Our data do not support the idea that transient changes of tonic 5-HT affect value-based decision-making. We provide evidence for an association of 5-HTTLPR with risk-seeking for losses, independent of acute 5-HT levels. This indicates that the association of 5-HTTLPR and risk-seeking for losses is mediated via other mechanisms, possibly by differences in the structural development of neural circuits of the 5-HT system during early life phases.

No abstract

It has been shown that the functional architecture of the default mode network (DMN) can be affected by serotonergic challenges and these effects may provide insights on the neurobiological bases of depressive symptomatology. To deepen our understanding of this possible interplay, we used a double‐blind, randomized, cross‐over design, with a control condition and two interventions to decrease (tryptophan depletion) and increase (tryptophan loading) brain serotonin synthesis. Resting‐state fMRI from 85 healthy subjects was acquired for all conditions 3 hr after the ingestion of an amino acid mixture containing different amounts of tryptophan, the dietary precursor of serotonin. The DMN was derived for each participant and session. Permutation testing was performed to detect connectivity changes within the DMN as well as between the DMN and other brain regions elicited by the interventions. We found that tryptophan loading increased tryptophan plasma levels and decreased DMN connectivity with visual cortices and several brain regions involved in emotion and affect regulation (i.e., putamen, subcallosal cortex, thalamus, and frontal cortex). Tryptophan depletion significantly reduced tryptophan levels but did not affect brain connectivity. Subjective ratings of mood, anxiety, sleepiness, and impulsive choice were not strongly affected by any intervention. Our data indicate that connectivity between the DMN and emotion‐related brain regions might be modulated by changes in the serotonergic system. These results suggest that functional changes in the brain associated with different brain serotonin levels may be relevant to understand the neural bases of depressive symptoms.

Value-based decision making (VBDM) is a principle that states that humans and other species adapt their behavior according to the dynamic subjective values of the chosen or unchosen options. The neural bases of this process have been extensively investigated using task-based fMRI and lesion studies. However, the growing field of resting-state functional connectivity (RSFC) may shed light on the organization and function of brain connections across different decision-making domains. With this aim, we used independent component analysis to study the brain network dynamics in a large cohort of young males (N = 145) and the relationship of these dynamics with VBDM. Participants completed a battery of behavioral tests that evaluated delay aversion, risk seeking for losses, risk aversion for gains, and loss aversion, followed by an RSFC scan session. We identified a set of large-scale brain networks and conducted our analysis only on the default mode network (DMN) and networks comprising cognitive control, appetitive-driven, and reward-processing regions. Higher risk seeking for losses was associated with increased connectivity between medial temporal regions, frontal regions, and the DMN. Higher risk seeking for losses was also associated with increased coupling between the left frontoparietal network and occipital cortices. These associations illustrate the participation of brain regions involved in prospective thinking, affective decision making, and visual processing in participants who are greater risk-seekers, and they demonstrate the sensitivity of RSFC to detect brain connectivity differences associated with distinct VBDM parameters.

In an inter-temporal choice (IteCh) task, subjects are offered a smaller amount of money immediately or a larger amount at a later time point. Here, we are using trial-by-trial fMRI data from 363 recording sessions and machine learning in an attempt to build a classifier that would ideally outperform established behavioral model given that it has access to brain activity specific to a single trial. Such methods could allow for future investigations of state-like factors that influence IteCh choices.To investigate this, coefficients of a GLM with one regressor per trial were used as features for a support vector machine (SVM) in combination with a searchlight approach for feature selection and cross-validation. We then compare the results to the performance of four different behavioral models.We found that the behavioral models reached mean accuracies of 90% and above, while the fMRI model only reached 54.84% at the best location in the brain with a spatial distribution similar to the well-known value-tracking network. This low, though significant, accuracy is in line with simulations showing that classifying based on signals with realistic correlations with subjective value produces comparable, low accuracies. These results emphasize the limitations of fMRI recordings from single events to predict human choices, especially when compared to conventional behavioral models. Better performance may be obtained with paradigms that allow the construction of miniblocks to improve the available signal-to-noise ratio.