The interactions between neocortical areas are fluid and state-dependent, but how individual neurons couple to cortex-wide network dynamics remains poorly understood. We correlated the spiking of neurons in primary visual (V1) and retrosplenial (RSP) cortex to activity across dorsal cortex, recorded simultaneously by widefield calcium imaging. Neurons were correlated with distinct and reproducible patterns of activity across the cortical surface; while some fired predominantly with their local area, others coupled to activity in distal areas. The extent of distal coupling was predicted by how strongly neurons correlated with the local network. Changes in brain state triggered by locomotion strengthened affiliations of V1 neurons with higher visual and motor areas, while strengthening distal affiliations of RSP neurons with sensory cortices. Thus, the diverse coupling of individual neurons to cortex-wide activity patterns is restructured by running in an area-specific manner, resulting in a shift in the mode of cortical processing during locomotion.
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The authors thank A. Garner and G. Keller for help with two-photon imaging in RSP, L. Hoermann for performing surgeries and histology for this project, and S. Hofer, P. Znamenskiy and A. Naka for feedback and discussions on this project and comments on the manuscript. This work was supported by the Swiss National Science Foundation (SNSF 31003A_169802 to T.D.M.-F.), the Wellcome Trust (090843/E/09/Z core grant to the Sainsbury Wellcome Centre), the EMBO Long-term Fellowship (ALTF 1481-2014 to K.B.C.), the HFSP Postdoctoral Fellowship (LT000414/2015-L to K.B.C.) and the Branco Weiss-Society in Science grant (K.B.C.).
The authors declare no competing interests.
Journal peer review information: Nature Neuroscience thanks Carsen Stringer, Igor Timofeev, and other anonymous reviewer(s) for their contribution to the peer review of this work.
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Integrated supplementary information
a. Histogram of firing rates for units recorded in V1 (left) and RSP (right). Fast-spiking cells dominated the high firing rate population for both V1 and RSP units. b. Example waveform characteristics used for clustering for example units recorded in V1. Points are colored according to their clustering. Energy is the square of the detected spike amplitude on each channel, averaged for 1 ms centered around spike time.c. Same as b, for several units in RSP. d. Recordings of the highest firing rate unit recorded in V1 (spike rate = 30 Hz, isolation distance = 224), from four neighboring sites. e. Self-triggered spike histogram of same V1 unit as shown in d. f. Same as d, for the highest firing unit recorded in RSP (spike rate = 39 Hz, isolation distance = 180). g. Self-triggered spike histogram of same RSP unit as shown in f. h. Spikes on each channel shown for unit displayed in f.
a. Silicon probes were coated in 1% DiI dissolved in ethanol before insertion to confirm recording site and allow for recovery of tracks. DiI tracks in V1 (top) and RSP (bottom). Scale bar 1 mm. b. Profile of units recorded at different depths from the cortical surface. Depths were recovered with ~100 um accuracy.
a. Example SpAMs calculated using raw spike trains (left) or spike trains convolved with a decaying exponential (right), for four example units (Pearson correlation). b. 2-dimensional correlation of SpAMs calculated using raw vs. convolved spike trains (left, V1 units; right, RSP units). c. 2-dimensional correlation of SpAMs calculated using Pearson correlations vs. those calculated using the partial correlation with respect to running. d. Population SpAMs were calculated by correlating all recorded spiking activity with each imaged pixel (Pearson correlation). These maps were very similar to maps made by correlating a seed pixel from the recording area with all other recorded pixels (Pearson correlation). Left two maps are for recordings in V1, rightmost are for recordings in RSP. The median 2-dimensional correlation for population maps calculated using these two methods was 0.97 (N = 16 animals). e. Activity maps associated with different behavioral parameters. Locomotion engaged lower limb somatosensory and motor cortex and frontal cortex. Visual stimulation engaged V1. Licking engaged anterior lateral motor cortex and forepaw somatosensory and motor cortex (likely reflecting the fact that animals often manipulated the water spout with their forepaws during water collection).
a. Correlation of uncorrected ΔF/F movie, and the same movie corrected for hemodynamic signal, average of 3 mice (Pearson correlation). b. (Top trace) ΔF/F of hemodynamic-corrected (orange) and uncorrected (blue) data. c. Fluorescence data deconvolved using nerds algorithm. d. Simulated spike trains generated by randomly removing spikes from the thresholded deconvolved data to produce a range of spike rates similar to the real spiking data. e. Example SpAMs for 2 units generated using the hemodynamic-corrected and uncorrected ΔF/F. f. 2-dimensional correlation of SpAMs generated using hemodynamic corrected vs. uncorrected fluorescence trace, across quiescent and active epochs (N = 60 units V1, N = 60 units RSP, two-sided t-test).
