Comparer des méthodes

	Analyse multivariée de patrons	Analyse de similarité représentationnelle
Domaine	Neuro-imagerie	Neuro-imagerie
Famille	Process / pipeline	Process / pipeline
Année d'origine	2001	2008
Auteur d'origine	James V. Haxby	Nikolaus Kriegeskorte
Type	fMRI pattern classification pipeline	fMRI similarity structure comparison
Source fondatrice	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.	Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis—connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.
Alias	MVPA, brain decoding, pattern classification	RSA, representational geometry, similarity structure analysis
Apparentées	3	3
Résumé	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?	Representational Similarity Analysis (RSA) is a framework for comparing representational geometry across brain regions, computational models, and behavioral measures. Introduced by Kriegeskorte and colleagues in 2008, RSA measures how similarly a brain region represents different stimuli or concepts by examining pairwise similarity structure rather than absolute activity patterns.