Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Dinamiskā funkcionālā savienojamība× | Smadzeņu tīklu grafu analīze× | |
|---|---|---|
| Nozare | Neiroattēlveidošana | Neiroattēlveidošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2013 | 2009 |
| Autors≠ | Ryan M. Hutchison | Ed Bullmore |
| Tips≠ | Resting-state fMRI connectivity pipeline | Brain network graph analysis pipeline |
| Pirmavots≠ | Hutchison, R. M., Womelsdorf, T., Allen, E. A., et al. (2013). Dynamic functional connectivity: promise, problems, and perspectives. NeuroImage, 80, 360–378. link ↗ | Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. DOI ↗ |
| Citi nosaukumi | dFC, time-varying connectivity, sliding window connectivity | graph theory, brain network analysis, network neuroscience |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | Dynamic Functional Connectivity (dFC) is an analytical framework that tracks changes in functional connectivity between brain regions over time, rather than averaging connectivity across an entire scanning session. Systematized by Hutchison and colleagues in 2013, dFC reveals how brain networks reorganize moment-to-moment, providing insights into transient brain states and cognitive flexibility. | Graph Theoretical Brain Network Analysis applies network science to understand brain organization, treating the brain as a complex network of interconnected nodes (regions) and edges (connections). Formalized by Bullmore and Sporns in 2009, graph analysis reveals fundamental organizational principles—modularity, efficiency, resilience—that characterize healthy and diseased brains. |
| ScholarGateDatu kopa ↗ |
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