Gamma band rhythms may synchronize distributed cell assemblies to facilitate information transfer within and across brain areas, yet their underlying mechanisms remain hotly debated. Most circuit models postulate that soma-targeting parvalbumin-positive GABAergic neurons are the essential inhibitory neuron subtype necessary for gamma rhythms. Using cell-type-specific optogenetic manipulations in behaving animals, we show that dendrite-targeting somatostatin (SOM) interneurons are critical for a visually induced, context-dependent gamma rhythm in visual cortex. A computational model independently predicts that context-dependent gamma rhythms depend critically on SOM interneurons. Further in vivo experiments show that SOM neurons are required for long-distance coherence across the visual cortex. Taken together, these data establish an alternative mechanism for synchronizing distributed networks in visual cortex. By operating through dendritic and not just somatic inhibition, SOM-mediated oscillations may expand the computational power of gamma rhythms for optimizing the synthesis and storage of visual perceptions.
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The authors thank D. Taylor for technical assistance, K. Chesnov and D. Abdelhalim for help with histology, and J. Isaacson, M. Feller, D. Feldman, Y. Dan and B. Atallah for comments on the manuscript. This work was supported by NEI grant R01EY023756-01. H.A. is a New York Stem Cell Foundation – Robertson Investigator. This work was supported by The New York Stem Cell Foundation. J.V. was supported by grants from the Swiss National Foundation (P300PA_164719 and P2FRP3_155172). M.P.J. was supported by NEI grant 5K99EY025026-02 and Howard Hughes Medical Institute (HHMI). T.J.S. was supported by HHMI.
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Features of narrowband high and low gamma power in awake mice.
a) Plot of normalized high gamma power versus visual stimulus size (n = 25 mice, significant effect of size on center-frequency power, p < 0.001, Kruskal-Wallis ANOVA). Error bars are s.e.m. b) As for a) but for increasing visual stimulus contrast and fixed size of 12 degrees (n = 8 mice, significant effect of contrast on center-frequency power, p = 0.002, Kruskal-Wallis ANOVA). c) As for a) but for increasing luminance at 0% contrast (significant effect of luminance on high gamma power, p = 0.003, Kruskal-Wallis ANOVA, n = 4 mice). d) Example power spectra for increasing luminance and 0% contrast. e) Histogram of the center frequency of the narrowband high gamma peak across 25 mice. f) Representative power spectrum in a mouse while running and while still, in the absence of any visual stimulus (grey screen). Line width denotes mean ± s.e.m. g) Plot of the high gamma power between running and still conditions (p < 0.001, n = 22 mice, signed rank test). h) Example power spectra for visually-induced gamma across several stimulus sizes demonstrated a decrease in peak frequency with grating diameter. i) Population data for peak gamma frequency versus stimulus size (p = 0.014, n = 32 mice, significant effect of stimulus size on peak frequency, Kruskal-Wallis ANOVA, error bars are s.e.m).
Supplementary Figure 2 Preferred gamma phases of the spiking of different cell types.
a) Histogram of mean spike phases for each RS cell during stimulation with the largest size grating. 0˚ and 180˚ represent the peak and trough of the oscillation, respectively. The red arrow signifies the resultant vector length. The number denotes the circular mean of the distribution in degrees. b) Same as a) but for PV/FS cells. c) Same as a) but for SOM cells.
Supplementary Figure 3 Impact of SOM neuron suppression on cortical dynamics.
a) Example image of a V1 brain section from a SOM-Cre;Rosa-LSL-tdtomato mouse injected with a Cre-dependent AAV virus driving eNpHR3.0-YFP. Scale bar is 130μm. b) Scatter plots of gamma power in control conditions (black) and during optogenetic suppression of SOM cells. From left to right: relative gamma power (n = 14 mice, p < 0.001), peak/trough power (n = 13 mice, p = 0.03), absolute power when the illumination light turned on after the visual stimulus (n = 14 mice, p < 0.001), absolute power when the light turned on before the visual stimulus (n = 7 mice, p = 0.02), and absolute power in the high gamma band ~60Hz (n = 11 mice, p = 0.08). All tests: Wilcoxon signed rank test. c) Example PSTH from a putative SOM neuron expressing eNpHR3.0 in the absence (black) and presence (red) of light. d) Population average change of the power spectrum in SOM-Cre mice during optogenetic suppression. Upper and lower edge of filled area denote mean ± standard error (n = 14 mice). e) Scatter plot of the peak frequency of the visually-driven gamma rhythm and the peak frequency of the change in gamma power during SOM neuron optogenetic suppression (r = 0.78, p < 0.001, n = 14 mice). Data points have been jittered with Gaussian noise for display purposes only to avoid overlap. f) Average pairwise phase consistency spectrum for FS units in SOM-Cre mice in the absence (black) and presence (red) of light, n = 31 cells, thickness of line denotes mean ± standard error.
