ArticleNeuroImage2018
Estimating dynamic brain functional networks using multi-subject fMRI data.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine learning-based multimodal biomarkers enable accurate diagnosis and early detection of pancreatic ductal adenocarcinoma.Scientific reports · 2026Article
- Identifying covariate-related subnetworks for whole-brain connectome analysis.Biostatistics (Oxford, England) · 2024Article
- A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.PloS one · 2024Article
- Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence.Human brain mapping · 2023Article
- Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Extracting brain disease-related connectome subgraphs by adaptive dense subgraph discovery.Biometrics · 2022Article
- FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation.Proceedings of machine learning research · 2022Article
- Developing Multimodal Dynamic Functional Connectivity as a Neuroimaging Biomarker.Brain connectivity · 2021Article
- Article
- [Brain functional network reconstruction based on compressed sensing and fast iterative shrinkage-thresholding algorithm].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis.Frontiers in neuroscience · 2019Review
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Authors and funding
5 authors.
Funding
Abstract
A common assumption in the study of brain functional connectivity is that the brain network is stationary. However it is increasingly recognized that the brain organization is prone to variations across the scanning session, fueling the need for dynamic connectivity approaches. One of the main challenges in developing such approaches is that the frequency and change points for the brain organization are unknown, with these changes potentially occurring frequently during the scanning session. In order to provide greater power to detect rapid connectivity changes, we propose a fully automated two-stage approach which pools information across multiple subjects to estimate change points in functional connectivity, and subsequently estimates the brain networks within each state phase lying between consecutive change points. The number and positioning of the change points are unknown and learned from the data in the first stage, by modeling a time-dependent connectivity metric under a fused lasso approach. In the second stage, the brain functional network for each state phase is inferred via sparse inverse covariance matrices. We compare the performance of the method with existing dynamic connectivity approaches via extensive simulation studies, and apply the proposed approach to a saccade block task fMRI data.
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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.