ArticleNeuroImage2022
Stability of spectral estimates in resting-state magnetoencephalography: Recommendations for minimal data duration with neuroanatomical specificity.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Tracking electrophysiological signatures of Alzheimer's disease: a systematic review of multimodal studies.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- A Systematic Review of Aperiodic Neural Activity in Clinical Investigations.The European journal of neuroscience · 2025Pooled it
- Article
- Magnetoencephalography in human neuroscience research: planning, piloting, implementation and quality assurance.Nature protocols · 2026Review
- Peripheral Mitochondrial Energetics are Associated with Cortical Neurophysiological Alterations in Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2026Article
- Hierarchical Flows of Human Cortical Activity.bioRxiv : the preprint server for biology · 2026Article
- Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.GeroScience · 2026Review
- Dynamic causal modeling of low-density resting-state EEG in long-term meditation practitioners.Scientific reports · 2026Article
- Reduced cortical excitability is associated with head impact severity and cognitive symptoms in adolescent football players.iScience · 2026Article
- Unsupervised Representation Learning Generates Differentiable Neurophysiological Profiles.bioRxiv : the preprint server for biology · 2026Article
- Magnetoencephalographic Spectral Abnormalities and Explainable Machine Learning of Neuropathic-Like Pain in Knee Osteoarthritis.Journal of pain research · 2026Article
- EEG Signatures of COVID-19 Survival compared to close contacts and the Cuban EEG normative database.Clinical neurophysiology practice · 2026Article
- Capturing individual variation in children's electroencephalograms during nREM sleep.PLoS computational biology · 2026Article
- Aperiodic Activity Reflects Pathologic Waveform Shapes in Focal Epilepsy.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Temporal stability and neural complexity in resting-state MEG predict migraine phenotypes.The journal of headache and pain · 2025Article
- Aberrant Functional Connectivity and Brain Network Organization in High-Schizotypy Individuals: An Electroencephalography Study.Schizophrenia bulletin · 2025Article
- Cortical Effects of Dopamine Replacement Account for Clinical Response Variability in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Assessing the stability of evoked, induced, and passive MEG responses for repeat testing with optically pumped magnetometers.Scientific reports · 2025Article
- Neural Electrical Correlates of Subjective Happiness.Human brain mapping · 2025Article
- Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia.EBioMedicine · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The principle of resting-state paradigms is appealing and practical for collecting data from impaired patients and special populations, especially if data collection times can be minimized. To achieve this goal, researchers need to ensure estimated signal features of interest are robust. In electro- and magnetoencephalography (EEG, MEG) we are not aware of any studies of the minimal length of data required to yield a robust one-session snapshot of the frequency-spectrum derivatives that are typically used to characterize the complex dynamics of the brain's resting-state. We aimed to fill this knowledge gap by studying the stability of common spectral measures of resting-state MEG source time series obtained from large samples of single-session recordings from shared data repositories featuring different recording conditions and instrument technologies (OMEGA: N = 107; Cam-CAN: N = 50). We discovered that the rhythmic and arrhythmic spectral properties of intrinsic brain activity can be robustly estimated in most cortical regions when derived from relatively short segments of 30-s to 120-s of resting-state data, regardless of instrument technology and resting-state paradigm. Using an adapted leave-one-out approach and Bayesian analysis, we also provide evidence that the stability of spectral features over time is unaffected by age, sex, handedness, and general cognitive function. In summary, short MEG sessions are sufficient to yield robust estimates of frequency-defined brain activity during resting-state. This study may help guide future empirical designs in the field, particularly when recording times need to be minimized, such as with patient or special populations.
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