Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy

Author:

Conrad Erin C12ORCID,Lucas Alfredo234ORCID,Ojemann William K S23,Aguila Carlos A23,Mojena Marissa2,LaRocque Joshua J12ORCID,Pattnaik Akash R23ORCID,Gallagher Ryan24ORCID,Greenblatt Adam5,Tranquille Ashley2,Parashos Alexandra6,Gleichgerrcht Ezequiel7ORCID,Bonilha Leonardo7,Litt Brian123,Sinha Saurabh R1ORCID,Ungar Lyle8,Davis Kathryn A12ORCID

Affiliation:

1. Department of Neurology, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 , USA

2. Center for Neuroengineering and Therapeutics, University of Pennsylvania , Philadelphia, PA 19104 , USA

3. Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania , Philadelphia, PA 19104 , USA

4. Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 , USA

5. Department of Neurology, Washington University in St. Louis , St. Louis, MO 63110 , USA

6. Department of Neurology, Medical University of South Carolina , Charleston, SC 29425 , USA

7. Department of Neurology, Emory University , Atlanta, GA 30325 , USA

8. Department of Computer and Information Science, University of Pennsylvania , Philadelphia, PA 19104 , USA

Abstract

Abstract Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data are captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-centre study for model development; two-centre study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using several interictal EEG features derived from recent publications. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. Forty-seven patients (30 female; ages 20–69; 20 left-sided, 10 right-sided and 17 bilateral seizure onsets) were analysed for model development and internal validation. Nineteen patients (10 female; ages 23–73; 5 left-sided, 10 right-sided, 4 bilateral) were analysed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. We replicated the finding that right temporal lobe epilepsy was harder to distinguish in a separate modality of resting-state functional MRI. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome. A potential clinical application of these findings could be to either support or oppose a hypothesis of unilateral temporal lobe epilepsy when deciding to pursue surgical resection or ablation as opposed to device implantation.

Funder

National Institute of Neurological Disorders and Stroke

Burroughs Wellcome Fund

National Science Foundation

National Institutes of Health Grant

Georgia Clinical and Translational Science Awards

National Institutes of Health

Publisher

Oxford University Press (OUP)

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