Near-real-time connectivity estimation for multivariate neural data

Abstract

Optical imaging in vivo is an important tool for allowing researchers to understand neural ensemble interactions during awake behavior, sleep, anesthesia and during seizure activity. A major bottleneck in the overall efficiency of neural imaging experiments is the need for post-hoc analysis of imaging data. Computational capabilities are now at the point where real- or near-real-time multivariate analysis of imaging data is possible as data is acquired. In this paper we address the feasibility of performing real-time data analysis with a desktop computer, MATLAB, and a graphics processing unit (GPU). Important components of any real-time functional imaging analysis system are 1) dimensional reduction of the data, 2) visualization of the reduced vector space and 3) rapid calculation of functional connectivities. The ability to assess sources of variability in the data, and connectivity estimates on the fly, are potentially transformative for the way imaging laboratories perform their work. Here, we present benchmarks for analysis of functional imaging data using dimensional reduction methods and estimation of functional connectivities using least-squares and ridge regression methods.