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Found 5 papers, 4 papers with code
Hierarchical Bayesian inference for community detection and connectivity of functional brain networks
The method can robustly detect the group-level community structure of weighted functional networks that give rise to hidden brain states with an unknown number of communities and retain the variability of individual networks.
A mathematical perspective on edge-centric brain functional connectivity
Edge time series are increasingly used in brain functional imaging to study the node functional connectivity (nFC) dynamics at the finest temporal resolution while avoiding sliding windows.
Inferring network properties from time series via transfer entropy and mutual information: validation of bivariate versus multivariate approaches
Functional and effective networks inferred from time series are at the core of network neuroscience.
Neurons and Cognition Information Theory Social and Information Networks Information Theory Data Analysis, Statistics and Probability
Assessing the Significance of Directed and Multivariate Measures of Linear Dependence Between Time Series
Inferring linear dependence between time series is central to our understanding of natural and artificial systems.
Methodology Information Theory Information Theory Statistics Theory Data Analysis, Statistics and Probability Neurons and Cognition Applications Statistics Theory
Deriving pairwise transfer entropy from network structure and motifs
The pairwise (or bivariate) transfer entropy from a source to a target node in a network does not depend solely on the local source-target link weight, but on the wider network structure that the link is embedded in.
Information Theory Social and Information Networks Information Theory Data Analysis, Statistics and Probability Neurons and Cognition
