The Striatum Organizes 3D Behavior via Moment-to-Moment Action Selection

Abstract

Many naturalistic behaviors are built from modular components that are expressed sequentially. Although striatal circuits have been implicated in action selection and implementation, the neural mechanisms that compose behavior in unrestrained animals are not well understood. Here, we record bulk and cellular neural activity in the direct and indirect pathways of dorsolateral striatum (DLS) as mice spontaneously express action sequences. These experiments reveal that DLS neurons systematically encode information about the identity and ordering of sub-second 3D behavioral motifs; this encoding is facilitated by fast-timescale decorrelations between the direct and indirect pathways. Furthermore, lesioning the DLS prevents appropriate sequence assembly during exploratory or odor-evoked behaviors. By characterizing naturalistic behavior at neural timescales, these experiments identify a code for elemental 3D pose dynamics built from complementary pathway dynamics, support a role for DLS in constructing meaningful behavioral sequences, and suggest models for how actions are sculpted over time.

Keywords: basal ganglia; behavior; coding; direct pathway; ethology; indirect pathway; machine learning; mouse; photometry; striatum.

Figure 1. Motion Sequencing during neural recordings

A. AAVs expressing Cre-On jRCaMP1b and Cre-Off GCaMP6s were injected into the DLS to assess direct (dSPN, red) and indirect (iSPN, green) pathway activity via multicolor photometry (see Methods). Top, sagittal schematic of injection site. Middle and bottom, histological verification of jRCaMP1b (dSPN, red) and GCaMP6s (iSPN, green) expression (sagittal sections, SNr = substantia nigra pars reticulata, GP = globus pallidus). Scale bar, 1 mm. B. An example of simultaneous electrophysiological (top, black), firing rate (middle, yellow) and photometry recording (bottom, green, GCaMP6s, red, jRCaMP1b). C. Top, correlation between electrophysiologically-acquired firing rates and cre-independent photometry signals (red, jRCaMP1b; green, GCaMP6s), compared to time-shuffled electrophysiology (shading, 95% confidence interval). Bottom, same as top panel using the derivative of the photometry signals. D. Left, Experimental schematic. Three-dimensional (3D) imaging data is fed to the MoSeq algorithm, which outputs identified behavioral syllables and their transition statistics (color bars, lower and right, top). Middle, three examples of syllables, occurring successively over time, depicted as “spinograms,” in which the spine of the mouse is depicted as if looking at the mouse from the side, with time indicated as increasing color darkness. Bottom, isometric-view illustrations of the 3D imaging data associated with the “reared pause,” “dive,” and “locomotion” behavioral syllables. E. Example of MoSeq-defined syllables (top, individual syllables labeled with unique colors) aligned to dSPN (middle, red) and iSPN-related (bottom, green) photometry signals.

Figure 2. Direct and indirect pathway activity correlates with fast behavioral transitions

A. Correlation between velocity, direct pathway activity (red), and indirect pathway activity (green) at indicated time bins. B. Correlations between scalar variables and their derivatives to dSPN fluorescence signals (Pearson r, n = 189315 comparisons), iSPN fluorescence signals (n = 173577 comparisons), and to the difference between the two signals binned at the timescale of individual syllables (n = 146636 comparisons). * = p < 0.001, ** = p < 1×10e−10. C. Top, grand-averaged, z-scored dSPN (red) and iSPN (green) activity aligned to all syllable transitions. Bottom, derivative of top panel. Shading, time-shuffled 95% confidence interval. Note that all syllable-triggered averages are z-scored relative to the time-shuffled control (see Methods). D. Average z-scored dSPN and iSPN fluorescence levels for individual syllables. Top, syllable-triggered averages for each syllable shown for the direct and indirect pathways, sorted by the averages in the direct pathway (first positive then negative peaks). Bottom, same as Top except sorted by indirect pathway syllable-triggered averages. E. Top left, average peak z-scored direct and indirect pathway fluorescence signal (ΔF/F0) associated with each syllable (individual syllables identified via an arbitrary color code, with coding preserved across all top panels to illustrate relative dSPN and iSPN activity across individuals; Pearson r = 0.76, p < 1×10e−7, n = 41 comparisons). Top middle, example peak z-scored dSPN and iSPN fluorescence signals (ΔF/F0) from a single mouse. Top right, result of randomly shuffling syllable onsets. Bottom, z-scored dSPN and iSPN fluorescence signal for each syllable is plotted similarly to the top left but each syllable is now instead shaded by their average height (left), 2D velocity (middle), and 3D velocity (right) during the execution of the indicated syllable. F. Average dSPN (red) and iSPN (green) activity for four example syllables, each of whose pose dynamics were described by a human observer.

