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.
Highlights
Magneto- and electroencephalography (M/EEG) based source localization is widely utilized in basic and clinical neuroscience research, and has had critical clinical impact for applications such as preoperative functional mapping of neurosurgery patients and noninvasive localization of epileptic foci in patients suffering from drug-resistant epilepsy (Fischer et al, 2005; Gross et al, 2013; RamachandranNair et al, 2007)
The most widely utilized distributed source estimation methods developed to date include the minimum norm estimate (MNE), the normalized MNE estimates dynamic statistical parametric mapping and standardized low resolution brain electromagnetic tomography, as well as exact low resolution brain electromagnetic tomography (Dale et al, 2000; Hamalainen and Ilmoniemi, 1994; Pascual-Marqui, 2002; Pascual-Marqui et al, 2011)
Because we study patch activations, the point source vertex i that defines the center of the patch is determined by the point source in the patch that is closest to the center of gravity of the patch in the spherically inflated surface of the hemisphere; i = argmin k rc′ g − rk′ 2 k ∈ P, where r′k denotes the position of vertex k on the spherical surface, P is the set of source dipoles in the activated patch and rc′ g is the center of gravity, or average, of all source positions in the activated patch in the spherically inflated surface space
Summary
Magneto- and electroencephalography (M/EEG) based source localization is widely utilized in basic and clinical neuroscience research, and has had critical clinical impact for applications such as preoperative functional mapping of neurosurgery patients and noninvasive localization of epileptic foci in patients suffering from drug-resistant epilepsy (Fischer et al, 2005; Gross et al, 2013; RamachandranNair et al, 2007). Hauk et al (2019) summarized important resolution metrics that have been used in the past to quantify different aspects of spatial fidelity and discussed how they relate to each-other These metrics were quantified for MEG, EEG, as well as for combined MEG and EEG, respectively, as a function of source location for some common linear estimation methods. It was noted that parameters that provide intuitive measures of spatial fidelity for easy comparisons of source estimation methods are warranted but should be interpreted with caution since much information is lost in the transformation from a distribution to a single scalar value These studies assumed point sources and investigated linear estimation methods under ideal scenarios without noise. Under these conditions, closed-form expressions of the resolution matrix are attainable. Babiloni et al (2004) conducted a simulation study that assessed the added benefit of integrating MEG with EEG for varying SNR but spatial fidelity was only evaluated for five selected regions of interest in the brain
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