GCCA

A MATLAB toolbox for Granger causal connectivity analysis. Assessing directed functional connectivity from time series data is a key challenge in neuroscience. One approach to this problem leverages a combination of Granger causality analysis and network theory. This article describes a freely available MATLAB toolbox – ‘Granger causal connectivity analysis’ (GCCA) – which provides a core set of methods for performing this analysis on a variety of neuroscience data types including neuroelectric, neuromagnetic, functional MRI, and other neural signals. The toolbox includes core functions for Granger causality analysis of multivariate steady-state and event-related data, functions to preprocess data, assess statistical significance and validate results, and to compute and display network-level indices of causal connectivity including ‘causal density’ and ‘causal flow’. The toolbox is deliberately small, enabling its easy assimilation into the repertoire of researchers. It is however readily extensible given proficiency with the MATLAB language.


References in zbMATH (referenced in 11 articles )

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  1. Papana, Angeliki; Siggiridou, Elsa; Kugiumtzis, Dimitris: Detecting direct causality in multivariate time series: a comparative study (2021)
  2. Lin, Tiger W.; Chen, Yusi; Bukhari, Qasim; Krishnan, Giri P.; Bazhenov, Maxim; Sejnowski, Terrence J.: Differential covariance: a new method to estimate functional connectivity in fMRI (2020)
  3. Ren, Weijie; Li, Baisong; Han, Min: A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series (2020)
  4. Cekic, Sezen; Grandjean, Didier; Renaud, Olivier: Multiscale Bayesian state-space model for Granger causality analysis of brain signal (2019)
  5. Nunes, Ronaldo V.; Reyes, Marcelo B.; de Camargo, Raphael Y.: Evaluation of connectivity estimates using spiking neuronal network models (2019)
  6. Peró-Cebollero, Maribel; Guàrdia-Olmos, Joan; Mancho-Fora, Núria: A systematic review of simulation procedures for fMRI connectivity studies (2018)
  7. Shao, Pei-Chiang; Huang, Jian-Jia; Shann, Wei-Chang; Yen, Chen-Tung; Tsai, Meng-Li; Yen, Chien-Chang: Granger causality-based synaptic weights estimation for analyzing neuronal networks (2015)
  8. Held, Pascal; Moewes, Christian; Braune, Christian; Kruse, Rudolf; Sabel, Bernhard A.: Advanced analysis of dynamic graphs in social and neural networks (2013) ioport
  9. Liu, Ying; Aviyente, Selin: Quantification of effective connectivity in the brain using a measure of directed information (2012)
  10. Seth, Anil K.; Barrett, Adam B.; Barnett, Lionel: Causal density and integrated information as measures of conscious level (2011)
  11. Yan, Xiaodan: Dissociated emergent-response system and fine-processing system in human neural network and a heuristic neural architecture for autonomous humanoid robots (2010) ioport