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Retrosplenial cortex maps the conjunction of internal and external spaces

Nature Neuroscience volume 18, pages 1143–1151 (2015)Cite this article

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Abstract

Intelligent behavior demands not only multiple forms of spatial representation, but also coordination among the brain regions mediating those representations. Retrosplenial cortex is densely interconnected with the majority of cortical and subcortical brain structures that register an animal's position in multiple internal and external spatial frames of reference. This unique anatomy suggests that it functions to integrate distinct forms of spatial information and provides an interface for transformations between them. Evidence for this was found in rats traversing two different routes placed at different environmental locations. Retrosplenial ensembles robustly encoded conjunctions of progress through the current route, position in the larger environment and the left versus right turning behavior of the animal. Thus, the retrosplenial cortex has the requisite dynamics to serve as an intermediary between brain regions generating different forms of spatial mapping, a result that is consistent with navigational and episodic memory impairments following damage to this region in humans.

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Figure 1: Framework for assessing the contribution of distinct spatial frames of reference.
Figure 2: RSC neurons map position in egocentric space.
Figure 3: RSC neurons map position in route-centered space.
Figure 4: RSC activity is sensitive to allocentric positioning of the track.
Figure 5: Sensitivity to distinct reference frames in simultaneously recorded RSC neurons.
Figure 6: RSC ensemble activity patterns are sensitive to allocentric positioning of the track.

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Acknowledgements

We thank S. Cowen, L. Quinn, L. Rangel, J. Olson and L. Shelley for reviewing the manuscript. Special thanks to A. Chiba, B. La and S. Kolbu for helping with the design and implementation of experiments and analyses. This work funded by National Science Foundation IOS-1149718 and National Science Foundation Graduate Research Fellowship DGE-1144086.

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  1. Department of Cognitive Science, University of California, San Diego, San Diego, California, USA.,

    Andrew S Alexander & Douglas A Nitz

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  1. Andrew S Alexander
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A.S.A. and D.A.N. each contributed to all components of the work (design, experimentation, analysis and writing).

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Correspondence to Douglas A Nitz.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Recording room setup and track layout.

Left panels: photographs of recording room from opposing viewpoints. Upper right panel: Diagram of recording room layout. Blue circles indicate the two viewpoints from which the photographs in the left panels were taken. The recording room is 13.5′ × 13.5′. Fixed and salient distal cues are schematized. The numbered squares in the bottom portion of the room correspond to a desk with the recording computer (1), and a storage/utility desk (2). Bottom right panel, photograph of ‘W’ track with rat for scale. White numbers indicate the distances of each of the turns and full distance of the track.

Supplementary Figure 2 Histology and tetrode placements.

Histology depicting final tetrode placements for bilateral RSC implants in all rats (n=6, KB3 had no left hemisphere implant). Tetrode trajectories are schematized in upper left panel as in Figure 1. 4 tetrodes were typically bundled into a single microdrive cannula. Black triangles depict final electrode depth of each bundle. RSD, retrosplenial dysgranular; RSGc, retrosplenial granular c; RSGb, retrosplenial granular b; RSGa, retrosplenial granular a; ml, midline; V2, secondary visual; cg, cingulum; DS, dorsal subiculum.

Supplementary Figure 3 Sort quality metrics and quantification.

(a-b) Top panels: Representative RSC cluster identification for two tetrodes in different animals. Different color clusters represent individually isolated neurons. Grey points correspond to unsorted spikes. Below, Mean waveform shape and standard deviation for neurons of top graphs in corresponding colors. Associated patterns of firing activity across each route and track location are shown beneath each waveform plot. (c) For data from all recording datasets, plot of the peak amplitude for the mean waveform of sorted neurons versus the peak amplitude of the unsorted waveforms recorded on the same tetrode. The average peak amplitude for sorted waveforms was 5.5 (± 3.6 SD) times greater than that for corresponding unsorted waveforms. (d) Additional mean waveform for an RSC neuron with corresponding firing rate profile. The waveform schematizes the method for calculating the coefficient of variation (CVs) at the peak, in which the standard deviation at the peak is divided by the mean value across all spike waveforms at the peak. Mean peak-amplitude CVs for waveforms grouped as individual (sorted) neurons were 0.16 ± 0.13, a value significantly lower than that for the remaining, unsorted waveforms of each tetrode (0.36 ± 0.17, one-tailed Wilcoxon rank-sum test, n = 243, z = -16.7, p = 9.52x10-63).

