Σύγκριση μεθόδων

	Εντοπισμός πηγών MEG	Δυναμική Αιτιακή Μοντελοποίηση
Πεδίο	Νευροαπεικόνιση	Νευροαπεικόνιση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1972	2003
Δημιουργός	David Cohen	Karl J. Friston
Τύπος	MEG neuroimaging analysis pipeline	Causal modeling pipeline for neuroimaging
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	MEG localization, magnetic source imaging, MSI	DCM, Dynamic Causal Model
Συναφείς	3	2
Σύνοψη	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.