Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Fāzes sasaistes vērtība× | Smadzeņu tīklu grafu analīze× | |
|---|---|---|
| Nozare | Neiroattēlveidošana | Neiroattēlveidošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1999 | 2009 |
| Autors≠ | Jean-Philippe Lachaux | Ed Bullmore |
| Tips≠ | EEG/MEG functional connectivity analysis | Brain network graph analysis pipeline |
| Pirmavots≠ | Lachaux, J. P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194–208. DOI ↗ | 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 | PLV, phase synchronization, phase coupling | graph theory, brain network analysis, network neuroscience |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | Phase-Locking Value (PLV) is a frequency-domain measure of neural synchronization that quantifies the stability of phase difference between two signals. Introduced by Lachaux and colleagues in 1999, PLV detects phase coupling between brain regions independent of signal amplitude, enabling researchers to characterize functional connectivity from EEG and MEG recordings. | 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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