Diffusion weighted magnetic resonance imaging (DW-MRI) is a powerful but still relatively new in vivo imaging technique that allows an unprecedented level of insight into brain connectivity. Despite numerous studies, the field of quantitative diffusion imaging analysis has not fully matured. In particular, the acute interest in fiber tractography, fueled by its ability to capture connectivity information in diffusion images, has not been fully realized in group studies because of open problems that involve normalization and quantitative comparison of fiber tracts across individuals. The majority of today's diffusion imaging studies either employ techniques inspired by Voxel-Based Morphometry (VBM) or the more recent Tract-Based Spatial Statistics (TBSS) approach. Neither of these approaches incorporates the connectivity information captured by fiber tractography, nor are the inferences gained by these approaches associated with specific anatomical structures. Yet, in recent years, there has been an increased interest in statistical brain mapping techniques that are structure-specific. Analysis that takes into account the unique properties of specific anatomical structures can be reasonably expected to have greater statistical specificity, and even sensitivity, than analysis performed pointwise over the whole brain. A key feature of structure-specific analysis is its ability to combine or average data along anatomically meaningful directions while respecting the boundaries between structures, as opposed to uniform smoothing over the whole brain. Furthermore, analysis that restricts its attention to structures of interest produces inferences that can be communicated and visualized more effectively, contextualized by the underlying anatomy. The overall aim of this application is to develop, validate and distribute a statistical analysis framework for DW- MRI that is structure-specific and fully leverages the connectivity information encoded in diffusion imagery. Our approach is based primarily on a recently published method that uses surface-based representation to model sheet-like white matter tracts, allowing tract-specific representation, smoothing and statistical inference. Our approach gained immediate enthusiasm from the leaders in the field, and the letters of support from the respective creators of TBSS and DTIstudio speak to that fact. If funded, this application would lead to a set of carefully validated tools that would allow clinical investigators to easily leverage tract-specific analysis in studies of white matter.
Specific Aim 1. A Turnkey Framework for Tract-Specific Analysis (TSA) of Brain White Matter This aim will deliver an end-user software application that enables clinical investigators to perform tract-specific analysis. Proposed enhancements to the current TSA framework in [121] include (1) incorporation of additional white matter tracts;(2) use of fiber-crossing resolution for more accurate tractography;(3) advanced tract segmentation methodology;(4) a flexible interface for specifying a wide range of statistical designs;(5) implementation using software engineering best practices;(6) interoperability with existing tools and nomenclatures.
Specific Aim 2. Validation and Clinical Applications in Amyotrophic Lateral Sclerosis (ALS) This aim will evaluate and refine the developed methodology within the real-world clinical context of a significant neuroimaging study of white matter integrity under neurodegenerative conditions. Specifically, the proposed study of upper motor neuron disease in ALS, with specific biological hypotheses, will help define the effects of the proposed tract-specific modeling and normalization strategy on neuroimaging analysis of DW- MRI data. Relevant to the American Recovery and Reinvestment Act of 2009, Penn Medicine contributes substantially to the local economy. In 2008, Penn Medicine created 37,000 jobs and $5.4 billion in regional economic activity, with the area's highly trained workforce producing more than 24,600 applications for just 840 open Penn staff research positions. The current proposal will help create or retain 6 highly skilled jobs in the Philadelphia region.

Public Health Relevance

This project will improve scientists'ability to use medical imaging to quantitatively assess the effects of healthy development, aging and disease on the connectivity between different regions of the human brain. Scientific interest in brain connectivity has been rising in recent years, but the computational tools that are currently available lack the specificity needed to study how the individual tracts composing the brain white matter are affected by disease. By providing open-source, user-friendly, widely interoperable, and extensively validated tools for tract-specific brain connectivity analysis, the project will enable a wide field of scientists to leverage white matter imaging more effectively in diagnosing disease, monitoring the effects of interventions on white matter tracts, and answering basic science questions about brain connectivity.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Biomedical Imaging Technology Study Section (BMIT)
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Liu, Yuan
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University of Pennsylvania
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United States
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