  Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur
both during natural sleep and anesthesia and are preserved across species. Even
though electrophysiological recordings have been largely used to characterize
brain states, they are limited in the spatial resolution and cannot target
specific neuronal population. Recently, large-scale optical imaging techniques
coupled with functional indicators overcame these restrictions, and new
pipelines of analysis and novel approaches of SWs modelling are needed to
extract relevant features of the spatio-temporal dynamics of SWs from these
highly spatially resolved data-sets. Here we combined wide-field fluorescence
microscopy and a transgenic mouse model expressing a calcium indicator
(GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale
under ketamine anesthesia. We developed a versatile analysis pipeline to
identify and quantify the spatio-temporal propagation of the SWs. Moreover, we
designed a computational simulator based on a simple theoretical model, which
takes into account the statistics of neuronal activity, the response of
fluorescence proteins and the slow waves dynamics. The simulator was capable of
synthesizing artificial signals that could reliably reproduce several features
of the SWs observed in vivo, thus enabling a calibration tool for the analysis
pipeline. Comparison of experimental and simulated data shows the robustness of
the analysis tools and its potential to uncover mechanistic insights of the
Slow Wave Activity (SWA).
