Magnetoencephalography Source Estimation Research Articles

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature of the analysis, which requires several decisions at each step of the analysis pipeline, such as the choice of a source estimation algorithm, a connectivity metric and a cortical parcellation, to name but a few. Recent studies have emphasized the importance of selecting the regularization parameter in minimum norm estimates with caution, as variations in its value can result in significant differences in connectivity estimates. In particular, the amount of regularization that is optimal for MEG source estimation can actually be suboptimal for coherence-based MEG connectivity analysis. In this study, we expand upon previous work by examining a broader range of commonly used connectivity metrics, including the imaginary part of coherence, corrected imaginary part of Phase Locking Value, and weighted Phase Lag Index, within a larger and more realistic simulation scenario. Our results show that the best estimate of connectivity is achieved using a regularization parameter that is 1 or 2 orders of magnitude smaller than the one that yields the best source estimation. This remarkable difference may imply that previous work assessing source-space connectivity using minimum-norm may have benefited from using less regularization, as this may have helped reduce false positives. Importantly, we provide the code for MEG data simulation and analysis, offering the research community a valuable open source tool for informed selections of the regularization parameter when using minimum-norm for source space connectivity analyses.

Source localization using virtual magnetoencephalography helmets: A simulation study toward a prior-based tailored scheme.

Magnetoencephalography (MEG) source estimation of brain electromagnetic fields is an ill-posed problem. A virtual MEG helmet (VMH), can be constructed by recording in different head positions and then transforming the multiple head-MEG coordinates into one head frame (i.e., as though the MEG helmet was moving while the head remained static). The constructed VMH has sensors placed in various distances and angles, thus improving the spatial sampling of neuromagnetic fields. VMH has been previously shown to increase total information in comparison to a standard MEG helmet. The aim of this study was to examine whether VMH can improve source estimation accuracy. To this end, controlled simulations were carried out, in which the source characteristics are predefined. A series of VMHs were constructed by applying two or three translations and rotations to a standard 248 channel MEG array. In each simulation, the magnetic field generated by 1 to 5 dipoles was forward projected, alongside noise components. The results of this study showed that at low noise levels (e.g., averaged data of similar signals), VMHs can significantly improve the accuracy of source estimations, compared to the standard MEG array. Moreover, when utilizing a priori information, tailoring the constructed VMHs to specific sets of postulated neuronal sources can further improve the accuracy. This is shown to be a robust and stable method, even for proximate locations. Overall, VMH may add significant precision to MEG source estimation, for research and clinical benefits, such as in challenging epilepsy cases, aiding in surgical design.

Large-scale cortico-cerebellar computations for horizontal and vertical vergence in humans

Horizontal and vertical vergence eye movements play a central role in binocular coordination. Neurophysiological studies suggest that cortical and subcortical regions in animals and humans are involved in horizontal vergence. However, little is known about the extent to which the neural mechanism underlying vertical vergence overlaps with that of horizontal vergence. In this study, to explore neural computation for horizontal and vertical vergence, we simultaneously recorded electrooculography (EOG) and whole-head magnetoencephalography (MEG) while presenting large-field stereograms for 29 healthy human adults. The stereograms were designed to produce vergence responses by manipulating horizontal and vertical binocular disparities. A model-based approach was used to assess neural sensitivity to horizontal and vertical disparities via MEG source estimation and the theta-band (4 Hz) coherence between brain activity and EOG vergence velocity. We found similar time-locked neural responses to horizontal and vertical disparity in cortical and cerebellar areas at around 100–250 ms after stimulus onset. In contrast, the low-frequency oscillatory neural activity associated with the execution of vertical vergence differed from that of horizontal vergence. These findings indicate that horizontal and vertical vergence involve partially shared but distinct computations in large-scale cortico-cerebellar networks.

Information spreading by a combination of MEG source estimation and multivariate pattern classification.

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

MEG biomarker of Alzheimer's disease: Absence of a prefrontal generator during auditory sensory gating.

Magnetoencephalography (MEG), a direct measure of neuronal activity, is an underexplored tool in the search for biomarkers of Alzheimer's disease (AD). In this study, we used MEG source estimates of auditory gating generators, nonlinear correlations with neuropsychological results, and multivariate analyses to examine the sensitivity and specificity of gating topology modulation to detect AD. Our results demonstrated the use of MEG localization of a medial prefrontal (mPFC) gating generator as a discrete (binary) detector of AD at the individual level and resulted in recategorizing the participant categories in: (1) controls with mPFC generator localized in response to both the standard and deviant tones; (2) a possible preclinical stage of AD participants (a lower functioning group of controls) in which mPFC activation was localized to the deviant tone only; and (3) symptomatic AD in which mPFC activation was not localized to either the deviant or standard tones. This approach showed a large effect size (0.9) and high accuracy, sensitivity, and specificity (100%) in identifying symptomatic AD patients within a limited research sample. The present results demonstrate high potential of mPFC activation as a noninvasive biomarker of AD pathology during putative preclinical and clinical stages. Hum Brain Mapp 38:5180-5194, 2017. © 2017 Wiley Periodicals, Inc.

MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters.

Minimum Norm Estimation (MNE) is an inverse solution method widely used to reconstruct the source time series that underlie magnetoencephalography (MEG) data. MNE addresses the ill-posed nature of MEG source estimation through regularization (e.g., Tikhonov regularization). Selecting the best regularization parameter is a critical step. Generally, once set, it is common practice to keep the same coefficient throughout a study. However, it is yet to be known whether the optimal lambda for spectral power analysis of MEG source data coincides with the optimal regularization for source-level oscillatory coupling analysis. We addressed this question via extensive Monte-Carlo simulations of MEG data, where we generated 21,600 configurations of pairs of coupled sources with varying sizes, signal-to-noise ratio (SNR), and coupling strengths. Then, we searched for the Tikhonov regularization coefficients (lambda) that maximize detection performance for (a) power and (b) coherence. For coherence, the optimal lambda was two orders of magnitude smaller than the best lambda for power. Moreover, we found that the spatial extent of the interacting sources and SNR, but not the extent of coupling, were the main parameters affecting the best choice for lambda. Our findings suggest using less regularization when measuring oscillatory coupling compared to power estimation.

