Ensemble encoding of action speed by striatal fast-spiking interneurons

Abstract

Striatal fast-spiking interneurons (FSIs) potently inhibit the output neurons of the striatum and, as such, powerfully modulate action learning. Through electrical synaptic coupling, FSIs are theorized to temporally coordinate their activity. This has important implications for their ability to temporally summate inhibition on downstream striatal projection neurons. While some in vivo single-unit electrophysiological recordings of putative FSIs support coordinated firing, others do not. Moreover, it is unclear as to what aspect of action FSIs encode. To address this, we used in vivo calcium imaging of genetically identified FSIs in freely moving mice and applied machine learning analyses to decipher the relationship between FSI activity and movement. We report that FSIs exhibit ensemble activity that encodes the speed of action sub-components, including ambulation and head movements. These results suggest FSI population dynamics fit within a Hebbian model for ensemble inhibition of striatal output guiding action.

Keywords: Basal ganglia; Calcium imaging; Endoscope; Ensemble; GABA; Inhibition; Kinematics; Striatum.

Figure 1:. Coordinated activity of fast-spiking interneurons (FSIs) in vivo correlates with movement speed.

(A) Left: Representative ΔF/F signals from a striatal FSI expressing GCaMP6f along with representative action potential firing in FSIs recorded using whole cell patch clamp recordings in current clamp configuration. Increasing firing frequencies were induced by progressively larger current steps (translating to depolarization increments of 10 mV). Right: Relationship between firing rate and maximum ΔF/F signal plotted per cell. The average within cell r was 0.99. Scale bars: 30 mV (vertical) and 125 ms (horizontal). (B) Immunostaining of GCaMP6f and the placement of the gradient refractive index lens (GRIN lens) in the dorsal striatum. CC: corpus callosum. (C) Left: Representative image of striatal FSIs expressing GCaMP6f in vivo. The colored numbers correspond to the Ca2+ transients in these neurons at right. Scale bar: 50 μm. Right: Speed trace for one representative animal (black) and GCaMP6f traces for FSIs demonstrating pairs of FSIs have similar firing patterns (cells 1 and 2; cells 3 and 4; cells 5, 6 and 7). Scale bars: 4 cm/s, 10% ΔF/F, 10 s. (D) Left: Cross-correlogram of ΔF/F signal from FSI pairs shows a significant coordination of FSI-FSI ΔF/F signals (mean ± SEM, shaded region). Dotted line represents level of significance (p < 0.001). Center: Average Fisher z transformation of maximal FSI-FSI cross-correlation r values across mice. Right: Distribution of Fisher z transformation values significantly deviated from a normal distribution. (E) Left: Cross-correlogram of ΔF/F signal to speed (FSI-speed) shows a significant correlation of FSI ΔF/F signals and speed (mean ± SEM, shaded region). Dotted line represents level of significance (p < 0.001). Center: Average Fisher z transformation of maximal FSI-speed cross-correlation r values across mice. Right: The distribution of Fisher z transformation values did not deviate from a normal distribution. (F) Average FSI ΔF/F signal across mice (red traces) was examined within 2 s epochs (dotted lines) of two mobility states: around local speed peaks (gray trace; left) and inactive periods (gray trace, center). Right: Average FSI ΔF/F signal was significantly greater around speed peaks relative to periods of inactivity. ** p < 0.005.

Figure 2:. Ensembles of FSIs encode distinct movements.

(A) Left: Example of an ambulatory movement. Right: Example traces illustrating the relationship between ΔF/F signal from a striatal FSI preferentially responding to ambulation speed (red) and ambulation speed isolated from the overall speed signal (gray). (B) Left: Example of a head movement from the same mouse shown in (A). Right: Example traces illustrating the relationship between ΔF/F signal from a striatal FSI preferentially responding to head movement speed (blue) and head movement speed isolated from the overall speed signal (gray). (C) The breakdown of striatal FSIs for which ΔF/F signal preferentially encoded ambulation (ambulation) or head movements (head), as well as FSIs that encoded both ambulation and head movements equally (ambulation/head) or FSIs that did not encode either ambulation or head movement (stochastic). (D) For ambulation FSIs, the cross-correlation between ΔF/F signal and isolated ambulation speed, as well as overall speed, was greater than the cross-correlation to head movement speed. **** p < 0.0001. (E) For head FSIs, the cross-correlation between ΔF/F signal and isolated head movement speed was greater than the cross-correlation to ambulation speed. In addition, for these FSIs the cross-correlation of ΔF/F signal to overall speed was greater than to ambulation speed. *p < 0.05, **** p < 0.0001. (F)For ambulation/head FSIs, the cross-correlation between ΔF/F signal and overall speed was greater than the cross-correlation to ambulation speed and to head movement speed. *p < 0.05. (G)For stochastic FSIs, cross-correlations between ΔF/F signals and overall speed, ambulation speed, and head movement speed did not differ. (H) FSI-FSI cross-correlations of ΔF/F signals were significantly greater between ambulation FSIs and other ambulation FSIs (ambulation - ambulation) compared to ambulation - head and ambulation - stochastic cross-correlations; whereas ambulation - ambulation and ambulation - ambulation/head cross-correlations were not different. *** p < 0.001, **** p < 0.0001.

Figure 3:. FSI population activity accurately predicts action speed.

(A) Schematic of the workflow for applying a generalized boosted model (GBM) machine learning algorithm to predict movement using in vivo Ca2+ imaging data. (B). Left: Representative traces are shown of observed speed (black trace) and GBM predicted speed (gray trace). Right: Dot plot of the correlation between observed and predicted speed for each animal. Scale bars: 2 cm/s, 500 s. (C) Left: Root mean square error (RMSE) magnitude is plotted for each mouse for the GBM predicting speed using Ca2+ imaging data from FSI ensembles as predictors (red bars); using shuffled data of all FSIs as predictors (10 different permutations, gray bars); performing the GBM with each FSI individually as a predictor and taking the mean of the resulting RMSEs (white bars); and using the mean activity of all FSIs as the sole predictor (dark gray bars). Right: Mean RMSE across animals for the four different GBM analyses shown on the left. One-way ANOVA revealed significantly less error between the observed and predicted speed for the GBM using all FSIs as predictors compared to the shuffled, individual, and mean FSI activity models. *** p < 0.001, ** p < 0.01. (D) Left: Representative traces are shown of observed acceleration (black trace) and GBM predicted acceleration (gray trace). Right: Dot plot of the correlation between observed and predicted acceleration for each animal. Scale bars: 10 cm/s2, 500 s. (E) Left: RMSE is plotted for each mouse for the GBM predicting acceleration using Ca2+ imaging data from FSI ensembles as predictors (red bars); using shuffled data of all FSIs as predictors (10 different permutations, gray bars); performing the GBM with each FSI individually as a predictor and taking the mean of the resulting RMSEs (white bars); and using the mean activity of all FSIs as the sole predictor (dark gray bars). Right: Mean RMSE across animals for the four different GBM analyses shown on the left. One-way ANOVA revealed significantly less error between the observed and predicted acceleration for the GBM using all FSIs as predictors compared to the shuffled, individual, and mean FSI activity models. ** p < 0.01.