ArticleFrontiers in computational neuroscience2022
Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder.
Article in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Thermodynamic Biomarkers of Neuroinflammation: Nanothermometry, Energy-Stress Dynamics, and Predictive Entropy in Glial-Vascular Networks.International journal of molecular sciences · 2025Review
- Personalized EEG-guided brain stimulation targeting in major depression via network controllability and multi-objective optimization.BMC psychiatry · 2025Article
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8 authors.
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Abstract
Organized patterns of system-wide neural activity adapt fluently within the brain to adjust behavioral performance to environmental demands. In major depressive disorder (MD), markedly different co-activation patterns across the brain emerge from a rather similar structural substrate. Despite the application of advanced methods to describe the functional architecture, e.g., between intrinsic brain networks (IBNs), the underlying mechanisms mediating these differences remain elusive. Here we propose a novel complementary approach for quantifying the functional relations between IBNs based on the Kuramoto model. We directly estimate the Kuramoto coupling parameters (
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