השוואת שיטות

	מודלים סיבתיים דינמיים	eLORETA	ניתוח פוטנציאלים הקשורים לאירוע
תחום	הדמיה עצבית	הדמיה עצבית	הדמיה עצבית
משפחה	Process / pipeline	Process / pipeline	Process / pipeline
שנת המקור	2003	2002	1969
הוגה השיטה	Karl J. Friston	Roberto D. Pascual-Marqui	George Sutherland
סוג	Causal modeling pipeline for neuroimaging	EEG/MEG source localization algorithm	Time-locked EEG analysis pipeline
מקור מכונן	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.	Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique. MIT Press.
כינויים	DCM, Dynamic Causal Model	Exact LORETA, eLORETA source reconstruction	ERP, evoked potential, averaged EEG
קשורות	2	2	3
תקציר	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.	Event-Related Potential (ERP) analysis is a method for extracting stereotyped brain electrical responses time-locked to stimulus presentation or behavioral events from EEG recordings. Formalized in the cognitive neuroscience literature by researchers including Sutherland and Picton, ERP analysis enables millisecond-level temporal resolution of neural processing and has become foundational for studying perception, attention, memory, and decision-making.