Supplementary Figure 4 Impact of PV neuron suppression on cortical dynamics.
a) Example image of a V1 brain section from a PV-Cre;Rosa-LSL-tdtomato mouse injected with a Cre-dependent AAV virus driving eNpHR3.0-YFP. Scale bar is 130μm. b) Scatter plots of gamma power in control conditions (black) and during optogenetic suppression of PV cells. From left to right: relative power (n = 18 mice, p = 0.06), peak/trough power (n = 18 mice, p = 0.95), absolute power when the illumination light turned on after the visual stimulus (n = 18 mice, p < 0.001), absolute power when the light turned on before the visual stimulus (n = 8 mice, p = 0.008), and absolute power in the high gamma band ~60Hz (n = 13 mice, p = 0.008). All tests: Wilcoxon signed rank test. c) Average pairwise phase consistency spectrum for RS units in PV-Cre mice in the absence (black) and presence (red) of light, n=105, thickness of line denotes mean ± standard error. d) Example PSTH of a putative eNpHR3.0-expressing FS/PV unit during visual stimulation in the absence (black) and presence (red) of light. e) Population average change of the power spectrum in PV-Cre mice during optogenetic suppression. Upper and lower edge of filled area denote mean ± standard error (n = 18 mice). f) Histogram of the optogenetic modulation index (OMI) for isolated regular spiking, putative excitatory units in SOM-Cre;eNpHR3.0 mice. g) Dose response curve for the change in absolute visually induced gamma power in four different conditions as a function of light intensity. Yellow: SOM-Cre;eNpHR3.0 mice; Light green: PV-Cre;eNpHR3.0 mice; Dark green: PV-Cre:eArCH3.0 mice; black: control mice not expressing any opsin. Error bars are s.e.m. h) As in g) but for induced gamma power. i) Example power spectra of the visual responses to a large grating with (red) and without (black) light in a SOM-Cre mouse not expressing eNpHR3.0. Thickness of line denotes mean ± standard error. j) Left: Example power spectra form a PV-CRE:eArCH3.0 mouse under control (black) and illumination (red). Right: Example LFP traces during visual stimulation with a large grating under control (black) and during illumination (red). Thickness of line denotes mean ± standard error. k) As in j), but in another mouse.
Supplementary Figure 5 Impact of SOM suppression on firing rates and synaptic currents in the model and in vivo.
a) Model prediction for the simulated optogenetic modulation index of PCs for various levels of SOM suppression with increasing surround levels in the model. b) As in a) but for PV neurons during SOM suppression. c) Histogram of the optogenetic modulation index (OMI) for isolated regular spiking, putative excitatory units in SOM-Cre;eNpHR3.0 mice. d) As in c) but for fast spiking units. e) Model prediction for the firing rate of PCs (black), PVs (green) and SOMs (yellow) as a function of total gamma power in the model with the standard parameters (same plot as Fig 3d). f) As in e) but the connection from SOM to PV has been set to zero. g) Example single trial traces of IPSCs in the absence (black) and presence (red) of light to suppress SOM cells. h) Average power spectra for IPSCs in control and photo-suppression conditions for the example cell in e).
Supplementary Figure 6 Impact of brain state and anesthesia.
a) Photo-stimulation of PV neurons in V1 of awake (black, n = 6 mice, same curve as Fig 4b), V1 of isoflurane-anesthetized (gray, n = 4 mice) and S1 of isoflurane anesthetized (blue, n = 3 mice) PV-Cre mice. Anesthesia shifts the peak resonant frequency (p<0.001, significant effect of anesthesia on fold increase (F = 10.01, d.f. = 1) and p<0.001 frequency-anesthesia interaction (F = 7.54, d.f. = 10), 2-way-ANOVA) but the curves for the different brain regions both peak at 48Hz (p=0.44, for effect of brain region on fold increase, 2-way-ANOVA, F = 0.6, d.f. = 1). Error bars denote s.e.m. b) Scatter plot of the absolute peak gamma power for optogenetic suppression of SOM and PV neurons in awake, but non-running conditions (SOM n = 11 mice, p < 0.001; PV: n = 13 mice, p = 0.001, Wilcoxon signed rank test). c) Scatter plot of the fold change of optogenetic suppression for SOM and PV mice comparing running and quiescence conditions.
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Veit, J., Hakim, R., Jadi, M. et al. Cortical gamma band synchronization through somatostatin interneurons. Nat Neurosci 20, 951–959 (2017). https://doi.org/10.1038/nn.4562
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