Research Papers:

Resting-state functional connectivity of the dorsal frontal cortex predicts subcortical vascular cognition impairment

Xiaopeng Hu, Xia Zhou, Chao Zhang, Haibao Wang, Yongqiang Yu _ and Zhongwu Sun

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Oncotarget. 2017; 8:93079-93086. https://doi.org/10.18632/oncotarget.21855

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Xiaopeng Hu1,*, Xia Zhou2,*, Chao Zhang2, Haibao Wang1, Yongqiang Yu1 and Zhongwu Sun2

1Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Anhui, China

2Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui, China

*These authors have contributed equally to this work

Correspondence to:

Yongqiang Yu, email: [email protected]

Zhongwu Sun, email: [email protected]

Keywords: multivariate pattern analysis, dorsal frontal cortex, vascular cognition impairment, subcortical

Received: August 05, 2016     Accepted: August 26, 2017     Published: October 16, 2017


Functional magnetic resonance imaging (fMRI) studies have revealed group differences in the frontal area between the subcortical vascular cognition impairment (SVCI) patients and the controls. However, most of the existing research focused on average differences between the two groups, and therefore had limited clinical applicability. The aim of our study was to investigate whether inter-regions functional connectivity of the dorsal frontal cortex (DFC) can be used to discriminate the SVCI from the controls at the level of the individual. Thirty-two SVCI patients and 32 demographically similar healthy individuals underwent resting-state functional magnetic resonance imaging. The DFC, derived from a prior atlas, was divided into 10 clusters. Features based on DFC were obtained through functional connectivity analysis between pairs of DFC. A nonlinear kernel support vector machine was used for classification and validated using 8-fold cross validation. An excellent classification accuracy was obtained from both the left and the right DFC functional connectivity (accuracy=75.07%, sensitivity=81.57% and specificity=61.71%; accuracy=45.38%, sensitivity=60.74% and specificity=39.48%; P<0.001). These findings shed further light on the pathogenesis of SVCI and showed promising classification performance using machine learning analysis based on DFC fMRI data, which may be useful for the differentiation of SVCI.

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