MSPA-MCS: Mathematical and Computational Algorithms for Visualization of Human Brain Neural Pathways

  • Zhang, Jun (PI)

Grants and Contracts Details


Project Summary We propose to develop new mathematical models and computational techniques in the application of a novel magnetic resonance imaging (MRI) modality. The project contains two main thrusts: one is to develop new mathematical and computational models for the high angular resolution diffusion weighted (HARD) imaging technique, and the other is to develop computing techniques for noninvasive in vivo white matter neural fiber tracking in human brain. The interdisciplinary research team will take a systematic approach to advancing white matter fiber tractography for in vivo visualization of neural fiber pathways of the human brain. Intellectual Merits: We will develop mathematical model and numerical techniques with simultaneous smoothing and estimation capability to compute accurate HARD data and develop robust diffusion-based fiber tracking algorithm to in vivo visualize and reconstruct major neural fiber networks in the human brain noninvasively. This project involves researchers from mathematics, computer science, and neuroscience with diverse background and expertise. This research work will enhance our understanding of the information pathways of the human brain. The team tackles the human brain visualization and information extraction problem systematically, from data processing to fiber tractography. The integration of a new mathematical model of HARD imaging with the generalized diffusion-based fiber tracking algorithm promises high fidelity in vivo elucidation of fine details of the human brain connectivity. The new fiber tracking algorithm directly simulates the water diffusion in brain, which is the fundamental characteristics of the diffusion-based MRI modalities. Broader Impacts: The research project is across mathematics, computer science, and neuroscience and will establish a new connection between mathematics and computer science, bridged by the common interest in computational neuroscience. A great deal of diverse knowledge and expertise can be fused to generate new knowledge and understanding of human brain. The team members from different disciplines can learn from and complement each other. Graduate students will be trained in an interdisciplinary environment with diverse knowledge and skills. The results of this study will aid our understanding of the human brain and its various functions. This understanding can help us design biologically motivated computing systems and combat many brain diseases. The research team will train Ph.D. students in applied mathematics and computer science and disseminate results across mathematics, computer science, and neuroscience.
Effective start/end date10/1/059/30/08


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