The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread

Area of Science:

• Neuroscience
• Computational Biology
• Medical Imaging

Background:

• Individual variability significantly impacts treatment efficacy.
• Personalized healthcare strategies are crucial for improving patient outcomes.
• Current brain intervention methods lack patient-specific customization.

Observation:

• We present a novel approach for brain interventions using personalized brain network models.
• These models are derived from non-invasive structural neuroimaging data.
• We illustrate the process using a patient with bitemporal epilepsy.

Findings:

• A Virtual Epileptic Patient (VEP) model was developed integrating patient-specific connectivity, epileptogenic zone, and MRI lesions.
• High-performance computing enabled systematic parameter space exploration.
• The VEP model was fitted and validated against empirical stereotactic EEG (SEEG) data.

Implications:

• This methodology allows for the development of novel, personalized therapeutic and intervention strategies.
• Virtual patient models can advance precision medicine in neurology.
• Optimized treatment plans can be created based on individual brain network dynamics.