Non-gaussian probabilistic MEG source localisation based on kernel density estimation

NeuroImage

Volumn 87, Issue , 2014, Pages 444-464

  Mohseni, Hamid R ^a   Kringelbach, Morten L ^a,b,c,d   Woolrich, Mark W ^b   Baker, Adam ^a,b   Aziz, Tipu Z ^c   Probert Smith, Penny ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c JOHN RADCLIFFE HOSPITAL   (United Kingdom)

^d AARHUS UNIVERSITY   (Denmark)

Author keywords

Beamformer; Magnetoencephalography; Non Gaussian; Null beamformer; Source reconstruction

Indexed keywords

ARTICLE; BAYES THEOREM; BRAIN FUNCTION; CORRELATION ANALYSIS; DENSITY FUNCTIONAL THEORY; EVOKED AUDITORY RESPONSE; EVOKED VISUAL RESPONSE; INFORMATION PROCESSING; KERNEL METHOD; MAGNETOENCEPHALOGRAPHY; MATHEMATICAL ANALYSIS; MULTIVARIATE ANALYSIS; PRIORITY JOURNAL; PROBABILITY; SIMULATION; VARIANCE;

BEAMFORMER; MAGNETOENCEPHALOGRAPHY; NON-GAUSSIAN; NULL-BEAMFORMER; SOURCE RECONSTRUCTION;

ALGORITHMS; BAYES THEOREM; BRAIN; COMPUTER SIMULATION; HUMANS; MAGNETOENCEPHALOGRAPHY; MODELS, NEUROLOGICAL; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84892531756     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2013.09.012     Document Type: Article

References (53)

