ArticleNetwork neuroscience (Cambridge, Mass.)2019
A mixed-modeling framework for analyzing multitask whole-brain network data.
Article in Network neuroscience (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Identifying covariate-related subnetworks for whole-brain connectome analysis.Biostatistics (Oxford, England) · 2024Article
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- 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
- Extracting brain disease-related connectome subgraphs by adaptive dense subgraph discovery.Biometrics · 2022Article
- A mixed-modeling framework for whole-brain dynamic network analysis.Network neuroscience (Cambridge, Mass.) · 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
- Link predictions for incomplete network data with outcome misclassification.Statistics in medicine · 2021Article
- 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
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The emerging area of brain network analysis considers the brain as a system, providing profound insight into links between system-level properties and health outcomes. Network science has facilitated these analyses and our understanding of how the brain is organized. While network science has catalyzed a paradigmatic shift in neuroscience, methods for statistically analyzing networks have lagged behind. To address this for cross-sectional network data, we developed a mixed-modeling framework that enables quantifying the relationship between phenotype and connectivity patterns, predicting connectivity structure based on phenotype, simulating networks to gain a better understanding of topological variability, and thresholding individual networks leveraging group information. Here we extend this comprehensive approach to enable studying system-level brain properties across multiple tasks. We focus on rest-to-task network changes, but this extension is equally applicable to the assessment of network changes for any repeated task paradigm. Our approach allows (a) assessing population network differences in changes between tasks, and how these changes relate to health outcomes; (b) assessing individual variability in network differences in changes between tasks, and how this variability relates to health outcomes; and (c) deriving more accurate and precise estimates of the relationships between phenotype and health outcomes within a given task.
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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.