Wischnewski Kevin J, Jarre Florian, Eickhoff Simon B, Popovych Oleksandr V
Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Forschungszentrum Jülich, Germany.
Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany.
PLoS One. 2025 May 12;20(5):e0322983. doi: 10.1371/journal.pone.0322983. eCollection 2025.
Personalized modeling of the resting-state brain activity implies the usage of dynamical whole-brain models with high-dimensional model parameter spaces. However, the practical benefits and mathematical challenges originating from such approaches have not been thoroughly documented, leaving the question of the value and utility of high-dimensional approaches unanswered. Studying a whole-brain model of coupled phase oscillators, we proceeded from low-dimensional scenarios featuring 2-3 global model parameters only to high-dimensional cases, where we additionally equipped every brain region with a specific local model parameter. To enable the parameter optimizations for the high-dimensional model fitting to empirical data, we applied two dedicated mathematical optimization algorithms (Bayesian Optimization, Covariance Matrix Adaptation Evolution Strategy). We thereby optimized up to 103 parameters simultaneously with the aim to maximize the correlation between simulated and empirical functional connectivity separately for 272 subjects. The obtained model parameters demonstrated increased variability within subjects and reduced reliability across repeated optimization runs in high-dimensional spaces. Nevertheless, the quality of the model validation (goodness-of-fit, GoF) improved considerably and remained very stable and reliable together with the simulated functional connectivity. Applying the modeling results to phenotypical data, we found significantly higher prediction accuracies for sex classification when the GoF or coupling parameter values optimized in the high-dimensional spaces were considered as features. Our results elucidate the model fitting in high-dimensional parameter spaces and can contribute to an improved dynamical brain modeling as well as its application to the frameworks of inter-individual variability and brain-behavior relationships.
静息态脑活动的个性化建模意味着使用具有高维模型参数空间的动态全脑模型。然而，源于此类方法的实际益处和数学挑战尚未得到充分记录，使得高维方法的价值和效用问题悬而未决。在研究耦合相位振荡器的全脑模型时，我们从仅具有2 - 3个全局模型参数的低维场景入手，进而研究高维情况，在高维情况下，我们还为每个脑区配备了特定的局部模型参数。为了能够针对高维模型拟合经验数据进行参数优化，我们应用了两种专门的数学优化算法（贝叶斯优化、协方差矩阵自适应进化策略）。我们由此同时优化多达103个参数，目的是分别针对272名受试者最大化模拟功能连接与经验功能连接之间的相关性。在高维空间中，所获得的模型参数显示出受试者内部变异性增加，并且在重复优化运行中可靠性降低。尽管如此，模型验证（拟合优度，GoF）的质量显著提高，并且与模拟功能连接一起保持非常稳定和可靠。将建模结果应用于表型数据时，我们发现当将在高维空间中优化的GoF或耦合参数值作为特征时，性别分类的预测准确率显著更高。我们的结果阐明了在高维参数空间中的模型拟合，并且有助于改进动态脑建模及其在个体间变异性和脑 - 行为关系框架中的应用。
