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

You might also read

Related Articles

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

Sort by
Same authorSame journal

Group Joint ICA (gjICA): A Method for Multimodal Fusion of Concurrent EEG and fMRI Data.

Human brain mapping·2026
Same author

Converting negative symptom dimension scores across SANS and PANSS.

Schizophrenia research·2026
Same author

Robustness of NeuroMark-derived functional networks to fMRI spatial normalization across the human lifespan.

NeuroImage·2026
Same author

AI-associated delusions: terminology.

The lancet. Psychiatry·2026
Same author

Dynamic Estimation of Spatially Interactive Networks (DESINE) Reveals Constrained Brain Repertoire in Schizophrenia Linked to Clinical and Cognitive Symptoms.

bioRxiv : the preprint server for biology·2026
Same author

Electroencephalography Microstate Instability and Clinical Outcomes in Individuals at Clinical High Risk of Psychosis.

JAMA psychiatry·2026

Related Experiment Video

Updated: Sep 6, 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

18.1K

Nonlinear functional network connectivity in resting functional magnetic resonance imaging data.

Sara M Motlaghian1, Aysenil Belger2, Juan R Bustillo3

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

Human Brain Mapping
|June 28, 2022
PubMed
Summary

This study introduces a novel method using normalized mutual information (NMI) to detect nonlinear relationships in brain functional networks. The findings reveal distinct nonlinear functional connectivity patterns in schizophrenia patients compared to healthy controls, particularly in visual cortex networks.

Keywords:
functional network connectivitymutual informationnonlinear functional network connectivitytime courses

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

7.3K
Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS
07:56

Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS

Published on: June 24, 2025

323

Related Experiment Videos

Last Updated: Sep 6, 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

18.1K
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

7.3K
Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS
07:56

Resting-State Connectivity and Neuroimaging of Prefrontal Cortex Activity During a Block-Design Yoga Asana Practice Using fNIRS

Published on: June 24, 2025

323

Area of Science:

  • Neuroscience
  • Network Science
  • Data Analysis

Background:

  • Functional brain networks exhibit complex relationships, including nonlinear interactions.
  • Traditional linear methods may overlook crucial nonlinear dependencies in functional connectivity.
  • Understanding these nonlinearities is vital for characterizing brain function and dysfunction.

Purpose of the Study:

  • To introduce and validate a novel technique for quantifying explicitly nonlinear relationships in functional brain networks using normalized mutual information (NMI).
  • To investigate differences in nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls.
  • To explore the modularity and distinctiveness of nonlinear FNC patterns compared to linear approaches.

Main Methods:

  • Utilized simulated data and a resting-state fMRI dataset from schizophrenia patients and healthy controls.
  • Applied group independent component analysis (ICA) to decompose fMRI data into intrinsic connectivity networks.
  • Calculated nonlinear relationships between brain region time courses using normalized mutual information (NMI), after removing linear effects.

Main Results:

  • Identified modularized nonlinear relationships in functional brain networks, especially in sensory and visual cortex.
  • Observed significant differences in nonlinear FNC between schizophrenia patients and healthy controls, with controls exhibiting higher nonlinearity in visual cortex.
  • Found that certain brain domains (subcortical, auditory) showed less nonlinear FNC, while visual cortex links displayed substantial nonlinear and modular properties.

Conclusions:

  • Quantifying nonlinear functional connectivity provides a complementary approach to linear methods, revealing important variations in brain function.
  • The proposed NMI-based method highlights differences in nonlinear FNC between schizophrenia patients and controls.
  • The findings suggest that nonlinear FNC analysis is a valuable tool for understanding brain function and identifying neurobiological markers of psychiatric disorders.