| 基于内在功能连接推定抑郁症脑网络效率的改变 |
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| 引用本文： | 李淮周,周海燕,杨阳,杨孝敬,王海渊,钟宁. 基于内在功能连接推定抑郁症脑网络效率的改变[J]. 生物化学与生物物理进展, 2018, 45(1): 43-50 |
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| 作者姓名： | 李淮周 周海燕 杨阳 杨孝敬 王海渊 钟宁 |
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| 作者单位： | 北京工业大学信息学部，北京 100124；北京工业大学未来网络科技高精尖创新中心，北京 100124；脑信息智慧服务北京市国际科技合作基地，北京 100124；磁共振成像脑信息学北京市重点实验室，北京 100124,北京工业大学信息学部，北京 100124；脑信息智慧服务北京市国际科技合作基地，北京 100124；磁共振成像脑信息学北京市重点实验室，北京 100124,北京工业大学未来网络科技高精尖创新中心，北京 100124；脑信息智慧服务北京市国际科技合作基地，北京 100124；磁共振成像脑信息学北京市重点实验室，北京 100124；Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Gunma, Japan,北京工业大学信息学部，北京 100124；北京工业大学未来网络科技高精尖创新中心，北京 100124；脑信息智慧服务北京市国际科技合作基地，北京 100124；磁共振成像脑信息学北京市重点实验室，北京 100124,北京工业大学信息学部，北京 100124；北京工业大学未来网络科技高精尖创新中心，北京 100124；脑信息智慧服务北京市国际科技合作基地，北京 100124；磁共振成像脑信息学北京市重点实验室，北京 100124,北京工业大学信息学部，北京 100124；北京工业大学未来网络科技高精尖创新中心，北京 100124；脑信息智慧服务北京市国际科技合作基地，北京 100124；磁共振成像脑信息学北京市重点实验室，北京 100124；Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Gunma, Japan |
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| 基金项目： | 国家重点基础研究发展计划资助(2014CB744600), 国家自然科学基金资助项目(61420106005, 61272345), 磁共振成像脑信息学北京市重点实验室资助(2014CGZC03), 北京市自然科学基金(4164080)和国家国际科技合作专项资助(2013DFA32180) |
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| 摘 要： | 本文研究了在保留最大化内在功能连接条件下抑郁症患者脑网络效率的改变,并探索了改变的拓扑效率和抑郁症病理学之间的关系.为此,我们收集了20例抑郁症患者和20例在年龄、性别和教育水平相匹配的健康被试的静息态功能磁共振图像数据.图论分析显示,与健康对照组比较,抑郁症患者的节点效率减少在左海马旁回、右杏仁核,左颞横回和左颞极(颞中回)减少.减少的节点效率表明,在抑郁症患者脑网络中这些区域传送信息到其他区域的能力减弱.此外,发现局部效率降低在左内侧额上回、左眶部额上回、右回直肌、左杏仁核、右顶上回、左丘脑和左颞极(颞中回).并且发现左内侧额上回、左杏仁核、左丘脑与PHQ-9得分呈负相关.降低的局部效率表明抑郁症患者脑网络中这些区域的局部网络信息传送能力受到抑制.这些结果进一步确认在抑郁症患者中涉及情感信息处理的前额-丘脑-边缘区域被破坏.我们的发现为抑郁症病人的辅助诊断提供了新的潜在生物学标记物.
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| 关 键 词： | 抑郁症，静息态功能磁共振，网络效率，图论 |
| 收稿时间： | 2017-05-01 |
| 修稿时间： | 2017-08-22 |
The Changes of Brain Network Efficiency in Patients With Major Depressive Disorder Estimated by Intrinsic Functional Connectivity |
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| LI Huai-Zhou,ZHOU Hai-Yan,YANG Yang,YANG Xiao-Jing,WANG Hai-Yuan and ZHONG Ning. The Changes of Brain Network Efficiency in Patients With Major Depressive Disorder Estimated by Intrinsic Functional Connectivity[J]. Progress In Biochemistry and Biophysics, 2018, 45(1): 43-50 |
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| Authors: | LI Huai-Zhou ZHOU Hai-Yan YANG Yang YANG Xiao-Jing WANG Hai-Yuan ZHONG Ning |
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| Affiliation: | Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China;Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China;Beijing Key Laboratory of MRI and Brain Informatics, Beijing 100124, China,Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China;Beijing Key Laboratory of MRI and Brain Informatics, Beijing 100124, China,Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China;Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China;Beijing Key Laboratory of MRI and Brain Informatics, Beijing 100124, China;Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Gunma, Japan,Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China;Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China;Beijing Key Laboratory of MRI and Brain Informatics, Beijing 100124, China,Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China;Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China;Beijing Key Laboratory of MRI and Brain Informatics, Beijing 100124, China and Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China;Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China;Beijing Key Laboratory of MRI and Brain Informatics, Beijing 100124, China;Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Gunma, Japan |
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| Abstract: | This study focused on the changes of network topological efficiency under the condition of maximizing the intrinsic functional connectivity, and explored the relationships between altered topological efficiency and depressive psychopathology. For this purpose, we collected the resting-state functional MRI data from 20 major depressive disorder (MDD) patients and 20 healthy control (HC) individuals with matching of age, gender and education level. Graph theory analysis showed that the patients with MDD exhibited significantly reduced nodal efficiency in the left parahippocampal gyrus, right amygdala, left heschl and left temporal pole (middle temporal gyrus) compared with the HC group. The reduced nodal efficiency indicated that the function of transmitting information to other regions was weakened in MDD patients. The local efficiency of the left medial superior frontal gyrus, left orbital superior frontal gyrus, right rectus, left amygdala, right superior parietal gyrus, left thalamus, and left temporal pole (middle temporal gyrus) were also significantly reduced. And the local efficiency of the left medial superior frontal gyrus, left amygdala, left thalamus had negative correlation with PHQ-9. The reduced local efficiency implied that the ability of information transmission at the local level was damaged in the depressed brain network. These results suggested that the prefrontal-thalamo-limbic system involving affective processing was damaged in MDD patients. Our findings might provide a potential biomarker for the clinical diagnosis of depressed patients. |
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| Keywords: | major depressive disorder resting state functional magnetic resonance imaging network efficiency graph theory |
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