Padova Neuroscience Center, University of Padova, 35131, Padova, Italy.
Department of Information Engineering, University of Padova, Via G. Gradenigo 6/B, 35131, Padova, Italy.
Sci Rep. 2023 Jun 27;13(1):10389. doi: 10.1038/s41598-023-37478-2.
Resting state fMRI has been used in many studies to investigate the impact of brain tumours on functional connectivity (FC). However, these studies have so far assumed that FC is stationary, disregarding the fact that the brain fluctuates over dynamic states. Here we utilised resting state fMRI data from 33 patients with high-grade gliomas and 33 healthy controls to examine the dynamic interplay between resting-state networks and to gain insights into the impact of brain tumours on functional dynamics. By employing Hidden Markov Models, we demonstrated that functional dynamics persist even in the presence of a high-grade glioma, and that patients exhibited a global decrease of connections strength, as well as of network segregation. Furthermore, through a multivariate analysis, we demonstrated that patients' cognitive scores are highly predictive of pathological dynamics, thus supporting our hypothesis that functional dynamics could serve as valuable biomarkers for better understanding the traits of high-grade gliomas.
静息态功能磁共振成像已被广泛应用于研究脑肿瘤对功能连接的影响。然而，这些研究迄今为止都假设功能连接是稳定的，忽略了大脑在动态状态下波动的事实。在这里，我们利用来自 33 名高级别胶质瘤患者和 33 名健康对照者的静息态 fMRI 数据，来研究静息态网络之间的动态相互作用，并深入了解脑肿瘤对功能动力学的影响。通过使用隐马尔可夫模型，我们证明了即使存在高级别胶质瘤，功能动力学仍然存在，并且患者表现出连接强度以及网络分离的全局降低。此外，通过多元分析，我们证明了患者的认知评分可以高度预测病理性动力学，从而支持我们的假设，即功能动力学可以作为有价值的生物标志物，用于更好地理解高级别胶质瘤的特征。
