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| Dynamic Causal Modeling× | eLORETA× | |
|---|---|---|
| Dziedzina | Neuroobrazowanie | Neuroobrazowanie |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2003 | 2002 |
| Twórca≠ | Karl J. Friston | Roberto D. Pascual-Marqui |
| Typ≠ | Causal modeling pipeline for neuroimaging | EEG/MEG source localization algorithm |
| Źródło pierwotne≠ | 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 ↗ |
| Inne nazwy | DCM, Dynamic Causal Model | Exact LORETA, eLORETA source reconstruction |
| Pokrewne | 2 | 2 |
| Podsumowanie≠ | 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. |
| ScholarGateZbiór danych ↗ |
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