e.g., horizontal lines in Fig. 9) yields a direction tuning curve for each subfield. These tuning curves were fit with a modified wrapped Gaussian function, r(
) (Yang and Maunsell 2004)
 | (2) |
pref indicates the preferred direction, 
represents the tuning bandwidth, a denotes the tuning curve amplitude, and b indicates the baseline firing rate.
|
In addition to quantifying the direction selectivity in each subfield, we estimated the overall spatial RF profile of each MSTd neuron by quantifying how the strength of directional tuning varied across subfields. Specifically, we plotted the amplitude a of the wrapped Gaussian fit to each direction tuning curve as a function of the center location of each subfield (e.g., Fig. 12A). These amplitude data were then fitted with a two-dimensional Gaussian function
 | (3) |
x and y are the tuning widths along the horizontal (x) and vertical (y) dimensions, respectively; and b is the baseline response level. The horizontal and vertical sizes of the RF were computed by measuring the full width at half-maximum (FWHM), which can be written as: FWHM = 2
= 2.35 (Wennekers 2001).
|
To assess the degree of overlap of SU and MU RF profiles, we computed normalized separations between the centers of the SU and MU spatial RFs. The normalized separations between SU and MU RFs were defined as
 |
 |
x_SU and x_MU reflect the horizontal spread of the RFs for SU and MU, respectively, whereas y_SU and y_MU indicate the vertical spread of the RFs. If the spatial separation between the centers of the SU and MU RFs is small relative to the sizes of the RFs, then these normalized separation metrics will be <1. |
|
RESULTS |
|---|
|
; Takahashi et al. 2007) in which SU responses were examined in detail. Due to the length of the experimental protocols in those studies, we generally did not collect data from multiple recording sites along penetrations through MSTd. Hence our analyses are limited to a comparison of SU and MU responses at the same recording site and we do not directly address variations in tuning as a function of distance along electrode penetrations. Clustering of 3D translation and rotation tuning in MSTd
In the translation protocol (see METHODS), each MSTd neuron was tested with 26 directions of translation, consisting of all combinations of azimuth and elevation separated by 45° on a sphere (Fig. 1, A and B). Figure 2A shows an example of 3D translation tuning for simultaneously recorded SU and MU activity in MSTd. The data are shown as contour maps in which mean firing rate (represented by color) is plotted as a function of azimuth (abscissa) and elevation (ordinate) (see also Gu et al. 2006). Data in the top row show responses obtained in the vestibular stimulus condition and data in the bottom row are from the visual condition. Note that the MU responses are about twofold larger than the SU responses. In the vestibular condition, the SU shows clear spatial tuning for translation, with a preferred direction at 190° azimuth and –1° elevation. A nearly opposite translation preference was seen in the visual condition for this SU, with the direction preference occurring at 47° azimuth and 9° elevation. This pattern of results is typical of an "opposite" cell, as described previously (Gu et al. 2006). The MU activity recorded simultaneously with this SU (Fig. 2A, right column) shows similar tuning for translational motion with a preferred direction of (181°, 0°) for the vestibular condition and (9°, –33°) for the visual condition. This suggests that nearby neurons in MSTd have similar direction preferences.
The similarity in tuning between MU and SU responses cannot simply be due to the same single unit contributing to both signals. To avoid this confound, we have excluded SU spikes from the MU signal (see METHODS), so that the MU response reflects the combined activity of several other nearby SUs. This was verified by cross-correlation analysis, as shown in Fig. 2B. Before SU spikes were removed, there was a sharp peak in the cross-correlogram centered around 0 ms. After SU spikes were removed, the cross-correlogram was relatively flat, indicating that spikes from the single cell were effectively excluded from the MU signal. Thus the observed similarity in SU and MU tuning for the example in Fig. 2A is not attributable to a common source of spikes. All MU data reported herein had the corresponding SU spikes removed.
