在操纵认知负载和训练的条件下活动流
Wanyun Zhao1, Kaiqiang Su1, Hengcheng Zhu2
1Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China.
NeuroImage
|July 28, 2024
概括
这项研究揭示了工作记忆训练如何增强大脑网络通信. 适应性培训改善了执行控制网络中的分布式处理,为认知灵活性提供了新的见解.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
背景情况:
- 灵活的认知功能,如工作记忆 (WM),依赖于平衡局部和分布式的大脑处理.
- 了解局部与分布式处理对任务诱导的大脑活动的具体贡献仍然是一个挑战.
- 活动流程映射提供了一种评估分布式处理的方法,但其在自适应性认知变化中的作用尚未得到充分探索.
研究的目的:
- 调查工作记忆负载和适应性训练如何调节前对系统中的活动流和大脑激活.
- 区分网络内部与网络间活动流向对认知处理的贡献.
- 建立研究认知可塑性和信息整合的方法框架.
主要方法:
- 招募51名健康志愿者 (31名女性) 进行工作记忆任务,使用不同的负载条件和适应性训练.
- 利用活动流量映射来分析脑活性和连接模式在前对对联网络.
- 在培训前后检查网络内部和网络间活动流程的变化.
主要成果:
- 在基线时,执行控制网络 (ECN) 和背部注意网络 (DAN) 的激活随着WM负载而增加;只有DAN显示了线性分布式处理响应 (网络内).
- 适应性培训增加了ECN中的分布式处理,并诱导了线性负载响应,主要是通过网络之间的活动流.
- 活动流预测表明训练,连接和大脑活动之间存在因果关系.
结论:
- 活动流量映射提供了超越传统激活措施的神经处理的独特见解,特别是在认知负载和训练下.
- 适应性培训通过增加网络间的通信来增强ECN中的分布式处理.
- 本研究引入了一种新的方法论方法,用于调查认知系统中的信息整合和分离.
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