ArticleNeuroImage2021
Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed.
- Robust probabilistic measurement of structural-functional module consistency in infant brain development.Brain structure & function · 2026Article
- Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.GeroScience · 2026Review
- Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges.Statistics and computing · 2026Article
- Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling.Medical image analysis · 2025Article
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- Secondary analysis: Graph analysis of brain connectivity network in autism spectrum disorder.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2024Article
- A Bayesian multilevel model for populations of networks using exponential-family random graphs.Statistics and computing · 2024Article
- Brain Network Analysis: A Review on Multivariate Analytical Methods.Brain connectivity · 2023Review
- 3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics.Network neuroscience (Cambridge, Mass.) · 2023Article
- A regression framework for brain network distance metrics.Network neuroscience (Cambridge, Mass.) · 2022Article
- Individual prediction of hemispheric similarity of functional connectivity during normal aging.Frontiers in psychiatry · 2022Article
- A Hierarchical Graph Learning Model for Brain Network Regression Analysis.Frontiers in neuroscience · 2022Article
- Exploratory factor analysis with structured residuals for brain network data.Network neuroscience (Cambridge, Mass.) · 2021Article
- Testing biological network motif significance with exponential random graph models.Applied network science · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of an entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brain's functional connectivity structure across a group of young individuals and a group of old individuals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.