Evolution With Purpose: Hierarchy-Informed Optimization of Whole-Brain Models

2026-02-11

AI Summary

This study addresses the challenge that traditional evolutionary optimization in whole-brain biophysical models tends to overfit individual subjects, limiting generalizability and behavioral prediction. To overcome this, the authors propose Hierarchical-guided Curriculum Optimization (HICO), which, for the first time, leverages the biological hierarchical organization of brain networks to define a curriculum sequence for evolutionary optimization. By progressively optimizing heterogeneous regional parameters in stages aligned with this hierarchy, HICO operates within a dynamic mean-field (DMF) whole-brain modeling framework. The method maintains high fidelity to neuroimaging data while substantially enhancing cross-subject generalization. Furthermore, the optimized model parameters successfully predict individual behavioral performance, demonstrating the approach’s potential for linking biophysically grounded brain dynamics to cognition.

Technology Category

• Search and Optimization: Evolutionary Computation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Evolutionary Learning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• evolutionary optimization
• whole-brain modeling
• overfitting
• generalization
• biophysical models

Innovation

• hierarchy-informed optimization
• evolutionary search
• whole-brain modeling
• curricular learning
• Dynamic Mean Field