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A Connectome Computation System for discovery science of brain

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  • Life & Medical Sciences
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Science Bulletin

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 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.

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Acknowledgments

This work was partially supported by the National Basic Research Program (973) of China (2015CB351702), the National Natural Science Foundation of China (81220108014, 81471740, 81201153, 81171409, and 81270023), the Key Research Program (KSZD-EW-TZ-002) and the Hundred Talents Program of the Chinese Academy of Sciences. Dr. Xiu-Xia Xing acknowledges the Beijing Higher Education Young Elite Teacher Project (No. YETP1593). Dr. Zhi Yang acknowledges the Foundation of Beijing Key Laboratory of Mental Disorders (2014JSJB03) and the Outstanding Young Researcher Award from Institute of Psychology, Chinese Academy of Sciences (Y4CX062008). We thank all members of the Laboratory for Functional Connectome and Development, Institute of Psychology at CAS and the attendees of the first CCS education course for their helpful feedback and suggestions for the improvement of the CCS.

Conflict of interest

The authors declare that they have no conflicts of interest.

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    Authors and Affiliations

    1. Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China

      Ting Xu, Zhi Yang, Lili Jiang & Xi-Nian Zuo

    2. Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China

      Ting Xu, Zhi Yang, Lili Jiang & Xi-Nian Zuo

    3. Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China

      Ting Xu, Zhi Yang, Lili Jiang & Xi-Nian Zuo

    4. Department of Applied and Computational Mathematics, College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China

      Xiu-Xia Xing

    5. Faculty of Psychology, Southwest University, Chongqing, 400715, China

      Xi-Nian Zuo

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    1. Ting Xu
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    3. Lili Jiang
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    Correspondence to Xi-Nian Zuo.

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    Ting Xu and Zhi Yang contributed equally to this work.

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    Xu, T., Yang, Z., Jiang, L. et al. A Connectome Computation System for discovery science of brain. Sci. Bull. 60, 86–95 (2015). https://doi.org/10.1007/s11434-014-0698-3

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