Browse
Submit Data
Databases
API
Help

Dataset Information

30 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Three-year reliability of MEG resting-state oscillatory power.

ABSTRACT:

Introduction

Resting-state oscillatory activity has been extensively studied across a wide array of disorders. Establishing which spectrally- and spatially-specific oscillatory components exhibit test-retest reliability is essential to move the field forward. While studies have shown short-term reliability of MEG resting-state activity, no studies have examined test-retest reliability across an extended period of time to establish the stability of these signals, which is critical for reproducibility.

Methods

We examined 18 healthy adults age 23 - 61 who completed three visits across three years. For each visit, participants completed both a resting state MEG and structural MRI scan. MEG data were source imaged, and the cortical power in canonical frequency bands (delta, theta, alpha, beta, low gamma, high gamma) was computed. Intra-class correlation coefficients (ICC) were then calculated across the cortex for each frequency band.

Results

Over three years, power in the alpha and beta bands displayed the highest reliability estimates, while gamma showed the lowest estimates of three-year reliability. Spatially, delta, alpha, and beta all showed the highest degrees of reliability in the parietal cortex. Interestingly, the peak signal for each of these frequency bands was located outside of the parietal cortex, suggesting that reliability estimates were not solely dependent on the signal-to-noise ratio.

Conclusion

Oscillatory resting-state power in parietal delta, posterior beta, and alpha across most of the cortex are reliable across three years and future MEEG studies may focus on these measures for the development of specific markers.

SUBMITTER: Lew BJ

PROVIDER: S-EPMC8590732 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Resting state EEG oscillatory power differences in ADHD college students and their peers.

Project description:BackgroundAmong the most robust neural abnormalities differentiating individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) from typically developing controls are elevated levels of slow oscillatory activity (e.g., theta) and reduced fast oscillatory activity (e.g., alpha and beta) during resting-state electroencephalography (EEG). However, studies of resting state EEG in adults with ADHD are scarce and yield inconsistent findings.MethodsEEG profiles, recorded during a resting-state with eyes-open and eyes-closed conditions, were compared for college students with ADHD (n?=?18) and a nonclinical comparison group (n?=?17).ResultsThe ADHD group showed decreased power for fast frequencies, especially alpha. This group also showed increased power in the slow frequency bands, however, these effects were strongest using relative power computations. Furthermore, the theta/beta ratio measure was reliably higher for the ADHD group. All effects were more pronounced for the eyes-closed compared to the eyes-open condition. Measures of intra-individual variability suggested that brains of the ADHD group were less variable than those of controls.ConclusionsThe findings of this pilot study reveal that college students with ADHD show a distinct neural pattern during resting state, suggesting that oscillatory power, especially alpha, is a useful index for reflecting differences in neural communication of ADHD in early adulthood.

| S-EPMC3537601 | biostudies-literature

Aberrant resting-state oscillatory brain activity in Parkinson's disease patients with visual hallucinations: An MEG source-space study.

Project description:To gain insight into possible underlying mechanism(s) of visual hallucinations (VH) in Parkinson's disease (PD), we explored changes in local oscillatory activity in different frequency bands with source-space magnetoencephalography (MEG). Eyes-closed resting-state MEG recordings were obtained from 20 PD patients with hallucinations (Hall+) and 20 PD patients without hallucinations (Hall-), matched for age, gender and disease severity. The Hall+ group was subdivided into 10 patients with VH only (unimodal Hall+) and 10 patients with multimodal hallucinations (multimodal Hall+). Subsequently, neuronal activity at source-level was reconstructed using an atlas-based beamforming approach resulting in source-space time series for 78 cortical and 12 subcortical regions of interest in the automated anatomical labeling (AAL) atlas. Peak frequency (PF) and relative power in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) were compared between Hall+ and Hall-, unimodal Hall+ and Hall-, multimodal Hall+ and Hall-, and unimodal Hall+ and multimodal Hall+ patients. PF and relative power per frequency band did not differ between Hall+ and Hall-, and multimodal Hall+ and Hall- patients. Compared to the Hall- group, unimodal Hall+ patients showed significantly higher relative power in the theta band (p = 0.005), and significantly lower relative power in the beta (p = 0.029) and gamma (p = 0.007) band, and lower PF (p = 0.011). Compared to the unimodal Hall+, multimodal Hall+ showed significantly higher PF (p = 0.007). In conclusion, a subset of PD patients with only VH showed slowing of MEG-based resting-state brain activity with an increase in theta activity, and a concomitant decrease in beta and gamma activity, which could indicate central cholinergic dysfunction as underlying mechanism of VH in PD. This signature was absent in PD patients with multimodal hallucinations.

