विधियों की तुलना करें

	MEG स्रोत स्थानीयकरण	डायनामिक कारणात्मक मॉडलिंग
क्षेत्र	तंत्रिका प्रतिबिंबन	तंत्रिका प्रतिबिंबन
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1972	2003
प्रवर्तक	David Cohen	Karl J. Friston
प्रकार	MEG neuroimaging analysis pipeline	Causal modeling pipeline for neuroimaging
मौलिक स्रोत	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.
उपनाम	MEG localization, magnetic source imaging, MSI	DCM, Dynamic Causal Model
संबंधित	3	2
सारांश	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.