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，其中许多参数存在非线性相互作用且无法获得可处理的梯度。标准进化方法能够极好地拟合MRI数据；然而，在众多可能的此类解中，它会找到那些对个体被试过拟合且预测能力有限的解。本文研究了利用生物学知识引导进化是否有所帮助。聚焦于全脑动态平均场（DMF）模型，我们比较了一个基线模型（其中20个参数在全脑共享）与一个异质化模型（其中七个典型脑区使用不同的20个参数集）。异质化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（HICO）、反向课程式方法以及随机打乱的课程式方法。虽然所有异质化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，只有HICO方法还能利用参数集预测被试的行为能力。因此，通过利用关于脑区层级的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世界领域中的优化目的。