Evidence map›Paper›PMID 38545514›Full record

ArticleFrontiers in human neuroscience2024

The SESAMEEG package: a probabilistic tool for source localization and uncertainty quantification in M/EEG.

Gianvittorio Luria, Alessandro Viani, Annalisa Pascarella, Harald Bornfleth, Sara Sommariva, Alberto Sorrentino

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in human neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 36% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 3 institutions in 1 country.

Gianvittorio LuriaBayesian Estimation for Engineering Solutions srl, Genoa, Italy.
Alessandro VianiDepartment of Mathematics, University of Genoa, Genoa, Italy.
Annalisa PascarellaCNR, Institute for Applied Mathematics "Mauro Picone", Rome, Italy.
Harald BornflethBESA GmbH, Gräfelfing, Germany.
Sara SommarivaDepartment of Mathematics, University of Genoa, Genoa, Italy.
Alberto SorrentinoDepartment of Mathematics, University of Genoa, Genoa, Italy.
University of Genoa · ITEngineering (Italy) · ITIstituto per le Applicazioni del Calcolo Mauro Picone · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Source localization from M/EEG data is a fundamental step in many analysis pipelines, including those aiming at clinical applications such as the pre-surgical evaluation in epilepsy. Among the many available source localization algorithms, SESAME (SEquential SemiAnalytic Montecarlo Estimator) is a Bayesian method that distinguishes itself for several good reasons: it is highly accurate in localizing focal sources with comparably little sensitivity to input parameters; it allows the quantification of the uncertainty of the reconstructed source(s); it accepts user-defined

Indexed as

Bayesian inferenceEEGinverse problemsMATLABMEGopen-source softwarePython

Identifiers

PMID38545514
PMCPMC10965712
OpenAlexW4392755712

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.