eprintid: 3320
rev_number: 7
eprint_status: archive
userid: 69
dir: disk0/00/00/33/20
datestamp: 2016-03-23 13:07:11
lastmod: 2016-09-13 10:18:01
status_changed: 2016-03-23 13:07:11
type: conference_item
metadata_visibility: show
creators_name: Vanello, Nicola
creators_name: Positano, Vincenzo
creators_name: Ricciardi, Emiliano
creators_name: Santarelli, Maria Filomena
creators_name: Guazzelli, Mario
creators_name: Pietrini, Pietro
creators_name: Landini, Luigi
creators_id: 
creators_id: 
creators_id: emiliano.ricciardi@imtlucca.it
creators_id: 
creators_id: 
creators_id: pietro.pietrini@imtlucca.it
creators_id: 
title: Independent component analysis of fMRI data: a model based approach for artifacts separation
ispublished: pub
subjects: RC0321
divisions: CSA
full_text_status: none
pres_type: paper
keywords: biomedical MRI; brain; independent component analysis; noise; FastICA; correlated movement; fMRI data; fMRI simulation; functional magnetic resonance imaging; high noise level; image artifacts; model based approach; non invasive study; rician distributed noise; simulated data sets; simulated subject movement; spatial ICA decomposition; spatial data smoothing; Blood; Brain modeling; Data analysis; Gaussian noise; Heart beat; Magnetic resonance imaging; Physiology
abstract: Independent component analysis applied to functional magnetic resonance imaging is a promising technique for non invasive study of brain function. We examine the behavior of spatial ICA decomposition applying ICA to simulated data sets. We study the ICA performances in presence of movement correlated and uncorrelated with activation task, also taking into account the presence of rician distributed noise. We show that the presence of image artifacts due to simulated subject movement and MRI noise greatly affects the method ability to reveal the activation, especially in the presence of movement correlated with activation task. Spatial smoothing of data, before ICA, seems to overcome this problem, allowing us to retrieve the original sources also in the presence of both correlated movement and high noise level.
date: 2003-03
publisher: IEEE
pagerange: 529-532
event_title: First International IEEE EMBS Conference on Neural Engineering
event_location: Capri, Italy
event_dates: May 20-22, 2003
event_type: conference
id_number: 10.1109/CNE.2003.1196880
refereed: TRUE
isbn: 0-7803-7579-3
book_title: First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings.
official_url: http://dx.doi.org/10.1109/CNE.2003.1196880
citation:   Vanello, Nicola and Positano, Vincenzo and Ricciardi, Emiliano and Santarelli, Maria Filomena and Guazzelli, Mario and Pietrini, Pietro and Landini, Luigi  Independent component analysis of fMRI data: a model based approach for artifacts separation.  In: First International IEEE EMBS Conference on Neural Engineering, May 20-22, 2003, Capri, Italy pp. 529-532.  ISBN 0-7803-7579-3.      (2003)