Hierarchical Bayesian estimates of distributed MEG sources: Theoretical aspects and comparison of variational and MCMC methods

NeuroImage

Volumn 35, Issue 2, 2007, Pages 669-685

  Nummenmaa, Aapo ^a   Auranen, Toni ^a   Hämäläinen, Matti S ^b   Jääskeläinen, Iiro P ^a   Lampinen, Jouko ^a   Sams, Mikko ^a   Vehtari, Aki ^a  

^a AALTO UNIVERSITY   (Finland)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

Hierarchical modeling; Markov chain Monte Carlo; MEG source reconstruction; Sensitivity analysis; Variational Bayes

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; MAGNETOENCEPHALOGRAPHY; MATHEMATICAL MODEL; MONTE CARLO METHOD; PRIORITY JOURNAL; PROBABILITY; SENSITIVITY ANALYSIS; SIMULATION;

ALGORITHMS; BAYES THEOREM; COMPUTER SIMULATION; MAGNETOENCEPHALOGRAPHY; MARKOV CHAINS; MONTE CARLO METHOD; SENSITIVITY AND SPECIFICITY;

EID: 33947121763     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2006.05.001     Document Type: Article

References (40)

1. Ahlfors S.P., and Simpson G.V. Geometrical interpretation of fMRI-guided MEG/EEG inverse estimates. NeuroImage 22 (2004) 323-332
2. p- norm priors. NeuroImage 26 3 (2005) 870-884
3. Baillet S., and Garnero L. A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem. IEEE Trans. Biom. Eng. 44 5 (1997)
4. Bernardo J.M., and Smith A.F.M. Bayesian Theory (1994), Wiley, Chichester
5. Bertrand C., Hamada Y., and Kado H. MRI prior computation and parallel tempering algorithm: a probabilistic resolution of the MEG/EEG inverse problem. Brain Topogr. 14 1 (2001)
6. Bertrand C., Ohmi M., Suzuki R., and Kado H. A probabilistic solution to the MEG inverse problem via MCMC methods: The reversible jump and parallel tempering algorithms. IEEE Trans. Biomed. Eng. 48 5 (2001)
7. Dale A.M., and Sereno M.I. Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cogn. Neurosci. 5 2 (1993) 162-176
8. Dale A.M., Fischl B., and Sereno M.I. Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage 9 (1999) 179-194
9. Dale A.M., Liu A.K., Fischl B.R., Buckner R.L., Belliveau J.W., Lewine J.D., and Halgren E. Dynamic statistical parametric mapping: Combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26 (2000) 55-67
10. Fischl B., Sereno M.I., and Dale A.M. Cortical surface-based analysis: II. Inflation, flattening, and a surface-based coordinate system. NeuroImage 9 (1999) 195-207
11. Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1 2 (2005) 1-19
12. Gelman A., and Meng X.-L. Simulating normalizing constants: from importance sampling to bridge sampling to path sampling. Stat. Sci. 13 2 (1998) 163-185
13. Gelman A., Carlin J.B., Stern H.S., and Rubin D.B. Bayesian data analysis. Chapman and Hall/CRC. 2nd ed. (2003)
14. Ghahramani Z., and Beal M. Graphical models and variational methods. In: Opper M., and Saad D. (Eds). Advanced Mean Field Methods-Theory and Practice (2001), MIT Press
15. Gilks W.R., Richardson S., and Spiegelhalter D.J. Markov Chain Monte Carlo in Practice (1996), Chapman and Hall
16. Harville D.A. Matrix Algebra From a Statistician's Perspective (1999), Springer-Verlag
17. Hämäläinen, M.S., Ilmoniemi, R.J., 1984 Interpreting measured magnetic fields of the brain: Estimates of current distributions. Technical Report TKK-F-A559, Helsinki University of Technology, Department of Technical Physics.
18. Hämäläinen M.S., Hari R., Ilmoniemi R.J., Knuutila J., and Lounasmaa O.V. Magnetoencephalography-Theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Modern Phys. 65 2 (1993)
19. Johnson N.L., Kotz S., and Balakrishnan N. Continuous univariate distributions, volume 1. Wiley Series in Probability and Mathematical Statistics. 2nd ed. (1994), John Wiley and Sons, Inc
20. Kaipio J.P., and Somersalo E. Statistical and Computational Inverse Problems (2005), Springer
21. Köhler T., Wagner M., Fuchs M., Wischmann H.-A., Drenckhahn R., and Theissen A. Depth nornalization in MEG/EEG current density imaging. Conference Proceedings of the 18th Annual International Conference of the Engineering in Medicine and Biology Society of the IEEE. (1996)
22. Kincses W.E., Braun C., Kaiser S., Grodd W., Ackermann H., and Mathiak K. Reconstruction of extended cortical sources for EEG and MEG based on a Monte-Carlo-Markov-chain estimator. Hum. Brain Mapp. 18 (2003) 100-110
23. Lin F.-H., Belliveau J.W., Dale A.M., and Hämäläinen M.S. Distributed current estimates using cortical orientation constraints. Hum. Brain Mapp. 27 (2006) 1-13
24. Liu J.S. Monte Carlo Strategies in Scientific Computing (2001), Springer
25. Liu A.K., Belliveau J.W., and Dale A.M. Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. Proc. Natl. Acad. Sci. 95 15 (1998) 8945-8950
26. Liu A.K., Dale A.M., and Belliveau J.W. Monte Carlo simulation studies of EEG and MEG localization accuracy. Hum. Brain Mapp. 16 (2002) 47-62
27. Matsuura K., and Okabe Y. Selective minimum-norm solution of the biomagnetic inverse problem. IEEE Trans. Biomed. Eng. 42 6 (1995) 608-615
28. Mosher J.C., Lewis P.S., and Leahy R.M. Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans. Biomed. Eng. 39 6 (1992) 541-557
29. Neal R.M. Bayesian Learning for Neural Networks (1996), Springer-Verlag
30. Neal R.M. Annealed importance sampling. Stat. Comput. 11 (2001) 125-139
31. Neal R.M. Slice sampling. Ann. Stat. 31 3 (2003) 705-767
32. Pascual-Marqui R.D. Standardized low resolution brain electromagnetic tomography (sLORETA): Technical details. Methods Find. Exp. Clin. Pharmacol. 24 (2002) 5-12
33. Phillips C., Rugg M.D., and Friston K.J. Anatomically informed basis functions for EEG source localization: Combining functional and anatomical constraints. NeuroImage 16 (2002) 678-695
34. Robert C.P., and Casella G. Monte Carlo Statistical Methods. 2nd ed. (2004), Springer
35. 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 (2004) 806-826
36. Schmidt D.M., George J.S., and Wood C.C. Bayesian inference applied to the electro-magnetic inverse problem. Hum. Brain Mapp. 7 (1999) 195-212
37. Tanaka T. Information geometry of mean-field approximation. In: Opper M., and Saad D. (Eds). Advanced Mean Field Methods-Theory and Practice (2001), MIT Press
38. Trujillo-Barreto N.J., Aubert-Vázquez E., and Valdés-Sosa P.A. Bayesian model averaging in EEG/MEG imaging. NeuroImage 21 (2004) 1300-1319
39. Uutela K., Hämäläinen M., and Somersalo E. Visualization of magnetoencephalographic data using minimum current estimates. NeuroImage 10 (1999) 173-180
40. von Helmholtz, H., 1853. Ueber einige Gesetze der Vertheilung elektrischer Ströme in körperlichen Leitern, mit Anwendung auf die thierisch-elektrischen Versuche. Annalen der Physik und Chemie, 89 211-233, 353-377.