Suppr超能文献

ASH:一种用于生成逼真且个性化的慢性中风容积传导头部模型的自动管道。

ASH: an Automatic pipeline to generate realistic and individualized chronic Stroke volume conduction Head models.

作者信息

Carla Piastra Maria, van der Cruijsen Joris, Piai Vitória, Jeukens Floor E M, Manoochehri Mana, Schouten Alfred C, Selles Ruud W, Oostendorp Thom

机构信息

Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Center, Donders Institute for Brain Cognition and Behaviour, Nijmegen, The Netherlands.

Department of Neuroinformatics, Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.

出版信息

J Neural Eng. 2021 Apr 27;18(4). doi: 10.1088/1741-2552/abf00b.

Abstract

Large structural brain changes, such as chronic stroke lesions, alter the current pathways throughout the patients' head and therefore have to be taken into account when performing transcranial direct current stimulation simulations.We implement, test and distribute the first MATLAB pipeline that automatically generates realistic and individualized volume conduction head models of chronic stroke patients, by combining the already existing software SimNIBS, for the mesh generation, and lesion identification with neighborhood data analysis, for the lesion identification. To highlight the impact of our pipeline, we investigated the sensitivity of the electric field distribution to the lesion location and lesion conductivity in 16 stroke patients' datasets.Our pipeline automatically generates 1 mm-resolution tetrahedral meshes including the lesion compartment in less than three hours. Moreover, for large lesions, we found a high sensitivity of the electric field distribution to the lesion conductivity value and location.This work facilitates optimizing electrode configurations with the goal to obtain more focal brain stimulations of the target volumes in rehabilitation for chronic stroke patients.

摘要

大脑的大型结构变化,如慢性中风损伤,会改变患者头部的电流通路,因此在进行经颅直流电刺激模拟时必须予以考虑。我们实现、测试并发布了首个MATLAB管道,该管道通过结合现有的用于网格生成的软件SimNIBS和用于损伤识别的邻域数据分析来识别损伤,从而自动生成慢性中风患者逼真的个体化容积传导头部模型。为了突出我们管道的影响,我们在16例中风患者的数据集中研究了电场分布对损伤位置和损伤电导率的敏感性。我们的管道在不到三小时内自动生成包括损伤区域在内的1毫米分辨率的四面体网格。此外,对于大型损伤,我们发现电场分布对损伤电导率值和位置具有高度敏感性。这项工作有助于优化电极配置,目标是在慢性中风患者的康复中对目标体积进行更聚焦的脑刺激。

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