在使用多变量结构共变性分析的精神病患者中,脑网络退化和失调
Rositsa Paunova1,2, Cristina Ramponi3, Sevdalina Kandilarova1,2
1Department of Psychiatry and Medical Psychology, Medical University Plovdiv, Plovdiv, Bulgaria.
Frontiers in psychiatry
|November 1, 2023
概括
这项研究使用了多变量方法来发现脑部区域对于精神障碍如精神分裂症,严重抑郁症和双相情感障碍至关重要. 特定的大脑网络模式在这些诊断和健康个体之间有所不同.
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.
- 医疗成像医学成像
背景情况:
- 精神分裂症,主要抑郁症和双相情感障碍等主要精神疾病与大脑连接的改变有关.
- 了解这些疾病背后的特定大脑区域和网络模式对于诊断和治疗至关重要.
研究的目的:
- 为了确定大脑区域在塑造神经网络连接模式在精神分裂症,严重抑郁症和双相情感障碍中至关重要.
- 使用多变量方法调查诊断特定的结构共变性模式.
主要方法:
- 应用多变量结构共变量分析T1wMRI图像来自164个受试者 (17个精神分裂症,25个双相情感障碍,68个主要抑郁症,54个健康对照).
- 使用SPM12工具箱的SHOOT算法提取感兴趣的区域 (ROI).
- 为显著的ROI计算了自身中心性,并进行了集群分析以确定诊断特定的网络配置文件.
主要成果:
- 确定了四个不同的大脑区域集群,具有特定于诊断的结构共变性模式.
- 集群1显示精神分裂症和健康对照/双极性障碍之间存在强烈的歧视.
- 集群2和3分别针对严重抑郁症和双相情感障碍;集群4有助于歧视所有群体.
结论:
- 多变量结构共变在识别精神病诊断中具有预测价值的大脑区域方面是有效的.
- 退化在主要精神疾病内和跨越主要精神疾病时,以不同的方式塑造大脑网络,揭示出独特的大脑特征.
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