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Authors: M. Bartés-Serrallonga 1 ; J. Solé-Casals 1 ; A. Adan 2 ; C. Falcón 3 ; N. Bargalló 4 and J. M. Serra-Grabulosa 5

Affiliations: 1 University of Vic, Spain ; 2 Universitat de Barcelona, Institute for Brain and Cognition and Behaviour (IR3C), Spain ; 3 Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and CIBER-BBN, Spain ; 4 Hospital Clínic de Barcelona, Spain ; 5 Universitat de Barcelona and Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Spain

ISBN: 978-989-8425-84-3

Keyword(s): Functional magnetic resonance imaging, Independent component analysis, BOLD.

Abstract: Functional magnetic resonance imaging (fMRI) is a technique to map the brain, anatomically as well as physiologically, which does not require any invasive analysis. In order to obtain brain activation maps, the subject under study must perform a task or be exposed to an external stimulus. At the same time a large amount of images are acquired using ultra-fast sequences through magnetic resonance. Afterwards, these images are processed and analyzed with statistical algorithms. This study was made in collaboration with the consolidated Neuropsychology Research Group of the University of Barcelona, focusing on applications of fMRI for the study of brain function in images obtained with various subjects. This group performed a study which analyzed fMRI data, acquired with various subjects, using the General Linear Model (GLM). The aim of our work was to analyze the same fMRI data using Independent Component Analysis (ICA) and compare the results with those obtained through GLM. Results sh owed that ICA was able to find more active networks than GLM. The activations were found in frontal, parietal, occipital and temporal areas. (More)

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Paper citation in several formats:
Bartés-Serrallonga, M.; Solé-Casals, J.; Adan, A.; Falcón, C.; Bargalló, N. and Serra-Grabulosa, J. (2011). STATISTICAL ANALYSIS OF FUNCTIONAL MRI DATA USING INDEPENDENT COMPONENT ANALYSIS.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: Special Session on Challenges in Neuroengineering, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 430-436. DOI: 10.5220/0003723504300436

@conference{special session on challenges in neuroengineering11,
author={M. Bartés{-}Serrallonga. and J. Solé{-}Casals. and A. Adan. and C. Falcón. and N. Bargalló. and J. M. Serra{-}Grabulosa.},
title={STATISTICAL ANALYSIS OF FUNCTIONAL MRI DATA USING INDEPENDENT COMPONENT ANALYSIS},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: Special Session on Challenges in Neuroengineering, (IJCCI 2011)},
year={2011},
pages={430-436},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003723504300436},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: Special Session on Challenges in Neuroengineering, (IJCCI 2011)
TI - STATISTICAL ANALYSIS OF FUNCTIONAL MRI DATA USING INDEPENDENT COMPONENT ANALYSIS
SN - 978-989-8425-84-3
AU - Bartés-Serrallonga, M.
AU - Solé-Casals, J.
AU - Adan, A.
AU - Falcón, C.
AU - Bargalló, N.
AU - Serra-Grabulosa, J.
PY - 2011
SP - 430
EP - 436
DO - 10.5220/0003723504300436

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