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

Autor:	Luria G; Bayesian Estimation for Engineering Solutions srl, Genoa, Italy., Viani A; Department of Mathematics, University of Genoa, Genoa, Italy., Pascarella A; CNR, Institute for Applied Mathematics 'Mauro Picone', Rome, Italy., Bornfleth H; BESA GmbH, Gräfelfing, Germany., Sommariva S; Department of Mathematics, University of Genoa, Genoa, Italy., Sorrentino A; Department of Mathematics, University of Genoa, Genoa, Italy.
Jazyk:	angličtina
Zdroj:	Frontiers in human neuroscience [Front Hum Neurosci] 2024 Mar 13; Vol. 18, pp. 1359753. Date of Electronic Publication: 2024 Mar 13 (Print Publication: 2024).
DOI:	10.3389/fnhum.2024.1359753
Abstrakt:	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 a priori high- and low-probability search regions in input; it can localize the generators of neural oscillations in the frequency domain. Both a Python and a MATLAB implementation of SESAME are available as open-source packages under the name of SESAMEEG and are well integrated with the main software packages used by the M/EEG community; moreover, the algorithm is part of the commercial software BESA Research (from version 7.0 onwards). While SESAMEEG is arguably simpler to use than other source modeling methods, it has a much richer output that deserves to be described thoroughly. In this article, after a gentle mathematical introduction to the algorithm, we provide a complete description of the available output and show several use cases on experimental M/EEG data. Competing Interests: GL was employed by Bayesian Estimation for Engineering Solutions srl. HB was employed by BESA GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Luria, Viani, Pascarella, Bornfleth, Sommariva and Sorrentino.)
Databáze:	MEDLINE
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