MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes

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.

[1]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[2]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[3]  Jose M. Sanchez-Bornot,et al.  Model driven EEG/fMRI fusion of brain oscillations , 2009, Human brain mapping.

[4]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[5]  D. M. Schmidt,et al.  Bayesian inference applied to the electromagnetic inverse problem , 1998, Human brain mapping.

[6]  Thiagalingam Kirubarajan,et al.  Out-of-sequence measurement processing for tracking ground target using particle filters , 2002, Proceedings, IEEE Aerospace Conference.

[7]  E. Halgren,et al.  Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity , 2000, Neuron.

[8]  B. Rosen,et al.  Evidence of a Cerebrovascular Postarteriole Windkessel with Delayed Compliance , 1999, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[9]  M. Hämäläinen,et al.  Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data , 1989, IEEE Transactions on Biomedical Engineering.

[10]  Sergey M. Plis,et al.  Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks , 2009, ECML/PKDD.

[11]  Klaus-Robert Müller,et al.  Temporal kernel CCA and its application in multimodal neuronal data analysis , 2010, Machine Learning.

[12]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[13]  Jorge J. Riera,et al.  Nonlinear Local Electro-Vascular Coupling. Part I: A Theoretical Model , 2006 .

[14]  J. C. Jimenez,et al.  Simulation of Stochastic Differential Equations Through the Local Linearization Method. A Comparative Study , 1999 .

[15]  Sergey M. Plis,et al.  Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC , 2008, NeuroImage.

[16]  Ron Borowsky,et al.  Inferring neural activity from BOLD signals through nonlinear optimization , 2007, NeuroImage.

[17]  Juan Zhou,et al.  Learning effective brain connectivity with dynamic Bayesian networks , 2007, NeuroImage.

[18]  Kenneth Hugdahl,et al.  Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Karl J. Friston,et al.  Dynamic causal modeling , 2010, Scholarpedia.

[20]  Tim Hesterberg,et al.  Monte Carlo Strategies in Scientific Computing , 2002, Technometrics.

[21]  Olivier D. Faugeras,et al.  EEG-fMRI fusion of non-triggered data using Kalman filtering , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[22]  R. Leahy,et al.  EEG and MEG: forward solutions for inverse methods , 1999, IEEE Transactions on Biomedical Engineering.

[23]  Iven M. Y. Mareels,et al.  Nonlinear estimation of the BOLD signal , 2008, NeuroImage.

[24]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[25]  Hamilton E. Link,et al.  Discrete dynamic Bayesian network analysis of fMRI data , 2009, Human brain mapping.

[26]  L. Shah,et al.  Functional magnetic resonance imaging. , 2010, Seminars in roentgenology.

[27]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[28]  A. Brix.  Bayesian Data Analysis, 2nd edn , 2005 .

[29]  Karl J. Friston,et al.  Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics , 2000, NeuroImage.

[30]  Yul-Wan Sung,et al.  Functional magnetic resonance imaging , 2004, Scholarpedia.

[31]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[32]  Karl J. Friston,et al.  DEM: A variational treatment of dynamic systems , 2008, NeuroImage.

[33]  Karl J. Friston,et al.  Comparing hemodynamic models with DCM , 2007, NeuroImage.

[34]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[35]  Hamid Soltanian-Zadeh,et al.  Integrated MEG/EEG and fMRI model based on neural masses , 2006, IEEE Transactions on Biomedical Engineering.

[36]  Kristine L. Bell,et al.  A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking , 2007 .

[37]  Sergey M. Plis,et al.  Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data , 2005, NeuroImage.

[38]  Thomas T. Liu,et al.  Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the balloon model to the interpretation of BOLD transients , 2004, NeuroImage.

[39]  V. Calhoun,et al.  Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[40]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[41]  Jean-Francois Mangin,et al.  What is the best similarity measure for motion correction in fMRI time series? , 2002, IEEE Transactions on Medical Imaging.

[42]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[43]  L. Freire,et al.  Motion Correction Algorithms May Create Spurious Brain Activations in the Absence of Subject Motion , 2001, NeuroImage.

[44]  M. Mintun,et al.  Brain work and brain imaging. , 2006, Annual review of neuroscience.

[45]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[46]  Huafeng Liu,et al.  Nonlinear Analysis of the BOLD Signal , 2009, EURASIP J. Adv. Signal Process..

[47]  M. Torrens.  Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[48]  Nikos K Logothetis,et al.  Interpreting the BOLD signal. , 2004, Annual review of physiology.

[49]  J. Sarvas.  Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. , 1987, Physics in medicine and biology.

[50]  R. Quiroga,et al.  Unmixing concurrent EEG-fMRI with parallel independent component analysis. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[51]  I. Fried,et al.  Coupling Between Neuronal Firing, Field Potentials, and fMRI in Human Auditory Cortex , 2005, Science.

[52]  Amos J. Storkey,et al.  Continuous Time Particle Filtering for fMRI , 2007, NIPS.

[53]  Vince D. Calhoun,et al.  Neuronal chronometry of target detection: Fusion of hemodynamic and event-related potential data , 2005, NeuroImage.

[54]  Ryoji Suzuki,et al.  A probabilistic solution to the MEG inverse problem via MCMC methods: the reversible jump and parallel tempering algorithms , 2001, IEEE Transactions on Biomedical Engineering.

[55]  A. Marrs,et al.  A Bayesian approach to multi-target tracking and data fusion with out-of-sequence measurements , 2001 .

[56]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[57]  Jérémie Mattout,et al.  Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework , 2007, NeuroImage.

[58]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[59]  R. Buxton,et al.  Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.

[60]  Dirk Ostwald,et al.  An information theoretic approach to EEG–fMRI integration of visually evoked responses , 2010, NeuroImage.

[61]  Naoki Miura,et al.  A state-space model of the hemodynamic approach: nonlinear filtering of BOLD signals , 2004, NeuroImage.

[62]  Dimitris Samaras,et al.  Modeling Neuronal Interactivity using Dynamic Bayesian Networks , 2005, NIPS.

[63]  E. Somersalo,et al.  Non-stationary magnetoencephalography by Bayesian filtering of dipole models , 2003 .

[64]  David Poeppel,et al.  Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique , 2001, IEEE Transactions on Biomedical Engineering.

[65]  David Poeppel,et al.  Reconstructing spatio-temporal activities of neural sources using MEG vector beamformer , 2000, NeuroImage.

[66]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[67]  R Kawashima,et al.  Nonlinear local electrovascular coupling. II: From data to neuronal masses , 2007, Human brain mapping.