[en] This paper describes an application of hierarchical or empirical Bayes to the distributed source reconstruction problem in electro- and magnetoencephalography (EEG and MEG). The key contribution is the automatic selection of multiple cortical sources with compact spatial support that are specified in terms of empirical priors. This obviates the need to use priors with a specific form (e.g., smoothness or minimum norm) or with spatial structure (e.g., priors based on depth constraints or functional magnetic resonance imaging results). Furthermore, the inversion scheme allows for a sparse solution for distributed sources, of the sort enforced by equivalent current dipole (ECD) models. This means the approach automatically selects either a sparse or a distributed model, depending on the data. The scheme is compared with conventional applications of Bayesian solutions to quantify the improvement in performance.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
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Bibliography
Baillet S., and Garnero L. A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem. IEEE Trans. Biomed. Eng. 44 5 (1997) 374-385
Cointepas Y., Mangin J.-F., Garnero L., Poline J.-P., and Benali H. BrainVISA: Software Platform for Visualization and Analysis of Multi-modality Brain Data (2001), Proc. 7th HBM, Brighton, UK S98
Daunizeau, J., Friston, K.J., 2007. A mesostate-space model for EEG and MEG. NeuroImage. 2007 Jul 24; [Epub ahead of print].
Daunizeau J., Grova C., Marrelec G., Mattout J., Jbabdi S., Pelegrini-Issac M., Lina J.M., and Benali H. Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework. NeuroImage 36 1 (2007) 69-87
Dempster A.P., Laird N.M., and Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc., Ser. B 39 (1977) 1-38
Efron B., and Morris C. Stein's estimation rule and its competitors-an empirical Bayes approach. J. Am. Stat. Assoc. 68 (1973) 117-130
Friston K.J., Henson R., Phillips C., and Mattout J. Bayesian estimation of evoked and induced responses. Hum. Brain Mapp. 27 9 (2006) 722-735
Friston K.J., Mattout J., Trujillo-Barreto N., Ashburner J., and Penny W. Variational free energy and the Laplace approximation. NeuroImage 34 1 (2007) 220-234
Friston K.J., Chu C., Mourão-Miranda J., Hulme O., Rees G., Penny W., and Ashburner J. Bayesian decoding of brain images. NeuroImage (2007) Aug 24; [Epub ahead of print]
Fuchs M., Wagner M., Kohler T., and Wischmann HA. Linear and nonlinear current density reconstructions. J. Clin. Neurophysiol. 16 3 (1999) 267-295
Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Anal. 1 3 (2006) 515-533
Harrison L.M., Penny W., Ashburner J., Trujillo-Barreto N., and Friston K.J. Diffusion-based spatial priors for imaging. NeuroImage (2007) (Aug 8; electronic publication ahead of print)
Harville D.A. Maximum likelihood approaches to variance component estimation and to related problems. J. Am. Stat. Assoc. 72 (1977) 320-338
Henson R.N., Goshen-Gottstein Y., Ganel T., Otten L.J., Quayle A., and Rugg M.D. Electrophysiological and haemodynamic correlates of face perception, recognition and priming. Cereb. Cortex 13 7 (2003) 793-805
Henson R.N., Mattout J., Singh K.D., Barnes G.R., Hillebrand A., and Friston K.J. Population-level inferences for distributed MEG source localization under multiple constraints: application to face-evoked fields. NeuroImage 38 3 (2007) 422-438
Jun S.C., George J.S., Plis S.M., Ranken D.M., Schmidt D.M., and Wood CC. Improving source detection and separation in a spatiotemporal Bayesian inference dipole analysis. Phys. Med. Biol. 51 10 (2006) 2395-2414
Kass R.E., and Steffey D. Approximate Bayesian inference in conditionally independent hierarchical models (parametric empirical Bayes models). J. Am. Stat. Assoc. 407 (1989) 717-726
Kass R.E., and Raftery A.E. Bayes factors. J. Am. Stat. Assoc. 90 (1995) 773-795
