Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing

Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing

Network inference algorithms are valuable tools for the study of large-scale neuroimaging datasets. Multivariate transfer entropy is well suited for this task, being a model-free measure that captures nonlinear and lagged dependencies between time series to infer a minimal directed network model. Gr...

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Bibliographic Details
Main Authors: Leonardo Novelli, Patricia Wollstadt, Pedro Mediano, Michael Wibral, Joseph T. Lizier
Format: Article
Language:English
Published: The MIT Press 2019-07-01
Series:Network Neuroscience
Subjects:
Neuroimaging
Directed connectivity
Effective network
Multivariate transfer entropy
Information theory
Nonlinear dynamics
Statistical inference
Nonparametric tests
Online Access:https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00092

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https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00092

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