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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.


Graphical structure of the DBN that is used in this paper – displayed is a window for a single TR. Observations are denoted by squares (MEG and BOLD); hidden variables are denoted by circles (neural activity in an ROI and unobserved BOLD time points); arrows denote transition and observation models. Due to the sampling frequency difference, many MEG observations are available per single fMRI TR. Time between ti and ti+1 corresponds to the MEG sampling time period.
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Figure 2: Graphical structure of the DBN that is used in this paper – displayed is a window for a single TR. Observations are denoted by squares (MEG and BOLD); hidden variables are denoted by circles (neural activity in an ROI and unobserved BOLD time points); arrows denote transition and observation models. Due to the sampling frequency difference, many MEG observations are available per single fMRI TR. Time between ti and ti+1 corresponds to the MEG sampling time period.

Mentions: The graphical structure of the DBN used in this paper is shown in Figure 2.


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)

Graphical structure of the DBN that is used in this paper – displayed is a window for a single TR. Observations are denoted by squares (MEG and BOLD); hidden variables are denoted by circles (neural activity in an ROI and unobserved BOLD time points); arrows denote transition and observation models. Due to the sampling frequency difference, many MEG observations are available per single fMRI TR. Time between ti and ti+1 corresponds to the MEG sampling time period.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Graphical structure of the DBN that is used in this paper – displayed is a window for a single TR. Observations are denoted by squares (MEG and BOLD); hidden variables are denoted by circles (neural activity in an ROI and unobserved BOLD time points); arrows denote transition and observation models. Due to the sampling frequency difference, many MEG observations are available per single fMRI TR. Time between ti and ti+1 corresponds to the MEG sampling time period.
Mentions: The graphical structure of the DBN used in this paper is shown in Figure 2.

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.