Figure 3. The direct and indirect pathways contain complementary codes for behavioral syllables

A. Syllables hierarchically ordered by model-based distance (see Methods). Dashed lines indicate human observer-specified boundaries between syllable classes. B. Six examples of average z-scored photometry signals, aligned to syllable onset (red, dSPNs; green, iSPNs). Colored boxes designate the syllable in panel A represented by the associated waveform. C. Left, correlations between behavioral distances and syllable-associated dSPN (red), iSPN (green), and combined (yellow) photometry signals (Pearson r, direct pathway p < 1×10e−10; indirect pathway p < 1×10e−9; both pathways p < 1×10e−10; n = 820 comparisons). Right, histograms describing residual correlations after shuffling relationships between syllable identities and photometry signals (1000 random shuffles, see Methods). D. Same as C bottom right, except here pathway identities were shuffled and the analysis was repeated 1,000 times (pathway shuffle, see Methods). E. Left, correlation between photometry signals and behavioral distance (here, defined using MoSeq, height, angle, and velocity, with each parameter weighted using LASSO regression) (Pearson r = 0.53, p < 1×10e−10, n = 820 comparisons, see Methods). Right, recovered LASSO regression weights that maximize the neural-behavioral correlations in left. F. Left, same dendrogram shown in A, with 4 example hierarchical cuts used for the decoding shown on the left displayed (see Methods). Middle, classifier decoding hit rate (y-axis) of syllable identity based upon either dSPN (red) or iSPN (green) waveforms alone, or their combination (yellow); classifier performance was evaluated at progressively deeper levels of the hierarchical clustering (left), with the outermost branch (the full set of 41 syllables) represented as cut 1. Shuffle represents the random assignment of syllable identity to the classifier. Note that the number of classes to decode decreases at higher hierarchical cuts, leading to increased chance performance. Right, ratio between the performance using both pathways and either dSPNs (red) or iSPNs (green) alone.

Figure 4. Sequence-dependent syllable representations in DLS

A. Schematic of an example syllable sequence. Throughout all panels in this figure, waveforms are aligned to syllable B (starred) and then sorted based on the likelihood of either the incoming transition, or both the incoming and outgoing transitions. B. Grand average photometry waveforms (red colors, dSPNs; green colors, iSPNs; n = 8 mice) for all syllables, separated by high or low probability of expression (here defined as above or below the 50^th percentile probability, respectively). Top, waveforms sorted based upon the summed probability of incoming and outgoing syllables. Bottom, waveforms sorted based upon the probability of the incoming syllable. C and D. Same as B but controlling for the 3D velocity of the incoming syllable (n = 8 mice). Velocities were grouped by whether they were above or below the 50^th percentile of average velocity prior to syllable onset. Syllables were sorted by either high or low incoming velocity, and the associated neural waveforms were then sorted by either high or low probability of combined (C) or incoming (D) syllable expression. E. Individual syllables analyzed using the same scheme shown in A.

Figure 5. Direct and indirect pathway neural ensembles encode syllable identity

A. Example miniscope field of view (FOV) from a mouse with both dSPNs and iSPNs labeled. Scale bar = 100 µm. B. Normalized fluorescence of individual neurons extracted using CNMF-e (top, see Methods) aligned to behavioral syllables (bottom, each syllable uniquely color coded). C. Proportion of active direct (red), indirect (green), and both pathway (yellow) neurons aligned to syllable onset (see Methods). Time-shuffled data is shown in gray (95% confidence interval). Shading indicates bootstrap SEM. D. Left columns, peak-normalized fluorescence averaged across all instances of the same syllable, in this case a “rear up.” All neurons from all mice are included in this representation. Cells with positive peaks were sorted from earliest to latest, then cells with negative-going peaks were sorted from latest to earliest. Single trials (all other columns) are shown for all neurons, merged across mice (see Methods). In each panel here (and in E) cellular traces are normalized individually to emphasize responses. E. Top, peak-normalized fluorescence traces for five different syllables aligned to syllable onset, with human annotations above. Cells are sorted as in D. Bottom, proportion of dSPNs (red) and iSPNs (green) active aligned to syllable onset (defined as exceeding two SDs above the mean ΔF/F0). Shading indicates 95% bootstrap confidence interval. F. Left, cumulative distribution function of ensemble correlations, computed using different examples of the same syllable (black) or examples of different syllables (gray) for the direct pathway (top), indirect pathway (middle), or both (bottom). Right, histogram of the average within syllable correlation after shuffling syllable identities. G. Histogram of correlations between neural activity distance and Moseq-defined behavioral distance for dSPNs (red), iSPNs (green), or both cell types (yellow). H. Left, Decoding accuracy along different cuts of the behavioral hierarchy for dSPNs (red), iSPNs (green), and both cell types (yellow) (as in Figure 3F, syllables are clustered into larger groups moving from cut 1 to cut 6). Right, decoding accuracy for individual syllables as a function of the number of neurons provided to the decoder. Shading indicates 99% bootstrap confidence interval. Chance performance increased at higher hierarchical cuts given fewer classes to decode. I, Left, Distribution of single neuron modulation with respect to syllable sequence frequency. Lower indices indicate higher activity for low probability transitions, while higher indices indicate higher activity for high probability transitions (see Methods). Cells significantly modulated relative to a shuffle control (gray line) are highlighted in dark and light gray. Middle, Each pair of dots indicates the proportion of trials a given neuron was active across all syllables for high (HiP) and low (LoP) transition probabilities. Shown are neurons that increase activity for high transition probability examples (left, light gray) and low transition probability examples (middle, dark gray) from Left. J. Percent of cells that have a significant modulation index (p<.05 relative to shuffle controls) reveals a greater number of inhibited than activated neurons during high probability sequences.

