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.


Non-parametric summary of parameter behavior by box and whisker (1. 5 IQR) and bar-plots comparing all parameters optimized by the PF on simulated data using fMRI-only vs. fusion data. These include seven hemodynamic parameters (Θ and V0) and the estimate of neural activity. Bar-plots on the right summarize two important differences between results produced using fMRI-only and fusion data: ratio of IQR of fMRI over fusion, and difference of fMRI and fusion medians.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Non-parametric summary of parameter behavior by box and whisker (1. 5 IQR) and bar-plots comparing all parameters optimized by the PF on simulated data using fMRI-only vs. fusion data. These include seven hemodynamic parameters (Θ and V0) and the estimate of neural activity. Bar-plots on the right summarize two important differences between results produced using fMRI-only and fusion data: ratio of IQR of fMRI over fusion, and difference of fMRI and fusion medians.

Mentions: The behavior of parameters across the tracking run for both approaches is summarized in Figure 10. The left column plots show a complete non-parametric representation of all distributions. The right column plots emphasize the differences by demonstrating important details, which may not be clearly visible on the box-plot due to differences in the parameter scale. The ratio of the inter quartile ranges (IQR) of fMRI-only to fusion experiment shows that for all parameters but the two (the neural efficacy and the resting oxygen extraction fraction) fMRI-only run produces significantly wider distributions of up to seven times for the hemodynamic transit time and the neural activity estimate. This is a clear demonstration of the underdetermined nature of the problem of inferring neural activity from the BOLD response. The PF algorithm using fMRI-only data allows the parameters to freely vary within a wide range while maintaining good fit to the data, whereas, in the case of fusion, parameters are constrained by MEG.


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)

Non-parametric summary of parameter behavior by box and whisker (1. 5 IQR) and bar-plots comparing all parameters optimized by the PF on simulated data using fMRI-only vs. fusion data. These include seven hemodynamic parameters (Θ and V0) and the estimate of neural activity. Bar-plots on the right summarize two important differences between results produced using fMRI-only and fusion data: ratio of IQR of fMRI over fusion, and difference of fMRI and fusion medians.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Non-parametric summary of parameter behavior by box and whisker (1. 5 IQR) and bar-plots comparing all parameters optimized by the PF on simulated data using fMRI-only vs. fusion data. These include seven hemodynamic parameters (Θ and V0) and the estimate of neural activity. Bar-plots on the right summarize two important differences between results produced using fMRI-only and fusion data: ratio of IQR of fMRI over fusion, and difference of fMRI and fusion medians.
Mentions: The behavior of parameters across the tracking run for both approaches is summarized in Figure 10. The left column plots show a complete non-parametric representation of all distributions. The right column plots emphasize the differences by demonstrating important details, which may not be clearly visible on the box-plot due to differences in the parameter scale. The ratio of the inter quartile ranges (IQR) of fMRI-only to fusion experiment shows that for all parameters but the two (the neural efficacy and the resting oxygen extraction fraction) fMRI-only run produces significantly wider distributions of up to seven times for the hemodynamic transit time and the neural activity estimate. This is a clear demonstration of the underdetermined nature of the problem of inferring neural activity from the BOLD response. The PF algorithm using fMRI-only data allows the parameters to freely vary within a wide range while maintaining good fit to the data, whereas, in the case of fusion, parameters are constrained by MEG.

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.