ArticleHuman brain mapping2019
A MATLAB toolbox for multivariate analysis of brain networks.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
The trial behind it
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
19 citing papers in PubMed.
- Neural compensation in persons with HIV and marijuana use: Insights from a reorganized DMN.Network neuroscience (Cambridge, Mass.) · 2026Article
- Altered Basal Ganglia Network Topology Associated With Auditory-Motor Synchronization.Brain and behavior · 2025Article
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- Contrasting topologies of synchronous and asynchronous functional brain networks.Network neuroscience (Cambridge, Mass.) · 2024Article
- Differences in Brain Network Topology Based on Alcohol Use History in Adolescents.Brain sciences · 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
- Longitudinal relationship of baseline functional brain networks with intentional weight loss in older adults.Obesity (Silver Spring, Md.) · 2022Article
- 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
- Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care.Frontiers in pediatrics · 2021Review
- Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity.Obesity (Silver Spring, Md.) · 2020Article
- Analysis of brain subnetworks within the context of their whole-brain networks.Human brain mapping · 2019Article
- Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks.Frontiers in human neuroscience · 2019Article
- A MATLAB toolbox for multivariate analysis of brain networks.Human brain mapping · 2019Article
- A mixed-modeling framework for analyzing multitask whole-brain network data.Network neuroscience (Cambridge, Mass.) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed-effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole-brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering-based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets.
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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.