A Hierarchical Bayesian Model for Differential Connectivity in Multi-trial Brain Signals
KAUST DepartmentBiostatistics Group
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Embargo End Date2022-03-16
Permanent link to this recordhttp://hdl.handle.net/10754/662151
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AbstractThere is a strong interest in the neuroscience community to measure brain connectivity and develop methods that can differentiate connectivity across patient groups and across different experimental stimuli. The development of such statistical tools is critical to understand the dynamics of functional relationships among brain structures supporting memory encoding and retrieval. However, challenges arise by providing from the need to incorporate within-condition similarity with between-conditions heterogeneity in modeling connectivity, as well as how to provide a natural way to conduct trial- and condition-level inference on effective connectivity. A Bayesian hierarchical vector autoregressive (BH-VAR) model is proposed to characterize brain connectivity and infer differences in connectivity across conditions. Within-condition connectivity similarity and between-conditions connectivity heterogeneity are accounted for by the priors on trial-specific models. In addition to the fully Bayesian framework, an alternative two-stage computational approach is also proposed which still allows straightforward uncertainty quantification of between-trial conditions via MCMC posterior sampling, but provides a fast approximate procedure for the estimation of trial-specific VAR parameters. A novel aspect of the approach is the use of a frequency-specific measure, partial directed coherence (PDC), to characterize effective connectivity under the Bayesian framework. More specifically, PDC allows inferring directionality and explaining the extent to which the present oscillatory activity at a certain frequency in a sender channel influences the future oscillatory activity in a specific receiver channel relative to all possible receivers in the brain network. The proposed model is applied to a large electrophysiological dataset collected as rats performed a complex sequence memory task. This unique dataset includes local field potentials (LFPs) activity recorded from an array of electrodes across the hippocampal region CA1 while animals were presented with multiple trials from two main conditions. The proposed modeling approach provided novel insights into hippocampal connectivity during memory performance. Specifically, it separated CA1 into two functional units, a lateral and a medial segment, each showing stronger functional connectivity to itself than to the other. This approach also revealed that information primarily flowed in a lateral-to-medial direction across trials (within-condition), and suggested this effect was stronger on one trial condition than the other (between-conditions effect). Collectively, these results indicate that the proposed model is a promising approach to quantify the variation of functional connectivity, both within- and between-conditions, and thus should have broad applications in neuroscience research.
CitationHu, L., Guindani, M., Fortin, N. J., & Ombao, H. (2020). A hierarchical bayesian model for differential connectivity in multi-trial brain signals. Econometrics and Statistics, 15, 117–135. doi:10.1016/j.ecosta.2020.03.009
SponsorsN.J. Fortin’s research was supported in part by NIH grants R01-MH115697 and R01-DC017687, NSF awards IOS- 1150292 and BCS-1439267, and Whitehall Foundation award 2010-05-84. M. Guindani was supported by the NSF grant SES-1659921. H. Ombao was supported in part by the NIH grant R01MH115697 and the KAUST Fund.
JournalEconometrics and Statistics