1. Introduction

The BrainSuite BIDS-App provides a portable, streamlined method for performing BrainSuite (http://brainsuite.org) analysis workflows for processing and analyzing anatomical, diffusion, and functional MRI data. This release of BrainSuite BIDS-App is based on version 21a of BrainSuite. The BrainSuite BIDS-App implements three major BrainSuite pipelines for subject-level analysis, as well as corresponding group-level analysis functionality.

1.1. Development Team

The BrainSuite BIDS App lead developer is Yeun Kim in the Shattuck Lab at the Ahmanson-Lovelace Brain Mapping Center at UCLA. The project is supervised by David Shattuck, with contributions by Shantanu Joshi, Anand Joshi, and Soyoung Choi.

BrainSuite is produced and distributed as a collaborative project between David Shattuck at UCLA and Richard Leahy at the Biomedical Imaging Group at the University of Southern California.

Major contributors to the BrainSuite project include Chitresh Bhushan, Soyoung Choi, Hanna Damasio, Justin P. Haldar, Anand A. Joshi, Shantanu H. Joshi, Yeun Kim, Divya Varadarajan, and Jessica L. Wisnowski.

1.2. BrainSuite BIDS App

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1.2.1. Subject-level Analysis

Alternative text BrainSuite Anatomical Pipeline

The BrainSuite Anatomical Pipeline (BAP) processes T1-weighted (T1w) MRI to generate brain surfaces and volumes that are consistently registered and labeled according to a reference anatomical atlas. The major stages in BAP comprise:

  • Cortical surface extraction (CSE).

  • Cortical thickness estimation based on partial volume estimates and the anisotropic diffusion equation ().

  • Surface-constrained volumetric registration (SVReg) to generate a mapping to a labeled reference atlas and label the cortical surface and brain volume.

  • Mapping of cortical thickness estimates to the atlas space

  • Computation of subject-level statistics (e.g., mean GM volume within ROIs, cortical thickness within surface ROIs) BrainSuite Diffusion Pipeline

The BrainSuite Diffusion Pipeline (BDP) performs several steps to process diffusion MRI. these include:

  • Processing of diffusion weighted imaging (DWI) to correct image distortion (based on either field maps or nonlinear registration to a corresponding T1-weighted MRI)

  • Coregistration of the DWI to the T1w scan

  • Fitting of diffusion tensor models to the DWI data

  • Fitting of orientation distribution functions to the DWI data (using FRT, FRACT, GQI, 3D-SHORE, or ERFO as appropriate)

  • Computation of diffusion indices (FA, MD, AxD, RD, GFA) BrainSuite Functional Pipeline

The BrainSuite Functional Pipeline (BFP) processes resting-state and task-based fMRI data.

  • BFP processes 4D fMRI datasets using a combination of tools from AFNI, FSL, BrainSuite and additional in-house tools developed for BrainSuite

  • Performs motion correction and outlier detection

  • Registers the fMRI data to the corresponding T1w anatomical data

  • Generates a representation of the fMRI data in grayordinate space in preparation for group-level analysis

1.2.2. Group-level Analysis

Alternative text bssr

  • Group-level statistical analysis of structural data is performed using the BrainSuite Statistics Toolbox in R (bssr). Bssr supports the following analyses:
    • tensor based morphometry (TBM) analysis of voxel-wise magnitudes of the 3D deformation fields of MRI images registered to the atlas

    • cortical surface analysis of the vertex-wise thickness in the atlas space

    • diffusion parameter maps analysis (e.g., fractional anisotropy, mean diffusivity, radial diffusivity)

    • region of interest (ROI)-based analysis of average gray matter thickness, surface area, and gray matter volume within cortical ROIs BrainSync+bfp stats

  • Group-level statistical analysis of fMRI data (functional connectivity) is performed using BrainSync, a tool that temporally aligns spatially registered fMRI datasets for direct timeseries comparisons between subjects.
    • atlas-based linear modeling using a reference dataset created from multiple input datasets

    • atlas-free pairwise testing of all pairs of subjects is performed and used as test statistics for regression or group difference studies

1.2.3. QC and BrainSuite Dashboard functionalities

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  • Quality check (QC) component of the BrainSuite BIDS App generates snapshots of key stages in the participant-level workflows for quick visualization and assessment

  • BrainSuite Dashboard is an interactive web-page that is updated in real time while BrainSuite BIDS App

1.2.4. Citation

Related BrainSuite Papers:

[Kim2023] bioRxiv preprint on BrainSuite BIDS App

[Shattuck2001a] Skull stripping, bias field correction, tissue classification.

[Shattuck2001b] Topology correction.

[Shattuck2002] Skull stripping, bias field correction, tissue classification, topology correction, surface generation, GUI.

[Bhushan2015] INVERSION: Co-registration and Distortion Correction of Diffusion and Anatomical Images

[Pantazis2010] Comparison of landmark-based and automatic methods for cortical surface registration

[Joshi2007] Surface-Constrained Volumetric Brain Registration

[Joshi2020bssr] The BrainSuite Statistics in R (bssr) Toolbox

[Joshi2018bfp] BrainSuite fMRI Pipeline

[Joshi2018ale] Cortical Thickness using the Anisotropic Diffusion

[Joshi2022] USC Brain Atlas

1.2.5. Support

Questions about usage can be submitted to http://forums.brainsuite.org/. Issues or suggestions can be directly submitted as an issue to this Github Repository.

1.2.6. Acknowledgements

This project is supported in part by NIH Grant R01-NS074980.

1.2.7. Licenses

GPLv2 only for most code.