功能连接图理论分析口语文字处理效率在前额叶皮层激活前额叶皮层激活
bioRxiv : the preprint server for biology
|January 23, 2026
概括
功能近红外光谱学 (fNIRS) 和图形理论揭示了大脑网络在口语语言处理中的差异. 高绩效的人表现出高效的脑网络,而低绩效的人表现出低效的网络.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算语言学 计算语言学
背景情况:
- 口语识别受到单词频率和语音学概率的影响.
- 了解口语文字处理中的个体差异的神经基础至关重要.
- 功能连接测量提供了对大脑网络效率的见解.
研究的目的:
- 调查功能近红外光谱学 (fNIRS) 和图形理论是否可以在神经类型听众中描述口语文字处理效率.
- 通过使用功能连接测量 (全球效率,本地效率,模块化,枢纽性) 在背侧前额皮层 (DLPFC) 中评估口语语言处理中的个体差异.
- 探索心理语言模型与认知处理努力的直接测量之间的关系.
主要方法:
- 20名神经类型参与者 (18-21岁) 完成了一项听觉工作记忆任务,涉及不同频率和声学概率的单词.
- fNIRS记录了前额叶皮层的血液动力学变化 (HbO,HbR).
- 使用部分相关系数创建了功能连接矩阵,并应用了图形理论措施来分析前线网络.
主要成果:
- 任务性能,而不是单词频率,与大脑测量相关:更高的前额功能连接 (FC) 与更差的准确性相关,高的模块化与更慢的响应时间相关.
- 高性能个体 (High-P) 显示低FC强度和高效率,而低性能个体 (Low-P) 显示高模块化和低效率.
- 确定了中部大脑区域 ("中心"),高P个体显示中心重叠与小组级一致的中心,与低P个体不同.
结论:
- 识别的网络属性与典型个体的高效和低效的口语文字处理有关.
- 这些发现为评估非典型人群中的语言功能提供了基础.
- 大脑网络特征,如效率和模块化,与口语文字处理中的行为表现有关.
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