Principles and Operation of Virtual Brain Twins

Area of Science:

• Computational Neuroscience
• Personalized Medicine
• Neuroimaging

Background:

• Current clinical neuroscience trials often overlook individual variability.
• Mechanism-based trials are underutilized due to the brain's complexity.

Purpose of the Study:

• Introduce Virtual Brain Twins (VBTs) as personalized digital replicas of individual brains.
• Demonstrate how VBTs bridge molecular mechanisms, whole-brain dynamics, and imaging data.
• Advance understanding of healthy and disordered brain function through personalized computational models.

Main Methods:

• Integrate structural and functional brain data into advanced computational models.
• Utilize network modeling to couple mesoscopic neuronal activity via white matter connectivity.
• Employ Bayesian inference for VBT construction and simulation.

Main Results:

• VBTs provide interpretable predictive capabilities for brain dynamics.
• Applications demonstrated in resting-state, healthy aging, multiple sclerosis, and epilepsy.
• Potential for personalized treatment optimization and intervention.

Conclusions:

• VBTs represent a transformative approach to understanding and treating neurological disorders.
• Future extensions include Parkinson's disease, consciousness research, and brain-computer interfaces.
• Pave the way for advancements in personalized medicine and brain-machine integration.