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Discriminative analysis of early Alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI

Abstract : In 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.
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https://hal.inria.fr/inria-00123878
Contributor : Chine Publications Liama <>
Submitted on : Thursday, January 11, 2007 - 11:58:40 AM
Last modification on : Wednesday, June 3, 2020 - 9:53:28 PM

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Kun Wang, Tianzi Jiang, Meng Liang, Liang Wang, Lixia Tian, et al.. Discriminative analysis of early Alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI. Proceedings of Medical Image Computing and Computer Assisted Intervention -MICCAI'06, Oct 2006, Copenhague, Danemark, Denmark. pp.340-347, ⟨10.1007/11866763_42⟩. ⟨inria-00123878⟩

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