MEG and fMRI fusion for non-linear estimation of neural and BOLD signal changes

Sergey M. Plis, Vince D. Calhoun, Michael P. Weisend, Tom Eichele, Terran Lane

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number114
JournalFrontiers in Neuroinformatics
Volume4
Issue numberNOV
DOIs
StatePublished - Nov 11 2010
Externally publishedYes

Keywords

  • Dynamic Bayesian networks
  • Latent variable inference
  • Multimodal data fusion
  • Particle filtering

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

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