Multivariate Pattern Analysis
Multivariate Pattern Analysis (MVPA) on fMRI and other neuroimaging data is a machine learning approach 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?
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Allikad
- 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: 10.1016/j.tics.2006.07.005 ↗
- Haxby, J. V., Gobbini, M. I., Furey, M. L., et al. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539), 2425–2430. DOI: 10.1126/science.1063736 ↗
Kuidas sellele lehele viidata
ScholarGate. (2026, June 3). Multivariate Pattern Analysis (MVPA). ScholarGate. https://scholargate.app/et/neuroimaging/multivariate-pattern-analysis
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