Background: We propose a mathematical model for multichannel assessment of the\ntrial-to-trial variability of auditory evoked brain responses in magnetoencephalography\n(MEG).\nMethods: Following the work of de Munck et al., our approach is based on the\nmaximum likelihood estimation and involves an approximation of the spatio-temporal\ncovariance of the contaminating background noise by means of the Kronecker product\nof its spatial and temporal covariance matrices. Extending the work of de Munck et al.,\nwhere the trial-to-trial variability of the responses was considered identical to all\nchannels, we evaluate it for each individual channel.\nResults: Simulations with two equivalent current dipoles (ECDs) with different\ntrial-to-trial variability, one seeded in each of the auditory cortices, were used to study\nthe applicability of the proposed methodology on the sensor level and revealed spatial\nselectivity of the trial-to-trial estimates. In addition, we simulated a scenario with\nneighboring ECDs, to show limitations of the method. We also present an illustrative\nexample of the application of this methodology to real MEG data taken from an\nauditory experimental paradigm, where we found hemispheric lateralization of the\nhabituation effect to multiple stimulus presentation.\nConclusions: The proposed algorithm is capable of reconstructing lateralization\neffects of the trial-to-trial variability of evoked responses, i.e. when an ECD of only one\nhemisphere habituates, whereas the activity of the other hemisphere is not subject to\nhabituation. Hence, it may be a useful tool in paradigms
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