ArticleChaos (Woodbury, N.Y.)2019
Causality detection in cortical seizure dynamics using cross-dynamical delay differential analysis.
Article in Chaos (Woodbury, N.Y.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.Scientific reports · 2025Article
- EEG spectral attractors identify a geometric core of brain dynamics.Patterns (New York, N.Y.) · 2024Article
- Decoding imagined speech with delay differential analysis.Frontiers in human neuroscience · 2024Article
- Network-motif delay differential analysis of brain activity during seizures.Chaos (Woodbury, N.Y.) · 2023Article
- Predicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA.Briefings in bioinformatics · 2023Article
- Dynamical differential covariance recovers directional network structure in multiscale neural systems.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Dynamical ergodicity DDA reveals causal structure in time series.Chaos (Woodbury, N.Y.) · 2021Article
- Cortical chimera states predict epileptic seizures.Chaos (Woodbury, N.Y.) · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Most natural systems, including the brain, are highly nonlinear and complex, and determining information flow among the components that make up these dynamic systems is challenging. One such example is identifying abnormal causal interactions among different brain areas that give rise to epileptic activities. Here, we introduce cross-dynamical delay differential analysis, an extension of delay differential analysis, as a tool to establish causal relationships from time series signals. Our method can infer causality from short time series signals as well as in the presence of noise. Furthermore, we can determine the onset of generalized synchronization directly from time series data, without having to consult the underlying equations. We first validate our method on simulated datasets from coupled dynamical systems and apply the method to intracranial electroencephalography data obtained from epilepsy patients to better characterize large-scale information flow during epilepsy.
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Registered trials
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