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Abstract #3061

Removing instantaneous correlations between BOLD fMRI time series to improve connectivity estimation

Roberta Sclocco 1 , Elisa Marchetta 2,3 , Viviana Casaleggi 1 , Marco Tettamanti 4 , Anna Maria Bianchi 1 , and Giovanna Rizzo 3

1 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy, 2 Department of Neuroradiology, San Raffaele Scientific Institute, Milano, Italy, 3 Istituto di Bioimmagini e Fisiologia Molecolare, CNR, Segrate (MI), Italy, 4 Nuclear Medicine Department & Division of Neuroscience, San Raffaele Scientific Institute, Milano, Italy

The investigation of brain connectivity through spectral Granger causality indices relies on the estimation of the coefficients of a multivariate autoregressive model (MVAR). In the original formulation, though, the estimated model only includes lagged terms, therefore omitting the potential contribution of instantaneous correlations between the analyzed time series. In this work, we applied a method for removing zero-lag interactions on BOLD fMRI time series from a public dataset, and our results showed how this procedure is able to improve the estimation of the causal relationships, allowing to correctly identify the driving node of the network.

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