Multi-area neural mass modeling of EEG and MEG signals

NeuroImage

Volumn 52, Issue 3, 2010, Pages 793-811

  Babajani Feremi, Abbas ^a   Soltanian Zadeh, Hamid ^a,b  

^a HENRY FORD HOSPITAL   (United States)

^b UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Effective connectivity; Electroencephalography (EEG); Functional magnetic resonance imaging (fMRI); Magnetoencephalography (MEG); Neural Mass Model

Indexed keywords

ARTICLE; BRAIN CORTEX; ELECTROENCEPHALOGRAPHY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; MAGNETOENCEPHALOGRAPHY; NERVE CELL EXCITABILITY; NERVE CELL NETWORK; NEUROIMAGING; PRIORITY JOURNAL; PYRAMIDAL NERVE CELL; SENSORY NERVE CELL; SIGNAL DETECTION; STATISTICAL ANALYSIS; STATISTICAL MODEL; BIOLOGICAL MODEL; BRAIN; BRAIN MAPPING; HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES;

BRAIN; BRAIN MAPPING; ELECTROENCEPHALOGRAPHY; HUMANS; MAGNETIC RESONANCE IMAGING; MAGNETOENCEPHALOGRAPHY; MODELS, NEUROLOGICAL; NERVE NET;

EID: 77954387306     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2010.01.034     Document Type: Article

References (98)

