网络状态动态是跨诊断人口中渴望的基础
Jean Ye1, Kathleen A Garrison2, Cheryl Lacadie3
1Interdepartmental Neuroscience Program, Yale School of Medicine.
medRxiv : the preprint server for health sciences
|October 24, 2023
概括
新的大脑成像方法显示,特定的大脑网络状态与渴望强度有关. 了解这些动态的大脑模式,可以了解成和心理健康.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 计算神经科学是一种神经科学.
背景情况:
- 情感和动机状态对于福祉和精神病理学至关重要.
- 了解这些状态背后的动态大脑机制至关重要.
- 功能性磁共振成像 (fMRI) 提供了新的方法来研究随时间推移的大脑相互作用.
研究的目的:
- 开发一种用于识别特定于情感状态的大脑网络状态的新框架.
- 检查即时网络参与对情绪状态体验的贡献.
- 为了调查潜在的渴望的网络状态动态.
主要方法:
- 利用基于连接组的预测建模 (CPM) 来识别渴望预测的大脑网络边缘.
- 采用以边缘为中心的时间序列方法来量化即时网络参与.
- 从跨诊断样本 (N=252) 分析数据,并在独立队列 (N=173) 中复制发现.
主要成果:
- 渴望更高的人在渴望积极的网络状态中花费了更多的时间.
- 这些个人在渴望负网络状态中花费的时间更少.
- 结果是强大的,并在不同的数据集和刺激中复制.
结论:
- 渴望特定的大脑网络状态的动态招募是渴望的个人差异的基础.
- 开发的框架提供了一种新的方法来探索网络状态在情感体验中的瞬间参与.
- 这些见解对理解和潜在的治疗渴望相关疾病有影响.
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