Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity
from neuroimaging data. In the 15 years since its introduction, the neural models and …

The brain creates a model of the world around us. We can use this representation to
perceive and comprehend what we see at any given moment, but also to vividly re-experience …

This paper provides a worked example of using Dynamic Causal Modelling (DCM) and
Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (…

Psychedelics exert unique effects on human consciousness. The thalamic filter model suggests
that core effects of psychedelics may result from gating deficits, based on a disintegration …

This technical note describes some Bayesian procedures for the analysis of group studies
that use nonlinear models at the first (within-subject) level – eg, dynamic causal models – and …

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

Research suggests that perception and imagination engage neuronal representations in
the same visual areas. However, the underlying mechanisms that differentiate sensory …

Importance Major depressive disorder (MDD) is associated with deficits in representing
reward prediction errors (RPEs), which are the difference between experienced and predicted …

This paper considers the identification of large directed graphs for resting-state brain networks
based on biophysical models of distributed neuronal activity, that is, effective connectivity. …

In recent years, evidence has accumulated to suggest the hippocampus plays a role beyond
memory. A strong hippocampal response to scenes has been noted, and patients with …