ArticleNetwork neuroscience (Cambridge, Mass.)2022
A regression framework for brain network distance metrics.
Article in Network neuroscience (Cambridge, Mass.), 2022. 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.
- Connectivity Regression.Biostatistics (Oxford, England) · 2025Article
- Insulin resistance, cognition, and functional brain network topology in older adults with obesity.Scientific reports · 2025Article
- Contrast Sensitivity Predicts 30-month Functional Brain Network Integrity in Cognitively Unimpaired Older Adults: the Brain Networks and Mobility Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Moderation between resting-state connectivity and brain amyloid levels on speed of cognitive and physical function in older adults: Evidence for network-based cognitive reserve.Aperture neuro · 2025Article
- Observational
- Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data.Network neuroscience (Cambridge, Mass.) · 2024Article
- Article
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data.bioRxiv : the preprint server for biology · 2023Article
- Associations of physical function and body mass index with functional brain networks in community-dwelling older adults.Neurobiology of aging · 2023Observational
- 3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics.Network neuroscience (Cambridge, Mass.) · 2023Article
- A Hierarchical Graph Learning Model for Brain Network Regression Analysis.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances (or similarities) between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances (or similarities) between connection matrices, and adapt several standard methods for estimation and inference within our framework: standard
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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.