A ventral striatal prediction error signal in human fear extinction learning

Animal studies have shown that the prediction error (PE) signal that drives fear extinction learning is encoded by phasic activity of midbrain dopamine (DA) neurons. Thus, the extinction PE resembles the appetitive PE that drives reward learning. In humans, fear extinction learning is less well understood. Using computational neuroimaging, a previous study from our group reported hemodynamic activity in the left ventral putamen, a subregion of the ventral striatum (VS), to correlate with a PE function derived from a formal associative learning model. The activity was modulated by genetic variation in a DA-related gene. To conceptually replicate and extend this finding, we here asked whether an extinction PE (EPE) signal in the left ventral putamen can also be observed when genotype information is not taken into account. Using an optimized experimental design for model estimation, we again observed EPE-related activity in the same striatal region, indicating that activation of this region is a feature of human extinction learning. We further observed significant EPE signals across wider parts of the VS as well as in frontal cortical areas. These results may suggest that the prediction errors during extinction learning are available to larger parts of the brain, as has also been observed in human neuroimaging studies of reward PE signaling. Conclusive evidence that the human EPE signal is of DAergic nature is still outstanding

Cognitive variability in bipolar I disorder: A cluster-analytic approach informed by resting-state data

Item does not contain fulltextBackground: While the presence of cognitive performance deficits in bipolar disorder I (BD-I) is well established, there is no consensus about which cognitive abilities are affected. Heterogeneous phenotypes displayed in BD-I further suggest the existence of subgroups among the disorder. The present study sought to identify different cognitive profiles among BD-I patients as well as potentially underlying neuronal network changes. Methods: 54 euthymic BD-I patients underwent cognitive testing and resting state neuroimaging. Hierarchical cluster-analysis was performed on executive function scores of bipolar patients. The derived clusters were compared against 54 age-, gender- and IQ-matched healthy controls (HC) to facilitate the interpretation of results. Further, resting state network properties were compared to identify differences probably underlying cognitive profiles. Results: A three-cluster solution emerged. Cluster 1 (n = 22) was characterized by deficits in cognitive flexibility and motor inhibition, cluster 2 (n = 12) displayed impulsive decision-making, while cluster 3 (n = 20) showed good visuospatial planning. Weaker connections in cluster 1 compared to cluster 2 were found between regions activated during tasks cluster 1 showed deficits on. Cluster 3 had a higher modularity than cluster 2, which correlated positively with problem solving performance and risk-taking in this cluster. Conclusion: Obtained clusters showed distinct cognitive profiles, characterized by deficits and strengths, most of which remained precluded in a general comparison. Weaker interregional connections and separated subnetworks might underly behavioral deficits and strengths, respectively. The findings help explain the phenotypic heterogeneity observed in BD-I.14 p

Pattern classification predicts individuals' responses to affective stimuli

Since the successful demonstration of "brain reading" of fMRI BOLD signals using multivoxel pattern classification (MVPA) techniques, the neuroimaging community has made vigorous attempts to exploit the technique in order to identify the signature patterns of brain activities associated with different cognitive processes or mental states. In the current study, we tested whether the valence and arousal dimensions of the affective information could be used to successfully predict individual's active affective states. Using a whole-brain MVPA approach, together with feature elimination procedures, we are able to discriminate between brain activation patterns associated with the processing of positive or negative valence and cross validate the discriminant function with an independent data set. Arousal information, on the other hand, failed to provide such discriminating power. With an independent sample, we test further whether the MVPA identified brain network could be used for inter-individual classification. Although the inter-subject classification success was only marginal, we found correlations with individual differences in affective processing. We discuss the implications of our findings for future attempts to classify patients based on their responses to affective stimuli

Increased Neural Activity in Mesostriatal Regions after Prefrontal Transcranial Direct Current Stimulation and L-DOPA Administration

Dopamine dysfunction is associated with a wide range of neuropsychiatric disorders commonly treated pharmacologically or invasively. Recent studies provide evidence for a nonpharmacological and noninvasive alternative that allows similar manipulation of the dopaminergic system: transcranial direct current stimulation (tDCS). In rodents, tDCS has been shown to increase neural activity in subcortical parts of the dopaminergic system, and recent studies in humans provide evidence that tDCS over prefrontal regions induces striatal dopamine release and affects reward-related behavior. Based on these findings, we used fMRI in healthy human participants and measured the fractional amplitude of low-frequency fluctuations to assess spontaneous neural activity strength in regions of the mesostriatal dopamine system before and after tDCS over prefrontal regions (n = 40, 22 females). In a second study, we examined the effect of a single dose of the dopamine precursor levodopa (l-DOPA) on mesostriatal fractional amplitude of low-frequency fluctuation values in male humans (n = 22) and compared the results between both studies. We found that prefrontal tDCS and l-DOPA both enhance neural activity in core regions of the dopaminergic system and show similar subcortical activation patterns. We furthermore assessed the spatial similarity of whole-brain statistical parametric maps, indicating tDCS- and l-DOPA-induced activation, and >100 neuronal receptor gene expression maps based on transcriptional data from the Allen Institute for Brain Science. In line with a specific activation of the dopaminergic system, we found that both interventions predominantly activated regions with high expression levels of the dopamine receptors D2 and D3