Rxnat: An Open-Source R Package for XNAT-Based Repositories

Abstract

The extensible neuroimaging archive toolkit (XNAT) is a common platform for storing and distributing neuroimaging data and is used by many key repositories of public neuroimaging data. Some examples include the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC, https://nitrc.org/), the ConnectomeDB for the Human Connectome Project (https://db.humanconnectome.org/), and XNAT Central (https://central.xnat.org/). We introduce Rxnat (https://github.com/adigherman/Rxnat), an open-source R package designed to interact with any XNAT-based repository. The program has similar capabilities with PyXNAT and XNATpy, which were developed for Python users. Rxnat was developed to address the increased popularity of R among neuroimaging researchers. The Rxnat package can query multiple XNAT repositories and download all or a specific subset of images for further processing. This provides a lingua franca for the large community of R analysts to interface with multiple XNAT-based publicly available neuroimaging repositories. The potential of Rxnat is illustrated using an example of neuroimaging data normalization from two neuroimaging repositories, NITRC and HCP.

Keywords: MRI; R; XNAT; connectome; neuroconductor; neuroimaging; nitrc; normalization.

Figure 1

Image processing pipeline: neck removal, inhomogeneity correction, skull stripping via registration and label fusion, tissue class segmentation, and intensity normalization across studies.

Figure 2

Pipeline image processing. (A) T1-weighted image after bias-field correction and neck removal. (B) Brain mask (red) estimated using multi-atlas label fusion. (C) Brain image plotted next to a three-class tissue segmentation in white matter (WM, color-labeled white), gray matter (GM, color labeled gray), and cerebrospinal fluid (CSF, color labeled black).

Figure 3

Tissue intensity densities in raw (first row) vs. WhiteStripe intensity normalized images (second row). The distribution of intensities for each study participant and tissue type is represented by one density (line) by tissue type: Cerebrospinal Fluid (CSF, left), Gray Matter (GM, middle), White Matter (WM, right). The density color coding corresponds to the different repositories: blue for NITRC and red for HCP.