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：进化搜索非常适合大规模生物物理脑建模，其中许多参数存在非线性相互作用且无易处理的梯度，需要进行优化。标准进化方法能很好地拟合MRI数据；然而，在众多可能的解中，它会找到过度拟合个体被试且预测能力有限的解。本文探讨了以生物学知识引导进化是否有所帮助。聚焦于全脑动力学平均场（DMF）模型，将20个参数在全脑共享的基线方案与异质性方案进行比较——后者对七个经典脑区分别使用不同的20个参数集。异质性模型采用四种策略优化：同时优化所有参数、遵循脑网络层级的课程式方法（HICO）、反向课程式方法以及随机打乱的课程式方法。尽管所有异质性策略都能良好拟合数据，但仅课程式方法可泛化至新被试。最重要的是，只有HICO能利用参数集预测被试的行为能力。因此，通过引入脑区层级生物学知识引导进化，HICO展示了如何利用领域知识服务于现实领域的优化目标。