Skip to main content
SpringerLink
Log in

Functional Connectivity in the Resting Brain: An Analysis Based on ICA

  • Conference paper
Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4232))

Included in the following conference series:

Abstract

The functional connectivity of the resting state, or default mode, of the human brain has been a research focus, because it is reportedly altered in many neurological and psychiatric disorders. Among the methods to assess the functional connectivity of the resting brain, independent component analysis (ICA) has been very useful. But how to choose the optimal number of separated components and the best-fit component of default mode network are still problems left. In this paper, we used three different numbers of independent components to separate the fMRI data of resting brain and three criterions to choose the best-fit component. Furthermore, we proposed a new approach to get the best-fit component. The result of the new approach is consistent with the default-mode network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Friston, K.J.: Statistical parametric mapping and other analyses of functional imaging data, pp. 363–396. Academic Press, San Diego (1996)

    Google Scholar 

  2. Biswal, B.B., Yekin, F.Z., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995)

    Article  Google Scholar 

  3. Goebel, R., Linden, D.E., Lanfermann, H., Zanella, F.E., Singer, W.: Functional imaging of mirror inverse reading reveals separate coactivated networks for oculomotion spatial transformations. Neuroreport 9, 713–719 (1998)

    Article  Google Scholar 

  4. Cordes, D., Haughton, V., Carew, J.D., Arfanakis, K., Maravilla, K.: Hierarchical clustering to measure connectivity in fMRI resting-state data. Magn. Reson. Imaging 20, 305–317 (2002)

    Article  Google Scholar 

  5. Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S.: Functional connectivity: the principle-component analysis of large (PET) data sets. J. Cere Blood Flow Metab. 13, 5–14 (1993)

    Article  Google Scholar 

  6. Peltier, S.J., Polk, T.A., Noil, D.C.: Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm. Hum. Brain. Mapp. 20, 220–226 (2003)

    Article  Google Scholar 

  7. Biswal, B.B., Ulmer, J.L.: Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. J. Comp. Assist Tomogr. 23, 265–271 (1999)

    Article  Google Scholar 

  8. Kiviniemi, V., Kantola, J.H., Jauhiainen, J., Hyvarinen, A., Tervonen, O.: Independent component analysis of nondeterministic fMRI signal sources. Neuroimage 19, 253–260 (2003)

    Article  Google Scholar 

  9. van de Ven, V.G., Formisano, E., Prvulovic, D., Roeder, C.H., Linden, D.E.J.: Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest. Hum. Brain. Mapp. 22, 165–178 (2004)

    Article  Google Scholar 

  10. Greicius, M.D., Srivastava, G., Reiss, A.L., Menon, V.: Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: Evidence from functional MRI. Proc. Natl. Acad. Sci. USA 100, 253–258 (2004)

    Article  Google Scholar 

  11. McKeown, M.J., Humphries, C., Achermann, P., Borbely, A., Sejnowski, T.J.: A new method for detecting state changes in the EEG: Exploratory application to sleep data. J. Sleep. Res. 7(suppl. 1), 48–56 (1998a)

    Article  Google Scholar 

  12. McKeown, M.J., Jung, T.-P., Makeig, S., Brown, G.G., Kindermann, S.S., Lee, T.-W., Sejnowski, T.J.: Spatially independent activity patters in functional magnetic resonance imaging data during the stroop color-naming task. Proc. Natl. Acad. Sci. USA. 95, 803–810 (1998b)

    Article  Google Scholar 

  13. 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 decomposition into independent spatial components. Hum. Brain. Mapp. 6, 160–188 (1998c)

    Article  Google Scholar 

  14. McKeown, M.J., Sejnowski, T.J.: Independent component analysis of fMRI data: Examining the assumptions. Hum. Brain. Mapp. 6, 368–372 (1998)

    Article  Google Scholar 

  15. Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural. Comput. 7, 1129–1159 (1995)

    Article  Google Scholar 

  16. Hyvarinen, A.: Fast robust fixed-point algorithms for independent component analysis. IEEE. Trans. Neural. Netw. 10, 626–634 (1999)

    Article  Google Scholar 

  17. Calhoun, V.D., Adali, T., McGinty, V.B., Pekar, J.J., Watson, T.D., Pearlson, G.D.: fMRI activation in a visual-perception task: Network of areas detected using the general linear model and independent component analysis. NeuroImage 14, 1080–1088 (2001)

    Article  Google Scholar 

  18. Gu, H., Engelien, W., Feng, H.H., Silbersweig, D.A., Stern, E., Yang, Y.H.: Mapping Transient, randomly occurring neuropsychological events using independent component analysis. NeuroImage 14, 1432–1443 (2001)

    Article  Google Scholar 

  19. Long, Z.Y., Yao, L., Zhao, X.J.: Spatial Independent Component Analysis of Multitaskrelated Activation in fMRI Data. Lecture Notes Computer Science, vol. 2714, pp. 515–522. Springer, Heidelberg (2003)

    Google Scholar 

  20. Wu, X., Long, Z.Y., Yao, L., Chen, K.W.: A new post-processing method of applying independent component analysis to fMRI data. In: Medical Imaging 2006.2, Proceeding of SPIE 6144: 61446U-1- 5, USA (2006)

    Google Scholar 

  21. Wu, X., Yao, L., Long, Z.Y.: Improved Infomax Algorithm of Independent Component Analysis Applied to fMRI data. In: Medical Imaging 2004.1, Proceeding of SPIE 5370, U.S.A, pp. 1880–1889 (2004)

    Google Scholar 

  22. Raczkowski, D., Kalat, J.W., Nebes, R.: Reliability validity of some handedness questionnaire items. Neuropsychologia 6, 43–47 (1974)

    Article  Google Scholar 

  23. Friston, K.J., Ashburner, J., Frith, C.D., Poline, J.B., Heather, J.D., Frachowiak, R.S.D.: Spatial registration and normalization of images. Hum. Brain. Mapp. 3, 165–189 (1995)

    Article  Google Scholar 

  24. Talairach, J., Tournoux, P.: Co-planar stereotaxic atlas of the human brain. Thieme Medical, New York (1988)

    Google Scholar 

  25. Maldjian, J.A., Laurienti, P.J., Burdette, J.B., Kraft, R.A.: An automated method for neuroanatomic and cytoachitectonic atlas-based interrogation of fMRI data sets. NeuroImage 19, 1233–1239 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China

    Xia Wu & Li Yao

  2. College of Information and Computer Technology, Beijing Normal University, Beijing, China

    Li Yao

  3. Center for Human Development, University of California at San Diego, CA, 92093, USA

    Zhi-ying Long

  4. Department of Radiology, Xuan Wu Hospital of Beijing, Beijing, 100053, China

    Jie Lu & Kun-cheng Li

Authors
  1. Xia Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhi-ying Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kun-cheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wu, X., Yao, L., Long, Zy., Lu, J., Li, Kc. (2006). Functional Connectivity in the Resting Brain: An Analysis Based on ICA. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_20

Download citation

  • DOI: https://doi.org/10.1007/11893028_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46479-2

  • Online ISBN: 978-3-540-46480-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation