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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

137
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,...
137
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

68
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...
68
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

38
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...
38
Protein Networks02:26

Protein Networks

3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.9K

You might also read

Related Articles

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

Sort by
Same author

Distance-weighted Sinkhorn loss for Alzheimer's disease classification.

iScience·2024
Same author

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach.

Scientific reports·2024
Same author

Plasma Biomarkers as Predictors of Progression to Dementia in Individuals with Mild Cognitive Impairment.

Journal of Alzheimer's disease : JAD·2024
Same author

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

JAMA psychiatry·2024
Same author

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

ArXiv·2024
Same author

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.

medRxiv : the preprint server for health sciences·2024
Same journal

A human-specific genetic modifier reconfigures large-scale cortical network dynamics underlying behavioral performance.

bioRxiv : the preprint server for biology·2026
Same journal

<i>Staphylococcus aureus</i> uses a eukaryotic-like uridyltransferase to make UDP-GlcNAc for cell wall synthesis.

bioRxiv : the preprint server for biology·2026
Same journal

Dynamic redistribution of eIF4F controls cap-dependent translation initiation.

bioRxiv : the preprint server for biology·2026
Same journal

When does additional information improve accuracy of RNA secondary structure prediction?

bioRxiv : the preprint server for biology·2026
Same journal

Normative brain-state trajectories reveal deviation from healthy aging in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same journal

Noradrenergic infraslow rhythm during sleep is the critical link between heart-rate dynamics and memory consolidation.

bioRxiv : the preprint server for biology·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

pNet: A toolbox for personalized functional networks modeling.

Yuncong Ma1,2, Hongming Li1,2, Zhen Zhou1,2

  • 1Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA.

Biorxiv : the Preprint Server for Biology
|May 15, 2024
PubMed
Summary
This summary is machine-generated.

We developed pNet, an open-source toolbox for personalized functional networks (FNs) from fMRI data. It enhances reliability and reproducibility for brain research across development, aging, and disorders.

Keywords:
Personalized functional networkfunctional coherence optimizationfunctional magnetic resonance imagingindependence enhancementopen-source toolboxquality control

More Related Videos

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

5.6K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.3K

Related Experiment Videos

Last Updated: Jun 26, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K
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

5.6K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.3K

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Mapping

Background:

  • Personalized functional networks (FNs) from functional magnetic resonance imaging (fMRI) reveal individual brain functional topography.
  • Understanding variations in FNs is crucial for studying brain development, aging, and neurological disorders.
  • Current methods for deriving FNs may lack reliability and reproducibility for clinical applications.

Approach:

  • Developed an open-source toolbox, pNet, for user-friendly and extendable personalized functional network modeling.
  • pNet supports volumetric and surface fMRI data, ensuring broad compatibility.
  • Incorporates rigorous quality control, multiple user interfaces (GUI, CLI), and job-scheduling for high-performance computing.

Key Points:

  • pNet computes personalized FNs using two distinct modeling approaches: optimizing functional coherence and enhancing independence.
  • The toolbox generates HTML-based reports for quality control and visualization of personalized FNs.
  • Developed in both MATLAB and Python with a modular design for user extensibility.

Conclusions:

  • pNet enhances the reliability and reproducibility of personalized functional networks derived from fMRI data.
  • The toolbox is effective and user-friendly, demonstrated through evaluations on two fMRI datasets.
  • pNet facilitates advanced research into individual brain variations and their association with various conditions.