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

被引:455
作者
Mukamel, Eran A. [1 ]
Nimmerjahn, Axel [1 ]
Schnitzer, Mark J. [1 ,2 ]
机构
[1] Stanford Univ, James H Clark Ctr Biomed Engn & Sci, Stanford, CA 94305 USA
[2] Stanford Univ, Howard Hughes Med Inst, Stanford, CA 94305 USA
关键词
INDEPENDENT COMPONENT ANALYSIS; CEREBELLAR ANTERIOR LOBE; IN-VIVO; BLIND SEPARATION; PURKINJE-CELLS; VISUAL-CORTEX; FIRING RATE; FMRI DATA; PROJECTION; AWAKE;
D O I
10.1016/j.neuron.2009.08.009
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Recent advances in fluorescence imaging permit studies of Ca2+ 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 Ca2+ imaging in the cerebellar vermis of awake behaving mice. Our analysis yielded simultaneous Ca2+ 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 Ca2+ spiking rose significantly during locomotion.
引用
收藏
页码:747 / 760
页数:14
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