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

Neuroinformatics
Bingye LeiKai Wu

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 ne...Continue Reading

References

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Methods Mentioned

BETA
feature extraction

Software Mentioned

ViPAR Pooling Analysis
NiPy Ecosystem
NEURO
NLD
- DOCKER
DPABI ( Data Processing & Analysis for Brain Imaging )
Django
Python
NAPR ( Neuroanatomical Age Prediction using R
PANDA ( Pipeline for Analyzing

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