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	Analiza Podobieństwa Reprezentacyjnego	Dynamic Causal Modeling
Dziedzina	Neuroobrazowanie	Neuroobrazowanie
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2008	2003
Twórca	Nikolaus Kriegeskorte	Karl J. Friston
Typ	fMRI similarity structure comparison	Causal modeling pipeline for neuroimaging
Źródło pierwotne	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.
Inne nazwy	RSA, representational geometry, similarity structure analysis	DCM, Dynamic Causal Model
Pokrewne	3	2
Podsumowanie	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.