Supplementary Figure 4 Reliability in turn-related activity.

(a) Left panel, the mean firing activity, at each of the 12 turns (6 left, 6 right across both routes and allocentric track locations), for individual RSC neurons that exhibited a significant bias for left or right turning (n = 104). Each row represents the mean of the max-normalized firing rate discharge of a single RSC neuron across all trials for each of the 12 turns. Middle panel, same data as left, with each row max-normalized. For both left and middle panels, neurons are sorted from lowest coefficient of variation on left turns (CVL), at top, to lowest coefficient of variation on right turns (CVR), at the bottom. Right panel, mean coefficient of variation (and SD) for all turn-sensitive neurons was 0.52 ± 0.33, which was significantly less than a distribution of CV values generated by randomizing the entire matrix and recalculating (0.73 ± 0.24, one-tailed Wilcoxon rank-sum test, n = 104, p = 6.55x10-11). (b) Same figures as above, but for HPC neurons that exhibited statistically significant biases for left versus right turning (n = 39). Although these HPC neurons exhibited a significant egocentric bias, the CV for the preferred turn type was nearly 3 times greater than for RSC neurons (µ±SD = 1.50 ± 0.53) and was not significantly different than CV values calculated after randomization of the same data (µ±SD = 1.57 ± 0.42, Wilcoxon rank-sum test, n = 39, p = 0.24). Thus, HPC neurons with statistically significant left versus right turn firing rates do not exhibit reliable turn-related activity across route and allocentric positions. We conclude that the HPC neurons in this subset largely exhibited unidirectional place fields that happened to overlap with a turn site.

Supplementary Figure 5 Trial-by-trial spatial reliability of RSC neuron activation is significantly greater than chance.

(a-c) Mean activation patterns across track space for three RSC neurons (top trace in each). Bottom three traces in each depict, in corresponding colors (RLR blue, LRL black), the firing rate profiles for three randomly selected route traversals from the same track running session (trials 4, 8, and 12 in each). To assess spatial reliability across individual trials, the firing rate profiles for every trial was correlated (Pearson’s r) with the mean firing rate profile for the same route and same track location. Correlation values for the representative trials are given (rµ). (d) i-iii. correspond to the RSC neurons depicted in A-C, respectively. Here, distributions of actual trial-to-mean correlation values (black) are shown in conjunction with two ‘chance’ distributions; First, a ‘random’ distribution of trial-to-mean profile correlation values generated by correlating randomly selected trials taken from any route or track location among the entire population of recorded RSC neurons (red); Second, a ‘spatial shuffle’ distribution of correlation values generated by randomizing the spatial position of individual firing rate values within a given trial and correlating to the corresponding mean firing rate profile (purple). Mean correlation values for each distribution are represented by filled circles of the corresponding color. iv. Full distribution of all correlation values, (actual, black; random, red; spatial shuffle, purple) across all neurons, track locations, and routes. Means and standard deviations are depicted as dashed lines and shaded boundaries of the corresponding color. The actual trial-to-mean profile correlation distribution was significantly greater than both the random and spatial shuffle correlation distributions for the full population (one-tailed Wilcoxon rank-sum test, z = 121.3 and z = 135.6 for ‘random’ and ‘spatial shuffle,’ p = 0 for both). 100% of RSC neurons had significantly greater actual correlation distributions than both of the ‘chance’ correlation distributions (one-tailed Wilcoxon rank-sum test, p < 0.01).

Supplementary Figure 6 RSC firing rate activity is not sensitive to trial-by-trial fluctuation in angular velocity.

