Eigenvector centrality mapping based on low-frequency phase alignment

Lohmann, G; Grigutsch, M; Margulies, D; Horstmann, A; Pleger, B; Lepsien, J; Goldhahn , D; Schloegl, H; Stumvoll, M; Villringer, A; Turner, R

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Eigenvector centrality mapping based on low-frequency phase alignment

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Lohmann, G., Grigutsch, M., Margulies, D., Horstmann, A., Pleger, B., Lepsien, J., et al. (2011). Eigenvector centrality mapping based on low-frequency phase alignment. Poster presented at 19th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2011), Montréal, Canada.

Cite as: https://hdl.handle.net/21.11116/0000-0002-4F73-5

Abstract

We have previously introduced a new analysis method for fMRI data called “eigenvector centrality mapping (ECM)” (Lohmann et al, 2010). In ECM, each voxel receives a rank describing its centrality within the brain using a method similar to Google's PageRank algorithm. In this context, we have previously used spectral coherence as a similarity metric. However, this ignores phase shifts so that time series may receive high coherence values even though they are separated by large phase shifts. Here, we investigate whether changes in brain states manifest themselves not only in centrality changes of spectral coherence but also of phase alignment.