Figure 5. Functional connectivity across spatial scales encodes behavior.

a, Schematic illustrating the setup for simultaneous mesoscopic and 2-photon imaging. b, Left, example mesoscopic imaging frame and schematic of microprism placement in the contralateral hemisphere. Right, example 2-photon imaging frame collected through the prism. c, Population data (n=7 independent mice) showing average (±SEM) prediction accuracy (R²) for modeling behavior variables using either activity (yellow) or ${\varphi }_t$ (red) for mesoscopic or 2-photon data. * indicates p<0.05 for two sided paired t-test (see main text). Comparing ${\varphi }_t^{\left(20\right)}$ to ${\mathit{x}}_t$: for mesoscopic data, pupil: p=0.002; face: p=0.004; wheel: p=0.0002. For cellular data, pupil: p=0.0001; face: p=0.001; wheel: p=0.0004. d, Example sequential multimodal correlation matrices, derived from a sliding window applied to neural activity from mesoscopic (parcels) and 2-photon (cells) imaging, used for diffusion embedding. e, Dynamic multimodal correlation time series for three example cells, where each row represents a mesoscopic parcel. The standard deviation of correlation values over time, averaged across all rows is indicated. f, Example of the first 20 diffusion embedding components from the same animal (n=243 cells, 47 parcels). g, Time series for behavioral metrics corresponding to data in (e-g). h, Population data (n=7 independent mice) showing average (±SEM) prediction accuracy (R²) for modeling behavior variables using ${\varphi }_t$ derived from the embedding of dual mesoscopic and 2-photon correlations. i, Example maps for the cells in (e) showing R² values for modeling the correlation of the cell with each parcel using the overall diffusion embedding. j, Grand average map showing R² values as in (i) collapsed across all animals (n=6) and all cells and cortical parcels.