ArticleeLife2024
osl-dynamics, a toolbox for modeling fast dynamic brain activity.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Temporal neural dynamics patterns in episodic and chronic migraine: a magnetoencephalography study.The journal of headache and pain · 2026Article
- Dynamic neural states underpin motor symptom severity in Parkinson's disease: a longitudinal analysis of chronic cortico-subthalamic nucleus recordings.EBioMedicine · 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
- MEG state dynamics of sentence generation: evidence for a compensatory segmentation mechanism in healthy aging.Frontiers in computational neuroscience · 2026Article
- Dynamic, state-dependent characteristics of cognitive fluctuations in Lewy body dementia: a magnetoencephalography study.Brain communications · 2026Article
- Large-scale cortical functional networks are organized in structured cycles.Nature neuroscience · 2025Article
- Divergent Brain Network Activity in Asymptomatic C9orf72 and SOD1 Variant Carriers Compared With Established Amyotrophic Lateral Sclerosis.Human brain mapping · 2025Article
- Distinct alpha networks modulate different aspects of perceptual decision-making.PLoS biology · 2025Article
- Gamma activation spread reflects disease activity in amyotrophic lateral sclerosis.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 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
- Lifespan oscillatory dynamics in lexical production: A population-based MEG resting-state analysis.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Decoding the neural dynamics of everyday prospective remembering: a hidden Markov model approach.Frontiers in human neuroscience · 2025Article
- osl-ephys: a Python toolbox for the analysis of electrophysiology data.Frontiers in neuroscience · 2025Article
- The Gaussian-linear hidden Markov model: A Python package.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Changes in sensorimotor network dynamics in resting-state recordings in Parkinson's disease.Brain communications · 2025Article
- Comparison between EEG and MEG of static and dynamic resting-state networks.Human brain mapping · 2024Article
- Dynamic network analysis of electrophysiological task data.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- The cortical neurophysiological signature of amyotrophic lateral sclerosis.Brain communications · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.
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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.