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：演化搜索非常适合大规模生物物理脑建模，此类模型需要优化大量存在非线性相互作用且无可行梯度的参数。标准演化方法虽然能对MRI数据实现极佳拟合，但在众多可能解中，往往会产生对个体被试过拟合且预测能力有限的方案。本文探究以生物学知识引导演化能否带来改善。聚焦全脑动态平均场（DMF）模型，我们将20个参数在全脑共享的基线方案，与针对七个标准脑区分别采用20个不同参数的异质性方案进行对比。异质性模型通过四种策略优化：一次性优化全部参数、遵循脑网络层级结构的课程式方法（HICO）、反向课程式方法及随机打乱课程式方法。所有异质性策略虽均能良好拟合数据，但仅课程式方法可泛化至新被试。更重要的是，唯HICO方法使参数集可用于预测被试的行为能力。因此，通过利用脑区层级结构这一生物学知识引导演化，HICO证明了领域知识如何能在现实场景中服务于优化目的。