NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data

Bingye Lei, Fengchun Wu, Jing Zhou, Dongsheng Xiong, Kaixi Wang, Lingyin Kong, Pengfei Ke, Jun Chen, Yuping Ning, Xiaobo Li, Zhiming Xiang, Kai Wu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)79-91
Number of pages13
Issue number1
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Software
  • General Neuroscience
  • Information Systems


  • Collaboration
  • Machine learning
  • Neuroimaging
  • Pattern analysis


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