1. Allen P., Mechelli A., Stephan K.E., Day F., Dalton J., Williams S., McGuire P.K. Fronto-temporal interactions during overt verbal initiation and suppression. J. Cogn. Neurosci. 2008, 20:1656-1669.
2. Archambeau C., Opper M., Shen Y., Cornford D., Shawe-Taylor J. Variational inference for diffusion processes. Neural Information Processing Systems (NIPS) 2008, 20:17-24. The MIT Press, Cambridge. Massachusetts. C. Platt, D. Koller, Y. Singer, S. Roweis (Eds.).
3. Babajani A., Nekooei M.H., Soltanian-Zadeh H. Integrated MEG and fMRI model: synthesis and analysis. Brain Topogr 2005, 18:101-113.
4. Babajani A., Soltanian-Zadeh H. Integrated MEG/EEG and fMRI model based on neural masses. IEEE Trans. Biomed. Eng. 2006, 53:1794-1801.
5. Babajani-Feremi A., Soltanian-Zadeh H., Moran J.E. Integrated MEG/fMRI model validated using real auditory data. Brain Topogr. 2008, 21:61-74.
6. Babiloni F., Babiloni C., Carducci F., Romani G.L., Rossini P.M., Angelone L.M., Cincotti F. Multimodal integration of high-resolution EEG and functional magnetic resonance imaging data: a simulation study. NeuroImage 2003, 19:1-15.
7. Beal M.J. Variational Algorithms for Approximate Bayesian Inference. Gatsby Computational Neuroscience Unit 2003, University College London.
8. Beal, M., Ghahramani, Z., 2001a. The Variational Kalman Smoother. Gatsby Unit technical reports.
9. Beal M.J., Ghahramani Z. The Variational Kalman Smoother. Technical Report GCNU TR 2001-003. Computational and Theoretical Neuroscience and Machine Learning, Gatsby Computational Neuroscience Unit 2001, University College, London.
10. Beal M.J., Ghahramani Z. The variational Bayesian EM algorithm for incomplete data: with application to scoring graphical model structures. Bayesian Statistics 7 2003, 453-464. Oxford University Press.
11. Booth J.R., Wood L., Lu D., Houk J.C., Bitan T. The role of the basal ganglia and cerebellum in language processing. Brain Res. 2007, 1133:136-144.
12. Booth J.R., Mehdiratta N., Burman D.D., Bitan T. Developmental increases in effective connectivity to brain regions involved in phonological processing during tasks with orthographic demands. Brain Res. 2008, 1189:78-89.
13. Brazdil M., Mikl M., Marecek R., Krupa P., Rektor I. Effective connectivity in target stimulus processing: a dynamic causal modeling study of visual oddball task. NeuroImage 2007, 35:827-835.
14. Buxhoeveden D.P., Casanova M.F. The minicolumn hypothesis in neuroscience. Brain 2002, 125:935-951.
15. Cao F., Bitan T., Booth J.R. Effective brain connectivity in children with reading difficulties during phonological processing. Brain Lang. 2008, 107:91-101.
16. Chen, Z., 2003. Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond. http://users.isr.ist.utl.pt/.
17. Chen C.C., Kiebel S.J., Friston K.J. Dynamic causal modelling of induced responses. NeuroImage 2008, 41:1293-1312.
18. Daunizeau J., Grova C., Marrelec G., Mattout J., Jbabdi S., Pelegrini-Issac M., Lina J.M., Benali H. Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework. NeuroImage 2007, 36:69-87.
19. Daunizeau J., Friston K.J., Kiebel S.J. Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models. Physica D. 2009, 238:2089-2118.
20. David O., Friston K.J. A neural mass model for MEG/EEG: coupling and neuronal dynamics. NeuroImage 2003, 20:1743-1755.
21. David O., Harrison L., Friston K.J. Modelling event-related responses in the brain. NeuroImage 2005, 25:756-770.
22. David O., Kiebel S.J., Harrison L.M., Mattout J., Kilner J.M., Friston K.J. Dynamic causal modeling of evoked responses in EEG and MEG. NeuroImage 2006, 30:1255-1272.
23. David O., Wozniak A., Minotti L., Kahane P. Preictal short-term plasticity induced by intracerebral 1 Hz stimulation. NeuroImage 2008, 39:1633-1646.
24. Deco G., Rolls E.T., Horwitz B. "What" and "where" in visual working memory: a computational neurodynamical perspective for integrating FMRI and single-neuron data. J. Cogn. Neurosci. 2004, 16:683-701.
