Fusing EEG and fMRI based on a bottom-up model: Inferring activation and effective connectivity in neural masses

Philosophical Transactions of the Royal Society B: Biological Sciences

Volumn 360, Issue 1457, 2005, Pages 1025-1041

  Riera, J ^a   Aubert, E ^b   Iwata, K ^a   Kawashima, R ^a   Wan, X ^a   Ozaki, T ^c  

^a TOHOKU UNIVERSITY   (Japan)

^b CUBAN NEUROSCIENCE CENTER   (Cuba)

^c INSTITUTE OF STATISTICAL MATHEMATICS   (Japan)

Author keywords

Autoregressive models; Bottom up models; Dynamic systems; EEG and fMRI data fusion; Motor coordination

Indexed keywords

BIOLOGY;

ARTICLE; BIOLOGICAL MODEL; BRAIN; BRAIN MAPPING; COMPARATIVE STUDY; ELECTROENCEPHALOGRAPHY; HISTOLOGY; HUMAN; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PSYCHOMOTOR PERFORMANCE; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; TIME;

BRAIN; BRAIN MAPPING; DATA INTERPRETATION, STATISTICAL; ELECTROENCEPHALOGRAPHY; HUMANS; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; PSYCHOMOTOR PERFORMANCE; REGRESSION ANALYSIS; TIME FACTORS;

EID: 25444530035     PISSN: 09628436     EISSN: None     Source Type: Journal    
DOI: 10.1098/rstb.2005.1646     Document Type: Article

References (56)

