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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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Habituation in real-data analysis. The blue line depicts the 1st-degree polynomial p(k) = -0.0042 k + 1.2079 fitted to ψ(k) for a right-hemisphere channel revealing a strong signal and a clear habituation (see figure 9).
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Figure 10: Habituation in real-data analysis. The blue line depicts the 1st-degree polynomial p(k) = -0.0042 k + 1.2079 fitted to ψ(k) for a right-hemisphere channel revealing a strong signal and a clear habituation (see figure 9).

Mentions: figure 10 for the real-data analysis is fully analogous to figure 4 for the simulation. Alternatively to the 1st-degree polynomial, one might fit an exponential function. However, given the observed level of the inter-trial variance of ψ and the fact that in the real-data scenario the exact characteristics of habituation is not known, we decided to follow the Ockham’s razor principle and hence opt for linear fits. Besides, exponential fits resulted in exactly the same channels revealing significant habituation.


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 in real-data analysis. The blue line depicts the 1st-degree polynomial p(k) = -0.0042 k + 1.2079 fitted to ψ(k) for a right-hemisphere channel revealing a strong signal and a clear habituation (see figure 9).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Habituation in real-data analysis. The blue line depicts the 1st-degree polynomial p(k) = -0.0042 k + 1.2079 fitted to ψ(k) for a right-hemisphere channel revealing a strong signal and a clear habituation (see figure 9).
Mentions: figure 10 for the real-data analysis is fully analogous to figure 4 for the simulation. Alternatively to the 1st-degree polynomial, one might fit an exponential function. However, given the observed level of the inter-trial variance of ψ and the fact that in the real-data scenario the exact characteristics of habituation is not known, we decided to follow the Ockham’s razor principle and hence opt for linear fits. Besides, exponential fits resulted in exactly the same channels revealing significant habituation.

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