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 author

Detecting abnormal dynamic patterns of phase changes in schizophrenia from complex-valued fMRI data.

Journal of neuroscience methods·2025
Same author

[Magnetic one-step purification-high performance liquid chromatography-triple quadrupole/composite linear ion trap mass spectrometry for the determination of 54 veterinary drug residues in carp].

Se pu = Chinese journal of chromatography·2025
Same author

Detecting low-amplitude biomarker activations via decomposition of complex-valued fMRI data with collaborative phase and magnitude sparsity.

Medical image analysis·2025
Same author

Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data.

Journal of neuroscience methods·2024
Same author

A Millimeter-Wave Broadband Multi-Mode Substrate-Integrated Gap Waveguide Traveling-Wave Antenna with Orbit Angular Momentum.

Sensors (Basel, Switzerland)·2024
Same author

Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia.

Journal of neuroscience methods·2023
Same journal

Relationship between spontaneous EEG oscillations at 7 and 45 days of acute plateau exposure and the plateau acclimatization index.

Frontiers in neuroscience·2026
Same journal

Neuroprotective effects of paederoside against mitochondrial dysfunction in rotenone-induced cell models of Parkinson's disease.

Frontiers in neuroscience·2026
Same journal

Covariance-based analysis of spindle-band EEG during declarative and non-declarative odor cueing in sleep.

Frontiers in neuroscience·2026
Same journal

Correction: Physiological determinants of cortical P100 responses in pattern visual evoked potentials: a scoping review.

Frontiers in neuroscience·2026
Same journal

Transcranial magnetic stimulation and motor overflow: a systematic review in neurological disorders.

Frontiers in neuroscience·2026
Same journal

Editorial: Advancing neurodegenerative disease biomarkers: the role of neuroimaging in TDP-43 and tau proteinopathies.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data.

Wei-Xing Li1, Qiu-Hua Lin1, Chao-Ying Zhang1

  • 1School of Information and Communication Engineering, Dalian University of Technology, Dalian, China.

Frontiers in Neuroscience
|July 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new complex-valued transfer entropy (CTE) method for analyzing brain connectivity using functional magnetic resonance imaging (fMRI) data. CTE enhances the prediction of mental disorders like schizophrenia by utilizing both magnitude and phase information from fMRI scans.

Keywords:
complex-valued fMRI datadirected connectivityfunctional connectivitypartial transfer entropytransfer entropy

More Related Videos

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

2.4K
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

18.0K

Related Experiment Videos

Last Updated: Jun 19, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K
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

2.4K
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

18.0K

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biophysics

Background:

  • Functional magnetic resonance imaging (fMRI) is crucial for understanding brain connectivity and predicting mental disorders.
  • Existing methods often ignore phase data in complex-valued fMRI, potentially missing vital information for connectivity analysis.

Purpose of the Study:

  • To introduce a novel complex-valued transfer entropy (CTE) method for analyzing causal links in complex-valued fMRI data.
  • To leverage both magnitude and phase information for a more comprehensive brain connectivity analysis.

Main Methods:

  • Developed a complex-valued transfer entropy (CTE) method to measure causal links in complex-valued fMRI data.
  • Utilized partial transfer entropy to assess magnitude-phase and phase-magnitude causality.
  • Defined causality significance using statistical tests and signal shuffling.

Main Results:

  • CTE demonstrated higher accuracy than existing methods in simulations.
  • Applied to fMRI data from schizophrenia patients and controls, CTE revealed significant group differences and new directed connectivity patterns.
  • Achieved 10.2-20.9% higher classification accuracy for schizophrenia prediction using CTE-derived connectivity features.

Conclusions:

  • The proposed CTE method offers a comprehensive approach to detecting predictive directed connectivity from complex-valued fMRI data.
  • CTE can be applied to both complex-valued and magnitude-only fMRI data, offering a versatile tool for neuroscience research.