Data-driven filtration and segmentation of mesoscale neural dynamics

Recording neuronal group activity across the cortical hemispheres from awake, behaving mice is essential for understanding information flow across cerebral networks. Video recordings of cerebral function comes with challenges, including optical and movement-associated vessel artifacts, and limited references for time series extraction. Here we present a data-driven workflow that isolates artifacts from calcium activity patterns, and segments independent functional units across the cortical surface. Independent Component Analysis utilizes the statistical interdependence of pixel activation to completely unmix signals from background noise, given sufficient spatial and temporal samples. We also utilize isolated signal components to produce segmentations of the cortical surface, unique to each individual’s functional patterning. Time series extraction from these maps maximally represent the underlying signal in a highly compressed format. These improved techniques for data pre-processing, spatial segmentation, and time series extraction result in optimal signals for further analysis. ### Competing Interest Statement The authors have declared no competing interest. * ΔF/F (dFoF) : change in fluorescence over mean fluorescence ICA : Independent Component Analysis PCA : Principal Component Analysis Domain Map : maximum projection map of ICA components Domain : A single contiguous unit from a domain map, represents an ICA component’s maximal region of influence Mosiac Movie : a video representation of the time series extracted under each domain in the domain map