A Connectome Computation System for discovery science of brain
作者： Ting Xu , Zhi Yang , Lili Jiang , Xiu-Xia Xing , Xi-Nian Zuo
DOI: 10.1007/S11434-014-0698-3
关键词:
摘要: Much like genomics, brain connectomics has rapidly become a core component of most national projects around the world. Beyond ambitious aims these projects, fundamental challenge is need for an efficient, robust, reliable and easy-to-use pipeline to mine such large neuroscience datasets. Here, we introduce computational pipeline-namely Connectome Computation System (CCS)-for discovery science human connectomes at macroscale with multimodal magnetic resonance imaging technologies. The CCS designed three-level hierarchical structure that includes data cleaning preprocessing, individual connectome mapping mining, knowledge discovery. Several functional modules are embedded into this hierarchy implement quality control procedures, reliability analysis visualization. We demonstrate utility based upon publicly available dataset, NKI-Rockland Sample, delineate normative trajectories well-known large-scale neural networks across natural life span (6-85 years age). been made freely public via GitHub ( https://github.com/zuoxinian/CCS ) our laboratorys Web site http://lfcd.psych.ac.cn/ccs.html facilitate progress in field connectomics.
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