ArticleIEEE transactions on bio-medical engineering2018
Capturing Dynamic Connectivity from Resting State fMRI using Time-Varying Graphical Lasso.
Article in IEEE transactions on bio-medical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Targeted Time-Varying Functional Connectivity.Human brain mapping · 2025Article
- An Explainable Unified Framework of Spatio-Temporal Coupling Learning With Application to Dynamic Brain Functional Connectivity Analysis.IEEE transactions on medical imaging · 2025Article
- Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks.IEEE transactions on medical imaging · 2024Article
- Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI.Cognitive neurodynamics · 2023Article
- Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI Paradigms.IEEE transactions on bio-medical engineering · 2023Article
- An explainable autoencoder with multi-paradigm fMRI fusion for identifying differences in dynamic functional connectivity during brain development.Neural networks : the official journal of the International Neural Network Society · 2023Article
- Brain Functional Connectivity Analysis via Graphical Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences.Journal of neuroscience methods · 2022Article
- The Brain Circuits and Dynamics of Curiosity-Driven Behavior in Naturally Curious Marmosets.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder.Human brain mapping · 2021Article
- Multi-Paradigm fMRI Fusion via Sparse Tensor Decomposition in Brain Functional Connectivity Study.IEEE journal of biomedical and health informatics · 2021Article
- A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks.Network neuroscience (Cambridge, Mass.) · 2021Article
- Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions.Human brain mapping · 2020Article
- A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity.Journal of neuroscience methods · 2020Article
- Joint Bayesian-Incorporating Estimation of Multiple Gaussian Graphical Models to Study Brain Connectivity Development in Adolescence.IEEE transactions on medical imaging · 2020Article
- Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data.Scientific reports · 2020Article
- Early childhood developmental functional connectivity of autistic brains with non-negative matrix factorization.NeuroImage. Clinical · 2020Article
- Refined measure of functional connectomes for improved identifiability and prediction.Human brain mapping · 2019Article
- Multilayer feature selection method for polyp classification via computed tomographic colonography.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Functional fingerprinting for the developing brain using deep metric learning.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
7 authors.
Funding
Abstract
Functional connectivity (FC) within the human brain evaluated through functional magnetic resonance imaging (fMRI) data has attracted increasing attention and has been employed to study the development of the brain or health conditions of the brain. Many different approaches have been proposed to estimate FC from fMRI data, whereas many of them rely on an implicit assumption that functional connectivity should be static throughout the fMRI scan session. Recently, the fMRI community has realized the limitation of assuming static connectivity and dynamic approaches are more prominent in the resting state fMRI (rs-fMRI) analysis. The sliding window technique has been widely used in many studies to capture network dynamics, but has a number of limitations. In this study, we apply a time-varying graphical lasso (TVGL) model, an extension from the traditional graphical lasso, to address the challenge, which can greatly improve the estimation of FC. The performance of estimating dynamic FC is evaluated with the TVGL through both simulated experiments and real rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) project. Improved performance is achieved over the sliding window technique. In particular, group differences and transition behaviours between young adults and children are investigated using the estimated dynamic connectivity networks, which help us to better unveil the mechanisms underlying the evolution of the brain over time.
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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.