Abstract
We consider the problem of estimating brain effective connectivity from electroencephalographic (EEG) measurements, which is challenging due to instantaneous correlations in the sensor data caused by volume conduction in the head. We present selected results of a larger realistic simulation study in which we tested the ability of various measures of effective connectivity to recover the information flow between the underlying sources, as well as the ability of linear and nonlinear inverse source reconstruction approaches to improve the estimation. It turns out that factors related to volume conduction dramatically limit the neurophysiological interpretability of sensor-space connectivity maps and may even (depending on the connectivity measure used) lead to conflicting results. The success of connectivity estimation on inverse source estimates crucially depends on the correctness of the source demixing. This in turn depends on the capability of the method to model (multiple) interacting sources, which is in general not achievable by linear inverses.
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Haufe, S., Nikulin, V.V., Nolte, G., Müller, KR. (2012). Pitfalls in EEG-Based Brain Effective Connectivity Analysis. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_26
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DOI: https://doi.org/10.1007/978-3-642-34713-9_26
Publisher Name: Springer, Berlin, Heidelberg
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