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. 2019 Apr 1;20(2):218-239.
doi: 10.1093/biostatistics/kxx068.

Neuroconductor: an R platform for medical imaging analysis

John Muschelli  1 , Adrian Gherman  1 , Jean-Philippe Fortin  2 , Brian Avants  2 , Brandon Whitcher  3 , Jonathan D Clayden  4 , Brian S Caffo  1 , Ciprian M Crainiceanu  1
Affiliations

Neuroconductor: an R platform for medical imaging analysis

John Muschelli et al. Biostatistics. .

Erratum in

Abstract

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.

Keywords: Bioinformatics; Image analysis; Statistical modelling.

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Figures

Fig. 1.
Fig. 1.
Typical processing pipeline for multi-sequence structural MRI data.
Fig. 2.
Fig. 2.
Visualization of the diffusion tensor imaging (DTI) analysis results in R. (a) Visualization of the anatomical structures in template space (Eve template, 1-mm isotropic T1-weighted modality) in coronal, sagittal and axial planes (coordinates formula image, formula image and formula image). (b) T-statistics characterizing the differences between males and females in the mean diffusivity (MD) values for grey matter (GM) and white matter (WM) voxels, in template space. The DTI analysis was performed using data from the Human Connectome Project (HCP). Blue and red regions represent higher and lower values of MD in males, respectively. The areas in the central areas of the WM show lower areas of MD in males, such as the area given the cross-hairs, whereas areas of higher MD values are located near the cortical GM and occipital horns of the lateral ventricles. (c) Visualization of the Eve atlas white matter parcellation map (WMPM) with selected structures.
Fig. 3.
Fig. 3.
Neuroconductor initial package submission. If the DESCRIPTION file is present, it will parsed and the submitted version of the package is checked against existing Neuroconductor packages. If this version is not already in Neuroconductor, the email-based verification process is started. Once the maintainer verifies the submission the package is ready to be tested.
Fig. 4.
Fig. 4.
Neuroconductor package code testing. The package is cloned/updated on the Neuroconductor server and Travis CI/AppVeyor checks are initiated for the original version of the package together with a stable and current version of this package. The DESCRIPTION, travis.yml and appveyor.yml files are updated to reflect the stable/current package status. Once the Travis CI / AppVeyor checks are done the package listing is updated to reflect the continuous integration test results.
See this image and copyright information in PMC

References

    1. Adler D., Murdoch D., Nenadic O., Urbanek S., Chen M., Gebhardt A., Bolker B., Csardi G., Strzelecki A. and Senger A. (2016). rgl: 3D Visualization Using OpenGL. R package version 0.96.0.
    1. Allaire J., Cheng J., Xie Y., McPherson J., Chang W., Allen J., Wickham H. and Hyndman R. (2015). rmarkdown: Dynamic Documents for Rr. R package version 0.5.
    1. Allaire J. J., Ushey K., Tang Y. and Eddelbuettel D. (2017). reticulate: R Interface to Python.
    1. Avants B. B., Epstein C. L., Grossman M. and Gee J. C.. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. 12, 26–41. - PMC - PubMed
    1. Avants B. B., Libon D. J., Rascovsky K., Boller A., McMillan C. T., Massimo L., Coslett H. B., Chatterjee A., Gross R. G. and Grossman M. (2014a). Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population. Neuroimage 84, 698–711. - PMC - PubMed

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