A Gaussian Dynamic Convolution Models of the FMRI BOLD Response

  • Huafu Chen
  • Ling Zeng
  • Dezhong Yao
  • Qing Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


Blood oxygenation level dependent (BOLD) contrast based functional magnetic resonance imaging (fMRI) has been widely utilized to detect brain neural activities and great efforts are now stressed on the hemodynamic processes of different brain regions activated by a stimulus. The focus of this paper is Gaussian dynamic convolution models of the fMRI BOLD response. The convolutions are between the perfusion function of the neural response to a stimulus and a Gaussian function. The parameters of the models are estimated by a nonlinear least-squares optimal algorithm for the fMRI data of eight subjects collected in a visual stimulus experiment. The results show that the Gaussian model is better in fitting the data.


Gaussian Model Neural Response Blood Oxygenation Level Dependent fMRI Data Blood Oxygenation Level Dependent Signal 
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  1. 1.
    Kwong, K., Belliveau, J., Chesler, D.: Dynamic Magnetic Resonance Imaging of Human Brain Activity During Primary Sensory Stimulation. Proc. Natl. Acad. Sci. USA 89, 5675–5679 (1992)CrossRefGoogle Scholar
  2. 2.
    Ogawa, S., Tank, D.W., Menon, R., Ellermann, J.M., Kim, S.G., Merkle, H., Ugurbil, K.: Intrinsic Signal Changes Accompanying Sensory Stimulation: Functional Brain Mapping with Magnetic Resonance Imaging. Proc. Natl. Acad. Sci. USA 89, 5951–5955 (1992)CrossRefGoogle Scholar
  3. 3.
    Bandettini, P.A., Jesmanowicz, A., Wong, E.C., Hyde, J.S.: Processing Strategies for Time-Course Data Sets in Functional MRI of the Human Brain. Magnetic Resonance in Medicine 30(2), 161–173 (1993)CrossRefGoogle Scholar
  4. 4.
    Rasmus, M.B., Ziad, S.S., Peter, A.N.: Spatial Heterogeneity of the Nonlinear Dynamics in the FMRI BOLD Response. NeuroImage 14(5), 817–826 (2001)Google Scholar
  5. 5.
    Yao, D.: Highresoltion EEG Mappings:A Spherical Harmonic Spectra Theory and Simulation Results.  Clinical Neurophysiology 111(1), 81–92 (2000)Google Scholar
  6. 6.
    Boynton, G.M., Engel, S.A., Glover, G.H., Heeger, D.J.: Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1. J. of Neuroscience 16(13), 4207–4221 (1996)Google Scholar
  7. 7.
    Rao, S.M., Bandettini, P.A., Binder, J.R., Bobholz, J.A., Hammeke, T.A., Stein, v., Hyde, v.: Relationship Between Finger Movement Rate and Functional Magnetic Resonance Signal Change in Human Primary Motor Cortex. J. of Cerebral Blood Flow and Metabolis 16(6), 1250–1254 (1996)CrossRefGoogle Scholar
  8. 8.
    Miller., V., Luh, W.M., Liu, T.T., Martinez, A., Obata, T., Wong, E.C., Frank, L.R., Buxton, R.B.: Nonlinear Temporal Dynamics of the Cerebral Blood Flow Response. Human Brain Mapping 13(1), 1–12 (2001)CrossRefGoogle Scholar
  9. 9.
    Buxton, R.B., Liu, T.T., Wong, E.C.: Nonlinearity of the Hemodynamic Response: Modeling the Neural and BOLD Contributions. In: Proc. 9th ISMRM, vol. 1164 (2001)Google Scholar
  10. 10.
    Liu, H.L., Gao, J.H.: An investigation of the Impulse Functions for the Nonlinear BOLD Response in functional MRI. Magnetic Resonance Imaging 18(8), 931–938 (2000)CrossRefGoogle Scholar
  11. 11.
    Vazquez, A.L., Noll, D.C.: Nonlinear Aspects of the BOLD Response in Functional MRI. Neuroimage 7(2), 108–118 (1998)CrossRefGoogle Scholar
  12. 12.
    Aguirre, G.K., Zarahn, E., Esposito, M.D.: The Variability of Human BOLD Hemodynamic Responses. NeuroImage 8(4), 360–369 (1998)CrossRefGoogle Scholar
  13. 13.
    Chen, H., Yao, D.: An Extended Gamma Dynamic Model of fMRI BOLD Response. Neurocomputing 61, 395–400 (2004)CrossRefGoogle Scholar
  14. 14.
    Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A.: Neurophysiological Investigation of the Basis of the FMRI Signal. Nature 412, 150–157 (2001)CrossRefGoogle Scholar
  15. 15.
    Rajapakse, J.C., Kruggel, F., Maisog, J.M., VonCramon, D.Y.: Modelling Hemodynamic Response for Analysis of Functional MRI Time-series. Human Brain Mapping 6(4), 283–300 (1998)CrossRefGoogle Scholar
  16. 16.
    Rosen, B.R., Buckner, R.L., Dale, A.: Event-related Functional MRI: Past, Present, and Future. Proc. Natl. Acad. Sci. USA 95, 773–780 (1998)CrossRefGoogle Scholar
  17. 17.
    Chen, H., Yao, D., Liu, Z.: A Study on Asymmetry of Spatial Visual Field by Analysis of the FMRI BOLD Response. Brain topography 17(1), 39–46 (2004)CrossRefMATHGoogle Scholar
  18. 18.
    Chen, H., Yao, D., Zhuo, Y., Chen, L.: Analysis of FMRI Data by Blind Separation into Independent Temporal Component. Brain Topography 15(4), 223–232 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huafu Chen
    • 1
  • Ling Zeng
    • 1
  • Dezhong Yao
    • 1
  • Qing Gao
    • 1
  1. 1.School of Applied Mathematics, School of Life Science & TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina

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