Open Access
December 2018 SCALPEL: Extracting neurons from calcium imaging data
Ashley Petersen, Noah Simon, Daniela Witten
Ann. Appl. Stat. 12(4): 2430-2456 (December 2018). DOI: 10.1214/18-AOAS1159


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 R package scalpel, which is available on CRAN.


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Ashley Petersen. Noah Simon. Daniela Witten. "SCALPEL: Extracting neurons from calcium imaging data." Ann. Appl. Stat. 12 (4) 2430 - 2456, December 2018.


Received: 1 March 2017; Revised: 1 December 2017; Published: December 2018
First available in Project Euclid: 13 November 2018

zbMATH: 07029461
MathSciNet: MR3875707
Digital Object Identifier: 10.1214/18-AOAS1159

Keywords: Calcium imaging , cell sorting , clustering , dictionary learning , neuron identification , segmentation , sparse group lasso

Rights: Copyright © 2018 Institute of Mathematical Statistics

Vol.12 • No. 4 • December 2018
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