Extracting neuronal functional network dynamics via adaptive Granger causality analysis

Alireza Sheikhattar, Sina Miran, Ji Liu, Jonathan B. Fritz, Shihab A. Shamma, Patrick O. Kanold, Behtash Babadi

Research output: Contribution to journalArticlepeer-review

Abstract

Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

Original languageEnglish (US)
Pages (from-to)E3869-E3878
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue number17
DOIs
StatePublished - Apr 24 2018
Externally publishedYes

Keywords

  • Adaptive filtering
  • Functional network dynamics
  • Granger causality
  • Point processes
  • Sparsity

ASJC Scopus subject areas

  • General

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