网络状态动态是跨诊断人口中基础渴望的基础
Jean Ye1, Kathleen A Garrison2, Cheryl Lacadie3
1Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA. jean.ye@yale.edu.
Molecular psychiatry
|August 25, 2024
概括
使用功能磁共振成像 (fMRI) 测量的大脑网络动态,揭示了大脑状态与渴望的关系. 持续参与渴望积极的大脑网络和减少时间渴望负网络的特征是更高的渴望水平.
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:
- 情绪状态及其调节在精神病理学中至关重要,但潜在的动态大脑反应尚未得到充分理解.
- 情感和动机状态的个体差异可能源于大脑反应的波动.
研究的目的:
- 引入一种用于识别与情感体验相关的大脑网络状态的新框架.
- 调查这些网络状态的动态参与如何促进情感体验,特别是基本渴望.
主要方法:
- 利用基于连接体的预测建模 (CPM) 来识别在跨诊断样本 (N=252) 中预测基础渴望的大脑网络.
- 采用以边缘为中心的时间序列方法来量化渴望相关子网络的瞬间参与.
- 在一个独立的数据集 (N=173) 中使用不同的参与者和刺激重复发现.
主要成果:
- 较高的基础渴望与渴望积极的大脑网络状态的持久性有关.
- 减少对渴望负面大脑网络状态的居住也特征为渴望更高的人.
- 这些关联在数据集和网络定义中是强大的和一致的.
结论:
- 大脑网络状态的动态参与显著地支着基本渴望的个体差异.
- 开发的框架提供了一种新的方法来探索网络状态动态在情感体验中的作用.
- 这些发现对理解和潜在的治疗渴望相关疾病有影响.
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