【摘要】 与基因组学非常相似,脑连接组学迅速成为世界大多数国家脑计划的核心组成.除了这些脑计划雄心勃勃的目标,挖掘如此巨大的神经影像数据的一个最基本挑战是如何开发高效、稳健、可靠和易用计算流水线.本文引入一个计算流水线——连接组计算系统(CCS)——来开展基于多模态功能磁共振成像技术的宏观尺度人脑连接组"探索科学".CCS具备3层等级结构:(1)数据清理和预处理,(2)个体连接组绘制和(3)连接组挖掘和知识发现.若干功能模块嵌入到此等级架构,用来实现质量控制、可靠性分析和连接组可视化等功能.基于一个公开的毕生发展样本(6~85岁),我们展示了CCS的具体应用:绘制七大脑功能网络皮层厚度和表面积的毕生发展变化轨线.CCS已经通过GITHUB(https://github.com/zuoxinian/CCS)和我们实验室网站(http://lfcd.psych.ac.cn/ccs.html)向公众免费公开,用于加快人脑连接组学领域的发现科学进程.更多还原

【Abstract】 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 Computation 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 and connectome 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 Git Hub(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.更多还原