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

Authors:
Kun Wang;Tianzi Jiang;Meng Liang;Liang Wang;Lixia Tian;Xinqing Zhang;Kuncheng Li;Zhening Liu
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Department of Radiology, Neurology, Xuanwu Hospital of Capital University of Medical Science, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Department of Radiology, Neurology, Xuanwu Hospital of Capital University of Medical Science, Beijing, China;Department of Radiology, Neurology, Xuanwu Hospital of Capital University of Medical Science, Beijing, China;Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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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.