Striatal activity topographically reflects cortical activity

Abstract

The cortex projects to the dorsal striatum topographically^1,2 to regulate behaviour^3-5, but spiking activity in the two structures has previously been reported to have markedly different relations to sensorimotor events^6-9. Here we show that the relationship between activity in the cortex and striatum is spatiotemporally precise, topographic, causal and invariant to behaviour. We simultaneously recorded activity across large regions of the cortex and across the width of the dorsal striatum in mice that performed a visually guided task. Striatal activity followed a mediolateral gradient in which behavioural correlates progressed from visual cue to response movement to reward licking. The summed activity in each part of the striatum closely and specifically mirrored activity in topographically associated cortical regions, regardless of task engagement. This relationship held for medium spiny neurons and fast-spiking interneurons, whereas the activity of tonically active neurons differed from cortical activity with stereotypical responses to sensory or reward events. Inactivation of the visual cortex abolished striatal responses to visual stimuli, supporting a causal role of cortical inputs in driving the striatum. Striatal visual responses were larger in trained mice than untrained mice, with no corresponding change in overall activity in the visual cortex. Striatal activity therefore reflects a consistent, causal and scalable topographical mapping of cortical activity.

Extended Data Fig. 1. Task performance.

a, Timeline of events in a trial. After 0.5 s with no wheel movement, a stimulus appears. The mouse may turn the wheel immediately, but it only becomes yoked to the stimulus after a further 0.5 s, at which time an auditory Go cue is played. If the mouse drives the stimulus into the centre, a water reward is delivered and a new trial begins after 1s; if the mouse drives the stimulus off the screen away from the centre, a white noise sound is played and a new trial begins after 2 s. b, Psychometric curve showing task performance: the fraction of choices as a function of stimulus contrast and side. Curve and shaded region show mean ± s.e. across sessions. c, Median reaction time as a function of stimulus contrast and side as in (b) (mean ± s.e. across each session’s median). Dotted horizontal line indicates time of auditory Go cue. d, Histogram of times from stimulus to movement onset (reaction time) by trial quartile within sessions (first quarter of trials in the session in black, last quarter in beige; mean ± s.e. across mice). In early trials, mice typically begin moving the wheel before the Go cue. Later in the sessions, they waited more often for the Go cue.

Extended Data Fig. 2. Cortical widefield alignment.

a, Example widefield images from one mouse, used to align vasculature. b, Retinotopic visual field sign maps corresponding to the sessions in (a). c, Retinotopic maps averaged across all sessions for three example mice, used to align widefield images across mice. d, Retinotopic map averaged across mice and symmetrized, used to align widefield images to the Allen CCF atlas (used only for figure overlay purposes). e, Cortical seed pixels (left) and corresponding pixel-pixel correlation maps (right). Each pixel-pixel correlation map (right) is made by correlating a given pixel with all other pixels, which reveals clusters of pixels belonging to correlated cortical regions (e.g. the central circles corresponding to limb somatomotor cortex). f, Summed edge-filtered pixel-pixel correlation maps (as in e) showing the outlines of correlated clusters of pixels. Each pixel-pixel correlation map (as in e) highlights a correlated cluster of pixels. Edge-filtering each pixelpixel correlation map then draws a boundary around the highlighted correlated cluster. Summing these edges across all pixel-pixel correlation maps illustrates the boundaries of all correlated clusters of pixels, prominently including limb somatomotor cortex (central circles), visual cortex (posterior lateral triangular regions), retrosplenial cortex (posterior medial region), and orofacial somatomotor cortex (frontal lateral regions). The Allen CCF regions aligned using retinotopy (red) align well to correlation borders, indicating that our alignment methods based on posterior retinotopy also successfully align anterior regions.

