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MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes.

Plis SM, Calhoun VD, Weisend MP, Eichele T, Lane T - Front Neuroinform (2010)

Bottom Line: Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique.The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity.We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources.

View Article: PubMed Central - PubMed

Affiliation: The Mind Research Network Albuquerque, NM, USA.

ABSTRACT
The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources.

No MeSH data available.


Related in: MedlinePlus

Stimulus-locked averaged data. Stimulus presentation is shown with the gray bar from 0 to 1 s, where 8 Hz flashing checkerboard was presented. fMRI was linearly interpolated to MEG sampling rate (1200 Hz) before averaging (A). For MEG spatial standard deviation in the sensor space is shown (B), (the global field power).
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Figure 13: Stimulus-locked averaged data. Stimulus presentation is shown with the gray bar from 0 to 1 s, where 8 Hz flashing checkerboard was presented. fMRI was linearly interpolated to MEG sampling rate (1200 Hz) before averaging (A). For MEG spatial standard deviation in the sensor space is shown (B), (the global field power).

Mentions: The data for both modalities were averaged with reference to the stimulus onset, discarding the first and the last 10 s of the run. fMRI data was linearly interpolated to the MEG resolution (1200 Hz) before averaging (see Figure 13). Although it is a different setup from what was used in simulations and it leads to sample-by-sample symmetric treatment of both modalities, fMRI information content is not increased by interpolation and MEG still provides the most information about the dynamics of the latent activity. The averaging of fMRI time courses was performed over all of the voxels belonging to cuneus, precuneus, and pericalcarine ROIs of the FreeSurfer atlas.


MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes.

Plis SM, Calhoun VD, Weisend MP, Eichele T, Lane T - Front Neuroinform (2010)

Stimulus-locked averaged data. Stimulus presentation is shown with the gray bar from 0 to 1 s, where 8 Hz flashing checkerboard was presented. fMRI was linearly interpolated to MEG sampling rate (1200 Hz) before averaging (A). For MEG spatial standard deviation in the sensor space is shown (B), (the global field power).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2991230&req=5

Figure 13: Stimulus-locked averaged data. Stimulus presentation is shown with the gray bar from 0 to 1 s, where 8 Hz flashing checkerboard was presented. fMRI was linearly interpolated to MEG sampling rate (1200 Hz) before averaging (A). For MEG spatial standard deviation in the sensor space is shown (B), (the global field power).
Mentions: The data for both modalities were averaged with reference to the stimulus onset, discarding the first and the last 10 s of the run. fMRI data was linearly interpolated to the MEG resolution (1200 Hz) before averaging (see Figure 13). Although it is a different setup from what was used in simulations and it leads to sample-by-sample symmetric treatment of both modalities, fMRI information content is not increased by interpolation and MEG still provides the most information about the dynamics of the latent activity. The averaging of fMRI time courses was performed over all of the voxels belonging to cuneus, precuneus, and pericalcarine ROIs of the FreeSurfer atlas.

Bottom Line: Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique.The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity.We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources.

View Article: PubMed Central - PubMed

Affiliation: The Mind Research Network Albuquerque, NM, USA.

ABSTRACT
The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources.

No MeSH data available.


Related in: MedlinePlus