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

Brain Imaging01:14

Brain Imaging

947
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
947
Organization of the Brain01:30

Organization of the Brain

3.7K
The brain is an integral component of the nervous system and serves as the center for processing sensory inputs, making decisions, and directing bodily actions. This complex organ is organized into three primary sections: the hindbrain, midbrain, and forebrain, each responsible for a range of vital functions.
Hindbrain
The hindbrain, located at the base of the brain, plays a vital role in regulating automatic processes that sustain life. It includes the medulla oblongata, which is essential for...
3.7K

You might also read

Related Articles

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

Sort by
Same author

A New Era of Precision Neuromodulation in Psychiatry.

JAMA psychiatry·2026
Same author

Corrigendum to "Revisiting subcallosal cingulate deep brain stimulation for depression: Long-term safety and effectiveness outcomes from a pooled analysis of 172 implanted patients" [Brain Stimul 18 (2025) 1632-1640].

Brain stimulation·2026
Same author

Deep brain stimulation induces white matter remodeling and functional changes to brain-wide networks.

Nature neuroscience·2026
Same author

Common Electrophysiology Biomarkers Collected at Home Robustly Track Depression Recovery With Deep Brain Stimulation.

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

Globus pallidus externus (GPe) alpha band activity decreases after deep brain stimulation in clinically responsive obsessive-compulsive disorder patients.

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

Age-dependent acceleration of structural brain aging in medication-free major depressive disorder linked to neuroanatomical phenotype findings from COORDINATE-MDD consortium.

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

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
Same journal

A Bayesian phase I/II platform design with data augmentation accounting for delayed outcomes.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Mar 31, 2026

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

1.6K

A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data.

Shuo Chen1, F DuBois Bowman2, Helen S Mayberg3

  • 1Department of Epidemiology and Biostatistics, University of Maryland, College Park, Maryland 20742, U.S.A.

Biometrics
|October 27, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model for brain imaging, unifying voxel and region analyses for better connectivity insights. The method quantifies connectivity strength and assesses clinical covariate impacts in major depression.

Keywords:
Bayesian hierarchical modelBrain imagingFunctional connectivityMCMCResting-state fMRI

More Related Videos

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

6.2K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

4.1K

Related Experiment Videos

Last Updated: Mar 31, 2026

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

1.6K
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

6.2K
Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

4.1K

Area of Science:

  • Neuroimaging
  • Statistical modeling
  • Computational neuroscience

Background:

  • Functional connectivity analysis in brain imaging is crucial for understanding brain function.
  • Existing methods often analyze brain connectivity at either the voxel or region level, limiting comprehensive insights.
  • Population-level inferences are essential for clinical applications in neuroscience.

Purpose of the Study:

  • To develop a novel Bayesian hierarchical model for brain imaging data.
  • To unify voxel-level and region-level brain connectivity analyses.
  • To enable population-level inferences on brain connectivity and the impact of covariates.

Main Methods:

  • A two-component mixture model is used at the first level to summarize connectivity for cross-region voxel pairs.
  • A new measure of connectivity strength is defined based on the proportion of connected voxel pairs.
  • Parameter estimation is performed using efficient Markov chain Monte Carlo (MCMC) techniques.
  • The model is applied to resting-state functional magnetic resonance imaging (fMRI) data and simulated data.

Main Results:

  • The proposed model successfully unifies voxel and region-level analyses, providing population-level inferences.
  • A novel measure of connectivity strength, reflecting the breadth of between-region connectivity, was established.
  • The impact of clinical covariates on connectivity was evaluated at the population level.
  • The method demonstrated efficient parameter estimation using MCMC techniques.

Conclusions:

  • The developed Bayesian hierarchical model offers a unified framework for analyzing brain connectivity from imaging data.
  • The new connectivity strength measure provides a valuable metric for assessing the breadth of functional connections.
  • The approach facilitates population-level inferences, crucial for clinical neuroscience research, particularly in conditions like major depression.