Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

531
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
531

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Right‑DLPFC rTMS Transiently Modulates Risky Decision-Making and EEG Oscillatory Activity.

Brain and behavior·2026
Same author

Reconstruction of MRI from undersampled k-spaces of double-contrast volume acquisitions using deep neural networks.

Magnetic resonance imaging·2026
Same author

Validation-Aware Retrospective EEG Treatment-Response Modelling Using Chaotic Pattern of Prime Numbers Features: Segment-Level Separability and Subject-Wise Generalisation.

Bioengineering (Basel, Switzerland)·2026
Same author

Sex-related structural alterations across common epilepsies: a worldwide ENIGMA study.

bioRxiv : the preprint server for biology·2026
Same author

Explainable EEG-based prediction of depression therapy outcomes using local Fibonacci pattern analysis.

Psychiatry research. Neuroimaging·2026
Same author

Psychosocial stressors and SLE flares: A grounded theory based on Iranian women's lived experiences.

Chronic illness·2026

Related Experiment Video

Updated: Jul 12, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

Effective Connectivity Estimation by a Hybrid Neural Network, Empirical Wavelet Transform, and Bayesian Optimization.

Milad Esmaeil-Zadeh, Morteza Fattahi, Mohammad Soltani-Gol

    IEEE Journal of Biomedical and Health Informatics
    |October 26, 2023
    PubMed
    Summary

    This study introduces a novel hybrid neural network model for measuring nonlinear effective brain connectivity, outperforming existing methods in accuracy and noise robustness. The model effectively handles non-stationary EEG signals, advancing brain function research.

    More Related Videos

    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
    11:14

    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

    Published on: October 4, 2015

    11.0K
    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
    06:04

    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

    Published on: January 17, 2025

    565

    Related Experiment Videos

    Last Updated: Jul 12, 2025

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
    08:51

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

    Published on: November 1, 2019

    5.7K
    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
    11:14

    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

    Published on: October 4, 2015

    11.0K
    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
    06:04

    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

    Published on: January 17, 2025

    565

    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Signal Processing

    Background:

    • Accurate measurement of nonlinear effective brain connectivity is vital for understanding brain function.
    • Existing methods struggle with non-stationary signals, hyperparameter selection, and time lag determination.
    • Electroencephalography (EEG) signals are inherently non-stationary, posing challenges for connectivity analysis.

    Purpose of the Study:

    • To propose a novel hybrid neural network model for enhanced nonlinear effective connectivity measurement.
    • To address limitations of existing methods in handling non-stationary data and parameter selection.
    • To evaluate the model's performance on simulated and real EEG data, including clinical applications.

    Main Methods:

    • A hybrid model combining Empirical Wavelet Transform (EWT) and Long Short-Term Memory (LSTM) networks.
    • Bayesian Optimization (BO) for automated selection of optimal hyperparameters and time lags.
    • A novel algorithm for selecting generalizable weights to improve model robustness.
    • Evaluation using simulated data and real EEG data from ADHD and healthy subjects.

    Main Results:

    • The proposed EWT-LSTM model demonstrated superior performance compared to various neural networks (LSTM, CNN-LSTM, GRU, RNN, MLP) and traditional methods (LGC, KGC, PDC, DTF).
    • The model exhibited significant robustness against noise, accurately identifying brain connections under noisy conditions.
    • Analysis of effective connectivity in ADHD patients revealed patterns consistent with previous research.

    Conclusions:

    • The developed hybrid EWT-LSTM model offers a powerful and robust solution for nonlinear effective connectivity analysis in non-stationary EEG data.
    • This approach advances the investigation of brain dynamics and has potential applications in clinical neuroscience, particularly in understanding disorders like ADHD.
    • The model's ability to handle noise and optimize parameters makes it a valuable tool for future brain connectivity research.