For the translation protocol, this analysis was performed on a total of 285 MSTd neurons for the vestibular condition and 270 MSTd neurons for the visual condition. All SU/MU pairs were recorded simultaneously from a single microelectrode. Table 1 summarizes the proportions of SU and MU responses with significant spatial tuning for translation. In the vestibular condition, 56% (161/285) of SUs and 32% (92/285) of MUs had significant spatial tuning (ANOVA, P < 0.05, Table 1). In contrast, 97% (261/270) of SUs and 82% (222/270) of MUs were significantly tuned in the visual translation condition. When the SU was significantly tuned, the MU was also significantly tuned in 45% of cases for the vestibular condition, compared with 84% of cases in the visual condition. When the MU was significantly tuned, the SU was also significantly tuned in 78% of cases for the vestibular condition, compared with 98% cases in the visual condition (see Table 1). Both SU and MU selectivities for translation were less prevalent in the vestibular condition.
|
|
). Vestibular rotation responses may also reflect a contribution of retinal slip of the faint visual background since these rotation responses were found to be slightly weaker during viewing in total darkness (Takahashi et al. 2007).
|
We next summarize the similarity of SU and MU tuning across the population of MSTd neurons. To summarize response strength, we computed the difference between the peak response and spontaneous activity (Rmax – spont). Figure 5A shows this metric for SU and MU responses recorded simultaneously during the translation protocol. The vast majority of data points lie above the diagonal for both the vestibular (red) and visual (cyan) conditions. The average MU/SU peak response ratios were 4.1 and 3.4 for the vestibular and visual conditions, respectively. Figure 5B shows analogous data for the rotation protocol. The average MU/SU peak response ratios were 3.6 and 3.1 for the vestibular and visual rotation conditions, respectively. Thus for both the translation and rotation protocols, the average MU response was significantly stronger than the average SU response (paired t-test, P << 0.001). Thus even if we failed to exclude every SU spike from the MU activity, any residual SU activity would account for little of the MU response.
|
). Figure 5C compares DDI values from SU and MU responses measured during the translation protocol (red: vestibular; cyan: visual). Filled symbols represent data sets with significant MU tuning (ANOVA, P < 0.05), whereas open symbols indicate nonsignificant MU tuning (ANOVA, P > 0.05). Most data points tend to fall near or slightly below the unity-slope diagonal. As a result, the mean DDI values for SU activity in the vestibular (0.54) and visual (0.76) conditions were significantly larger than the corresponding mean DDI values for MU activity in the vestibular (0.48) and visual (0.66) conditions (Wilcoxon rank-sum tests, P < 0.001). MU and SU DDI values were significantly correlated for both the vestibular (r = 0.32; P < 0.0001) and visual (r = 0.51; P < 0.0001) translation conditions, indicating that SUs tend to exhibit weaker tuning at sites where the MU response is poorly tuned. These findings argue against the possibility that flat MU tuning results solely from a combination of SUs that are individually well tuned, but to different directions. Rather, it appears that weak MU tuning is associated, to some degree, with weaker SU tuning. Figure 5D shows a similar comparison of SU and MU DDI values for the rotation protocol. Again, mean DDI values for SU activity significantly exceeded those for MU activity in both the vestibular (0.66 vs. 0.58) and visual (0.77 vs. 0.69) conditions (Wilcoxon rank-sum tests, P < 0.001). Despite the smaller sample sizes, MU and SU DDI values were again significantly correlated for both vestibular (r = 0.48; P < 0.0001) and visual (r = 0.41; P < 0.001) conditions. Overall, the extent of clustering appears to be comparable for rotation and translation.
We next consider the matching of 3D direction preferences between SU and MU activity. For the translation protocol, 72 data sets (25%) had significant tuning for both SU and MU responses in the vestibular condition, whereas 219 data sets (81%) showed significant SU and MU tuning in the visual condition. For these data sets, we computed the smallest angle in 3D between the preferred direction vectors for SU and MU activity (|
preferred direction|). If SU and MU responses have similar direction preferences, this metric should tend toward zero; if there is no clustering of SU and MU preferences, the distribution of | preferred direction| should be uniform as plotted here. Figure 6A shows the distribution of | preferred direction| for the vestibular (red) and visual (cyan) translation conditions. Both distributions were significantly nonuniform (permutation test, P < 0.001), with peaks close to 0°. For the rotation protocol, analogous data were available from 56 recordings in the vestibular condition and 57 recordings in the visual condition. As shown in Fig. 6B, the distributions of | preferred direction| were again clearly nonuniform (permutation test, P < 0.001), with peaks close to 0°. Thus when both SU and MU activity in MSTd are significantly tuned, the preferred direction vectors tend to be very similar. Occasionally, there are large differences in direction preference between SU and MU responses and these might occur, for example, when the electrode is located near the boundary between two clusters of neurons that have nearly opposite preferences. Such reversals in direction preference have been observed in area MT, for example (Albright et al. 1984; DeAngelis and Newsome 1999; Malonek et al. 1994).
