Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden
neuronal states from measurements of brain activity. It provides posterior estimates of …

One of the primary technical challenges facing magnetoencephalography (MEG) is that the
magnitude of neuromagnetic fields is several orders of magnitude lower than interfering …

This chapter focuses on the imaging (or distributed) method for implementing EEG/MEG
source reconstruction in SPM. This approach results in a spatial projection of sensor data …

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In
addition to standard M/EEG preprocessing, we presently offer three main analysis tools:(i) …

Mathematical models of scientific data can be formally compared using Bayesian model
evidence. Previous applications in the biological sciences have mainly focussed on model …

In neuroimaging it is now becoming standard practise to fit multiple models to data and
compare them using a model selection criterion. This is especially prevalent in the analysis …

The spatial resolution of EEG/MEG source estimates, often described in terms of source
leakage in the context of the inverse problem, poses constraints on the inferences that can …

Acetylcholine (ACh) is a neuromodulatory transmitter implicated in perception and learning
under uncertainty. This study combined computational simulations and pharmaco …

Abstract Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of
functional magnetic resonance imaging (fMRI) to quantify effective connectivity between …

Magnetoencephalography (MEG) and electroencephalography (EEG) allow functional brain
imaging with high temporal resolution. While solving the inverse problem independently at …