Evidence map›Paper›PMID 37274829›Full record

ArticleBrain communications2023

An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application.

Pablo Cuesta, Ricardo Bruña, Ekta Shah, Christopher Laohathai, Stephanie Garcia-Tarodo, Michael Funke, Gretchen Von Allmen, Fernando Maestú

Open access · goldAbstract read
In one paragraph

Article in Brain communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 47% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

8 authors at 4 institutions in 3 countries.

Pablo CuestaDepartment of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, 28040, Spain.ORCID https://orcid.org/0000-0002-8459-3354
Ricardo BruñaDepartment of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, Madrid, 28040, Spain.
Ekta ShahDepartment of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Christopher LaohathaiDepartment of Neurology, Saint Louis University, Saint Louis, MO 63110, USA.
Stephanie Garcia-TarodoDépartement de la femme, de l'enfant et de l'adolescent, Hôpital des Enfants - Hôpitaux Universitaires de Genève, Geneva, 1211 Genève 14, Switzerland.
Michael FunkeDepartment of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Gretchen Von AllmenDepartment of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Fernando MaestúCenter for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, 28040, Spain.
The University of Texas Health Science Center at Houston · USUniversidad Complutense de Madrid · ESHôpital de l'enfance · CHSaint Louis University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30-70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient's magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography-MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery.

Indexed as

children epilepsycomputational neurosurgeryinterictal activitymagnetoencephalography

Identifiers

PMID37274829
PMCPMC10236945
OpenAlexW4378227907

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.