Figure 6. Simultaneous imaging of dSPNs and iSPNs reveals pathway decorrelations

A. Average cross-correlations compared to the average cross-correlation of cell-type-identity-shuffled traces when the animal’s 3D velocity exceeded the 50^th percentile. For all comparisons between the observed correlations and identity-shuffled traces, p<.01. B. Left, similar to Figure 5F, cumulative distribution function of ensemble correlations for separate examples of the same syllable (black, within syllable) and examples of different syllables (gray, between syllable). Right, histogram of average within syllable correlation after shuffling syllable identities. Red line shows the observed average correlation before shuffling. C. Top, decoding accuracy of all syllables in psuedopopulations of separately recorded iSPNs (green) and dSPNs (red) as a function of neuron number. Decoding performance using both iSPNs and dSPNs shown in yellow. Middle, decoding accuracy using psudopopulations of simultaneously recorded iSPNs and dSPNs (see Methods). Bottom, example decoding accuracy using simultaneously recorded iSPNs and dSPNs from the same mouse (orange), and from a pseudopopulation of iSPNs and dSPNs where both the iSPNs and the dSPNs used were from a single animal (yellow). D. Left, performance of a classifier predicting pathway identity of separately recorded dSPNs and iSPNs (see Methods) using MoSeq (blue dashed line), scalars (blue dotted line), or both combined (blue solid line), as a function of the confidence criterion for the classifier (percent of neurons meeting criterion shown in red, see Methods). Right, same as left, except predicting the class of simultaneously recorded dSPNs and iSPNs. Note that the noise in percent correct is due to finite size effects (i.e. there are fewer neurons to classify in the test set).

Figure 7. Excitotoxic lesions of the DLS alter the expression and sequencing (but not number or content) of behavioral syllables

A. Coronal schematic of NMDA and saline injection sites. Scale bar = 1 mm. B. Syllables expressed in saline and DLS lesioned mice, ordered by differential usage, sorted with the most “lesion-downregulated” syllables on the left and “lesion-enriched” syllables on the right (asterisks = p < 0.05, two-sample t-test corrected for false discovery rate. C. Average distance between the 3D pose dynamics that define each syllable (behavioral distance) for each instance of the same syllable within a condition (i.e., sham or lesion), for each syllable instance in both conditions, and for all instances of a given syllable and for all other syllables in both conditions (see Methods). Each circle indicates a different syllable. D. Statemap depiction of syllables (as nodes) and transition probabilities (as edges, here weighted by bigram probability, the probability of one syllable occurring after the other) from control (sham, left, n = 3 mice) injections. Heatmap depiction of the difference (red = upregulated transition, blue = downregulated transition) between this control statemap and the DLS lesion statemap shown on right (n = 5 lesioned mice). E. The first-order transition entropy rate between syllables in sham and lesioned cases, demonstrating that DLS lesioned mice exhibit less predictable behavioral sequences (p < 0.01 ranksum, z = −2.79; n = 24 lesion trials; n = 15 sham trials). Each circle represents a trial. F. Top, average occupancy heatmap for sham mice during a session without odor (left), during an odor presentation session (middle), and the difference between these two sessions (right). Bottom, same as top but for lesioned mice. Gray circles mark the approximate location of the odor source. G. The difference in the amount of time spent within 10 cm of the odor source (dot in F), between pre-odor and odor trials for sham and lesioned mice (p < 0.01 ranksum; ranksum value 16; n = 5 sham trials; n = 8 lesion trials).