ArticleJournal of computational neuroscience2018
A method for decomposing multivariate time series into a causal hierarchy within specific frequency bands.
Article in Journal of computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Spinal Cord Stimulation for Prolonged Disorders of Consciousness: A Study on Scalp Electroencephalography.CNS neuroscience & therapeutics · 2024Article
- Electrophysiological correlates of thalamocortical function in acute severe traumatic brain injury.Cortex; a journal devoted to the study of the nervous system and behavior · 2022Article
- Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies.Nature reviews. Neurology · 2021Review
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Authors and funding
2 authors.
Funding
Abstract
We propose a method - Frequency extracted hierarchical decomposition (FEHD) - for studying multivariate time series that identifies linear combinations of its components that possess a causally hierarchical structure - the method orders the components so that those at the "top" of the hierarchy drive those below. The method shares many of the features of the "hierarchical decomposition" method of Repucci et al. (Annals of Biomedical Engineering, 29, 1135-1149, 2001) but makes a crucial advance - the proposed method is capable of determining this causal hierarchy over arbitrarily specified frequency bands. Additionally, a novel minimization strategy is used to generate the decomposition resulting in an increase in stability, reliability, and an improvement in the sensitivity to model parameters. We demonstrate the utility of the method by applying it to both artificial time series constructed to have specific causal graphs, and to the EEG of healthy volunteers and patient subjects who are recovering from a severe brain injury.
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Registered trials
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