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Abstract
Recent advances in fluorescence imaging permit studies of Ca(2+) dynamics in large
numbers of cells, in anesthetized and awake behaving animals. However, unlike for
electrophysiological signals, standardized algorithms for assigning optically recorded
signals to individual cells have not yet emerged. Here, we describe an automated sorting
procedure that combines independent component analysis and image segmentation for
extracting cells' locations and their dynamics with minimal human supervision. In
validation studies using simulated data, automated sorting significantly improved
estimation of cellular signals compared to conventional analysis based on image regions
of interest. We used automated procedures to analyze data recorded by two-photon Ca(2+)
imaging in the cerebellar vermis of awake behaving mice. Our analysis yielded simultaneous
Ca(2+) activity traces for up to >100 Purkinje cells and Bergmann glia from single
recordings. Using this approach, we found microzones of Purkinje cells that were stable
across behavioral states and in which synchronous Ca(2+) spiking rose significantly
during locomotion.