Knowledge Commons of Institute of Automation，CAS
| 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
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| 2017 | |
| 会议名称 | International Conference on Biomedical Image and Signal Processing (ICBISP) |
| 会议录名称 | ICBISP conference proceedings |
| 会议日期 | 2017-05-13 |
| 会议地点 | 武汉 |
| 会议主办者 | The Institution of Engineering and Technology (IET) |
| 摘要 | Schizophrenia 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. |
| 关键词 | Functional Mri Schizophrenia Multi-task Learning Feature Selection |
| URL | 查看原文 |
| 收录类别 | EI |
| 语种 | 英语 |
| 文献类型 | 会议论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/14511 |
| 专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
| 作者单位 | 1.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 |
| 第一作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
| 推荐引用方式 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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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| ICBISP2017_fullpaper（980KB） | 会议论文 | 开放获取 | No License | 浏览 下载 | ||
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