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Scalpel: extracting neurons from calcium imaging data. (English) Zbl 1412.62171

Summary: In the past few years, new technologies in the field of neuroscience have made it possible to simultaneously image activity in large populations of neurons at cellular resolution in behaving animals. In mid-2016, a huge repository of this so-called “calcium imaging” data was made publicly available. The availability of this large-scale data resource opens the door to a host of scientific questions for which new statistical methods must be developed.
In this paper we consider the first step in the analysis of calcium imaging data – namely, identifying the neurons in a calcium imaging video. We propose a dictionary learning approach for this task. First, we perform image segmentation to develop a dictionary containing a huge number of candidate neurons. Next, we refine the dictionary using clustering. Finally, we apply the dictionary to select neurons and estimate their corresponding activity over time, using a sparse group lasso optimization problem. We assess performance on simulated calcium imaging data and apply our proposal to three calcium imaging data sets.
Our proposed approach is implemented in the \(\mathtt R\) package \(\mathtt {scalpel}\), which is available on \(\mathtt {CRAN}\).

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62J07 Ridge regression; shrinkage estimators (Lasso)
68T05 Learning and adaptive systems in artificial intelligence
62H12 Estimation in multivariate analysis
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Full Text: DOI arXiv Euclid

References:

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