1. Baillet S., Mosher J.C., Leahy R.M. Electromagnetic brain mapping. IEEE Signal Proc. Mag. 2001, 18(6):14-30.
2. Barnes G.R., Hillebrand A. Statistical flattening of MEG beamformer images. Hum. Brain Mapp. 2003, 18(1):1-12.
3. Brookes M.J., Gibson A.M., Hall S.D., Furlong P.L., Barnes G.R., Hillebrand A., Singh K.D., Holliday I.E., Francis S.T., Morris P.G. A general linear model for MEG beamformer imaging. NeuroImage 2004, 23(3):936-946.
4. Brookes M.J., Gibson A.M., Hall S.D., Furlong P.L., Barnes G.R., Hillebrand A., Singh K.D., Holliday I.E., Francis S.T., Morris P.G. GLM-beamformer method demonstrates stationary field, alpha ERD and gamma ERS co-localisation with fMRI BOLD response in visual cortex. NeuroImage 2005, 1:302-308.
5. Brookes M.J., Stevenson C.M., Barnes G.R., Hillebrand A., Simpson M.I., Francis S.T., Morris P.G. Beamformer reconstruction of correlated sources using a modified source model. NeuroImage 2007, 34(4):1454-1465.
6. Brookes M.J., Hale J.R., Zumer J.M., Stevenson C.M., Francis S.T., Barnes G.R., Owen J.P., Morris P.G., Nagarajan S.S. Measuring functional connectivity using MEG: methodology and comparison with fcMRI. NeuroImage 2011, 3:1082-1104.
7. Cheng P., Serfling R. Asymptotic mean integrated squared errors of some nonparametric density estimators (corresp.). IEEE Trans. Inf. Theory 1981, 2:239-242.
8. Cox H., Zeskind R.M., Owen M.M. Robust adaptive beamforming. IEEE Trans. Acoust. Speech Signal Process. 1987, ASSP-35(Suppl. 1):1365-1376.
9. Dalal S.S., Zumer J., Agrawal V., Hild K., Sekihara K., Nagarajan S. Nutmeg: a neuromagnetic source reconstruction toolbox. Neurol. Clin. Neurophysiol. 2004, 2004:52.
10. Dalal S.S., Sekihara K., Nagarajan S.S. Modifed beamformers for coherent source region suppression. IEEE Trans. Biomed. Eng. 2006, 53(7):1357-1363.
11. Davis A.W. Statistical distributions in univariate and multivariate Edgeworth populations. Biometrika 1976, 63(3):661-670.
12. Dogandzic A., Nehorai A. Estimating evoked dipole responses in unknown spatially correlated noise with EEG/MEG arrays. IEEE Trans. Signal Process. 2000, 1:13-25.
13. Ehrenberg L., Gannot S., Leshem A., Zehavi E. Sensitivity analysis of MVDR and MPDR beamformers. Electrical and Electronics Engineers in Israel (IEEEI), IEEE 26th Convention of 2010, 416-420.
14. Elul R., Hanley J., Simmons J.Q. Non-Gaussian behavior of the EEG in down's syndrome suggests decreased neuronal connections. Acta Neurol. Scand. 1975, 51:21-28.
15. Friedman J. On bias, variance, 0/1 loss, and the curse-of-dimensionality. Data Min. Knowl. Disc. 1997, 1(1):55-77.
16. Friston K., Glaser D., Henson R., Kiebel S., Phillips C., Ashburner J. Classical and Bayesian inference in neuroimaging: applications. NeuroImage 2002, 2:484-512.
17. Grimmett G., Stirzaker D. Probability and Random Processes 2001, Oxford University Press.
18. Hamalainen M., Hari R., Ilmoniemi R.J., Knuutila J., Lounasmaa O.V. Magnetoencephalographytheory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 1993, 413-497. 10.1103/RevModPhys.65.413.
19. Hansen P., Kringelbach M.L., Salmelin R. MEG. An Introduction to Methods 2010, Oxford University Press, Oxford.
20. Haykin S. Adaptive Filter Theory 2002, Prentice Hall.
21. Hillebrand A. The use of anatomical constraints with MEG beamformers. NeuroImage 2003, 4:2302-2313.
22. Hudson J.E. Adaptive Array Principles 1981, Peter Peregrinus, London, U.K.
23. Hui H.B., Leahy R. Linearly constrained MEG beamformers for MVAR modeling of cortical interactions. IEEE Inter. Symp. on Biomedical Imaging: Nano to Macro, Buenos Aires, Argentina 2006.
24. Jarque C.M., Bera A.K. A test for normality of observations and regression residuals. Int. Stat. Rev. 1987, 55(2):163-172.
25. Jun S., George J., Paré-Blagoev J., Plis S., Ranken D., Schmidt D., Wood C. Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data. NeuroImage 2005, 28(1):84-98.
26. Lee T.-W., Lewicki M., Sejnowski T. ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22(10):1078-1089.
27. Limpiti T., Van Veen B., Wakai R. Cortical patch basis model for spatially extended neural activity. IEEE Trans. Biomed. Eng. 2006, 53(9):1740-1754.
28. Mattout J., Phillips C., Penny W.D., Rugg M.D., Friston K.J. MEG source localization under multiple constraints: an extended Bayesian framework. NeuroImage 2006, 30(3):753-767.
29. Mohseni H.R., Kringelbach M., Smith P.P., Stein J., Aziz T.Z. Application of null-beamformer to source localization of MEG data in deep brain stimulation. IEEE EMBC Conference, Buenos Aires, Argentina 2010.
30. Mohseni H.R., Smith P.P., Parsons C.E., Young K.S., Hyam J.A., Stein A., Stein J.F., Green A.L., Aziz T.Z., Kringelbach M.L. MEG can map short and long-term changes in brain activity following deep brain stimulation for chronic pain. PLoS One 2012, 7(6):e37993.
31. Morosan P., Rademacher J., Schleicher A., Amunts K., Schormann T., Zilles K. Human primary auditory cortex: cytoarchitectonic subdivisions and mapping into a spatial reference system. NeuroImage 2001, 13(4):684-701.
32. Mosher J.C., Lewis P.S., Leahy R.M. Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans. Biomed. Eng. 1992, 39(6):541-557.
33. Mosher J., Leahy R., Lewis P. EEG and MEG: forward solutions for inverse methods. IEEE Trans. Biomed. Eng. 1999, 3:245-259.
34. Nagarajan S., Attias H., Hild K., Sekihara K. Stimulus evoked independent factor analysis of MEG data with large background activity. Adv. Neural Inf. Process 2005, 18.
35. Nagarajan S., Attias H., Hild K., Sekihara K. A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity. NeuroImage 2006, 30(2):400-416.
36. Nummenmaa A., Auranen T., Hamalainen M., Jaaskelainen I., Lampinen J., Sams M., Vehtari A. Hierarchical Bayesian estimates of distributed meg sources: theoretical aspects and comparison of variational and MCMC methods. NeuroImage 2007, 35(2):669-685.
37. Parzen E. On estimation of a probability density function and mode. Ann. Math. Stat. 1962, 33(3):1065-1076.
38. Popescu M., Popescu E., Chan T., Blunt S.D., Lewine J.D. Spatio temporal reconstruction of bilateral auditory steady-state responses using MEG beamformers. IEEE Trans. Biomed. Eng. 2008, 55(3):1092-1102.
39. Robinson S., Vrba J., et al. Functional neuroimaging by synthetic aperture magnetometry (SAM). Recent Adv. Biomagnetism 1999, 302-305.
40. Scott D. Multivariate Density Estimation: Theory, Practice, and Visualization 1992, John Wiley, New York.
41. Scott D., Sain S. Multi-dimensional density estimation. Handbook of Statistics 2004, 1-39. (August).
42. Sekihara K., Nagarajan S., Poeppel D., Marantz A., Miyashita Y. Application of an MEG eigenspace beamformer to reconstructing spatio-temporal activities of neural sources. Hum. Brain Mapp. 2002, 15(4):199-215.
43. Sekihara K., Nagarajan S.S., Poeppel D., Marantz A. Performance of an MEG adaptive-beamformer technique in the presence of correlated neural activities: effects on signal intensity and time-course estimates. IEEE Trans. Biomed. Eng. 2006, 49(12):1534-1546.
44. Silverman B.W. Density Estimation for Statistics and Data Analysis 1999, Chapman and Hall/CRC.
45. Taulu S., Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys. Med. Biol. 2006, 7:1759.
46. Van Trees H. Detection, Estimation, and Modulation Theory, Radar-Sonar Signal Processing and Gaussian Signals in Noise, Part IV 2004, Wiley-Interscience.
47. Van Veen B.D., Buckley K. Beamforming: a versatile approach to spatial filtering. IEEE ASSP Mag. 1988, 5(2):4-24.
48. Van Veen B.D., Dronglen W.V., Yuchtman M., Suzuki A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans. Biomed. Eng. 1997, 44(9):867-880.
49. Wipf D., Nagarajan S. Beamforming using the relevance vector machine. Proceedings of the 24th International Conference on Machine Learning 2007, 1023-1030.
50. Wipf D., Nagarajan S. A unified Bayesian framework for MEG/EEG source imaging. NeuroImage 2009, 44(3):400-416.
51. Woolrich M., Hunt L., Groves A., Barnes G. MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularisation. NeuroImage 2011, 57(4):1466-1479.
52. Zumer J., Attias H., Sekihara K., Nagarajan S. A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data. NeuroImage 2007, 37(1):102-115.
53. Zumer J., Attias H., Sekihara K., Nagarajan S. Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data. NeuroImage 2008, 41(3):924-940.