Personalised virtual brain models in epilepsy

Individuals with drug-resistant focal epilepsy are candidates for surgical treatment as a curative option. Before surgery can take place, the patient must have a presurgical evaluation to establish whether and how surgical treatment might stop their seizures without causing neurological deficits. Virtual brains are a new digital modelling technology that map the brain network of a person with epilepsy, using data derived from MRI. This technique produces a computer simulation of seizures and brain imaging signals, such as those that would be recorded with intracranial EEG. When combined with machine learning, virtual brains can be used to estimate the extent and organisation of the epileptogenic zone (ie, the brain regions related to seizure generation and the spatiotemporal dynamics during seizure onset). Virtual brains could, in the future, be used for clinical decision making, to improve precision in localisation of seizure activity, and for surgical planning, but at the moment these models have some limitations, such as low spatial resolution. As evidence accumulates in support of the predictive power of personalised virtual brain models, and as methods are tested in clinical trials, virtual brains might inform clinical practice in the near future.

Automated summary

Individuals with drug-resistant focal epilepsy can benefit from surgical treatment, but a presurgical evaluation is necessary to assess the feasibility of surgery without causing neurological deficits. Virtual brains, created using data from MRI, offer a computer simulation of seizures and brain imaging signals. By combining virtual brains with machine learning, it is possible to estimate the epileptogenic zone and understand seizure onset dynamics. However, the current models have limitations such as low spatial resolution, and further research is needed to validate their predictive power and potential clinical applications.