Comparer des méthodes

	Localisation de sources en magnétoencéphalographie (MEG)	Modélisation Causale Dynamique	Analyse des potentiels évoqués liés à l'événement
Domaine	Neuro-imagerie	Neuro-imagerie	Neuro-imagerie
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	1972	2003	1969
Auteur d'origine	David Cohen	Karl J. Friston	George Sutherland
Type	MEG neuroimaging analysis pipeline	Causal modeling pipeline for neuroimaging	Time-locked EEG analysis pipeline
Source fondatrice	Hauk, O., Friston, K. J., & Leff, A. (2019). Functional neuroimaging of language: understanding the complex relationships between localization and function. Journal of Neurolinguistics, 50, 236–250.	Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.	Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique. MIT Press.
Alias	MEG localization, magnetic source imaging, MSI	DCM, Dynamic Causal Model	ERP, evoked potential, averaged EEG
Apparentées	3	2	3
Résumé	Magnetoencephalography (MEG) source localization is the inverse problem of estimating where in the brain neural currents originate from magnetic field measurements at the scalp. Introduced by David Cohen in 1972, MEG offers superior temporal resolution (milliseconds) and spatial specificity compared to EEG, as magnetic fields are less distorted by tissue conductivity, enabling researchers to pinpoint neural activity with high precision.	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.	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.