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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

313
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
313
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

376
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
376
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

301
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...
301
Longitudinal Studies01:26

Longitudinal Studies

593
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
593
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

1.3K
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...
1.3K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

614
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
614

You might also read

Related Articles

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

Sort by
Same author

Statistics and AI - A Fireside Conversation.

Harvard data science review·2026
Same author

Cardiovascular-Kidney-Metabolic Syndrome: Conceptualising an Approach to Health Economic Modelling.

Diabetes, obesity & metabolism·2026
Same author

Artificial Intelligence in Image-Based Cardiovascular Disease Analysis.

Annual review of biomedical data science·2026
Same author

Multi-organ imaging and genetics show the impact of sleep patterns on the human brain and body.

Communications medicine·2026
Same author

Scalable subclonal reconstruction of cancer cells in DNA sequencing data using a penalized likelihood model.

bioRxiv : the preprint server for biology·2026
Same author

Connectome-based spatial statistics enabling large-scale population analyses of human connectome across cohorts.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Mar 8, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies.

Zhao-Hua Lu1, Zakaria Khondker2, Joseph G Ibrahim2

  • 1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.

Neuroimage
|February 2, 2017
PubMed
Summary

This study introduces a new Bayesian model for analyzing brain imaging data and genetic markers over time. The model effectively identifies genetic influences on Alzheimer's disease progression and brain changes.

Keywords:
Genetic variantsLongitudinal imaging phenotypesLow-rank regressionMarkov chain Monte CarloSpatiotemporal correlation

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K

Related Experiment Videos

Last Updated: Mar 8, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K

Area of Science:

  • Neuroscience
  • Genetics
  • Biostatistics

Background:

  • Longitudinal neuroimaging studies are crucial for understanding Alzheimer's disease (AD) progression.
  • Analyzing complex relationships between genetic markers and evolving brain phenotypes presents significant statistical challenges.

Purpose of the Study:

  • To develop a novel statistical framework for joint analysis of longitudinal neuroimaging data and genetic markers.
  • To investigate the impact of specific genetic variations on brain structure changes in Alzheimer's disease.

Main Methods:

  • A Bayesian longitudinal low-rank regression (L2R2) model was developed, integrating low-rank matrices, penalized splines, and sparse factor analysis with random effects.
  • The model captures genetic main effects, their time interactions, overall time effects, and within-subject correlations.
  • An efficient Markov chain Monte Carlo algorithm was employed for posterior computation.

Main Results:

  • Simulation studies demonstrated that the L2R2 model outperforms existing methods.
  • The model successfully identified associations between single nucleotide polymorphisms (SNPs) in AD-associated genes and brain tissue volumes.
  • Significant interactions between SNPs, patient age, and regional brain volumes were detected.

Conclusions:

  • The L2R2 model provides a robust approach for joint analysis of high-dimensional longitudinal neuroimaging and genetic data.
  • This method enhances our understanding of the genetic underpinnings of Alzheimer's disease progression and its impact on brain structure.