Jean Daunizeau

..The result supports the hypothesis of different neurophysiological mechanisms underlying the generation of the spike versus wave components of GSW discharges...

..We apply the method on EEG/fMRI recordings from a patient with epilepsy, in order to identify brain areas involved during the generation of epileptic spikes. The results are validated using intracranial EEG measurements...

..Finally, we illustrate how our approach can be used to quantify subjective beliefs and preferences that underlie inter-individual differences in behaviour...

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..Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs...

..Here, we describe the distributed spatial model and present a comparative evaluation with conventional equivalent current dipole (ECD) models of auditory processing, as measured with EEG...

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..This finding substantially advances our understanding of striatal function and provides direct empirical evidence for formal learning theories that posit a central role for prediction error-dependent plasticity...

..LEA is evaluated through numerical simulations and compared to a classical Weighted Minimum Norm estimation...

..Finally, we note that the ability to model endogenous or random fluctuations on hidden neuronal (and physiological) states provides a new and possibly more plausible perspective on how regionally specific signals in fMRI are generated...

..We then discuss analysis strategies to combine both techniques by reviewing studies in epilepsy, current methodologic development, and future directions of this fast-developing field...

..This means the approach automatically selects either a sparse or a distributed model, depending on the data. The scheme is compared with conventional applications of Bayesian solutions to quantify the improvement in performance...

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..In both practical examples, Bayesian model selection favoured the nonlinear models over corresponding bilinear ones...

..The importance of implicit variance in neuronal states for neural-mass models of cortical dynamics is illustrated using both synthetic data and real EEG measurements of sensory evoked responses...

..We illustrate the methods using Dynamic Causal Models of brain imaging data...

..We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies...

..We illustrate the advantage of our Bayesian scheme, using a multi-subject EEG auditory study, where we compare competing models for the generation of the N100 component...

..The approach is evaluated and compared to standard inverse EEG techniques, using synthetic data and real data. The results demonstrate the added-value of the mesostate-space model and its variational inversion...

..In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception...

..The mean-field model presented here will form the basis of a dynamic causal model of observed electromagnetic signals in future work...

..Finally, we discuss limitations and potential extensions of this work...

..By presenting anomalous stimuli, we find that the resulting recognition dynamics disclose inference at multiple time scales and are reminiscent of neuronal dynamics seen in the real brain...

..The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain...

..The model furnishes a potentially powerful approach to characterising SCR that exploits knowledge about how these signals are generated...

..g. comparing different source reconstruction methods for EEG/MEG or selecting among competing computational models of learning and decision-making...

..The framework provides predictions about, and principled constraints on, cortical structure-function relationships, which can be tested by manipulating the time-scales of sensory input...

..We conclude that, even without conscious processing of contextual cues, our brain can learn their reward value and use them to provide a bias on decision making...

..Using simulated EEG data, the proposed inverse approach is evaluated and compared with standard distributed methods using both classical criteria and ROC curves...