The IAS-MEEG Package: A Flexible Inverse Source Reconstruction Platform for Reconstruction and Visualization of Brain Activity from M/EEG Data.
Bayesian framework
Brain activity reconstruction
Conditionally Gaussian prior
Iterative Krylov solver
Sensitivity weighting
Sliced visualization
Journal
Brain topography
ISSN: 1573-6792
Titre abrégé: Brain Topogr
Pays: United States
ID NLM: 8903034
Informations de publication
Date de publication:
01 2023
01 2023
Historique:
received:
23
05
2022
accepted:
31
10
2022
pubmed:
3
12
2022
medline:
14
1
2023
entrez:
2
12
2022
Statut:
ppublish
Résumé
We present a standalone Matlab software platform complete with visualization for the reconstruction of the neural activity in the brain from MEG or EEG data. The underlying inversion combines hierarchical Bayesian models and Krylov subspace iterative least squares solvers. The Bayesian framework of the underlying inversion algorithm allows to account for anatomical information and possible a priori belief about the focality of the reconstruction. The computational efficiency makes the software suitable for the reconstruction of lengthy time series on standard computing equipment. The algorithm requires minimal user provided input parameters, although the user can express the desired focality and accuracy of the solution. The code has been designed so as to favor the parallelization performed automatically by Matlab, according to the resources of the host computer. We demonstrate the flexibility of the platform by reconstructing activity patterns with supports of different sizes from MEG and EEG data. Moreover, we show that the software reconstructs well activity patches located either in the subcortical brain structures or on the cortex. The inverse solver and visualization modules can be used either individually or in combination. We also provide a version of the inverse solver that can be used within Brainstorm toolbox. All the software is available online by Github, including the Brainstorm plugin, with accompanying documentation and test data.
Identifiants
pubmed: 36460892
doi: 10.1007/s10548-022-00926-9
pii: 10.1007/s10548-022-00926-9
pmc: PMC9834133
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
10-22Informations de copyright
© 2022. The Author(s).
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