The natural frequencies of the resting human brain: An MEG-based atlas

Capilla, Almudena; Arana, Lydia; García-Huéscar, Marta; Melcón, María; Groß, Joachim; Campo, Pablo

doi:10.1016/j.neuroimage.2022.119373

Cited by 40 publications

(29 citation statements)

References 62 publications

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“…Our study also found that MEG estimated spectra were dominated by oscillations in the alpha band, especially in anterior and deeper regions, unlike the actual in situ measurements from the MNI IEEG atlas (Fig 4, Fig 7, Fig S3). This observation is consistent with the finding of dominance in alpha oscillations reported in previous studies (Capilla et al, 2022; Keitel & Gross, 2016; Mahjoory et al, 2020). In our study, the MNI IEEG atlas as ground truth enabled us to quantify the extent of overestimation or underestimation of alpha dominance in MEG estimated spectra.…”

Section: Discussionsupporting

confidence: 94%

“…A quantitative comparison between the spectra from MEG estimated VIEEG and the MNI IEEG atlas (Fig 5B) showed, in most of the lateral frontal regions and medial regions, an average overlap in the alpha band of less than 0.5. A similar dominance of alpha oscillations in MEG was also reported in previous MEG resting state studies (Capilla et al, 2022; Keitel & Gross, 2016; Mahjoory et al, 2020). Using eyes open MEG data, Capilla et al (2022) reported the dominance of alpha oscillations in all posterior regions.…”

Section: Discussionsupporting

confidence: 88%

“…A similar dominance of alpha oscillations in MEG was also reported in previous MEG resting state studies (Capilla et al, 2022; Keitel & Gross, 2016; Mahjoory et al, 2020). Using eyes open MEG data, Capilla et al (2022) reported the dominance of alpha oscillations in all posterior regions. Alpha is also of much higher amplitude with eyes closed than with eyes open.…”

Section: Discussionsupporting

confidence: 88%

“…To tackle the dominance of alpha oscillations, a few previous studies normalized each ROI spectrum by considering the average spectra of all other brain regions. Such normalization gives a measure of the characteristic features of each ROI spectrum compared to other brain regions, resulting in a less widespread influence of alpha oscillations in all brain regions (Capilla et al, 2022; Keitel & Gross, 2016). We did not incorporate such normalization in this study, as we aimed to compare the MEG estimated spectra for each ROI with the MNI IEEG atlas, not with other ROIs.…”

Section: Discussionmentioning

confidence: 99%

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Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

Afnan

Ellenrieder

Lina

et al. 2022

Preprint

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Background: Magnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. Method: We validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (IEEG) atlas (https://mni-open-ieegatlas.research.mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space, by applying a forward model to the MEG reconstructed source maps and estimated virtual IEEG (VIEEG) potentials on each IEEG channel location and quantitatively compared those with actual IEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. Results: The MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the VIEEG than in the IEEG were more accurately recovered. In the deep regions, MEG estimated amplitudes were largely underestimated and the spectra were poorly recovered. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of IEEG but detected by MEG. Importantly, we found that MEG estimated spectra were more comparable to spectra from the IEEG atlas after the aperiodic components were removed. Conclusion: This study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

Afnan

Ellenrieder

Lina

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Modes 9 and 10 exhibit low-frequency oscillations in the delta/theta band (1-7 Hz). The PSD of each mode is consistent with the expected oscillations at the highpower regions in each mode [68]. A comparison with states inferred with this dataset using an HMM is presented in the section "Large-scale resting-state networks can be formed from a linear mixture of modes".…”

Section: Resting-state Meg Datasupporting

confidence: 65%

Mixtures of large-scale dynamic functional brain network modes

Gohil

Roberts

Timms

et al. 2022

Preprint

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Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical ''modes''. The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100-150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMM's while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMM's assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments.

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Spectral Topography of the Subthalamic Nucleus to Inform Next‐Generation Deep Brain Stimulation

et al. 2023

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Background The landscape of neurophysiological symptoms and behavioral biomarkers in basal ganglia signals for movement disorders is expanding. The clinical translation of sensing‐based deep brain stimulation (DBS) also requires a thorough understanding of the anatomical organization of spectral biomarkers within the subthalamic nucleus (STN). Objectives The aims were to systematically investigate the spectral topography, including a wide range of sub‐bands in STN local field potentials (LFP) of Parkinson's disease (PD) patients, and to evaluate its predictive performance for clinical response to DBS. Methods STN‐LFPs were recorded from 70 PD patients (130 hemispheres) awake and at rest using multicontact DBS electrodes. A comprehensive spatial characterization, including hot spot localization and focality estimation, was performed for multiple sub‐bands (delta, theta, alpha, low‐beta, high‐beta, low‐gamma, high‐gamma, and fast‐gamma (FG) as well as low‐ and fast high‐frequency oscillations [HFO]) and compared to the clinical hot spot for rigidity response to DBS. A spectral biomarker map was established and used to predict the clinical response to DBS. Results The STN shows a heterogeneous topographic distribution of different spectral biomarkers, with the strongest segregation in the inferior‐superior axis. Relative to the superiorly localized beta hot spot, HFOs (FG, slow HFO) were localized up to 2 mm more inferiorly. Beta oscillations are spatially more spread compared to other sub‐bands. Both the spatial proximity of contacts to the beta hot spot and the distance to higher‐frequency hot spots were predictive for the best rigidity response to DBS. Conclusions The spatial segregation and properties of spectral biomarkers within the DBS target structure can additionally be informative for the implementation of next‐generation sensing‐based DBS. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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The natural frequencies of the resting human brain: An MEG-based atlas

Cited by 40 publications

References 62 publications

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

Mixtures of large-scale dynamic functional brain network modes

Spectral Topography of the Subthalamic Nucleus to Inform Next‐Generation Deep Brain Stimulation

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