BrainSuite BIDS App: Containerized Workflows for MRI Analysis

Abstract

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.

Simulating rigid head motion artifacts on brain magnitude MRI data–Outcome on image quality and segmentation of the cerebral cortex

“…Complementing the MRIQC comparison, automatic brain segmentation was performed to compare differences in segmentation performance between real and simulated motion-corrupted data. Whole brain segmentation of the cerebral cortex was performed using 6 freely available segmentation tools: FreeSurfer [ 23 ], BrainSuite [ 24 ], ANTs [ 25 ], SAMSEG [ 26 ], FastSurfer [ 27 ], and SynthSeg+ [ 28 , 29 ]. FreeSurfer, SAMSEG, FastSurfer, and SynthSeg+ all use the same labeling system for segmented brain structures, making comparison between segmentations of the cerebral cortex straightforward.…”

Diffusion tensor brain imaging at 0.55T: A feasibility study

“…Following reconstruction, we used a fieldmap‐based distortion correction technique from the BrainSuite Diffusion Pipeline 38 to compensate for susceptibility‐induced geometric distortions. (Note that, due to phase wrapping issues that occured in our 0.55T fieldmap acquisition, it was necessary to correct the fieldmap using a phase unwrapping approach 39 ).…”

“…Gradient nonlinearity correction was then applied to both the diffusion results and the T1‐weighted anatomical image using software from the Human Connectome Project's preprocessing pipeline 40 together with spherical harmonic coefficients describing each gradient system. Diffusion tensors were then using the BrainSuite Diffusion Pipeline 38 . From the estimated tensors, we extracted the orientation of the primary eigenvector for each voxel, and calculated quantitative parameters like mean diffusivity (MD) and fractional anisotropy (FA).…”

“…Diffusion tensors were then using the BrainSuite Diffusion Pipeline. 38 From the estimated tensors, we extracted the orientation of the primary eigenvector for each voxel, and calculated quantitative parameters like mean diffusivity (MD) and fractional anisotropy (FA). The diffusion-weighted images were rigidly coregistered to the corresponding T1-weighted anatomical image.…”

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity

“…They can be used to build a fully automated approach (e.g., the NeuroMark pipeline (Du et al, 2020)) that does not require post hoc ICN selection or network matching (Du et al, 2020), and also automatically provides ICN ordering, providing modular functional network connectivity. These approaches have been used in many prior studies and offer a fully automated framework that can be integrated within a larger framework (e.g., a containerized version is available from http://trendscenter.org/software/gift, as well as a BIDSapp (Kim et al, 2023)), making them more easily comparable across studies.…”