Virtual brain models: a roadmap towards effective brain digital twins

Virtual Brain Models (VBMs) are emerging computational tools promising to revolutionize personalised and precision medicine. VBMs reproduce brain functional dynamics through a generative model that can be constructed and optimized against single subject data, thereby generating what is called a brain digital twin (BDT). VBMs can provide critical information about parameters of network functioning and connectivity but this ability depends on their design, raising some fundamental questions. Can VBM parameters identify key physiological features of the brain otherwise not accessible? Are VBM parameters correlated with sensorimotor and cognitive functions? What is still needed to make a VBM more precise in its physiological mechanisms? Simulations in patients affected by neurological diseases are indeed showing that VBMs can operate as effective BDTs capturing essential aspects of the physiopathology of single patients along with relevant physio-psychological correlations. However, the current VBM theory and technology still pose some limitations. We maintain that the evolution of VBM holds the key for a deeper understanding of brain physiology and pathology fostering the development of powerful computational tools capable of supporting personalized clinical decision making. Here, we will consider a roadmap addressing what VBM already do well, why this is not enough, what must change, and what becomes possible if it does.