Constructing brain functional networks from EEG: partial and unpartial correlations

We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed th...

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Bibliographic Details
Published inJournal of integrative neuroscience Vol. 10; no. 2; p. 213
Main Authors Jalili, Mahdi, Knyazeva, Maria G
Format Journal Article
LanguageEnglish
Published England 01.06.2011
Subjects
Adult
Brain - physiology
Brain Mapping - methods
Electroencephalography - methods
Female
Humans
Male
Middle Aged
Models, Neurological
Nerve Net - physiology
Signal Processing, Computer-Assisted
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Summary:We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed through unpartial correlations in terms of graph metrics. In particular, they have completely different connection efficiency, clustering coefficient, assortativity, degree variability, and synchronization properties. Unpartial correlations are simple to compute and they can be easily applied to large-scale systems, yet they cannot prevent the prediction of non-direct edges. In contrast, partial correlations, which are often expensive to compute, reduce predicting such edges. We suggest combining these alternative methods in order to have complementary information on brain functional networks.
ISSN:0219-6352
DOI:10.1142/S0219635211002725