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Corticothalamic feedback sculpts visual spatial integration in mouse thalamus

Nature Neuroscience volume 24, pages 1711–1720 (2021)Cite this article

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Abstract

En route from the retina to the cortex, visual information passes through the dorsolateral geniculate nucleus (dLGN) of the thalamus, where extensive corticothalamic (CT) feedback has been suggested to modulate spatial processing. How this modulation arises from direct excitatory and indirect inhibitory CT feedback pathways remains enigmatic. Here, we show that in awake mice, retinotopically organized cortical feedback sharpens receptive fields (RFs) and increases surround suppression in the dLGN. Guided by a network model indicating that widespread inhibitory CT feedback is necessary to reproduce these effects, we targeted the visual sector of the thalamic reticular nucleus (visTRN) for recordings. We found that visTRN neurons have large RFs, show little surround suppression and exhibit strong feedback-dependent responses to large stimuli. These features make them an ideal candidate for mediating feedback-enhanced surround suppression in the dLGN. We conclude that cortical feedback sculpts spatial integration in the dLGN, likely via recruitment of neurons in the visTRN.

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Fig. 1: Anatomical and functional mapping of L6CT feedback.
Fig. 2: CT feedback effects on dLGN size tuning.
Fig. 3: Model of CT feedback effects predicts wide inhibitory feedback coupling kernel.
Fig. 4: Retinotopic organization and RF properties of visTRN neurons.
Fig. 5: Suppression of cortical feedback reduces responses and increases preferred stimulus size in the visTRN.

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Data availability

Except for Fig. 3, all figures were generated from processed data. The data sets are available from https://gin.g-node.org/busse_lab/corticothalamic_spatial_integration.

Code availability

Preprocessed data were analyzed in Matlab and Python using custom-written code. The code to reproduce the figures is available at https://gin.g-node.org/busse_lab/corticothalamic_spatial_integration.

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Acknowledgements

This research was supported by DFG BU 1808/5-1 (L.B.), DFG SFB870 TP19 (118803580) (L.B.), by an add-on fellowship from the Joachim Herz Stiftung (G.B.), by DFG SFB870 Z04 (118803580) (M. Götz) and by funds awarded to the Centre for Integrative Neuroscience within the framework of German Excellence Initiative (DFG EXC 307). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the Viral Vector Facility of the LMU, in particular I. Mühlhahn for CsCl DNA preparation and A. Wal for recording some of the data in Fig. 2. Confocal microscopy was performed in the bioimaging core facility of the LMU Biomedical Center. We are grateful to M. Sotgia for lab management and support with animal handling, M. Sotgia and H. Wolfrohm for contributing to histology, S. Schörnich for IT support and B. Grothe for providing an excellent research infrastructure.

Author information

Author notes

  1. These authors contributed equally: Gregory Born, Felix A. Schneider-Soupiadis, Sinem Erisken.

Authors and Affiliations

  1. Division of Neurobiology, Faculty of Biology, LMU Munich, Munich, Germany

    Gregory Born, Felix A. Schneider-Soupiadis, Sinem Erisken, Martin A. Spacek & Laura Busse

  2. Graduate School of Systemic Neurosciences (GSN), LMU Munich, Munich, Germany

    Gregory Born & Felix A. Schneider-Soupiadis

  3. Werner Reichardt Centre for Integrative Neuroscience, University of Tubingen, Tübingen, Germany

    Sinem Erisken & Agne Vaiceliunaite

  4. Graduate School of Neural & Behavioural Sciences, International Max Planck Research School, University of Tubingen, Tübingen, Germany