25. Dormand J.R., Prince P.J. A family of embedded Runge-Kutta formulae. J. Comput. Appl. Math. 1980, 6:19-26.
26. Ethofer T., Anders S., Erb M., Herbert C., Wiethoff S., Kissler J., Grodd W., Wildgruber D. Cerebral pathways in processing of affective prosody: a dynamic causal modeling study. NeuroImage 2006, 30:580-587.
27. Evensen G. The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dyn. 2003, 53:343-367.
28. Evensen G., van Leeuwen P.J. An ensemble Kalman smoother for nonlinear dynamics. Mon. Weather Rev. 2000, 128:1852-1867.
29. Fastenrath M., Friston K.J., Kiebel S.J. Dynamical causal modelling for M/EEG: spatial and temporal symmetry constraints. NeuroImage 2009, 44:154-163.
30. Freeman W.J. Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Biol. Cybern. 1987, 56:139-150.
31. Friston K.J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 1994, 2:56-78.
32. Friston K.J., Frith C.D., Liddle P.F., Frackowiak R.S. Functional connectivity: the principal-component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 1993, 13:5-14.
33. Friston K.J., Mechelli A., Turner R., Price C.J. Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. NeuroImage 2000, 12:466-477.
34. Friston K.J., Harrison L., Penny W. Dynamic causal modelling. NeuroImage 2003, 19:1273-1302.
35. Friston K.J., Trujillo-Barreto N., Daunizeau J. DEM: a variational treatment of dynamic systems. NeuroImage 2008, 41:849-885.
36. Garrido M.I., Kilner J.M., Kiebel S.J., Stephan K.E., Friston K.J. Dynamic causal modelling of evoked potentials: a reproducibility study. NeuroImage 2007, 36:571-580.
37. Ghahramani Z., Hinton G.E. Parameter Estimation for Linear Dynamical Systems. Department of Computer Science 1996, University of Toronto.
38. Ghahramani Z., Beal M. Variational inference for Bayesian mixtures of factor analysers. Adv. Neural Inform. Process. Syst. 2000, 12:449-455.
39. Ghahramani Z., Beal M. Propagation algorithms for variational Bayesian learning. Adv. Neural Inform. Process. Syst. 2001, 13:507-513.
40. Grefkes C., Eickhoff S.B., Nowak D.A., Dafotakis M., Fink G.R. Dynamic intra- and interhemispheric interactions during unilateral and bilateral hand movements assessed with fMRI and DCM. NeuroImage 2008, 41:1382-1394.
41. Horwitz B., Tagamets M.A., McIntosh A.R. Neural modeling, functional brain imaging, and cognition. Trends Cogn. Sci. 1999, 3:91-98.
42. Husain F.T., Tagamets M.A., Fromm S.J., Braun A.R., Horwitz B. Relating neuronal dynamics for auditory object processing to neuroimaging activity: a computational modeling and an fMRI study. NeuroImage 2004, 21:1701-1720.
43. Iturria-Medina Y., Canales-Rodriguez E.J., Melie-Garcia L., Valdes-Hernandez P.A., Martinez-Montes E., Aleman-Gomez Y., Sanchez-Bornot J.M. Characterizing brain anatomical connections using diffusion weighted MRI and graph theory. NeuroImage 2007, 36:645-660.
44. Jansen B.H., Rit V.G. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol. Cybern. 1995, 73:357-366.
45. Jimenez J.C. A simple algebraic expression to evaluate the local linearization schemes for stochastic differential equations. Appl. Math. Lett. 2002, 15:775-780.
46. Jimenez J.C., Carbonell F. Rate of convergence of local linearization schemes for initial-value problems. Appl. Math. Comput. 2005, 171:1282-1295.
47. Jimenez J.C., Ozaki T. An approximate innovation method for the estimation of diffusion processes from discrete data. J. Time Ser. Anal. 2006, 27:77-97.
48. Jimenez J.C., Ozaki T. Simulation of stochastic differential equations through the local linearization method. A comparative study. J. Time Ser. Anal. 2006, 27:77-97.
49. Jimenez J.C., Shoji I., Ozaki T. Simulation of stochastic differential equations through the local linearization method. A comparative study. J. Stat. Phys. 1999, 94:587-602.
50. Jirsa V.K., Haken H. A derivation of a macroscopic field theory of the brain from the quasi-microscopic neural dynamics. Physica D 1997, 99:503-526.
