Scharwächter Leon, Schmitt Felix J, Pallast Niklas, Fink Gereon R, Aswendt Markus
University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany.
University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; University of Cologne, Institute of Zoology, Dept. of Computational Systems Neuroscience, Cologne, Germany.
Neuroimage. 2022 Jun;253:119110. doi: 10.1016/j.neuroimage.2022.119110. Epub 2022 Mar 17.
Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.
图论能够评估神经元连接性的变化以及脑区之间的相互作用，这些变化和相互作用是由局部损伤（如中风后）以及全局性干扰（如精神功能障碍或神经退行性疾病所致）引起的。因此，基于从结构和功能磁共振成像连接矩阵构建图的网络分析在临床研究中越来越常用。相比之下，在小鼠神经成像中，重点主要放在基本连接参数上，即相关系数或纤维计数，而更先进的网络分析则很少使用。本综述总结了图论测量方法及其解释，以描述从近期体内小鼠脑研究中得出的网络。为便于读者进入该主题，我们解释了相关的数学定义，提供了一个专用软件工具包，并讨论了应用于静息态功能磁共振成像（rs-fMRI）和扩散张量成像（DTI）的实际注意事项。通过这种方式，我们旨在促进跨物种比较，并应用标准化测量方法对转化性脑疾病研究中的网络变化进行分类和解释。
