Evidence map›Paper›PMID 39440305›Full record

ArticleBrain communications2024

The sixth sense: how much does interictal intracranial EEG add to determining the focality of epileptic networks?

Ryan S Gallagher, Nishant Sinha, Akash R Pattnaik, William K S Ojemann, Alfredo Lucas, Joshua J LaRocque, John M Bernabei, Adam S Greenblatt, Elizabeth M Sweeney, Iahn Cajigas and 4 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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Ryan S GallagherCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-1789-9142
Nishant SinhaCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-2090-4889
Akash R PattnaikCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-2474-1685
William K S OjemannCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Alfredo LucasCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0001-9439-735X
Joshua J LaRocqueCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-3697-9965
John M BernabeiCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-3012-1263
Adam S GreenblattDepartment of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Elizabeth M SweeneyDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Iahn CajigasDepartment of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-5808-453X
H Isaac ChenDepartment of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA.
Kathryn A DavisCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0002-7020-6480
Erin C ConradCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID https://orcid.org/0000-0001-8910-1817
Brian LittCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Funding

Training Program in Neuroengineering and MedicineT32NS091006 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Brian Litt · 2015 to 2026
$4.1M
Optimized Intracranial EEG Targeting in Focal Epilepsy based upon Neuroimaging ConnectomicsR01NS116504 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVIS, KATHRYN ADAMIAK · 2021 to 2025
$3.3M
Guiding epilepsy surgery using network models and Stereo EEGR01NS125137 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2022 to 2025
$2.8M
Quantitative interictal networks to localize seizure generatorsK23NS121401 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Erin Conrad · 2022 to 2026
$1.2M
Scalable methods to quantify epileptic network and guide epilepsy surgeryK99NS138680 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI SINHA, NISHANT · 2024 to 2025
$298k
NINDS NIH HHS K23 NS121401NINDS NIH HHS K99 NS138680NINDS NIH HHS R01 NS116504NINDS NIH HHS R01 NS125137NINDS NIH HHS T32 NS091006
6 · The paper itself

Abstract

Intracranial EEG is used for two main purposes: to determine (i) if epileptic networks are amenable to focal treatment and (ii) where to intervene. Currently, these questions are answered qualitatively and differently across centres. There is a need to quantify the focality of epileptic networks systematically, which may guide surgical decision-making, enable large-scale data analysis and facilitate multi-centre prospective clinical trials. We analysed interictal data from 101 patients with drug-resistant epilepsy who underwent pre-surgical evaluation with intracranial EEG at a single centre. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. Sixty-five patients had unifocal seizure onset on intracranial EEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal intracranial EEG abnormalities for each patient. We compared these measures against the '5 Sense Score,' a pre-implant prediction of the likelihood of focal seizure onset, assessed the ability to predict unifocal seizure onset by combining these metrics and evaluated how predicted focality relates to subsequent treatment and outcomes. The spatial dispersion of intracranial EEG electrodes predicted network focality with similar performance to the 5-SENSE score [area under the receiver operating characteristic curve = 0.68 (95% confidence interval 0.57, 0.78)], indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5-SENSE score and the spatial dispersion of interictal intracranial EEG abnormalities significantly improved this prediction [area under the receiver operating characteristic curve = 0.79 (95% confidence interval 0.70, 0.88);

Indexed as

epilepsy surgeryintracranial EEGnormative atlasseizure onset zonespatial coverage

Identifiers

PMID39440305
PMCPMC11495218

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Registered trials

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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.