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

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Abstract For the past 20 years, we have developed and distributed BrainSuite, a collection of open-source software tools that provide advanced capabilities for analysis and visualization of brain MRI. Since 2011, this effort has been largely supported by the parent NINDS Grant R01-NS078940. The parent grant was awarded under the “Extended Development, Hardening and Dissemination of Technologies in Biomedical Computing, Informatics and Big Data Science” mechanism (PA-14-156), and is largely focused on developing new tools for coregistration of multimodal imaging data, modeling and analysis of diffusion data, and quantitative analysis of functional and structural connectivity. These tools have been developed with the goal of supporting a broad a range of neuroimaging studies, but we have not previously had an Alzheimer’s disease (AD) on this project. In the present application, we identified key areas where BrainSuite can be applied and extended to create new methods specifically designed for probing brain imaging data to identify functional and structural changes that indicate neurodegeneration in subjects in the early stages of Alzheimer’s disease. These new methods will focus on two areas of the brain, the transentorhinal and entorhinal region of the medial temporal lobe, that are known to show tau tangles in the earliest stages of the disease. The transentorhinal cortex in particular is one that has not often been analyzed using computational anatomy methods despite its implication in early-stage AD. Our proposal builds on recently developed longitudinal diffeomorphic analysis methods and applying them to the study of the transentorhinal cortex (TEC) and entorhinal cortex (ERC), which relied upon manual delineation to identify TEC and ERC. Given the high potential for these new methods to be of value in AD research, we have developed Aim 1 of the supplement proposal to adapt BrainSuite’s registration and labeling tools, developed under the parent grant, to automatically identify TEC and ERC. This will accelerate the use of the longitudinal diffeomorphic analysis methods. Under Aim 2 of the supplement proposal, we will develop the longitudinal diffeomorphic pipeline as a tool integrated into BrainSuite. This will accelerate the ability of investigators to apply this framework in the research setting. In Aim 2, we will also develop tools to examine AD-related functional changes in resting-state fMRI data. This will build on the methods we have developed in the parent grant for analyzing fMRI data, and enable the examination of changes in functional connectivity associated with TEC and ERC that are likely to result as a product of neurodegeneration associated with AD. We anticipate that the new research supported by this supplement will lead to new tools that will provide an AD biomarker that provides an early indicator of AD, as well as a mechanism for tracking AD progression. As with all BrainSuite tools, these new methods will be made available freely as open source software. In this way, we expect that these tools will lead to new studies, both by other investigators as well as our own team, that use these tools in imaging studies of AD.