Bayesian model averaging in EEG/MEG imaging

NeuroImage

Volumn 21, Issue 4, 2004, Pages 1300-1319

  Trujillo Barreto, Nelson J ^a   Aubert Vázquez, Eduardo ^a   Valdés Sosa, Pedro A ^a  

^a CUBAN NEUROSCIENCE CENTER   (Cuba)

Author keywords

Bayesian inference; Bayesian model averaging; EEG; Inverse problem; MEG; Model comparison

Indexed keywords

ANATOMY; ARTICLE; BAYES THEOREM; DATA ANALYSIS; DENSITY; ELECTRIC CURRENT; ELECTROENCEPHALOGRAM; ELECTROMAGNETIC FIELD; INTERMETHOD COMPARISON; LOW RESOLUTION ELECTROMAGNETIC TOMOGRAPHY; MATHEMATICAL MODEL; NEUROIMAGING; PARAMETER; PRIORITY JOURNAL; SIMULATION; SYSTEM ANALYSIS; TECHNIQUE; TOMOGRAPHY;

ARTIFACTS; BAYES THEOREM; BRAIN; BRAIN MAPPING; DATA COLLECTION; DOMINANCE, CEREBRAL; ELECTROENCEPHALOGRAPHY; EVOKED POTENTIALS, AUDITORY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MAGNETOENCEPHALOGRAPHY; MATHEMATICAL COMPUTING; MODELS, NEUROLOGICAL; NERVE NET; OCCIPITAL LOBE; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED; THALAMUS;

EID: 1842557587     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2003.11.008     Document Type: Article

References (65)

