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bioRxiv · 10.1101/230672

Locating highly correlated sources from MEG with (recursive) (R)DS-MUSIC

Makela, N.·Stenroos, M.·Sarvas, J.·Ilmoniemi, R.
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Abstract

We introduce a source localization method of the MUltiple Signal Classification (MUSIC) family that can locate brain-signal sources robustly and reliably, irrespective of their temporal correlations. The method, double-scanning (DS) MUSIC, is based on projecting out the topographies of source candidates during topographical scanning in a way that breaks the mutual dependence of highly correlated sources, but keeps the uncorrelated sources intact. We also provide a recursive version of DS-MUSIC (RDS-MUSIC), which overcomes the peak detection problem present in the non-recursive methods. We compare DS-MUSIC and RDS-MUSIC with other localization techniques in numerous simulations with varying source configurations, correlations, and signal-to-noise ratios. DS- and RDS-MUSIC were the most robust localization methods; they had a high success rate and localization accuracy for both uncorrelated and highly correlated sources. In addition, we validated RDS-MUSIC by showing that it successfully locates bilateral synchronous activity from measured auditory-evoked MEG.

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Makela, N., Stenroos, M., Sarvas, J., Ilmoniemi, R.. 2017-12-08. Locating highly correlated sources from MEG with (recursive) (R)DS-MUSIC. https://doi.org/10.1101/230672

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