Extended Data Fig. 7: Results of influence regression are robust to potential artefacts from data processing and off-target photostimulation.
From: Single-neuron perturbations reveal feature-specific competition in V1
a, Analysis of influence effects directly in ΔF/F traces. ΔFluorescence was calculated as for ΔActivity, but using ΔF/F traces rather than trial-averaged deconvolved activity. ΔFluorescence was significantly negative in the 1 s following neuron photostimulation relative to control; n = 153,689 neuron site pairs and 90,705 control site pairs. Neuron versus control site: P = 6.79 × 10−15, Mann–Whitney U-test. Data in all plots shown as mean ± s.e.m. calculated by bootstrap. b, ΔFluorescence in non-targeted neurons following photostimulation of neurons at varying distances. n = 1,822 near pairs, 35,541 mid-range pairs, 35,882 far pairs. Near versus mid-range: P = 7.62 × 10−19; near versus far: P = 5.0 × 10−6; mid-range versus far: P = 1.21 × 10−47; Mann–Whitney U-test. c, As in b, but without neuropil subtraction, or any source de-mixing from CNMF; traces were extracted by projecting raw movies onto neuron ROIs. n = 1,822 near pairs, 35,541 mid-range pairs, 35,882 far pairs. Near versus mid-range: P = 5.96 × 10−28; near versus far: P = 5.21 × 10−38; mid-range versus far: P = 4.15 × 10−13; Mann–Whitney U-test. This indicates that distance-dependent influence effects were not an artefact of source extraction algorithms. d, The influence regression from Fig. 3d was applied to ΔFluorescence traces. This regression resulted in beta coefficients for traces at each time frame relative to photostimulation onset, which are plotted over time. Coefficients for slopes for the three distance bins are plotted. The same size and ordering of effects is apparent as when using deconvolved data and the ΔActivity metric (compare to Fig. 3). Mean ± s.e.m. calculated using 10,000 coefficient estimates by bootstrap resampling. All coefficients were significantly different from zero, averaged over 0–1,000 ms from photostimulation onset, with P < 1 × 10−4 by bootstrap. e, As in a but for signal and noise correlation coefficients. Averaged over 0–1,000 ms from photostimulation onset, signal correlation coefficients were significantly less than zero with P = 0.0008 and noise correlation was greater than zero with P = 0.0154, estimated by bootstrap. f, Similar to regression analysis in Fig. 3d, e, but as a test of potential off-target effects. Instead of using only the photostimulated neuron’s activity and tuning properties to calculate correlations with the non-targeted neuron, properties of multiple nearby neurons were used, to test whether off-target photostimulation of nearby cells could underlie the observed effects (see Methods). This is equivalent to influence regression using identical influence values and distance predictors as in Fig. 3e, but changing all activity predictors. Only distance effects were apparent, as expected, whereas activity-related effects were absent. This suggests that the properties of the individually targeted neuron were responsible for the influence relationships we observed. Plots show bootstrap distribution with median estimate as grey line, 25–75% interval as box, 1–99% interval as whiskers. Left, coefficients for piece-wise linear distance predictors from the model. Significance estimated by bootstrap: 25–100 μm, offset P = 0.0982, slope P < 1 × 10−4; 100–300 μm, offset P < 1 × 10−4, slope P < 1 × 10−4; >300 μm, offset P = 0.0018, slope P = 0.0316. Right, coefficients for activity predictors from the same model. Signal correlation, P = 0.9370; signal × distance interaction, P = 0.4072; noise correlation P = 0.8772; noise × distance interaction, P = 0.5138; signal × noise interaction P = 0.5260; n = 64,485 pairs.
