自然科学版
陕西师范大学学报(自然科学版)
生物医学与信息工程专题
个体脑功能网络在长时间尺度上的动态复杂度分析
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倪黄晶1, 秦姣龙2*
(1 南京邮电大学 江苏省智慧健康大数据分析与位置服务工程实验室,江苏 南京 210023;2 南京理工大学 计算机科学与工程学院,江苏 南京 210094)
秦姣龙,女,副教授,博士,主要从事神经影像建模与分析。E-mail: jiaolongq@njust.edu.cn
摘要:
为人脑功能网络在长时间尺度上的动态复杂度变化情况,基于单个被试在18个月内近百次密集扫描的纵向静息态功能磁共振成像的数据集,采用排列熵对全脑功能网络的动态复杂度进行定量分析。结果表明,所有功能网络的复杂度会随着时间变化而发生一定的动态波动变化,但这种变化的幅度范围较为有限;不同功能网络的表现各异,其中小脑网络和皮下核团网络的复杂度最高,而额顶网络复杂度最低。
关键词:
脑功能网络;长时间尺度;动态;复杂度
收稿日期:
2019-09-11
中图分类号:
O231.5
文献标识码:
A
文章编号:
1672-4291(2020)06-0056-07
基金项目:
南京邮电大学引进人才科研启动基金(NY218138);国家自然科学基金(81701346);江苏省自然科学基金(BK20190736)
Doi:
Dynamic complexity analysis of individual brain functional networks over long-term time scales
NI Huangjing1, QIN Jiaolong2*
(1 Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu, China;2 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China)
Abstract:
The dynamic complexity of human brain functional networks on a long-term time scale from day to month to year are investigated. Based on the unique longitudinal resting-state functional magnetic resonance imaging dataset scanned on a single subject for nearly 100 times within 18 months, permutation entropy is adopted to quantitatively analyze the dynamic complexity of whole brain functional networks. The results show that the complexity of all functional networks will fluctuate dynamically with the scanning time, but their fluctuation ranges are relatively limited. Meanwhile,it can be found that the performances of different functional networks are different. The cerebellar and subcortical networks demonstrate the highest complexity, while the fronto-parietal network shows the lowest.
KeyWords:
brain functional network; long-term time scale; dynamic; complexity