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Spatial fidelity of MEG/EEG source estimates: A general evaluation approach.
Spatial fidelity of MEG/EEG source estimates: A general evaluation approach.
- Source :
-
NeuroImage [Neuroimage] 2021 Jan 01; Vol. 224, pp. 117430. Date of Electronic Publication: 2020 Oct 07. - Publication Year :
- 2021
-
Abstract
- 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.<br /> (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)
- Subjects :
- Adult
Brain diagnostic imaging
Brain physiology
Female
Humans
Linear Models
Magnetic Resonance Imaging
Male
Neocortex diagnostic imaging
Nonlinear Dynamics
Rest
Signal-To-Noise Ratio
Young Adult
Electroencephalography methods
Magnetoencephalography methods
Neocortex physiology
Signal Processing, Computer-Assisted
Spatial Analysis
Subjects
Details
- Language :
- English
- ISSN :
- 1095-9572
- Volume :
- 224
- Database :
- MEDLINE
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 33038537
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2020.117430