Epilepsy affects ~50 million people globally, and nearly one-third do not respond to medication. Surgical resection has long been an alternative but remains highly invasive with significant risks. Responsive neurostimulation offers a less invasive option, but its lack of specificity limits effectiveness. Adaptive neurostimulation instead responds directly to epileptic features to maximize seizure-stopping capabilities. Recent breakthroughs in patient-specific computational brain modeling enable simulation of brain dynamics and responses to neurostimulation in silico, offering a powerful framework to design and optimize adaptive stimulation strategies before clinical deployment. Individualized models built from neuroimaging and electrophysiological data help identify seizure onset zones, simulate propagation, and test targeted interventions under controlled conditions. This enables precise tuning of parameters, such as timing, amplitude, and location, to maximize seizure suppression and minimize side effects. Compared to traditional approaches, virtual testing accelerates development of safer, more effective, and personalized therapies for epilepsy.
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