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

Biological Causes of Schizophrenia01:29

Biological Causes of Schizophrenia

103
Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
Genetic Factors in Schizophrenia
The genetic basis of schizophrenia is strongly supported by family and twin...
103

You might also read

Related Articles

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

Sort by
Same author

Multiscale Permutation Time Irreversibility Analysis of MEG in Patients with Schizophrenia.

Entropy (Basel, Switzerland)·2025
Same author

Threshold distribution of equal states for quantitative amplitude fluctuations.

Physiological measurement·2023
Same author

Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms.

Entropy (Basel, Switzerland)·2022
Same author

Intravenous transplantation of mesenchymal stem cells improves cardiac performance after acute myocardial ischemia in female rats.

Transplant international : official journal of the European Society for Organ Transplantation·2006
Same author

[Effects of mechanical tensile stress on the expression of ICAM-1 mRNA in osteoblasts differentiated from rBMSCs].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2006
Same author

[Effects of osteoporosis on experimental tooth movement in aged rats].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2006

Related Experiment Video

Updated: Jul 21, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K

Schizophrenia MEG Network Analysis Based on Kernel Granger Causality.

Qiong Wang1,2, Wenpo Yao3, Dengxuan Bai1

  • 1School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Entropy (Basel, Switzerland)
|July 29, 2023
PubMed
Summary

This study introduces a new method, multivariate inhomogeneous polynomial kernel Granger causality (MKGC), to analyze brain networks in schizophrenia using magnetoencephalography (MEG). MKGC reveals distinct network differences between healthy controls and schizophrenia patients.

Keywords:
complexityeffective networkkernel Granger causalitynonequilibriumschizophrenia MEG

More Related Videos

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
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.6K

Related Experiment Videos

Last Updated: Jul 21, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K
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
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.6K

Area of Science:

  • Neuroscience
  • Network Science
  • Biophysics

Background:

  • Brain network analysis is crucial for understanding neurological disorders like schizophrenia.
  • Magnetoencephalography (MEG) provides valuable data for studying brain connectivity.

Purpose of the Study:

  • To introduce and validate the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) method for constructing directed weighted brain networks.
  • To characterize differences in brain network topology between individuals with schizophrenia and healthy controls using MEG data.

Main Methods:

  • Developed and tested MKGC against existing Granger causality methods using simulated data.
  • Applied MKGC to magnetoencephalography (MEG) data from schizophrenia patients (SCZs) and healthy controls (HCs).
  • Quantified network features including strength, nonequilibrium, and complexity (Shannon entropy).

Main Results:

  • MKGC demonstrated superior performance compared to bivariate linear and inhomogeneous polynomial kernel Granger causality methods.
  • Schizophrenia patients exhibited less dense effective connectivity networks than healthy controls.
  • Significant differences in in-connectivity strength (right frontal) and out-connectivity strength (left occipital) were observed.
  • Schizophrenia networks showed higher nonequilibrium but lower complexity (Shannon entropy) than healthy networks.

Conclusions:

  • MKGC is a reliable method for constructing and analyzing MEG-based brain networks.
  • Network characteristics derived from MKGC can effectively differentiate between schizophrenia and healthy individuals.
  • Findings highlight altered brain connectivity patterns in schizophrenia, with implications for understanding the pathophysiology of the disorder.