Abstract
The development of neuroimaging instrumentation has boosted neuroscience researches. Consequently, both the fineness and the cost of data acquisition have profoundly increased, leading to the main bottleneck of this field: limited sample size and high dimensionality of neuroimaging data. Therefore, the emphasis of ideas of data pooling and research collaboration has increased over the past decade. Collaborative analysis techniques emerge as the idea developed. In this paper, we present NEURO-LEARN, a solution for collaborative pattern analysis of neuroimaging data. Its collaboration scheme consists of four parts: projects, data, analysis, and reports. While data preparation workflows defined in projects reduce the high dimensionality of neuroimaging data by collaborative computation, pooling of derived data and sharing of pattern analysis workflows along with generated reports on the Web enlarge the sample size and ensure the reliability and reproducibility of pattern analysis. Incorporating this scheme, NEURO-LEARN provides an easy-to-use Web application that allows users from different sites to share projects and processed data, perform pattern analysis, and obtain result reports. We anticipate that this solution will help neuroscientists to enlarge sample size, conquer the curse of dimensionality and conduct reproducible studies on neuroimaging data with efficiency and validity.
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Acknowledgements
The NEURO-LEARN project was inspired by Prof. Kai Wu’s work on MVPA of multi-modal neuroimaging. Part of the pattern analysis models was initially coded by Yue Zhang. We acknowledge the help from Kaixi Wang during the development of the Web application of NEURO-LEARN. And we are further thankful for the imaging data provided by Dr. Fengchun Wu and Prof. Yuping Ning. This work was supported by the National Natural Science Foundation of China (31771074, 81802230), the Science and Technology Program of Guangdong (2016B010108003, 2016A020216004, 2017A040405059, 2018B030335001), the Science and Technology Program of Guangzhou (201604020170, 201704020168, 201704020113, 201807010064, 201803010100, 201903010032).
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The Web application of NEURO-LEARN has been developed and coded by Bingye Lei, with substantial contributions and instructions from Kaixi Wang, Prof. Kai Wu and Prof. Jun Chen. Dr. Fengchun Wu and Prof. Yuping Ning were in charge of the acquisition and processing of raw imaging data. Dr. Jing Zhou and Prof. Dongsheng Xiong were responsible for the data preprocessing and feature computing. Lingyin Kong and Pengfei Ke developed the modules of local workflows of the example project and tested them. All authors contributed to the documentation. Bingye Lei, Prof. Xiaobo Li, Prof. Zhiming Xiang, and Prof. Kai Wu wrote and revised the manuscript. All authors contributed to and approved the final version of the paper and agreed to be accountable for the content of this work.
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Lei, B., Wu, F., Zhou, J. et al. NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data. Neuroinform 19, 79–91 (2021). https://doi.org/10.1007/s12021-020-09468-6
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DOI: https://doi.org/10.1007/s12021-020-09468-6