Salīdzināt metodes
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| Smadzeņu tīklu grafu analīze× | Daudzvariēblu modeļu analīze× | |
|---|---|---|
| Nozare | Neiroattēlveidošana | Neiroattēlveidošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2009 | 2001 |
| Autors≠ | Ed Bullmore | James V. Haxby |
| Tips≠ | Brain network graph analysis pipeline | fMRI pattern classification pipeline |
| Pirmavots≠ | Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. DOI ↗ | Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430. DOI ↗ |
| Citi nosaukumi | graph theory, brain network analysis, network neuroscience | MVPA, brain decoding, pattern classification |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | 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. | Multivariate Pattern Analysis (MVPA) is a machine learning approach to fMRI that decodes cognitive states, stimuli, or behavior from whole-brain spatial patterns of neural activity. Pioneered by Haxby and colleagues in 2001, MVPA treats fMRI as a classification problem: can a trained decoder predict what a person is perceiving or thinking based solely on their brain activity pattern? |
| ScholarGateDatu kopa ↗ |
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