抑郁症的客观判别:基于光学脑成像的静息态功能性连接检测和分析

doi:10.3969/j.issn.0258-8021.2018.03.004

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Abstract Recently, resting-state functional connectivity (RSFC) has gradually been studied in patients with mental disorders by functional near-infrared spectroscopy (fNIRS). However, it is still unknown whether RSFC derived from fNIRS is predictable for depressive disorders. In this work, we employed fNIRS(42 channels) to measure 8-minute spontaneous hemodynamic activity in the prefrontal cortex (PFC) of 28 patients having depressive disorders and 30 healthy controls. After filtering irrelative components by independent component and band-pass filter (0.008-0.09 Hz), we calculated left-right correlations in the prefrontal cortex which included inferior prefrontal cortex (IFG), middle prefrontal cortex (MFG) and superior prefrontal cortex (SFG).Then we selected two significant parameters (left-right correlations in the IFG and MFG as a participant’s two features for further classification (75% of the participants) and prediction (25% of the participants) using linear discriminant analysis (LDA) and support vector machine (SVM). Finally, a sensitivity of 73-74% and specificity of 83-87%was yielded. These results supported that RSFC derived from fNIRS is a feasible and effective technique to identify whether someone is suffered from depressive disorders.

Key words： functional near-infrared spectroscopy resting-state functional connectivity major depressive disorders linear discriminant analysis support vector machine

Received: 06 April 2017

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R318

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	Articles by authors
	Zhu Huilin
	Xu Jie
	Li Jiangxue
	Peng Hongjun

Cite this article:

Zhu Huilin,Xu Jie,Li Jiangxue, et al. Objective Discrimination of Depression: Detection and Analysis of Resting State Functional Connectivity Based on Optical Brain Imaging[J]. Chinese Journal of Biomedical Engineering, 2018, 37(3): 283-289.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2018.03.004 OR http://cjbme.csbme.org/EN/Y2018/V37/I3/283

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