|
The analyses of Fig. 6 consider the similarity of direction tuning between SU and MU responses separately for the visual and vestibular conditions. We now consider the congruency of visual and vestibular selectivity. Figure 7A shows the distribution of differences in direction preference between visual and vestibular responses for SUs recorded in the translation condition. As reported previously (Fetsch et al. 2007; Gu et al. 2006, 2007), this distribution is clearly bimodal. Roughly half of MSTd neurons have similar visual and vestibular translation preferences ("congruent" cells) and roughly half have widely different preferences ("opposite" cells). A very similar pattern is seen for MU activity from the translation condition, as shown in Fig. 7B, indicating that these two extremes of visual/vestibular congruency are preserved in MU activity. For the subset of recordings with significant translation tuning in both visual and vestibular responses, Fig. 7C compares the congruency of SU and MU tuning. With the exception of a handful of recording sites, the congruency of MU responses generally matches well with the congruency of SU responses (R = 0.57, P < 0.001). Thus both congruent and opposite cells appear to be clustered in area MSTd.
|
), almost all SUs are opposite cells in response to rotation stimuli (Fig. 7D) and MU responses in area MSTd exhibit a very similar pattern of results (Fig. 7E). The SU and MU measures of congruency again generally agree well (Fig. 7F; R = 0.34, P = 0.024), even though there is relatively little variation in these data across recording sites compared with the translation condition.
To provide a simple graphical summary of the results quantified in Figs. 5–7, we computed population tuning curves for SU and MU activity. To simplify the presentation, we constructed tuning curves from the subset of stimulus directions that lie in the horizontal plane (azimuth varies with elevation fixed at zero). This yields clear tuning for most neurons (Fetsch et al. 2007; Gu et al. 2006) and allows us to average tuning curves across neurons. Open symbols in Fig. 8A show the average tuning curve for SUs in the vestibular translation condition. For each SU with significant tuning (ANOVA, P < 0.05), the data were shifted such that the maximal response occurs at zero azimuth, spontaneous activity was subtracted, and the resulting curves were averaged across neurons. For comparison, filled symbols in Fig. 8A show the average tuning curve for the corresponding MU responses. In this case, spontaneous activity was again subtracted from each MU curve and the MU data were aligned to the azimuth preferred by each SU. Thus if there was no clustering of tuning in MSTd, the MU population tuning curve should be flat. As seen in Fig. 8A, average MU responses to vestibular translation have tuning consistent with SU responses, although the modulation depth of this MU tuning is modest and the error bars are substantially larger than those of the SU activity. By comparison, in the visual translation condition (Fig. 8B), average MU responses have considerably stronger modulation consistent with the analyses of Fig. 5. Finally, population tuning curves in Fig. 8, C and D show average selectivity for SU and MU responses in the vestibular and visual rotation conditions, respectively. In both rotation conditions, average MU responses show strong tuning that aligns well with the SU responses.
|
The measurements described earlier strongly suggest that neurons in area MSTd are clustered according to their 3D preferences for translational and rotational movements. Because the optic flow stimuli were large-field (90 x 90°) and the 3D tuning may reflect a complex interaction of visual motion signals across space, we were also interested in whether more basic visual response properties (receptive fields) are clustered in MSTd. Thus for a subset of 70 recordings, we used a reverse-correlation technique (see METHODS) to quantitatively characterize the directional RF structure of SU and MU responses.
