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Cortico-fugal output from visual cortex promotes plasticity of innate motor behaviour

Abstract

The mammalian visual cortex massively innervates the brainstem, a phylogenetically older structure, via cortico-fugal axonal projections1. Many cortico-fugal projections target brainstem nuclei that mediate innate motor behaviours, but the function of these projections remains poorly understood1,2,3,4. A prime example of such behaviours is the optokinetic reflex (OKR), an innate eye movement mediated by the brainstem accessory optic system3,5,6, that stabilizes images on the retina as the animal moves through the environment and is thus crucial for vision5. The OKR is plastic, allowing the amplitude of this reflex to be adaptively adjusted relative to other oculomotor reflexes and thereby ensuring image stability throughout life7,8,9,10,11. Although the plasticity of the OKR is thought to involve subcortical structures such as the cerebellum and vestibular nuclei10,11,12,13, cortical lesions have suggested that the visual cortex might also be involved9,14,15. Here we show that projections from the mouse visual cortex to the accessory optic system promote the adaptive plasticity of the OKR. OKR potentiation, a compensatory plastic increase in the amplitude of the OKR in response to vestibular impairment11,16,17,18, is diminished by silencing visual cortex. Furthermore, targeted ablation of a sparse population of cortico-fugal neurons that specifically project to the accessory optic system severely impairs OKR potentiation. Finally, OKR potentiation results from an enhanced drive exerted by the visual cortex onto the accessory optic system. Thus, cortico-fugal projections to the brainstem enable the visual cortex, an area that has been principally studied for its sensory processing function19, to plastically adapt the execution of innate motor behaviours.

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Figure 1: Visual cortex contributes to OKR potentiation.
Figure 2: Cortico-fugal projection from mouse visual cortex to NOT-DTN.
Figure 3: Cortico-fugal projection to NOT-DTN is necessary for OKR potentiation.
Figure 4: Enhanced cortical modulation of NOT-DTN activity with OKR potentiation.
Figure 5: Impact of cortex on NOT-DTN activity matches cortical contribution to OKR potentiation.

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Acknowledgements

We thank members of the Scanziani and Isaacson laboratories for advice on this project; J. Evora, N. Kim, M. Chan and A. Linder for technical support; T. M. Jessell for sharing the Flex.DTR.GFP plasmid and virus; M. Faulstich and S. du Lac for sharing eye tracking codes and advice on vestibular lesions; D. Li for advice on statistical analysis; J. Isaacson for comments on the manuscript; S. R. Olsen for sharing codes for in vivo recordings and unit isolation; and M. Xue for help with in vitro recordings. M.S. is an investigator of the Howard Hughes Medical Institute. This work was supported by the Gatsby Charitable Foundation and the US National Institutes of Health (R01 EY025668).

Author information

Authors and Affiliations

Authors

Contributions

B.L. and M.S. designed the study. B.L. performed all experiments and data analysis. A.D.H. shared the Hoxd10–GFP mouse line. B.L. and M.S. wrote the manuscript.

Corresponding authors

Correspondence to Bao-hua Liu or Massimo Scanziani.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Quantification of mouse OKR.

a, Transformation of sinusoidal gratings from cylindrical coordinates of the virtual drum to Cartesian coordinates of the monitor. xpix is the horizontal pixel position in Cartesian coordinates. D is the distance from the centre of monitors to the eye. xdeg is the azimuth angle of pixels in cylindrical coordinates. Note that the spatial period of the grating on the monitor is not uniform. See Methods for details. b, Schematic of calibration of the measurement of eye position. The camera is moved along a circumference centred on the image of the eye by ± 10°. ce, Example traces of OKR eye trajectory and corresponding fast Fourier transform (FFT) spectra. c, Left, raw trace of one individual eye trajectory with both slow OKR component and fast saccade-like component (red arrows; T, temporal; N, nasal). Right, isolated OKR component after removal of the saccade-like component. Spatial frequency, 0.08 cpd; oscillation frequency, 0.2 Hz. d, Eye trajectories in horizontal azimuth (left) and vertical elevation (right) overlaid with corresponding drum trajectories (the same example as in c). Note that OKR eye movement is mainly restricted to the axis of the drum movement. D, down; U, up. e, Fourier transform spectra of eye trajectory and drum trajectory in d (left). The amplitude of the OKR trajectory peaks at the principal frequency (dotted line). f, OKR gain derived from OKR velocity versus OKR eye trajectory. Each point is one trial. Solid line, linear regression. g, Population summary of OKR gain evoked by five oscillation frequencies (left, spatial frequency 0.08 cpd) and five spatial frequencies (right, oscillation frequency 0.4 Hz). Each point is one mouse (n = 39 for oscillation frequency and 49 for spatial frequency). Data shown as mean ± s.d.

