Suppr超能文献

基于磁共振成像的心脏驱动脑生物力学定量分析用于神经系统疾病的早期检测

MRI-based quantification of cardiac-driven brain biomechanics for early detection of neurological disorders.

作者信息

Diorio Tyler C, Abderezzai Javid, Nauman Eric, Kurt Mehmet, Tong Yunjie, Rayz Vitaliy L

机构信息

Purdue University, (Weldon School of Biomedical Engineering), West Lafayette, (IN), USA.

University of Washington, (Department of Mechanical Engineering), Seattle, (WA), USA.

出版信息

bioRxiv. 2024 Aug 6:2024.08.01.606246. doi: 10.1101/2024.08.01.606246.

Abstract

We present a pipeline to quantify biomechanical environment of the brain using solely MRI-derived data in order to elucidate the role of biomechanical factors in neurodegenerative disorders. Neurological disorders, like Alzheimer's and Parkinson's diseases, are associated with physical changes, including the accumulation of amyloid-β and tau proteins, damage to the cerebral vasculature, hypertension, atrophy of the cortical gray matter, and lesions of the periventricular white matter. Alterations in the external mechanical environment of cells can trigger pathological processes, and it is known that AD causes reduced stiffness in the brain tissue during degeneration. However, there appears to be a significant lag time between microscale changes and macroscale obstruction of neurological function in the brain. Here, we present a pipeline to quantify the whole brain biomechanical environment to bridge the gap in understanding how underlying brain changes affect macroscale brain biomechanics. This pipeline enables image-based quantification of subject-specific displacement field of the whole brain to subject-specific strain, strain rate, and stress across 133 labeled functional brain regions. We have focused our development efforts on utilizing solely MRI-derived data to facilitate clinical applicability of our approach and have emphasized automation in all aspects of our methods to reduce operator dependance. Our pipeline has the potential to improve early detection of neurological disorders and facilitate the identification of disease before widespread, irreversible damage has occurred.

摘要

我们提出了一种仅使用磁共振成像(MRI)衍生数据来量化大脑生物力学环境的流程,以阐明生物力学因素在神经退行性疾病中的作用。神经疾病,如阿尔茨海默病和帕金森病,与身体变化有关,包括淀粉样β蛋白和tau蛋白的积累、脑血管损伤、高血压、皮质灰质萎缩以及脑室周围白质病变。细胞外部机械环境的改变可引发病理过程,并且已知在神经退行性变期间,阿尔茨海默病会导致脑组织硬度降低。然而,在大脑中微观变化与神经功能的宏观障碍之间似乎存在显著的时间滞后。在此,我们提出一种量化全脑生物力学环境的流程,以弥合在理解潜在的大脑变化如何影响宏观脑生物力学方面的差距。该流程能够基于图像对全脑特定于个体的位移场进行量化,以得出133个标记的功能性脑区的特定于个体的应变、应变率和应力。我们将开发工作重点放在仅利用MRI衍生数据上,以促进我们方法的临床应用,并在方法的各个方面强调自动化以减少对操作人员的依赖。我们的流程有潜力改善神经疾病的早期检测,并在广泛的不可逆损伤发生之前促进疾病的识别。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d42/11326150/f0f9473c7c5e/nihpp-2024.08.01.606246v1-f0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验