Methods to Estimate Functional and Effective Brain Connectivity from MEG Data Robust to Artifacts of Volume Conduction
Due to the high temporal resolution of MEG data, they are well suited to study brain dynamics, while the limited spatial resolution constitutes a major confounder when one wants to estimate brain connectivity. To a very large extent, functional relationships between MEG sensors and estimated sources are caused by incomplete demixing of the brain sources. Many measures of functional and effective connectivity are highly sensitive to such mixing artifacts. In this chapter, we review methods that address this problem. They are all based on the insight that the imaginary part of the cross-spectra cannot be explained as a mixing artifact. Several variants of this idea will be presented. We will present three different methods adapted to localize source interactions: (a) minimum overlap component analysis (MOCA) decomposes linear estimates of the P most relevant singular vectors of the imaginary parts of the cross-spectra, (b) the MUSIC algorithm can be applied to this same subspace, and (c) the estimated sources can be analyzed further using multivariate generalizations of the imaginary part of coherency. Finally, a causal relation between these sources can be estimated using the phase slope index (PSI). The methods will be illustrated for empirical MEG data of a single subject under resting state condition.
This work was supported by grants from the EU (ERC-2010-AdG-269716), the DFG (SFB 936/A3), the BMBF (031A130), and from the Human Connectome Project (1U54MH091657-01) funded by the 16 National Institutes of Health Institutes and Centers that support the NIH Blueprint for Neuroscience Research.
- Marzetti L, Della Penna S, Snyder AZ, Pizzella V, Nolte G, de Pasquale F, Romani GL, Corbetta M (2013) Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure. NeuroImage 79:172–183PubMedPubMedCentralCrossRefGoogle Scholar
- Matsuda Y, Yamaguchi K (2004) Semi-invariant function of Jacobi algorithm in independent component analysis. In: Proceedings of the international joint conference on neural networksGoogle Scholar
- Pascual-Marqui RD, Lehmann D, Koukkou M, Kochi K, Anderer P, Saletu B, Tanaka H, Hirata K, John ER, Prichep L, Biscay-Lirio R, Kinoshita T (2011) Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos Transact A Math Phys Eng Sci 369(1952):3768–3784CrossRefGoogle Scholar