(a-c) i. Max-normalized mean firing rate and mean absolute angular velocity for a single neuron along the RLR or LRL route (as indicated). ii. Max-normalized firing rate and absolute angular velocity for representative individual trials. iii. Route position modulation of turn-related activity (e.g., greater mean firing rate at the first right turn versus the second right turn) could potentially be explained by systematic angular velocity differences across the two similar turns within a given route. To determine if this was the case, the difference in firing rate between the first and second like turns (rights on RLR route and lefts on LRL route) was calculated for each trial (y-axis). This data was then correlated with the difference in angular velocity at the same turn sites for corresponding trials (x-axis, example trials in red). iv. Based on the preferred turn type (higher firing for left versus right turns) of the neuron, activity in the relevant turn window (horizontal green bars in ii.) was integrated for every trial for both routes and for both track locations (x-axis), then correlated with the integrated absolute angular velocity for the same route positions across corresponding trials (y-axis). (d) Upper left panel, distribution of trial firing rate and angular velocity correlations across the entire recorded RSC population (black, µ = 0.01). For each cell, the trial order was randomized and correlations were recalculated to generate a second distribution approximating the angular velocity correlation values expected by chance (shown in red, µ = 0.00). The actual angular velocity correlation distribution was not significantly greater than the randomized distribution (one-tailed Wilcoxon rank-sum test, z = 1.02, n = 228, p = 0.15). Upper right panel, ratios of mean rates across all left versus all right turns for each RSC neuron (x-axis, truncated at 6.5) are plotted against each neuron’s corresponding absolute angular velocity correlation (y-axis) demonstrating that there is no relationship between the magnitude of turn preference and angular velocity correlation. Lower panels, Same as above two panels but shown solely for the population of RSC neurons that exhibited significant turn-related activation (µactual = 0.01, µrand = −0.01, one-tailed Wilcoxon rank-sum test, n = 104, z = 0.66, p = 0.25).

Supplementary Figure 7 RSC firing rate activity is not sensitive to trial-by-trial fluctuation in linear velocity.

(a-c) i. Max-normalized mean firing rate and mean linear velocity for a single neuron along the RLR or LRL route (as indicated). ii. Max-normalized firing rate and linear velocity for representative individual trials. iii. Route position modulation of turn-related activity (e.g., greater mean firing rate at the first right turn versus the second right turn) could potentially be explained by systematic linear velocity differences across the two similar turns within a given route. To determine if this was the case, the difference in firing rate between the first and second like turns (rights on RLR route and lefts on LRL route) was calculated for each trial (y-axis). This data was then correlated with the difference in linear velocity at the same turn sites for corresponding trials (x-axis, example trials in red). iv. Based on the preferred turn type (higher firing for left versus right turns) of the neuron, activity in the relevant turn window (horizontal green bars in ii.) was integrated for every trial for both routes and for both track locations (x-axis), then correlated with the integrated linear velocity for the same route positions across corresponding trials (y-axis). (d) Upper left panel, Distribution of trial firing rate and linear velocity correlations across the entire recorded RSC population (black, µ = −0.02). For each cell, the trial order was randomized and correlations were recalculated to generate a second distribution approximating the linear velocity correlation values expected by chance (shown in red, µ = 0.01). The actual linear velocity correlation distribution was not significantly greater than the randomized distribution (one-tailed Wilcoxon rank-sum test, n = 228, z = −2.01, p = 0.98). Upper right panel, Ratios of mean rates across all left versus all right turns for each RSC neuron (x-axis) are plotted against each neuron’s corresponding linear velocity correlation (y-axis) demonstrating that there is no relationship between the magnitude of turn preference and linear velocity correlation. Lower panels, Same as above two panels but shown solely for the population of RSC neurons that exhibited significant turn-related activation (µactual = −0.04, µrand = 0.004, one-tailed Wilcoxon rank-sum test, n = 104, z = −2.56, p = 0.99).

Supplementary Figure 8 Schematic of ensemble correlative reconstruction method.