    Sinem Erisken

  5. Department of Physiological Genomics, Biomedical Center (BMC), LMU Munich, Munich, Germany

    Chu Lan Lao

  6. Institute of Stem Cell Research, Helmholtz Center Munich, Biomedical Center (BMC), Munich, Germany

    Chu Lan Lao

  7. Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway

    Milad H. Mobarhan

  8. Department of Biosciences, University of Oslo, Oslo, Norway

    Milad H. Mobarhan

  9. Faculty of Science and Technology, Norwegian University of Life Sciences, Aas, Norway

    Gaute T. Einevoll

  10. Department of Physics, University of Oslo, Oslo, Norway

    Gaute T. Einevoll

  11. Bernstein Centre for Computational Neuroscience, Munich, Germany

    Laura Busse

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Contributions

L.B., S.E., G.B., F.A.S.-S. and A.V. conceptualized the study. M.H.M. and G.T.E. developed the methodology. G.B., S.E., F.A.S.-S., M.A.S., M.H.M. and L.B. developed the software. G.B., F.A.S.-S., S.E. and L.B. performed the formal analysis. G.B., F.A.S.-S., S.E., A.V. and M.A.S. performed the experimental investigations. C.L.L. provided resources. M.A.S., G.B., S.E., L.B. and F.A.S.-S. curated the data. G.B., S.E., F.A.S.-S. and L.B. wrote the original draft. All authors wrote and edited the manuscript. G.B., F.A.S.-S., S.E. and L.B. visualized the data. L.B. supervised the project, and L.B. and G.B. acquired funding.

Corresponding author

Correspondence to Laura Busse.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Results of additional mice used for the analysis of retinotopy of corticothalamic projections.

(a) Cartoon of V1 injection sites along the elevation axis. (b) Representative coronal slices with fluorophore expression along the V1 elevation axis. Images are ordered posterior to anterior. (c) Labeled L6CT axonal terminal fields in dLGN. (d–f). Same as (a–c) for another mouse, injected along the V1 azimuth axis. (g–i) Results of another mouse, where V1 injections were placed within a single coronal plane. Narrow-field images of mTurquoise2 in b,e and eGFP, mScarlet and mTurquoise2 in h were acquired with a confocal microscope and manually aligned with the wide-field epifluorescence images of the corresponding brain slices. All panels: numbers indicate distance from bregma. (b,e,h) Scale bar: 1 mm. (c,f,h) Scale bar: 250 µm. Observations in (b-i) were reproduced in 5 mice.

Extended Data Fig. 2 Quantification of expression volumes.

(a) Schematic of the injection: Extended Data Fig. 1). expression of ChR2-eYFP (n = 3), eGFP (n = 3) or mScarlet (n = 3) in a localized population of V1 L6CT pyramidal cells (see also Fig. 1a,b and Extended Data Fig. 1) (b) Pipeline for quantification of expression volumes. (b) Left: Manually chosen reference points (green circles) on salient features of an example brain-slice image. Blue: DAPI; fainter green: eYFP. Middle: Corresponding locations marked on the manually chosen atlas section from the Allen CCF (see Methods). (c) Right: Brain-slice image registered and transformed to the CCF. White points outline the expression zone and are extracted as CCF coordinates. (c–f) Computation of the relative volumes of transduced V1 CT pyramidal cells within L6 (‘source volume’) and those of their dLGN projections (‘target volume’) for a representative Ntsr1-Cre mouse. (c) Coronal sections of the V1 injection site, overlaid with fitted area boundaries from the Allen CCF (gray). Green: ChR2-eYFP. (d) Top: Top view of V1 L6 (blue) within the cortex (black contour). Bottom: 3D reconstruction of the expression volume (green) within V1 L6 (blue), seen from the same perspective as the upper panel (‘source volume’). Relative volume: 25%. (e) Coronal sections with transduced L6CT neurons projecting to a restricted volume the dLGN. (f) Top: Coronal schematic of dLGN (blue) within the brain section (black contour). Bottom: 3D reconstruction of the expression volume (green) within the dLGN (blue), seen from the same perspective as the upper panel (‘target volume’). Relative volume: 15%. In (c,e), numbers in bottom right corner indicate distance from bregma in mm; scale bar: 0.5 mm. (g) Comparison of the relative expression volumes within V1 L6 ((expression volume within V1 L6)/(total volume of V1 L6)) and dLGN ((expression volume within dLGN)/(total volume of dLGN)) for each mouse. Local injections in V1 yield restricted, spatially specific expression in dLGN with similar relative volumes (mean difference = 0.017, p = 0.55, resampling, n = 9 mice). Black: mice used for viral tracing experiments; green: mice used for ChR2-assisted functional mapping). (h) Example close-up image of L6CT neurons expressing eGFP (green). Scale bar: 0.5 mm. (i) Example confocal image of dLGN with eGFP signal in projections from L6CT neurons. Scale bar: 250 µm. (j) Close-up confocal image of L6CT projections in dLGN for same slice as in (i). Scale bar: 25 µm.