51. Kappen, H.J., 2006. An Introduction to Stochastic Control Theory, Path Integrals and Reinforcement Learning. http://www.snn.ru.nl/.
52. Kasess C.H., Windischberger C., Cunnington R., Lanzenberger R., Pezawas L., Moser E. The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling. NeuroImage 2008, 40:828-837.
53. Kiebel S.J., David O., Friston K.J. Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization. NeuroImage 2006, 30:1273-1284.
54. Kiebel S.J., Garrido M.I., Friston K.J. Dynamic causal modelling of evoked responses: the role of intrinsic connections. NeuroImage 2007, 36:332-345.
55. Kiebel S.J., Kloppel S., Weiskopf N., Friston K.J. Dynamic causal modeling: a generative model of slice timing in fMRI. NeuroImage 2007, 34:1487-1496.
56. Kim J., Zhu W., Chang L., Bentler P.M., Ernst T. Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Hum. Brain Mapp. 2007, 28:85-93.
57. Kloeden P.E., Platen E. Numerical Solution of Stochastic Differential Equations, Stochastic Modeling and Applied Probability 1999, Springer. third ed.
58. Lee L., Friston K., Horwitz B. Large-scale neural models and dynamic causal modelling. NeuroImage 2006, 30:1243-1254.
59. Lee L., Harrison L.M., Mechelli A. The functional brain connectivity workshop: report and commentary. Network 2003, 14:R1-15.
60. Lee L., Harrison L.M., Mechelli A. A report of the functional connectivity workshop, Dusseldorf 2002. NeuroImage 2003, 19:457-465.
61. Lopes da Silva F.H., Hoeks A., Smits H., Zetterberg L.H. Model of brain rhythmic activity. The alpha-rhythm of the thalamus. Kybernetik 1974, 15:27-37.
62. Makarov V.A., Panetsos F., de Feo O. A method for determining neural connectivity and inferring the underlying network dynamics using extracellular spike recordings. J. Neurosci. Methods 2005, 144:265-279.
63. Marreiros A.C., Kiebel S.J., Friston K.J. Dynamic causal modelling for fMRI: a two-state model. NeuroImage 2008, 39:269-278.
64. Mechelli A., Price C.J., Noppeney U., Friston K.J. A dynamic causal modeling study on category effects: bottom-up or top-down mediation?. J. Cogn. Neurosci. 2003, 15:925-934.
65. Moran R.J., Kiebel S.J., Stephan K.E., Reilly R.B., Daunizeau J., Friston K.J. A neural mass model of spectral responses in electrophysiology. NeuroImage 2007, 37:706-720.
66. Moran R.J., Stephan K.E., Kiebel S.J., Rombach N., O'Connor W.T., Murphy K.J., Reilly R.B., Friston K.J. Bayesian estimation of synaptic physiology from the spectral responses of neural masses. NeuroImage 2008, 42:272-284.
67. Mosher J.C., Leahy R.M., Lewis P.S. EEG and MEG: forward solutions for inverse methods. IEEE Trans. Biomed. Eng. 1999, 46:245-259.
68. Mountcastle V.B. The columnar organization of the neocortex. Brain 1997, 120(Pt. 4):701-722.
69. Ozaki T. A bridge between nonlinear time series models and nonlinear stochastic dynamical systems: a local linearization approach. Stat. Sin. 1992, 2:113-135.
70. Ozaki T. Identification of nonlinearities and non-Gaussianities in time series. New Directions in Time Series Analysis, Part I. IMA Volumes in Mathematics and Its Application 1992, 227-264. Springer Verlag, NY. D.R. Brillinger (Ed.).
71. Ozaki T. A local linearization approach to nonlinear filtering. Int. J. Control 1993, 57:75-96.
72. Peters A., Sethares C. Myelinated axons and the pyramidal cell modules in monkey primary visual cortex. J. Comp. Neurol. 1996, 365:232-255.
73. Rauch H.E., Tung F., Striebel C.T. On the maximum likelihood estimates for linear dynamic systems. Technical Report 6-90-63-62 1963, Lockheed Missiles and Space Co., Palo Alto, California.
74. Rennie C.J., Robinson P.A., Wright J.J. Unified neurophysical model of EEG spectra and evoked potentials. Biol. Cybern. 2002, 86:457-471.
75. Riera J., Aubert E., Iwata K., Kawashima R., Wan X., Ozaki T. Fusing EEG and fMRI based on a bottom-up model: inferring activation and effective connectivity in neural masses. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2005, 360:1025-1041.
