Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction
By K. Sekihara, Maneesh Sahani, Srikantan S. Nagarajan. NeuroImage, 2005
480 citations recorded by the source corpus.
This paper discusses the location bias and the spatial resolution in the reconstruction of a single dipole source by various spatial filtering techniques used for neuromagnetic imaging. We first analyze the location bias for several representative adaptive and non-adaptive spatial filters using their resolution kernels. This analysis theoretically validates previously reported empirical findings that standardized low-resolution electromagnetic tomography (sLORETA) has no location bias. We also find that the minimum-variance spatial filter does exhibit bias in the reconstructed location of a single source, but that this bias is eliminated by using the normalized lead field. We then focus on the comparison of sLORETA and the lead-field normalized minimum-variance spatial filter, and analyze the effect of noise on source location bias. We find that the signal-to-noise ratio (SNR) in the measurements determines whether the sLORETA reconstruction has source location bias, while the lead-field normalized minimum-variance spatial filter has no location bias even in the presence of noise. Finally, we compare the spatial resolution for sLORETA and the minimum-variance filter, and show that the minimum-variance filter attains much higher resolution than sLORETA does. The results of these analyses are validated by numerical experiments as well as by reconstructions based on two sets of evoked magnetic responses.
Subfields: Artificial Intelligence, Oceanography, Signal Processing
View Paper: https://www.semanticscholar.org/paper/5782cf7b32e48556824c4ab21b15bfec1455e3cc
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Citations in the corpus
- Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization (2007, result): “…is equal to , thus demonstrating that sLORETA produces exactly localized maxima to point-test sources anywhere in the brain, even in the presence of noise, i.e. sLORETA is unbiased. iK jK This new result is to be contrasted with those published by Sekihara et al (2005) and Greenblatt et al (2005).”
- A unified Bayesian framework for MEG/EEG source imaging (2009, unknown): “Recently it has been shown, both empirically (Pascual-Marqui, 2002) and theoretically (Sekihara et al., 2005), that the sLORETA algorithm has zero location bias under this condition at high SNR.”
- A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG data (2006, unknown): “Of many tomographic algorithms, the adaptive beamformer has been shown to have the best spatial resolution and zero localization bias [1, 2].”
- Measuring functional connectivity using MEG: Methodology and comparison with fcMRI (2011, unknown): “The resolution kernel (see Sekihara et al., 2005 for a complete description) is a means to assess the contribution of distant sources to the estimated activity at a predetermined location.”