最大的模型揭示了频繁的大脑状态切换在早期精神病的多元大脑功能分析中
bioRxiv : the preprint server for biology
|February 6, 2026
概括
最大模型 (MEM) 提供了一种新的方法来研究精神病中的大脑网络动态. MEM分析显示,网络稳定性发生变化和精神病的过渡,与认知缺陷和症状严重程度有关.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 精神病学神经科学 精神病学神经科学
背景情况:
- 传统的神经成像方法,如区域激活和对对应,在捕捉动态大脑网络相互作用方面存在局限性.
- 最大模型 (MEM) 提供了一种有前途的整合方法,通过整合区域和区域间活动来研究不断发展的功能性大脑网络.
研究的目的:
- 为了比较MEM的疗效与传统方法在精神病中大脑网络改变的特征.
- 研究精神病患者的默认模式网络 (DMN) 和背部注意网络 (DAN) 的动态变化.
主要方法:
- 应用于仅激活,仅对联同激活和MEM分析的人类连接组项目-早期精神病数据集.
- 利用HCP/Glasser地图来定义DMN和DAN区域,量化节点频段功率,功能连接 (FC) 和MEM特征中的群体差异.
- 研究了能源景观盆地之间的过渡率及其与认知和精神病理学的关联.
主要成果:
- 精神病与减少的BOLD频段功率和FC图表指标有关.
- MEM分析显示,在精神病中,DMN和DAN节点状态之间的切换更频繁,盆地过渡更高,停留时间更短.
- MEM指标显示了与工作记忆有显著的负相关性,与症状严重程度有积极的正相关性.
结论:
- MEM提供了对节点激活/失活模式,盆地过渡和网络能量的独特见解,超越了传统方法.
- MEM分析更好地描述了与精神病相关的网络变化,包括减弱的网络间连接和减少的DMN/DAN稳定性.
- 这些网络不稳定可能是工作记忆受损和精神病变的严重程度增加的基础.
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