Extended Data Figure 2 Optogentic silencing of visual cortex.

a, Left, schematic of experimental setup. IN, inhibitory neurons; VC, visual cortex. Middle, raster plot and PSTH of a single unit. Black, control condition; blue, cortical silencing. Blue bar, duration of blue light illumination (15 s). Control and photostimulation trials were interleaved (see b), but are separated here for clarity. Right, summary of firing rate of regular spiking units (n = 40). Data shown as mean ± s.e.m. b, Top, block design to examine the impact of cortical silencing on OKR performance. LED off, control trials; LED on, cortical silencing trials. Bottom, cycle averages of one individual OKR eye trajectory. T, temporal; N, nasal.

Extended Data Figure 3 Visual cortex contributes to OKR potentiation across spatial frequencies.

a, Data from example mice. OKR performance of a naive animal (no vestibular lesion, left) and an animal with vestibular lesion (right). Top, schematic experimental setup. Bottom, cycle averages of all eye trajectories evoked by five spatial frequencies (oscillation frequency 0.4 Hz), and the corresponding OKR gains. Thickness of traces shows s.e.m. Data shown as mean ± s.e.m. b, Population average of cortical contribution to OKR gain at five spatial frequencies for animals with vestibular lesion (VL, solid line, n = 17 mice) and naive animals (no VL, dotted line, n = 51 mice). Data shown as mean ± s.e.m. c, Population average of cortical contribution to OKR gain at five oscillation frequencies before vestibular lesion (Pre VL, dotted line) and after vestibular lesion (Post VL, solid black line). The grey line is the maximal possible cortical contribution to OKR gain after vestibular lesion assuming the entire OKR potentiation depends on visual cortex (Max. possible) (n = 13 animals). Data shown as mean ± s.e.m. d, Population average of cortical contribution to OKR potentiation (potentiation index, PI) measured as the ratio between a and b (illustrated in c) at each oscillation frequency. Data shown as mean ± s.e.m. e, Population averages of pseudo-OKR potentiation following sham lesions. Black data points: no cortical silencing, normalized by OKR gain before sham lesions without cortical silencing. Blue data points: cortical silencing, normalized by OKR gain before sham lesions during cortical silencing (n = 6 mice). Data shown as mean ± s.e.m. f, Population summary of cortical contribution to OKR gain before (Pre SL) and after sham lesions (Post SL). Data shown as mean ± s.e.m.

Extended Data Figure 4 Visual cortex contributes to OKR potentiation induced by continuous OKR stimulation.

a, Schematic of experimental design. OKR gain before stimulation was measured twice, 1 day before and 1 h before continuous OKR stimulation (see Methods for details). b, Data from example mouse. Cycle averages of all eye trajectories and corresponding OKR gain before (Pre stim.) and after (Post stim.) continuous OKR stimulation (n = 576 cycles, spatial frequency 0.1 cpd, oscillation frequency 0.4 Hz). The thickness of the trace shows s.e.m. Note that following OKR stimulation cortical silencing leads to a larger reduction in OKR gain. Data shown as mean ± s.e.m. c, Population averaged time course of OKR potentiation induced by continuous OKR stimulation. Black, no cortical silencing (control), normalized by OKR gain before stimulation (Pre stim.) without cortical silencing. Blue, cortical silencing, normalized by OKR gain before stimulation during cortical silencing (n = 11 mice). Red arrow: the cortical contribution to OKR potentiation; magenta arrow: OKR potentiation. Data shown as mean ± s.e.m. d, Population summary of cortical contribution to OKR gain before (Pre stim.) and after stimulation (Post stim.) (n = 11 mice). Red data points: the animal in b. Data shown as mean ± s.e.m. e, Population summary of cortical contribution to OKR potentiation (potentiation index, PI) (n = 11 mice). Red data point: the animal in b. Data shown as mean ± s.e.m.

