转移到核心:异常的核心-外围全球地形在单极和双极抑郁症
Andrea Scalabrini1, Mariagrazia Palladini2, Sara Poletti3
1University of Bergamo, Department of Human and Social Sciences, Bergamo, Italy; The Royal's Institute of Mental Health Research, University of Ottawa Institute of Mental Health Research, Ottawa, Canada.
Journal of affective disorders
|March 5, 2026
概括
大脑网络活动在重大抑郁障碍 (MDD) 和双极障碍 (BD) 患者中发生变化,核心网络过度活跃,外围网络不太活跃. 这种不平衡可能成为抑郁状态的神经生物学标志物.
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.
- 脑部成像 脑部成像
背景情况:
- 重度抑郁症 (MDD) 和双极性障碍 (BD) 是一种复杂的情绪障碍,伴有大脑功能变化.
- 了解这些疾病的神经生物学基础对于开发有效的治疗方法至关重要.
研究的目的:
- 研究MDD和BD中核心和外围大脑网络的全球信号拓.
- 使用静止状态fMRI识别抑郁状态的潜在神经生物学标志物.
主要方法:
- 休息状态功能磁共振成像 (fMRI) 用于140名患有MDD或BD的患者和70名健康对照 (HC).
- 机器学习和部分最小平方 (PLS) 分析被用来检查大脑网络活动和诊断群体歧视.
- 分析了与症状严重程度和诊断组相关的全球信号相关性和核心-外围 (C-) 比率.
主要成果:
- 与HC患者相比,MDD和BD患者在跨模态核心网络 (例如,默认模式网络,前端对象网络) 的活性增加,在单模态外围网络 (例如,视觉,感觉运动皮层) 的活性降低.
- 机器学习模型根据改变的全球信号动态 (ACC = 79%和77%) 准确地区分了MDD和BD与HC.
- 在外围网络的全球信号相关性和抑郁症状严重程度之间发现了负相关性. 在躁狂期间,在BD中观察到明显的模式,随着外围网络活动的增加.
结论:
- 全球信号地形,特别是核心和外围大脑网络之间的不平衡,在情绪障碍中起着关键作用.
- 核心-外围 (C-) 比率是一个强有力的预测指标,表明其作为抑郁状态的神经生物学标志物的潜力.
- 大脑网络组织中的情绪状态依赖的变化凸显了这些疾病的动态性质.
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