Ontology type: schema:Chapter Open Access: True
2006
AUTHORSKun Wang , Tianzi Jiang , Meng Liang , Liang Wang , Lixia Tian , Xinqing Zhang , Kuncheng Li , Zhening Liu
ABSTRACTIn this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD. More... »
PAGES340-347
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006
ISBN
978-3-540-44727-6
978-3-540-44728-3
http://scigraph.springernature.com/pub.10.1007/11866763_42
DOIhttp://dx.doi.org/10.1007/11866763_42
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PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/17354790
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