Only two decades ago, it would have been difficult to imagine a Research Topic on the use of digital twins in neurosciences. Since then, rapid developments in brain imaging, brain modeling, and the availability of computing power changed our way of operating in neuroscience, leading to a novel culture of open science, data sharing, and interoperation in the field. The integration of concepts, simulation technologies, and data is the foundation of a digital twin. It digitally represents an intended or actual real-world physical system or process, which integrates empirical data and simulates its behavior for purposes of testing, prediction, monitoring, and optimization. In neurosciences, the applications are clinical (eg, personalized brain modeling) and technological (eg, brain-derived architecture for body control or interfaces with external effectors). The Human Brain Project (HBP), after almost 10 years of research and development in computational neurosciences, has placed the digital twin at the core of its Scientific Vision, as presented in its statement paper entitled The coming decade of digital brain research. The European digital neuroscience infrastructure EBRAINS integrates data, multiscale models and methods to create workflows operating digital twins for research and clinical translation. It speaks to the increasing maturity of digital neurosciences that the engineering concept of the digital twin finds such prominent entry in these domains.
Historically, the concept of the digital twin originated in the realm of industry and manufacturing and comprises three components: the physical object, its virtual counterpart, and the data flow back and forth between the two. Empirical data measured for the physical object are passed to the model, and information and processes from the model are passed to the physical object. Such dialectic needs to be operationalized, which entails the use of appropriate sensors to measure the environmental inputs; the appropriate implementation to represent/convey such inputs in the virtual space, where the digital twin operates; the data handling, which is needed to represent the environmental inputs for the digital twin; and the modeling, in the digital space, of the real-world responses to the potential actions of the digital twin (for example, as to inform external effectors, in the case of Brain-Computer interfaces).
This Research Topic aims at representing the state-of-the-art of digital twin technology in brain sciences, ranging from sophisticated research tools, personalized brain models aiding in diagnostics and therapy, and novel brain-derived cognitive architectures for use in technical applications such as neurorobotics. Submissions in domains related to digital twins such as ethical issues, the use of novel high performance computing, and neurotechnologies are welcome.
Only two decades ago, it would have been difficult to imagine a Research Topic on the use of digital twins in neurosciences. Since then, rapid developments in brain imaging, brain modeling, and the availability of computing power changed our way of operating in neuroscience, leading to a novel culture of open science, data sharing, and interoperation in the field. The integration of concepts, simulation technologies, and data is the foundation of a digital twin. It digitally represents an intended or actual real-world physical system or process, which integrates empirical data and simulates its behavior for purposes of testing, prediction, monitoring, and optimization. In neurosciences, the applications are clinical (eg, personalized brain modeling) and technological (eg, brain-derived architecture for body control or interfaces with external effectors). The Human Brain Project (HBP), after almost 10 years of research and development in computational neurosciences, has placed the digital twin at the core of its Scientific Vision, as presented in its statement paper entitled The coming decade of digital brain research. The European digital neuroscience infrastructure EBRAINS integrates data, multiscale models and methods to create workflows operating digital twins for research and clinical translation. It speaks to the increasing maturity of digital neurosciences that the engineering concept of the digital twin finds such prominent entry in these domains.
Historically, the concept of the digital twin originated in the realm of industry and manufacturing and comprises three components: the physical object, its virtual counterpart, and the data flow back and forth between the two. Empirical data measured for the physical object are passed to the model, and information and processes from the model are passed to the physical object. Such dialectic needs to be operationalized, which entails the use of appropriate sensors to measure the environmental inputs; the appropriate implementation to represent/convey such inputs in the virtual space, where the digital twin operates; the data handling, which is needed to represent the environmental inputs for the digital twin; and the modeling, in the digital space, of the real-world responses to the potential actions of the digital twin (for example, as to inform external effectors, in the case of Brain-Computer interfaces).
This Research Topic aims at representing the state-of-the-art of digital twin technology in brain sciences, ranging from sophisticated research tools, personalized brain models aiding in diagnostics and therapy, and novel brain-derived cognitive architectures for use in technical applications such as neurorobotics. Submissions in domains related to digital twins such as ethical issues, the use of novel high performance computing, and neurotechnologies are welcome.
