一个空间有限的独立组件分析,由结构和功能网络连接性共同提供信息.
Mahshid Fouladivanda1,2, Armin Iraji1,2, Lei Wu1
1Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
bioRxiv : the preprint server for biology
|June 10, 2024
概括
这项研究引入了一种新的多模式独立组件分析 (ICA) 模型,将结构和功能大脑连接整合起来. 该模型增强了内在连接网络 (ICNs) 的估计,显示了精神分裂症中改进的网络区分.
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.
- 精神疾病 精神疾病
背景情况:
- 对结构和功能大脑连接的联合分析为大脑组织提供了更深入的见解.
- 精神分裂症是一种复杂的疾病,理解被破坏的大脑网络至关重要.
- 现有的方法经常单独分析模式,可能缺少集成网络信息.
研究的目的:
- 为估计内在连接网络 (ICNs) 提出和验证一种多模式独立组件分析 (ICA) 模型.
- 整合结构连接 (来自dMRI) 和功能连接 (来自rs-fMRI) 以提高ICN估计.
- 评估模型在区分大脑网络方面的表现,特别是在精神分裂症中.
主要方法:
- 开发了一个结构功能连接和空间受限制的ICA (sfCICA) 模型.
- 使用扩散权重MRI (dMRI) 通过全脑通道图进行结构连接.
- 在休息状态下使用功能性MRI (rs-fMRI) 进行功能连接.
- 应用了多目标优化框架来进行主体级ICN估计.
- 在合成和真实数据集上验证了模型,包括精神分裂症患者和健康对照.
主要成果:
- 多模体sfCICA模型揭示了ICNs之间增强的功能合,与结构连接相关.
- 观察到改善的模块化和网络区分,特别是在精神分裂症患者中.
- 统计分析显示,与单模式方法相比,群体差异明显更大.
- 整合结构信息改善了对功能网络的估计.
结论:
- sfCICA模型有效地利用来自结构和功能大脑连接的互补信息.
- 从两种模式同时学习可以提高连接性估计和网络特征.
- 这种方法为研究精神分裂症等脑疾病提供了优势,因为它提供了更敏感和更明显的网络测量.
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