Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Багатовимірний аналіз патернів× | Аналіз мозкових мереж на основі графів× | |
|---|---|---|
| Галузь | Нейровізуалізація | Нейровізуалізація |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2001 | 2009 |
| Автор методу≠ | James V. Haxby | Ed Bullmore |
| Тип≠ | fMRI pattern classification pipeline | Brain network graph analysis pipeline |
| Основоположне джерело≠ | 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 ↗ | Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. DOI ↗ |
| Інші назви | MVPA, brain decoding, pattern classification | graph theory, brain network analysis, network neuroscience |
| Пов'язані | 3 | 3 |
| Підсумок≠ | 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? | 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. |
| ScholarGateНабір даних ↗ |
|
|