SUBBOTIN GRAPHICAL MODELS FOR EXTREME VALUE DEPENDENCIES WITH APPLICATIONS TO FUNCTIONAL NEURONAL CONNECTIVITY

• 成果类型: Article
• 署名作者: Chang, Andersen; Allen, Genevera I.
• 署名单位: Rice University; Rice University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1723
• 发表日期: 2023
• 页码: 2364-2386
• 关键词: spike train inference; exponential family; regression; selection; deconvolution

摘要

With modern calcium imaging technology, activities of thousands of neurons can be recorded in vivo. These experiments can potentially provide new insights into intrinsic functional neuronal connectivity, defined as contemporaneous correlations between neuronal activities. As a common tool for estimating conditional dependencies in high-dimensional settings, graphical models are a natural choice for estimating functional connectivity networks. However, raw neuronal activity data presents a unique challenge: the relevant information in the data lies in rare extreme value observations that indicate neuronal firing rather than in the observations near the mean. Existing graphical modeling techniques for extreme values rely on binning or thresholding observations which may not be appropriate for calcium imaging data. In this paper we develop a novel class of graphical models, called the Subbotin graphical model, which finds sparse conditional dependency structures with respect to the extreme value observations without requiring data preprocessing. We first derive the form of the Subbotin graphical model and show the conditions under which it is normalizable. We then study the empirical performance of the Subbotin graphical model and compare it to existing extreme value graphical modeling techniques and functional connectivity models from neuroscience through several simulation studies as well as a real-world calcium imaging data example.