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International Conference on Neural Information Processing

ICONIP 2012: Neural Information Processing pp 624–631Cite as

RAFNI: Robust Analysis of Functional NeuroImages with Non–normal α-Stable Error

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Abstract

Functional Magnetic Resonance Imaging (fMRI) is a non-inasive neuro-imaging method that is widely used in cognitive neuroscience. It relies on the measurement of changes in the blood oxygenation level resulting from neural activity. The technique is widely used in cognitive neuroscience. fMRI is known to be contaminated by artifacts. Artifacts are known to have fat tails and are often skewed therefore modeling the error using a Gaussian distribution is a not enough. In this paper, we introduce RAFNI, an extention of AFNI, which is an fMRI open source software for the Analysis of Functional NeuroImages. We are modeling the error introduced by artifacts using α-stable distribution. We demonstrate the applicability and efficiency of stable distributions on real fMRI. We show that the α-stable estimator gives better results than the OLS-based estimators.

Keywords

  • Functional Magnetic Resonance Imaging
  • α-stable distribution
  • General Linear Model (GLM)

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Author information

Authors and Affiliations

  1. Qatar Computing Research Institute, Qatar

    Halima Bensmail, Samreen Anjum & Mohammed El Anbari

  2. Texas A & M in Qatar, Qatar

    Othmane Bouhali

Authors
  1. Halima Bensmail
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  2. Samreen Anjum
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  3. Othmane Bouhali
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  4. Mohammed El Anbari
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Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Education City, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074, Wuhan, Hubei, China

    Zhigang Zeng

  3. College of Computer Science, Chongqing University, 174 Shazhengjie Street, 400044, Chongqing, China

    Chuandong Li

  4. Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Chi Sing Leung

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© 2012 Springer-Verlag Berlin Heidelberg

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Bensmail, H., Anjum, S., Bouhali, O., El Anbari, M. (2012). RAFNI: Robust Analysis of Functional NeuroImages with Non–normal α-Stable Error. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_75

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  • DOI: https://doi.org/10.1007/978-3-642-34475-6_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34474-9

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