[FieldTrip] Measures of time series coupling based on generalized weighted multiple regression

pascualm at key.uzh.ch pascualm at key.uzh.ch
Tue Dec 19 16:32:49 CET 2017

Dear Colleagues,
The pre-print at:
entitled "Measures of time series coupling based on generalized
weighted multiple regression"
might be of interest to those working in the field of brain
connectivity based on signals of electric neuronal activity.
The abstract can be found below, under the signature.
Roberto D. Pascual-Marqui, PhD, PD
The KEY Institute for Brain-Mind Research, University of Zurich
Visiting Professor at Neuropsychiatry, Kansai Medical University, Osaka
[www.keyinst.uzh.ch/loreta] [scholar.google.com/citations?user=pascualmarqui]

The sharing and the transmission of information between cortical brain
regions is carried out by mechanisms that are still not fully
understood. A deeper understanding should shed light on how
consciousness and cognition are implemented in the brain. Research
activity in this field has recently been focusing on the discovery of
non-conventional coupling mechanisms, such as all forms of
cross-frequency couplings between diverse combinations of amplitudes
and phases, applied to measured or estimated cortical signals of
electric neuronal activity. However, all coupling measures that
involve phase computation have poor statistical properties. In this
work, the conventional estimators for the well-known phase-phase
(phase synchronization or locking), phase-amplitude, and
phase-amplitude-amplitude couplings are generalized by means of the
weighted multiple regression model. The choice of appropriate weights
produces estimators that bypass the need for computing the
complex-valued phase. In addition, a new coupling, denoted as the
inhibitory coupling (InhCo), is introduced and defined as the
dependence of one complex-valued variable on the inverse and on the
conjugate inverse of another complex-valued variable. A weighted
version denoted as wInhCo is also introduced, bypassing the need for
computing the inverse of a complex variable, which has very poor
statistical properties. The importance of this form of inhibitory
coupling is that it may capture well-known processes, such as the
observed inverse alpha/gamma relation within the same cortical region,
or the inverse alpha/alpha relation between distant cortical regions.

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