Evolutionary search is well suited for large-scale biophysical brain modeling, where many parameters with nonlinear interactions and no tractable gradients need to be optimized. Standard evolutionary approaches achieve an excellent fit to MRI data; however, among many possible such solutions, it finds ones that overfit to individual subjects and provide limited predictive power. This paper investigates whether guiding evolution with biological knowledge can help. Focusing on whole-brain Dynamic Mean Field (DMF) models, a baseline where 20 parameters were shared across the brain was compared against a heterogeneous formulation where different sets of 20 parameters were used for the seven canonical brain regions. The heterogeneous model was optimized using four strategies: optimizing all parameters at once, a curricular approach following the hierarchy of brain networks (HICO), a reversed curricular approach, and a randomly shuffled curricular approach. While all heterogeneous strategies fit the data well, only curricular approaches generalized to new subjects. Most importantly, only HICO made it possible to use the parameter sets to predict the subjects' behavioral abilities as well. Thus, by guiding evolution with biological knowledge about the hierarchy of brain regions, HICO demonstrated how domain knowledge can be harnessed to serve the purpose of optimization in real-world domains.

翻译：进化搜索非常适用于大规模生物物理脑建模，因为许多参数之间存在非线性相互作用且无法获得可处理的梯度。标准进化方法能够很好地拟合MRI数据；然而，在众多可能的解中，这些方法找到的解往往对个体被试过拟合，且预测能力有限。本文探讨了利用生物学知识引导进化过程是否有所帮助。聚焦于全脑动态平均场模型，我们比较了两种方案：一种基线方案（全脑共享20个参数）与一种异质性方案（七个典型脑区各自使用不同的20个参数集）。异质性模型采用四种策略进行优化：一次性优化所有参数、遵循脑网络层级的课程式方法、反向课程式方法以及随机重排的课程式方法。虽然所有异质性策略都能很好地拟合数据，但只有课程式方法能够泛化到新被试。最重要的是，仅HICO方法能够进一步利用参数集预测被试的行为能力。因此，通过利用脑区层级结构的生物学知识引导进化过程，HICO展示了如何在实际领域中运用领域知识来服务于优化目标。