Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study

Ana P Millán; Elisabeth C W van Straaten; Cornelis J Stam; Ida A Nissen; Sander Idema; Piet Van Mieghem; Arjan Hillebrand

doi:10.1162/netn_a_00361

Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study

Netw Neurosci. 2024 Jul 1;8(2):437-465. doi: 10.1162/netn_a_00361. eCollection 2024.

Authors

Ana P Millán^{1

2}, Elisabeth C W van Straaten^{1

3

4}, Cornelis J Stam^{1

5

4}, Ida A Nissen¹, Sander Idema^{6

3

7}, Piet Van Mieghem⁸, Arjan Hillebrand^{1

5

3}

Affiliations

¹ Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam, The Netherlands.
² Institute "Carlos I" for Theoretical and Computational Physics, and Electromagnetism and Matter Physics Department, University of Granada, Granada, Spain.
³ Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands.
⁴ Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
⁵ Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands.
⁶ Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurosurgery, Amsterdam, The Netherlands.
⁷ Amsterdam Neuroscience, Cancer Biology and Immonology, Amsterdam, The Netherlands.
⁸ Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.

Abstract

Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (N = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a pseudo-prospective study (N = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after any resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.

Keywords: Epidemic spreading model; Epilepsy; Epilepsy surgery; Large-scale brain network; Magnetoencephalography; Personalized medicine; Seizure modeling; Whole-brain modeling.

Plain language summary

Individualized computational models of epilepsy surgery capture some of the key aspects of seizure propagation and the resective surgery. It is to be established whether this information can be integrated during the presurgical evaluation of the patient to improve surgical planning and the chances of a good surgical outcome. Here we address this question with a pseudo-prospective study that applies a computational framework of seizure propagation and epilepsy surgery—the ESSES framework—in a pseudo-prospective study mimicking the presurgical conditions. We found that within this pseudo-prospective setting, ESSES could correctly predict 75% of NSF and 80.8% of SF cases. This finding suggests the potential of individualised computational models to inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection.