Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality.

By Andrea Brovelli, Mingzhou Ding, A. Ledberg, Yonghong Chen, R. Nakamura, Steven L. Bressler. Proceedings of the National Academy of Sciences of the United States of America, 2004

1,122 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/f119513c7ee212e7c74f3e6e7c5a4b1197df24f4

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Citations in the corpus

Network Topology Inference from Spectral Templates (2016, methodology): “ternative methods rely on partial correlations [4], [17], Gaussian graphical models [18]–[21], structural equation models [22], [23], Granger causality [16], [24], or their nonlinear (kernel-”
Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics (2014, methodology): “It has been applied to analyze times series obtained through invasive techniques, such as single-unit, multi-unit (Passaro et al., 2008), LFP (Brovelli et al., 2004; Bressler et al., 2007) recordings as well as non-invasive techniques, such as EEG (Astolﬁ et al., 2006), MEG (Gow et al., 2008), and f”
What can Statistics Contribute to a Causal Understanding? (2007, methodology): “It was, for instance, used by Brovelli et al. (2004) to analyse how the brain controls muscle contractions, including an understanding of which parts of the brain were involved.”
Nonlinear multivariate analysis of neurophysiological signals. (2005, methodology): “Recently, a frequency specific Granger causality measure was utilized in LFP recordings from somatosensory and motor cortices of macaque monkeys as they performed a motor maintenance in a visual discrimination task (Brovelli et al., 2004).”
Motifs in Brain Networks (2004, background): “Fluctuating and distributed patterns of dynamical interactions among functionally specialized areas result in rapid switches in functional and effective connectivity (McIntosh et al. 1999; Bu¨chel and Friston 2000; McIntosh et al., 2003; Brovelli et al. 2004).”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition (2010, methodology): “Analysis methods have been developed to interrogate the directionality of the flow of signals between two or multiple recorded areas, such as the Granger causality analysis (93, 118, 244, 371, 381, 560) and its frequency-domain representation (38, 244).”
Mapping directed influence over the brain using Granger causality and fMRI (2005, methodology): “Previous invasive electrophysiological studies have shown that statistical techniques based on VAR-modeling and Granger causality are capable of detecting directed interactions between neuronal populations as reflected in the dynamic structure of LFP signals (Bernasconi and Konig, 1999; Bernasconi e”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “There is mounting evidence against such a parcellation scheme of brain functions and in favor of a more distributed mode of information processing in the primate cortex (96, 253, 497, 583, 675).”
FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data (2010, background): “…details related to the functionality of the di ﬀ erent modules, without the aim of being exhaustive. imaginary part of coherency [11], phase slope index [12], partial directed coherence [13], directed transfer function [14], and Geweke’s extension of Granger causality to the frequency domain [15].”
Analysing connectivity with Granger causality and dynamic causal modelling (2013, methodology): “GC applied to local-field potentials (LFPs) recorded from cat visual cortex; important early clarification of statistical issues [23] GC analysis of LFP data obtained from awake monkeys reveal directional beta-frequency interactions in a large –scale network during motor maintenance behaviour [25] N”
Cortex and Memory: Emergence of a New Paradigm (2009, methodology): “…working-memory performance (Axmacher, Schmitz, Wagner, Elger, & Fell, 2008; Sehatpour et al., 2008; Buschman & Miller, 2007; Saalmann et al., 2007; Brovelli et al., 2004; Gross et al., 2004; Brovelli, Battaglini, Naranjo, & Budai, 2002; Tallon- Baudry et al., 2001; Stein, Rappelsberger,…”
A study of problems encountered in Granger causality analysis from a neuroscience perspective (2017, background): “Consequently, the many possible points of failure for causality methods are also hidden, such as inappropriate model structure (2) and poor model estimation (3), poor representation of the features of interest (4), and poor statistical properties (5).”