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MEG Source Localization×動的因果モデリング×eLORETA×
分野神経画像学神経画像学神経画像学
系統Process / pipelineProcess / pipelineProcess / pipeline
提唱年197220032002
提唱者David CohenKarl J. FristonRoberto D. Pascual-Marqui
種類MEG neuroimaging analysis pipelineCausal modeling pipeline for neuroimagingEEG/MEG source localization algorithm
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. link ↗Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. DOI ↗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. link ↗
別名MEG localization, magnetic source imaging, MSIDCM, Dynamic Causal ModelExact LORETA, eLORETA source reconstruction
関連322
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.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.
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ScholarGate手法を比較: MEG Source Localization · Dynamic Causal Modeling · eLORETA. 2026-06-18に以下より取得 https://scholargate.app/ja/compare