Evidence map›Paper›PMID 39234168›Full record

ArticleBrain communications2024

Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy.

Erin C Conrad, Alfredo Lucas, William K S Ojemann, Carlos A Aguila, Marissa Mojena, Joshua J LaRocque, Akash R Pattnaik, Ryan Gallagher, Adam Greenblatt, Ashley Tranquille and 7 more

Abstract read
In one paragraph

Article in Brain communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Transcranial Focused Ultrasound Stimulation Targeting White Matter Inhibits Seizures in a Rat Model of Epilepsy.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026
    Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Erin C ConradDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0001-8910-1817
Alfredo LucasCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0001-9439-735X
William K S OjemannCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Carlos A AguilaCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Marissa MojenaCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Joshua J LaRocqueDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-3697-9965
Akash R PattnaikCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-2474-1685
Ryan GallagherCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-1789-9142
Adam GreenblattDepartment of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA.
Ashley TranquilleCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Alexandra ParashosDepartment of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA.
Ezequiel GleichgerrchtDepartment of Neurology, Emory University, Atlanta, GA 30325, USA.ORCID https://orcid.org/0000-0002-4212-4146
Leonardo BonilhaDepartment of Neurology, Emory University, Atlanta, GA 30325, USA.
Brian LittDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Saurabh R SinhaDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0003-0305-6731
Lyle UngarDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
Kathryn A DavisDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-7020-6480

Funding

Institutional Career Development CoreKL2TR002381 · NCATS · EMORY UNIVERSITY · PI HENRY M BLUMBERG, Anandi Nayan Sheth · 2017 to 2026
$14.1M
Training Program in Neuroengineering and MedicineT32NS091006 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Brian Litt · 2015 to 2026
$4.1M
Biomarkers to Predict Outcome from Responsive Brain Stimulation for EpilepsyR61NS125568 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVIS, KATHRYN ADAMIAK · 2023 to 2025
$3.6M
Optimized Intracranial EEG Targeting in Focal Epilepsy based upon Neuroimaging ConnectomicsR01NS116504 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVIS, KATHRYN ADAMIAK · 2021 to 2025
$3.3M
Quantitative interictal networks to localize seizure generatorsK23NS121401 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Erin Conrad · 2022 to 2026
$1.2M
NCATS NIH HHS KL2 TR002381NINDS NIH HHS K23 NS121401NINDS NIH HHS R01 NS116504NINDS NIH HHS R61 NS125568NINDS NIH HHS T32 NS091006
6 · The paper itself

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.

Indexed as

EEGinterictal EEGintracranial EEGtemporal lobe epilepsy

Identifiers

PMID39234168
PMCPMC11372416

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.