A Bayesian Approach to MR Tractography in the Developing Brain

大脑发育中磁共振纤维束成像的贝叶斯方法

• 批准号: 7766294
• 负责人: JOSHUA S SHIMONY
• 金额: $ 12.27万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-15 至 2012-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7766294
• 关键词: Adrenal Glands; Affect; Age; Algorithms; Anisotropy; Area; Bayesian Method; Behavioral; Biophysics; Brain; Cell Size; Cell membrane; Cerebral cortex; Cerebrum; Cognitive; Consensus; Corpus Callosum; Data; Data Analyses; Data Set; Dependence; Development; Diffusion; Disease; Evaluation; Face; Fiber; Goals; Histologic; Histology; Human; Image; Impaired cognition; Incidence; Individual; Infant; Injury; Intervention; Magnetic Resonance Imaging; Maps; Measurement; Measures; Methods; Modeling; Monitor; Motor; Motor Pathways; Multiple Sclerosis; Neonatal; Neurons; Noise; Outcome; Papio; Physiological; Premature Infant; Probability; Probability Theory; Process; Qualitative Methods; Quantitative Evaluations; Research Personnel; Research Project Grants; Risk; Role; Simulate; Software Tools; Structure; Survival Rate; System; Techniques; Testing; Tissues; United States; Variant; Ventricular; Very Low Birth Weight Infant; Visual system structure; Water; base; cohort; diffusion anisotropy; imaging modality; improved; indexing; injured; leukodystrophy; mortality; neonate; neuropathology; premature; programs; software development; tool; water diffusion; white matter; white matter damage; white matter injury

DESCRIPTION (provided by applicant): While survival rates for premature infants have improved steadily over the last decade, the incidence of adverse neurodevelopmental outcomes has remained essentially unchanged. Approximately 50% of the half million very low birth-weight infants born each year in the United States will face motor, cognitive, and/or behavioral challenges. The principal neuropathology associated with prematurity occurs in the cerebral white matter (WM), with secondary impact on the developing cerebral cortex. The mortality rate in this cohort is very low, limiting the amount of pathological material available for study. Thus, methods for quantitative evaluation of cerebral WM in preterm infants are urgently required. Such methods could be used to define normal WM development, which would allow the monitoring of neonatal interventions aimed at optimizing cerebral development as well as identifying infants at risk for later cognitive impairment. MR diffusion measurements can provide information on WM microstructure and on neuronal fiber tracts. At present, it is not clear which parameters are the best indicators of white matter integrity or quality. Similarly, there is no consensus on the best means by which to identify or follow WM tracts. Currently, the diffusion tensor model is the most commonly used and is usually separately applied to individual voxels. White matter fiber bundles in the brain extend over many voxels and could be better modeled with extension of the diffusion tensor model to include local connectivity with neighboring voxels. Bayesian probability theory provides us with the tools for optimal model selection and parameter estimation that can better evaluate WM connectivity and provide a consistent probability theory basis for neuronal fiber tracts and their evaluation. The candidate's long-term goal is to develop diffusion MR imaging methods to provide an accurate evaluation of WM development and maturation using Bayesian probability theory. The central hypothesis is that Bayesian probability theory will provide a means for optimal parameter estimation that will provide accurate information on the status of WM connectivity. The objective in this application is to develop the software tools needed for Bayesian based analysis and to apply it initially to simulated data, followed by application to normal ex vivo baboon brain, followed by a study of normal human infants. The study will conclude with an evaluation of WM injury in ex vivo baboon brains with histological correlates. Bayesian probability theory has not been applied to the evaluation of WM development, and the candidate is able to compare human neonate results with normal and abnormal ex vivo baboon brains, which is a well-established model for human brain maturation.

描述（由申请人提供）：虽然在过去的十年中，早产婴儿的存活率稳步提高，但不良神经发育结果的发生率基本上保持不变。 在美国，每年出生的非常低的出生体重婴儿中，大约有50％面临运动，认知和/或行为挑战。 与早产相关的主要神经病理发生在大脑白质（WM）中，对发展中的脑皮质有次要影响。 该队列中的死亡率非常低，限制了可用于研究的病理材料的量。 因此，迫切需要对早产儿的脑WM进行定量评估的方法。 这种方法可用于定义正常的WM发育，这将允许监测旨在优化脑发育的新生儿干预措施，并确定有后来认知障碍的婴儿。 MR扩散测量可以提供有关WM微结构和神经元纤维区域的信息。 目前，尚不清楚哪些参数是白质完整性或质量的最佳指标。 同样，在最佳手段上也没有达成共识，可以识别或遵循WM区域。 当前，扩散张量模型是最常用的，通常分别应用于单个体素。 大脑中的白质纤维束在许多体素上延伸，可以通过扩散张量模型的扩展来更好地建模，以包括与相邻体素的局部连接。 贝叶斯概率理论为我们提供了最佳模型选择和参数估计的工具，这些工具可以更好地评估WM连接性，并为神经元素纤维区域及其评估提供一致的概率理论基础。 候选人的长期目标是开发传播MR​​成像方法，以使用贝叶斯概率理论对WM的发展和成熟进行准确评估。 中心假设是贝叶斯概率理论将为最佳参数估计提供一种手段，该理论将提供有关WM连接状态的准确信息。 此应用程序的目的是开发基于贝叶斯分析所需的软件工具，并最初将其应用于模拟数据，然后将其应用于正常的离体狒狒大脑，然后对正常的人类婴儿进行研究。 该研究将以组织学相关的离体狒狒大脑的WM损伤评估。 贝叶斯概率理论尚未应用于WM发展的评估，候选人能够将人类新生儿的结果与正常和异常的外体狒狒大脑进行比较，这是人类脑部成熟的良好模型。