Extended Data Fig. 3. Striatal recording locations and electrophysiological borders.

a, Top, widefield images used to approximate probe location (red line); middle/bottom, horizontal and coronal views of the brain with widefield-estimated probe location (red line) and histologically verified probe location (green line). Black outline, brain; blue outline; dorsal striatum, purple outline; ventral striatum. Widefield-estimated probe locations closely match histologically verified probe locations. b, Widefield-estimated probe location of all trained mice plotted in Allen CCF coordinates. c, Example histology showing GCaMP6s fluorescence (green) and dye from the probe (red). d, Example multiunit correlation matrix by location along the probe for multiple sessions in the mouse from (c), with the borders of the striatum approximated medially by the lack of spikes in the ventricle and laterally by the sudden drop in local multiunit correlation. Dye from (c) corresponds to session 1 in (d) and histology-validated regions are labelled.

Extended Data Fig. 4. Relationship of cortical spiking with cortical fluorescence and with striatal spiking.

a, Example triple recording with widefield imaging, VISam electrophysiology, and striatal electrophysiology during the task. b, Deconvolution kernel obtained by predicting cortical multiunit spikes from cortical fluorescence around the probe (black, mean; grey, individual sessions). c, Current source density (CSD) from average stimulus responses aligned and averaged across sessions, used to identify superficial and deep cortical layers. Horizontal dashed line represents the estimated border between superficial and deep layers. d, Correlation of VISam spiking with deconvolved fluorescence (green) and DMS spiking (black) (mean ± s.e. across sessions). Cortical fluorescence and striatal spiking are both correlated to cortical spiking along a similar depth profile (correlation between fluorescence and striatal depth profiles compared to depth-shifted distribution, r = 0.57 ± 0.14 mean ± s.e. across 10 sessions, p = 9.0*10^-4). e, Correlation of VISam spiking with fluorescence deconvolved with a kernel created only using superficial spikes (blue) or deep spikes (orange) (mean ± s.e. across sessions). Inset: deconvolution kernels as in (b) created using only superficial or deep spikes. Kernels created from superficial or deep spikes are not different (2-way ANOVA on time and depth, depth p = 1 across 10 sessions) and correlation between spikes and deconvolved fluorescence does not depend on kernel (2way ANOVA on depth and kernel, kernel p = 0.97), indicating the deconvolution kernel is related to GcaMP6s dynamics consistently across depths. f, Cross-correlation of multiunit activity across superficial cortex, deep cortex, and DMS. Inset: zoomed-in plot. Deep cortical spiking leads striatal spiking by ~3ms (orange vertical line).

Extended Data Fig. 5. Striatal activity during trials with ipsilateral stimuli and ipsilaterally-orienting movements.

a, Activity for each striatal domain across all trials from all sessions with ipsilateral stimuli, ipsilaterally-orienting movements, and rewards, formatted as in Fig. 3a. Trials are sorted vertically by reaction time; blue line: stimulus onset, orange curve: movement onset, yellow line: Go cue. Activity within each timepoint is smoothed with a running average of 100 trials to display across-trial trends. b, Prediction of activity in each striatal domain by summing kernels for task events, formatted as in Fig. 3c. c, Prediction of striatal activity from cortical activity, formatted as in Fig. 3d. d, Trial-averaged activity in each striatal domain (black), predicted from task events (blue), and predicted from cortical activity (green), aligned to stimulus (blue line), movement (orange line), and reward (cyan line) (mean ± s.e. across sessions), formatted as in Fig. 3e.

Extended Data Fig. 6. Visual responses in dorsomedial striatum do not depend on upcoming movement choice and responses to the auditory Go cue are suppressed by ongoing movement.

a, Curves show average stimulus response (0-0.2 s after stimulus onset) in DMS, as a function of contrast and side, for trials with < 500 ms reaction times and contralateral-orienting (purple) or ipsilateral-orienting (orange) movements (mean ± s.e. across sessions). Movement choice does not affect stimulus responses, indicating that stimulus responses are purely sensory, rather than linked to decisions (2-way ANOVA on stimulus and choice, interaction p = 0.56 for 77 sessions). b, Go cue kernel (lag = 50 ms after Go cue shown) obtained when fitting cortical activity from task events, for trials with movement onset before the Go cue (top) and after the Go cue (bottom). c, Go cue kernel obtained when fitting activity in each striatal domain from task events as in Fig. 3b, for trials with movement onset before the Go cue (black) and after the Go cue (grey). Note that responses to the Go cue are much larger in parietal cortex and DMS when the mouse is not moving.