Extended Data Figure 5 Identification of the NOT-DTN based on retinal input and c-Fos expression.

a, Left, coronal section of NOT-DTN of Hoxd10–GFP mouse. The distribution of GFP-expressing RGC axons delineates the NOT-DTN (dotted box). Right, delineation of NOT-DTN and surrounding nuclei (modified from Paxinos, G. & Franklin, K. The Mouse Brain in Stereotaxic Coordinates (Elsevier, 2007)) for the corresponding coronal plane. D, dorsal; L, lateral. b, c-Fos immunostaining of coronal slices containing NOT-DTN of Hoxd10–GFP mice. Left, section from an animal that underwent OKR stimulation. Note that the distribution of GFP RGC axons overlaps with that of c-Fos-positive cells. Right, section from an animal that did not undergo OKR stimulation (control). c, Quantification of the extent of overlap between GFP RGC axons and c-Fos-positive cells in b (left). Top left, boundary of the domain of RGC axons. Top right, boundary of the domain of c-Fos-positive cells. Bottom left, overlay of those two boundaries. Bottom right, calculation of overlap coefficient r of those two domains (see Methods). d, Left, histogram of fluorescence intensity of c-Fos-positive cells. Data shown as mean ± s.d. P < 10−20. Right, summary of density of c-Fos-positive cells in NOT-DTN. Each data point represents one slice. Data shown as mean ± s.d. P < 10−20. n = 50 slices from 4 mice of OKR group and 59 slices from 4 mice of control group. e, c-Fos immunostaining of coronal slices containing superior colliculus (SC, top) or vLGN (bottom). Blue, DAPI; red, c-Fos. f, Summary of density of c-Fos-positive cells in superior colliculus (left) and vLGN (right). Each data point represents one slice. Data shown as mean ± s.d. n = 4 mice for both OKR group and control group.

Extended Data Figure 6 Structures projecting to NOT-DTN and monosynaptic transmission between visual cortex and NOT-DTN.

a, Subcortical structures labelled by retro-beads injected into the NOT-DTN. Top left, injection site. SC, superior colliculus; dLGN, dorsal lateral geniculate nucleus; IGL, intergeniculate leaflet; vLGN, ventral lateral geniculate nucleus; LTN, lateral terminal nucleus; MTN, medial terminal nucleus. b, Schematic drawing of the two pathways relaying visual information to the AOS. Thalamo-cortical-NOT-DTN pathway is outlined in blue, retinal pathway outlined in green. c, Spatial distribution of NOT-DTN-projecting neurons in visual cortex (visual cortex injected with Flex–tdTomato and NOT-DTN with Cav2–Cre) for two coronal sections. Boundaries between primary and secondary areas are drawn according to Paxinos, G. & Franklin, K. The Mouse Brain in Stereotaxic Coordinates (Elsevier, 2007). Inset on the right, higher magnification of the region shown in the red box. Blue, DAPI; white, tdTomato. d, Left, schematic of the setup for in vitro whole-cell voltage-clamp recording from NOT-DTN neurons in acute slices. Green, patched NOT-DTN neurons; red, axons from visual cortex. D, dorsal; L, lateral. Middle, summary of success rate of EPSCs evoked by optogenetic stimulation of cortico-fugal axons. Right, peak amplitude of AMPA receptor mediated EPSCs. Data shown as mean ± s.d. Each data point represents one NOT-DTN recording. e, Left, AMPA receptor-mediated EPSCs evoked by optogenetic stimulation of cortico-fugal axons for three NOT-DTN neurons voltage-clamped at −65 mV. Right, AMPA receptor-mediated EPSCs of the same cells after blocking multi-synaptic components with TTX (sCRACM). Black, individual trials; red, average; blue, time course of blue light illumination.

Extended Data Figure 7 Cortical contribution to OKR gain at different oscillation frequencies in animals with spared or ablated cortical projection to the NOT-DTN.

a, Population averages of cortical contribution to OKR gain at five different oscillation frequencies before (dotted line, Pre VL) and after (solid line, Post VL) vestibular lesion for mice in which the cortico-fugal projection was ablated (n = 18 animals). Data shown as mean ± s.e.m. b, c, Population averages of cortical contribution to OKR gain at five different oscillation frequencies for mice in which the infection with diphtheria toxin receptor (DTR) (b) or injection of diphtheria toxin (DT) (c) was omitted (n = 8 and 6 animals, respectively). Data shown as mean ± s.e.m.

