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

2026-02-13 · Source: cs.NE updates on arXiv.org · Field: Science & Research — Life Sciences & Biology, Mathematics & Computational Sciences, Health & Medical Research · Depth: Expert, extended

Summary

Hormoz Shahrzad's research introduces Hierarchy-Informed Curriculum Optimization (HICO), a novel evolutionary search strategy for optimizing whole-brain Dynamic Mean Field (DMF) models. This method guides parameter optimization using biological knowledge about the hierarchical organization of brain networks, specifically the seven canonical brain regions. The study compared HICO against homogeneous, flat heterogeneous, reverse-phased, and randomly shuffled curricular approaches. While all heterogeneous models achieved good fits to individual MRI data, only curricular approaches, particularly HICO, demonstrated robust generalization to new subjects and enabled the prediction of subjects' behavioral abilities, including fluid reasoning and psychopathology. HICO's phased optimization, which introduces 20 parameters per region sequentially based on cortical hierarchy, significantly outperformed other methods in producing stable, generalizable, and behaviorally informative solutions, despite all methods having an equal computational budget of 120 generations.

Key takeaway

For AI Researchers and Computational Neuroscientists developing large-scale brain models, adopting hierarchy-informed curriculum optimization (HICO) is crucial. This approach yields models that not only fit individual neuroimaging data but also generalize across subjects and reliably predict behavioral outcomes. Your work will benefit from integrating biological domain knowledge into evolutionary search strategies to achieve more stable and interpretable solutions, moving beyond mere data fitting to create truly predictive models.

Key insights

Guiding evolutionary optimization with biological hierarchy improves whole-brain model generalization and behavioral predictability.

Principles

• Biological domain knowledge enhances optimization.
• Curricular parameter introduction improves generalization.
• Increased model complexity alone does not guarantee generalization.

Method

HICO optimizes Dynamic Mean Field (DMF) model parameters in phases, introducing RSN-specific parameter blocks sequentially based on the cortical hierarchy from global to unimodal regions, while holding previously optimized parameters fixed.

In practice

• Apply HICO to scale high-dimensional brain models.
• Use HICO to predict behavioral abilities from brain parameters.
• Consider hierarchical curricula for complex system optimization.

Topics

• Evolutionary Optimization
• Whole-Brain Modeling
• Dynamic Mean Field Models
• Cortical Hierarchy
• Behavioral Prediction

Best for: AI Researcher, AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.