CCS Released

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By Xi-Nian Zuo (Xi-Nian Zuo)

CCS Paper 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.

Notes: the paper can be downloaded at http://lfcd.psych.ac.cn/publications.html. The codes are under developing stage but all usable for you to explore the connectomics with multimodal imaging data. Please cite the CCS paper if you feel it helps your research.

Online version of this post: http://rfmri.org/content/ccs-released

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Commented by YAN Chao-Gan (YAN Chao-Gan)

Great work! ;)

Best,

Chao-Gan

On Thu, Feb 26, 2015 at 9:12 PM, The R-fMRI Network <rfmri.org@gmail.com> wrote:

[To post a comment, please reply to rfmri.org@gmail.com ABOVE this line]

By Xi-Nian Zuo (Xi-Nian Zuo)

CCS Paper 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.

Notes: the paper can be downloaded at http://lfcd.psych.ac.cn/publications.html. The codes are under developing stage but all usable for you to explore the connectomics with multimodal imaging data. Please cite the CCS paper if you feel it helps your research.

Online version of this post: http://rfmri.org/content/ccs-released

Many a little makes a mickle -- your kind contributions shall make our efforts not perish from the earth. Please help The R-fMRI Network at http://rfmri.org/#overlay=HelpUs
To manage subscriptions, please visit: http://rnet.co/mailman/listinfo/rfmri.org_rnet.co
Mail comment ID: http://rfmri.org/mailcomment/redirect/%3C31.1962.0.1425003155.bcb953711bda4b0ec56f665cc0823f6e%40www.rfmri.org%3E

Online version of this post: http://www.rfmri.org/comment/3553#comment-3553

Many a little makes a mickle -- your kind contributions shall make our efforts not perish from the earth. Please help The R-fMRI Network at http://rfmri.org/#overlay=HelpUs
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