Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI

被引:276
作者
Shen, Hui [1 ]
Wang, Lubin [1 ]
Liu, Yadong [1 ]
Hu, Dewen [1 ]
机构
[1] Natl Univ Def Technol, Coll Mechatron & Automat, Changsha 410073, Hunan, Peoples R China
基金
美国国家科学基金会;
关键词
Schizophrenia; Discriminative analysis; Functional magnetic resonance imaging (fMRI); Functional network; Resting-state; Machine learning; STRUCTURAL-CHANGES; BRAIN ACTIVITY; DISCONNECTIVITY; CLASSIFICATION; ASSOCIATION; MORPHOMETRY; UPDATE;
D O I
10.1016/j.neuroimage.2009.11.011
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Recently, a functional disconnectivity hypothesis of schizophrenia has been proposed for the physiological explanation of behavioral syndromes of this complex mental disorder. In this paper, we aim at further examining whether syndromes of schizophrenia could be decoded by some special spatiotemporal patterns of resting-state functional connectivity. We designed a data-dFiven classifier based on machine learning to extract highly discriminative functional connectivity features and to discriminate schizophrenic patients from healthy controls. The proposed classifier consisted of two separate steps. First, we used feature selection based on a correlation coefficient method to extract highly discriminative regions and construct the optimal feature set for classification. Then, an unsupervised-learning classifier combining low-dimensional embedding and self-organized clustering of fMRI was trained to discriminate schizophrenic patients from healthy controls. The performance of the classifier was tested using a leave-one-out cross-validation strategy. The experimental results demonstrated not only high classification accuracy (93.75% for schizophrenic patients, 75.0% for healthy controls), but also good generalization and stability with respect to the number of extracted features. In addition, some functional connectivities between certain brain regions of the cerebellum and frontal cortex were found to exhibit the highest discriminative power, which might provide further evidence for the cognitive dysmetria hypothesis of schizophrenia. This primary study demonstrated that machine learning could extract exciting new information from the resting-state activity of a brain with schizophrenia, which might have potential ability to improve current diagnosis and treatment evaluation of schizophrenia. (C) 2009 Elsevier Inc. All rights reserved.
引用
收藏
页码:3110 / 3121
页数:12
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