Ten years of the digital twin brain: Perspectives and challenges

Abstract Over the past decade, the digital twin brain (DTB) has emerged as a transformative brain science paradigm, integrating multimodal data to construct dynamic models closely simulating biological brain function. This approach has advanced understanding of structure-function relationships, cognitive behaviors, and disease mechanisms, while supporting personalized therapies. Recent progress highlights DTB's potential in capturing functional heterogeneity, simulating information integration, and predicting individual cognitive and pathological variations. Looking forward, the development of a high-precision DTB is expected to drive breakthroughs in understanding brain mechanisms and enabling precision medicine. This perspective summarizes DTB modeling strategies, including multimodal data integration and optimization, while addressing challenges such as model granularity, and biological interpretability. Future efforts should focus on refining modeling techniques and integrating with brain cognition and disease. We believe these advancements will pave the way for breakthroughs in brain science and precision medicine, ushering in a new era of neuroscience and personalized healthcare.