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基于多模态影像下的抑郁症大脑异常

Brain abnormalities in depression based on multimodal imaging

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【作者】 李姗李永超邹颖杨琳王茵姚志军胡斌

【Author】 LI Shan;LI Yongchao;ZOU Ying;YANG Lin;WANG Yin;YAO Zhijun;HU bin;Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University;CAS Center for Excellence in Brain Science and Intelligence Technology;Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences;Engineering Research Center of Open Source Software and Real-Time Systems (Lanzhou University), Ministry of Education;

【通讯作者】 胡斌;

【机构】 兰州大学信息科学与工程学院甘肃省可穿戴装备重点实验室中国科学院脑科学与智能技术卓越创新中心中国科学院半导体研究所——兰州大学认知神经传感技术联合研究中心兰州大学开源软件与实时系统教育部工程中心

【摘要】 近年来,结构性磁共振成像(sMRI)和功能性磁共振成像(fMRI)被广泛应用于抑郁症研究。从结构形态学、结构网络、功能网络3个角度探索抑郁症患者的大脑异常,了解其发病机制,辅助医生临床诊断、治疗和预后。目前大量研究发现抑郁症患者的海马体、杏仁核出现不同程度的萎缩,脑网络的连接强度、图论属性等均出现显著异常,且出现异常的脑区对应于人的情绪调节、注意力和认知控制等功能,异常的程度与抑郁的严重程度呈现高度相关性。从不同角度对抑郁症的研究现状进行综述,并对未来的研究提出了建议。

【Abstract】 In recent years, structural magnetic resonance imaging(sMRI) and functional magnetic resonance imaging(fMRI) are widely used in depression research. From the perspectives of morphology, structural network and functional network, the brain abnormalities of depression were explored to understand the pathogenesis, and to assist doctors in clinical diagnosis, treatment and prognosis. A large number of researches have found that the hippocampus and amygdala of depression showed different degrees of atrophy, and the connection strength of brain network and graph theory attributes showed significant abnormal. Moreover, the abnormal brain areas were related to emotional regulation, attention and cognitive control, and the degree of abnormalities were highly correlated with the severity of depression. The research actuality of depression from different perspectives was reviewed, and some suggestions for future research were put forward.

【基金】 国家重点研发计划基金资助项目(No.2019YFA0706200);国家自然科学基金资助项目(No.61632014,No.61627808,No.61210010);国家重点基础研究发展计划(973计划)基金资助项目(No.2014CB744600);北京市科技计划项目(No.Z171100000117005)~~
  • 【文献出处】 智能科学与技术学报 ,Chinese Journal of Intelligent Science and Technology , 编辑部邮箱 ,2020年02期
  • 【分类号】R749.4
  • 【被引频次】4
  • 【下载频次】437
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