130 citations · 424 across the 9 of their papers we have counts for
9 papers
Minimum-phase property of the hemodynamic response function, and implications for Granger Causality in fMRI
Leonardo Novelli, Lionel Barnett, Anil Seth +1
Granger Causality (GC) is widely used in neuroimaging to estimate directed statistical dependence among brain regions using time series of brain activity. An important issue is tha…
Spectral Dynamic Causal Modelling: A Didactic Introduction and its Relationship with Functional Connectivity
Leonardo Novelli, Karl Friston, Adeel Razi
We present a didactic introduction to spectral Dynamic Causal Modelling (DCM), a Bayesian state-space modelling approach used to infer effective connectivity from non-invasive neur…
Hierarchical Bayesian inference for community detection and connectivity of functional brain networks
Lingbin Bian, Nizhuan Wang, Leonardo Novelli +2
Most functional magnetic resonance imaging studies rely on estimates of hierarchically organized functional brain networks whose segregation and integration reflect the cognitive a…
A mathematical perspective on edge-centric brain functional connectivity
Leonardo Novelli, Adeel Razi
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…
Inferring network properties from time series using transfer entropy and mutual information: validation of multivariate versus bivariate approaches
Leonardo Novelli, Joseph T. Lizier
Functional and effective networks inferred from time series are at the core of network neuroscience. Interpreting their properties requires inferred network models to reflect key u…
Assessing the Significance of Directed and Multivariate Measures of Linear Dependence Between Time Series
Oliver M. Cliff, Leonardo Novelli, Ben D. Fulcher +2
Inferring linear dependence between time series is central to our understanding of natural and artificial systems. Unfortunately, the hypothesis tests that are used to determine st…
Deriving pairwise transfer entropy from network structure and motifs
Leonardo Novelli, Fatihcan M. Atay, Jürgen Jost +1
Transfer entropy is an established method for quantifying directed statistical dependencies in neuroimaging and complex systems datasets. The pairwise (or bivariate) transfer entro…
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
Leonardo Novelli, Patricia Wollstadt, Pedro Mediano +2
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 m…
IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks
Patricia Wollstadt, Joseph T. Lizier, Raul Vicente +5
The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using…