ArticleScientific reports2019
Inferring correlations associated to causal interactions in brain signals using autoregressive models.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Inferring effective neuronal circuits via network flux counting.PLoS computational biology · 2026Article
- Spatially structured heterogeneity shapes large-scale cortical dynamics in a model of the human cortex.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Irrational herding persists in human-bot interactions.Scientific reports · 2025Article
- Asymmetry of Directed Brain Connectivity at Birth in Low-Risk Full-Term Newborns.Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2024Article
- Community detection in the human connectome: Method types, differences and their impact on inference.Human brain mapping · 2024Article
- Single voxel autocorrelation uncovers gradients of temporal dynamics in the hippocampus and entorhinal cortex during rest and navigation.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Functional Interactions between Entorhinal Cortical Pathways Modulate Theta Activity in the Hippocampus.Biology · 2021Article
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Authors and funding
5 authors.
Funding
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Abstract
The specific connectivity of a neuronal network is reflected in the dynamics of the signals recorded on its nodes. The analysis of how the activity in one node predicts the behaviour of another gives the directionality in their relationship. However, each node is composed of many different elements which define the properties of the links. For instance, excitatory and inhibitory neuronal subtypes determine the functionality of the connection. Classic indexes such as the Granger causality (GC) quantifies these interactions, but they do not infer into the mechanism behind them. Here, we introduce an extension of the well-known GC that analyses the correlation associated to the specific influence that a transmitter node has over the receiver. This way, the G-causal link has a positive or negative effect if the predicted activity follows directly or inversely, respectively, the dynamics of the sender. The method is validated in a neuronal population model, testing the paradigm that excitatory and inhibitory neurons have a differential effect in the connectivity. Our approach correctly infers the positive or negative coupling produced by different types of neurons. Our results suggest that the proposed approach provides additional information on the characterization of G-causal connections, which is potentially relevant when it comes to understanding interactions in the brain circuits.
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