提前规划:可预测的切换招募任务活跃和静止状态网络.
Danielle L Kurtin1,2, Garazi Araña-Oiarbide2, Romy Lorenz3,4,5
1NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
Human brain mapping
|July 20, 2023
概括
切换任务会产生性能成本,但可预测的序列可能会将默认模式网络 (DMN) 与多重需求皮质 (MDC) 连接起来,以提高准确性.
科学领域:
- 认知神经科学 认知神经科学
- 神经成像是一种神经成像.
- 人类大脑功能 人类大脑功能
背景情况:
- 任务切换是一种复杂的认知过程,与性能成本有关,包括反应时间减慢和精度降低.
- 切换的神经相关物涉及多重需求皮质 (MDC),有时是默认模式网络 (DMN).
- 在任务切换过程中DMN的确切作用仍然不完全理解.
研究的目的:
- 研究默认模式网络 (DMN) 在认知切换中的作用.
- 检查在任务切换期间不同程度的可预测性如何影响大脑活动和功能连接.
- 阐明导致切换成本减轻的神经机制.
主要方法:
- 从24名参与者中收集了功能磁共振成像 (fMRI) 数据,他们执行了一个任务交换范式.
- 该范式操纵了顺序,感知和空间维度的可预测性.
- 分析涉及计算活动地图用于切换与停留试验,并使用对对相互信息功能连接 (miFC) 评估功能连接.
主要成果:
- 交换试验显示反应时间增加和更广泛的神经招募,包括DMN,MDC和其他任务积极网络.
- 增加了顺序可预测性,提高了任务准确性和体运动和突出/腹部注意力网络中的调节活动.
- 顺序和感知可预测性的变化,但不是空间,显著影响了miFC,顺序可预测性增加了几个关键大脑网络之间的连接性.
结论:
- 在任务切换期间DMN的参与,特别是随着序列可预测性增加,表明在执行职能中发挥了作用.
- 在顺序可预测性下,DMN和MDC之间的miFC增加表明了协调活动.
- DMN可以通过生成即将发生事件的时间图表来促进执行任务的执行,从而降低切换成本.
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