The white matter of the human brain is made up of axons that typically travel together in bundles called fascicles. Recent advances in Magnetic Resonance Imaging (MRI) has made it possible to image the self- diffusion of water molecules through Diffusion-Weighted MRI (DWMRI). Because water diffuses significantly more along axons than across them, DWMRI allows one to probe the microarchitecture of the white matter of the living human brain for the first time. In this project, we propose to build models of fascicles by grouping individual fiber models derived from DWMRI data based on the surrounding anatomy. We hypothesize that the anatomy through which a tract passes is predictive of the location of the tract, thus providing information about what fascicle each fiber should be assigned to. The resulting algorithms will provide automated tools for the modeling of the major fascicles in the human brain.

Public Health Relevance

Connectivity tools are difficult for clinicians and researchers to use as they require manual interventions to generate reliable tract models. Automation will facilitate the analysis of connectivity in disorders such as schizophrenia, AD, autism and aging for subtle effects that occur early in brain disorders.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-NT-B (08))
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Liu, Yuan
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Massachusetts General Hospital
United States
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