Search Results for author: Leonardo Novelli

Found 7 papers, 4 papers with code

Minimum-phase property of the hemodynamic response function, and implications for Granger Causality in fMRI

no code implementations4 Dec 2023 Leonardo Novelli, Lionel Barnett, Anil Seth, Adeel Razi

We find that these models are minimum-phase for a wide range of physiologically plausible parameter values.

Spectral Dynamic Causal Modelling: A Didactic Introduction and its Relationship with Functional Connectivity

no code implementations23 Jun 2023 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 neuroimaging data.

Hierarchical Bayesian inference for community detection and connectivity of functional brain networks

no code implementations18 Jan 2023 Lingbin Bian, Nizhuan Wang, Leonardo Novelli, Jonathan Keith, Adeel Razi

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.

Bayesian Inference Community Detection

A mathematical perspective on edge-centric brain functional connectivity

1 code implementation20 Jun 2021 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 windows.

Time Series Time Series Analysis

Inferring network properties from time series via transfer entropy and mutual information: validation of bivariate versus multivariate approaches

1 code implementation15 Jul 2020 Leonardo Novelli, Joseph T. Lizier

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

1 code implementation9 Mar 2020 Oliver M. Cliff, Leonardo Novelli, Ben D. Fulcher, James M. Shine, Joseph T. Lizier

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

2 code implementations7 Nov 2019 Leonardo Novelli, Fatihcan M. Atay, Jürgen Jost, Joseph T. Lizier

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

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