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.

Original languageEnglish
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages624-631
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 19 Nov 2012
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7663 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Neural Information Processing, ICONIP 2012
CountryQatar
CityDoha
Period12/11/1215/11/12

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Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Bensmail, H., Anjum, S., Bouhali, O., & El Anbari, M. (2012). RAFNI: Robust analysis of functional neuroimages with non-normal α-stable error. In Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings (PART 1 ed., pp. 624-631). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7663 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-34475-6_75