BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure

Zwiers, Marcel P.; Moia, Stefano; Oostenveld, Robert

doi:10.3389/fninf.2021.770608

Cited by 33 publications

(22 citation statements)

References 20 publications

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“…While very flexible and powerful, their learning curves can be steep. We decided to use BIDScoin (9) for two reasons: its user-friendly GUI allows researchers to define appropriate mapping strategies after the software has made intelligent guesses. it includes a plugin to run spec2nii, a command-line conversion tool from vendor proprietary formats into NIfTI-MRS. …”

Section: Methodsmentioning

confidence: 99%

Continuous automated analysis workflow for MRS studies

Zöllner

Davies-Jenkins

Lee

et al. 2022

Preprint

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Purpose: Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review. Methods: The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary dashboard for all analysis steps. Results: The automated architecture successfully completed for a demonstration dataset. The only manual step required is to copy a raw data folder into a monitored directory. Conclusion: Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and large-scale studies.

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Section: Methodsmentioning

confidence: 99%

Continuous automated analysis workflow for MRS studies

Zöllner

Davies-Jenkins

Lee

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For some users, this may require conversion of study data into NIfTI (e.g., from DICOM), generation of JSON files that include descriptive information regarding the study data, and copying or moving of data into the BIDS directory hierarchy. There are several freely available BIDS converters, including BIDScoin (Zwiers et al, 2022), heudiconv (Halchenko et al, 2022) and Dcm2Bids (Bedetti et al, 2022). A basic example of a BIDS data directory is shown in Fig.…”

Section: Brainsuite Bids App Inputsmentioning

confidence: 99%

BrainSuite BIDS App: Containerized Workflows for MRI Analysis

Kim¹,

Joshi

Choi

et al. 2023

Preprint

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There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

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“…All raw MRI data were converted to BIDS (Brain Imaging Data Structure; Gorgolewski et al, 2016 ) using BIDScoin (version 1.5; Zwiers et al, 2021 ), including conversion to NiFTi format, and supplemented with standardised metadata. All anatomical MRI scans were defaced to remove identifiable features using a wrapper tool around pydeface (DOI: https://zenodo.org/badge/latestdoi/47563497 ).…”

Section: Transforming Fmri Data Into Bids Structurementioning

confidence: 99%