摘要
目的基于弥散张量成像及纤维束追踪技术构建大脑结构网络,研究大脑结构网络增龄性变化。方法分别构建30例青年人和30例老年人的脑结构网络。运用图论分析方法,计算脑结构网络的全局以及局部网络拓扑参数,定量分析年龄对大脑网络全局特性及局部特性变化的影响。结果研究显示人脑结构网络具有小世界属性,脑网络全局特征参数与年龄具有强相关性(P<0.05),平均节点度、局部效率以及全局效率随年龄增长呈明显下降趋势,而平均聚类系数和平均最短路径长度呈上升趋势。脑网络局部特性与大脑全局特性的变化具有一致性,其中右侧楔前叶、左侧颞中回、双侧前扣带和旁扣带脑回、双侧内侧和旁扣带脑回、左侧梭状回、左侧舌回、左侧距状裂等脑区存在显著老化现象。结论大脑网络信息传输效率随年龄的增长而降低,整体机能随年龄增长呈下降趋势,但在局部脑区有着不同的老化机制。
Objective Based on diffusion tensor imaging and fiber bundle tracking technology,a brain structure network was constructed to study age-related changes in the brain structure network.Methods The brain structure networks of 30 young people and 30 old people were respectively constructed.The global and local network topological parameters were calculated by using graph theory analysis method,and the influence of age on global and local characteristics of brain network was quantitatively analyzed.Results The results show that the human brain structure network has a small world property,and the global characteristic parameters of the brain network have strong correlation with age(P<0.05).The average node degree,local efficiency and global efficiency decrease with age,while the average clustering coefficient and the average shortest path length are on the rise.The local characteristics of brain networks are consistent with the changes in the global brain characteristics.There are significant aging phenomena in the right precuneus,the left middle temporal gyrus,the anterior cingulate gyrus,the medial cingulate gyrus,the left fusiform gyrus,the left lingual gyrus and the left calcarine fissure.Conclusion The efficiency of brain network information transmission decreases with age,and the overall function decreases with age,but there are different aging mechanisms in local brain areas.
作者
黄干
姚旭峰
于同刚
黄钢
HUANG Gan;YAO Xufeng;YU Tonggang;HUANG Gang(School of Medical Instrument and Food Engineering,University of Shanghai for Science and Technology,Shanghai 200082,P.R.China;College of Medical Imaging,Shanghai University of Medicine and Health Sciences,Shanghai 200120,P.R.China;Laboratory of Molecular Imaging in Shanghai,Shanghai 200120,P.R.China;Department of Radiology,Huashan Hospital Affiliated Shanghai Gamma Hospital,Fudan University,Shanghai 200235,P.R.China)
出处
《医学影像学杂志》
2019年第9期1441-1445,共5页
Journal of Medical Imaging
基金
上海市自然科学基金(编号:16ZR1416000)
上海健康医学院协同创新项目(编号:HMCI-16-11-002)
关键词
弥散张量成像
脑结构网络
图论分析
磁共振成像
Diffusion tensor imaging
Brain structure network
Graph theory analysis
Magnetic resonance imaging
