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Retinal origin of direction selectivity in the superior colliculus

Abstract

Detecting visual features in the environment, such as motion direction, is crucial for survival. The circuit mechanisms that give rise to direction selectivity in a major visual center, the superior colliculus (SC), are entirely unknown. We optogenetically isolate the retinal inputs that individual direction-selective SC neurons receive and find that they are already selective as a result of precisely converging inputs from similarly tuned retinal ganglion cells. The direction-selective retinal input is linearly amplified by intracollicular circuits without changing its preferred direction or level of selectivity. Finally, using two-photon calcium imaging, we show that SC direction selectivity is dramatically reduced in transgenic mice that have decreased retinal selectivity. Together, our studies demonstrate a retinal origin of direction selectivity in the SC and reveal a central visual deficit as a consequence of altered feature selectivity in the retina.

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Figure 1: Membrane potential responses of SGS neurons to sweeping bars.
Figure 2: Voltage-clamp recording and optogenetic silencing to isolate retinal excitation to SGS neurons.
Figure 3: Retinal excitation and total excitation are similarly tuned in SGS neurons.
Figure 4: SGS direction selectivity originates from individually tuned retinal inputs.
Figure 5: Genetic disruption of retinal direction selectivity reduces selectivity in the SGS.

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Acknowledgements

We thank X. Zhao and H. Chen for their help with data analysis. For the use of GCaMP6s, we gratefully acknowledge V. Jayaraman, R.A. Kerr, D.S. Kim, L.L. Looger and K. Svoboda from the GENIE Project, Janelia Farm Research Campus, Howard Hughes Medical Institute. This research was supported by US National Institutes of Health (NIH) grants (EY026286 to J.C. and X.L., and EY024016 to W.W.), National Natural Science Foundation of China (NSFC) grant (81371049 to X.S.), China Scholarship Council (CSC) scholarship (201309120003 to X.S.) and Tianjin 131 Innovative Talent Project first-level talent scholarship (to X.S.).

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Authors and Affiliations

Authors

Contributions

X.S., J.B., X.L., W.W. and J.C. designed the experiments. X.S. performed in vivo whole-cell recording experiments and analyzed the data. J.B. performed in vivo two-photon imaging experiments and analyzed the data. H.A.L. and D.K. performed retinal imaging experiments and analyzed the data. J.C. performed intrinsic imaging. Y.J. performed histology. W.W. and J.C. guided data analysis and oversaw the project. All authors discussed the results and wrote the manuscript.

Corresponding author

Correspondence to Jianhua Cang.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Spike and Vm responses of an example SGS neuron to sweeping bars of different directions.

a, Peri-stimulus spike time histograms (left) and trial-averaged Vm (right) of an example SGS neuron in response to sweeping bars of 12 directions. The red dotted lines indicate the resting membrane potential of -55 mV. b, The direction tuning curves for peak Vm (black, left axis) and spike rate (red, right axis) of the same neuron. Note that the two tuning curves are nearly identical. Data are presented as mean ± s.e.m., and n = 7, 6, 7, 6, 6, 7, 5, 6, 7, 6, 5, 7 trials for the 12 directions, respectively.

Supplementary Figure 2 Blue LED light effectively silenced excitatory SGS neurons.

a-b, Peri-stimulus spike time histograms (PSTHs) of two example excitatory SGS neurons in response to sweeping bars of different directions in the absence (left) or presence (right) of LED illumination. There was no spike response to bars of any moving directions under LED. c-d, PSTHs of two example inhibitory neurons in response to sweeping bars of different directions in the absence (left) or presence (right) of LED. Note that LED illumination evoked sustained firing in these cells. e-f, Mean firing rates without and with LED illumination for excitatory (n = 16) and inhibitory (n = 5) SGS neurons. The mean firing rates were calculated for the 3-sec stimulation period for each of the 12 conditions, and the maximum value under LED-off was used to compare with the value of the same stimulus condition under LED-on for each cell. g, A schematic of experimental setup to examine whether optogenetic activation of GABAergic neurons could affect retinal transmission by potentially acting presynaptically through GABAB receptors. Visually-evoked local field potentials (LFP) were recorded in the SGS before and 15-45 minutes after administration of the GABAB receptor antagonist CGP54626 (10 μM) to the SC surface. h, An example of LFP responses to a 20° diameter circle flashing on and off. Responses are the average across 40–60 trials. Time zero represents the onset of each flashing on or off. i, Average LFP N1 response (the first large negative waveform) amplitudes (black) and initial slopes (gray) during no LED illumination (Pre), LED illumination (LED) and LED with CGP54626 administration (LED+CGP). The initial slope is the average slope of all points from the response onset to the 30% of the N1 peak amplitude. CGP54626 did not affect the average response (LED: 0.04 ± 0.01; LED+CGP: 0.04 ± 0.01; p = 0.81, W = 3, Wilcoxon test, 5 mice) nor the initial slope (1.70 ± 0.43; LED+CGP: 1.40 ±0.29; p = 0.31, W = 9, Wilcoxon test, 5 mice) when the SC was silenced by LED photoactivation of GABAergic neurons.

