Bayesian Hierarchical Latent Variable Model for Time-Varying Connectivity Analysis of Local Field Potentials

• Activity Number: 173 - Bayesian Methods Applied to Biometric Problems
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Dustin Pluta* and Lingge Li and Klaus Telkmann and Gabriel Elias and Norbert Fortin and Hernando Ombao and Babak Shahbaba
• Companies: University of California Irvine and University of California, Irvine and University of California Irvine and University of California, Irvine and University of California, Irvine and King Abdullah University of Science and Technology (KAUST) and University of California Irvine
• Keywords: Time series; Neuroimaging; Bayesian hierarchical models; Latent variables; High-dimensional data

Abstract

Local field potentials (LFP) measured across a set of tetrodes implanted in the hippocampus of a rat naturally produce connectivity measures that are approximately low-rank. We here present a hierarchical latent variable model for time series of connectivity matrices arising from LFP measurements during a complex cognitive task. By leveraging the low-rank structure of the connectivity data through a latent variable representation, the proposed method efficiently models the time-varying covariance with minimal loss of information. A simulation study verifies this performance in scientifically realistic settings. Applying our model to LFP data from four rats during an odor-based sequence memory task identifies a small set of connectivity features that are most strongly related to the experimental conditions. We conclude with an interpretation of our results with respect to the "memory replay" hypothesis of hippocampal function during a sequence memory task.