Extended Data Fig. 7. Task kernels for cortical activity associated with each striatal domain match task kernels for striatal activity.

a, Cortical maps used to define cortical activity associated with each striatal domain (from Fig. 2f, producing activity in Fig. 3d). b, Temporal kernels obtained when fitting cortical activity from task events for stimuli (left), movements (middle), and outcome (right) (mean ± s.e. across sessions), formatted as in Fig. 3b. c, Correlation of task kernels for striatal and cortical activity. Columns from left to right: correlation of associated striatal and cortical kernels, within the same session; correlation of striatal kernels for different domains within the same session; correlation of striatal kernels from different sessions but the same striatal domain, and correlation of cortical kernels obtained from different sessions, but associated with the same striatal domain. Grey lines show single sessions; black points and error bars show mean ± s.e. across mice. The kernels obtained for associated striatal domains and cortical regions are more correlated than kernels for different striatal domains (signed-rank test, p = 6.1*10^-5 for 15 mice), indicating task kernels are domain-specific and shared between associated cortical and striatal regions. Correlations are also higher between associated striatal and cortical activity within-sessions, than between kernels fit to the same striatal domain on different sessions (signed-rank test, p = 1.2*10^-4), indicating that differences between cortical and striatal task responses are smaller than session-to-session variability. d, Cross-validated fraction of variance explained by task events for striatal activity vs. associated cortical activity. Small dots, sessions; large dots, mean ± s.e. across sessions; colour, striatal domain. Explained variance from task events is correlated between the cortex and striatum (correlation, r = 0.68 p = 1.14*10^-10 across 77 sessions).

Extended Data Fig. 8. Prediction of striatal activity from subregions of cortex, from other striatal domains, and from the cortex during passive periods.

a, Activity in each striatal domain predicted from subregions of cortex (indicated by white regions in diagrams below x-axis) or from the other two striatal domains (far right). Each curve shows the relative cross-validated fraction of explained variance ((R²region - R²full cortex) /R²full cortex) for the color-coded striatal domain (mean ± s.e. across sessions). Predictions are best from the associated cortical regions (2-way ANOVA on session and cortical subregion, subregion DMS p = 4.6*10^-3, DCS p = 4.8*10^-5, DLS p = 3.1*10^-85 across 77 sessions) and striatal activity is less well predicted from other striatal domains than from cortex (signed-rank test, DMS p = 1.6*10^-10, DCS p = 2.1*10^-5, DLS p = 8.4*10^-10 across 77 sessions). b, Example wheel trace, deconvolved cortical fluorescence, visual cortical electrophysiology, and striatal electrophysiology session in the passive context (viewing visual noise stimuli), showing coherent low-frequency oscillations in VISam and DMS (formatted as in Extended Data Fig. 4a, from the same session session). c, Cross-validated fraction of striatal variance explained from cortex in task vs. passive contexts. Small dots, sessions; large dots, mean ± s.e. across sessions; colour, striatal domain. DMS is predicted slightly better from cortex in the passive state, but DCS and DLS are predicted slightly worse the passive state (signed-rank test, DMS p = 6.2*10^-7, DCS p = 1.9*10^-4, DLS p = 1.6*10^-5 across 77 sessions). d, Variance of striatal activity across task and passive states, legend as in (b). During the passive state, DMS exhibits more variance while DCS and DLS exhibit less variance (signed-rank test, DMS p = 3.0*10^-6, DCS p = 1.3*10^-4, DLS p = 1.1*10^-10 across 77 sessions), matching the differences in predictability between states. e, Cross-validated fraction of striatal explained variance from the cortex vs. variance of striatal activity during task performance, legend as in (b). Cortex-explained variance is consistently related to activity variance across domains (ANCOVA, domain p = 0.22, domain-activity variance interaction p = 0.76).

