ArticleNeuroImage2015
A two-part mixed-effects modeling framework for analyzing whole-brain network data.
Article in NeuroImage, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed.
- Altered Basal Ganglia Network Topology Associated With Auditory-Motor Synchronization.Brain and behavior · 2025Article
- The Effect of Combining Transcranial Direct Current Stimulation and Pain Neuroscience Education in Patients With Chronic Low Back Pain and High Pain Catastrophizing: An Exploratory Clinical, Cognitive, and fMRI Study.Brain and behavior · 2025Article
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- Differences in Brain Network Topology Based on Alcohol Use History in Adolescents.Brain sciences · 2023Article
- Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables.Biostatistics (Oxford, England) · 2023Article
- Brain working memory network indices as landmarks of intelligence.Neuroimage. Reports · 2023Article
- Desire and craving measured using behavioral ratings and brain network topology differ significantly among moderate to heavy alcohol consumers.Alcohol, clinical & experimental research · 2023Article
- Brain Network Analysis: A Review on Multivariate Analytical Methods.Brain connectivity · 2023Review
- Altered default mode network associated with pesticide exposure in Latinx children from rural farmworker families.NeuroImage · 2022Article
- A mixed-modeling framework for whole-brain dynamic network analysis.Network neuroscience (Cambridge, Mass.) · 2022Article
- Functional Connectivity Methods and Their Applications in fMRI Data.Entropy (Basel, Switzerland) · 2022Review
- Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Influence of Heart Rate Variability on Abstinence-Related Changes in Brain State in Everyday Drinkers.Brain sciences · 2021Article
- Link predictions for incomplete network data with outcome misclassification.Statistics in medicine · 2021Article
- Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models.NeuroImage · 2021Article
- Bayesian modeling of dependence in brain connectivity data.Biostatistics (Oxford, England) · 2020Article
- Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology.Computational statistics & data analysis · 2020Article
- Analysis of brain subnetworks within the context of their whole-brain networks.Human brain mapping · 2019Article
- A mixed-modeling framework for analyzing multitask whole-brain network data.Network neuroscience (Cambridge, Mass.) · 2019Article
- A MATLAB toolbox for multivariate analysis of brain networks.Human brain mapping · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Whole-brain network analyses remain the vanguard in neuroimaging research, coming to prominence within the last decade. Network science approaches have facilitated these analyses and allowed examining the brain as an integrated system. However, statistical methods for modeling and comparing groups of networks have lagged behind. Fusing multivariate statistical approaches with network science presents the best path to develop these methods. Toward this end, we propose a two-part mixed-effects modeling framework that allows modeling both the probability of a connection (presence/absence of an edge) and the strength of a connection if it exists. Models within this framework enable quantifying the relationship between an outcome (e.g., disease status) and connectivity patterns in the brain while reducing spurious correlations through inclusion of confounding covariates. They also enable prediction about an outcome based on connectivity structure and vice versa, simulating networks to gain a better understanding of normal ranges of topological variability, and thresholding networks leveraging group information. Thus, they provide a comprehensive approach to studying system level brain properties to further our understanding of normal and abnormal brain function.
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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.