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

Mental Health Outcomes of Foster and Adopted Individuals with Adverse Childhood Experiences: A Validation of Known Risks Using EHR Data.

medRxiv : the preprint server for health sciences·2026
Same author

Before Birth, Beyond Childhood: Understanding the Influence of Prenatal Substance Exposure on Psychiatric Diagnoses.

medRxiv : the preprint server for health sciences·2026
Same author

Utilization and Costs Among Bladder Cancer, Other Cancer, and Non-Cancer Populations in the United States.

Urology·2026
Same author

Gastrointestinal bleeding risk among antithrombotics users: a propensity score matched study of drug classes.

Expert opinion on drug metabolism & toxicology·2026
Same author

Neuroimaging and neurophysiologic biomarkers for diagnosis and prognosis of depressive disorders, bipolar disorder, anxiety disorders, obsessive compulsive disorder, posttraumatic stress disorder, and substance use disorder: an evidence map.

BMC psychiatry·2026
Same author

Social Science Strengthens One Health: A Participatory Approach to Codevelop a Guidebook on Countering Zoonotic Spillover in Southeast Asia, 2022-2024.

Health security·2026
Same journal

What do LLMs value? An evaluation framework for revealing subjective trade-offs in assessment of glycemic control.

Proceedings of machine learning research·2026
Same journal

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift.

Proceedings of machine learning research·2026
Same journal

Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video.

Proceedings of machine learning research·2026
Same journal

Perspective: Machine Learning for Health Should Consider Social Drivers of Health.

Proceedings of machine learning research·2026
Same journal

Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression.

Proceedings of machine learning research·2026
Same journal

Does Domain-Specific Retrieval Augmented Generation Help LLMs Answer Consumer Health Questions?

Proceedings of machine learning research·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K

A Computational Framework for EEG Causal Oscillatory Connectivity.

Eric Rawls1, Casey Gilmore2, Erich Kummerfeld3

  • 1Psychiatry and Behavioral Sciences, University of Minnesota.

Proceedings of Machine Learning Research
|November 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to measure brain wave connectivity using causal discovery. Transcranial direct current stimulation (tDCS) altered theta and alpha band connectivity in mild Traumatic Brain Injury (mTBI) patients.

Keywords:
Causal DiscoveryEEGOscillationsTranscranial Direct Current StimulationTraumatic Brain Injury

More Related Videos

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.4K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.3K

Related Experiment Videos

Last Updated: Jun 7, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.4K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.3K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Electroencephalography (EEG) is crucial for studying brain function.
  • Measuring causal oscillatory connectivity in EEG data presents challenges.
  • Recent advancements in causal discovery and spectral analysis offer new possibilities.

Purpose of the Study:

  • To develop and validate a novel approach for quantifying EEG causal oscillatory connectivity.
  • To apply this method to analyze brain activity changes following transcranial direct current stimulation (tDCS) in mild Traumatic Brain Injury (mTBI) patients.
  • To investigate the impact of tDCS on prefrontal theta and alpha band oscillatory networks.

Main Methods:

  • Parameterizing EEG time-frequency data into oscillatory and aperiodic components.
  • Utilizing the Greedy Adjacencies and Non-Gaussian Orientations (GANGO) method for causal discovery on oscillatory data.
  • Extending GANGO to lagged time series to account for temporal autocorrelation.
  • Applying community detection to analyze whole-scalp oscillatory connectivity patterns.

Main Results:

  • The novel approach successfully measured causal oscillatory connectivity in EEG data.
  • Transcranial direct current stimulation (tDCS) significantly increased causal theta-band oscillatory connections originating from prefrontal sensors.
  • Simultaneously, tDCS decreased causal alpha-band oscillatory connections between prefrontal sensors and the rest of the scalp.

Conclusions:

  • The developed method provides a robust way to assess EEG causal oscillatory connectivity.
  • tDCS modulates prefrontal theta and alpha band connectivity, potentially underlying improvements in executive function after mTBI.
  • Findings suggest a neurophysiological mechanism for tDCS efficacy in treating executive dysfunction post-mTBI.