[FieldTrip] Measuring Granger-causal effects in multivariate time series by system editing

pascualm at key.uzh.ch pascualm at key.uzh.ch
Mon Dec 31 04:06:56 CET 2018

Dear Colleagues,
The preprint entitled
"Measuring Granger-causal effects in multivariate time series by system editing"
is available at bioRxiv
and includes supplementary material for the sake of reproducible
research: program codes (PASCAL), executable file, & toy data in human
readable format.
The abstract can be found below.
Cordially, Roberto
Roberto D. Pascual-Marqui, PhD, PD
The KEY Institute for Brain-Mind Research, University of Zurich
Visiting Professor at Neuropsychiatry, Kansai Medical University, Osaka

Abstract: What is the role of each node in a system of many
interconnected nodes? This can be quantified by comparing the dynamics
of the nodes in the intact system, with their modified dynamics in the
edited system, where one node is deleted. In detail, the spectra are
calculated from a causal multivariate autoregressive model for the
intact system. Next, without re-estimation, one node is deleted from
the model and the modified spectra at all other nodes are
re-calculated. The change in spectra from the edited system to the
intact system quantifies the role of the deleted node, giving a
measure of its Granger-causal effects (CFX) on the system. A
generalization of this novel measure is available for networks (i.e.
for groups of nodes), which quantifies the role of each network in a
system of many networks. For the sake of reproducible research,
program codes (PASCAL), executable file, and toy data in human
readable format are included in the supplementary material.

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