Developments in M/EEG analysis allows for models that are sophisticated enough to capture the full richness of the data. This chapter focuses on dynamic causal modeling (DCM) for M/EEG, which entails the inversion of informed spatiotemporal models of observed responses. The idea is to model condition-specific responses over channels and peristimulus time with the same model, where the differences among conditions are explained by changes in only a few key parameters. The face and predictive validity of DCM have been established, which makes it a potentially useful tool for group studies
Complex processes resulting from interaction of multiple elements can rarely be understood by analyt...
Dynamic Causal Modeling (DCM) uses dynamical systems to represent the high-level neural processing s...
International audienceComplex processes resulting from interaction of multiple elements can rarely b...
Developments in M/EEG analysis allows for models that are sophisticated enough to capture the full r...
Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnet...
Abstract: We present a review of dynamic causal modeling (DCM) for magneto-and electroencephalograph...
Abstract—Recent advances in neurophysiology have led to the development of complex dynamical models ...
International audienceThe goal of dynamic causal modelling (DCM) of neuroimaging data is to study ex...
Abstract — The use of complex dynamical models have been proposed for describing the connections and...
International audienceDynamical causal modeling (DCM) of evoked responses is a new approach to makin...
We describe the use of spatial and temporal constraints in dynamic causal modelling (DCM) of magneto...
International audienceDynamic causal modeling (DCM) is a methodological approach to study effective ...
This dataset consists of BIDS-formatted M/EEG data from Wakeman & Henson (2015) that have been proce...
Complex processes resulting from interaction of multiple elements can rarely be understood by analyt...
Dynamic Causal Modeling (DCM) uses dynamical systems to represent the high-level neural processing s...
International audienceComplex processes resulting from interaction of multiple elements can rarely b...
Developments in M/EEG analysis allows for models that are sophisticated enough to capture the full r...
Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnet...
Abstract: We present a review of dynamic causal modeling (DCM) for magneto-and electroencephalograph...
Abstract—Recent advances in neurophysiology have led to the development of complex dynamical models ...
International audienceThe goal of dynamic causal modelling (DCM) of neuroimaging data is to study ex...
Abstract — The use of complex dynamical models have been proposed for describing the connections and...
International audienceDynamical causal modeling (DCM) of evoked responses is a new approach to makin...
We describe the use of spatial and temporal constraints in dynamic causal modelling (DCM) of magneto...
International audienceDynamic causal modeling (DCM) is a methodological approach to study effective ...
This dataset consists of BIDS-formatted M/EEG data from Wakeman & Henson (2015) that have been proce...
Complex processes resulting from interaction of multiple elements can rarely be understood by analyt...
Dynamic Causal Modeling (DCM) uses dynamical systems to represent the high-level neural processing s...
International audienceComplex processes resulting from interaction of multiple elements can rarely b...