[Eeglablist] New sLORETA, eLORETA, and Brain Connectivity Tools

Roberto D. Pascual-Marqui pascualm at key.uzh.ch
Thu Nov 15 05:33:44 PST 2007


There are two new items (2007-Nov-15) that might be of interest to
colleagues working in EEG/MEG tomography and brain connectivity.

1. A technical report with new measures of connectivity:
"Instantaneous and lagged measurements of linear and nonlinear
dependence between groups of multivariate time series: frequency
decomposition"
can be downloaded from:
http://arxiv.org/abs/0711.1455
The abstract can be found below.

2. The new sLORETA/eLORETA software package (2007-Nov-15) is now
available. It includes:
2.a. The new eLORETA method
2.b. Tools for defining cortical regions of interest (ROIs)
2.c. New instantaneous and lagged measurements of linear and nonlinear
dependence between groups of multivariate time series. Connectivity is
computed between cortical regions of interest (ROIs)
2.d. Statistical tools (SnPM methodology) for hypothesis testing on
the new connectivity measures.

Download URL and password for installation remain the same, and can be
obtained (if you write your email address correctly) at:
http://www.uzh.ch/keyinst/NewLORETA/PassWord/PassWordSloreta.htm

Presently, the software runs on Windows. Hopefully, it can all be
re-implemented soon in JAVA, which would make it platform independent
(and open source).

Cordially,
Roberto
-- 
R.D. Pascual-Marqui
The KEY Institute for Brain-Mind Research
University Hospital of Psychiatry
pascualm at key.uzh.ch
www.keyinst.uzh.ch/loreta
-----------------------------------------------------

Abstract: Measures of linear dependence (coherence) and nonlinear
dependence (phase synchronization) between any number of multivariate
time series are defined. The measures are expressed as the sum of
lagged dependence and instantaneous dependence. The measures are
non-negative, and take the value zero only when there is independence
of the pertinent type. These measures are defined in the frequency
domain and are applicable to stationary and non-stationary time
series. These new results extend and refine significantly those
presented in a previous technical report (Pascual-Marqui 2007,
arXiv:0706.1776 [stat.ME], http://arxiv.org/abs/0706.1776 ), and have
been largely motivated by the seminal paper on linear feedback by
Geweke (1982 JASA 77:304-313). One important field of application is
neurophysiology, where the time series consist of electric neuronal
activity at several brain locations. Coherence and phase
synchronization are interpreted as "connectivity" between locations.
However, any measure of dependence is highly contaminated with an
instantaneous, non-physiological contribution due to volume conduction
and low spatial resolution. The new techniques remove this confounding
factor considerably. Moreover, the measures of dependence can be
applied to any number of brain areas jointly, i.e. distributed
cortical networks, whose activity can be estimated with eLORETA
(Pascual-Marqui 2007, arXiv:0710.3341 [math-ph],
http://arxiv.org/abs/0710.3341 ).


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