ArticleIEEE transactions on medical imaging2019
Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Fusion of Multi-Task fMRI Data: Guided Solutions for IVA and Transposed IVA.Sensors (Basel, Switzerland) · 2026Article
- Multidimensional Comparisons Between Constrained ICA/IVA Algorithms for Multi-Subject fMRI Data Analysis.IEEE access : practical innovations, open solutions · 2026Article
- Exploring synergies: Advancing neuroscience with machine learning.Signal processing · 2026Article
- Constrained Independent Vector Analysis With Reference for Multi-Subject fMRI Analysis.IEEE transactions on bio-medical engineering · 2024Article
- Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.NeuroImage · 2024Article
- Identifying the Relationship Structure Among Multiple Datasets Using Independent Vector Analysis: Application to Multi-Task fMRI Data.IEEE access : practical innovations, open solutions · 2024Article
- A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis.Sensors (Basel, Switzerland) · 2023Article
- Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data.Sensors (Basel, Switzerland) · 2022Article
- Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches.Frontiers in neuroscience · 2022Article
- Relationship between Dynamic Blood-Oxygen-Level-Dependent Activity and Functional Network Connectivity: Characterization of Schizophrenia Subgroups.Brain connectivity · 2021Article
- Disjoint subspaces for common and distinct component analysis: Application to the fusion of multi-task FMRI data.Journal of neuroscience methods · 2021Article
- Graph-theoretical analysis identifies transient spatial states of resting-state dynamic functional network connectivity and reveals dysconnectivity in schizophrenia.Journal of neuroscience methods · 2021Article
- Multidataset Independent Subspace Analysis With Application to Multimodal Fusion.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2021Article
- Adaptive constrained independent vector analysis: An effective solution for analysis of large-scale medical imaging data.IEEE journal of selected topics in signal processing · 2020Article
- Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia.NeuroImage · 2020Article
- Space: A Missing Piece of the Dynamic Puzzle.Trends in cognitive sciences · 2020Review
- Dynamic Modeling of Common Brain Neural Activity in Motor Imagery Tasks.Frontiers in neuroscience · 2020Article
- Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia.Frontiers in neuroscience · 2019Article
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4 authors.
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Abstract
Dynamic functional connectivity analysis is an effective way to capture the networks that are functionally associated and continuously changing over the scanning period. However, these methods mostly analyze the dynamic associations across the activation patterns of the spatial networks while assuming that the spatial networks are stationary. Hence, a model that allows for the variability in both domains and reduces the assumptions imposed on the data provides an effective way for extracting spatiotemporal networks. Independent vector analysis (IVA) is a joint blind source separation technique that allows for estimation of spatial and temporal features while successfully preserving variability. However, its performance is affected for higher number of datasets. Hence, we develop an effective two-stage method to extract time-varying spatial and temporal features using IVA, mitigating the problems with higher number of datasets while preserving the variability across subjects and time. The first stage is used to extract reference signals using group-independent component analysis (GICA) that are used in a parameter-tuned constrained IVA framework to estimate time-varying representations of these signals by preserving the variability through tuning the constraint parameter. This approach effectively captures variability across time from a large-scale resting-state fMRI data acquired from healthy controls and patients with schizophrenia and identifies more functionally relevant connections that are significantly different among healthy controls and patients with schizophrenia, compared with the widely used GICA method alone.
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