Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Neuroimage. 2022 Aug 15;257:119321. doi: 10.1016/j.neuroimage.2022.119321. Epub 2022 May 14.
Dynamical whole-brain models were developed to link structural (SC) and functional connectivity (FC) together into one framework. Nowadays, they are used to investigate the dynamical regimes of the brain and how these relate to behavioral, clinical and demographic traits. However, there is no comprehensive investigation on how reliable and subject specific the modeling results are given the variability of the empirical FC. In this study, we show that the parameters of these models can be fitted with a "poor" to "good" reliability depending on the exact implementation of the modeling paradigm. We find, as a general rule of thumb, that enhanced model personalization leads to increasingly reliable model parameters. In addition, we observe no clear effect of the model complexity evaluated by separately sampling results for linear, phase oscillator and neural mass network models. In fact, the most complex neural mass model often yields modeling results with "poor" reliability comparable to the simple linear model, but demonstrates an enhanced subject specificity of the model similarity maps. Subsequently, we show that the FC simulated by these models can outperform the empirical FC in terms of both reliability and subject specificity. For the structure-function relationship, simulated FC of individual subjects may be identified from the correlations with the empirical SC with an accuracy up to 70%, but not vice versa for non-linear models. We sample all our findings for 8 distinct brain parcellations and 6 modeling conditions and show that the parcellation-induced effect is much more pronounced for the modeling results than for the empirical data. In sum, this study provides an exploratory account on the reliability and subject specificity of dynamical whole-brain models and may be relevant for their further development and application. In particular, our findings suggest that the application of the dynamical whole-brain modeling should be tightly connected with an estimate of the reliability of the results.
动态全脑模型被开发出来，将结构（SC）和功能连接（FC）联系在一起，形成一个框架。如今，它们被用于研究大脑的动力学状态，以及这些状态如何与行为、临床和人口统计学特征相关。然而，目前还没有关于在考虑到经验性 FC 的可变性的情况下，这些建模结果的可靠性和个体特异性的综合研究。在本研究中，我们表明，这些模型的参数可以根据建模范例的确切实现，具有从“差”到“好”的可靠性。我们发现，作为一般经验法则，增强模型个性化会导致模型参数越来越可靠。此外，我们观察到，通过分别对线性、相位振荡器和神经质量网络模型的结果进行采样来评估模型复杂度，并没有明显的效果。事实上，最复杂的神经质量模型通常会产生与简单线性模型可比的“差”可靠性的建模结果，但表现出模型相似性图的增强个体特异性。随后，我们表明，这些模型模拟的 FC 在可靠性和个体特异性方面都优于经验 FC。对于结构-功能关系，个体受试者的模拟 FC 可以通过与经验 SC 的相关性来识别，其准确率高达 70%，但对于非线性模型则不行。我们对 8 种不同的脑区划分和 6 种建模条件进行了所有发现的采样，并表明分区诱导的效应对于建模结果比对经验数据更为明显。总之，本研究对动态全脑模型的可靠性和个体特异性进行了探索性的研究，可能对其进一步的发展和应用具有重要意义。特别是，我们的发现表明，动态全脑建模的应用应该与对结果可靠性的估计紧密联系起来。
