WMG academic leads review on digital twin brains
Thursday 3 September 2026
WMG academic leads review on digital twin brains
, Assistant Professor in Applied Artificial Intelligence at 海角社区 Manufacturing Group (WMG), University of 海角社区, has led a major commissioned review for Nature Reviews on digital twin brains.
Published in Nature Reviews Electrical Engineering, "鈥� draws on research from WMG, the University of 海角社区鈥檚 Department of Computer Science, and Fudan University to tackle one of biology鈥檚 biggest questions: how close are we to building a digital twin of a living human brain?
The review reveals that the key to faithfully building a digital twin lies not in computing power, but in how much of the living brain we can measure, validate, and update over time. A digital twin brain is not just a detailed simulation; it鈥檚 a living, unique digital copy of a person鈥檚 brain which is updated throughout their life.
鈥淎 digital twin brain is more than a large-scale simulation of the brain鈥�, explains Dr Zhang. 鈥淭he key distinction is that it represents a specific individual and remains connected to the biological brain through data.
"The reality of creating a digital twin brain changes the question from 鈥楬ow many brain cells can we simulate?鈥� to 鈥楬ow much of an individual living brain can we actually observe, constrain, and update in order to create a true digital twin?鈥欌��
The clearest illustration of this challenge is scale. In simple nervous systems, scientists can already map every synapse (brain connection); however, human brains have around 86 billion neurons. Full synaptic-level mapping at that scale is currently far beyond technical and economic feasibility.
The research formalises this idea in a new framework 鈥� the measurement-defined emulation scale 鈥� which describes how the achievable fidelity of a digital twin brain is shaped by the resolution, completeness and updatability of the biological measurements available to constrain it, rather than simply by neuron count.
In practice, that means creating a feasible human digital twin brain will require combining several imperfect tools rather than waiting for a single perfect one: detailed electron microscopy in small local regions of the brain, broader statistical mapping of cell types and circuits, and whole-brain MRI to tie it all together.
But mapping structure is only half the challenge. Brains change constantly through learning, ageing, disease, and experience, so a meaningful digital twin must also keep pace over time, rather than being frozen at the moment it is built.
The review charts a path from today's largely static models towards ones that update with new data, eventually working towards closed-loop systems where a digital twin's behaviour is checked against and corrected by the brain it mirrors.
of the University of 海角社区 and Fudan University added: 鈥淚t is important to distinguish what digital twin brains can do today from the longer-term vision. Current models can already reproduce selected structural and functional features using individualised biological data.
鈥淭he next challenge is to make these models increasingly adaptive: able to update as new measurements arrive and eventually interact with the biological system in closed-loop settings.鈥�
The researchers set out three ways the technology could be used:
- Neuroscience: using digital twin brains as virtual platforms for running experiments that would be impossible or unethical on a living brain, potentially providing one of the earliest mature applications of the technology
- Healthcare: using virtual patient twins for individualised disease prediction, treatment evaluation, and virtual intervention testing
- AI: developing brain-inspired systems for studying perception, action, adaptation, and interaction
鈥淭he long-term potential of digital twins is not to build larger computational models, but models that are meaningfully connected to individuals and can help us understand how biological systems change over time,鈥� concludes Dr Ruohan Zhang. 鈥淚f developed responsibly, digital twin brains could become a shared technological and scientific infrastructure spanning healthcare, neuroscience and brain-inspired artificial intelligence.鈥�
Read the full review through the Nature Reviews website: