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Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders

文献类型：期刊论文

作者Zhao, Jianlong1,2,12; Huang, Jinjie1; Zhi, Dongmei2,11,12; Yan, Weizheng2,11,12; Ma, Xiaohong8,9,10; Yang, Xiao8,9,10; Li, Xianbin7; Ke, Qing6; Jiang, Tianzi2,3,11,12; Calhoun, Vince D.4,5
刊名JOURNAL OF NEUROSCIENCE METHODS
出版日期2020-07-15
卷号341页码:10
关键词Resting-state fMRI Generative adversarial networks (GAN) Deep learning Classification Major depressive disorders Schizophrenia
ISSN号0165-0270
DOI10.1016/j.jneumeth.2020.108756
通讯作者Huang, Jinjie(jjhuang@hrbust.edu.cn) ; Sui, Jing(kittysj@gmail.com)
英文摘要As a popular deep learning method, generative adversarial networks (GAN) have achieved outstanding performance in multiple classifications and segmentation tasks. However, the application of GANs to fMRI data is relatively rare. In this work, we proposed a functional network connectivity (FNC) based GAN for classifying psychotic disorders from healthy controls (HCs), in which FNC matrices were calculated by correlation of time courses derived from non-artefactual fMRI independent components (ICs). The proposed GAN model consisted of one discriminator (real FNCs) and one generator (fake FNCs), each has four fully-connected layers. The generator was trained to match the discriminator in the intermediate layers while simultaneously a new objective loss was determined for the generator to improve the whole classification performance. In a case for classifying 269 major depressive disorder (MDD) patients from 286 HCs, an average accuracy of 70.1% was achieved in 10-fold cross-validation, with at least 6% higher compared to the other 6 popular classification approaches (54.5-64.2%). In another application to discriminating 558 schizophrenia patients from 542 HCs from 7 sites, the proposed GAN model achieved 80.7% accuracy in leave-one-site-out prediction, outperforming support vector machine (SVM) and deep neural net (DNN) by 3%-6%. More importantly, we are able to identify the most contributing FNC nodes and edges with the strategy of leave-one-FNC-out recursively. To the best of our knowledge, this is the first attempt to apply the GAN model on the FNC-based classification of mental disorders. Such a framework promises wide utility and great potential in neuroimaging biomarker identification.
WOS关键词MAJOR DEPRESSIVE DISORDER ; PREFRONTAL CORTEX ; MOOD DISORDERS ; GROUP ICA ; SCHIZOPHRENIA ; FMRI ; BIPOLAR ; FRAMEWORK ; PATTERNS ; SUBJECT
资助项目Natural Science Foundation of China[61773380] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32040100] ; Beijing Municipal Science and Technology Commission[Z181100001518005] ; National Institute of Health[R01MH117107] ; National Institute of Health[R01EB005846] ; National Institute of Health[P20GM103472] ; National Science Foundation[1539067]
WOS研究方向Biochemistry & Molecular Biology ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000546302200020
出版者ELSEVIER
资助机构Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Beijing Municipal Science and Technology Commission ; National Institute of Health ; National Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/40056]  
专题自动化研究所_脑网络组研究中心
通讯作者Huang, Jinjie; Sui, Jing
作者单位1.Harbin Univ Sci & Technol, Dept Automat, 52 Xuefu Rd, Harbin 150080, Peoples R China
2.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing 100190, Peoples R China
3.Chinese Acad Sci, CAS Ctr Excellence Brain Sci, Inst Automat, Beijing 100190, Peoples R China
4.Emory Univ, Atlanta, GA 30303 USA
5.Georgia State Univ, Triinst Ctr Translat Res Neuroimaging & Data Sci, Georgia Inst Technol, Atlanta, GA 30303 USA
6.Zhejiang Univ, Affiliated Hosp 1, Dept Neurol, Sch Med, Hangzhou, Zhejiang, Peoples R China
7.Capital Med Univ, Beijing Anding Hosp, Beijing Key Lab Mental Disorders, Beijing, Peoples R China
8.West China Hosp Sichuan, Mental Hlth Ctr, State Key Lab Biotherapy, Chengdu 610041, Peoples R China
9.Sichuan Univ, Huaxi Brain Res Ctr, West China Hosp, Chengdu 610041, Peoples R China
10.West China Hosp Sichuan, Psychiat Lab, State Key Lab Biotherapy, Chengdu 610041, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Jianlong,Huang, Jinjie,Zhi, Dongmei,et al. Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders[J]. JOURNAL OF NEUROSCIENCE METHODS,2020,341:10.
APA Zhao, Jianlong.,Huang, Jinjie.,Zhi, Dongmei.,Yan, Weizheng.,Ma, Xiaohong.,...&Sui, Jing.(2020).Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders.JOURNAL OF NEUROSCIENCE METHODS,341,10.
MLA Zhao, Jianlong,et al."Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders".JOURNAL OF NEUROSCIENCE METHODS 341(2020):10.

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来源：自动化研究所

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