Extended Data Fig. 9. Visual cortical inactivation selectively eliminates striatal visual responses.

a, Cortical unit firing rate change across topical muscimol application. Horizontal dotted line indicates bottom edge of cortex. Topical muscimol effectively silences the full cortical depth. b, Deconvolved cortical fluorescence standard deviation (top) and retinotopic visual field sign (bottom) before and after muscimol application. Muscimol was centred on VISam and spread laterally to other visual areas. c, Relative firing rate change in each striatal domain before and after cortical inactivation (dots are sessions). Firing rate increases slightly after cortical inactivation (signed-rank test, DMS p = 0.04, DCS p = 0.02, DLS p = 0.02 across 22 sessions). d, Passive responses to visual stimuli in cortical ROIs (left) and corresponding striatal domains (right) before (black) and after (yellow) inactivation of visual cortex. Muscimol reduced the stimulus response in VISam and DMS proportionally within each session (correlation between fractional reduction of each area: r = 0.48, p = 0.04 across 22 sessions). e, Psychometric curve (left) and median reaction (right) as a function of stimulus contrast and side as in Extended Data Fig. 1b, before (black) and after (yellow) muscimol in visual cortex (mean ± s.e. across sessions). Task performance becomes worse asymmetrically across stimuli (2-way ANOVA on stimulus and condition, interaction p = 0.04 across 22 sessions) and reaction times become longer across stimuli (2-way ANOVA on stimulus and condition, condition p = 1.1*10^-26 across 22 sessions). f, Kernels using task events to predict striatal activity before (black) and after (yellow) visual cortical muscimol (mean ± s.e. across sessions). Stimulus kernel weights decrease after muscimol while other kernel weights do not change significantly (2-way ANOVA on regressor and condition, condition effect on stimuli regressors DMS p = 2.0*10^-7, DCS p = 8.4*10^-6, DLS p = 0.03, p > 0.05 for other domains and regressors across 22 sessions).

Extended Data Fig. 10. Identifying striatal cell types with electrophysiology, and unidentified interneuron (UIN) activity.

a, Electrophysiological properties used to classify striatal cell types. Striatal cells were identified as medium spiny neurons (MSNs), fast-spiking interneurons (FSIs), tonically-active neurons (TANs) and a fourth class of unidentified interneurons (UINs), according to waveform duration, length of post-spike suppression, and fraction of long inter-spike intervals. b, Histogram of firing rates across all units within each cell type. c, Number of units in each striatal domain classified as each cell type. d, Averaged and smoothed firing rates (lines) and raster plots across trials (dots) for one example cell of each type in each domain, aligned to the indicated task event. Top row: DMS; middle row, DCS; bottom row, DLS. e, Waveform and autocorrelogram of unidentified interneurons (UINs) (mean ± s.d. across cells). f, Heatmaps, spiking in individual cells aligned to contralateral stimuli (left), contralaterally-orienting movements (middle), and rewards (right), averaged across trials with reaction times less than 500 ms, max-normalized, and sorted by time of maximum activity using half of the trials and plotting the other half of trials, formatted as in Fig. 4b. Line plots, average activity across neurons, formatted as in Fig. 4c. g, Correlation of the activity of each neuron (rows within heatmaps of c) with the average activity within cell types or cortical activity from an ROI corresponding to each domain, calculated from non-overlapping sessions to account for interneuron sparsity (mean ± s.e. across sessions), formatted as in Fig. 4d. UINs were equally correlated to other UINs, MSNs, FSIs, and cortical activity (2-way ANOVA on firing rate and type, type p = 0.56 across 77 sessions) and uncorrelated to TAN activity (2-way ANOVA on firing rate and type, type p = 2.7*10^-12 across 77 sessions). h, Activity during passive stimulus presentations in untrained (black) and trained (yellow) mice (mean ± s.e. across sessions), activity increases in DMS and DCS (time window 0-0.2 s, rank-sum test, DMS: p = 5.5*10^-4, DCS: p = 1.4*10^-4 across 77 sessions).