Figure 9A shows the RF map obtained for an example SU. The visual stimulus was a dynamic random-dot display consisting of a 4 x 4 grid of subfields that spanned the 90 x 90° visual display (Fig. 1C). Each subfield contained a random-dot pattern moving coherently in one of eight possible directions (0°: rightward; 90°: upward). Every 100 ms, the direction of motion was chosen randomly for each of the 16 subfields, such that each subregion of the receptive field was probed with all eight directions of motion. The resulting spike train was cross-correlated to the stimulus sequence for each subfield, resulting in a distinct direction–time map for each individual subfield (Fig. 9A). If the portion of the neuron's RF overlying a particular stimulus subfield produces a direction-selective visual response, then that subfield will show structure in the direction–time map. If no directional response is elicited by the stimulus in a particular subfield, the direction–time map will be unstructured. For the example neuron in Fig. 9A, the central four subfields show clear directional structure, with a preference for rightward and slightly upward motion. Note that this MSTd RF is clearly bilateral and includes the fovea. Figure 9B shows the RF map for simultaneously recorded MU activity. The pattern of selectivity for the MU is nearly identical, suggesting that RF properties are locally clustered.
To further quantify the similarity of RF structure between SU and MU responses, we reduced each direction–time map to a single direction tuning curve by taking a cross section through the data at a correlation delay of Tpeak 
80 ms (horizontal lines in Fig. 9). This peak delay (Tpeak) was chosen as the delay that maximized the variance in direction tuning across all subfields of the RF map (estimated separately for SU and MU activities). The resulting direction tuning curves for each subfield are shown in Fig. 10 for the same example recording (MU: filled symbols; SU: open symbols). Solid and dashed curves show the best fits of a wrapped Gaussian function (see METHODS). Two characteristics were used to quantify each direction tuning curve: 1) the vector sum of normalized responses, which reflects the strength of direction selectivity, and 2) the preferred direction determined by the peak of the Gaussian fit. For the example data set in Fig. 10, the normalized vector sum is significantly greater than zero for both SU and MU responses in the central four subfields (P < 0.05; see METHODS).
|
|
Whereas the preceding analysis focused on the direction tuning of SU/MU activity in each subfield of the mapping grid, the reverse-correlation data can also be used to estimate the overall spatial profile of the receptive field. For this purpose, we plotted the amplitude of the wrapped Gaussian fit as a function of the location of each subfield (e.g., Fig. 12A). Note that this provides a spatial map of the regions of the RF that produce direction-selective responses; nondirectional portions of the receptive field, should they exist, would not be represented. Figure 12B shows the analogous spatial map for the simultaneously recorded MU activity. The SU and MU responses have very similar spatial profiles. To quantify the location and extent of these spatial RFs, the data were fit with a two-dimensional (2D) Gaussian (see METHODS). For the example SU, the best-fitting Gaussian (Fig. 12C) is centered at (–1.5°, –5.4°) and has dimensions of 38 x 42° (full width at half-maximum [FWHM], horizontal x vertical). For the corresponding MU profile, the center is located at (2.5°, –0.9°) and the dimensions are 41 x 38°.
Among the 70 data sets obtained using reverse correlation, spatial RF maps with statistically significant structure (permutation test, P < 0.05; see METHODS) were obtained for 64% (45/70) of SUs and 66% (46/70) of MUs. Forty-four percent (31/70) had significant spatial structure for both SU and MU responses. Three SU/MU pairs were excluded because of poor fits with the 2D Gaussian function (correlation coefficient, r < 0.8, between measured spatial profile and 2D Gaussian fit). For the 28 data sets that met the above-cited criteria, we quantified the degree of overlap of the SU and MU RFs by taking the distance between the centers of the Gaussian fits and normalizing by the average SD of the Gaussian (see METHODS). The distribution of this normalized RF separation, along both horizontal and vertical dimensions, is shown in Fig. 13A. If the SU and MU RFs were completely overlapping, these values would all be very close to zero. Mean values are 0.26 ± 0.04 SE for the horizontal separation and 0.27 ± 0.04 SE for vertical. Thus on average the distance between centers of the SU and MU RFs is approximately one fourth of the SD of the Gaussian fit, or about one tenth of the FWHM. This indicates that the spatial profiles of SU and MU responses overlap extensively.
|
Figure 13B compares the size of corresponding SU and MU RFs (FWHM along horizontal and vertical dimensions). There was no significant difference between SU and MU responses in terms of these size metrics (paired t-test, P > 0.05, n = 28). Together with the separation data of Fig. 13A, these results indicate that the spatial RFs of SU and MU responses overlap heavily, although it should be noted that our measurements may miss regions of the RFs that are not directionally selective. The average RF size in our population was 39 x 42° for SUs and 36 x 38° for MUs (FWHM dimensions). These values are largely in agreement with previous reports (Desimone and Ungerleider 1986; Komatsu and Wurtz 1988; Van Essen et al. 1981).
