A dataset of densely sampled activity across mouse sensorimotor cortex.

(A) Top, dorsal view of the Allen CCF, with the area investigated here indicated by a box. Bottom, summary of several previously-identified subareas of mouse sensorimotor cortex (Methods). Orientation legend: A, anterior, P, posterior, L, lateral, M, medial. (B) Widefield calcium imaging in a 5 mm cranial window superimposed on the Allen CCF to show location. Activation elicited by paw vibration (Methods). (C) Counts of neurons imaged for each area, with identities colored in inset. (D) Field of view locations of the 98 sessions of two-photon imaging collected in layer 2/3 for 6 mice (colors). (E) Locations of the 39,398 neurons extracted from all sessions. Mouse identity colored as in D.

Mice performed delayed reach-to-grasp-to-drink movements to 15 distinct targets.

(A) Task timeline. ITI, inter-trial interval. (B) Infrared image of the mouse during the inter-trial interval from one of two high-speed cameras. The 15 possible target locations illustrated with colored dots. (C) Paw centroid trajectories from a frontal (left panel) and dorsal (right panel) view, illustrating kinematic separation by target. Target locations and average nose and mouth locations superimposed over centroid traces for clearer visualization. (D) Box plots of lift reaction times (RTs) for each mouse. Boxes show interquartile range (IQR) with a line for median; whiskers show 1.5 times the IQR; dots show outliers. Negative RTs indicate reaches before the Go cue, and were excluded from further analysis. (E) Time between lift and spout contact across mice. Three consecutive sessions per animal shown for both D and E.

Neurons in mouse sensorimotor cortex exhibited heterogeneous tuning profiles.

(A) Extracted neuron locations superimposed on the Allen CCF. Red indicates a neuron that was modulated according to our statistical test (Methods), gray indicates a neuron that was not. (B) Binned and Gaussian-smoothed (s.d. 150 µm) map derived from A. (C) Venn diagram depicting the number of modulated cells for each event alignment. (D-H) Peri-event time histograms (PETHs) of five example neurons in each area. Successful trials to each target were averaged and smoothed. Colors as in Figure 2B. Shaded regions, SEMs; scale bars, 10 events/second. Neurons were chosen to be clear and representative examples of response profiles observed in each area.

Onset of neural activity across sensorimotor cortex followed both area borders and a somatotopic organization.

(A) Smoothed histogram of latency to half-max activation for neurons by anatomical area. (B) Latency to half-max activation map. The metric was computed on individual neurons, neurons were binned into pixels, then the map was Gaussian smoothed (s.d. 150 µm).

PETH features were organized into distinct spatial patterns in sensorimotor cortex.

(A) Top left, schematic for the response duration metric, which measures the autocorrelation width of the trial-averaged trace for each neuron and target. Bottom left, histogram of response duration values for all modulated neurons grouped based on anatomical region. Right, metric map of the response duration values where bright regions correspond to higher response duration values and darker regions to lower. (B-F) Analogous to A for other metrics. (B) Tuning sharpness, computed as one minus the ratio of the average peak response (excluding the strongest response condition) normalized by the strongest peak response. (C) Target tuning linearity, the coefficient of determination of the peak response with physical distance of the target from the target that evoked the strongest response. (D) Tuning persistence, corresponding to how consistent the tuning was across targets at all lags from the peak (Methods). (E) Peak-time variation, computed as the standard deviation of peak times across targets. Only statistically-modulated targets were included for each neuron. (F) “Local dimensionality”, the participation ratio of the 25 nearest neighbors to each neuron. Star indicates that this metric was not used for analyses below, because it is not independent for each neuron.

Derivatives of PETH feature maps produced response-property boundaries aligned with anatomy and somatotopy.

(A) Side-by-side comparison of a PETH feature map and its gradient magnitude map. Right, bright blue values represent large gradient magnitudes and dark values represent small gradients. (B-E) Same as A right, for the other PETH features. (F) Quadratic mean over the five PETH features of the gradient magnitude maps. (G) Top, 10,000 1-mm random boundaries plotted in gray within the sampled region. Bottom, for each random boundary, neurons that were within 500 µm on either side of the boundary were included; neurons within 50 µm of the boundary were excluded. The target tuning linearity feature map is shown underneath for illustration. An SVM was trained to predict which side of the boundary a neuron was on using its PETH feature vector. (H) The top-performing 2.5% of random boundaries (assessed via cross-validation) plotted in yellow on top of the average gradient magnitude map.

