Abstract #3822

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

Andrea Duggento¹, Gaetano Valenza^2,3, Luca Passamonti^4,5, Maria Guerrisi¹, Riccardo Barbieri^3,6, and Nicola Toschi^1,7

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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