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

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Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4222))

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Abstract

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.

Supported by NSFC#90208003, #30200059, 30470510, TRAPOYT, the 973 Project No. 2003CB716100, Doctor training Fund of MOE, PRC, Fok Ying Tong Education Foundation (91041).

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References

  1. Friston, K.J., Frith, C.D., Turner, R., Frackowiak, R.S.: Characterizing evoked hemodynamics with fMRI. NeuroImage 2, 157–165 (1995)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  3. Adler, R.J.: The geometry of random fields. John Wiley & Sons, Inc., New York (1981)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  8. Backfrieder, W.: Quantification of intensity variations in functional MR images using rotated principal components. Phys. Med. Biol. 41, 1425–1438 (1996)

    Article  Google Scholar 

  9. Stone, J.V.: Blind source separation using temporal predictability. Neural Compute. 13, 1559–1574 (2001)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  13. Friston, K.J., Jezzard, P., Turner, R.: Analysis of functional MRI time series. Hum. Brain Mapp. 1, 153–171 (1994)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Center of Neuroinformatics, School of Applied Mathmatics, University of Electronic Science and Technology of China, Chengdu, 610054, PR China

    Qiyi Song, Huafu Chen, Yi Zhang, Qiaoli Hu & Dezhong Yao

  2. Department of Mathematics, Sichuan University of Science and Engineering, Zigong, 643000, PR China

    Feng Yin

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  1. Qiyi Song
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  2. Feng Yin
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  3. Huafu Chen
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  4. Yi Zhang
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  5. Qiaoli Hu
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  6. Dezhong Yao
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Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., P.O. Box, 710071, Xi’an, P.R. China

    Xinbo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Song, Q., Yin, F., Chen, H., Zhang, Y., Hu, Q., Yao, D. (2006). A Novel Unified SPM-ICA-PCA Method for Detecting Epileptic Activities in Resting-State fMRI. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_77

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  • DOI: https://doi.org/10.1007/11881223_77

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45907-1

  • Online ISBN: 978-3-540-45909-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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