Evidence map›Paper›PMID 39546895›Full record

ReviewNeuroImage. Clinical2024

Electroencephalography derived connectivity informing epilepsy surgical planning: Towards clinical applications and future perspectives.

Giulia Salvatici, Giovanni Pellegrino, Marco Perulli, Alberto Danieli, Paolo Bonanni, Gian Marco Duma

Abstract readReview
In one paragraph

Review in NeuroImage. Clinical, 2024. 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
–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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Giulia SalvaticiScientific Institute IRCCS E.Medea, Epilepsy and Clinical Neurophysiology Unit, Conegliano 31015, Italy. Electronic address: giulia.salvatici@studenti.unipd.it.
Giovanni PellegrinoClinical Neurological Sciences Department, Schulich School of Medicine and Dentistry, Western University, London N6A5C1, Canada. Electronic address: giovannipellegrino@gmail.com.
Marco PerulliFondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Alberto DanieliScientific Institute IRCCS E.Medea, Epilepsy and Clinical Neurophysiology Unit, Conegliano 31015, Italy. Electronic address: alberto.danieli@lanostrafamiglia.it.
Paolo BonanniScientific Institute IRCCS E.Medea, Epilepsy and Clinical Neurophysiology Unit, Conegliano 31015, Italy. Electronic address: paolo.bonanni@lanostrafamiglia.it.
Gian Marco DumaScientific Institute IRCCS E.Medea, Epilepsy and Clinical Neurophysiology Unit, Conegliano 31015, Italy. Electronic address: gianmarco.duma@lanostrafamiglia.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epilepsy is one of the most diffused neurological disorders, affecting 50 million people worldwide. Around 30% of patients have drug-resistant epilepsy (DRE), defined as failure of at least two tolerated antiseizure medications (ASMs) to achieve sustained seizure freedom. Brain surgery is an effective therapeutic approach in this group, hinging on the accurate localization of the epileptic focus. The latter task is complex and requires multimodal investigation methods. Epilepsy is also a network disorder and represents one of the best application scenarios of methods leveraging brain functional organization at large scales. Connectivity analysis represents a promising tool for improving surgical assessment, enabling better identification of candidates who could benefit the most from epilepsy surgery. The scalp electroencephalography (EEG) is the most relevant tool to characterize epileptic activity. The EEG has benefited significantly from technological advancement across the last decades. Firstly, electrical source imaging (ESI) allows the reconstruction of electrical activity detected by EEG at the cortex level; secondly, functional connectivity (FC) allows the assessment of functional dependencies across brain areas. The EEG has therefore expanded potential applications in the localization and characterization of the epileptogenic network for surgical planning. As the translation of these methods in clinical practice is little discussed in the literature, we reviewed the investigations using EEG-derived FC. We showed that the FC-informed identification of the epileptic networks improves the localization precision in focal epilepsy. We discussed the heterogeneity in the results and methodology preventing prompt research-to-clinic translation. We finally provided practical suggestions for promoting the applicability of FC-based research in real clinical practice, looking for future research.

Indexed as

ElectroencephalographyBrainDrug Resistant EpilepsyEpilepsyHumansNeurosurgical ProceduresBrain dynamicsDrug-resistant epilepsyEEGEpilepsy surgeryFunctional connectivity

Identifiers

PMID39546895
PMCPMC11613172

What OpenQuestion holds

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Registered trials

None linked

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.