This project advances the state-of-the-art in High-Angular Resolution Diffusion Imaging (HARDI), a powerful new imaging approach that can resolve fiber pathways in the brain with spectacular precision. Uniting expertise at an NIH-funded National Neuroimaging Resource (at UCLA), the University of Minnesota Center for Magnetic Resonance Research, and Siemens Corporate Research, we aim to demonstrate that HARDI provides new and vital information in assessing clinically important brain degeneration in HIV/AIDS and Alzheimer's Disease (AD), extending our initial findings that revealed how these diseases spread dynamically in the living brain. HARDI applies magnetic field gradients to the brain, in up to 256 different directions, to precisely detail the directions, pathways, and integrity of fibers in the brain. HARDI datasets cannot yet be compared across subjects without new mathematics that treats these signals as lying in Riemannian manifolds. This project provides those tools. Our research will (1) advance the mathematics - based in part on geometry, statistics, and Riemannian manifolds - to extract information from HARDI, and (2) quantify how much HARDI can improve our understanding of brain degeneration, and what factors affect it. Using the extra detail in HARDI images, we will develop a method to enable large-scale multi-subject comparison of HARDI images, by fluidly aligning 3D images across subjects (Aim 1;Multi-subject Alignment). This is the first step towards population studies of disease, e.g., comparing fiber integrity across patient populations to examine gene or treatment effects, or comparing a patient with a normative database. Validation on phantoms and synthetic data is a key part of all Aims.
In Aim 2 (Segmentation and Connectivity Mapping), we will develop algorithms to map white matter connectivity, and identify clinically important fiber pathways in the brain, based on the full angular information of HARDI.
In Aim 3 (HARDI Statistics), optimized voxel-based statistics will compare HARDI data, point-by-point, across populations, to identify systematic fiber deficits, comparing fiber integrity and connectivity with a normal reference population.
In Aim 4 (HARDI Maps of Brain Degeneration), we will evaluate HARDI for revealing new descriptors of AD and HIV-related brain degeneration: two illnesses on which we have published prolifically, where measures of white matter degeneration are sorely lacking. The societal burden of AD and HIV is growing;HIV affects 40 million people worldwide, and AD affects 4.5 million individuals in the U.S. alone;everyone is at risk. Our powerful markers of brain white matter degeneration will help us determine how much benefit HARDI's added resolution provides. This new analytic approach will greatly advance our ability to understand pathological brain degeneration, providing sensitive new measures to track it. This has immediate value for drug trials and patient monitoring. As always, we will share all algorithms, protocols, and images, with 50+ collaborating laboratories.
This project develops tools that unleash the full power of HARDI (high-angular resolution diffusion imaging) to advance clinical studies of the brain. HARDI applies magnetic field gradients to the brain in up to 256 different directions to precisely detail the directions, pathways, and integrity of fibers and their connections. We will evaluate HARDI for understanding and revealing new descriptors of Alzheimer's Disease and HIV-related brain white matter degeneration - with immediate value for drug trials and patient monitoring in HIV, which affects 40 million people worldwide, and in AD, which affects 4.5 million individuals in the U.S. alone.
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|Cetingul, H Ertan; Wright, Margaret J; Thompson, Paul M et al. (2014) Segmentation of high angular resolution diffusion MRI using sparse riemannian manifold clustering. IEEE Trans Med Imaging 33:301-17|
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|Kochunov, Peter; Jahanshad, Neda; Sprooten, Emma et al. (2014) Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling. Neuroimage 95:136-50|
|Kim, Jinyoung; Lenglet, Christophe; Duchin, Yuval et al. (2014) Semiautomatic segmentation of brain subcortical structures from high-field MRI. IEEE J Biomed Health Inform 18:1678-95|
|Prasad, Gautam; Joshi, Shantanu H; Thompson, Paul M (2014) OPTIMIZING BRAIN CONNECTIVITY NETWORKS FOR DISEASE CLASSIFICATION USING EPIC. Proc IEEE Int Symp Biomed Imaging 2014:834-837|
|Dennis, Emily L; Thompson, Paul M (2014) Functional brain connectivity using fMRI in aging and Alzheimer's disease. Neuropsychol Rev 24:49-62|
|Warstadt, Nicholus M; Dennis, Emily L; Jahanshad, Neda et al. (2014) Serum cholesterol and variant in cholesterol-related gene CETP predict white matter microstructure. Neurobiol Aging 35:2504-13|
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