ArticleProceedings of the National Academy of Sciences of the United States of America2018
Extracting neuronal functional network dynamics via adaptive Granger causality analysis.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it, 104 citations in OpenAlex.
- Ferret as a model system for studying the anatomy and function of the prefrontal cortex: A systematic review.Neuroscience and biobehavioral reviews · 2024Pooled it
- Laminar architecture of a decision circuit in the mouse orbitofrontal cortex.Nature communications · 2026Article
- Inferring effective neuronal circuits via network flux counting.PLoS computational biology · 2026Article
- Granger sensori-behavioral functional taxonomy of neuronal ensemble activity from two-photon calcium imaging data.PLoS computational biology · 2026Article
- Similar Response Dynamics Represent Opposite Behaviors and Rewards in the Frontal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
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- Uncovering Dynamic Neural Information Flow with Continuous-Time Weighted Dynamic Bayesian Networks.bioRxiv : the preprint server for biology · 2026Article
- Reciprocal brain-behavior associations in gambling disorder revealed by theta cordance and directional predictive modeling.Frontiers in behavioral neuroscience · 2026Article
- Jacobian Granger causality for count and binary data with applications to causal network inference.Scientific reports · 2025Article
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- Similar Dynamic Frontal Cortex Representations of Auditory Stimuli Cueing Opposite Actions and Rewards.bioRxiv : the preprint server for biology · 2025Article
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- VARX Granger analysis: Models for neuroscience, physiology, sociology and econometrics.PloS one · 2025Article
- In Vivo Two-Photon Imaging of the Olfactory System in Insects.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems.Entropy (Basel, Switzerland) · 2024Article
- Orbitofrontal cortex modulates auditory cortical sensitivity and sound perception in Mongolian gerbils.Current biology : CB · 2024Article
- Adaptive modeling and inference of higher-order coordination in neuronal assemblies: A dynamic greedy estimation approach.PLoS computational biology · 2024Article
- Fractured columnar small-world functional network organization in volumes of L2/3 of mouse auditory cortex.PNAS nexus · 2024Article
- Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas.Frontiers in computational neuroscience · 2024Review
- Time-dependent homeostatic mechanisms underlie brain-derived neurotrophic factor action on neural circuitry.Communications biology · 2023Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
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 Ca
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