方法对比

	动态因果建模	eLORETA	事件相关电位分析
领域	神经影像	神经影像	神经影像
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	2003	2002	1969
提出者	Karl J. Friston	Roberto D. Pascual-Marqui	George Sutherland
类型	Causal modeling pipeline for neuroimaging	EEG/MEG source localization algorithm	Time-locked EEG analysis pipeline
开创性文献	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.
别名	DCM, Dynamic Causal Model	Exact LORETA, eLORETA source reconstruction	ERP, evoked potential, averaged EEG
相关	2	2	3
摘要	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.