Identifying Complex Brain Networks Using Penalized Regression Methods
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The recorded electrical activity of complex brain networks through the EEG reflects their intrinsic spatial, temporal and spectral properties. In this work we study the application of new penalized regression methods to i) the spatial characterization of the brain networks associated with the identification of faces and ii) the PARAFAC analysis of resting-state EEG. The use of appropriate constraints through non-convex penalties allowed three types of inverse solutions (Loreta, Lasso Fusion and ENet L) to spatially localize networks in agreement with previous studies with fMRI. Furthermore, we propose a new penalty based in the Information Entropy for the constrained PARAFAC analysis of resting EEG that allowed the identification in time, frequency and space of those brain networks with minimum spectral entropy. This study is an initial attempt to explicitly include complexity descriptors as a constraint in multilinear EEG analysis.
KeywordsInformation Entropy PARAFAC EEG inverse problem Multiple penalized least squares model Complex brain networks
The authors thank Mark Cohen and Jhoanna Pérez-Hidalgo-Gato for kindly providing the data of the resting EEG and face identification experiment used in this study.
- 1.Pascual-Marqui, R.D.: Review of methods for solving the EEG inverse problem. Int. J. Bioelectromagn. 1, (1), 75–86 (1999)Google Scholar
- 13.Valdés-Sosa, P.A., Sánchez-Bornot, J.M., Vega-Hernández, M., Melie-García, L., Lage-Castellanos, A., Canales-Rodríguez, E.: Granger causality on spatial manifolds: applications to neuroimaging. In: Schelter, B., Winterhalter, M., Timmer, J. (eds.) Handbook of Time Series Analysis: Recent Theoretical Developments and Applications, pp. 461–492. Wiley-VCH, Weinheim (2006)Google Scholar
- 14.Land, S., Friedman, J.: Variable fusion: a new method of adaptive signal regression. Technical Report. Department of Statistics, Stanford University, Stanford (1996)Google Scholar
- 16.Vega-Hernández, M., Sánchez-Bornot, J.M., Lage-Castellanos, A., Martínez-Montes, E., Valdés-Sosa, P.A.: Penalized regression methods for solving the EEG inverse problem. NeuroImage 27(1) (2006) (CD-ROM)Google Scholar
- 18.Bro, R.: Multi-way analysis in the food industry: Models, algorithms and applications. PhD thesis, University of Amsterdam and Royal Veterinary and Agricultural University, Denmark, (1998)Google Scholar
- 19.Martínez-Montes, E., Sánchez-Bornot, J.M., Valdés-Sosa, P.A.: Generalized penalized PARAFAC analysis of EEG time series. NeuroImage, 36(S1), (2007) (CD-ROM)Google Scholar
- 21.Kanwisher, N., McDermott, J., Chon, M.M.: The fusiform area: a module in human extrastriate cortex specialized for face perception. J. Neurosci. 17, 4302–4311 (1997)Google Scholar