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

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's proficiency in drug...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

You might also read

Related Articles

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

Sort by
Same author

A specific computational role for early-life unpredictability, and not lifelong traumatic experience, in decision-making under uncertainty.

bioRxiv : the preprint server for biology·2026
Same author

Anhedonia buffers the effects of early-life unpredictability on threat-reward decision-making.

bioRxiv : the preprint server for biology·2026
Same authorSame journal

Neurodatascience: Past, Present, and Future.

Data science in science·2026
Same author

Structure Inference in Complex Environments Improves From Childhood to Adulthood.

Developmental science·2026
Same author

Multistep inference across the human lifespan can be improved with individualized memory interventions.

The journals of gerontology. Series B, Psychological sciences and social sciences·2026
Same author

Cognitive Graphs: Representational Substrates for Planning.

Decision (Washington, D.C.)·2025
Same journal

A Bayesian Integrative Mixed Modeling Framework for Analysis of the Multi-Site Adolescent Brain and Cognitive Development Study.

Data science in science·2026
Same journal

Enhancing Health Research with Machine Learning: Practical Case Studies Using the <i>All of Us</i> Researcher Workbench.

Data science in science·2025
Same journal

TIME-VARYING <i>ℓ</i> <sub>0</sub> OPTIMIZATION FOR SPIKE INFERENCE FROM MULTI-TRIAL CALCIUM RECORDINGS.

Data science in science·2025
Same journal

Assessment of Glioblastoma Multiforme Tumor Heterogeneity via MRI-derived Shape and Intensity Features.

Data science in science·2025
Same journal

Adaptive Sequential Singular Spectrum Analysis: Effective Signal Extraction with Application to Heart Rate Signals Related to E-cigarette Use.

Data science in science·2025
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

A Bayesian Time-Varying Psychophysiological Interaction Model.

Brian Schetzsle1, Jaylen Lee1, Aaron Bornstein2

  • 1Department of Statistics, University of California, Irvine, California, USA.

Data Science in Science
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian framework for analyzing brain functional connectivity, improving upon the standard Psychophysiological Interaction (PPI) model. The new method offers more robust and dynamic insights into brain region coordination.

Keywords:
Psychophysiological interactiondynamic covariancefunctional connectivitytime-varying parameter

Related Experiment Videos

Last Updated: Jun 13, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Area of Science:

  • Neuroscience
  • Brain Imaging
  • Computational Biology

Background:

  • Functional connectivity analysis is crucial for understanding brain coordination.
  • The standard Psychophysiological Interaction (PPI) model has limitations due to confounding effects.
  • Existing methods for inferring task-dependent functional connectivity require refinement.

Purpose of the Study:

  • To develop a more robust and dynamic method for analyzing functional connectivity.
  • To address confounding effects inherent in the standard PPI model.
  • To introduce a Bayesian extension of the PPI model for time-varying connectivity estimation.

Main Methods:

  • Utilizing partial correlations derived from Gaussian Graphical Models (GGMs) to correct for confounding.
  • Implementing a Bayesian extension to the PPI model for dynamic functional connectivity analysis.
  • Employing scale-mixture shrinkage priors for sparsity and a Bayesian decision-theoretic framework for identifying structural zeros.

Main Results:

  • The proposed method demonstrates superior performance compared to the standard PPI model using simulated data.
  • The framework successfully identified dynamic functional connectivity patterns in human fMRI data.
  • Partial correlations provide a more accurate measure of functional connectivity than PPI regression coefficients.

Conclusions:

  • The novel Bayesian framework offers a more robust and dynamic approach to functional connectivity analysis.
  • This method enhances the understanding of task-dependent brain region coordination.
  • The findings have significant implications for neuroimaging research and the interpretation of brain activity.