Figure 1. Cortex and striatum show spatial gradients of sensorimotor activity during visually guided behaviour.

a, Cartoon of the task, showing a mouse turning a steering wheel surrounded by three screens. b, Timeline of task events from an example session, showing gratings of various contrasts appearing on the left or right of the animal (top) and velocity of the steering wheel (bottom). c, Cortical activity measured by widefield calcium imaging during the period in (b). Deconvolved fluorescence traces are shown for orofacial and limb primary motor cortex (MOp), secondary motor cortex (MOs), and primary visual cortex (VISp). d, Spikes measured simultaneously across the dorsal striatum during the same period. e, Deconvolved cortical fluorescence maps, at five timepoints averaged across all trials of all sessions with right-hand stimuli, correct counter-clockwise wheel turns, and reaction times < 500 ms. MOp and MOs: primary and secondary motor cortex; SSp: Primary somatosensory cortex; VIS: visual cortex; RSP: retrosplenial cortex. f, Mean multiunit firing rate in striatum as a function of location and time, averaged over the same events as in (e). The three grayscale panels represent activity aligned to visual stimulus onset (red line); contralaterally-orienting movements (purple line), and reward delivery (cyan line). Firing rates are arranged by distance from the lateral striatal border, averaged across sessions, and max-normalized.

Figure 2. Striatal domains are topographically correlated with connected cortical regions.

a, Spike-triggered average of deconvolved cortical fluorescence for multiunit activity at each striatal location (indexed by colours), averaged across sessions from all mice. b, Regressed spatial kernels corresponding to locations in (a). c, Centre-of-mass of the kernel weights in (b), using the same colour scale as (a). d, Ternary plot showing relative correlation of the cortical kernels for each 200 μm striatal segment in each session (dots) with the three template kernels used to assign each segment to a striatal domain (vertices). e, Left, centre-of-mass CCF location for each striatal domain estimated from all striatal segments in (d); right, cumulative fraction of striatal segments in (d) categorized into each domain relative to CCF location. f, Mean cortical spatial kernels for each striatal domain (lag = 0 s) during the task, averaged across sessions and max-normalized. g, Cortical spatial kernels as in (f) computed while mice passively viewed visual noise stimuli. h, Density of cortical locations projecting to each striatal domain, data from the Allen Mouse Brain Connectivity Atlas. i, Time course of spatially summed cortical kernels weights, measured in the behavioural task (black) or during passive viewing of visual noise (red). Curves show mean across domains and sessions ± s.e. across sessions. The kernel weights are larger for the cortex leading striatum (signed-rank test, p = 2.5*10^-6 across 77 sessions). j, Correlation of spatiotemporal cortical kernels across contexts (task vs. passive), across striatal domains, and across sessions (mean ± s.e. across mice). Cortical kernels fit from different domains are significantly less correlated than kernels from different behavioural contexts or recording sessions (signed-rank test, p = 6.1*10^-5 across 15 mice).

Figure 3. Striatal sensorimotor activity reflects associated cortical activity.

