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

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

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...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

You might also read

Related Articles

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

Sort by
Same author

A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation.

Cell reports methods·2026
Same author

A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation.

bioRxiv : the preprint server for biology·2026
Same author

Would You Agree If N Is Three? On Statistical Inference for Small N.

Journal of cognitive neuroscience·2025
Same author

Riemannian geometry boosts functional near-infrared spectroscopy-based brain-state classification accuracy.

Neurophotonics·2025
Same author

Sensitive and specific fNIRS-based approach for awareness detection in disorders of consciousness: proof of principle in healthy adults.

Neurophotonics·2025
Same author

In vivo magnetic recording of single-neuron action potentials.

Journal of neurophysiology·2025
Same journal

Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.

Neuron·2026
Same journal

TGF-β1-induced endothelial transcytosis drives blood-brain barrier leakage during aging.

Neuron·2026
Same journal

Image space opens up for visual neuroscience.

Neuron·2026
Same journal

Septal GLP-1 receptors control alcohol taking and seeking.

Neuron·2026
Same journal

Microglial fitness in moderation: Tuning TREM2 signaling through Ptpn6.

Neuron·2026
Same journal

Human astrocytes keep time with inflammation.

Neuron·2026
See all related articles

Related Experiment Video

Updated: Jun 17, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

The model- and the data-gamma.

Pascal Fries1

  • 1Ernst Strüngmann Institute in Cooperation with Max Planck Society, 60528 Frankfurt, Germany. pascal.fries@esi-frankfurt.de

Neuron
|December 17, 2009
PubMed
Summary
This summary is machine-generated.

Researchers found that gamma-band synchronization connects different brain regions in the rat hippocampus. This suggests a switching mechanism for communication, modulated by theta brain waves.

More Related Videos

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Published on: September 4, 2017

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Related Experiment Videos

Last Updated: Jun 17, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Published on: September 4, 2017

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Structural connections in the brain are thought to support functional communication.
  • Neural oscillations, particularly in the gamma-frequency band, are implicated in information processing and communication between brain regions.

Purpose of the Study:

  • To investigate the spatial and temporal dynamics of gamma-band synchronization within the hippocampal formation.
  • To explore the relationship between gamma-band synchronization and theta oscillations in mediating brain communication.

Main Methods:

  • Electrophysiological recordings in the rat hippocampal formation.
  • Analysis of neural oscillations, focusing on gamma-band synchronization.
  • Investigating the temporal coordination of gamma activity in relation to theta oscillations.

Main Results:

  • Demonstrated spatially and temporally fine-grained gamma-band synchronization between distinct parts of the rat hippocampal formation.
  • Showed that gamma-band synchronization is modulated by the theta rhythm.
  • Provided evidence for a theta-modulated switching mechanism in gamma-mediated neural communication.

Conclusions:

  • Gamma-band synchronization plays a crucial role in functional connectivity between structurally connected brain regions.
  • Theta oscillations may act as a coordinating mechanism, orchestrating communication via gamma-band synchronization.
  • These findings offer insights into the neural basis of information processing in the hippocampus.