An Intelligent Neuro-Digital Twin Framework for Personalized Brain Modeling Using Artificial Intelligence

This paper presented an intelligent neuro-digital twin frame-work for personalized brain modelling using artificial intelli-gence and high-performance computing. The proposed architecture integrates multimodal medical imaging, AI-based feature extraction, neural connectivity mod-elling, computational simulation, and continuous model refine-ment to create an adaptive virtual representation of the human brain. The framework provides a foundation for applications in-cluding neurological disease analysis, personalized treatment planning, drug development, brain-computer interfaces, and medical education. The mathematical formulation provides a conceptual representation of neural connectivity, brain-state estimation, and disease-risk prediction. The charts and architecture presented in this work illustrate the relationship between neuronal connectivity, computational processing stages, and AI components within the proposed framework. However, the presented framework is conceptual and requires experimental implementation and clinical valida-tion before quantitative claims about clinical performance can be established. Future developments in artificial intelligence, multimodal learning, high-performance computing, privacy-preserving computation, and neuroscience may further improve the ca-pabilities of neuro-digital twins and contribute to the develop-ment of personalized computational healthcare.

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