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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