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.
Highlights
Magnetoencephalography (MEG) and functional magnetic resonance imaging both provide indirect views of the underlying neural activity
Simulation In order to find out how estimation of neural activity and tracking of the blood oxygenation level dependent (BOLD) response depends on the data modality and their combination, we have constructed the following simulation
Average functional magnetic resonance imaging (fMRI) activity of this region of interest (ROI) is generated by the hemodynamic forward model (Forward Models) from simulated neural activity formed by spaced weighted radial basis functions (RBF)
Summary
Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) both provide indirect views of the underlying neural activity. The neurovascular transformation of neural activity into the fMRI signal can be measured with full brain coverage with high spatial resolution without a spatial inverse problem. MEG analysis becomes an issue when the goal is localization of the The localized spatial resolution of fMRI allows one to concentrate on the relationship among brain regions. In this case, the dynamical properties of a modality gain high importance. Studying the temporal properties of neuronal causes via fMRI is hampered by the complexity of the blood oxygenation level dependent (BOLD) signal generation from the underlying neural activity. Analyzing fMRI and MEG simultaneously allows us to focus on statistical properties of brain activity and neural activity in particular and overcome limitations accompanying each of these modalities
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