|
|
DISCUSSION |
|---|
|
; Duffy and Wurtz 1991; Lagae et al. 1994; Saito et al. 1986) and also show that basic visual receptive field properties are clustered. Our main new finding is that the vestibular sensitivity of MSTd neurons to both translational and rotational motion is also clustered in MSTd. In general, the selectivity of MU activity for vestibular translation was weak compared with visual translation and rotation, as discussed further in the following text. However, when MU signals showed clear selectivity for direction of translation, the tuning was generally consistent with that of simultaneously recorded single units. These findings suggest that there is a topographic organization of vestibular signals in area MSTd.
It should be noted, however, that our data limited us to a comparison of SU and MU activity obtained from the same recording site. Thus although our results establish clustering of vestibular selectivity in MSTd, we cannot conclude that MSTd contains a columnar organization of vestibular signals. Although clustering is usually associated with columns (Mountcastle 1957, 1997), it may also be possible to have clustering without columns (Liu and Newsome 2003). We also cannot directly infer anything about the topographic organization of vestibular signals across the surface of the cortex because we did not record neural activity at multiple locations along electrode penetrations through MSTd (see Britten 1998). Our results therefore leave open a number of aspects of the organization of vestibular signals in MSTd, but we do clearly establish clustering of vestibular signals and compare this to clustering of optic flow responses.
The remainder of the discussion will consider 1) the strength of visual clustering in our data with respect to previous findings and 2) the difference in strength of clustering between visual and vestibular responses, particularly for translational motion.
Clustering of optic flow selectivity in area MSTd
In his quantitative study of clustering of optic flow properties in MSTd, Britten (1998) measured MU or SU tuning at intervals of 50–100 µm along electrode penetrations through MSTd. He correlated the optic flow tuning observed at a particular recording site with the tuning observed at other sites along the penetration and this revealed a modest, but statistically significant, tendency for optic flow tuning to be clustered. For recording sites separated by <500 µm the optic flow tuning curves were significantly correlated for about 30% of cases. Beyond 500 µm, significant correlations between tuning curves were rare.
The heading data of Britten (1998) (his Fig. 3A) can best be compared with our visual translation condition. In our data, we found significant MU tuning for >80% of recording sites and the heading preferences of SU and MU responses were closely matched in the vast majority of cases (Fig. 6A). Thus it may appear that we have found stronger evidence for clustering of optic flow selectivity than did Britten (1998). However, a couple of methodological differences may account for most of this apparent difference. First, all of our neurons were tested with a range of headings that sampled all possible directions in 3D (Fig. 1A), whereas Britten measured heading tuning curves over a much more restricted range of headings (around straightforward) in the horizontal plane (typically –40 to +40°). For recording sites tested over this restricted range, a weak correlation between tuning curves may result from either site having weak selectivity over the range of headings tested. Second, intersite distance may be a factor. Britten always compared heading tuning at sites separated by some distance, whereas we compared SU and MU activity from the same location. Given that correlations between tuning curves fall off with distance, as shown by Britten, it is not surprising that the strongest similarities in tuning would be observed among neurons close to the tip of the electrode. Given these methodological differences, our results do not appear to be inconsistent with Britten's.
Differences in clustering between visual and vestibular responses
When MU responses show significant tuning, there is generally good agreement between SU and MU preferences for both visual and vestibular stimuli (Fig. 6). This appears to suggest that visual and vestibular responses are clustered to a similar degree. For vestibular translation, on the other hand, only 32% of MU recordings show significant heading tuning, whereas 82% of MU recordings show significant tuning for the visual translation condition. If SU and MU preferences for vestibular translation are generally well matched (Fig. 6A), then why do MU responses seldom show significant tuning? One possible explanation is simply that SU responses to vestibular translation are less selective than SU responses to visual translation, as can be seen in Fig. 5C. Indeed, only 56% of SUs show significant tuning for vestibular translation, whereas 97% of SUs are significantly tuned for visual translation. However, when a SU is significantly tuned, the MU activity is also tuned in only 45% of cases for vestibular translation versus 84% for visual translation.