Extended Data Figure 8 Tuning properties of NOT-DTN neurons.

a, Left, schematic of experimental setup; mouse under anaesthesia. Right, raster plot and PSTH of an example single unit. Shades indicate the temporonasal phase of drum trajectory. b, Histogram of direction selectivity index (DSI) of single units in NOT-DTN stimulated by oscillatory drum movement. c, Example single unit. Left, raster plot and PSTH of responses evoked by moving gratings of 12 equally spaced directions (indicated by arrows, red arrow for temporonasal direction). Bar, duration of stimulation. Right, polar plot of the same unit. Green arrow, preferred direction. d, Top, example polar plots of weak DSI, medium DSI and strong DSI units. Bottom, histogram of DSI of NOT-DTN units stimulated by grating movement of 12 directions. e, Summary of preferred direction for NOT-DTN units with DSI greater than 0.1. Note the dominant preference for temporonasal direction. f, OKR gain and NOT-DTN multi-unit activity recorded before (closed) and after (open) silencing NOT-DTN with muscimol. Each colour represents one animal. Note that strong suppression of NOT-DTN activity leads to the abolishment of the OKR. Mice were awake during recording.

Extended Data Figure 9 Cortical silencing induces a larger shift along the transfer function after vestibular lesion.

a, Example spatial frequency tuning and oscillation frequency tuning curves of OKR peak velocity. Note that while the OKR peak velocity is modulated by the spatial frequency of the drum stimulus (black), it is constant across oscillation frequencies (grey). Data shown as mean ± s.e.m. b, Data from example mouse. Left, cycle averages of all eye trajectories triggered by five different spatial frequencies. Right, the corresponding PSTH of NOT-DTN multi-unit activity. Note the correlation between the amplitude of eye trajectory and the amplitude of activity. Shades indicate the temporonasal phase of drum trajectory. c, Spatial frequency tuning curves of OKR gain (black) and NOT-DTN activity (grey) from b. Data shown as mean ± s.e.m. d, Pseudo-transfer function from the animal shown in Fig. 5b using the firing rate during the nasotemporal instead of the temporonasal phase. Each data point represents one trial. Coloured triangles represent the same trials as illustrated in Fig. 5b (middle). Note the lack of correlation between OKR gain and the nasotemporal phase of multi-unit activity (MUA) recorded in NOT-DTN. e, Example spiking activity in superior colliculus (SC) during OKR stimulation. e1, Image of coronal slice containing superior colliculus. Red, electrode track labelled with DiI. e2, PSTH of superior colliculus MUA. e3, Spatial frequency tuning curves of OKR gain (black) and superior colliculus activity (grey). Data shown as mean ± s.e.m. e4, Pseudo-transfer function using superior colliculus activity. Note the lack of correlation between OKR gain and MUA recorded in superior colliculus. f, As in e, except for spiking activity in ventral lateral geniculate nucleus (vLGN) during OKR stimulation. Note the lack of correlation between OKR gain and MUA recorded in vLGN. Data shown as mean ± s.e.m. g, Data from example mouse. Recording from NOT-DTN. Shift along the transfer function upon cortical silencing for data points obtained at two different spatial frequencies (SF; left, 0.04 cpd; right, 0.08 cpd) in a naive animal (no vestibular lesion). The vector (arrow) connects the centres of mass of control (grey) and cortical silencing trials (blue) obtained at the same spatial frequency. Red line, transfer function computed with data obtained at all tested spatial frequencies under control conditions (that is, without cortical silencing). h, As in g, except for an animal with vestibular lesion. Note longer vectors as compared to g. i, Population summary of vectors for five different spatial frequencies computed on averaged normalized transfer functions in naive animals (no VL; left; n = 17) and animals with vestibular lesion (VL; right; n = 17). j, Population averages of vector lengths (left) and slopes (right) for naive animals (no VL; black; n = 17) and animals with vestibular lesion (VL; red; n = 17). Data shown as mean ± s.e.m.

Extended Data Figure 10 Presence and absence of collaterals from NOT-DTN-projecting cortical neurons in selected brain areas.

a, Absence of collaterals from NOT-DTN-projecting cortical neurons in flocculus (FL); inferior olive (IO); periaqueductal grey (PAG); medial accessory oculomotor nucleus (MA3); and vestibular nuclei (VN). For each coronal section the left panel is the DAPI fluorescence signal (blue channel) and the right panel is the tdTomato fluorescence signal (red channel). b, Presence of collaterals from NOT-DTN-projecting cortical neurons in the striatum and superior colliculus (SC).

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Liu, Bh., Huberman, A. & Scanziani, M. Cortico-fugal output from visual cortex promotes plasticity of innate motor behaviour. Nature 538, 383–387 (2016). https://doi.org/10.1038/nature19818

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