A Novel Unified SPM-ICA-PCA Method for Detecting Epileptic Activities in Resting-State fMRI

  • Qiyi Song
  • Feng Yin
  • Huafu Chen
  • Yi Zhang
  • Qiaoli Hu
  • Dezhong Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


In this paper, it is reported that the method and primary application of a novel noninvasive technique, resting functional magnetic resonance imaging (fMRI) with unified statistical parameter mapping (SPM) independent component analysis (ICA), and principal component analysis( PCA), for localizing interictal epileptic activities of glioma foci. SPM is based on the general linear model (GLM). ICA combined PCA was firstly applied to fMRI datasets to disclose independent components, which is specified as the equivalent stimulus response patterns in the design matrix of a GLM. Then, parameters were estimated and regionally-specific statistical inferences were made about activations in the usual way. The validity is tested by simulation experiment. Finally, the fMRI data of two glioma patients is analyzed, whose results are consisting with the clinical estimate.


fMRI Data Independent Component Analysis Statistical Parameter Mapping Bold Signal Blind Source Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Friston, K.J., Frith, C.D., Turner, R., Frackowiak, R.S.: Characterizing evoked hemodynamics with fMRI. NeuroImage 2, 157–165 (1995)CrossRefGoogle Scholar
  2. 2.
    Friston, K.J., Holmes, A.P., Poline, J.B., Grasby, P.J., Williams, S.C., Frackowiak, R.S., Turner, R.: Analysis of fMRI time-series revisited. NeuroImage 2, 45–53 (1995)CrossRefGoogle Scholar
  3. 3.
    Adler, R.J.: The geometry of random fields. John Wiley & Sons, Inc., New York (1981)MATHGoogle Scholar
  4. 4.
    Dewen, H., Lirong, Y., Yadong, L., Zongtan, Z., Friston, K.J., Changlian, T., Daxing, W.: Unified SPM-ICA for fMRI analysis. Neuroimage 25, 746–755 (2005)CrossRefGoogle Scholar
  5. 5.
    Della-Maggiore, V., Chau, W., Peres-Neto, P.R., McIntosh, A.R.: An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data. NeuroImage 17, 19–28 (2002)CrossRefGoogle Scholar
  6. 6.
    Friston, K.J., Fletcher, P., Josephs, O., Holmes, A., Rugg, M.D., Turner, R.: Event-related fMRI: characterizing differential responses. NeuroImage 7, 30–40 (1998)CrossRefGoogle Scholar
  7. 7.
    Fadili, M.J., Ruan, S., Bloyet, D., Mazoyer, B.: A multistep unsupervised fuzzy clustering analysis of fMRI time series. Hum. Brain Mapp. 10, 160–178 (2000)CrossRefGoogle Scholar
  8. 8.
    Backfrieder, W.: Quantification of intensity variations in functional MR images using rotated principal components. Phys. Med. Biol. 41, 1425–1438 (1996)CrossRefGoogle Scholar
  9. 9.
    Stone, J.V.: Blind source separation using temporal predictability. Neural Compute. 13, 1559–1574 (2001)MATHCrossRefGoogle Scholar
  10. 10.
    Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J.: Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Hum. Brain Mapp. 13, 43–53 (2001)CrossRefGoogle Scholar
  11. 11.
    McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.P., Kindermann, S.S., Bell, A.J., Sejnowski, T.J.: Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 6, 160–188 (1998)CrossRefGoogle Scholar
  12. 12.
    Fransson, P.: Spontaneous Low-frequency BOLD Signal Fluctuations: An fMRI Investigation of the Resting-state Default Mode of Brain Function Hypothesis. Human Brain Mapping 26, 15–29 (2005)CrossRefGoogle Scholar
  13. 13.
    Friston, K.J., Jezzard, P., Turner, R.: Analysis of functional MRI time series. Hum. Brain Mapp. 1, 153–171 (1994)CrossRefGoogle Scholar
  14. 14.
    Quyang, X., Pike, G.B., Evance, A.C.: FMRI of human visual cortex using temporal correlation and spatial coherence analysis. In: Proc., SMR, 2nd Annual Meeting, San Franciseco, p. 633 (1994)Google Scholar
  15. 15.
    Turner, R., Grinvald, A.: Direct visualization of patterns of deoxygenating and deoxygenating in monkey cortical vas-culture during functional brain activation. In: Proc., SMR, 2nd Annual Meeting, San Franciseco, p. 430 (1994)Google Scholar
  16. 16.
    Zhang, Y., Ye, M., Lv, J.C., Tan, K.K.: Convergence analysis of a deterministic discrete time system of Oja’s PCA learning algorithm. IEEE Trans. on Neural Networks 16(6), 1318–1328 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qiyi Song
    • 1
  • Feng Yin
    • 2
  • Huafu Chen
    • 1
  • Yi Zhang
    • 1
  • Qiaoli Hu
    • 1
  • Dezhong Yao
    • 1
  1. 1.Center of Neuroinformatics, School of Applied MathmaticsUniversity of Electronic Science and Technology of ChinaChengduPR China
  2. 2.Department of MathematicsSichuan University of Science and EngineeringZigongPR China

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