Dynamic Causal Modeling of Spatiotemporal Integration of ...

Spatiotemporal integration of phonological and semantic processes. Group source reconstruction showed activation of a predominant left cerebral ...

Dynamic causal modeling of spatiotemporal integration of ... - PubMed

Integration of phonological and lexicosemantic processes is essential for visual word recognition. Here we used dynamic causal modeling of ...

Dynamic Causal Modeling of Spatiotemporal Integration of Phonological and Semantic Processes: An Electroencephalographic Study. Gaëtan Yvert ...

(PDF) Dynamic Causal Modeling of Spatiotemporal Integration of ...

PDF | Integration of phonological and lexicosemantic processes is essential for visual word recognition. Here we used dynamic causal ...

Dynamic causal modeling of spatiotemporal integration of ...

Abstract: Integration of phonological and lexicosemantic processes is essential for visual word recognition. Here we used dynamic causal modeling of...

(PDF) Dynamic Causal Modeling of Spatiotemporal Integration of ...

Dynamic Causal Modeling of Spatiotemporal Integration of Phonological and Semantic Processes: An Electroencephalographic Study.

Comparison of two integration methods for dynamic causal ...

Dynamic causal modeling (DCM) is a model-driven approach used to infer causal relationships between brain areas (Friston, 2011). Based upon a realistic local ...

Dynamic causal modelling of distributed electromagnetic responses

We depart from equivalent current dipole formulations of DCM, and extend it to provide spatiotemporal source estimates that are spatially distributed. The ...

Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based ...

Dynamic causal modeling (DCM) is an analysis technique that has been successfully used to infer about directed connectivity between brain ...

Dynamic causal modelling revisited - ScienceDirect.com

This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model.

Spectral dynamic causal modeling: A didactic introduction and its ...

K. E.. (. 2022. ). An introduction to thermodynamic integration and application to dynamic causal models ... Local information transfer as a spatiotemporal ...

Neural masses and fields in dynamic causal modeling - Frontiers

For M/EEG, neural mass and neural field models in particular are used, to support this analysis by quantifying the temporal and spatiotemporal ...

Dynamic Causal Modeling for fMRI - SPM Documentation

Depending on the spatio-temporal properties of a given measurement technique, one needs to define an adequate state equation and an observation model. See Fig ...

Dynamic causal modeling - Scholarpedia

Modelling functional integration: a comparison of structural equation and dynamic causal models. Neuroimage 23: S264-274. Kiebel, S.J. ...

Dynamic causal modelling for EEG and MEG

Dynamic Causal Modelling provides a generative spatiotemporal model for M/EEG responses. ... The integration of this model, to form ...

Dynamic Causal Modeling

This stems from the notion of functional integration, which views function as an emergent property of brain networks. Dynamic causal modelling or DCM was ...

Dynamic causal modeling - Wikipedia

Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison.

Thermodynamic integration for dynamic causal models | bioRxiv

In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either ...

Dynamic causal modelling

An important con- ceptual aspect of dynamic causal models, for neuroimaging, pertains to how the experimental inputs enter the model and cause neuronal ...

Dynamic causal modeling analysis reveals the modulation of motor ...

Dynamic causal modeling analysis reveals the modulation of motor cortex and integration in superior temporal gyrus during multisensory speech ...