Another possibility is that weaker selectivity in MU responses to vestibular translation may be affected by the visual–vestibular congruency of tuning for SUs. As shown previously and in Fig. 7, A and B, approximately half of MSTd neurons have congruent preferences for visual and vestibular translation, whereas the other half have opposite preferences (Gu et al. 2006, 2007). This creates an interesting issue for functional organization of these neurons because opposite cells could be clustered with congruent cells according to either their visual preference or their vestibular preference (this issue is much less relevant for rotation because there are very few congruent cells for rotation; Takahashi et al. 2007). If, for example, MSTd neurons were predominantly clustered according to their visual preference for translation, then the vestibular preferences of nearby congruent and opposite neurons will not be consistent. This would contribute to weak MU tuning in the vestibular translation condition while preserving strong MU tuning in the visual translation condition. At present, our data do not allow us to resolve this issue. Additional experiments, involving recording from multiple isolated SUs, may be necessary to fully understand how congruent and opposite neurons are organized within MSTd. This issue may have important consequences for understanding how the responses of congruent and opposite neurons are decoded during perceptual tasks.
|
|
GRANTS |
|---|
|
|
|
ACKNOWLEDGMENTS |
|---|
|
|
|
FOOTNOTES |
|---|
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Address for reprint requests and other correspondence: G. C. DeAngelis, Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, 245 Meliora Hall, Rochester, NY 14627 (E-mail: gdeangelis{at}cvs.rochester.edu)
|
|
REFERENCES |
|---|
|
Borghuis BG, Perge JA, Vajda I, van Wezel RJ, van de Grind WA, Lankheet MJ. The motion reverse correlation (MRC) method: a linear systems approach in the motion domain. J Neurosci Methods 123: 153–166, 2003.[CrossRef][Web of Science][Medline]
Born RT, Bradley DC. Structure and function of visual area MT. Annu Rev Neurosci 28: 157–189, 2005.[CrossRef][Web of Science][Medline]
Born RT, Tootell RB. Segregation of global and local motion processing in primate middle temporal visual area. Nature 357: 497–499, 1992.[CrossRef][Web of Science][Medline]
Bremmer F, Kubischik M, Pekel M, Lappe M, Hoffmann KP. Linear vestibular self-motion signals in monkey medial superior temporal area. Ann NY Acad Sci 871: 272–281, 1999.[CrossRef][Web of Science][Medline]
Britten KH. Clustering of response selectivity in the medial superior temporal area of extrastriate cortex in the macaque monkey. Vis Neurosci 15: 553–558, 1998.[CrossRef][Web of Science][Medline]
Churchland AK, Lisberger SG. Relationship between extraretinal component of firing rate and eye speed in area MST of macaque monkeys. J Neurophysiol 94: 2416–2426, 2005.
DeAngelis GC, Newsome WT. Organization of disparity-selective neurons in macaque area MT. J Neurosci 19: 1398–1415, 1999.
DeAngelis GC, Ohzawa I, Freeman RD. Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. I. General characteristics and postnatal development. J Neurophysiol 69: 1091–1117, 1993.
DeAngelis GC, Uka T. Coding of horizontal disparity and velocity by MT neurons in the alert macaque. J Neurophysiol 89: 1094–1111, 2003.
Desimone R, Ungerleider LG. Multiple visual areas in the caudal superior temporal sulcus of the macaque. J Comp Neurol 248: 164–189, 1986.[CrossRef][Web of Science][Medline]
Duffy CJ. MST neurons respond to optic flow and translational movement. J Neurophysiol 80: 1816–1827, 1998.
Duffy CJ, Wurtz RH. Sensitivity of MST neurons to optic flow stimuli. I. A continuum of response selectivity to large-field stimuli. J Neurophysiol 65: 1329–1345, 1991.
Duffy CJ, Wurtz RH. Response of monkey MST neurons to optic flow stimuli with shifted centers of motion. J Neurosci 15: 5192–5208, 1995.[Abstract]
Eckhorn R, Krause F, Nelson JI. The RF-cinematogram. A cross-correlation technique for mapping several visual receptive fields at once. Biol Cybern 69: 37–55, 1993.[CrossRef][Web of Science][Medline]
Fetsch CR, Wang S, Gu Y, Deangelis GC, Angelaki DE. Spatial reference frames of visual, vestibular, and multimodal heading signals in the dorsal subdivision of the medial superior temporal area. J Neurosci 27: 700–712, 2007.
