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International Conference on Artificial Neural Networks

International Conference on Neural Information Processing

ICANN 2003, ICONIP 2003: Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 pp 515–522Cite as

Spatial Independent Component Analysis of Multitask-Related Activation in fMRI Data

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2714))

Abstract

Independent component analysis (ICA) is a technique to separate the mixed signal into independent components without priori assumptions about the hemodynamic response to the task. Spatial ICA (SICA) is applied widely in fMRI data because the spatial dimension of fMRI data is larger than their temporal dimension. The general linear model (GLM) is based on a priori knowledge about stimulation paradigm. In our study, a two-task cognitive experiment was designed, and SICA and GLM were applied to analyze these fMRI data. Both methods could easily find some common areas activated by two tasks. However, SICA could also find more accurate areas activated by different single task in specific brain areas than GLM. The results demonstrate that ICA methodology can supply us more information or the intrinsic structure of the data especially when multitask-related components are presented in the data.

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Author information

Authors and Affiliations

  1. Department of Electronics, Beijing Normal University, Beijing, P.R.China, 100088

    Zhi-ying Long, Li Yao, Xiao-jie Zhao & Liu-qing Pei

  2. Department of Psychology, Beijing Normal University, Beijing, P.R.China, 100088

    Gui Xue, Qi Dong & Dan-ling Peng

Authors
  1. Zhi-ying Long
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  2. Li Yao
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  3. Xiao-jie Zhao
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  4. Liu-qing Pei
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  5. Gui Xue
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  6. Qi Dong
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  7. Dan-ling Peng
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Editor information

Editors and Affiliations

  1. Bogazici University, Bebek, 34342, Istanbul, Turkey

    Okyay Kaynak  & Ethem Alpaydin  & 

  2. Laboratory of Computer and Information Science, Helsinki University of Technology, P.O.B. 5400, 02015, Finland

    Erkki Oja

  3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Lei Xu

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© 2003 Springer-Verlag Berlin Heidelberg

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Long, Zy. et al. (2003). Spatial Independent Component Analysis of Multitask-Related Activation in fMRI Data. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_61

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  • DOI: https://doi.org/10.1007/3-540-44989-2_61

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40408-8

  • Online ISBN: 978-3-540-44989-8

  • eBook Packages: Springer Book Archive

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