Evidence map›Paper›PMID 29164737›Full record

ArticleHuman brain mapping2018

Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy.

Rasheda Arman Chowdhury, Giovanni Pellegrino, Ümit Aydin, Jean-Marc Lina, François Dubeau, Eliane Kobayashi, Christophe Grova

Registry-linked trialAbstract read
In one paragraph

Article in Human brain mapping, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07681869 (A Randomized Controlled Study on the Effects of Nimodipine on Alcohol Drinking Among Adults Who Are Heavy Alcohol Drinkers), which is not on this map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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.

NCT07681869 phase2not yet recruitingnot on this mapstarted 2026, after this paper: background citation

A Randomized Controlled Study on the Effects of Nimodipine on Alcohol Drinking Among Adults Who Are Heavy Alcohol Drinkers

TypeinterventionalSponsorYale UniversityRan2026 to 2032Enrolled40ConditionsAlcohol Use DisorderArmsNimodipine, Placebo
3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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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

7 authors.

Rasheda Arman ChowdhuryMultimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Québec, Canada.ORCID 0000-0001-9691-594X
Giovanni PellegrinoSan Camillo Hospital IRCCS, 80 Via Alberoni, Venice, 30126, Italy.ORCID 0000-0002-1195-1421
Ümit AydinMultimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, Québec, Canada.
Jean-Marc LinaEcole de Technologie Supérieure, Montréal, Québec, Canada.
François DubeauNeurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
Eliane KobayashiNeurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
Christophe GrovaMultimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Québec, Canada.

Funding

CIHR MOP‐133619CIHR MOP‐93614
6 · The paper itself

Abstract

Fusion of electroencephalography (EEG) and magnetoencephalography (MEG) data using maximum entropy on the mean method (MEM-fusion) takes advantage of the complementarities between EEG and MEG to improve localization accuracy. Simulation studies demonstrated MEM-fusion to be robust especially in noisy conditions such as single spike source localizations (SSSL). Our objective was to assess the reliability of SSSL using MEM-fusion on clinical data. We proposed to cluster SSSL results to find the most reliable and consistent source map from the reconstructed sources, the so-called consensus map. Thirty-four types of interictal epileptic discharges (IEDs) were analyzed from 26 patients with well-defined epileptogenic focus. SSSLs were performed on EEG, MEG, and fusion data and consensus maps were estimated using hierarchical clustering. Qualitative (spike-to-spike reproducibility rate, SSR) and quantitative (localization error and spatial dispersion) assessments were performed using the epileptogenic focus as clinical reference. Fusion SSSL provided significantly better results than EEG or MEG alone. Fusion found at least one cluster concordant with the clinical reference in all cases. This concordant cluster was always the one involving the highest number of spikes. Fusion yielded highest reproducibility (SSR EEG = 55%, MEG = 71%, fusion = 90%) and lowest localization error. Also, using only few channels from either modality (21EEG + 272MEG or 54EEG + 25MEG) was sufficient to reach accurate fusion. MEM-fusion with consensus map approach provides an objective way of finding the most reliable and concordant generators of IEDs. We, therefore, suggest the pertinence of SSSL using MEM-fusion as a valuable clinical tool for presurgical evaluation of epilepsy.

Indexed as

Preoperative CareSignal Processing, Computer-AssistedBrainElectroencephalographyEpilepsyHumansMagnetic Resonance ImagingMagnetoencephalographyMultimodal ImagingReproducibility of Resultscoherent maximum entropy on the meanfusion of EEG and MEGinterictal epileptic spikespresurgical evaluation of epilepsyreproducibilitysingle trial localization

Identifiers

PMID29164737
PMCPMC6916740

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Registered trials

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.