Froehler MT, Duffy CJ. Cortical neurons encoding path and place: where you go is where you are. Science 295: 2462–2465, 2002.
Gu Y, DeAngelis GC, Angelaki DE. A functional link between area MSTd and heading perception based on vestibular signals. Nat Neurosci 10: 1038–1047, 2007.[CrossRef][Web of Science][Medline]
Gu Y, Watkins PV, Angelaki DE, DeAngelis GC. Visual and nonvisual contributions to three-dimensional heading selectivity in the medial superior temporal area. J Neurosci 26: 73–85, 2006.
Horton JC, Adams DL. The cortical column: a structure without a function. Philos Trans R Soc Lond B Biol Sci 360: 837–862, 2005.
Jones JP, Palmer LA. The two-dimensional spatial structure of simple receptive fields in cat striate cortex. J Neurophysiol 58: 1187–1211, 1987.
Komatsu H, Wurtz RH. Relation of cortical areas MT and MST to pursuit eye movements. I. Localization and visual properties of neurons.J Neurophysiol 60: 580–603, 1988.
Lagae L, Maes H, Raiguel S, Xiao DK, Orban GA. Responses of macaque STS neurons to optic flow components: a comparison of areas MT and MST. J Neurophysiol 71: 1597–1626, 1994.
Liu J, Newsome WT. Functional organization of speed tuned neurons in visual area MT. J Neurophysiol 89: 246–256, 2003.
Malonek D, Tootell RB, Grinvald A. Optical imaging reveals the functional architecture of neurons processing shape and motion in owl monkey area MT. Proc Biol Sci 258: 109–119, 1994.
Mountcastle V. Modality and topographic properties of single neurons of cat's somatic sensory area. J Neurophysiol 20: 408–438, 1957.
Mountcastle VB. The columnar organization of the neocortex. Brain 120: 701–722, 1997.
Page WK, Duffy CJ. Heading representation in MST: sensory interactions and population encoding. J Neurophysiol 89: 1994–2013, 2003.
Prince SJ, Pointon AD, Cumming BG, Parker AJ. Quantitative analysis of the responses of V1 neurons to horizontal disparity in dynamic random-dot stereograms. J Neurophysiol 87: 191–208, 2002.
Ringach DL, Sapiro G, Shapley R. A subspace reverse-correlation technique for the study of visual neurons. Vision Res 37: 2455–2464, 1997.[CrossRef][Web of Science][Medline]
Saito H, Yukie M, Tanaka K, Hikosaka K, Fukada Y, Iwai E. Integration of direction signals of image motion in the superior temporal sulcus of the macaque monkey. J Neurosci 6: 145–157, 1986.[Abstract]
Snyder JP. Map Projections: A Working Manual. Washington, DC: U.S. Government Printing Office, 1987, p. 182–190.
Takahashi K, Gu Y, May PJ, Newlands SD, DeAngelis GC, Angelaki DE. Multimodal coding of three-dimensional rotation and translation in area MSTd: comparison of visual and vestibular selectivity. J Neurosci 27: 9742–9756, 2007.
Van Essen DC, Maunsell JH, Bixby JL. The middle temporal visual area in the macaque: myeloarchitecture, connections, functional properties and topographic organization. J Comp Neurol 199: 293–326, 1981.[CrossRef][Web of Science][Medline]
Wennekers T. Orientation tuning properties of simple cells in area V1 derived from an approximate analysis of nonlinear neural field models. Neural Comput 13: 1721–1747, 2001.[CrossRef][Web of Science][Medline]
Yang T, Maunsell JH. The effect of perceptual learning on neuronal responses in monkey visual area V4. J Neurosci 24: 1617–1626, 2004.
Zeki SM. Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey. J Physiol 236: 549–573, 1974.
This article has been cited by other articles:
|
|
 |
|
  S. A. Chowdhury, K. Takahashi, G. C. DeAngelis, and D. E. Angelaki Does the Middle Temporal Area Carry Vestibular Signals Related to Self-Motion? J. Neurosci., September 23, 2009; 29(38): 12020 - 12030. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
TOP
ABSTRACT
INTRODUCTION
0.05)
