Contributions in Merlin

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Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity. It provides posterior estimates of neurobiologically interpretable quantities such as the effective strength of synaptic connections among neuronal populations and their context-dependent modulation. DCM is increasingly used in the analysis of a wide range of neuroimaging and electrophysiological data. Given the relative complexity of DCM, compared to conventional analysis techniques, a good knowledge of its theoretical foundations is needed to avoid pitfalls in its application and interpretation of results. By providing good practice recommendations for DCM, in the form of ten simple rules, we hope that this article serves as a helpful tutorial for the growing community of DCM users.

The aim of the present paper is to assess the effects of altered dopamine (DA) transmission on the functional connectivity among brain regions mediating aversive conditioning in humans. To this aim, we analyzed a previous published data set from a double-blind design combined with functional magnetic resonance imaging (fMRI) recordings in which healthy volunteers were randomly assigned to one of three drug groups: amphetamine (an indirect DA agonist), haloperidol (DA D2 receptor antagonist), and placebo. Participants were exposed to an aversive classical conditioning paradigm using cutaneous electrical stimulation as the unconditioned stimulus (US), and visual cues as the conditioned stimuli (CS) where one colour (CS+) was followed by the US in 33% of the trials and another colour (CS-) had no consequences. All participants reported awareness of stimulus contingencies. Group analysis of fMRI data revealed that the left ventral striatum (VS) and amygdala activated in response to the CS+ in all the three groups. Because of their activation patterns and documented involvement in aversive conditioning, both regions were used as seeds in the functional connectivity analysis. To constrain the functional networks obtained to relate to the conditioned response, we also correlated seed activity with the Galvanic Skin Response (GSR). In the placebo group, the right ventral tegmental area/substantia nigra (VTA/SN), bilateral caudate, right parahippocampal gyrus, left inferior parietal lobule (IPL), bilateral postcentral gyrus, bilateral middle frontal (BA 46), orbitofrontal, and ventromedial prefrontal cortices (PFC, BA 10/11) correlated with the VS and amygdala seeds in response to the CS+ compared to the CS-. Enhancing dopamine transmission via amphetamine was associated with reduced task differences and significant functional connectivity for both CS+ and CS- conditions between the left VS seed and regions modulated by DA, such as the left VTA/SN, right caudate, left amygdala, left middle frontal gyrus (BA 46), and bilateral ventromedial PFC (BA 10). Blocking dopamine transmission via haloperidol was associated with significant functional connectivity across an alternate network of regions including the left amygdala seed and the right insula, the left ACC (BA 24/32), bilateral IPL (BA 40), precuneus (BA 7), post-central gyrus, middle frontal gyrus (BA 46), and supplementary motor area (SMA, BA 6) to the CS+ versus the CS-. These data provide insight into the distinct effects of DA agents on the functional connectivity between striatal, limbic, and prefrontal areas.

In this paper, we axiomatize a target-based model of choice that allows decision makers to be both risk averse and risk seeking, depending on the payoff's position relative to a prespecified target. The approach can be viewed as a hybrid model, capturing in spirit two celebrated ideas: first, the satisficing concept of Simon (1955); second, the switch between risk aversion and risk seeking popularized by the prospect theory of Kahneman and Tversky (1979). Our axioms are simple and intuitive; in order to be implemented in practice, our approach requires only the specification of an aspiration level. We show that this approach is dual to a known approach using risk measures, thereby allowing us to connect to existing theory. Though our approach is intended to be normative, we also show that it resolves the classical examples of Allais (1953) and Ellsberg (1961).

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