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 Experiment Video

Updated: May 22, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Determining functional connectivity using fMRI data with diffusion-based anatomical weighting.

F DuBois Bowman1, Lijun Zhang, Gordana Derado

  • 1Department of Biostatistics and Bioinformatics, Center for Biomedical Imaging Statistics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. dbowma3@emory.edu

Neuroimage
|May 29, 2012
PubMed
Summary
This summary is machine-generated.

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

Estimated Burden of COVID-19 Illnesses, Medical Visits, Hospitalizations, and Deaths in the US From October 2022 to September 2024.

JAMA internal medicine·2026
Same author

Chronic Conditions as Risk Factors for COVID-19-Associated Hospitalization Among Adults, 2020-2023.

American journal of preventive medicine·2025
Same author

Letter to the Editor in Response to Zhao et al.

The Journal of infectious diseases·2025
Same author

Respiratory Virus Detection and Acute Respiratory Illness Rates in Students and Staff in Schools.

Pediatrics·2025
Same author

Effectiveness of 13-Valent Pneumococcal Conjugate Vaccine Against Pneumonia Hospitalization Among Medicare Beneficiaries Aged ≥65 Years in Long-Term Care.

The Journal of infectious diseases·2025
Same author

The epidemiology of bacterial meningitis in the United States during 2008-2023: an analysis of active, laboratory, population-based, multistate surveillance data.

Lancet regional health. Americas·2025
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
Same journal

Cognitive Strategy-based neuromodulation optimizes neural communication to improve working memory.

NeuroImage·2026
See all related articles

We developed a novel method combining functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) to better understand brain connectivity. This anatomically weighted functional connectivity (awFC) approach improves the accuracy and informativeness of neural network analysis.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Network Science

Background:

  • Functional connectivity (FC) via fMRI and structural connectivity (SC) via DTI are key research areas.
  • Evidence suggests overlap between functional and structural brain networks, but some regions show FC without SC.
  • This indicates FC may be influenced by anatomical connections, suggesting combined fMRI and DTI data could enhance FC determination.

Purpose of the Study:

  • To introduce a novel statistical method, anatomically weighted functional connectivity (awFC), integrating fMRI and DTI data.
  • To improve the accuracy and interpretability of neural processing network identification.
  • To provide a method that defaults to conventional analysis while offering enhanced structural insights.

Main Methods:

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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

Related Experiment Videos

Last Updated: May 22, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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

  • Developed a hierarchical clustering algorithm using a novel distance measure.
  • The distance measure combines a functional component (fMRI signal correlations) and an anatomical component (SC probabilities).
  • Optimized awFC parameters using functional criteria and applied to resting-state and task-based fMRI datasets.

    • Main Results:

    • awFC analysis resulted in more highly autocorrelated networks compared to conventional methods in applied datasets.
    • A simulation study confirmed the accurate performance of awFC.
    • awFC generally achieved comparable or superior accuracy to standard approaches.

    Conclusions:

    • The novel awFC method effectively integrates fMRI and DTI data for enhanced neural network analysis.
    • awFC provides more informative results by incorporating structural properties into functional network identification.
    • This approach offers a robust and potentially superior alternative to conventional connectivity analyses.