Jahanshad Neda, Prasad Gautam, Toga Arthur W, McMahon Katie L, de Zubicaray Greig I, Martin Nicholas G, Wright Margaret J, Thompson Paul M
Imaging Genetics Center - Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA.
University of Queensland, Centre for Advanced Imaging, Brisbane, Australia.
Multimodal Brain Image Anal (2012). 2012;7509:29-40. doi: 10.1007/978-3-642-33530-3_3.
Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra's algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer's risk gene, .
脑连接性分析在研究脑组织结构方面越来越受欢迎。许多连接性测量指标，包括路径长度，通常被定义为在图中连接一个节点与其他节点所经过的节点数量。尽管名为路径长度，但它纯粹是拓扑学上的，并未考虑连接的物理长度。轨迹的距离可能也高度相关，但在连接性分析中通常被忽视。在这里，我们结合基因分型、解剖磁共振成像和高分辨率扩散成像，利用全脑纤维束成像来了解我们的基因如何影响皮质连接。我们基于迪杰斯特拉算法定义了一种新的测量方法，以计算连接成对皮质区域的纤维束的路径长度。我们将这些测量指标整理成矩阵，其中元素代表沿纤维束行进的物理距离。然后，我们分析了一大群健康双胞胎，结果表明我们的路径长度测量方法是可靠的、可遗传的，并且即使在年轻人中也受到阿尔茨海默病风险基因的影响。
