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Maximum-likelihood estimation of channel-dependent trial-to-trial variability of auditory evoked brain responses in MEG.

Sielużycki C, Kordowski P - Biomed Eng Online (2014)

Bottom Line: Following the work of de Munck et al., our approach is based on the maximum likelihood estimation and involves an approximation of the spatio-temporal covariance of the contaminating background noise by means of the Kronecker product of its spatial and temporal covariance matrices.We also present an illustrative example of the application of this methodology to real MEG data taken from an auditory experimental paradigm, where we found hemispheric lateralization of the habituation effect to multiple stimulus presentation.Hence, it may be a useful tool in paradigms that assume lateralization effects, like, e.g., those involving language processing.

View Article: PubMed Central - HTML - PubMed

Affiliation: Special Lab Non-invasive Brain Imaging, Leibniz Institute for Neurobiology, Brenneckestr, 6, 39118 Magdeburg, Germany. cezary.sieluzycki@icm-institute.org.

ABSTRACT

Background: We propose a mathematical model for multichannel assessment of the trial-to-trial variability of auditory evoked brain responses in magnetoencephalography (MEG).

Methods: Following the work of de Munck et al., our approach is based on the maximum likelihood estimation and involves an approximation of the spatio-temporal covariance of the contaminating background noise by means of the Kronecker product of its spatial and temporal covariance matrices. Extending the work of de Munck et al., where the trial-to-trial variability of the responses was considered identical to all channels, we evaluate it for each individual channel.

Results: Simulations with two equivalent current dipoles (ECDs) with different trial-to-trial variability, one seeded in each of the auditory cortices, were used to study the applicability of the proposed methodology on the sensor level and revealed spatial selectivity of the trial-to-trial estimates. In addition, we simulated a scenario with neighboring ECDs, to show limitations of the method. We also present an illustrative example of the application of this methodology to real MEG data taken from an auditory experimental paradigm, where we found hemispheric lateralization of the habituation effect to multiple stimulus presentation.

Conclusions: The proposed algorithm is capable of reconstructing lateralization effects of the trial-to-trial variability of evoked responses, i.e. when an ECD of only one hemisphere habituates, whereas the activity of the other hemisphere is not subject to habituation. Hence, it may be a useful tool in paradigms that assume lateralization effects, like, e.g., those involving language processing.

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Related in: MedlinePlus

Habituation. 1st-degree polynomial (blue line) fitted to ψ(k) for a right-hemisphere channel revealing a strong signal and a clear habituation (see also figure 3). For this channel, the fitted line p(k) = -0.0058 k + 1.2681.
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Figure 4: Habituation. 1st-degree polynomial (blue line) fitted to ψ(k) for a right-hemisphere channel revealing a strong signal and a clear habituation (see also figure 3). For this channel, the fitted line p(k) = -0.0058 k + 1.2681.

Mentions: For illustrative purposes, figure 4 shows, for a selected channel revealing a strong signal and a clear habituation, the estimated ψ(k) (black dots) and the consecutively fitted 1st-degree polynomial.


Maximum-likelihood estimation of channel-dependent trial-to-trial variability of auditory evoked brain responses in MEG.

Sielużycki C, Kordowski P - Biomed Eng Online (2014)

Habituation. 1st-degree polynomial (blue line) fitted to ψ(k) for a right-hemisphere channel revealing a strong signal and a clear habituation (see also figure 3). For this channel, the fitted line p(k) = -0.0058 k + 1.2681.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4060856&req=5

Figure 4: Habituation. 1st-degree polynomial (blue line) fitted to ψ(k) for a right-hemisphere channel revealing a strong signal and a clear habituation (see also figure 3). For this channel, the fitted line p(k) = -0.0058 k + 1.2681.
Mentions: For illustrative purposes, figure 4 shows, for a selected channel revealing a strong signal and a clear habituation, the estimated ψ(k) (black dots) and the consecutively fitted 1st-degree polynomial.

Bottom Line: Following the work of de Munck et al., our approach is based on the maximum likelihood estimation and involves an approximation of the spatio-temporal covariance of the contaminating background noise by means of the Kronecker product of its spatial and temporal covariance matrices.We also present an illustrative example of the application of this methodology to real MEG data taken from an auditory experimental paradigm, where we found hemispheric lateralization of the habituation effect to multiple stimulus presentation.Hence, it may be a useful tool in paradigms that assume lateralization effects, like, e.g., those involving language processing.

View Article: PubMed Central - HTML - PubMed

Affiliation: Special Lab Non-invasive Brain Imaging, Leibniz Institute for Neurobiology, Brenneckestr, 6, 39118 Magdeburg, Germany. cezary.sieluzycki@icm-institute.org.

ABSTRACT

Background: We propose a mathematical model for multichannel assessment of the trial-to-trial variability of auditory evoked brain responses in magnetoencephalography (MEG).

Methods: Following the work of de Munck et al., our approach is based on the maximum likelihood estimation and involves an approximation of the spatio-temporal covariance of the contaminating background noise by means of the Kronecker product of its spatial and temporal covariance matrices. Extending the work of de Munck et al., where the trial-to-trial variability of the responses was considered identical to all channels, we evaluate it for each individual channel.

Results: Simulations with two equivalent current dipoles (ECDs) with different trial-to-trial variability, one seeded in each of the auditory cortices, were used to study the applicability of the proposed methodology on the sensor level and revealed spatial selectivity of the trial-to-trial estimates. In addition, we simulated a scenario with neighboring ECDs, to show limitations of the method. We also present an illustrative example of the application of this methodology to real MEG data taken from an auditory experimental paradigm, where we found hemispheric lateralization of the habituation effect to multiple stimulus presentation.

Conclusions: The proposed algorithm is capable of reconstructing lateralization effects of the trial-to-trial variability of evoked responses, i.e. when an ECD of only one hemisphere habituates, whereas the activity of the other hemisphere is not subject to habituation. Hence, it may be a useful tool in paradigms that assume lateralization effects, like, e.g., those involving language processing.

Show MeSH
Related in: MedlinePlus