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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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Results of simulations. Top: the trial-invariant spatial response pattern R at j = 25, i.e. for the time point at the middle of the analyzed time window (see also figure 2) in a 2D projection of the MEG sensors layout (open black circles), for the simulated data generated as the superposition of the habituating activity (see figure 2) of the ECDs depicted in figure 1 and the contaminating spatially and temporally correlated background noise, at the overall SNR=0.1. Middle: Spatial distribution of the slope coefficients of 1-st degree polynomials fitted to the multichannel estimates of ψ reflecting the trial-to-trial variability of R. Sensors marked with blue-filled circles denote the negative slopes significantly different from zero at the 0.05/245 significance level, where 245 is the number of analyzed channels, revealing a clear habituation. The exact color of a circle reflects the value of the slope. Bottom: The estimate of the temporal covariance T of the noise.
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Figure 3: Results of simulations. Top: the trial-invariant spatial response pattern R at j = 25, i.e. for the time point at the middle of the analyzed time window (see also figure 2) in a 2D projection of the MEG sensors layout (open black circles), for the simulated data generated as the superposition of the habituating activity (see figure 2) of the ECDs depicted in figure 1 and the contaminating spatially and temporally correlated background noise, at the overall SNR=0.1. Middle: Spatial distribution of the slope coefficients of 1-st degree polynomials fitted to the multichannel estimates of ψ reflecting the trial-to-trial variability of R. Sensors marked with blue-filled circles denote the negative slopes significantly different from zero at the 0.05/245 significance level, where 245 is the number of analyzed channels, revealing a clear habituation. The exact color of a circle reflects the value of the slope. Bottom: The estimate of the temporal covariance T of the noise.

Mentions: figure 3 shows the estimates obtained from the iterative solution of equations (13)–(16) after 20 iterations. The top panel shows the estimated trial-invariant pattern of the response, R, at j = 25, i.e. between the two M100 peaks originating from the two ECDs. A strong correspondence to the forward field of the simulated ECDs (see figure 1) can be seen. Since the estimated ψ, which scales R across trials (see (4)), is typically noisy, 1st-degree polynomials were fitted (see [11,17,18]) to the ψ estimates on each channel in order to reveal the main tendency in the overall trial-to-trial variability. figure 3 (middle) shows the slopes of the fitted polynomials for each channel. We only show the values for those sensors at which the value of the slopes was significantly different from zero at the Bonferroni-corrected significance level of 0.05/245, where 245 is the number of artifact-free channels (we excluded 3 sensors from the 248-channel set of our 4D MEG system due to strong artifacts on those channels; let us remind here that we used a real-measurement noise in these simulations). Sensors marked with empty circles denote the slopes which were not significantly different from zero. Note that those are predominantly channels with poor SNR, as revealed by the top panel. The significant slopes reveal the difference in habituation between the two auditory cortices, with the mean equal to -0.0041 for the left-hemisphere sensors and -0.0056 for the right hemisphere, compared to -0.0041 and -0.0060 at the source level. In both cases, the corresponding standard deviation σ was equal to 0.0007. Hence, the habituation lateralization on the source level was well reconstructed on the sensor level despite the demanding overall SNR of 0.1.


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)

Results of simulations. Top: the trial-invariant spatial response pattern R at j = 25, i.e. for the time point at the middle of the analyzed time window (see also figure 2) in a 2D projection of the MEG sensors layout (open black circles), for the simulated data generated as the superposition of the habituating activity (see figure 2) of the ECDs depicted in figure 1 and the contaminating spatially and temporally correlated background noise, at the overall SNR=0.1. Middle: Spatial distribution of the slope coefficients of 1-st degree polynomials fitted to the multichannel estimates of ψ reflecting the trial-to-trial variability of R. Sensors marked with blue-filled circles denote the negative slopes significantly different from zero at the 0.05/245 significance level, where 245 is the number of analyzed channels, revealing a clear habituation. The exact color of a circle reflects the value of the slope. Bottom: The estimate of the temporal covariance T of the noise.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Results of simulations. Top: the trial-invariant spatial response pattern R at j = 25, i.e. for the time point at the middle of the analyzed time window (see also figure 2) in a 2D projection of the MEG sensors layout (open black circles), for the simulated data generated as the superposition of the habituating activity (see figure 2) of the ECDs depicted in figure 1 and the contaminating spatially and temporally correlated background noise, at the overall SNR=0.1. Middle: Spatial distribution of the slope coefficients of 1-st degree polynomials fitted to the multichannel estimates of ψ reflecting the trial-to-trial variability of R. Sensors marked with blue-filled circles denote the negative slopes significantly different from zero at the 0.05/245 significance level, where 245 is the number of analyzed channels, revealing a clear habituation. The exact color of a circle reflects the value of the slope. Bottom: The estimate of the temporal covariance T of the noise.
Mentions: figure 3 shows the estimates obtained from the iterative solution of equations (13)–(16) after 20 iterations. The top panel shows the estimated trial-invariant pattern of the response, R, at j = 25, i.e. between the two M100 peaks originating from the two ECDs. A strong correspondence to the forward field of the simulated ECDs (see figure 1) can be seen. Since the estimated ψ, which scales R across trials (see (4)), is typically noisy, 1st-degree polynomials were fitted (see [11,17,18]) to the ψ estimates on each channel in order to reveal the main tendency in the overall trial-to-trial variability. figure 3 (middle) shows the slopes of the fitted polynomials for each channel. We only show the values for those sensors at which the value of the slopes was significantly different from zero at the Bonferroni-corrected significance level of 0.05/245, where 245 is the number of artifact-free channels (we excluded 3 sensors from the 248-channel set of our 4D MEG system due to strong artifacts on those channels; let us remind here that we used a real-measurement noise in these simulations). Sensors marked with empty circles denote the slopes which were not significantly different from zero. Note that those are predominantly channels with poor SNR, as revealed by the top panel. The significant slopes reveal the difference in habituation between the two auditory cortices, with the mean equal to -0.0041 for the left-hemisphere sensors and -0.0056 for the right hemisphere, compared to -0.0041 and -0.0060 at the source level. In both cases, the corresponding standard deviation σ was equal to 0.0007. Hence, the habituation lateralization on the source level was well reconstructed on the sensor level despite the demanding overall SNR of 0.1.

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