MEG and fMRI fusion for nonlinear estimation of neural and BOLD signal changes

• Main Authors: Sergey M Plis, Vince D Calhoun, Tom Eichele, Michael P Weisend, Terran Lane
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2010-11-01
• Series: Frontiers in Neuroinformatics
• Subjects: Dynamic Bayesian Networks; latent variable inference; multimodal data fusion; particle filtering
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00114/full

The combined analysis of MEG/EEG and functional MRI 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 BOLD response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater SNR, that confirms the expectation arising from the nature of the experiment. The highly nonlinear 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.