ArticleImaging neuroscience (Cambridge, Mass.)2024
Dynamic network analysis of electrophysiological task data.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Temporal neural dynamics patterns in episodic and chronic migraine: a magnetoencephalography study.The journal of headache and pain · 2026Article
- Varying patterns of association between cortical large-scale networks and subthalamic nucleus activity in Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Effects of Age on Resting-State Cortical Networks.Human brain mapping · 2026Article
- Temporally Defined Brain Network Activation Associated With Slowed Information Processing Speed in Multiple Sclerosis.Human brain mapping · 2026Article
- Dynamic, state-dependent characteristics of cognitive fluctuations in Lewy body dementia: a magnetoencephalography study.Brain communications · 2026Article
- Distinct alpha networks modulate different aspects of perceptual decision-making.PLoS biology · 2025Article
- Convergent neural dynamical systems for task control in artificial networks and human brains.bioRxiv : the preprint server for biology · 2025Article
- Differential Beta and Gamma Activity Modulation during Unimanual and Bimanual Motor Learning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Evidence for Transient, Uncoupled Power and Functional Connectivity Dynamics.Human brain mapping · 2025Article
- Canonical Hidden Markov Model Networks for studying M/EEG.Imaging neuroscience (Cambridge, Mass.)Article
- MEG-GPT: A transformer-based foundation model for magnetoencephalography data.Imaging neuroscience (Cambridge, Mass.)Article
- Modelling discrete states and long-term dynamics in functional brain networks.Imaging neuroscience (Cambridge, Mass.)Article
- Multidimensional dynamic characterization and decoding of finger movements using magnetoencephalography.Imaging neuroscience (Cambridge, Mass.)Article
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8 authors.
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Abstract
An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data, this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. While this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM, and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks.
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