Supplementary Figure 3 In vivo whole-cell voltage-clamp recording and optogenetic photostimulation.

a, A schematic of experimental setup. b, Trial-averaged current (Im) traces of example excitatory (cell #06) and inhibitory (cell #33) cells in response to blue LED illumination. Recordings were done in Gad2-Cre X floxed ChR2 transgenic mice, and the holding potential was -65 mV. Note that LED illumination induced a large sustained ChR2 current in the inhibitory cell, and no change in the excitatory cell. The red dotted lines indicate the mean Im in the absence of LED illumination. c, Mean Im caused by LED activation versus holding potential of an example excitatory SGS neuron. The reversal potential of inhibition was determined by adjusting the holding potential to minimize the amplitude of the inhibitory postsynaptic current evoked by photostimulation of Gad2+ neurons. The solid line indicates the linear regression (R2 = 0.99, F1,3 = 410.6, p < 0.001, linear regression; r = 0.996, p < 0.001, Pearson correlation).which crosses the zero-current line at the voltage of -65.08 mV, i.e., the reversal potential of inhibitory currents for this cell. d, Distribution of the reversal potential determined in excitatory cells (n = 43).

Supplementary Figure 4 Excitatory postsynaptic currents in response to sweeping bars.

a, Raw traces of excitatory postsynaptic currents (EPSCs) of an example SGS neuron in response to sweeping bars of different directions. Red dotted lines indicate the mean current level in the absence of visual stimulus. b, Direction tuning curve for the peak amplitude of the filtered and trial-averaged EPSCs. Note that the peak EPSC at the non-preferred direction is nearly zero in this cell, indicating a lack of excitatory inputs. Data are presented as mean ± s.e.m., and n = 7, 7, 6, 6, 8, 6, 7, 6, 6, 6, 6, 7 trials for the 12 directions, respectively.

Supplementary Figure 5 Isolating SGS neurons’ retinal inputs.

a, Trial-averaged current (Im) traces of an example excitatory cell to sweeping bars at its preferred (top) and opposite (bottom) directions, in the absence (left, red traces) or presence of LED illumination (right, blue traces). Black lines are the Im traces in the absence of visual stimulation. b, Trial-averaged Im traces of an example inhibitory cell of the same conditions. The black dotted line indicates the baseline Im level in the absence of LED photoactivation. For both excitatory and inhibitory cells, the Im under the condition without visual stimulus as indicated by the corresponding black lines was used as the baseline for calculating total and retinal EPSCs.

Supplementary Figure 6 Amplification of retinal excitation by intracollicular circuits.

a, Trial-averaged traces of total EPSC (black line, tEPSC) and retinal EPSC (blue line, rEPSC) of an example SGS neuron in response to a moving bar. Gray shade indicates the time window for calculation, which is defined as the duration in which both rEPSC and tEPSC were below their respective “cutoff” (blue dotted line for rEPSC and black for tEPSC). The cutoff was determined as two standard deviations below the baseline, which was the condition when a blank visual stimulus (gray screen) was shown instead of bars. b, Retinal-total EPSC transformation for this cell. Plotted are the corresponding rEPSC and tEPSC at each time point in the response window for all 12 stimulus directions. The black and white dots are the mean values in 10 evenly-divided bins from 0 to the maximum rEPSC. Black dotted line indicates 80% of the maximum bin-averaged tEPSC. The black solid line is the linear regression of the bin-averaged points that are below the 80% line (R2 = 0.99, F1,4 = 369.4, p < 0.001, linear regression; r = 0.99, p < 0.001, Pearson correlation). The bin-averaged points above the 80% line were not included in the linear regression analysis due to possible saturation of tEPSC responses. c, Distribution of correlation coefficient of linear regression between rEPSC and tEPSC (n = 41 cells). d, Amplification ratio (the one shown in Fig. 2), calculated as the ratio of peak tEPSC over peak rEPSC, each averaged over 12 stimulus directions, versus the linear regression slope as calculated in b (n = 41). The two methods gave similar results for the recorded cells. Note that some points are below the identity line possibly due to saturation of tEPSC responses in those cells. e, Similar slopes between DS (3.57 ± 0.58, n = 15 cells from 13 mice) and non-DS (3.21 ± 0.36, n = 26 cells from 20 mice) SGS neurons (p = 0.84, U = 187, Mann-Whitney U test).

Supplementary Figure 7 Analysis of intracollicular excitation of SGS neurons.

a, Trial-averaged total (left, black traces) and retinal (left, blue traces) EPSCs of an example SGS neuron to sweeping bars of different directions. b, Current traces of intracollicular excitation, calculated by point-by-point subtraction of the two traces in (a). Red dotted lines in (a) and (b) indicate the baseline EPSC level in the absence of visual stimulation. c, gDSI of intracollicular EPSC (cEPSC) integral and peak are correlated for direction selective SGS neurons (r = 0.85, p < 0.001, Pearson correlation; n = 15 cells from 13 mice). The gDSI of cEPSC peak were smaller than that of its integral in many cells (p = 0.003, W = 100, Wilcoxon test), possibly due to its saturation at preferred directions. Importantly, the fact that cEPSC integral is even more selective means it follows the scenario in Figure 4b, indicating that the selective intracollicular excitatory input is from individually tuned SGS neurons. d, The integral and peak of cEPSCs prefer similar directions in DS SGS neurons. The solid lines in both panel c and d are the lines of identity.

