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

Correlation and Regression00:53

Correlation and Regression

2.9K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
2.9K

You might also read

Related Articles

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

Sort by
Same author

LaRHP: latent-aware reconstruction via hypersphere projection for industrial image anomaly detection.

Scientific reports·2026
Same author

Determining acoustic impedance cube by inverting seismic data using feedforward and radial basis neural networks in an Iranian oilfield.

Scientific reports·2026
Same author

A physics-informed deep learning approach for 3D acoustic impedance estimation from seismic data: application to an offshore field in the Southwest Iran.

Scientific reports·2025
Same author

Breast cancer detection in mammography images using Neighborhood Attention transformer and Shearlet Transform.

Computers in biology and medicine·2025
Same author

Anomaly Detection Using Complete Cycle Consistent Generative Adversarial Network.

International journal of neural systems·2024
Same author

Revolutionizing endometriosis treatment: automated surgical operation through artificial intelligence and robotic vision.

Journal of robotic surgery·2024

Related Experiment Video

Updated: Dec 20, 2025

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.4K

Using autoregressive-dynamic conditional correlation model with residual analysis to extract dynamic functional

Hamidreza Hakimdavoodi1, Maryam Amirmazlaghani1,2

  • 1Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran.

Journal of Neural Engineering
|May 27, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new autoregressive model for dynamic functional connectivity (FC) in fMRI data, accurately capturing temporal changes without manual parameter tuning. The model revealed significant connectivity differences between typical and ADHD subjects.

More Related Videos

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.6K
Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
07:13

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

7.0K

Related Experiment Videos

Last Updated: Dec 20, 2025

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.4K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.6K
Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
07:13

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

7.0K

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Statistical Modeling

Background:

  • Dynamic functional connectivity (FC) analysis in fMRI is crucial for understanding brain function.
  • Existing methods often require manual parameter setting and may overlook fMRI data characteristics like autocorrelation.
  • A validated method for dynamic FC analysis is lacking.

Purpose of the Study:

  • To propose an autoregressive dynamic conditional correlation model for dynamic FC estimation in fMRI.
  • To address temporal autocorrelation and non-stationarity in fMRI time-series.
  • To introduce a novel index for validating dynamic FC analysis.

Main Methods:

  • Developed an autoregressive dynamic conditional correlation model assuming multivariate Gaussian distribution for brain time courses.
  • Incorporated autoregressive changes in conditional mean, variance, and covariances.
  • Proposed a new statistical consistency index for dynamic FC validation.
  • Tested the model on simulated and real fMRI data.

Main Results:

  • The model identified dynamic connectivity patterns with high precision, associated with independent Gaussian residuals.
  • Significant differences in brain connectivities were observed between typically developing and ADHD subjects.
  • The model effectively handled fMRI autocorrelation and eliminated variance changes from connectivity analysis.

Conclusions:

  • The proposed model offers an advanced approach to dynamic FC analysis in fMRI.
  • It accurately captures temporal dynamics and autocorrelation without manual window length adjustments.
  • The findings highlight potential neuroimaging biomarkers for conditions like ADHD.