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一种基于网络的方法来评估fMRI中的任务合规性

Radheshyam Stepponat¹, Marc Berger², Laurin Schäfer³

¹Developmental and Interventional Neuroimaging Lab (DINLAB), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; Comprehensive Center of Clinical Neurosciences and Mental Health (C3NMH), Austria.

Epilepsy & behavior : E&B

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概括

通过评估患者的任务参与度,功能性MRI (fMRI) 语言映射的可靠性得到改善. 使用语言网络 (LN) 和默认模式网络 (DMN) 活动,客观地测量合规性,提高术前规划的准确性.

科学领域:

神经成像是一种神经成像.
认知神经科学认知神经科学
临床神经学临床神经学

背景情况:

基于任务的功能性MRI (fMRI) 对于绘制语言网络和指导术前规划至关重要.
患者参与和任务执行直接影响fMRI激活图的有效性.
区分真正的语言组织与不良的任务执行对于可靠的临床fMRI至关重要.

研究的目的:

开发和验证临床fMRI期间任务参与的客观措施.
评估功能参与指数和认知表现之间的关系.
提高患者语言网络映射的可靠性.

主要方法:

对43名患者和25名健康对照者的fMRI数据进行了回顾性分析.
基于语言网络 (LN) 和默认模式网络 (DMN) 激活/抑制的定义的参与指数.
根据遵守水平对参与者进行分类,并在各组中比较注意力表现.

主要成果:

健康对照组在一个苛刻的理解任务中表现出比TLE患者更高的遵从性.
在多个测试中,LN参与强有力的预测了更好的注意力表现.
一个显著的线性趋势显示,注意力得分下降,合规性降低.

结论:

基于网络的参与指数 (LN和DMN) 提供了一种实用的方法,可以在fMRI中接近任务合规性.
LN 参与是引起注意力的强有力的预测因素,而 DMN 抑制则充当了补充的保护措施.
整合任务正和任务负标记可以提高fMRI预外科规划的可靠性.

关键词:

合规性合规性合规性默认模式网络默认模式网络功能性磁共振成像技术功能性磁共振成像技术语言网络语言网络负面的任务负面的任务时间叶时间叶

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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).