Limits...
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.


Estimator variance as a function of number of particles. This figure shows average (across all time points and 800 simulations) variance of estimated means of the BOLD response (A) and neural activity (B). Smaller values are better. It is clear that combined analysis requires much fewer particles to achieve lower variance of the estimate.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2991230&req=5

Figure 6: Estimator variance as a function of number of particles. This figure shows average (across all time points and 800 simulations) variance of estimated means of the BOLD response (A) and neural activity (B). Smaller values are better. It is clear that combined analysis requires much fewer particles to achieve lower variance of the estimate.

Mentions: For fMRI-only and the combined analysis we compute the estimate of the mean and its variance with M = 800 for number of particles = 100k with k∈{1,…,10}. Figure 6 demonstrates that fMRI-only filtering has much higher variance with the number of particles in the selected range and the variance is not stabilized even when 1000 particles are used. On the other hand the combined analysis provides stable results when number of particles is in the range [400,1000].


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)

Estimator variance as a function of number of particles. This figure shows average (across all time points and 800 simulations) variance of estimated means of the BOLD response (A) and neural activity (B). Smaller values are better. It is clear that combined analysis requires much fewer particles to achieve lower variance of the estimate.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Estimator variance as a function of number of particles. This figure shows average (across all time points and 800 simulations) variance of estimated means of the BOLD response (A) and neural activity (B). Smaller values are better. It is clear that combined analysis requires much fewer particles to achieve lower variance of the estimate.
Mentions: For fMRI-only and the combined analysis we compute the estimate of the mean and its variance with M = 800 for number of particles = 100k with k∈{1,…,10}. Figure 6 demonstrates that fMRI-only filtering has much higher variance with the number of particles in the selected range and the variance is not stabilized even when 1000 particles are used. On the other hand the combined analysis provides stable results when number of particles is in the range [400,1000].

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.