Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Normalized Dynamic Time Warping Increases Sensitivity in Differentiating Functional Network Connectivity in Schizophrenia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

A multi-frequency ICA-based approach for estimating voxelwise frequency difference patterns in fMRI data.

Aperture neuro·2025

Same author

NeuroMark-HiFi: A Data-Driven Method for Detecting High-Spatial-Frequency Functional Brain Networks.

bioRxiv : the preprint server for biology·2025

Same author

Professional challenges faced by student radiographers during COVID-19.

Radiography (London, England : 1995)·2025

Same author

Effects of estradiol administration on brain activation and anhedonia in perimenopausal women: A pharmaco-fMRI study.

Journal of affective disorders·2025

Same author

Impaired spatial dynamic functional network connectivity and neurophysiological correlates in functional hemiparesis.

NeuroImage. Clinical·2025

Same journal

Development and Standardization of a Simple Zebrafish Larval Model of Global Hypoxia-Reoxygenation: Recapitulating Key Pathological Features Associated with Cerebral Ischemia-Reperfusion Injury.

Journal of neuroscience methods·2026

Same journal

Metal Neurotoxicity assessment: a new imaging and analysis pipeline for primary neurons in co-cultures.

Journal of neuroscience methods·2026

Same journal

Corrigendum to "Human peripheral nerve xenografts and rat peripheral nerve allografts implanted to the striatum: Methodology and initial findings of cell-based therapy" [J. Neurosci. Methods 434 (2026) 110825].

Journal of neuroscience methods·2026

Same journal

Sombor-based graph-theoretic framework for the structural characterization of neuro-metabolic organic acids.

Journal of neuroscience methods·2026

Same journal

Pupil-DLC: An open-source deep learning pipeline for scalable, marker-less tracking of pupil dynamics across conscious and unconscious states.

Journal of neuroscience methods·2026

Same journal

Time as the language of Behavior: events, sequences, patterns and meanings.

Journal of neuroscience methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 14, 2025

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

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

Published on: March 21, 2019

A method for estimating and characterizing explicitly nonlinear dynamic functional network connectivity in

S M Motlaghian¹, V Vahidi², A Belger³

¹Tri-institutional Center for Translational Research in Neuroimaging and Data Science (Trends), Georgia State, Georgia Tech, and Emory, Atlanta, GA, USA.

Journal of Neuroscience Methods

|January 18, 2023

Summary

This summary is machine-generated.

This study introduces a novel method to analyze explicitly nonlinear dynamic functional network connectivity (EN dFNC), revealing unique brain activity patterns. Findings show significant differences in these nonlinear connections between schizophrenia patients and healthy controls.

Keywords:

Dynamic nonlinear functional network connectivity Explicitly nonlinear Independent component analysis (ICA)Intrinsic connectivity networks (ICNs)Mutual information

More Related Videos

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

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

Published on: August 5, 2014

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Aug 14, 2025

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

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

Published on: March 21, 2019

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

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

Published on: August 5, 2014

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Neuroscience
Computational Neuroscience
Brain Imaging

Background:

Functional connectivity (FC) analysis has evolved to study time-resolved changes.
Functional network connectivity (FNC) characterizes whole-brain network interactions.
Dynamic FNC (dFNC) captures transient, recurring whole-brain patterns.

Purpose of the Study:

To develop and validate a method for assessing explicitly nonlinear dynamic functional network connectivity (EN dFNC).
To investigate whether nonlinear relationships in brain activity contain unique information beyond linear correlations.
To explore group differences in EN dFNC between schizophrenia patients and healthy controls.

Main Methods:

Proposed an approach to assess EN dFNC using independent component analysis (ICA) time courses.
Removed linear relationships at each time point to isolate explicitly nonlinear dFNC.
Utilized normalized mutual information (NMI) to quantify nonlinear relationships.
Validated the method using simulations and applied it to fMRI data from 151 schizophrenia patients and 163 healthy controls.

Main Results:

The proposed method successfully estimated explicitly nonlinear dFNC over time, even with short data windows.
Identified three unique, structured, long-range functional states with significant group differences.
Observed that explicitly nonlinear relationships were generally more widespread than linear ones.
Found a specific state with long-range visual domain connections that was reduced in schizophrenia patients.

Conclusions:

Quantifying EN dFNC offers a valuable complementary tool for studying brain function.
This approach can reveal significant variations in brain activity typically overlooked by linear methods.
EN dFNC analysis shows potential for understanding complex neurological conditions like schizophrenia.