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

Beliefs about genetic influences on prosocial and antisocial behavior in a U.S. sample.

Public understanding of science (Bristol, England)·2025
Same author

Variability in trends of opioid-related hospital utilization among U.S. Adults, 2016-2021 check.

EClinicalMedicine·2025
Same author

Rates, Causes, and Predictive Factors of Hospital Readmissions After Spine Surgery for Lumbar Spinal Stenosis: A Nationwide Retrospective Cohort Study.

Neurospine·2025
Same author

How is volunteering associated with reduced mortality? A mediator-wide approach.

Health psychology : official journal of the Division of Health Psychology, American Psychological Association·2025
Same author

Multistate approach for stochastic interventions on a time-to-event mediator in the presence of competing risks: A new R command within the CMAverse R package.

Epidemiology (Cambridge, Mass.)·2024
Same author

Association of a pace of aging epigenetic clock with rate of cognitive decline in the Framingham Heart Study Offspring Cohort.

Alzheimer's & dementia (Amsterdam, Netherlands)·2024
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
Same journal

Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential.

Proceedings of machine learning research·2026
See all related articles

Related Experiment Video

Updated: Feb 24, 2026

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
10:43

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

15.9K

Network-Assisted Mediation Analysis with High-Dimensional Neuroimaging Mediators.

Baoyi Shi1, Ying Liu2, Shanghong Xie3

  • 1Department of Biostatistics, Columbia University, New York, NY, USA.

Proceedings of Machine Learning Research
|February 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel network-assisted mediation analysis for high-dimensional biomarkers. It identifies specific brain regions mediating maternal smoking

Keywords:
ABCD studyRDoCbrain imagingmediation analysismental healthpathway analysis

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study
07:30

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Published on: August 18, 2020

7.9K

Related Experiment Videos

Last Updated: Feb 24, 2026

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
10:43

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

15.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study
07:30

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study

Published on: August 18, 2020

7.9K

Area of Science:

  • Neuroscience
  • Biostatistics
  • Developmental Psychology

Background:

  • Mediation analysis estimates causal pathways via intermediate variables (mediators).
  • High-dimensional, correlated biomarkers (e.g., neuroimaging) challenge standard mediation methods.
  • Biomarkers like brain regions exhibit network structures that can enhance mediation analysis.

Purpose of the Study:

  • To investigate how brain cortical thickness mediates the effect of maternal smoking on children's cognitive abilities.
  • To develop a network-assisted mediation analysis approach leveraging hierarchical structures of neuroimaging mediators.
  • To address challenges posed by high-dimensional correlated mediators in mediation analysis.

Main Methods:

  • Proposed a network-assisted mediation analysis approach using conditional Gaussian graphical models.
  • Leveraged the star-shaped hierarchical network structure of brain cortical thickness.
  • Decomposed the joint indirect effect into effects through hub and leaf mediators.

Main Results:

  • Identified a specific brain region as a significant leaf mediator.
  • The proposed method successfully accounted for the star-shaped network structure of neuroimaging mediators.
  • Enabled individual identification and evaluation of indirect effects through leaf mediators after accounting for hub mediators.

Conclusions:

  • The network-assisted approach effectively analyzes mediation with high-dimensional, structured neuroimaging data.
  • This method provides novel insights into mediator-specific pathways, crucial for intervention design.
  • Identified a significant leaf mediator previously undiscoverable by existing methods.