a. Data were divided into alternating epochs (1-3 s duration), and SpAMs re-calculated for the divided data. Example maps are shown for units recorded in V1. b. Same as a, for RSP. c. SpAM stability was estimated by taking the 2-dimensional correlation of the SpAMs calculated for the divided epochs. Units with self-correlations less than 0.2 were excluded from analysis (N = 6 units).
a. Overlay of ΔF/F at recording site (green) and convolved multi-unit spike train (black) for V1 (left) and RSP (right) (r is Pearson correlation between convolved spike train and ΔF/F). b. Recording depth vs. the 2-dimensional correlation of a unit’s SpAM and the population SpAM for V1 (left, N = 141 units, linear regression, R = coefficient of correlation) and RSP (right, N = 215 units, linear regression, R = coefficient of correlation). Deep units in RSP were significantly more likely to be more similar to the population map. Error bars represent standard deviation, around mean at each depth bin.
a. Example ΔF/F trace (orange) and GLM fit (blue). b. Fraction of V1 ΔF/F signal explained using spiking, locomotion and LFP predictors (N = 5 mice). Grey lines from recordings from individual mice, blue indicates mean of each, with standard deviation. c. Same as b, for RSP (N = 5 mice). d. Fraction of V1 ΔF/F signal explained by a GLM modeled using spiking alone, for quiescence vs. locomotion (N = 5 mice, two-sided t-test). e. Same as d, for RSP (N = 5 mice, two-sided t-test).
a. Mean Pearson correlations with various cortical areas for all recorded V1 units during quiescence vs. locomotion. Units are sorted by population coupling, with highly-coupled units at the top. b. Same as a, for RSP. c. Summary of mean Pearson correlations for different areas for high- and low-coupled units recorded in V1. Significant differences indicated by * (p<0.05) or ** (p<0.01), (two-sided t-test, Bonferroni corrected for multiple comparisons, df = 54). d. Same as c, for RSP, (two-sided t-test, Bonferroni corrected for multiple comparisons, df = 84). e. Clusters representing RSP SpAMs for locomotion (L) vs. quiescence (Q) were determined for 5 mice using K-means clustering, and the optimal number of clusters was determined using the Calinski-Harabasz index for internal validation. For 4 out of the 5 mice, locomotion SpAMs yielded a higher optimal number of clusters than quiescence SpAMs, suggesting a richer overall diversity of locomotion SpAM patterns (two-sided Komogorov-Smirnov test, p = 0.04, N = 5 mice, 153 RSP units).
The mean SpAMs (Pearson correlation) of RSP units were more correlated with RSP during quiescence regardless of whether a. animals were in the dark (N = 3 mice, 2-dimensional correlation, two sided t-test, p = 5e-13) or b. navigating a closed-loop virtual reality corridor (N = 2 mice, 2-dimensional correlation, two sided t-test, p = 0.04), or shown visual stimuli (Fig. 3c).
a. (Left) LFP around a transition from aroused to passive brain state. (Right) Power in the delta band LFP over one recording session, indicating transitions between passive and aroused states. Velocity is shown in green; any epochs of velocity above 1 cm/s were excluded from the analysis. b. Example SpAMs, calculated for four units across passive and aroused epochs. c. 2-dimensional correlation of each RSP unit’s SpAMs across aroused and passive epochs (N = 171 units, 5 animals). d. Pairwise 2-dimensional correlations of each unit’s SpAM with all other SpAMs during passive and aroused epochs. e. RSP units showed no obvious remapping across licking and non-licking epochs. f. 2-dimensional correlation of RSP SpAMs across licking and non-licking epochs. Only SpAMs with >10% significant pixels in both lick and no lick conditions were included (N = 7 units, 3 animals). Mean value indicated with arrow.
