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Distributed Functional Connectivity Impairment in Schizophrenia: A Multi-site Study
Yang Y(杨勇)1,2; Cui,Yue1,2; Xu,Kaibing1,2; Liu,Bing1,2; Song,Ming1,2; Chen,Jun3; Wang,Huiling4; Chen,Yunchun5; Guo,Hua6; Li,Peng7,8; Lu,Lin7,8,14; Lv,Luxian9,10; Wan,Ping6; Wang,Huaning5; Yan,Hao7,8; Yan,Jun7,8; Zhang,Hongxing9,10,11; Zhang,Dai7,8,12; Jiang,Tianzi1,2,13,14
2017
Conference NameInternational Conference on Biomedical Image and Signal Processing (ICBISP)
Source PublicationICBISP conference proceedings
Conference Date2017-05-13
Conference Place武汉
Funding OrganizationThe Institution of Engineering and Technology (IET)
AbstractSchizophrenia has been considered as a dysconneciton syndrome, which means the disintegration, or over interaction between brain regions may underlie the pathophysiology of this disease. Noninvasive techniques like functional magnetic resonance imaging (fMRI) were utilized to test this hypothesis. However, there is no consensus on which brain areas and which functional network is related with it, mostly due to the small sample size of previous studies. Supervised machine learning techniques are able to examine fMRI connectivity data in a multivariate manner and extract features predictive of group membership. This technique requires large sample sizes and results from small sample study may not generalize well. By applying a multi-task classification framework to large size multi-site schizophrenia resting functional MRI (rsMRI) dataset, we were able to find consistent and robust features. We observed that schizophrenia patients had widespread deficits in the brain. The most informative and robustly selected functional connectivity (FC) features were between and within functional networks such as the default mode network (DMN), the fronto-parietal control network (FPN), the subcortical network, and the cingulo-opercular task control network (CON). Our finding validated the dysconnection hypothesis of schizophrenia and shed light on the details of the impaired functional connectivity.
KeywordFunctional Mri Schizophrenia Multi-task Learning Feature Selection
URL查看原文
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14511
Collection脑网络组研究中心
Affiliation1.Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3.Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
4.Center for Life Sciences / PKU-IDG / McGovern Institute for Brain Research, Peking University, Beijing 100871, China
5.Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
6.CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
7.Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
8.Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi’an 710032, China
9.Zhumadian Psychiatric Hospital, Zhumadian 463000, China
10.Peking University Sixth Hospital / Institute of Mental Health, Beijing 100191, China
11.Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
12.Department of Psychiatry, Henan Mental Hospital ,The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
13.Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China
14.Department of Psychology, Xinxiang Medical University, Xinxiang 453002, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yang Y,Cui,Yue,Xu,Kaibing,et al. Distributed Functional Connectivity Impairment in Schizophrenia: A Multi-site Study[C],2017.
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