a, Multiunit activity in each striatal domain, shown for all trials with contralateral stimuli, contralaterally-orienting movements, and rewards. Trials are aligned by stimulus onset (red line), which was followed 500 ms later by a Go cue (yellow line). Trials are combined across sessions and sorted by reaction time (time of movement onset, purple curve). For graphical purposes, activity at each time is smoothed with a running average of 100 trials to highlight features that are consistent across trials. b, Event kernels predicting activity in each striatal domain from task events. Left column: kernels for ipsilateral (blue) and contralateral (red) stimuli of different contrasts (colour saturation). Middle column: kernels for contralateral-orienting (purple) and ipsilateral-orienting (orange) movements. Right column: kernels for reward delivery on correct trials (cyan) and white noise on incorrect trials (black). Vertical black dotted lines indicate event onset, shading indicates mean ± s.e. across sessions. c, Prediction of striatal firing rate obtained from task events, formatted as in (a). d, Prediction of striatal firing rate from cortical activity, formatted as in (a). e, Trial-averaged activity in each striatal domain (black), predicted from task events (blue), and predicted from cortical activity (green), aligned to stimulus (red line), movement (purple line), and reward delivery (cyan line) (mean ± s.e. across sessions). f, Cross-validated explained variance (R²) of striatal activity predicted from the cortex and task. Small dots, sessions; large dots, mean ± s.e. across sessions; colour, striatal domain. The cortex explains more or the same amount of striatal activity as task events, indicating that striatal activity mirrors cortical activity (signed-rank test, DMS p = 5.8*10^-4, DCS p = 0.17, DLS p = 0.48). g, Passive responses to visual stimuli in DMS before (black) and after (yellow) inactivation of VISam with muscimol.

Figure 4. Striatal mirroring of cortical activity is cell-type specific.

a, Spike waveforms and autocorrelelograms for striatal medium spiny neurons (MSNs), fast spiking interneurons (FSIs), and tonically active neurons (TANs) (mean ± s.d. across cells). b, Heatmaps showing firing rates of individual cells of each class aligned to contralateral stimuli (red lines), contralaterally-orienting movements (purple lines), and rewards (blue lines), averaged across trials with reaction times < 500 ms, max-normalized, and sorted by time of maximum activity using half of the trials and plotting the other half of trials. Rows correspond to striatal domains: DMS, DCS, and DLS, columns to cell types. c, Activity as in (b) averaged across neurons of each cell type and domain (black). For reference, each row reports the cortical activity within a region of cortex associated with each domain (green), providing a close match with MSN and FSN activity but not with TAN activity. d, Correlations of activity in individual neurons (rows in b) with the average activity of each cell type in the same striatal domain (black curves in c), or with activity of the topographically associated cortical ROI (green curves in c), from non-overlapping sessions to account for interneuron sparsity (mean ± s.e. across sessions). MSNs and FSIs were equally correlated with themselves as with each other (shuffling MSN/FSI labels within sessions, MSN: p = 0.47, FSI: p = 0.99 across 77 sessions), or with cortical activity (2-way ANOVA on firing rate and type, type MSN p = 0.94, FSI p = 0.88 across 77 sessions). TANS were correlated with themselves and equally uncorrelated to MSNs, FSIs, and cortical activity (2-way ANOVA on firing rate and type, TAN vs. MSN p = 7.5*10^-48, TAN vs. FSI p = 2.0*10^-6, TAN vs. MSN, FSI, and cortex p = 0.68 across 77 sessions).

Figure 5. Striatal stimulus responses increase independently from visual cortex after training.

a, Cortical activity within regions associated with each striatal domain in untrained (black) and trained (yellow) mice to 100% contrast contralateral stimuli (mean ± s.e. across sessions). Stimulus responses in visual area VISam do not change with training, increase in the frontomedial cortex (Fr. M), and are not present in frontolateral cortex (Fr. L) (rank-sum test, VISam: p = 0.08, Fr. M: p = 6.2*10^-3 across 23 untrained and 48 trained sessions, time window 0-0.2 s). b, Striatal activity, plotted as in (a). Stimulus responses increase in DMS and DCS (rank-sum test, DMS: p = 2.1*10^-4, DCS: p = 9.7*10^-4, time window 0-0.2 s). c, Striatal activity as in (a) within each cell type. Stimulus activity within DMS and DCS increases for MSNs and TANs but not for FSIs (rank-sum test, DMS: MSN p = 2.8*10^-3, FSI p = 0.32, TAN p = 0.013, time window 0-0.2 s). d, Cortical kernels for each striatal domain computed from untrained mice passively viewed visual noise (as in Fig. 2g). Average domain kernels for each mouse are equally correlated across trained mice as they are between trained and untrained mice (rank-sum test p = 0.65).