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

Document Type

Article

Publication Date

1-1-2021

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.

Identifier

85086465209 (Scopus)

Publication Title

Neuroinformatics

External Full Text Location

https://doi.org/10.1007/s12021-020-09468-6

e-ISSN

15590089

ISSN

15392791

PubMed ID

32524429

First Page

79

Last Page

91

Issue

1

Volume

19

Grant

31771074

Fund Ref

National Natural Science Foundation of China

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