Comparer des méthodes

	Analyse de similarité représentationnelle	Modélisation Causale Dynamique
Domaine	Neuro-imagerie	Neuro-imagerie
Famille	Process / pipeline	Process / pipeline
Année d'origine	2008	2003
Auteur d'origine	Nikolaus Kriegeskorte	Karl J. Friston
Type	fMRI similarity structure comparison	Causal modeling pipeline for neuroimaging
Source fondatrice	Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis—connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.	Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.
Alias	RSA, representational geometry, similarity structure analysis	DCM, Dynamic Causal Model
Apparentées	3	2
Résumé	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.	Dynamic Causal Modeling (DCM) is a Bayesian framework for specifying and inverting generative models of brain connectivity from neuroimaging data. Introduced by Karl Friston and colleagues in 2003, DCM treats brain regions as dynamical systems and estimates effective connectivity by fitting observed fMRI time series to a biophysically plausible model of neuronal interactions.