手法を比較

	MEG Source Localization	動的因果モデリング
分野	神経画像学	神経画像学
系統	Process / pipeline	Process / pipeline
提唱年	1972	2003
提唱者	David Cohen	Karl J. Friston
種類	MEG neuroimaging analysis pipeline	Causal modeling pipeline for neuroimaging
原典	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.
別名	MEG localization, magnetic source imaging, MSI	DCM, Dynamic Causal Model
関連	3	2
概要	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.