DocumentCode :
2526539
Title :
fMRI brain mapping with kernels
Author :
Martínez-Ramón, Manel ; De Cassia Gomes-Vilela, Mariléa ; Gómez-Verdejo, Vanessa ; Oliviero, Antonio
Author_Institution :
Dept. de Teor. de la Senal y Comun., Univ. Carlos III de Madrid, Madrid, Spain
fYear :
2012
fDate :
28-30 May 2012
Firstpage :
1
Lastpage :
6
Abstract :
Functional Magnetic Resonance Imaging is a technique for the study of the human brain that can detect the regionally specific effects of brain stimuli or activity through the detection of the activity related BOLD signal. The standard fMRI techniques include the use of the so called General Linear Model (GLM), which assumes that the combination of different activity in the brain present linear behavior. We present here a nonlinear counterpart of the GLM that does not contain that assumption and that is based on the use of Mercer´s kernels, thus keeping the simplicity and reasonable computational burden of the of the linear model. We show the advantages of this model in analysis of real fMRI data in multistimuli experiments.
Keywords :
biomedical MRI; brain; BOLD signal; Mercer kernel; blood oxygenation level dependent; brain activity; brain stimuli; fMRI brain mapping; general linear model; human brain; magnetic resonance imaging; Brain modeling; Covariance matrix; Kernel; Time series analysis; Vectors; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cognitive Information Processing (CIP), 2012 3rd International Workshop on
Conference_Location :
Baiona
Print_ISBN :
978-1-4673-1877-8
Type :
conf
DOI :
10.1109/CIP.2012.6232910
Filename :
6232910
Link To Document :
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