Accurate prediction of AD patients using cortical

Authors: Dai Dai Huiguang He Joshua T Vogelstein Zengguang Hou

Publish Date: 2012/10/23

Volume: 24, Issue: 7, Pages: 1445-1457

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Abstract

It is widely believed that human brain is a complicated network and many neurological disorders such as Alzheimer’s disease AD are related to abnormal changes of the brain network architecture In this work we present a kernelbased method to establish a network for each subject using mean cortical thickness which we refer to hereafter as the individual’s network We construct individual networks for 83 subjects including AD patients and normal controls NC which are taken from the Open Access Series of Imaging Studies database The network edge features are used to make prediction of AD/NC through the sophisticated machine learning technology As the number of edge features is much more than that of samples feature selection is applied to avoid the adverse impact of highdimensional data on the performance of classifier We use a hybrid feature selection that combines filter and wrapper methods and compare the performance of six different combinations of them Finally support vector machines are trained using the selected features To obtain an unbiased evaluation of our method we use a nested cross validation framework to choose the optimal hyperparameters of classifier and evaluate the generalization of the method We report the best accuracy of 904  using the proposed method in the leaveoneout analysis outperforming that using the raw cortical thickness data by more than 10 This work was supported by the National Natural Science Foundation of China 61271151 61228103 61175076 and the Sci Tech Aiding the Disabled Program of the Chinese Academy of Sciences Grant KGCX2YW618 We thank Dr Hai Jiang for proofreading

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