Сравнение методов

	Анализ сходства репрезентаций	Динамическое каузальное моделирование
Область	Нейровизуализация	Нейровизуализация
Семейство	Process / pipeline	Process / pipeline
Год появления	2008	2003
Автор метода	Nikolaus Kriegeskorte	Karl J. Friston
Тип	fMRI similarity structure comparison	Causal modeling pipeline for neuroimaging
Основополагающий источник	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.
Другие названия	RSA, representational geometry, similarity structure analysis	DCM, Dynamic Causal Model
Связанные	3	2
Сводка	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.