ArticleCognitive neurodynamics2020
Investigating time-varying functional connectivity derived from the Jackknife Correlation method for distinguishing between emotions in fMRI data.
Article in Cognitive neurodynamics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses.Network neuroscience (Cambridge, Mass.) · 2026Article
- Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI.Cognitive neurodynamics · 2023Article
- A multi-stage dynamical fusion network for multimodal emotion recognition.Cognitive neurodynamics · 2023Article
- Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level.Cognitive neurodynamics · 2023Article
- Conflicts influence affects: an FMRI study of emotional effects in a conflict task.Cognitive neurodynamics · 2022Article
- Spatial Connectivity and Temporal Dynamic Functional Network Connectivity of Musical Emotions Evoked by Dynamically Changing Tempo.Frontiers in neuroscience · 2021Article
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4 authors.
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Abstract
Investigating human brain activity during expressing emotional states provides deep insight into complex cognitive functions and neurological correlations inside the brain. To be able to resemble the brain function in the best manner, a complex and natural stimulus should be applied as well, the method used for data analysis should have fewer assumptions, simplifications, and parameter adjustment. In this study, we examined a functional magnetic resonance imaging dataset obtained during an emotional audio-movie stimulus associated with human life. We used Jackknife Correlation (JC) method to derive a representation of time-varying functional connectivity. We applied different binary measures and thoroughly investigated two weighted measures to study different properties of binary and weighted temporal networks. Using this approach, we indicated different aspects of human brain function during expressing different emotions. The findings of global and nodal measures could demonstrate a significant difference between emotions and significant regions in each emotion, respectively. Also, the temporal centrality properties of nodes were different in emotional states. Ultimately, we showed that the resulting measures of temporal snapshots created by JC method can distinguish between different emotions.
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