1. Baillet S., Garnero L. A Bayeasian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem. IEEE Trans. Biomed. Eng. 44:1997;374-385.
2. Bosch-Bayard J., Valdés-Sosa P., Virués-Alba E., Aubert-Vázquez E., John R., Harmony T., Riera-Díaz J., Trujillo-Barreto N. 3D statistical parametric mapping of variable resolution electromagnetic tomography (VARETA). Clin. Electroencephalogr. 32(2):2001;47-66.
3. Brillinger, D.R., 1975. Time series: Data analysis and theory. (International Series in Decision Processes) Holt, Rinehart and Winston, Inc. Printed in the USA. ISBN 0-03-076975-2.
4. Clarke C.J.S. Probabilistic modeling of continuous current sources. Rajala H.-M., Katila T. Biomagnetic Localization and 3D Modelling. 1991;117-125 Helsinki University of Technology, Department of Technical Physics, Helsinki.
5. Clarke C.J.S. Error estimates in the biomagnetic inverse problem. Inverse Probl. 10:1994;77-86.
6. Collins D.L., Neelin P., Peters R.M., Evans A.C. Automatic 3D intersubject registration of MR volumetric in standardized Talairach space. J. Comput. Assist. Tomogr. 18:1994;192-205.
7. Dale A., Sereno M. Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cogn. 1993;162-176.
8. Dale A.M., Liu A.K., Fischl B.R., Buckner R.L., Belliveau J.W., Lewine J.D., Halgren E. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron. 26(1):2000;55-67.
9. De Munck J.C. Random dipoles as a model for spontaneous EEG and MEG activity. Williamson S.J.et al. Advances in Biomagnetism. 1989;595-598 Plenum, New York.
10. Draper D. Assessment and propagation of model uncertainty (with Discussion). J. R. Stat. Soc., B. 57:1995;45-97.
11. Evans A.C., Collins D.L., Mills S.R., Brown E.D., Kelly R.L., Peters T.M. 3D statistical neuroanatomical models from 305 MRI volumes. Proc. IEEE-Nuclear Science Symposium and Medical Imaging Conference. 95:1993;1813-1817 M.T.P Press, London.
12. Evans A.C., Collins D.L., Neelin P., MacDonald D., Kamber M., Marrett T.S. Three-dimensional correlative imaging: applications in human brain mapping. Thatcher R., Hallet M., Zeffiro T., John E.R., Huerta M. Functional Neuroimaging: Technical Foundations. 1994;145-161 Academic Press, New York.
13. Everitt B.S., Bullmore E.T. Mixture model mapping of brain activation in functional magnetic resonance images. Hum. Brain Mapp. 7:1999;1-14.
14. Friston K.J. Bayesian estimation of classical systems: an application to fMRI. NeuroImage. 16:2002;513-530.
15. Friston K.J., Glaser D.E., Henson R.N.A., Kiebel S., Phillips C., Ashburner J. Classical and Bayesian inference in neuroimaging: applications. NeuroImage. 16:2002;484-512.
16. Friston K.J., Penny W., Phillips C., Kiebel S., Hinton G., Ashburner J. Classical and Bayesian inference in neuroimaging: theory. NeuroImage. 16:2002;465-483.
17. Geweke J. Bayesian comparison of econometric models. Econometrica. 57:1994;1317-1340.
18. Fuchs, M., Wagner, M., Wischman, H.A., Dossel, O., 1995. Cortical current imaging by morphologically constrained reconstructions. In: Baumgartner, C., et al. (Eds.), Biomagnetism: Fundamental Research and Clinical Applications. Elsevier, IOS Press, Amsterdam, pp. 320-325.
19. Fuchs M., Wagner M., Kohler T., Wischman H.A. Linear and nonlinear current density reconstructions. J. Clin. Neurophysiol. 16(3):1999;267-295.
20. Gorodnitzky I., George J.S., Schlitt H.A., Lewis P.S. A weighted iterative algorithm for neuromagnetic imaging. Proc. IEEE Satellite Symp. Neurisci. Technology. 1992;60-64 Lyon, France.
21. Gorodnistky I., George J.S., Rao B. Neuromagnetic imaging with FOCUSS: a recursive weighted minimum-norm algorithm. Electroencephalogr. Clin. Neurophysiol. 95:1995;231-251.
22. Green P. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika. 82:1995;711-732.
23. Gull S.F. Developments in maximum entropy data analysis. Skilling J. Maximum Entropy and Bayesian Methods. Cambridge 1988. 1989;53-71 Kluwer, Dordrecht.
24. Hämäläinen, M., Ilmoniemi, R., 1984. Interpreting measured magnetic fields of the brain: estimates of current distributions. Tech. Rep. TKK-F-A620, Helsinki Univ., Helsinki, Finland.
25. Hämäläinen M.S., Ilmoniemi R.J. Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 32:1994;35-42.
26. Hansen P.C. Analysis of discrete illposed problems by means of the Lcurve. SIAM Rev. 34:1992;561-580.
27. Herdman A.T., Lins O., Van Roon P., Stapells D.R., Scherg M., Picton T.W. Intracerebral sources of human auditory steady-state responses. Brain Topogr. 15(2):2002;69-86.
28. Hoeting J.A., Madigan D., Raftery A.E., Volinsky C.T. Bayesian model averaging: a tutorial. Stat. Sci. 14(4):1999;382-417.
29. Ioannides A.A. Estimates of brain activity using magnetic field tomography and large scale communication within the brain. Ho M.W., Popp F.A., Warnke U. Bioelectrodynamics and Biocommunication. 1994;319-353 World Scientific, Singapore.
30. Ioannides A.A., Bolton J.P.R., Hasson R., Clarke C.J.S. Localized and distributed source solutions for the biomagnetic inverse problem II. Williamson S.J.et al. Advances in Biomagnetism. 1989;591-605 Plenum, New York.
31. Johnson B.W., Weinberg H., Ribary U., Cheyne D.O., Ancill R. Topographic distribution of the 40 Hz auditory evoked-related potential in normal and aged subjects. Brain Topogr. 1:1998;117-121.