| S-EPMC6425119 | biostudies-literature

Reliability of Static and Dynamic Network Metrics in the Resting-State: A MEG-Beamformed Connectivity Analysis.

Project description:The resting activity of the brain can be described by so-called intrinsic connectivity networks (ICNs), which consist of spatially and temporally distributed, but functionally connected, nodes. The coordinated activity of the resting state can be explored via magnetoencephalography (MEG) by studying frequency-dependent functional brain networks at the source level. Although many algorithms for the analysis of brain connectivity have been proposed, the reliability of network metrics derived from both static and dynamic functional connectivity is still unknown. This is a particular problem for studies of associations between ICN metrics and personality variables or other traits, and for studies of differences between patient and control groups, which both depend critically on the reliability of the metrics used. A detailed investigation of the reliability of metrics derived from resting-state MEG repeat scans is therefore a prerequisite for the development of connectomic biomarkers. Here, we first estimated both static (SFC) and dynamic functional connectivity (DFC) after beamforming source reconstruction using the imaginary part of the phase locking index (iPLV) and the correlation of the amplitude envelope (CorEnv). Using our approach, functional network microstates (FCμstates) were derived from the DFC and chronnectomics were computed from the evolution of FCμstates across experimental time. In both temporal scales, the reliability of network metrics (SFC), the FCμstates and the related chronnectomics were evaluated for every frequency band. Chronnectomic statistics and FCμstates were generally more reliable than node-wise static network metrics. CorEnv-based network metrics were more reproducible at the static approach. The reliability of chronnectomics have been evaluated also in a second dataset. This study encourages the analysis of MEG resting-state via DFC.

| S-EPMC6088195 | biostudies-literature

A three dimensional anatomical view of oscillatory resting-state activity and functional connectivity in Parkinson's disease related dementia: An MEG study using atlas-based beamforming.

Project description:Parkinson's disease (PD) related dementia (PDD) develops in up to 80% of PD patients. The present study was performed to further unravel the underlying pathophysiological mechanisms by applying a new analysis approach that uses an atlas-based MEG beamformer to provide a detailed anatomical mapping of cortical rhythms and functional interactions. Importantly, we used the phase lag index (PLI) as a measure of functional connectivity to avoid any biases due to effects of volume conduction. MEG recordings were obtained in 13 PDD and 13 non-demented PD patients. Beamforming was used to estimate spectral power and PLI in delta, theta, alpha, beta and gamma frequency bands. Compared to PD patients, PDD patients had more delta and theta power in parieto-occipital and fronto-parietal areas, respectively. The PDD patients had less alpha and beta power in parieto-temporo-occipital and frontal areas, respectively. Compared to PD patients, PDD patients had lower mean PLI values in the delta and alpha bands in fronto-temporal and parieto-temporo-occipital areas, respectively. In addition, in PDD patients connectivity between pairs of regions of interest (Brodmann areas) was stronger in the theta band and weaker in the delta, alpha and beta bands. The added value of the present results over previous studies analysing frequency-specific changes in neuronal activity in PD patients, is the anatomical framework in which we demonstrated a slowing in neuronal activity and a reduction in functional connectivity in PD related dementia. Moreover, this study shows a widespread reduction in functional connectivity between different regions in PDD.

| S-EPMC3777782 | biostudies-literature

How reliable are MEG resting-state connectivity metrics?

Project description:MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.

| S-EPMC5056955 | biostudies-literature

Predicting MEG resting-state functional connectivity from microstructural information.

Project description:Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

| S-EPMC8233113 | biostudies-literature

Characteristics of Resting State EEG Power in 80+-Year-Olds of Different Cognitive Status.

Project description:Compared with healthy older adults, patients with Alzheimer's disease show decreased alpha and beta power as well as increased delta and theta power during resting state electroencephalography (rsEEG). Findings for mild cognitive impairment (MCI), a stage of increased risk of conversion to dementia, are less conclusive. Cognitive status of 213 non-demented high-agers (mean age, 82.5 years) was classified according to a neuropsychological screening and a cognitive test battery. RsEEG was measured with eyes closed and open, and absolute power in delta, theta, alpha, and beta bands were calculated for nine regions. Results indicate no rsEEG power differences between healthy individuals and those with MCI. There were also no differences present between groups in EEG reactivity, the change in power from eyes closed to eyes open, or the topographical pattern of each frequency band. Overall, EEG reactivity was preserved in 80+-year-olds without dementia, and topographical patterns were described for each frequency band. The application of rsEEG power as a marker for the early detection of dementia might be less conclusive for high-agers.

| S-EPMC8387136 | biostudies-literature

Data-driven MEG analysis to extract fMRI resting-state networks.

Project description:The electrophysiological basis of resting-state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this article, we compare the two main existing data-driven analysis strategies for extracting RSNs from MEG data and introduce a third approach. The first approach uses phase-amplitude coupling to determine the RSN. The second approach extracts RSN through an independent component analysis of the Hilbert envelope in different frequency bands, while the third new approach uses a singular value decomposition instead. To evaluate these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract RSN with MEG using all three techniques, which matched the group-specific fMRI-RSN. Interestingly the new approach based on SVD yielded significantly higher correspondence to five out of seven fMRI-RSN than the two existing approaches. Importantly, with this approach, all networks-except for the visual network-had the highest correspondence to the fMRI networks within one frequency band. Thereby we provide further insights into the electrophysiological underpinnings of the fMRI-RSNs. This knowledge will be important for the analysis of the electrophysiological connectome.

| S-EPMC10915736 | biostudies-literature

Transient spectral events in resting state MEG predict individual task responses.

Project description:Even in response to simple tasks such as hand movement, human brain activity shows remarkable inter-subject variability. Recently, it has been shown that individual spatial variability in fMRI task responses can be predicted from measurements collected at rest; suggesting that the spatial variability is a stable feature, inherent to the individual's brain. However, it is not clear if this is also true for individual variability in the spatio-spectral content of oscillatory brain activity. Here, we show using MEG (N = 89) that we can predict the spatial and spectral content of an individual's task response using features estimated from the individual's resting MEG data. This works by learning when transient spectral 'bursts' or events in the resting state tend to reoccur in the task responses. We applied our method to motor, working memory and language comprehension tasks. All task conditions were predicted significantly above chance. Finally, we found a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. Our approach can predict individual differences in brain activity and suggests a link between transient spectral events in task and rest that can be captured at the level of individuals.

| S-EPMC7116674 | biostudies-literature

A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG.

Project description:ObjectiveFor people with idiopathic generalized epilepsy, functional networks derived from their resting-state scalp electrophysiological recordings have shown an inherent higher propensity to generate seizures than those from healthy controls when assessed using the concept of brain network ictogenicity (BNI). Herein we tested whether the BNI framework is applicable to resting-state magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME).MethodsThe BNI framework consists in deriving a functional network from apparently normal brain activity, placing a mathematical model of ictogenicity into the network and then computing how often such network generates seizures in silico. We considered data from 26 people with JME and 26 healthy controls.ResultsWe found that resting-state MEG functional networks from people with JME are characterized by a higher propensity to generate seizures (i.e., higher BNI) than those from healthy controls. We found a classification accuracy of 73%.ConclusionsThe BNI framework is applicable to MEG and was capable of differentiating people with epilepsy from healthy controls.SignificanceThe BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis.

| S-EPMC7992031 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data