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Zusammenfassung:
The bioelectromagnetic inverse problem cannot be solved based on EEG/MEG data alone and requires additional assumptions. In linear reconstruction methods, spatial smoothness is often used as an additional constraint. This is equivalent to the prior assumption of a particular source covariance structure. Recent publications (Knösche et al., NeuroImage 2013) have suggested altering this spatial correlation structure such that it reflects available knowledge on the functio-anatomical organization of the brain. In particular, it is possible to derive borders between different brain areas from various types of brain images. This allows assuming that sources located within the same area exhibit similar activity and sources in different areas are mutually uncorrelated. We present a technique based on the well-known LORETA method (Pascual-Marqui et al., Int. J. Psychophysiol. 1994), which is capable of incorporating such function-anatomical priors. We show that our method embodies the intended prior knowledge in the prior source covariance in an unbiased way. We present Monte-Carlo simulations, which provide a systematic evaluation of how the prior knowledge influences the estimate of different linear inverse procedures. The study answers questions like “What happens if the course of boundaries is uncertain?”, “What if our knowledge on functional areas is limited to certain cortical regions?” and “Can prior knowledge improve source localization?”. Besides presenting answers to these questions we demonstrate our method to localize auditory N100 activity from experimental EEG/MEG data. The results clearly suggest that spatially informed linear inverse methods provide very plausible reconstruction results.
