Comparing EEG/MEG neuroimaging methods based on localization error, false positive activity, and false positive connectivity

Abstract

Comparing EEG/MEG neuroimaging methods based on localization error, false positive activity, and false positive connectivity. 2018-02-22 Corresponding author: RD Pascual-Marqui pascualmarqui@key.uzh.ch ; www.uzh.ch/keyinst/loreta.htm scholar.google.com/citations?user=pascualmarqui 1.Abstract EEG/MEG neuroimaging consists of estimating the cortical distribution of time varying signals of electric neuronal activity, for the study of functional localization and connectivity. Currently, many different imaging methods are being used, with very different capabilities of correct localization of activity and of correct localization of connectivity. The aim here is to provide a guideline for choosing the best (i.e. least bad) imaging method. This first study is limited to the comparison of the following methods for EEG signals: sLORETA and eLORETA (standardized and exact low resolution electromagnetic tomography), MNE (minimum norm estimate), dSPM (dynamic statistical parametric mapping), and LCMVBs (linearly constrained minimum variance beamformers). These methods are linear, except for the LCMVBs that make use of the quadratic EEG covariances. To achieve a fair comparison, it is assumed here that the generators are independent and widely distributed (i.e. not few in number), giving a well-defined theoretical population EEG covariance matrix for use with the LCMVBs. Measures of localization error, false positive activity, and false positive connectivity are defined and computed under ideal no-noise conditions. It is empirically shown with extensive simulations that: (1) MNE, dSPM, and all LCMVBs are in general incapable of correct localization, while sLORETA and eLORETA have exact (zero-error) localization; (2) the brain volume with false positive activity is significantly larger for MN, dSPM, and all LCMVBs, as compared to sLORETA and eLORETA; and (3) the number of false positive connections is significantly larger for MN, dSPM, all LCMVBs, and sLORETA, as compared to eLORETA. Non-vague and fully detailed equations are given. PASCAL program codes and data files are available. It is noted that the results reported here do not apply to the LCMVBs based on EEG covariance matrices generated from extremely few generators, such as only one or two independent point sources. 2.Pascual-Marqui, Faber, Kinoshita, Kochi, Milz, Nishida, Yoshimura: Comparing EEG/MEG neuroimaging methods based on localization error, false positive activity, and false positive connectivity.

Cortical source imaging of resting-state MEG with a high resolution atlas: An evaluation of methods

“…This suggests that the cortical dynamics estimated by eLORETA are representative of the macroscopic neural dynamics generating the measured MEG. Furthermore, in line with theoretical results (Pascual-Marqui, 2007) and simulated point dipoles (Pascual-Marqui et al, 2018), we found that eLORETA had zero LE and relatively lower leakage than LCMV, wLCMV and sLORETA. The fact these results were consistent across a number of conditions indicates that eLORETA is appropriate as a general tool for source reconstruction of resting-state MEG data.…”

“…There was a significant effect of algorithm on LE ( Figure 3D and Figure 4A, χ 2 = 55, p = 1.31 × 10 −10 ), with significant differences between all pairs of algorithms (p = 0.0012 for all pairwise tests) except for comparisons between wLCMV, sLORETA, and eLORETA (p = 1). As previously reported numerically (Pascual-Marqui et al, 2018;Hauk et al, 2019) and theoretically expected (Pascual-Marqui, 2007), eLORETA and sLORETA had zero localization error. Interestingly, wLCMV also had zero LE in our results.…”

“…Much effort has been made to assess the quality of source reconstruction algorithms in the literature, which mainly focuses on source localization, i.e. identifying the origin of a small number of sources, for example evoked potentials with ground truth based on known task-relevant activity (Bai et al, 2007;Hassan et al, 2014;Seeland et al, 2018;Halder et al, 2019), or simulated activity at a small number of dipoles (Bradley et al, 2016;Finger et al, 2016;Barzegaran and Knyazeva, 2017;Hassan et al, 2017;Hincapié et al, 2017;Bonaiuto et al, 2018;Pascual-Marqui et al, 2018;Anzolin et al, 2019;Halder et al, 2019).…”

Towards an Objective Evaluation of EEG/MEG Source Estimation Methods: The Linear Tool Kit

“…), or about the shape of CTFs (Hauk et al, 2011). We do not evaluate eLORETA, also a method with zero peak localization error, in the present study (Pascual-Marqui et al, 2018). This should be done in a future study.…”

Functional Connectivity of the Caudate in Schizophrenia Evaluated with Simultaneous Resting-State Functional MRI and Electroencephalography Recordings

“…A fifth limitation is that the version of sLORETA that we used in this study may allow localization errors and result in false positive activity and false positive connectivity [61]. Recent advances in ICA of current source activity should provide a more robust and direct way to visualize these networks in the brain [62].…”