Search research articles

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

Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.

GeroScience·2026

Same author

Traumatic Experiences, Psychological Distress and Suicide-Related Behaviors in Autistic Adults.

Autism research : official journal of the International Society for Autism Research·2025

Same author

Evaluating Models of the Ageing BOLD Response.

Human brain mapping·2024

Same author

Access to and perceived unmet need for mental health services and support in a community sample of UK adolescents with and without experience of childhood adversity.

Epidemiology and psychiatric sciences·2024

Same author

Does functional system segregation mediate the effects of lifestyle on cognition in older adults?

Neurobiology of aging·2023

Same author

Cognition's dependence on functional network integrity with age is conditional on structural network integrity.

Neurobiology of aging·2023

Same journal

Investigating the Neural Origins of Ear-EEG: A Correlation Study Using Scalp EEG Source Reconstruction.

NeuroImage·2026

Same journal

Hysteresis effects in visual and auditory perception and the comparison of underlying neural mechanisms - an EEG study.

NeuroImage·2026

Same journal

Short-term audio-tactile training affects cortical auditory speech-envelope tracking for incongruent but not congruent stimuli.

NeuroImage·2026

Same journal

Dissociable Neurocognitive Mechanisms of State and Trait Anxiety in Working Memory: Threat-Induced Alterations in Decision Dynamics and Attenuation of Large-Scale Network Reconfiguration.

NeuroImage·2026

Same journal

Neuro-Ocular Amyloid Characterization in Alzheimer's Disease via Cross-Site PET-MRI and Hierarchical Cross-Attention Driven Multimodal Representation Learning.

NeuroImage·2026

Same journal

Whole-brain network dynamics underlying intolerance of uncertainty.

NeuroImage·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Dec 4, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models.

B C L Lehmann¹, R N Henson², L Geerligs³

¹MRC Biostatistics Unit, University of Cambridge, UK; Big Data Institute, University of Oxford, UK; Department of Statistics, University of Oxford, UK.

|October 25, 2020

Summary

This summary is machine-generated.

This study introduces a new Bayesian framework to analyze brain connectivity networks across groups. The method effectively characterizes and compares functional brain networks in young versus old individuals, advancing network neuroscience.

Keywords:

Bayesian ERGM Exponential Random Graph Model (ERGM)Fmri Group studies Network neuroscience

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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

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

Published on: October 30, 2018

Related Experiment Videos

Last Updated: Dec 4, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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

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

Published on: October 30, 2018

Area of Science:

Neuroscience
Network Science
Statistical Modeling

Background:

The brain is modeled as a network where nodes represent brain regions and edges represent interactions.
Analyzing group-level brain connectivity requires understanding common network features while accounting for individual variability.
Existing methods either oversimplify by creating a group-representative network (GRN) or miss shared information by analyzing individuals independently.

Purpose of the Study:

To develop a novel Bayesian framework for characterizing the distribution of an entire population of brain networks.
To extend exponential random graph models (ERGM) to handle multiple networks simultaneously.
To apply the framework to compare functional brain connectivity structures between young and old individuals.

Main Methods:

Utilized a Bayesian framework based on exponential random graph models (ERGM) extended for multiple networks.
Applied the method to resting-state functional magnetic resonance imaging (fMRI) data from the Cam-CAN project.
Compared functional connectivity structures between a group of young and a group of old healthy adults.

Main Results:

The proposed Bayesian framework successfully characterized the distribution of brain networks within the studied population.
The method allowed for reliable characterization and comparison of functional connectivity structures across age groups.
Demonstrated the framework's utility in identifying age-related differences in brain network properties.

Conclusions:

The developed Bayesian multi-network ERGM framework provides a robust approach to analyzing group-level brain connectivity.
This method effectively captures both commonalities and individual variations in network structure across populations.
The findings highlight the potential of this framework for studying brain network changes in aging and other conditions.