Comparison of noise-normalized minimum norm estimates for MEG analysis using multiple resolution metrics

• Published in: NeuroImage (Orlando, Fla.) Vol. 54; no. 3; pp. 1966 - 1974
• Main Authors: Hauk, Olaf, Wakeman, Daniel G., Henson, Richard
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2011; Elsevier Limited; Academic Press
• Subjects: Algorithms; Amplitude; Brain - anatomy & histology; Computer programs; Computer Simulation; Electroencephalography - statistics & numerical data; Estimates; Humans; Image Processing, Computer-Assisted - methods; Inverse problem; Localization error; Magnetic Resonance Imaging - statistics & numerical data; Magnetoencephalography - statistics & numerical data; Methods; Noise; Reference Values; Reproducibility of Results; Software; Software packages; Source analysis; Spatial dispersion; Studies; Tomography; Amplitude; Localization error; Spatial dispersion; Source analysis; Inverse problem
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2010.09.053

Noise-normalization has been shown to partly compensate for the localization bias towards superficial sources in minimum norm estimation. However, it has been argued that in order to make inferences for the case of multiple sources, localization properties alone are insufficient. Instead, multiple measures of resolution should be applied to both point-spread and cross-talk functions (PSFs and CTFs). Here, we demonstrate that noise-normalization affects the shapes of PSFs, but not of CTFs. We evaluated PSFs and CTFs for the MNE, dSPM and sLORETA inverse operators, on the metrics dipole localization error (DLE), spatial dispersion (SD) and overall amplitude (OA). We used 306-channel MEG configurations obtained from 17 subjects in a real experiment, including individual noise covariance matrices and head geometries. We confirmed that for PSFs DLE improved after noise normalization, and is zero for sLORETA. However, SD was generally lower for the unnormalized MNE. OA distributions were similar for all three methods, indicating that all three methods may greatly underestimate some sources relative to others. The reliability of differences between methods across subjects was demonstrated using distributions of standard deviations and p-values from paired t-tests. As predicted, the shapes of CTFs were the same for all methods, reflecting the general resolution limits of the inverse problem. This means that noise-normalization is of no consequence where linear estimation procedures are used as “spatial filters.” While low DLE is advantageous for the localization of a single source, or possibly a few spatially distinct sources, the benefit for the case of complex source distributions is not obvious. We suggest that software packages for source estimation should include comprehensive tools for evaluating the performance of different methods. ►Noise-normalization of inverse estimators affects the shapes of PSFs, but not CTFs. ►Spatial resolution of EEG/MEG should be assessed using multiple resolution metrics. ►Resolution metrics should describe localization, spatial extent and amplitude. ►dSPM and sLORETA have lower localization error than MNE, but larger spatial extent. ►The benefit of dSPM/sLORETA over MNE for complex source distributions is not clear.