Trait Anxiety Mediated by Amygdala Serotonin Transporter in the Common Marmoset.

High trait anxiety is associated with altered activity across emotion regulation circuitry and a higher risk of developing anxiety disorders and depression. This circuitry is extensively modulated by serotonin. Here, to understand why some people may be more vulnerable to developing affective disorders, we investigated whether serotonin-related gene expression across the brain's emotion regulation circuitry may underlie individual differences in trait anxiety using the common marmoset (Callithrix jacchus, mixed sexes) as a model. First, we assessed the association of region-specific expression of the serotonin transporter (SLC6A4) and serotonin receptor (HTR1A, HTR2A, HTR2C) genes with anxiety-like behavior; and second, we investigated their causal role in two key features of the high trait anxious phenotype: high responsivity to anxiety-provoking stimuli and an exaggerated conditioned threat response. While the expression of the serotonin receptors did not show a significant relationship with anxiety-like behavior in any of the targeted brain regions, serotonin transporter expression, specifically within the right ventrolateral prefrontal cortex (vlPFC) and most strongly in the right amygdala, was associated positively with anxiety-like behavior. The causal relationship between amygdala serotonin levels and an animal's sensitivity to threat was confirmed via direct amygdala infusions of a selective serotonin reuptake inhibitor (SSRI), citalopram. Both anxiety-like behaviors, and conditioned threat-induced responses were reduced by the blockade of serotonin reuptake in the amygdala. Together, these findings provide evidence that high amygdala serotonin transporter expression contributes to the high trait anxious phenotype and suggest that reduction of threat reactivity by SSRIs may be mediated by their actions in the amygdala.SIGNIFICANCE STATEMENT Findings here contribute to our understanding of how the serotonin system underlies an individual's expression of threat-elicited negative emotions such as anxiety and fear within nonhuman primates. Exploration of serotonergic gene expression across brain regions implicated in emotion regulation revealed that serotonin transporter gene expression in the ventrolateral prefrontal cortex (vlPFC) and most strongly in the amygdala, but none of the serotonin receptor genes, were predictive of interindividual differences in anxiety-like behavior. Targeting of amygdala serotonin reuptake with selective serotonin reuptake inhibitors (SSRIs) confirmed the causal relationship between amygdala serotonin transporter and an animal's sensitivity to threat by reversing expression of two key features of the high trait-like anxiety phenotype: high responsivity to anxiety-provoking uncertain threat and responsivity to certain conditioned threat.

A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework.

Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns of the RDoC framework, our study employed a latent variable approach, specifically utilizing bifactor analysis. We examined a total of 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with a total of 6,192 participants. Within this set of 84 maps, a curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set of our analysis, and the remaining held-out maps formed the internal validation set. External validation was performed with 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Our results indicate that a bifactor model with a task-general domain and splitting the cognitive systems domain into sub-domains better fits the current corpus of tfMRI data than the current RDoC framework. Our data-driven validation supports revising the RDoC framework to accurately reflect underlying brain circuitry.