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      Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data

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      Neuron

      Elsevier BV

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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.

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          Author and article information

          Journal
          Neuron
          Neuron
          Elsevier BV
          08966273
          September 2009
          September 2009
          : 63
          : 6
          : 747-760
          Article
          10.1016/j.neuron.2009.08.009
          3282191
          19778505
          0a5ec00e-7062-4beb-810d-56e114501f0a
          © 2009

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