在情感面部匹配任务中解开大规模的大脑动态及其与行为的联系
Cole Korponay1,2, Julia E Cohen-Gilbert1,2, You Cheng1,2
1McLean Imaging Center, McLean Hospital, Belmont, MA, USA.
Communications biology
|August 7, 2025
概括
这项研究表明,情绪处理涉及到动态的大脑网络,而不仅仅是固定区域. 识别这些网络比传统方法更好地解释了情感任务中的认知表现.
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
- 功能神经成像 功能神经成像
背景情况:
- 情绪处理依赖于复杂的大脑网络.
- 传统的fMRI分析可能会掩盖这些网络的动态性质.
- 了解网络动态对于解释认知表现至关重要.
研究的目的:
- 在情感面部处理过程中识别和跟踪并发的大脑过程,使用张量独立组件分析.
- 研究时间动态和大脑网络在情绪处理中的作用.
- 为了比较基于网络的分析与基于对比的模型的解释能力.
主要方法:
- 张量独立组件分析 (ICA) 应用于fMRI数据.
- 参与者 (n=413,复制 n=416) 执行了情感面部匹配任务 (EFMT).
- 分析了网络活动与认知和情感措施的关系.
主要成果:
- 通过EFMT招募的十个大规模大脑网络被确定.
- 这些网络参与了大脑皮层的74%,显示出灵活的重新合.
- 网络活动解释了比基于杏仁体的分析更多的认知和情绪干扰的变化.
结论:
- 情绪处理涉及到超越正规模型的动态网络交互.
- 基于网络的分析提供了对情感面部处理子组件的更深入的见解.
- 大脑网络的动态与认知变异性比情感变异性更为密切相关.
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