ArticleIEEE transactions on medical imaging2018
Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
- A Deep Spatio-Temporal Architecture for Dynamic ECN Analysis with Granger Causality based Causal Discovery.Pattern recognition · 2026Article
- Sparse covariate-driven factorization of high-dimensional brain connectivity with application to site effect correction.Biostatistics (Oxford, England) · 2026Article
- Tracking causal pathways in TMS-evoked brain responses.PLoS computational biology · 2025Article
- An Explainable Unified Framework of Spatio-Temporal Coupling Learning With Application to Dynamic Brain Functional Connectivity Analysis.IEEE transactions on medical imaging · 2025Article
- A Deep Dynamic Causal Learning Model to Study Changes in Dynamic Effective Connectivity During Brain Development.IEEE transactions on bio-medical engineering · 2024Article
- Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development.NeuroImage · 2024Article
- Topological state-space estimation of functional human brain networks.PLoS computational biology · 2024Article
- Unveiling hidden sources of dynamic functional connectome through a novel regularized blind source separation approach.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- 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
- Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences.Journal of neuroscience methods · 2022Article
- A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development.Journal of the American Statistical Association · 2022Article
- Validating dynamicity in resting state fMRI with activation-informed temporal segmentation.Human brain mapping · 2021Article
- Article
- Tools of the trade: estimating time-varying connectivity patterns from fMRI data.Social cognitive and affective neuroscience · 2021Review
- Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder.Human brain mapping · 2021Article
- Effect of APOE ε4 on multimodal brain connectomic traits: a persistent homology study.BMC bioinformatics · 2020Article
- Resolution-based spectral clustering for brain parcellation using functional MRI.Journal of neuroscience methods · 2020Article
- A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity.Journal of neuroscience methods · 2020Article
- Estimating Dynamic Functional Brain Connectivity With a Sparse Hidden Markov Model.IEEE transactions on medical imaging · 2020Article
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6 authors.
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Abstract
Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) time series, especially during resting state periods, provides a powerful tool to assess human brain functional architecture in health, disease, and developmental states. Recently, the focus of connectivity analysis has shifted toward the subnetworks of the brain, which reveals co-activating patterns over time. Most prior works produced a dense set of high-dimensional vectors, which are hard to interpret. In addition, their estimations to a large extent were based on an implicit assumption of spatial and temporal stationarity throughout the fMRI scanning session. In this paper, we propose an approach called dynamic sparse connectivity patterns (dSCPs), which takes advantage of both matrix factorization and time-varying fMRI time series to improve the estimation power of FC. The feasibility of analyzing dynamic FC with our model is first validated through simulated experiments. Then, we use our framework to measure the difference between young adults and children with real fMRI data set from the Philadelphia Neurodevelopmental Cohort (PNC). The results from the PNC data set showed significant FC differences between young adults and children in four different states. For instance, young adults had reduced connectivity between the default mode network and other subnetworks, as well as hyperconnectivity within the visual system in states 1 and 3, and hypoconnectivity in state 2. Meanwhile, they exhibited temporal correlation patterns that changed over time within functional subnetworks. In addition, the dSCPs model indicated that older people tend to spend more time within a relatively connected FC pattern. Overall, the proposed method provides a valid means to assess dynamic FC, which could facilitate the study of brain networks.
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