1. Allen, P. J., Josephs, O. & Turner, R. 2000 A method for removing imaging artifact from continuous EEG recorded during functional MRI. NeuroImage 12, 230-239.
2. Attwell, D. & Iadecola, C. 2002 The neural basis of functional brain imaging signals. Trends Neurosci. 25, 621-625.
3. Baillet, S., Mosher, J. C. & Leahy, R. M. 2001 Electromagnetic brain mapping. IEEE Signal Process. Mag., 14-30.
4. Behrens, T. E. J. & Johansen-Berg H. 2005 Relating connectional architecture to grey matter function using diffusion imaging. Phil. Trans. R. Soc. B 360, 903-911. (doi:10.1098/rstb.2005.1640.)
5. Brockwell, P. J. & Davis, R. A. 1987 Introduction to time series and forecasting. Springer texts in statistics, 2nd edn. New York: Springer Verlag.
6. Buxton, R. B., Wong, E. C. & Frank, L. R. 1998 Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn. Reson. Med. 39, 855-864.
7. Caesar, K., Gold, L. & Lauritzen, M. 2003 Context sensitivity of activity dependent increase in cerebral blood flow. Proc. Natl Acad. Sci. USA 100, 4239-4244.
8. Carew, J. D., Wahba, G., Xie, X., Nordheim, E. V. & Meyerand, E. 2003 Optimal spline smoothing of fMRI time series by generalized cross-validation. NeuroImage 18, 950-961.
9. Casey, B. J., Davidson, M. & Rosen, B. 2002 Functional magnetic resonance imaging basic principles of and applications to developmental sciences. Dev. Sci. 5, 301-309.
10. Dale, A. M., Liu, A. K. & Fischl, B. R. 2000 Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26, 55-67.
11. Ellingson, M. L., Liebenthal, E., Spanaki, M. V., Prieto, T. E., Binder, J. R. & Ropellaa, K. M. 2004 Ballistocardiogram artifact reduction in the simultaneous acquisition of auditory ERPS and fMRI. NeuroImage 22, 1534-1542.
12. Friston, K. J., Mechelli, A., Turner, R. & Price, C. J. 2000 Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics. NeuroImage 12, 466-477.
13. Friston, K. J., Harrison, L. & Penny, W. 2003 Dynamic causal modeling. NeuroImage 19, 1273-1302.
14. Fuster, J. M. 2000 Book review: the module: crisis of a paradigm. Neuron 26, 51-53.
15. Galka, A., Yamashita, O., Ozaki, T., Biscay, R. & Valdes-Sosa, P. 2004 A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering. NeuroImage 23, 435-453.
16. Gazzaniga, M. S. 2000 The new cognitive neurosciences, 2nd edn. Cambridge, MA: MIT Press.
17. Geweke, J. 1984 Inference and causality in economic time series. In Handbook of econometrics (ed. Z. Griliches & M. D. Intriligator), vol. 2, pp. 1101-1144. Amsterdam: North-Holland.
18. Goldman, R. I., Stern, J. M., Engel Jr, J. & Cohen, M. S. 2000 Acquiring simultaneous EEG and functional MRI. Clin. Neurophysiol. 111, 1974-1980.
19. Goldman, R. I., Stern, J. M., Engel Jr, J. & Cohen, M. S. 2002 Simultaneous EEG and fMRI of the alpha rhythm. NeuroReport 13, 2487-2492.
20. Harrison, R. V., Harel, N., Panesar, J. & Mount, R. J. 2002 Blood capillary distribution correlates with hemodynamic-based functional imaging in cerebral cortex. Cereb. Cortex 12, 225-233.
21. Hoeltzell, P. B. & Dykes, R. W. 1979 Conductivity in the somatosensory cortex of the cat. Evidence for cortical anisotropy. Brain Res. 117, 61-82.
22. Horwitz, B. 2003 The elusive concept of brain connectivity. NeuroImage 19, 466-470.
23. Horwitz, B., Warner, B., Fitzer, J., Tagamets, M.-A., Husain, F. T. & Long, T. W. 2005 Investigating the neural basis for functional and effective connectivity. Application to fMRI and PET. Phil. Trans. R. Soc. B 360, 1093-1108. (doi:10. 1098/rstb.2005.1647.)
24. Kawashima, R., et al. 2000 Human cerebellum plays an important role in memory-timed finger movement: an fMRI study. J. Neurophysiol. 83, 1079-1087.
25. Kim, S. G. 2003 Progress in understanding functional imaging signals. Proc. Natl. Acad. Sci. USA 100, 3550-3552.
26. Kruggel, F., Herrmann, C. S., Wiggins, C. J. & von Cramon, D. Y. 2001 Hemodynamic and electroencephalographic responses to illusory figures: recording of the evoked potentials during functional MRI. NeuroImage 14, 1327-1336.
27. Lee, L., Harrison, L. M. & Mechelli, A. 2003 The functional brain connectivity workshop: report and commentary. Network: Comput. Neural Syst. 14, R1-R5.
28. Leopold, D. A., Murayama, Y. & Logothetis, N. K. 2003 Very slow activity fluctuations in monkey visual cortex: implications for functional brain imaging. Cereb. Cortex 13, 422-433.
29. Logothetis, N. K. 2003a The underpinnings of the BOLD functional magnetic resonance imaging signal. J. Neurosci. 23, 3963-3971.
30. Logothetis, N. K. 2003b MR imaging in the non-human primate: studies of function and of dynamic connectivity. Curr. Opin. Neurobiol. 13, 630-642.
31. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. 2001 Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150-157.
32. MacDonald, J. D. 1997 A method for identifying geometrically simple surfaces from three dimensional images. Ph.D. dissertation, School of Computer Science, McGill University, Montreal, November.
33. Magistretti, P. J. & Pellerin, L. 1999 Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. Phil. Trans. R. Soc. B 354, 1155-1163.
34. Makeig, S., Jung, T.-P. & Sejnowski, T. J. 2002 Having your voxels and timing them too? In Exploratory analysis and data modeling in functional neuroimaging (ed. F. T. Sommer & A. Wichert). Cambridge, MA: MIT Press.
35. Marrelec, G., Benali, H., Ciuciu, P., Pélégrini-Issac, M. & Poline, J. B. 2003 Robust Bayesian estimation of the hemodynamic response function in event-related BOLD MRI using basic physiological information. Hum. Brain Mapp. 19, 1-17.
36. Martinez-Montes, E., Valdes-Sosa, P. A., Miwakeichi, F., Goldman, R. I. & Cohen, M. S. 2004 Concurrent EEG/fMRI analysis by multiway partial least squares. NeuroImage 22, 1023-1034.
37. Mazziotta, J., et al. 2001 A four-dimensional probabilistic atlas of the human brain. J. Am. Med. Inform. Assoc. 8, 401-430.
38. Miwakeichi, F., Martinez-Montes, E., Valdes-Sosa, P. A., Nishiyama, N., Mizuhara, H. & Yamaguchia, Y. 2004 Decomposing EEG data into space-time-frequency components using parallel factor analysis. NeuroImage 22, 1035-1045.
39. Niedermeyer, E. & Lopes Da Silva, F. H. 1999 Electroencephalography: basic principles, clinical applications, and related fields, 4th edn. Baltimore, MD: Williams & Wilkins.
40. Nunez, P. L. 1981 Electric fields of the brain: the neurophysics of EEG. New York: Oxford University Press.
41. Parker, G. J. M. & Alexander, D. C. 2005 Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue. Phil. Trans. R. Soc. B 360, 893-902. (doi:10.1098/rstb.2005. 1639.)
42. Penny, W. D., Stephan, K. E., Mechelli, A. & Friston, K. J. 2004 Comparing dynamic causal models. NeuroImage 22, 1157-1172.
43. Plonsey, R. & Heppner, D. B. 1967 Considerations of quasi-stationarity in electrophysiological systems. Bull. Math. Biophys. 29, 657-664.
44. Riera, J. & Fuentes, M. E. 1998 Electric lead field for a piece-wise homogeneous volume conductor model of the head. IEEE Trans. Biomed. Eng. 45, 746-753.
45. Riera, J., Fuentes, M. E., Valdés, P. & Ohárriz, Y. 1998 EEG distributed inverse solutions for a spherical head model. Inverse Probl. 14, 1009-1019.
46. Riera, J., Bosch, J., Yamashita, O., Kawashima, R., Sadato, N., Okada, T. & Ozaki, T. 2004 fMRI activation maps based on the NN-ARx model. NeuroImage 23, 680-697.
47. Robinson, P. A., Rennie, C. J., Rowe, D. L., O'Connor, S. C. & Gordon, E. 2005 Multiscale brain modeling. Phil. Trans. R. Soc. B 360, 1043-1050. (doi:10.1098/rstb.2005.1638.)
48. Salek-Haddadi, A., Friston, K. J., Lemieux, L. & Fish, D. R. 2003 Studying spontaneous EEG activity with fMRI. Brain Res. Rev. 43, 110-133.
49. Tagamets, M. A. & Horwitz, B. 2001 Interpreting PET and fMRI measures of functional neural activity: the effects of synaptic inhibition on cortical activation in human imaging studies. Brain Res. Bull. 54, 267-273.
50. Thompson, J. K., Peterson, M. R. & Freeman, R. D. 2003 Single-neuron activity and tissue oxygenation in the cerebral cortex. Science 299, 1070-1072.
51. Trujillo-Barreto, N. J., Aubert-Vazquez, E. & Valdes-Sosa, P. A. 2004 Bayesian model averaging in EEG/MEG imaging. NeuroImage 21, 1300-1319.
52. Vanhataloa, S., Tallgrena, P., Beckerc, C., Holmesc, M. D., Millerc, J. W., Kailaa, K. & Voipioa, J. 2003 Scalp-recorded slow EEG responses generated in response to hemodynamic changes in the human brain. Clin. Neurophysiol. 114, 1744-1754.
53. Wakana, S., Jiang, H., Nagae-Poetscher, L. M., van Zijl, P. C. M. & Mori, S. 2004 Fiber tract-based atlas of human white matter anatomy. Radiology 230, 77-87.
54. Waldvogel, D., van Gelderen, P. & Muellbacher, W. 2000 The relative metabolic demand of inhibition and excitation. Nature 406, 995-998.
55. Wan, X., Iwata, K., Riera, J., Kitamura, M. & Kawashima, R. 2005 Artifact reduction for EEG/fMRI recording part 1: nonlinear reduction of ballistocardiogram artifacts. ISM Research Memorandum No. 939. Tokyo, Japan: The Institute of Statistical Mathematics.
56. Yamashita, O., Galka, A., Ozaki, T., Biscay, R. & Valdes-Sosa, P. 2004 Recursive penalized least squares solution for dynamical inverse problems of EEG generation. Hum. Brain Mapp. 21, 221-235.