title: Constructing brain functional networks from EEG: partial and unpartial correlations.
abstract: 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.
authors.affiliation: Department of Computer Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran. MJalili@sharif.edu
authors.forename: Mahdi
authors.initials: M
authors.lastname: Jalili
authors.forename: Maria G
authors.initials: MG
authors.lastname: Knyazeva
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted