ArticleNeuroImage2020
Transient spectral events in resting state MEG predict individual task responses.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- FX ENTRAIN: scientific context, study design, and biomarker driven brain-computer interfaces in neurodevelopmental conditions.Frontiers in neuroscience · 2025Article
- Comparison between EEG and MEG of static and dynamic resting-state networks.Human brain mapping · 2024Article
- Post-stroke upper limb recovery is correlated with dynamic resting-state network connectivity.Brain communications · 2024Article
- Fronto-central resting-state 15-29 Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder.Scientific reports · 2023Article
- Article
- Mixtures of large-scale dynamic functional brain network modes.NeuroImage · 2022Article
- Individual Magnetoencephalography Response Profiles to Short-Duration L-Dopa in Parkinson's Disease.Frontiers in human neuroscience · 2021Article
- Spontaneous network activity <35 Hz accounts for variability in stimulus-induced gamma responses.NeuroImage · 2020Article
- Linking local and large-scale salient events with oscillatory and broadband arrhythmic activities in the resting human brain.Imaging neuroscience (Cambridge, Mass.)Article
- Modelling discrete states and long-term dynamics in functional brain networks.Imaging neuroscience (Cambridge, Mass.)Article
- Continuous deep brain stimulation of the subthalamic nucleus may not modulate beta bursts in patients with Parkinson's disease.Brain stimulationArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.