130 citations · 424 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…