Summary:
An integrated model for magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI) is proposed. In the model, the neural activity is related to the Post Synaptic Potentials (PSPs) which is common link between MEG and fMRI. Each PSP is modeled by the direction and strength of its current flow which are treated as random variables. The overall neural activity in each voxel is used for equivalent current dipole in MEG and as input of extended Balloon model in fMRI. The proposed model shows the possibility of detecting activation by fMRI in a voxel while the voxel is silent for MEG and vice versa. Parameters of the model can illustrate situations like closed field due to non-pyramidal cells, canceling effect of inhibitory PSP on excitatory PSP, and effect of synchronicity. In addition, the model shows that the crosstalk from neural activities of the adjacent voxels in fMRI may result in the detection of activations in these voxels that contain no neural activities. The proposed model is instrumental in evaluating and comparing different analysis methods of MEG and fMRI. It is also useful in characterizing the upcoming combined methods for simultaneous analysis of MEG and fMRI.
Similar content being viewed by others
References
Almeida, R. and Stetter, M. Modeling the link between functional imaging and neuronal activity: synaptic metabolic demand and spike rates. Neuroimage, 2002, 17:1065–1079.
Attwell, D. and Iadecola, C. The neural basis of functional brain imaging signals. Trends Neurosci., 2002, 25: 621–625.
Baillet, S., Mosher, J.C. and Leahy, R.M. Electromagnetic Brain Mapping. IEEE Signal Processing Magazine, 2001, 18:14–30.
Buxhoeveden, D.P. and Casanova, M.F. The minicolumn hypothesis in neuroscience. Brain, 2002, 125: 935–951.
Buxton, R.B., Wong, E.C. and Frank, L.R. Dynamics of blood flow and oxygenation changes during brain activation: the balloon mode. Magn. Reson. Med., 1998, 39: 855–864.
Caesar, K., Gold, L. and Lauritzen, M. Context sensitivity of activity dependent increases in cerebral blood flow. Proc. Nal. Acad. Sci. USA, 2003, 100: 4239–4244.
Curio, G., Mackert, B., Burghoff, M., Koetiz, R., Abraham-Fuchs, K. and Harer, W. Localization of evoked neuromagnetic 600 Hz activity in the cerebral somatosensory system. Electroenceph. Clin. Neurophysiol., 1994, 91: 483–487.
Dale, A.M. and Halgren, E. Spatiotemporal mapping of brain activity by integration of multiple imaging modalities. Curr. Opin. Neurobiol., 2001, 11: 202–208.
Dale, A.M., Liu, A.K. and Fischl, B.R. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron, 2000, 26: 55–67.
De Schutter, E. Dendritic voltage and calcium-gated channels amplify the variability of postsynaptic responses in a Purkinje cell model. J. Neurophysiol., 1998, 80: 504–519.
Friston, K.J., Mechelli, A., Turner, R. and Price, C.J. Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics. Neuroimage, 2000, 12: 466–477.
Hämäläinen, M., 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. of Modern Phys., 1993, 65: 413–497.
Hashimoto, I., Mashiko, T. and Imada, T. Somatic evoked high-frequency magnetic oscillations reflect activity of inhibitory interneurons in the human somatosensory cortex. Electroenceph. Clin. Neurophysiol., 1996, 100: 189–203.
Horwitz, B. and Poeppel, D. How can EEG/MEG and fMRI/PET data be combined? Human Brain Mapping, 2002, 17: 1–3.
Iadecola, C., Li, J., Xu, S. and Yang, G. Neural mechanisms of blood flow regulation during synaptic activity in cerebellar cortex. J. Neurophysiol., 1996, 75: 940–950.
Iadecola, C., Yang, G., Ebner, T.J. and Chen, G. Local and propagated vascular responses evoked by focal synaptic activity in cerebellar cortex. J. Neurophysiol., 1997, 78: 651–659.
Korvenoja, A., Aronen, H.J. and Ilmoniemi, R.J. Functional MRI as a constraint in multi-dipole models of MEG data. Int. Jour. Bioelec., 2001, Vol. 3.
Larkum, M.E., Launey, T., Dityatev, A. and Luscher, H.R. Integration of excitatory postsynaptic potentials in dendrites of motoneurons of rat spinal cord slice cultures. J. Neurophysiol., 1998, 80: 924–935.
Lauritzen, M. and Gold, L. Brain function and neurophysiological correlates of signals used in functional neuroimaging. Jour. Neurosci., 2003, 23: 3972–3980.
Liley, D.T.J. and Wright, J.J. Intracortical Connectivity of Pyramidal and Stellate Cells: Estimates of Synaptic Densities and Coupling Symmetry. Network: Computation in Neural systems, 1994.
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. USA, 1998, 95: 8945–8950.
Logothetis, N.K. The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. Philos. Trans. R. Soc. Lond. B. Biol. Sci., 2002, 357: 1003–1037.
Logothetis, N.K. MR imaging in the non-human primate: studies of function and of dynamic connectivity. Curr. Opin. Neurobiol., 2003, 13: 630–642.
Logothetis, N.K., Pauls, J., Augath, M., Trinath, T. and Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature, 2001, 412: 150–157.
Martinez-Montes, E., Valdes-Sosa, P.A., Miwakeichi, F., Goldman, R.I. and Cohen, M.S. Concurrent EEG/fMRI analysis by multiway partial least squares. NeuroImage, 2004, 22: 1023–1034.
Nunez, P.L. and Silberstein, R.B. On the relationship of synaptic activity to macroscopic measurements: does co-registration of EEG with fMRI make sense? Brain Topography, 2000, 13: 79–96.
Ogawa, S., Tank, D.W., Menon, R., Ellerman, J.M., Kim, S-G., Merkle, H. and Ugurbil, K. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Nat. Acad. Sci., 1992, 89: 5951–5955.
Riera, J., Aubert, E., Iwata, K., Kawashima, R., Wan, X. and Ozaki, T. Fusing EEG and fMRI based on a bottom-up model: Inferring activation and effective connectivity in neural masses. Philosophical Transactions: Biological Sciences, 2005, 360(1457): 1025–1041.
Riera, J., Bosch, J., Yamashita, O., Kawashima, R., Sadato, N., Okada, T. and Ozakic, T. fMRI activation maps based on the NN-ARx model. NeuroImage, 2004, 23: 680–697.
Tagamets, M.A. and Horwitz, B. Interpreting PET and fMRI measures of functional neural activity: the effects of synaptic inhibition on cortical activation in human imaging studies. Brain Research Bulletin, 2001, 54(3): 267–273.
Waldvogel, D., van Gelderen, P., Muellbacher, W., Ziemann, U., Immisch, I. and Hallett, M. The relative metabolic demand of inhibition and excitation. Nature, 2000, 406: 995–998.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported in part by the Research Council of the University of Tehran, Tehran, Iran. The authors would like to thank Dr. John Moran from the Neurology Department, Henry Ford Health System, Detroit, Michigan, USA for his helpful discussions and kind assistance.
Rights and permissions
About this article
Cite this article
Babajani, A., Nekooei, MH. & Soltanian-Zadeh, H. Integrated MEG and fMRI Model: Synthesis and Analysis. Brain Topogr 18, 101–113 (2005). https://doi.org/10.1007/s10548-005-0279-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10548-005-0279-5