Understanding how cortical networks encode behavior requires methods that capture fast dynamic changes in neural interactions. In this talk, I will present a novel computational framework for embedding dynamic functional connectivity. Using widefield mesoscopic and two-photon calcium imaging in awake spontaneously behaving mice, we quantified time-varying correlations between neuronal populations at cortical and cellular scales. Then we extracted their intrinsic structure using our "graph of graphs" approach, which relies on the Riemannian geometry of correlation matrices and non-Euclidean diffusion embedding. Our analysis demonstrates that spontaneous behaviors are represented by fast changes in the correlational structure of cortical networks that are distinct from fluctuations in the magnitude of neural activity. Combining simultaneous mesoscopic and two-photon imaging revealed that correlations between local and large-scale networks also encode behavior. Moreover, our framework uncovered functionally distinct cortical subnetworks not predicted by anatomical atlases.
Organizer
Devika Narain
d.narain@erasmusmc.nl