Title :
Data-driven fMRI group classification using connected components and Gaussian process classifiers
Author :
Lee, Sarah ; Zelaya, Fernando ; Samarasinghe, Yohan ; Amiel, Stephanie A. ; Brammer, Michael J.
Author_Institution :
Dept. of Neuroimaging, King´´s Coll. London, London, UK
Abstract :
Functional magnetic resonance imaging (fMRI) is a popular tool for studying brain activity due to its non-invasiveness. Convention ally an expected response needs to be available for correlating with fMRI time series in model-driven analysis, which limits experimental paradigms to blocked and event-related designs. To study neuronal responses due to slow physiological changes, such as after a glucose challenge or a drug administration, for which the expected response is unavailable, we had proposed a data-driven method: connected component analysis. In this paper, a novel group classification method is proposed by using both connected components and Gaussian process classifiers. The results demonstrate that the method is able to differentiate insulin resistant volunteers from insulin sensitive volunteers by their neuronal response to glucose ingestion with an accuracy of 77%.
Keywords :
biomedical MRI; drugs; image classification; medical image processing; sugar; time series; connected component analysis; data-driven FMRI group classification; drug administration; fMRI time series; functional magnetic resonance imaging; gaussian process classifier; glucose challenge; model-driven analysis; neuronal response; Brain; Gaussian processes; Immune system; Insulin; Magnetic resonance imaging; Sugar; Time series analysis; Gaussian process classifier; brain; connected component; data-driven; fMRI;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2011.5946504