Zayed Abdelrahman, Iturria-Medina Yasser, Villringer Arno, Sehm Bernhard, Steele Christopher J
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1701-1704. doi: 10.1109/EMBC44109.2020.9176229.
With an estimated five million new stroke survivors every year and a rapidly aging population suffering from hyperintensities and diseases of presumed vascular origin that affect white matter and contribute to cognitive decline, it is critical that we understand the impact of white matter damage on brain structure and behavior. Current techniques for assessing the impact of lesions consider only location, type, and extent, while ignoring how the affected region was connected to the rest of the brain. Regional brain function is a product of both local structure and its connectivity. Therefore, obtaining a map of white matter disconnection is a crucial step that could help us predict the behavioral deficits that patients exhibit. In the present work, we introduce a new practical method for computing lesion-based white matter disconnection maps that require only moderate computational resources. We achieve this by creating diffusion tractography models of the brains of healthy adults and assessing the connectivity between small regions. We then interrupt these connectivity models by projecting patients' lesions into them to compute predicted white matter disconnection. A quantified disconnection map can be computed for an individual patient in approximately 35 seconds using a single core CPU-based computation. In comparison, a similar quantification performed with other tools provided by MRtrix3 takes 5.47 minutes.
据估计，每年有500万新的中风幸存者，而且人口迅速老龄化，他们患有假定为血管源性的高强度病变和疾病，这些病变和疾病会影响白质并导致认知能力下降。因此，了解白质损伤对脑结构和行为的影响至关重要。目前评估病变影响的技术只考虑位置、类型和范围，而忽略了受影响区域与大脑其他部分的连接方式。局部脑功能是局部结构及其连接性的产物。因此，获取白质断开连接图是关键的一步，它可以帮助我们预测患者表现出的行为缺陷。在本研究中，我们介绍了一种新的实用方法，用于计算基于病变的白质断开连接图，该方法仅需要适度的计算资源。我们通过创建健康成年人脑部的扩散张量成像模型并评估小区域之间的连接性来实现这一目标。然后，我们将患者的病变投影到这些连接性模型中，中断这些模型，以计算预测的白质断开连接。使用基于单核CPU的计算，大约35秒就能为单个患者计算出量化的断开连接图。相比之下，使用MRtrix3提供的其他工具进行类似的量化需要5.47分钟。
