在叶中,记忆和语言网络重组的动态因果建模
Alireza Fallahi1,2, Narges Hoseini-Tabatabaei3, Fatemeh Eivazi4,5,6
1Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Annals of clinical and translational neurology
|September 30, 2023
概括
动态因果建模揭示了叶 (TLE) 中大脑连接对记忆和语言网络的显著改变. 这些发现有助于识别发性半球并了解TLE.
科学领域:
- 神经科学是一个神经科学.
- 认知神经学 认知神经学
- 的研究研究.
背景情况:
- (TLE) 显著影响认知功能,特别是记忆和语言.
- 识别发性半球对于有效的TLE管理至关重要.
- 了解TLE中的网络变化需要先进的神经成像分析.
研究的目的:
- 用动态因果建模 (DCM) 评估语言和记忆功能的变化.
- 在患有叶 (TLE) 的患者中识别发性半球.
- 为了比较TLE患者与健康个体的脑网络连接.
主要方法:
- 功能磁共振成像 (fMRI) 在记忆和语言任务中对22名左TLE和13名右TLE患者进行了功能磁共振成像.
- 动态因果建模 (DCM) 分析了记忆和语言网络中的有效定向连接.
- 在TLE患者的连接性改变与健康对照进行了比较.
主要成果:
- 与TLE中的语言网络相比,DCM对内存网络的影响更大.
- 在图片编码时从左半球观察到超连接性,在文字编码时从右半球观察到超连接性.
- 在TLE患者的语言网络中,在检索任务和半球之间注意到了低连接性.
- 神经心理测试显示DCM发现和TLE横向性之间的相关性.
结论:
- 动态因果模型有效地证明了TLE中的网络重组.
- 这些发现提高了对TLE对认知功能的影响的理解.
- 通过揭示特定的网络变化,DCM可以帮助识别发性半球.
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