Extended Data Fig. 3 L6CT photoactivation effects across various classifications of dLGN neurons and stability of distance dependence.

Change of firing rate (fold change, log2 ratio) as a function of (a) orientation selectivity index (OSI, see Methods), (b) direction selectivity index (DSI, see Methods), (c) estimated depth within dLGN, (d) RF area as obtained from sparse noise experiments, (e) contrast sensitivity (c50) and (f) exponent n of the contrast response function, (g) spontaneous firing rate obtained from interleaved blank trials, (h) mean response across all drift directions, (i) burst ratio, and (j) burst length (spikes/burst). Functional properties in (a,b,g–j) are computed from direction tuning experiments. (k,l) Spatial profile of modulations induced by photostimulation of CT feedback (see Fig. 1i) retested for the first half and second half of trials in each experiment (first half: p = 0.003, second half: p = 0.015). (m,n) Same as k-l for data partitioned into first and second half of each individual trial (first half: p = 0.016, second half: p = 0.009). (o) Variance in fold change values across distance in same overlapping bins as in Fig. 1i for all trials. Black line: regression fit; b: slope; p: significance of slope.

Extended Data Fig. 4 Neurons in L6 of mouse V1 prefer large stimulus sizes and experience little surround suppression.

(a) Distribution of preferred size for neurons (n = 177) recorded across layers of V1. (b) Same as (a) for suppression index. Dashed horizontal lines: borders between V1 layers, based on CSD analysis and histological estimates of relative layer thickness (see Methods). Red: Smoothed mean computed by local robust regression (MATLAB function ‘smooth’, method ‘rlowess’, window size = 0.28 mm).

Extended Data Fig. 5 Photoactivation of L6CT neurons promotes dLGN tonic firing mode.

(a) Representative image of a V1 coronal section from a Ntsr1-Cre mouse injected with Cre-dependent AAV-ChR2. Green: ChR2-YFP, blue: DAPI. Scale bar 100 µm. (b) Example orientation-tuning curves of cells located in putative L2/3 or putative L6 for trials during V1 L6CT photoactivation (blue) and under control conditions (black). Visual stimulus: Drifting gratings with temporal and spatial frequencies coarsely optimized for the recording, duration 0.75 s, photostimulation: starting 0.1 s before stimulus onset, lasting for 0.85 s. Data are presented as mean values ± s.e.m.. (c) Fold change (that is log2 ratio of average firing rates for V1 L6CT photoactivation and control conditions across tuning experiments) as a function of cortical depth relative to the base of L4, estimated by CSD (see Methods). Gold: layer-wise mean; pink: example neurons. Error bars: confidence intervals of the mean, determined by bootstrapping. n = 362 neurons. (d) Representative image of a dLGN coronal slice, with axons of Ntsr1+ neurons expressing ChR2 in green. (e,f) Recordings from dLGN. Raster plots of two example dLGN neurons during spontaneous activity aligned to V1 L6CT photoactivation (shaded blue). Red: burst spikes, black horizontal bar: 200 ms. (e) n = 31 trials, (f) n = 69 trials. (g) Firing rates during vs. before V1 L6CT photoactivation. Activation of L6CT neurons yielded diverse results (during: 4.2 sp/s vs. before: 2.7 sp/s; n = 167 neurons; p = 0.4, two-sided Wilcoxon signed-rank test), consistent with the interpretation of our functional mapping experiments (Fig. 1k). (h) Ratio of burst spikes during vs. before V1 L6CT photoactivation. Activating CT feedback decreased the fraction of spikes fired in bursts (before: 9.04%, during: 3.75%; n = 139 neurons; p = 1.7 × 10 − 7, Wilcoxon signed-rank test). Data points at marginals represent burst ratio = 0. Inset: cumulative distribution of burst lengths during (blue) vs. before (black) V1 L6CT photoactivation. Activating CT feedback shifted the distribution of spikes per burst towards lower values (p = 7.8×10−5, two-sample Kolmogorov-Smirnov test).

Extended Data Fig. 6 Size tuning curves of more dLGN example neurons.

Black: control condition; Blue: V1 suppression; horizontal lines: responses to blank screen (size 0 deg); vertical lines: preferred size; error bars represent s.e.m.

