Suppr超能文献

8岁以上脑结构网络的发育:基于扩散加权成像的初步研究

Development of Brain Structural Networks Over Age 8: A Preliminary Study Based on Diffusion Weighted Imaging.

作者信息

Wu Zhanxiong, Peng Yun, Selvaraj Sudhakar, Schulz Paul E, Zhang Yingchun

机构信息

School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China.

Department of Biomedical Engineering, University of Houston, Houston, TX, United States.

出版信息

Front Aging Neurosci. 2020 Mar 10;12:61. doi: 10.3389/fnagi.2020.00061. eCollection 2020.

Abstract

Brain structural network changes provide key information about the aging process of the brain. Unfortunately, there has yet to be a detailed characterization of these structural networks across different age groups. Efforts to classify these networks have also been hampered by their reliance on technically limited traditional methods, which are unable to track multiple fiber orientations within a voxel and consequently are prone to false detection and artifacts. In this study, a newly developed Ensemble Average Propagator (EAP) based probabilistic tractography method was applied to construct a structural network, with the strength of the link between any two brain functional regions estimated according to the alignment of the EAP along connecting pathways. Age-related changes in the topological organization of human brain structural networks were thereby characterized across a broad age range (ages 8-75 years). The data from 48 healthy participants were divided into four age groups (Group 1 aged 8-15 years; Group 2 aged 25-35 years; Group 3 aged 45-55 years; and, Group 4 aged 65-75 years; = 12 per group). We found that the brain structural network continues to strengthen during later adolescence and adulthood, through the first 20-30 years of life. Older adults, aged 65-75, had a significantly less optimized topological organization in their structural network, with decreased global efficiency and increased path lengths versus subjects in other groups. This study suggests that probabilistic tractography based on EAP provides a reliable method to construct macroscale structural connectivity networks to capture the age-associated changes of brain structures.

摘要

脑结构网络变化为大脑衰老过程提供了关键信息。不幸的是,目前尚未对不同年龄组的这些结构网络进行详细表征。对这些网络进行分类的努力也因依赖技术上有限的传统方法而受到阻碍,这些传统方法无法追踪体素内的多个纤维方向,因此容易出现错误检测和伪影。在本研究中,一种新开发的基于系综平均传播子(EAP)的概率纤维束成像方法被应用于构建结构网络,根据EAP沿连接通路的对齐情况估计任意两个脑功能区域之间连接的强度。从而在广泛的年龄范围(8 - 75岁)内表征了人类脑结构网络拓扑组织的年龄相关变化。来自48名健康参与者的数据被分为四个年龄组(第1组年龄为8 - 15岁;第2组年龄为25 - 35岁;第3组年龄为45 - 55岁;第4组年龄为65 - 75岁;每组n = 12)。我们发现,在生命的前20 - 30年中,脑结构网络在青春期后期和成年期持续强化。65 - 75岁的老年人在其结构网络中的拓扑组织优化程度明显较低,与其他组的受试者相比,全局效率降低,路径长度增加。这项研究表明,基于EAP的概率纤维束成像提供了一种可靠的方法来构建宏观尺度的结构连通性网络,以捕捉脑结构的年龄相关变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b058/7076118/5a9589b524ae/fnagi-12-00061-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验