Top two panels, Two separate mean spatial firing rate profiles for each neuron were calculated using odd and even trials. For all RSC neurons, odd-trial mean firing rate profiles (left figure) and even-trial mean firing rate profiles (right figure) are shown for a single-track location (α) and route (LRL). White vertical lines indicate turn sites and the space of the track is shown relative to the firing rate vector in blue. Middle panels, The mean RSC ensemble activity at each individual route position (bin 10 shown outlined in red) across odd trials was correlated with the mean RSC ensemble activity at every route position bin from even trials (e.g. even-bin number 20 shown outlined in black). Thus, for any individual route bin for odd trials, a 200-bin vector of correlation values was generated reflecting the similarity of the ensemble activity at that bin to the ensemble activity at all route positions taken from even trials (color mapped horizontal bar). Bottom panel, The full population of correlation values for each bin forms a correlation matrix. The column with the largest correlation value for each row represents the even-trial ensemble rate vector bin with the greatest similarity to the current odd-trial ensemble rate vector. Therefore, a reconstruction of the animal’s position on even trials can be estimated from the greatest correlation value across each row of the correlation matrix (see Figures 3E, 6A).

Supplementary Figure 9 RSC neurons exhibit head-direction sensitivity.

Left column, top and middle panels, 2D firing rate maps for a single neuron at track locations α and β (LRL and RLR route-running periods are shown adjacent to each other (approximate actual locations given by white (RLR) and red arrows (LRL)). The example RSC neuron exhibited increased activation when the animal was oriented towards the bottom-left of the camera’s view as it traversed two different route segments during RLR runs (white arrow). Left column, bottom panel, Gray trace is the animal’s position during a baseline session in a circular holding container. In accordance with the preferred directional tuning on the track, the cell spikes at locations (in blue) in which the animal is perched and facing towards the bottom-left of the camera’s view. Middle column: Polar mean firing rate tuning plots for the same RSC neuron. Direction and length of the resultant vector (in red) indicate the mean and magnitude of directional tuning for the neuron across track locations α and β, and in the holding container. Right column, top panel, For each RSC neuron, mean directional tuning at track location α is plotted against mean directional tuning preference at track location β. If the preferred tuning direction was the same at both track locations for a given neuron, its data point would fall along a diagonal moving from lower-left to upper-right (center dashed red line). Neurons with mean tuning preferences within 0.36 radians (~20 degree differences) across the two track locations were considered potential head-direction neurons. Right column, bottom panel, For each RSC neuron, the mean resultant length (tuning magnitude) at track location α is plotted against mean resultant computed at track location β. RSC neurons that had high resultants (> 0.5, red dashed line) were strongly directionally tuned across both track locations and considered potential head-direction neurons. Neurons that had both strong (resultants > 0.5) and consistent mean directional tuning across both track positions (within 0.36 radians) were determined to be allocentrically-referenced RSC head-direction neurons (6%, n=15/243, indicated by black data points in both plots; example neuron of left column shown in green).

Supplementary Figure 10 RSC neurons exhibit conjunctive encoding of position in multiple reference frames.

Mean positional firing patterns of 10 RSC neurons across individual routes (LRL in blue, RLR in black, dark lines means, light lines trials), for α (top), β (middle), and α’ (bottom) track running epochs. The 4 RSC neurons of columns 1 and 2 exemplify single reference frame encoding. The 6 neurons of columns 3-5 illustrate a variety of conjunctive patterns observed across the full RSC population, wherein highly complex spatial firing patterns vary according to the combinations of specific reference frames.

Supplementary Figure 11 Reconstruction of route position across track placements for RLR route.

Left and right panels depict RSC and HPC RLR route reconstructions, respectively. Ensemble firing rate vectors taken from track locations α, β, and α’ were utilized to reconstruct the animals position along the RLR route across allocentric track locations. Four correlation matrices were generated by correlating all track location α ensemble rate vectors with track location α and α’ ensemble rate vectors. Predicted location of the animal is the highest correlation in each row, here shown by the black line. Error of reconstruction for RLR routes across both regions is depicted in Figure 6B.

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Alexander, A., Nitz, D. Retrosplenial cortex maps the conjunction of internal and external spaces. Nat Neurosci 18, 1143–1151 (2015). https://doi.org/10.1038/nn.4058

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