Extended Data Fig. 7 In visTRN, elevation is predominantly encoded along the anterior-posterior axis.

(ai−iii) Innervation pattern in visTRN of axons from L6CT populations transduced with pAAV-CAG-FLEX-EGFP (green, left), pAAV-CAG-FLEX-mScarlet (red, middle), or pAAV-CAG-FLEX-mTurqoise (blue, right). V1 injections were performed along the retinotopic axis representing elevation (ai, inset, right), with EGFP labeling V1 regions representing higher elevations. Confocal images in (ai−iii) are arranged from posterior to anterior (number indicates distance from bregma in mm); images of each row were taken from the same slice, with separate visualization of the three fluorophores. Note that more anterior regions in visTRN contain terminal fields of L6CT axons labeled with mTurqoise, that is representing lower elevations in the visual field; middle regions along the AP axis in visTRN contain terminal fields of L6CT axons labeled with mScarlet, that is representing central elevations in the visual field; more posterior regions in visTRN contain terminal fields of L6CT axons labeled with EGFP, that is representing higher elevations in the visual field. (bi−iii) Same as (a) for a second example mouse. All scale bars 0.25 mm. We observed retinotopic CT projections from V1 to visTRN in 4 mice.

Extended Data Fig. 8 Size tuning curves of more visTRN example neurons.

(a-j) Black: control condition; Blue: V1 suppression; horizontal lines: responses to blank screen (size 0 deg); vertical lines: preferred size; error bars represent s.e.m. (k) Distribution of suppression indices for the visTRN neuron population (n = 61) during control (black) and V1 suppression (blue). Note that in both conditions the majority of visTRN neurons show little to no surround suppression (SI < 0.05).

Extended Data Fig. 9 The relationship between CT feedback effects on visTRN neurons and their response properties.

(a) Percent change in overall responsiveness by CT feedback as a function of SI (ai) and preferred size (aii) under control conditions, RF area as measured by a sparse noise stimulus (aiii), contrast sensitivity (c50, aiv) and steepness of the contrast response function (av), spontaneous firing rate (avi), mean response (avii), burst ratio (aviii), and burst length (aix) under control conditions of the size tuning experiments. While many relationships are not significant, CT feedback reduces overall responsiveness more for visTRN neurons with small compared to large RFs (aiii), but the explained variance is small, partially because there is a wide array of effects for visTRN neurons with rather small RF coverage. Second, visTRN neurons with higher firing rates, show stronger CT feedback related modulations of firing rate (avi−vii), pointing towards a multiplicative mechanism. (b,c) Same as (a), for CT feedback effects on preferred size and on surround suppression (SI), respectively. The observation that visTRN neurons with stronger surround suppression in control conditions show more pronounced changes in SI than those with weaker surround suppression (ci) could point towards an interesting subpopulation of visTRN neurons, which might represent spatial context and for which this representation is further enhanced by CT feedback. Black/red line: regression fit; b: slope; p: significance of slope.

Extended Data Fig. 10 Correlations between suppression index and distance of RF center to monitor or stimulus center, and between suppression index and preferred size.

(a) Suppression indices for visTRN population (n = 125) plotted against the normalized distance between stimulus center and their RF centers (Black line: linear regression; b: slope; p: significance of slope). (b) Suppression indices for visTRN population plotted against the distance between monitor center and their RF centers. (c,d) Strength of surround suppression in visTRN measured during size tuning as a function of RF area mapped with the sparse noise stimulus (c) and as a function of preferred size taken from the size tuning curve (d). Black: regression line including all data points, grey regression line including a restricted set (SI > 0.01 and RF area < 2000 deg2). (e,f) Same as (c-d), for dLGN neurons. Note that in both visTRN (d) and dLGN (f) neurons with larger preferred sizes also tend to have less surround suppression. One caveat regarding the interpretation of this anti-correlation is the limited size of our monitor, which for neurons with larger RFs might not allow for a sufficiently strong stimulation of the surround.

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Born, G., Schneider-Soupiadis, F.A., Erisken, S. et al. Corticothalamic feedback sculpts visual spatial integration in mouse thalamus. Nat Neurosci 24, 1711–1720 (2021). https://doi.org/10.1038/s41593-021-00943-0

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