Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany.
Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany.
Neuroimage. 2021 Aug 1;236:118201. doi: 10.1016/j.neuroimage.2021.118201. Epub 2021 May 24.
Modern approaches to investigate complex brain dynamics suggest to represent the brain as a functional network of brain regions defined by a brain atlas, while edges represent the structural or functional connectivity among them. This approach is also utilized for mathematical modeling of the resting-state brain dynamics, where the applied brain parcellation plays an essential role in deriving the model network and governing the modeling results. There is however no consensus and empirical evidence on how a given brain atlas affects the model outcome, and the choice of parcellation is still rather arbitrary. Accordingly, we explore the impact of brain parcellation on inter-subject and inter-parcellation variability of model fitting to empirical data. Our objective is to provide a comprehensive empirical evidence of potential influences of parcellation choice on resting-state whole-brain dynamical modeling. We show that brain atlases strongly influence the quality of model validation and propose several variables calculated from empirical data to account for the observed variability. A few classes of such data variables can be distinguished depending on their inter-subject and inter-parcellation explanatory power.
现代研究复杂大脑动力学的方法建议将大脑表示为一个由大脑图谱定义的脑区功能网络，而边缘则代表它们之间的结构或功能连接。这种方法也用于静息态大脑动力学的数学建模，其中应用的大脑分割在导出模型网络和控制建模结果方面起着至关重要的作用。然而，关于给定的大脑图谱如何影响模型结果，以及如何选择分割，还没有共识和经验证据，分割的选择仍然相当随意。因此，我们探讨了大脑分割对模型拟合经验数据的个体间和个体间变异性的影响。我们的目标是提供关于分割选择对静息态全脑动力建模潜在影响的全面经验证据。我们表明，大脑图谱强烈影响模型验证的质量，并提出了从经验数据中计算出的几个变量来解释观察到的变异性。根据其个体间和个体间的解释能力，可以区分几类这样的数据变量。
