ArticleJournal of neuroscience methods2020
A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity.
Article in Journal of neuroscience methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Sex differences in dynamic and static measures of brain integration derived from resting-state functional magnetic resonance imaging.Biology of sex differences · 2026Article
- Sex effects on dynamic structure-function coupling of intrinsic brain network.Brain imaging and behavior · 2025Article
- Common and unique network basis for externally and internally driven flexibility in cognition: From a developmental perspective.Developmental cognitive neuroscience · 2025Article
- Subsystem mechanisms of default mode network underlying white matter hyperintensity-related cognitive impairment.Human brain mapping · 2023Article
- Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives.Brain sciences · 2023Article
- Inter-Network Brain Functional Connectivity in Adolescents Assigned Female at Birth Who Experience Gender Dysphoria.Frontiers in endocrinology · 2022Article
- Sex-related differences in brain dynamism at rest as neural correlates of positive and negative valence system constructs.Cognitive neuroscienceArticle
- Sex differences in functional network dynamics observed using coactivation pattern analysis.Cognitive neuroscienceArticle
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Authors and funding
8 authors.
Funding
Abstract
backgroundFunctional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectivity. In particular, time-varying connectivity analysis has emerged as an important measure to uncover essential knowledge within the network. On the other hand, independent component analysis (ICA) has served as a powerful tool to preprocess fMRI data before performing network analysis. Together, they may lead to novel findings.
methodsWe propose a new framework (GICA-TVGL) that combines group ICA (GICA) with time-varying graphical LASSO (TVGL) to improve the power of analyzing functional connectivity (FNC) changes, which is then applied for neuro-developmental study. To investigate the performance of our proposed approach, we apply it to capture dynamic FNC using both the Philadelphia Neurodevelopmental Cohort (PNC) and the Pediatric Imaging, Neurocognition, and Genetics (PING) datasets.
resultsOur results indicate that females and males in young adult group possess substantial difference related to visual network. In addition, some other consistent conclusions have been reached by using these two datasets. Furthermore, the GICA-TVGL model indicated that females had a higher probability to stay in a stable state. Males had a higher tendency to remain in a globally disconnected mode. COMPARISON WITH EXISTING
methodThe performance of sliding window approach is largely affected by the window size selection. In addition, it also assumes temporal locality hypothesis.
conclusionOur proposed framework provides a feasible method to investigate brain dynamics and has the potential to become a widely used tool in neuroimaging studies.
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