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Artif Intell Med. 2019 May;96:25-32. doi: 10.1016/j.artmed.2019.03.007. Epub 2019 Mar 18.

Dynamic thresholding networks for schizophrenia diagnosis.

Author information

1
PCA Lab, Key Lab of Intelligent Perception and systems for High-Dimensional Information of Ministry of Education, and Jiangsu Key Lab of Image and Video Understanding for Social Security, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China. Electronic address: hongliangzou@126.com.
2
PCA Lab, Key Lab of Intelligent Perception and systems for High-Dimensional Information of Ministry of Education, and Jiangsu Key Lab of Image and Video Understanding for Social Security, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China.

Abstract

BACKGROUND AND OBJECTIVE:

Functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI) is an effective approach to describe the neural interaction between distributed brain regions. Recent progress in neuroimaging study reported that the connection between regions is time-varying, which may enhance understanding of normal cognition and alterations that result from brain disorders. However, conventional sliding window based dynamic FC (DFC) analysis has several drawbacks, including arbitrary choice of window length, inaccurate descriptor of FC, and the fact that many spurious connections were included in the fully-connected networks due to noise. This study aims to develop an effective dynamic thresholding brain networks method to diagnose schizophrenia.

METHODS:

In this study, we proposed a time-varying window length DFC method based on dynamic time warping to construct brain functional networks. To further eliminate the influence of spurious connections caused by noise, orthogonal minimum spanning tree was applied in these networks to generate time-varying window length dynamic thresholding FC (TVWDTFC) networks. To validate the effectiveness of our proposed method, experiments were conducted on a dataset, which including 56 individuals with schizophrenia and 74 healthy controls.

RESULTS:

We achieved a classification accuracy of 0.8077 (p < 0.001, permutation test) using support vector machine. Experimental results demonstrated that the proposed method outperforms several state-of-the-art approaches, which verified the effectiveness of our proposed TVWDTFC method in schizophrenia diagnosis. Additionally, we also found that the selected discriminative features were mostly distributed in frontal, parietal, and limbic area.

CONCLUSIONS:

The results suggest that our approach may be a promising tool for computer-aided diagnosis of schizophrenia.

KEYWORDS:

Dynamic time warping; Orthogonal minimum spanning tree; Schizophrenia; Time-varying window length DFC; rs-fMRI

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