# BrainSuite: Software for Analysis and Visualization of Multimodal Brain Imaging Data

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $553,040

## Abstract

Project Summary
Over the past 16 years, we have developed a collection of algorithms and software for the segmentation,
registration, labeling, and analysis of structural and diffusion MRI, integrated into the open source package
BrainSuite (http://brainsuite.org). Our approach emphasizes the development of separate, validated modules
addressing each aspect of the image analysis problem, which are then integrated through an interactive
interface, to provide fast automated or semi-automated processing and image visualization. Command line
tools using the same functions are also provided for large scale processing. The software runs on and is
consistent across Mac, Windows, and Linux platforms. This renewal application builds on these tools with a
continued emphasis on the ability to process large (now multimodal) data sets while simultaneously retaining
the ability to rapidly visualize, review, and where necessary modify intermediate results to optimize the fidelity
of each stage of processing. The renewal emphasizes development of new tools for coregistration of
multimodal data, modeling and analysis of diffusion data, and quantitative analysis of functional and structural
connectivity. The project has five specific aims. Aim 1 will develop advanced methods for intersubject
anatomical, diffusion, and functional MRI analysis that account for individual structural and functional
differences. This will improve upon existing methods that rely solely on structural (T1-weighed) images to
define homologies between subjects. Aim 2 will develop tools for intrasubject coregistration of multimodal
imaging data that explicitly account for and estimate resolution differences between modalities. In combination
with the intersubject methods in Aim 1, this will facilitate group pointwise and regional statistical multimodal
analysis. Aim 3 will develop tools to analyze diffusion data characterized by flexible sampling schemes and
multiple b-values, addressing the limited ability of current tools to model data produced by increasingly widely
used modern acquisition schemes such as those required by the Human Connectome Project and related NIH
projects. Aim 4 will expand the BrainSuite Statistics toolbox, which uses Python and R to provide an extensible
statistical framework for analyzing data; this aim will also facilitate the use of BrainSuite as part of larger image
analysis pipelines by continuing to support standard formats and developing our new tools as modular
command line programs. Distributions will be compatible with Nipype and NITRC-CE. Under Aim 5, we will
continue software development employing standard best practices. We will develop web-based interfaces for
rapidly visualizing and evaluating results from large, multisubject studies. User support will be provided through
online forums, tutorials, videos, documentation, and hands-on training. New analysis methods developed in the
above aims will be validated through simulation and evaluation on existi...

## Key facts

- **NIH application ID:** 10129436
- **Project number:** 5R01NS074980-10
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Richard M Leahy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $553,040
- **Award type:** 5
- **Project period:** 2011-05-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10129436

## Citation

> US National Institutes of Health, RePORTER application 10129436, BrainSuite: Software for Analysis and Visualization of Multimodal Brain Imaging Data (5R01NS074980-10). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10129436. Licensed CC0.

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