مقایسهٔ روش‌ها

	مدل‌سازی علی پویا	eLORETA
حوزه	تصویربرداری عصبی	تصویربرداری عصبی
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	2003	2002
پدیدآور	Karl J. Friston	Roberto D. Pascual-Marqui
نوع	Causal modeling pipeline for neuroimaging	EEG/MEG source localization algorithm
منبع بنیادین	Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.	Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods & Findings in Experimental & Clinical Pharmacology, 24(S-D), 5–12.
نام‌های دیگر	DCM, Dynamic Causal Model	Exact LORETA, eLORETA source reconstruction
مرتبط	2	2
خلاصه	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.	Exact Low-Resolution Electromagnetic Tomography (eLORETA) is a non-parametric solution to the inverse problem in EEG and MEG source localization. Developed by Roberto D. Pascual-Marqui in 2002, eLORETA reconstructs three-dimensional maps of electrical brain activity from scalp electrode recordings, offering zero localization error under ideal noise-free conditions.