| 基于多模态MRI精神分裂症脑复杂网络研究进展 |
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| 引用本文： | 孔令茵,吴逢春,雷炳业,柯鹏飞,陈军,吴凯.基于多模态MRI精神分裂症脑复杂网络研究进展[J].中国医学影像技术,2020,36(10):1550-1554. |
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| 作者姓名： | 孔令茵 吴逢春 雷炳业 柯鹏飞 陈军 吴凯 |
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| 作者单位： | 华南理工大学材料科学与工程学院生物医学工程系, 广东 广州 510006;广州医科大学附属脑科医院 广州市惠爱医院精神科, 广东 广州 510370;广东省精神疾病转化医学工程技术研究中心, 广东 广州 510370;华南理工大学材料科学与工程学院生物医学工程系, 广东 广州 510006;广东省精神疾病转化医学工程技术研究中心, 广东 广州 510370;广东省老年痴呆诊断与康复工程技术研究中心, 广东 广州 510500;国家医疗保健器具工程技术研究中心, 广东 广州 510500;华南理工大学材料科学与工程学院生物医学工程系, 广东 广州 510006;广州医科大学附属脑科医院 广州市惠爱医院精神科, 广东 广州 510370;广东省精神疾病转化医学工程技术研究中心, 广东 广州 510370;广东省老年痴呆诊断与康复工程技术研究中心, 广东 广州 510500;国家医疗保健器具工程技术研究中心, 广东 广州 510500;日本东北大学加龄医学研究所机能画像医学研究室, 日本宫城 仙台 980-8575 |
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| 基金项目： | 国家重点研发计划（2020YFC2004300、2020YFC2004301、2019YFC0118800、2019YFC0118802、2019YFC0118804、2019YFC0118805）、国家自然科学基金（31771074、81802230）、广东省科技重点领域研发计划（2018B030335001）、广州市产学研协同创新重大专项（201704020168、201704020113、201807010064、201803010100、201903010032）。 |
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| 摘 要： | 精神分裂症（SZ）为慢性精神疾病，常伴感知、思维、情感、行为等多方面障碍。MRI可用于观察SZ患者脑结构及功能异常，为识别精神障碍生物标记物提供重要支持。多项研究基于多模态MRI构建脑结构及功能网络，采用人脑连接组学分析方法，发现SZ脑复杂网络异常，如最短路径长度增大、聚类系数及网络效率下降、核心节点受损等，进一步支持SZ失连接假说。本文针对SZ脑结构及功能网络、多模态网络等最新研究进行综述，探讨SZ脑复杂网络拓扑结构及属性特异性的特点，讨论现有研究方法存在的问题以及未来发展方向。
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| 关 键 词： | 精神分裂症 磁共振成像 神经网络 计算机 |
| 收稿时间： | 2019/7/19 0:00:00 |
| 修稿时间： | 2020/4/15 0:00:00 |
Research progresses of brain complex network in schizophrenia based on multimodal MRI |
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| KONG Lingyin,WU Fengchun,LEI Bingye,KE Pengfei,CHEN Jun,WU Kai.Research progresses of brain complex network in schizophrenia based on multimodal MRI[J].Chinese Journal of Medical Imaging Technology,2020,36(10):1550-1554. |
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| Authors: | KONG Lingyin WU Fengchun LEI Bingye KE Pengfei CHEN Jun WU Kai |
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| Institution: | Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China;Department of Psychiatry, the Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou 510370, China;Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China;Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China;Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China;Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China;National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China; Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, China;Department of Psychiatry, the Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou 510370, China;Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China;Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, China;National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China;Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan |
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| Abstract: | Schizophrenia (SZ) is a group of chronic mental disorders, which is often accompanied by perception, thinking, emotion, behavior and other impairments. MRI techniques can be used to investigate brain structural and functional alterations in SZ, so as to provide significant support for the recognition of biomarkers for mental disorders. Structural and functional brain networks in SZ constructed with multimodal MRI have been analyzed by the human brain connectome using graph theory in numerous studies, which highlighted the abnormality of brain complex networks, such as increased shortest path length, decreased clustering coefficient and global efficiency, as well as deficits of global hub, providing further support for the hypothesis of dysconnection in SZ. The recent advancements of structural networks, functional networks and multimodal networks were reviewed, and the characteristics of brain complex networks in SZ were explored, the existing problems of analysis methods and future direction were discussed in this paper. |
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| Keywords: | schizophrenia magnetic resonance imaging neural networks computer |
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