Stable individualized brain computing model informed by spatiotemporal co-activity patterns.

ABSTRACT: Accurate simulation of the brain's intrinsic dynamic activity is essential for understanding human cognition and behavior and developing personalized brain disease therapies. Traditional neurodynamics models depend on structural connectivity to explain the emergence of functional connectivity (FC). However, achieving high-fidelity simulations at the individual level remains challenging, as the models fail to fully capture the brain information. To address these challenges, we introduce the Stable Individualized Brain Computing Model (SI-BCM), a data-driven reverse engineering framework designed to infer spatiotemporal co-activity patterns from fMRI data for simulating whole-brain activity. This model captures the dynamic interactions between brain regions by integrating spatiotemporal dime

SUBMITTER: Yang L 

PROVIDER: S-EPMC12716749 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature