在健康受试者中通过神经血管合进行认知大脑侧面化:统计复杂性分析
Héctor Rojas-Pescio1, Lucy Beishon2,3, Ronney B Panerai2,3
1Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago, Chile.
Physiological reports
|August 7, 2025
概括
统计复杂度测量 (SCM) 通过分析神经血管合 (NVC) 在认知任务期间有效检测大脑侧面化. SCM揭示了与半球主导和认知刺激相关的独特复杂性模式.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 信息理论 信息理论
背景情况:
- 人类大脑功能表现出横向化,通过神经血管合 (NVC) 可观察到不对称的大脑半球激活.
- 大脑侧面化通过使不同功能同时发挥作用来增强认知能力.
- 了解这些横向化机制对于认知神经科学和临床应用至关重要.
研究的目的:
- 在阿登布鲁克认知检查-III (ACE-III) 期间使用NVC响应调查认知横向化.
- 评估分散 (DE) 和统计复杂度度 (SCM) 在识别NVC信号半球差异方面的有效性.
- 探索认知刺激如何影响NVC复杂性模式和横向化.
主要方法:
- 应用分散 (DE) 和统计复杂度 (SCM) 测量到脑血流速度 (CBv),临界关闭压力 (CrCP) 和电阻面积产物 (RAP) 信号.
- 利用阿登布鲁克的认知检查-III (ACE-III) 来刺激认知功能,包括注意力,流性,语言,记忆和视觉空间任务.
- 进行统计分析,比较认知任务与基线期间NVC复杂性的半球差异.
主要成果:
- 统计复杂度测量 (SCM) 有效检测出显著的半球横向化 (最佳p值=0.001).
- 仅分散 (DE) 没有区分半球活动.
- 与基线相比,认知刺激通常导致SCM值较低,特别是在主导半球.
结论:
- 神经血管合 (NVC) 呈现出与半球主导和认知领域相关的独特复杂性模式.
- 像DE和SCM这样的高级信息理论方法为大脑横向化机制提供了宝贵的见解.
- 这些发现增强了我们对认知刺激和横向化过程中的NVC反应的理解.
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