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The functional organization of cortical feedback inputs to primary visual cortex

Nature Neurosciencevolume 21pages757764 (2018) | Download Citation

Abstract

Cortical feedback is thought to mediate cognitive processes like attention, prediction, and awareness. Understanding its function requires identifying the organizational logic of feedback axons relaying different signals. We measured retinotopic specificity in inputs from the lateromedial visual area in mouse primary visual cortex (V1) by mapping receptive fields in feedback boutons and relating them to those of neurons in their vicinity. Lateromedial visual area inputs in layer 1 targeted, on average, retinotopically matched locations in V1, but many of them relayed distal visual information. Orientation-selective axons overspread around the retinotopically matched location perpendicularly to their preferred orientation. Direction-selective axons were biased to visual areas shifted from the retinotopically matched position along the angle of their antipreferred direction. Our results show that feedback inputs show tuning-dependent retinotopic specificity. By targeting locations that would be activated by stimuli orthogonal to or opposite to a cell’s own tuning, feedback could potentially enhance visual representations in time and space.

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Acknowledgements

We thank C. Harvey, G. De Polavieja, A. Renart, B. Attalah, E. Chiappe, H. Young, and M. Fridman for critical comments on the manuscript. This work was supported by fellowships from Fundação para a Ciência e a Tecnologia to T.M and G.F., Marie Curie (PCIG12-GA-2012-334353) and Human Frontier Science Program (RGY0085/2013) grants to L.P., and by the Champalimaud Foundation.

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Author notes

  1. These authors contributed equally: Tiago Marques and Julia Nguyen.

Affiliations

  1. Champalimaud Research, Champalimaud Center for the Unknown, Lisbon, Portugal

    • Tiago Marques
    • , Julia Nguyen
    • , Gabriela Fioreze
    •  & Leopoldo Petreanu

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Contributions

T.M., J.N., and L.P. conceived the study. J.N. performed the experiments. T.M. set up and optimized intrinsic signal imaging experiments and visual stimuli. T.M. and L.P. analyzed the data. L.P. built the two-photon microscope. G.F. did histology. T.M., J.N., and L.P. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Leopoldo Petreanu.

Integrated supplementary information

  1. Supplementary Figure 1 Histology.

    a, Coronal sections of visual cortex showing infection site and labeled axons in V1 from an example mouse (out of 5). b, Blow-up from region in a (white rectangle) showing GCaMP6 expressing axons in V1. c, Confocal image of GCaMP6 expressing axons in L1 of V1. d, Injection site. Right, blow-up of region in L5 (black box).

  2. Supplementary Figure 2 Identification of LM boutons belonging to the same axon.

    a, Blow-up of a field of view showing two varicosities connected by an axonal shaft. b, Fluorescence signals from varicosities shown in a. Grey bars, visual stimuli. c, RFs from the same two varicosities. d, Matrix of correlation coefficients (Pearson’s R) for fluorescent traces from varicosities recorded in the same imaging session. e, Distribution of correlation coefficients (Pearson’s R, 9 fields of view). Black, pairs of varicosities belonging to the same axon (71 varicosities, 27 axons, 67 pairs); grey, all pairs (1317 varicosities, 119827 pairs). f, Distance between RF centers. g, Correlation coefficient (Pearson’s R) vs distance between RF centers. Black dots, pairs of varicosities belonging to the same axon (n = 67 pairs); grey, all pairs (n = 119827 pairs).

  3. Supplementary Figure 3 ΔAzimuth is larger than ΔElevation for both LM boutons and L2/3 somata.

    a, ΔRF of V1 L2/3 neurons measured with a square stimulus grid (azimuth in [−30°, 30°], elevation in [−30°, 30°]). b, Distribution of relative retinotopic position (left, ΔAzimuth; right, ΔElevation) of L2/3 V1 neurons. c, Cumulative distribution of ΔAzimuth (blue) and ΔElevation (red) distances. **, P = 1.6x10-22, two-sample Kolmogorov-Smirnov test, two-sided, n = 1979 L2/3 neurons. d-f, same as in a, b, and c but for LM boutons. **, P = 1.3x10-16, two-sample Kolmogorov-Smirnov test, two-sided. n = 3423 LM boutons. g, Angular histogram of deviation angle θ of LM boutons in d. Boutons less than 10° away from the origin are discarded for angular counts. Inner circle, expected fraction of a uniform distribution (0.125). Boutons are enriched in the horizontal bins even when RFs are measured using a square stimulus grid. h, Same as g but only for gratings responsive, non-selective boutons. Bottom, normalized population tuning curve. The group is slightly more tuned for horizontally-moving vertical stimuli. i, Same as h, after removing the most tuned boutons until tuning for horizontal and vertically-moving stimuli was equal (bottom). j, Cumulative distribution of ΔAzimuth (blue) and ΔElevation (red) distances for gratings responsive, non-selective boutons after removing the most tuned boutons for horizontally-moving vertical stimuli. *, P = 0.048, two sample Kolmogorov-Smirnov test, two-sided, n = 146 boutons.

  4. Supplementary Figure 4 Non-normalized angular counts of deviation angle θ.

    a, Angular bin counts for non-selective LM boutons. Boutons less than 10° away from the origin are discarded for angular counts. Inner circle, expected fraction of a uniform distribution (0.125). b, Same as in a but for the four subpopulations of OS LM boutons. c, Mean non-normalized bowtie angular distribution for the different subpopulations of OS boutons. Values are angles of the bowtie relative to the axis of preferred orientation. Colored lines, individual OS subpopulations. Colors correspond to b. Thick black line and circles, weighted mean across OS subpopulations. d, Difference in bouton counts across perpendicular bowtie bins. e, Same as in a,b but for the 45° and 90° DS subpopulations. f, Mean non-normalized angular distribution for the different subpopulations of DS boutons. Colors correspond to Fig. 6b. Thick magenta line and circles, weighted mean across DS subpopulations. Inner circle = 0.125. g, Difference in bouton counts between angular bins of opposing angles for all DS boutons. Shading in d,g, 95% confidence interval obtained by reshuffling group identities.

  5. Supplementary Figure 5 Tuning-dependent wiring biases are larger for ||ΔRF|| between 20° and 30° and increase with bouton’s selectivity.

    a, Difference in bouton abundance between perpendicular and parallel bowtie bins for OS LM boutons in different ΔRF distance bins. ΔRF distance bins contain same number of boutons (number on top). b, Same as in a but in different OSI bins. Slope = 54% (P = 0.056, two-sided permutation test, obtained by shuffling boutons’ OSI). The number of boutons in each bin is indicated at the top. c, Difference in bouton abundance between preferred motion direction and opposite bins for DS LM boutons in different ΔRF distance bins. d, Same as in c but in different DSI bins. Slope = 75% (P = 0.13, two-sided permutation test, obtained by shuffling boutons’ DSI). Shading in a, c, 95% confidence interval obtained by reshuffling group identities.

  6. Supplementary Figure 6 OS non-DS feedback inputs are enriched along the axis perpendicular to their preferred orientation.

    a, Mean normalized bowtie counts for OS non-DS boutons (**, 90°, P = 0.0096, two-sided permutation test, corrected for multiple comparisons, n = 960 OS non-DS boutons and 1362 Non-selective boutons). b, Difference in bouton abundance across perpendicular bowtie bins (**, P = 0.0004, two-sided permutation test, corrected for multiple comparisons, n = 960 OS non-DS boutons and 1362 Non-selective boutons). Shading in a,b, 95% confidence interval obtained by reshuffling group identities.

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https://doi.org/10.1038/s41593-018-0135-z

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