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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.


A single active ROI in the brain, shown with the solid color. It is the left hemisphere the bank of the superior temporal sulcus from the FreeSurfer atlas.
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Figure 1: A single active ROI in the brain, shown with the solid color. It is the left hemisphere the bank of the superior temporal sulcus from the FreeSurfer atlas.

Mentions: We want to demonstrate benefits of combined analysis of fMRI and MEG measurements. In order to produce clear and directly interpretable results, in this study we concentrate on a case with a single hidden variable. Of major importance in this paper is the task of inferring the values of this variable conditioned on the MEG and fMRI observations. Figure 1 displays an example region of interest (ROI) in the bank of the superior temporal sulcus of the left hemisphere, whose activity needs to be inferred.


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)

A single active ROI in the brain, shown with the solid color. It is the left hemisphere the bank of the superior temporal sulcus from the FreeSurfer atlas.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: A single active ROI in the brain, shown with the solid color. It is the left hemisphere the bank of the superior temporal sulcus from the FreeSurfer atlas.
Mentions: We want to demonstrate benefits of combined analysis of fMRI and MEG measurements. In order to produce clear and directly interpretable results, in this study we concentrate on a case with a single hidden variable. Of major importance in this paper is the task of inferring the values of this variable conditioned on the MEG and fMRI observations. Figure 1 displays an example region of interest (ROI) in the bank of the superior temporal sulcus of the left hemisphere, whose activity needs to be inferred.

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.