中文摘要：

就象 genomics 一样，大脑 connectomics 很快在全世界成为了很国家的大脑工程的一个核心部件。在这些工程的雄心勃勃的目的以外，基本挑战是对到我的一条有效、柔韧、可靠、容易使用的管道的需要如此的大神经科学数据集。这里，我们介绍一计算 pipelinenamely 为在有多模式的磁性的回声成像技术的 macroscale 的人的大脑 connectomes 的发现科学的 Connectome 计算系统(CCS ) 。CCS 与包括清洗的数据和预处理的三水平的层次结构被设计，印射的单个 connectome 和 connectome 采矿，和知识发现。几个功能的模块被嵌进这个层次实现质量控制过程，可靠性分析和 connectome 可视化。我们表明在公开可得到的数据集之上基于的 CCS 的实用程序， NKIRockland 样品，描出越过自然寿命的著名大规模神经网络的标准轨道(685 ? 岁) 。CCS 经由 GitHub (https://github.com/zuoxinian/CCS ) 和我们的实验室网络站点(http://lfcd.psych.ac.cn/ccs.html ) 被使自由地可得到到公众在人的大脑 connectomics 的地里在发现科学便于进步。

英文摘要：

Much like genomics, brain connectomics has rapidly become a core component of most national brain projects around the world. Beyond the ambitious aims of these projects, a fundamental challenge is the need for an efficient, robust, reliable and easy-to-use pipeline to mine such large neuroscience datasets. Here, we introduce a computational pipeline--namely the Connectome Compu- tation System （CCS）-for discovery science of human brain connectomes at the macroscale with multimodal magnetic resonance imaging technologies. The CCS is designed with a three-level hierarchical structure that includes data cleaning and preprocessing, individual connectome mapping andconnectome mining, and knowledge discovery. Several functional modules are embedded into this hierarchy to implement quality control procedures, reliability analysis and connectome visualization. We demonstrate the utility of the CCS based upon a publicly available dataset, the NKI- Rockland Sample, to delineate the normative trajectories of well-known large-scale neural networks across the natural life span （6-85 years of age）. The CCS has been made freely available to the public via GitHub （https：//github.com/ zuoxinian/CCS） and our laboratory＇s Web site （http：//lfcd. psych.ac.cn/ccs.html） to facilitate progress in discovery science in the field of human brain connectomics.