在日常生活中预测反复的负面思维:从基于大脑的图形理论预测建模的见解
Martino Schettino1, Rotem Dan2, Chiara Parrillo3
1Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychology, Sapienza University of Rome, Rome, Italy; IRCCS Institute of Neurological Science of Bologna, Bologna, Italy.
大脑网络异常预测每天重复的负面思维 (RNT). 图形理论建模确定了与RNT严重性和侵入性相关的特定网络特征,为RNT的出现提供了洞察力.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 精神病学是一个精神病学.
背景情况:
- 重复的负面思维 (RNT) 与大规模大脑网络中功能连接的改变有关.
- 这些网络异常对于每日RNT的预测潜力仍然不清楚.
研究的目的:
- 用基于大脑的图形理论预测建模 (GPM) 来预测日常生活中的RNT.
- 确定与RNT严重程度及其临床特征相关的特定功能性脑网络属性.
主要方法:
- 利用GPM与功能磁共振成像 (fMRI) 数据从54个人休息期间和RNT诱导的状态.
- 使用生态瞬间评估 (EMA) 评估RNT严重程度和波动.
主要成果:
- GPM确定了默认模式网络 (DMN),前端对称网络 (FPN) 和预测RNT的边缘网络的关键功能性质.
- 中间前额叶皮质的中心性预测了RNT侵入性;轨道前额叶皮质的强度/中心性预测了重复性;极分离预测了RNT严重性.
- 日常生活中的EMA评估提供了比实验室调查问卷更好的RNT预测.
结论:
- GPM有效地预测了日常生活中RNT的出现.
- 特定的网络属性,如中心性和分离,是RNT及其临床表现的基础.
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