a. (Top row) Mean spiking (with 95% confidence interval) for all RS V1 units at run onset (left) and offset (right), defined as any point where locomotion begins to exceed 3 cm/second (N = 108 units). Second row: mean spiking (with 95% confidence interval) for all FS units in V1 (N = 10 units). Third row: velocity profile at run onsets and offsets. Fourth row: ΔF/F profile at run onsets and offsets. b. Same as a, for RSP units (N = 131 RS units, N = 34 FS units). c. Example spike rasters at run onset for an example RS unit (top) and FS unit (bottom) in V1. d. Same as c, for RSP. e. Firing rate of V1 units when animals were stationary vs. during the first second after run onset (paired two-sided t-test, p < 0.01, FS units (N = 10 units), p < 0.01 RS units (N = 108 units)). f. Same as e, for RSP (paired two-sided t-test, p < 0.01, FS units (N = 34 units), p < 0.01 RS units (N = 131 units)).
a. Example activity traces from cells recorded in RSP (orange traces indicate PV+ units). b. Locomotion onset and offset-triggered activity traces in PV- and PV+ units (N = 4 mice). c. Mean traces (with 95% confidence interval) for PV+ and PV- neurons recorded in V1, averaged around locomotion onset. d. Mean traces (with 95% confidence interval) for PV+ and PV- neurons recorded in RSP, averaged around locomotion onset and offset. e. (Top) Example traces from 70 neurons, both PV+ and PV-, sorted by first principal component (PC1), with 35 chosen from either extreme end of the PC1 spectrum. (Bottom) Simultaneous velocity trace. f. More PV+ than PV- neurons were suppressed by locomotion (N = 1794 PV- neurons, N = 150 PV+ neurons, two-sided t-test, p = 0.007). g. Pairwise Pearson correlations for neurons during quiescence and locomotion (N = 492950 pairs, linear regression, R = coefficient of correlation, p = 8 e -11). Blue line indicates identity line for reference, black line indicates linear fit to data. h. More neurons with low correlations during quiescence (<25% percentile, N = 5 mice, 485 cells) had higher correlations during locomotion, and more units with high correlations (>75%, N = 5 mice, 486 cells) during quiescence had lower correlations during locomotion (all correlations Pearson). i. Same as h, from spiking data. More neurons with low correlations during quiescence (<25% percentile, N = 5 mice, 83 units) had higher correlations during locomotion, and more units with high correlations (>75%, N = 5 mice, 82 units) during quiescence had lower correlations during locomotion (all correlations Pearson).
a. Fold-change in firing rate induced by locomotion versus recording depth in V1 (N = 118 units, linear regression). b. Same as a, for RSP (N = 165 units, linear regression, R = coefficient of correlation). c. Mean (with 95% confidence interval) of all V1 units recorded at different depths triggered around run onset (left) and offset (right) (N = 118 units). d. Same as c, for RSP (N = 165 units). e. Mean (with 95% confidence interval) fluorescence traces for RSP cells recorded at different depths (N = 1942 cells, 4 animals), triggered around run onset (left) and offset (right).
a. Similarity of response to movie presentations during locomotion versus quiescence for V1 units (left, N = 105 units, two-sided paired t-test, mean Pearson correlation 0.05 ± 8e-4 during locomotion, 0.02 ± 3e-4 during quiescence, s.e.m.) and RSP units (right, N = 153 units, two-sided paired t-test, mean Pearson correlation 0.01 ± 2e-4 during locomotion, 0.005 ± 2e-4 during quiescence, s.e.m.). Mean values indicated by red bars. Unit responses were significantly more reliable during epochs of locomotion than quiescence. b. A unit’s response reliability plotted against its similarity to the population SpAM for V1 units (left) and RSP units (right). RSP units that were more similar to the population SpAM had more reliable visual responses than those anti- or uncorrelated with the population map. c. Pairwise noise correlations (Pearson) versus pairwise SpAM 2-dimensional correlation for V1 units (left, N = 1898 pairs, Bonferoni corrected two-sided t-test) and RSP units (right, N = 4359 pairs, Bonferoni corrected two-sided t-test).
a. Example SpAMs for 4 units recorded in medial superior colliculus (mSC), during quiescence and locomotion. b. Mean SpAMs for units located in superficial layers of mSC (left). Mean difference of SpAMs calculated during locomotion minus quiescence (right). On average, spiking activity in mSC was more correlated with RSP during locomotion than during quiescence (N = 3 animals, 5 recordings, mean difference SpAM for single and multi-unit activity). c. Same as b, for units located in deep layers of mSC.
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Clancy, K.B., Orsolic, I. & Mrsic-Flogel, T.D. Locomotion-dependent remapping of distributed cortical networks. Nat Neurosci 22, 778–786 (2019). https://doi.org/10.1038/s41593-019-0357-8
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