ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2024
Data-driven modeling of neural dynamics from EEG to track physiological changes.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2024. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Stabilizing fractional dynamical networks suppresses epileptic seizures.Scientific reports · 2026Article
- Blind identification of state transitions and latent neural dynamics from electrophysiological recordings.Journal of neuroscience methods · 2026Article
- Identification of modulated whole-brain dynamical models from nonstationary electrophysiological data.Journal of neural engineering · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A key challenge in studying brain function is gaining insight into the mechanisms which drive neural activity. In this paper, we seek to address this challenge by developing a framework for generative, individualized models based on EEG data which can give insight into the neural functions which drive observed electrophysiological activity. The models created using this framework are accurate, reliable, individualized, and capable of tracking changes in neural activity during the physiological changes occurring during cardiac arrest. Due to the biophysical significance of the model structure, we can gain insight into the mechanisms driving these changes in neural activity, e.g., lowered excitatory inputs across the brain.
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What OpenQuestion holds
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.