The new paper "Virtual Brain Twins: From Basic Neuroscience to Clinical Use" discusses the development of personalised brain models based on individual brain data, aiming to improve understanding and treatment of various brain disorders.
These models are generative and adaptive, using a person's brain imaging data to tailor interventions for conditions like epilepsy, Alzheimer's, multiple sclerosis, Parkinson's, psychiatric disorders and ageing. The approach uses machine learning and artificial intelligence for parameter estimation, integrating physiological and pathophysiological data to personalise treatments. The paper also explores challenges and future directions in the field.
For more details, consult the article by Wang & Triebkorn et al. here.
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