精神分裂症的认知决策机制基于动态广度锁定值和频段脑网络分析
Shuman Huang1,2, Jintao Yao1, Wanru Li1
1School of Computer and Information Engineering, Henan Normal University, Xinxiang, China.
Annals of the New York Academy of Sciences
|October 19, 2025
概括
精神分裂症患者表现出大脑活动和连接能力的改变. 这项研究使用了EEG数据的动态振幅锁定值 (DALV) 分析,揭示了精神分裂症患者的P300振幅降低和大脑网络效率受损.
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:
- 精神分裂症是一种严重的精神疾病,其特点是认知,情绪和行为障碍.
- 它的病理生理学越来越多地与大规模大脑网络合的损害有关.
- 虽然相位同步对于功能连接是常见的,但基于振幅的方法较少被探索.
研究的目的:
- 通过基于振幅的方法来研究精神分裂症中的功能性大脑网络改变.
- 与健康对照组 (HC) 相比,分析精神分裂症患者 (PSZ) 的神经振荡和网络特性.
主要方法:
- 利用动态振幅锁定值 (DALV) 方法,从头皮脑电图 (EEG) 信号构建功能连接网络.
- 对比了46个PSZ和31个HC的EEG数据.
- 分析了P300组件,太频带功率和大脑网络指标 (节点程度,集群系数,本地和全球效率).
主要成果:
- 与HC相比,PSZ在CPz和FCz电极位点显示P300振幅显著降低.
- 在整个大脑中观察到提达频带功率的增加,特别是在PSZ的顶部,前额头和左部区域.
- 基于DALV的脑网络分析显示,PSZ中节点程度,集群系数,局部效率和全球效率显著降低.
结论:
- 使用DALV的基于广度的功能连接分析有效地突出了精神分裂症中的网络异常.
- 精神分裂症与神经振荡 (甲频段) 和大脑网络拓学的明显变化有关.
- 这些发现有助于理解精神分裂症的神经支,并建议潜在的生物标志物.
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