Bayesian model selection (BMS) is a powerful method for determining the most likely among
a set of competing hypotheses about the mechanisms that generated observed data. BMS …

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

Psychedelic drugs produce profound changes in consciousness, but the underlying
neurobiological mechanisms for this remain unclear. Spontaneous and induced oscillatory activity …

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

Decades of neurobiological research have disclosed the diverse manners in which the
response properties of neurons are dynamically modulated to support adaptive cognitive …

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

We present a neural mass model of steady-state membrane potentials measured with local
field potentials or electroencephalography in the frequency domain. This model is an …

In this paper, we describe a dynamic causal model (DCM) of steady-state responses in
electrophysiological data that are summarised in terms of their cross-spectral density. These …

Cortico-basal ganglia-thalamocortical circuits are severely disrupted by the dopamine
depletion of Parkinson's disease (PD), leading to pathologically exaggerated beta oscillations. …

Functional integration in the brain rests on anatomical connectivity (the presence of axonal
connections) and effective connectivity (the causal influences mediated by these connections)…