Independence of Early Speech Processing from Word Meaning

We combined magnetoencephalography (MEG) with magnetic resonance imaging and electrocorticography to separate in anatomy and latency 2 fundamental stages underlying speech comprehension. The first acoustic-phonetic stage is selective for words relative to control stimuli individually matched on acoustic properties. It begins ∼60 ms after stimulus onset and is localized to middle superior temporal cortex. It was replicated in another experiment, but is strongly dissociated from the response to tones in the same subjects. Within the same task, semantic priming of the same words by a related picture modulates cortical processing in a broader network, but this does not begin until ∼217 ms. The earlier onset of acoustic-phonetic processing compared with lexico-semantic modulation was significant in each individual subject. The MEG source estimates were confirmed with intracranial local field potential and high gamma power responses acquired in 2 additional subjects performing the same task. These recordings further identified sites within superior temporal cortex that responded only to the acoustic-phonetic contrast at short latencies, or the lexico-semantic at long. The independence of the early acoustic-phonetic response from semantic context suggests a limited role for lexical feedback in early speech perception.

Cross-frequency coupling in parieto-frontal oscillatory networks during motor imagery revealed by magnetoencephalography

FRONTIERS COMMENTARY article Front. Neurosci., 01 May 2009 | https://doi.org/10.3389/neuro.01.011.2009

Head movements of children in MEG: Quantification, effects on source estimation, and compensation

Head movements during magnetoencephalography (MEG) recordings may lead to inaccurate localization of brain activity. This can be particularly problematic for studies with children. We quantified head movements in 8- to 12-year-old children performing a cognitive task and examined how the movements affected source estimation. Each child was presented auditory word stimuli in five 4-min runs. The mean change in the MEG sensor locations during the experiment ranged from 3 to 26mm across subjects. The variation in the head position was largest in the up–down direction. The mean localization error in equivalent current dipole (ECD) simulations was 12mm for runs with the most head movement, with the frontal cortex appearing to be most prone to errors due to head movements. In addition, we examined the effect of head movements on two types of source estimates, ECDs and minimum-norm estimates (MNE), for an auditory evoked response. Application of a recently introduced signal space separation (SSS) method to compensate for the head movements was found to increase the goodness-of-fit of the ECDs, reduce the spatial confidence intervals of the ECDs, and enhance the peak amplitude in the MNE. These results are indicative of the SSS method being able to compensate for the spatial smoothing of the signals caused by head movements. Overall, the results suggest that MEG source estimates are relatively robust against head movements in children, and that confounds due to head movements can be successfully dealt with in MEG studies of developmental cognition.

MEG source estimation from mesio-basal temporal areas in a child with a porencephalic cyst

A child whose left temporal lobe contained mesial, anterior and basal structures but lacked superio-lateral cortex had intractable epilepsy secondary to a porencephalic cyst. Magnetoencephalography (MEG) shows equivalent current dipoles (ECDs) as dipole modeling for temporal lobe epilepsy rather than in an exact location. We hypothesized that the magnetic fields generated by the epileptic discharges in mesio-basal temporal areas could be detected by MEG without interference from the superio-lateral temporal cortices. We analyzed MEG spikes using single dipole analysis and synthetic aperture magnetometry (SAM), and compared with EEG spike topography. Two MEG ECDs corresponding to T3 spikes localized to the anterior mesio-basal temporal region with vertical orientation. Sixteen MEG ECDs corresponding to T5 spikes localized to the middle to posterior mesio-basal temporal region with vertical orientation. SAM revealed maximum current density at hippocampus and anterior fusiform gyrus for T3 spikes, and at posterior hippocampus and fusiform gyrus for T5 spikes. Vertically oriented ECDs were obtained without superio-lateral temporal cortices because of temporo-parieto-occipital porencephalic cyst. The absence of superio-lateral temporal cortices, prominent temporal EEG spikes, less prominent MEG spikes, and mesio-basal SAM spikes indicated that the vertically oriented ECDs were projected directly from the mesio-basal temporal region.

MRI-constrained spectral imaging of benzodiazepine modulation of spontaneous neuromagnetic activity in human cortex

Spontaneous electromagnetic brain rhythms have been widely used in human neuropharmacology, but their applicability is complicated by the difficulties to localize their origins in the human cortex. Here, we used a novel multi-modal non-invasive imaging approach to localize lorazepam (30 μg/kg i.v.) modulation of cortical generators of spontaneous brain rhythms. Eight healthy subjects were measured with 306-channel magnetoencephalography (MEG) in a double-blind, randomized, placebo-controlled (saline), crossover design. For anatomically realistic source modeling, wavelet-transformed MEG data were combined with high-resolution MRI to constrain the current locations to the cortical mantle, after which individual data were co-registered to surface-based coordinate system for the calculation of group statistical parametric maps of drug effects. The distributed MRI-constrained MEG source estimates demonstrated decreased alpha (10 Hz) activity in and around the parieto-occipital sulcus and in the calcarine sulcus of the occipital lobe, following from increased GABAA-inhibition by lorazepam. Anatomically constrained spectral imaging displays the cortical loci of drug effects on oscillatory brain activity, providing a novel tool for human pharmacological neuroimaging.