Spatial leakage effects are particularly confounding for seed-based investigations of brain networks using source-level electroencephalography (EEG) or magnetoencephalography (MEG). Various methods designed to avoid this issue have been introduced but are limited to particular assumptions about its temporal characteristics. Here, we investigate the usefulness of a model-based geometric correction scheme (GCS) to suppress spatial leakage emanating from the seed location. We analyze its properties theoretically and then assess potential advantages and limitations with simulated and experimental MEG data (resting state and auditory-motor task). To do so, we apply Minimum Norm Estimation (MNE) for source reconstruction and use variation of error parameters, statistical gauging of spatial leakage correction and comparison with signal orthogonalization. Results show that the GCS has a local (i.e., near the seed) effect only, in line with the geometry of MNE spatial leakage, and is able to map spatially all types of brain interactions, including linear correlations eliminated after signal orthogonalization. Furthermore, it is robust against the introduction of forward model errors. On the other hand, the GCS can be affected by local overcorrection effects and seed mislocation. These issues arise with signal orthogonalization too, although significantly less extensively, so the two approaches complement each other. The GCS thus appears to be a valuable addition to the spatial leakage correction toolkits for seed-based FC analyses in source-projected MEG/EEG data.
Using a continuous listening task, we evaluated the coupling between the listener's cortical activity and the temporal envelopes of different sounds in a multitalker auditory scene using magnetoencephalography and corticovocal coherence analysis. Neuromagnetic signals were recorded from 20 right-handed healthy adult humans who listened to five different recorded stories (attended speech streams), one without any multitalker background (No noise) and four mixed with a "cocktail party" multitalker background noise at four signal-to-noise ratios (5, 0, Ϫ5, and Ϫ10 dB) to produce speech-in-noise mixtures, here referred to as Global scene. Coherence analysis revealed that the modulations of the attended speech stream, presented without multitalker background, were coupled at ϳ0.5 Hz to the activity of both superior temporal gyri, whereas the modulations at 4 -8 Hz were coupled to the activity of the right supratemporal auditory cortex. In cocktail party conditions, with the multitalker background noise, the coupling was at both frequencies stronger for the attended speech stream than for the unattended Multitalker background. The coupling strengths decreased as the Multitalker background increased. During the cocktail party conditions, the ϳ0.5 Hz coupling became left-hemisphere dominant, compared with bilateral coupling without the multitalker background, whereas the 4 -8 Hz coupling remained right-hemisphere lateralized in both conditions. The brain activity was not coupled to the multitalker background or to its individual talkers. The results highlight the key role of listener's left superior temporal gyri in extracting the slow ϳ0.5 Hz modulations, likely reflecting the attended speech stream within a multitalker auditory scene.
Functional connectivity studies conducted at the group level using magnetoencephalography (MEG) suggest that resting state networks (RSNs) emerge from the large-scale envelope correlation structure within spontaneous oscillatory brain activity. However, little is known about the consistency of MEG RSNs at the individual level. This paper investigates the inter- and intra-subject variability of three MEG RSNs (sensorimotor, auditory and visual) using seed-based source space envelope correlation analysis applied to 5 min of resting state MEG data acquired from a 306-channel whole-scalp neuromagnetometer (Elekta Oy, Helsinki, Finland) and source projected with minimum norm estimation. The main finding is that these three MEG RSNs exhibit substantial variability at the single-subject level across and within individuals, which depends on the RSN type, but can be reduced after averaging over subjects or sessions. Over- and under-estimations of true RSNs variability are respectively obtained using template seeds, which are potentially mislocated due to inter-subject variations, and a seed optimization method minimizing variability. In particular, bounds on the minimal number of subjects or sessions required to obtain highly consistent between- or within-subject averages of MEG RSNs are derived. Furthermore, MEG RSN topography positively correlates with their mean connectivity at the inter-subject level. These results indicate that MEG RSNs associated with primary cortices can be robustly extracted from seed-based envelope correlation and adequate averaging. MEG thus appears to be a valid technique to compare RSNs across subjects or conditions, at least when using the current methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.