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

What are Estimates?01:06

What are Estimates?

7.2K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
7.2K
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

889
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...
889
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

2.5K
The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
2.5K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

159
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
159
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

15.9K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
15.9K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

351
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,...
351

You might also read

Related Articles

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

Sort by
Same author

Microstructure-informed constitutive modeling of granular media under multidirectional loading: From particle-scale to continuum.

Communications engineering·2026
Same author

Depth normalization of small RNA sequencing: using data and biology to select a suitable method.

Nucleic acids research·2022
Same author

Statistical guarantees for regularized neural networks.

Neural networks : the official journal of the International Neural Network Society·2021
Same author

Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data.

Entropy (Basel, Switzerland)·2021
Same author

Primary and Repeated Respiratory Viral Infections Among Infants in Rural Nepal.

Journal of the Pediatric Infectious Diseases Society·2018
Same author

Biomarker-calibrated nutrient intake and healthy diet index associations with mortality risks among older and frail women from the Women's Health Initiative.

The American journal of clinical nutrition·2017

Related Experiment Video

Updated: Nov 11, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

872

Integrating additional knowledge into the estimation of graphical models.

Yunqi Bu1, Johannes Lederer1

  • 1Departments of Statistics and Biostatistics, University of Washington, Seattle, USA.

The International Journal of Biostatistics
|March 22, 2021
PubMed
Summary

This study introduces a novel method for analyzing brain connectivity using spatial information, improving accuracy in graphical models derived from functional magnetic resonance imaging (fMRI). The approach enhances statistical stability and reveals key brain network differences in Alzheimer's disease patients.

Keywords:
brain connectivity networksprior knowledgetuning parameters

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.5K

Related Experiment Videos

Last Updated: Nov 11, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

872
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.5K

Area of Science:

  • Neuroscience
  • Network Science
  • Biostatistics

Background:

  • Brain connectomes from functional magnetic resonance imaging (fMRI) are crucial for understanding network processes.
  • Standard graphical modeling methods can exhibit poor graph recovery, even with large datasets and optimal tuning.

Purpose of the Study:

  • To improve the accuracy and statistical stability of graphical models for brain connectomes.
  • To leverage underutilized spatial information in fMRI data for enhanced network analysis.

Main Methods:

  • Incorporation of spatial measurement positions (e.g., pairwise distances) into the tuning parameter of neighborhood selection.
  • Development of a computationally efficient and Bayesian-interpretable approach.

Main Results:

  • Demonstrated improvement over standard methods in statistical stability and graph recovery.
  • Application to Alzheimer's disease data highlighted the central role of brain lobes.
  • Identified increased cerebellar connectivity in Alzheimer's patients compared to controls.

Conclusions:

  • The proposed method offers a more robust approach to brain connectome analysis by integrating spatial data.
  • This technique provides valuable insights into neurological conditions like Alzheimer's disease by revealing specific connectivity patterns.