Kiebel S.J., and Friston KJ. Statistical parametric mapping for event-related potentials (II): a hierarchical temporal model. NeuroImage 22 2 (2004) 503-520
Kim H.-C., and Ghahramani Z. Bayesian Gaussian process classification with the EM-EP algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 28 12 (2006) 1948-1959
LeSage J.P., and Pace R.K. Using Matrix Exponentials to Explore Spatial Structure in Regression Relationships (2000), Working Paper, Department of Economics, University of Toledo
Mattout J., Phillips C., Penny W.D., Rugg M.D., and Friston KJ. MEG source localization under multiple constraints: an extended Bayesian framework. NeuroImage 30 (2006) 753-767
Mattout J., Henson R.N., and Friston K.J. Canonical Source Reconstruction for MEG. Computational Intelligence and Neuroscience (2007) Article ID 67613
Mosher J.C., Spencer M.E., Leahy R.M., and Lewis PS. Error bounds for EEG and MEG dipole source localization. Electroencephalogr. Clin. Neurophysiol. 86 5 (1993) 303-321
Neal R.M. Bayesian Learning for Neural Networks (1996), Springer-Verlag, New York
Neal R.M. Assessing relevance determination methods using DELVE. Neural Networks and Machine Learning (1998), Springer 97-129
Nagarajan S.S., Portniaguine O., Hwang D., Johnson C., and Sekihara K. Controlled support MEG imaging. NeuroImage 15 33(3) (2006) 878-885
Nummenmaa A., Auranen T., Hamalainen M.S., Jaaskelainen I.P., Lampinen J., Sams M., and Vehtari A. Hierarchical Bayesian estimates of distributed MEG sources: theoretical aspects and comparison of variational and MCMC methods. NeuroImage 35 2 (2007) 669-685
Patterson H.D., and Thompson R. Recovery of inter-block information when block sizes are unequal. Biometrika 58 (1971) 545-554
Phillips C., Rugg M.D., and Friston KJ. Anatomically informed basis functions for EEG source localization: combining functional and anatomical constraints. NeuroImage 16 3 Pt 1 (2002) 678-695
Phillips C., Rugg M., and Friston K.J. Systematic regularisation of linear inverse solutions of the EEG source localisation problem. NeuroImage 17 (2002) 287-301
Phillips C., Mattout J., Rugg M.D., Maquet P., and Friston K.J. An empirical Bayesian solution to the source reconstruction problem in EEG. NeuroImage 24 (2005) 997-1011
Rasmussen, C.E., 1996. Evaluation of Gaussian Processes and Other Methods for Non-Linear Regression. PhD thesis, Dept. of Computer Science, Univ. of Toronto, 1996. Available from http://www.cs.utoronto.ca~carl/.
Ripley B.D. Flexible non-linear approaches to classification. In: Cherkassy V., Friedman J.H., and Wechsler H. (Eds). From Statistics to Neural Networks (1994), Springer 105-126
Russell G.S., Srinivasan R., and Tucker DM. Bayesian estimates of error bounds for EEG source imaging. IEEE Trans. Med. Imag. 17 6 (1998) 1084-1089
Salin P.-A., and Bullier J. Corticocortical connections in the visual system: structure and function. Psychol. Bull. 75 (1995) 107-154
Sato M.A., Yoshioka T., Kajihara S., Toyama K., Goda N., Doya K., and Kawato M. Hierarchical Bayesian estimation for MEG inverse problem. NeuroImage 23 3 (2004) 806-826
Serinagaoglu Y., Brooks D.H., and MacLeod RS. Bayesian solutions and performance analysis in bioelectric inverse problems. IEEE Trans. Biomed. Eng. 52 6 (2005) 1009-1020
Talairach J., and Tournoux P. Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System-An Approach to Cerebral Imaging (1988), Thieme Medical Publishers, New York, NY
Tipping M.E. Sparse Bayesian learning and the Relevance Vector Machine. J. Mach. Learn. Res. 1 (2001) 211-244
Trujillo-Barreto N., Aubert-Vazquez E., and Valdes-Sosa P. Bayesian model averaging. NeuroImage 21 (2004) 1300-1319
Wipf D.P., Rami{dotless}rez R.R., Palmer J.A., Makeig S., and Rao B.D. Automatic Relevance Determination for Source Localization with MEG and EEG Data, Technical Report 21 (2006) University of California, San Diego
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