解码语音生产和感知过程中的特定状态连接
bioRxiv : the preprint server for biology
|November 24, 2025
概括
支持语言的动态大脑网络使用电皮质谱 (ECoG) 解码. 功能连接分析揭示了语音感知和发声的独特网络特征,突出显示了大脑不那么活跃的区域的作用.
科学领域:
- 认知神经科学 认知神经科学
- 系统神经科学 系统神经科学
- 神经成像是一种神经成像.
背景情况:
- 了解动态大脑网络对于语言处理至关重要.
- 使用fMRI的功能连接 (FC) 有局限性,特别是在语音生成方面.
- 电皮质谱 (ECoG) 为研究大脑网络提供了高分辨率的替代方案.
研究的目的:
- 通过ECoG.研究支持语音感知和语音产生的大脑网络.
- 在语言任务期间,从功能连接模式解码认知状态.
- 确定活跃和不太活跃的大脑区域对认知状态的贡献.
主要方法:
- 在受控语音制作任务中利用了42名患者的ECoG数据.
- 在五个认知状态中获得的神经活动:听觉,图像和阅读感知,语音产生和基线.
- 采用线性分类器来从单试 FC (皮尔森相关性) 解码认知状态.
主要成果:
- 从FC模式获得的认知状态的平均解码精度为64.4%.
- 识别了用于听觉和视觉感知以及语音生成的独特网络签名.
- 发现神经激活最小的区域对于区分认知状态至关重要.
结论:
- 功能连接分析提供了超越局部神经活动的补充见解.
- 动态的大脑网络,包括不太活跃的区域,对于语言感知和语言产生至关重要.
- ECoG是剖析语言等复杂认知功能的神经支柱的宝贵工具.
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