Spatial fidelity of MEG/EEG source estimates: A general evaluation approach.
Adult
Brain
/ diagnostic imaging
Electroencephalography
/ methods
Female
Humans
Linear Models
Magnetic Resonance Imaging
Magnetoencephalography
/ methods
Male
Neocortex
/ diagnostic imaging
Nonlinear Dynamics
Rest
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Spatial Analysis
Young Adult
EEG
MEG
Patch analysis
SNR
Source estimation
Spatial resolution
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
01 01 2021
01 01 2021
Historique:
received:
31
03
2020
revised:
15
09
2020
accepted:
29
09
2020
pubmed:
11
10
2020
medline:
9
3
2021
entrez:
10
10
2020
Statut:
ppublish
Résumé
Low spatial resolution is often cited as the most critical limitation of magneto- and electroencephalography (MEG and EEG), but a unifying framework for quantifying the spatial fidelity of M/EEG source estimates has yet to be established; previous studies have focused on linear estimation methods under ideal scenarios without noise. Here we present an approach that quantifies the spatial fidelity of M/EEG estimates from simulated patch activations over the entire neocortex superposed on measured resting-state data. This approach grants more generalizability in the evaluation process that allows for, e.g., comparing linear and non-linear estimates in the whole brain for different signal-to-noise ratios (SNR), number of active sources and activation waveforms. Using this framework, we evaluated the MNE, dSPM, sLORETA, eLORETA, and MxNE methods and found that the spatial fidelity varies significantly with SNR, following a largely sigmoidal curve whose shape varies depending on which aspect of spatial fidelity that is being quantified and the source estimation method. We believe that these methods and results will be useful when interpreting M/EEG source estimates as well as in methods development.
Identifiants
pubmed: 33038537
pii: S1053-8119(20)30915-0
doi: 10.1016/j.neuroimage.2020.117430
pmc: PMC7793168
mid: NIHMS1658689
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
117430Subventions
Organisme : NIDCD NIH HHS
ID : R01 DC016915
Pays : United States
Organisme : NIBIB NIH HHS
ID : T32 EB001680
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS104585
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB030006
Pays : United States
Organisme : NIDCD NIH HHS
ID : R01 DC016765
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB015896
Pays : United States
Informations de copyright
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.
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