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Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
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Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study.

Ana P Millán1,2, Elisabeth C W van Straaten1,3,4, Cornelis J Stam1,5,4

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A new computational model, the Epidemic Spreading Seizure and Epilepsy Surgery (ESSES) framework, predicts epilepsy surgery outcomes. ESSES aids presurgical planning by suggesting optimal resections and estimating success likelihood, even without invasive EEG data.

Keywords:
Epidemic spreading modelEpilepsyEpilepsy surgeryLarge-scale brain networkMagnetoencephalographyPersonalized medicineSeizure modelingWhole-brain modeling

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Area of Science:

  • Computational Neuroscience
  • Epileptology
  • Medical Imaging and Data Analysis

Background:

  • Epilepsy surgery is a key treatment for drug-resistant epilepsy, yet many patients experience persistent seizures post-resection.
  • Accurate presurgical planning is crucial for optimizing surgical outcomes and predicting patient-specific results.
  • Current methods often struggle to predict postsurgical seizure control effectively.

Purpose of the Study:

  • To develop and validate an individualized computational model, the Epidemic Spreading Seizure and Epilepsy Surgery (ESSES) framework, for predicting epilepsy surgery outcomes.
  • To assess ESSES's ability to aid presurgical planning by identifying optimal resection strategies and estimating the likelihood of seizure freedom.
  • To evaluate ESSES's performance in a pseudo-prospective, blinded setting without requiring invasive electroencephalography (iEEG) data.

Main Methods:

  • Developed the ESSES framework combining epidemic spreading principles with patient-specific connectivity and epileptogeneity maps.
  • Fitted ESSES parameters retrospectively using iEEG-recorded seizures from 15 patients to reproduce seizure dynamics.
  • Validated ESSES in a pseudo-prospective study of 34 patients, assessing its predictive accuracy for surgical outcomes (seizure-free vs. non-seizure-free) and comparing model-based resections with actual surgical plans.

Main Results:

  • ESSES accurately reproduced iEEG-recorded seizures, performing better for seizure-free (SF) than non-seizure-free (NSF) patients.
  • The model predicted good outcomes with 80.8% accuracy for SF and 75% for NSF cases in the pseudo-prospective validation.
  • Model-based optimal resections were smaller for SF patients, and actual surgical plans showed greater overlap and impact on seizure propagation for SF patients compared to NSF patients.

Conclusions:

  • Individualized computational models like ESSES can significantly inform epilepsy surgery planning by suggesting optimal resections and predicting postsurgical outcomes.
  • ESSES demonstrates clinical applicability by successfully predicting outcomes in a blinded, pseudo-prospective cohort without requiring iEEG data.
  • The findings suggest that ESSES can help identify patients likely to benefit from surgery and guide more effective resection strategies, potentially improving seizure control.