76. Riera J.J., Wan X., Jimenez J.C., Kawashima R. Nonlinear local electrovascular coupling: I. A theoretical model. Hum. Brain Mapp. 2006, 27:896-914.
77. Riera J.J., Jimenez J.C., Wan X., Kawashima R., Ozaki T. Nonlinear local electrovascular coupling: II. From data to neuronal masses. Hum. Brain Mapp. 2007, 28:335-354.
78. Sabatini B.L., Regehr W.G. Timing of synaptic transmission. Annu. Rev. Physiol. 1999, 61:521-542.
79. Schlosser R.G., Wagner G., Koch K., Dahnke R., Reichenbach J.R., Sauer H. Fronto-cingulate effective connectivity in major depression: a study with fMRI and dynamic causal modeling. NeuroImage 2008, 43:645-655.
80. Shumway R.H., Stoffer D.S. An approach to time series smoothing and forecasting using the em algorithm. J. Time Ser. Anal. 1982, 3:253-264.
81. Sonty S.P., Mesulam M.M., Weintraub S., Johnson N.A., Parrish T.B., Gitelman D.R. Altered effective connectivity within the language network in primary progressive aphasia. J. Neurosci. 2007, 27:1334-1345.
82. Sotero R.C., Trujillo-Barreto N.J. Biophysical model for integrating neuronal activity, EEG, fMRI and metabolism. NeuroImage 2008, 39:290-309.
83. Sotero R.C., Trujillo-Barreto N.J., Iturria-Medina Y., Carbonell F., Jimenez J.C. Realistically coupled neural mass models can generate EEG rhythms. Neural Comput. 2007, 19:478-512.
84. Stephan K.E., Harrison L.M., Kiebel S.J., David O., Penny W.D., Friston K.J. Dynamic causal models of neural system dynamics: current state and future extensions. J. Biosci. 2007, 32:129-144.
85. Stephan K.E., Weiskopf N., Drysdale P.M., Robinson P.A., Friston K.J. Comparing hemodynamic models with DCM. NeuroImage 2007, 38:387-401.
86. Stephan K.E., Kasper L., Harrison L.M., Daunizeau J., den Ouden H.E., Breakspear M., Friston K.J. Nonlinear dynamic causal models for fMRI. NeuroImage 2008, 42:649-662.
87. Stephan K.E., Riera J.J., Deco G., Horwitz B. The brain connectivity workshops: moving the frontiers of computational systems neuroscience. NeuroImage 2008, 42:1-9.
88. Tagamets M.A., Horwitz B. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cereb. Cortex 1998, 8:310-320.
89. Tagamets M.A., Horwitz B. A model of working memory: bridging the gap between electrophysiology and human brain imaging. Neural Netw. 2000, 13:941-952.
90. Valdes-Sosa P.A., Sanchez-Bornot J.M., Lage-Castellanos A., Vega-Hernandez M., Bosch-Bayard J., Melie-Garcia L., Canales-Rodriguez E. Estimating brain functional connectivity with sparse multivariate autoregression. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 2005, 360:969-981.
91. Valdes-Sosa P.A., Sanchez-Bornot J.M., Sotero R.C., Iturria-Medina Y., Aleman-Gomez Y., Bosch-Bayard J., Carbonell F., Ozaki T. Model driven EEG/fMRI fusion of brain oscillations. Hum. Brain Mapp. 2008.
92. Valdes P.A., Jimenez J.C., Riera J., Biscay R., Ozaki T. Nonlinear EEG analysis based on a neural mass model. Biol. Cybern. 1999, 81:415-424.
93. Wendling F., Bellanger J.J., Bartolomei F., Chauvel P. Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. Biol. Cybern. 2000, 83:367-378.
94. Wendling F., Bartolomei F., Bellanger J.J., Chauvel P. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur. J. Neurosci. 2002, 15:1499-1508.
95. Wiegerinck W., Kappen B. A Gaussian approximation for stochastic nonlinear dynamical processes with annihilation. NIPS 2006 Workshop on Dynamical Systems 2006, Stochastic Processes and Bayesian Inference, Whistler, BC, Canada.
96. Zavaglia M., Astolfi L., Babiloni F., Ursino M. The effect of connectivity on EEG rhythms, power spectral density and coherence among coupled neural populations: analysis with a neural mass model. IEEE Trans. Biomed. Eng. 2008, 55:69-77.
97. Zeki S., Shipp S. The functional logic of cortical connections. Nature 1988, 335:311-317.
98. Zetterberg L.H., Kristiansson L., Mossberg K. Performance of a model for a local neuron population. Biol. Cybern. 1978, 31:15-26.