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Abstract #3822

Estimating directed functional connectivity through autoregressive models and orthogonal Laguerre basis functions

Andrea Duggento1, Gaetano Valenza2,3, Luca Passamonti4,5, Maria Guerrisi1, Riccardo Barbieri3,6, and Nicola Toschi1,7

1Department of biomedicine and prevention, University of Rome "Tor Vergata", Rome, Italy, 2Department of Information Engineering, and Research Centre “E. Piaggio”, University of Pisa, Pisa, Italy, 3Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, 4Institute of Bioimaging and Molecular Physiology, National Research Council, Catanzaro, Italy, 5Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom, 6Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy, 7Department of Radiology, Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, United States

Classical multivariate Granger causality-based approaches to estimating effective functional connectivity are almost exclusively based on linear autoregressive models. In order to better represent the nonlinear, multiple-time scales interactions which concur to the formation of the BOLD signals, we present a novel approach to Granger causality based on a Volterra-Wiener decomposition with use of the discrete-time, orthogonal Laguerre basis. After validation in synthetic noisy oscillator networks, we analyze timeseries data from the "HCP-500-Subjects PTN Release", revealing a clear-cut, directed interactions between components which highlights strong driving roles of the posterior occipital-inferior parietal networks, superior parietal as well as of the novel “cognitive" cerebellar regions.

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