多模式共变网络反映了个人的认知灵活性.
Lin Jiang1,2, Simon B Eickhoff3,4, Sarah Genon3,4
1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China.
International journal of neural systems
|February 19, 2024
概括
休息期间的大脑网络协调预测了认知灵活性. 视觉和运动网络中的和视觉和运动网络之间的静止状态结构功能连接是基于任务的心理转移能力的关键.
科学领域:
- 神经科学是一个神经科学.
- 认知神经科学 认知神经科学
- 脑部成像 脑部成像
背景情况:
- 认知灵活性,即能够切换心理过程的能力,对于复杂的任务至关重要.
- 大脑的结构和功能组织是认知灵活性的基础.
- 休息状态大脑网络协变如何支持认知灵活性尚不清楚.
研究的目的:
- 研究多式共变网络 (MCN) 中静止状态结构功能共变与个人认知灵活性之间的关系.
- 识别特定的网络相互作用,预测认知灵活性表现.
主要方法:
- 利用了来自182名健康参与者的磁共振成像 (MRI) 和脑电图 (EEG) 数据.
- 在休息状态下分析了大规模大脑网络内部和大脑网络之间的结构功能共变.
- 采用多层感知子模型来利用MCN特征预测认知灵活性.
主要成果:
- 认知灵活性与视觉网络 (VN) 和体运动网络 (SMN) 中的子网络内共变有显著的相关性.
- 涉及SMN,VN,默认模式网络 (DMN),前端对象网络 (FPN),腹部注意网络 (VAN) 和背部注意网络 (DAN) 的跨子网络相互作用与认知灵活性有关.
- 使用静止状态MCN连接的预测模型准确地预测了个人的认知灵活性.
结论:
- 在特定的大脑网络内和之间休息状态结构功能协调与认知灵活性有关.
- 这些发现为支持认知灵活性的机制提供了神经生物学见解.
- 确定了预测个体认知灵活性的潜在神经生物学标志物.
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