Title
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings
Abstract
High-dimensional neural recordings across multiple brain regions can be used to establish functional connectivity with good spatial and temporal resolution. We designed and implemented a novel method, Latent Dynamic Factor Analysis of High-dimensional time series (LDFA-H), which combines (a) a new approach to estimating the covariance structure among high-dimensional time series (for the observed variables) and (b) a new extension of probabilistic CCA to dynamic time series (for the latent variables). Our interest is in the cross-correlations among the latent variables which, in neural recordings, may capture the flow of information from one brain region to another. Simulations show that LDFA-H outperforms existing methods in the sense that it captures target factors even when within-region correlation due to noise dominates cross-region correlation. We applied our method to local field potential (LFP) recordings from 192 electrodes in Prefrontal Cortex (PFC) and visual area V4 during a memory-guided saccade task. The results capture time-varying lead-lag dependencies between PFC and V4, and display the associated spatial distribution of the signals.
Year
Venue
DocType
2020
NIPS 2020
Conference
Volume
ISSN
Citations 
33
1049-5258
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Heejong Bong100.34
Zongge Liu221.39
Ren Zhao34815.88
Matthew A. Smith4265.09
Valérie Ventura525336.45
Robert E. Kass681.83