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

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.0K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.0K
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

1.1K
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...
1.1K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

7.7K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
7.7K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

474
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
474
Coefficient of Correlation01:12

Coefficient of Correlation

8.2K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.2K
Fascicle Arrangement in Skeletal Muscles01:25

Fascicle Arrangement in Skeletal Muscles

3.7K
Fascicles are bundles of muscle fibers in a skeletal muscle. Muscle fascicle arrangement is directly associated with the power and range of motion of various muscles. The configuration of these fascicles can vary, leading to different functional outcomes.
The four primary types of muscle based on fascicle arrangement are:
3.7K

You might also read

Related Articles

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

Sort by
Same author

Transcriptome and miRNome Analysis Provide New Insight Into Host Lipid Accumulation, Innate Immunity, and Viral Persistence in Hepatitis C Virus Infection <i>in vitro</i>.

Frontiers in microbiology·2020
Same author

Synergistic pathogenicity in sequential coinfection with fowl adenovirus type 4 and avian orthoreovirus.

Veterinary microbiology·2020
Same author

The Change Trend of Cause of Death in Patients With Stage I Non-Small Cell Lung Cancer After Surgery in US: A Long-Term Follow-Up Study Based on SEER Database.

Cancer control : journal of the Moffitt Cancer Center·2020
Same author

Changes in microstructure and rheological properties of konjac glucomannan/zein blend film-forming solution during drying.

Carbohydrate polymers·2020
Same author

Dux-Mediated Corrections of Aberrant H3K9ac during 2-Cell Genome Activation Optimize Efficiency of Somatic Cell Nuclear Transfer.

Cell stem cell·2020
Same author

Co-Digestion Biomethane Production and the Effect of Nanoparticle: Kinetics Modeling and Microcalorimetry Studies.

Applied biochemistry and biotechnology·2020
Same journal

Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

IEEE transactions on bio-medical engineering·2026
Same journal

Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

IEEE transactions on bio-medical engineering·2026
Same journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026
Same journal

Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

IEEE transactions on bio-medical engineering·2026
Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

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

6.0K

Structured and sparse partial least squares coherence for multivariate cortico-muscular analysis.

Jingyao Sun, Qilu Zhang, Di Ma

    IEEE Transactions on Bio-Medical Engineering
    |December 12, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A new algorithm, structured and sparse partial least squares coherence (ssPLSC), enhances cortico-muscular analysis for evaluating neural pathways. It excels with limited data and high noise, improving diagnostics for neurological disorders.

    More Related Videos

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.1K
    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    17.3K

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    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

    6.0K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.1K
    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    17.3K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Multivariate cortico-muscular analysis is key for assessing corticospinal pathways.
    • Existing methods struggle with high dimensionality and small sample sizes, limiting their use.

    Purpose of the Study:

    • To introduce a novel structured and sparse partial least squares coherence (ssPLSC) algorithm.
    • To extract shared latent space representations for cortico-muscular interactions.
    • To address generalizability, sparsity, and spatial structure in cortico-muscular analysis.

    Main Methods:

    • Developed an embedded optimization framework integrating a partial least squares (PLS)-based objective function.
    • Incorporated sparsity and connectivity-based structured constraints.
    • Designed an efficient alternating iterative algorithm to solve the optimization problem and verified its convergence.

    Main Results:

    • ssPLSC demonstrated competitive or superior performance compared to existing multivariate cortico-muscular fusion methods.
    • The algorithm showed particular effectiveness in scenarios with limited sample sizes and high noise levels.
    • Experimental results validated the algorithm's performance on synthetic and real-world datasets.

    Conclusions:

    • ssPLSC offers a robust multivariate fusion method for cortico-muscular analysis.
    • This approach can potentially aid in evaluating corticospinal pathway integrity.
    • The method shows promise for applications in diagnosing and understanding neurological disorders.