CONTRACK: FINDING THE MOST LIKELY PATHWAYS BETWEEN BRAIN REGIONS USING DFT

CONTRACK：使用 DFT 寻找大脑区域之间最有可能的通路

• 批准号: 8169886
• 负责人: Anthony J Sherbondy
• 金额: $ 1.23万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8169886
• 关键词: Algorithms; Brain; Brain region; Computer Retrieval of Information on Scientific Projects Database; Coupled; Diffusion weighted imaging; Fiber; Funding; Grant; Human; Institution; Life; Magnetic Resonance; Measures; Methods; Pathway interactions; Positioning Attribute; Research; Research Personnel; Resources; Sampling; Series; Source; United States National Institutes of Health; human data; research study; visual neuroscience; white matter

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Magnetic resonance diffusion-weighted imaging coupled with fiber tractography (DFT) is the only non-invasive method for measuring white matter pathways in the living human brain. DFT is often used to discover new pathways. But there are also many applications, particularly in visual neuroscience, in which we are confident that two brain regions are connected, and we wish to find the most likely pathway forming the connection. In several cases, current DFT algorithms fail to find these candidate pathways. To overcome this limitation, we have developed a probabilistic DFT algorithm (ConTrack) that identifies the most likely pathways between two regions. We introduce the algorithm in three parts: a sampler to generate a large set of potential pathways, a scoring algorithm that measures the likelihood of a pathway, and an inferential step to identify the most likely pathways connecting two regions. In a series of experiments using human data, we show that ConTrack estimates known pathways at positions that are consistent with those found using a high quality deterministic algorithm. Further we show that separating sampling and scoring enables ConTrack to identify valid pathways, known to exist, that are missed by other deterministic and probabilistic DFT algorithms.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 磁共振扩散加权成像与纤维束成像（DFT）相结合是测量活人大脑中白质通路的唯一非侵入性方法。 DFT 通常用于发现新路径。但也有许多应用，特别是在视觉神经科学中，我们确信两个大脑区域是连接的，并且我们希望找到形成连接的最可能的途径。在某些情况下，当前的 DFT 算法无法找到这些候选路径。为了克服这一限制，我们开发了一种概率 DFT 算法 (ConTrack)，可以识别两个区域之间最可能的路径。我们分三个部分介绍该算法：生成大量潜在路径的采样器、测量路径可能性的评分算法以及识别连接两个区域的最可能路径的推理步骤。在一系列使用人类数据的实验中，我们表明 ConTrack 估计的已知路径位置与使用高质量确定性算法发现的位置一致。此外，我们还表明，分离采样和评分使 ConTrack 能够识别已知存在的有效路径，而其他确定性和概率 DFT 算法会忽略这些路径。