Areas had distinct and multimodal distributions of PETH features.

(A) t-SNE of the five-dimensional PETH features for all neurons. Each gray point corresponds to a neuron. (B) Same embedding as in A, with neurons (points) colored by anatomical area. (C) Contour plots of the embedding from B, using two-dimensional Kernel Density Estimation to produce a smooth density estimate. Three contour levels (98%, 95%, 90%) are shown for each of the five anatomical areas of interest.

Neural response profiles were shared in patches that spanned anatomically defined regions.

(A) Schematic of analysis with hypothesis that neurons from different areas are well-separated in feature space. A Gaussian Mixture Model (GMM) was fit to the distribution of feature values for neurons belonging to a single anatomically-defined area, then a likelihood was computed for all neurons from all areas using the fit. (B) The resulting likelihood map for M2, where bright yellow pixels correspond to high likelihood regions and dark blue pixels to low likelihood regions. Colorbar scale is logarithmic. (C-F) Same as B, where the seed regions were M1, S1-fl, S1-hl, and S1-tr, respectively.

Four subpopulations with distinct response profiles form an overlapping patchwork across sensorimotor cortex.

(A) Schematic of GMM modeling with the hypothesis that components from different areas correspond. Only two GMM components in each area are illustrated for simplicity, with numbers corresponding to those in B. (B) Bhattacharyya distance calculated between all pairs of GMM components across all anatomical regions. The matrix is organized based on cluster identity from hierarchical clustering of the pairwise distances. Each cluster (colored box outlines) is referred to as a subpopulation. Colormap capped to better show any within-cluster detail. Cue-locked data, modulated neurons only. (C) Anterior subpopulation likelihood map. Plotting as in the other likelihood maps. (D-F) Same as C but for Forelimb motor (D), Forelimb somatomotor (E), and Hindlimb somatomotor (F) subpopulations. (G) Distributions of the five feature values (subpanels) for each of the four subpopulations (traces).

Subpopulations overlapped with their member neurons spatially intermingled.

(A) Neurons were classified into their most likely subpopulation, color coded and plotted on the CCF. (B) Prevalence of subpopulation by area. (C) Schematic for distinguishing two hypotheses, if subpopulations were identified separately using data from each of two areas and the identified high-density zones of neurons were plotted as contours of the likelihood maps. Left, expected result if subpopulations were homogeneous in their properties over cortical location; right, if subpopulations varied smoothly with cortical location. (D) Maps of prevalence of each subpopulation. (E) GMM component contours at 60% level, for each subpopulation identified separately using data from each area containing sufficient subpopulation members.

Widefield window images and vibration maps aligned to the Allen CCF for all mice, as in Figure 1B.

Binned and smoothed modulation map as in Figure 3, but with data aligned to Lift (left) or Contact (right). Lift alignment used neural data from 200 ms before to 400 ms after lift. Contact alignment used neural data from 300 ms before to 300 ms after first spout contact.

Principal component scores after VARIMAX rotation for each neuron, plotted as feature maps using the same method as Figure 5.

Feature value histograms for all GMM components and all areas.

(A) t-SNE as in Figure 7C but using the top 20 PCs of the PETHs as inputs instead of feature vectors. Multimodality was again strongly present. (B) Left, circular population of neurons chosen from an overlap zone in M1, to analyze for discrete subpopulations while avoiding spatial somatotopy. Upper right, t-SNE embedding of this M1 population using the PETH feature space. Lower right, t-SNE embedding of the same M1 population using the PCs. Both methods yielded clear multimodality. (C-G) Same as Figure 8B-F but using top 20 PCs instead of PETH features.

GMM on PETH feature space considering all cells together, agnostic to anatomical areas.

(A-D) Gradients as in Figure 6 but calculated using the subpopulation likelihood maps depicted in Figure 9C-F instead of the PETH feature maps. As in Figure 6, each subpopulation had a distinct gradient with boundaries approximately aligned with borders between anatomical regions, and other boundaries approximately separating somatotopic representations. (E) The quadratic mean of the four subpopulation gradient maps. This pooled gradient map closely resembles the pooled map in Figure 6F.