SCALPEL: EXTRACTING NEURONS FROM CALCIUM IMAGING DATA

• 成果类型: Article
• 署名作者: Petersen, Ashley; Simon, Noah; Witten, Daniela
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/18-AOAS1159
• 发表日期: 2018
• 页码: 2430-2456
• 关键词: spike train inference; Matrix Factorization; cellular resolution; neural activity; deconvolution

摘要

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.