32. Kass, R.E., Raftery, A.E., 1994. Bayes Factors. Technical Report no. 254. Department of Statistics, University of Washington.
33. Lütkenhöner B., Grave de Peralta R. The resolution-field concept. Electroencephalogr. Clin. Neurophysiol., Electroencephalogr. Clin. Neurophysiol. 102:1997;326-334.
34. MacKay D.J.C. Bayesian interpolation. Neural Comput. 4(3):1992;415-447.
35. Madigan D., Raftery A. Model selection and accounting for model uncertainty in graphical models using Occam's window. J. Am. Stat. Assoc. 89:1994;1535-1546.
36. Madigan, D., York, J., 1992. Bayesian graphical models for discrete data. Technical Report no. 259. Department of Statistics, University of Washington.
37. Matsuura K., Okabe Y. Selective minimum-norm solution of the biomagnetic inverse problem. Proc. IEEE Trans. Biomed. Eng. 42:1995;608-615.
38. Matsuura K., Okabe Y. Lp-normalized Minimum L1-norm solution of the biomagnetic inverse problem. Proc. IEEE Eng. Med. Biol. Soc. 1996;145 Nijmegen, The Netherlands.
39. Matsuura K., Okabe Y. A robust reconstruction of sparse biomagnetic sources. IEEE Trans. Biomed. Eng. 44:1997;720-726.
40. Mazziotta J.C., Toga A., Evans A.C., Fox P., Lancaster J. A probabilistic atlas of the human brain: theory and rationale for its development. NeuroImage. 2:1995;89-101.
41. Nunez P. Electric Fields of the Brain: The Neurophysics of EEG. 1981;Oxford Univ. Press, New York.
42. Pascual-Marqui R.D. Reply to comments by Hämäläinen, Ilmoniemi and Nunez. Skrandies W. Source Localization: Continuing Discussion of the Inverse Problem. ISBET Newsletter. vol. 6:1995;16-28. ISSN 0947-5133.
43. Pascual-Marqui R.D. Review of methods for solving the EEG inverse problem. Int. J. Bioelectromagn. 1(1):1999;75-86.
44. Pascual-Marqui R.D., Michel C.M., Lehman D. Low resolution electromagnetic tomography: a new method for localizing electrical activity of the brain. Int. J. Psychophysiol. 18:1994;49-65.
45. Picton T.W., Alain C., Woods D.L., John M.S., Scherg M., Valdés-Sosa P., Bosch-Bayard J., Trujillo N.J. Intracerebral sources of human auditory-evoked potentials. Audiol. Neuro-otol. 4:1999;64-79.
46. Picton T.W., John M.S., Dimitrijevic A., Purcell D. Human auditory steady-state responses. Rev. Pap. Int. J. Audiol. 42:2003;177-219.
47. Plonsey R. Reciprocity applied to volume conductors and the ECG. IEEE Trans. Bio-Med. Electron. 10:1963;9-12.
48. Raftery A.E. Bayesian model selection in structural equation models. Bollen K.A., Long J.S. Testing Structural Equation Models. 1993;163-180 Sage, Beverly Hills.
49. Raftery A.E., Madigan D. Bayesian model averaging for linear regression models. J. Am. Stat. Assoc. 92:1997;179-191.
50. Raftery, A.E., Madigan, D., Hoeting, J.A., 1993. Model selection and accounting for model uncertainty in linear regression models. Technical Report no 262, Department of Statistics, University of Washington.
51. Riera J.J., Fuentes M.E., Valdés P.A., Oharriz Y. Theoretical basis of the EEG spline inverse solutions for a spherical head model. Biomed. Tech. 42(1):1997;219-222.
52. Riera J., Valdés P., Fuentes M.E., Ohárris Y. Explicit Backus and Gilbert EEG inverse solution for a spherical head model. Biomed. Tech. 42(1):1997;216-218.
53. Rush S., Driscoll D.A. EEG electrode sensitivity - An application of reciprocity. IEEE Trans. Biomed. Eng. 16(1):1969;15-22.
54. Scherg M., von Cramon D. Evoked dipole source potentials of the human auditory cortex. Electroencephalogr. Clin. Neurophysiol. 65:1986;344-360.
55. Schmidt D.M., George J.S., Wood C.C. Bayesian inference applied to the biomagnetic inverse problem. Hum. Brain Mapp. 7:1999;195-212.
56. Scholz B., Schwierz G. Probability-based current dipole localization from biomagnetic fields. IEEE Trans. Biomed. Eng. 41:1994;735-742.
57. Smith A.F.M. Contribution to the discussion of "Posterior Bayes factors" J. R. Stat. Soc., Ser. B. 53:1991;132-133.
58. Srebro R. An iterative approach to the solution of the inverse problem. Electroencephalogr. Clin. Neurophysiol. 98:1996;349-362.
59. Tarantola A. Inverse Problem Theory. 1987;Elsevier, The Netherlands. ISBN 0-444-42765-1.
60. Taylor J.G., Ioannides A.A., Müller-Gärtner H.W. Mathematical analysis of lead field expansions. IEEE Trans. Med. Imag. 18:1999;151-163.
61. Tikhonov A.N., Arsenin V.Y. Solutions of Ill-Posed Problems. 1977;W.H. Winston, Washington, DC.
62. Trujillo-Barreto N.J., Martínez-Montes E., Melie-Gracía L., Valdés-Sosa E. A symmetrical Bayesian model for fMRI and EEG/MEG neuroimage fusion. Int. J. Bioelectromagn. 3(1):2001.
63. Valdes-Sosa P., Marti F., Garcia F., Casanova R. Variable resolution electric-magnetic tomography. Biomag 96': Proceedings of the Tenth International Conference on Biomagnetism. vol. II:2000;373-376 Springer-Verlag, New York.
64. Vidakovic B. Nonlinear wavelet shrinkage with Bayes rules and Bayes factors. J. Am. Stat. Assoc. 93:1998;173-179.
65. Wang J.Z., Williamson S.J., Kaufman L. Magnetic source images determined by a leadfield analysis - The unique minimum-norm least-squares estimation. IEEE Trans. Biomed. Eng. 39:1992;231-251.