与语言相关网络的模块化结构和网络集成的破坏预测叶的语言表现:基于图形的分析证据
Victor Karpychev1, Svetlana Malyutina1, Anna Zhuravleva1
1Center for Language and Brain, HSE University, Moscow, Russian Federation.
Epilepsy & behavior : E&B
|September 9, 2023
概括
叶 (TLE) 重组了大脑的语言网络,在任务中显示出改变的分离和整合. 在TLE患者中观察到健康半球中的补偿机制.
科学领域:
- 神经科学是一个神经科学.
- 的研究研究.
- 认知神经科学 认知神经科学
背景情况:
- 叶 (TLE) 是一种影响语言处理的网络疾病.
- 基于任务的功能磁共振成像 (fMRI) 对于理解语言网络重组至关重要.
- 以前的研究主要集中在静止状态分析上,在理解基于任务的网络动态方面存在差距.
研究的目的:
- 在语言任务期间使用基于图形的分析来研究TLE语言网络重组.
- 为了比较健康对照组和TLE个体之间的网络隔离和整合.
- 在TLE中识别网络集成和分离之间的平衡中出现的干扰.
主要方法:
- 收集了19名健康对照组和28名左TLE患者的句子完成期间基于任务的fMRI数据.
- 采用基于图形的分析来评估语言网络分成模块和模块间集成.
- 评估了总网络集成,模块内部集成和连接器枢纽的作用.
主要成果:
- 在TLE中,语言网络的分离方式不同,双边前模块和左叶模块显示了超连接性.
- 在TLE中,更强的模块内部集成比总网络集成的趋势预测了语言性能.
- 右半球连接器枢纽的增加表明TLE.在TLE中发挥了补偿作用.
结论:
- 在语言处理过程中,TLE显著改变了语言网络的分离和集成.
- 调查结果证实,TLE的全球连接性和半球补偿性减少.
- 这项研究增强了对在TLE.中基于网络的语言重组的理解.
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