Supplementary Figure 8 Retinal EPSC peak and integral have similar gDSI values, independent of analysis methods.

a-c, Diagrams of the 3 methods used for calculating retinal EPSC peak amplitudes. In each panel, the red trace represents a strong response, e.g., to the preferred direction, and the blue trace represents a weak response. The solid black line is the “baseline”, calculated as the mean of the EPSCs to the “blank stimulus” (gray screen). The dotted line is the “cutoff”, calculated as 2 (a), 3 (b) or 4 (c) standard deviations away from the baseline. In (a), the EPSC peak is calculated as the amplitude from the cutoff line, i.e., the dotted line. In (b) and (c), the EPSC peak is calculated as the amplitude from the baseline, i.e., the solid black line, but for conditions where the peak EPSC was weaker than the cutoff, the corresponding response magnitude were set as 0. Note that different ways of calculation may result in slight under- or over-estimation of weak responses due to spontaneous synaptic events. (d-f) gDSI of retinal EPSC integral and peak are similar for direction selective SGS neurons (n = 15) with the three calculation methods directly above. The EPSC integral in all panels was calculated for the response window determined as described in Methods. Panel (e) is the same one shown in Figure 4. The solid lines in panel d, e and f are the lines of identity.

Supplementary Figure 9 Normal retinotopic maps in ChAT-Vgat KO mice.

a, Visual stimulus protocol for generating the elevation (top) and azimuth (bottom) maps in the SC. The color scales represent the position of the moving bar on the stimulus monitor and the corresponding retinotopic locations in the SC. b, (top) Elevation map in a WT mouse. Both retinotopy (left) and response magnitude (right) are shown. The gray scale represents the response amplitude as a fractional change in reflection x 104. (bottom) Azimuth map for the same animal. c, Elevation and azimuth maps from two Vgat KO mice. The panel layout is the same as in (b). D, dorsal; V, ventral; N, nasal; T, temporal. See Cang et al (Journal of Neuroscience, 2008, 28(43):11015-23), for details of intrinsic imaging of retinotopic maps in the SC.

Supplementary Figure 10 Receptive field structure of superficial SGS neurons.

a, Distribution of ON receptive field (RF) sizes (in deg2) in WT (n = 316, 5 mice, mean ± s.e.m. = 85.84 ± 3.46, median = 75). b, Distribution of ON RF sizes in KO (n = 476, 8 mice, 128.31 ± 4.04, 125). c, Cumulative distributions of the data shown in (a) and (b) (asterisk (*) indicates p < 0.001, K-S statistic = 0.2500, Kolmogorov-Smirnov test). d, Distribution of OFF RF sizes in WT (n = 331, 5 mice, 118.20 ± 4.72, 100). e, Distribution of OFF RF sizes in KO (n=561, 8 mice, 138.19 ± 4.10, 125). f, Cumulative distributions of the data shown in (d) and (e) (asterisk (*) indicates p = 0.0137, K-S statistic = 0.1084, Kolmogorov-Smirnov test). g, ON-OFF overlap index in WT (n = 238, 5 mice, 0.50 ± 0.02, 0.50). h, ON-OFF overlap index in KO (n = 426, 8 mice, 0.51 ± 0.01, 0.50). i, Cumulative distribution of the data shown in (g) and (h) (p = 0.5909, K-S statistic = 0.0618, Kolmogorov-Smirnov test). See Inayat et al (Journal of Neuroscience, 2015, 35(20):7992-8003), for details of 2-photon imaging of SGS neurons’ RFs and quantification.

Supplementary Figure 11 Preferred directions of superficial SGS neurons as determined by two-photon calcium imaging.

a, Histogram of the preferred direction (prefD) distribution of superficial SGS neurons in WT mice, in response to drifting gratings. Only cells that had gDSI ≥ 0.2 were included (n = 252 out of 310 total responsive cells). This cutoff was applied to all panels. b, Histogram of the preferred direction distribution in Vgat KO mice, in response to drifting gratings (n = 132/407 total responsive cells). c-d, Histogram of the preferred direction distribution in WT (c) and Vgat KO (d) mice, in response to sweeping bars (n = 185/315 in WT and 80/505 in KOs).

Supplementary Figure 12 Relationship between DSI and gDSI.

a, Relationship between DSI and gDSI for whole-cell recording data (n = 52 for Vm from 41 mice, black; and n = 87 for EPSC from 58 mice, red). b, Relationship between DSI and gDSI for two-photon imaging data of the SC, in response to drifting gratings (n = 310, 5 WT mice, black; n = 407, 8 KO mice, red). The solid lines in both panels are the lines of identity.

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Shi, X., Barchini, J., Ledesma, H. et al. Retinal origin of direction selectivity in the superior colliculus. Nat Neurosci 20, 550–558 (2017). https://doi.org/10.1038/nn.4498

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