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Interoceptive feedback transmitted via the vagus nerve plays a vital role in motivation by tuning actions according to physiological needs. Whereas vagus nerve stimulation (VNS) reinforces actions in animals, motivational effects elicited by VNS in humans are still largely elusive. Here, we applied non-invasive transcutaneous auricular VNS (taVNS) on the left or right ear while participants exerted effort to earn rewards using a randomized cross-over design (vs. sham). In line with preclinical studies, acute taVNS enhances invigoration of effort, and stimulation on the left side primarily facilitates invigoration for food rewards. In contrast, we do not find conclusive evidence that acute taVNS affects effort maintenance or wanting ratings. Collectively, our results suggest that taVNS enhances reward-seeking by boosting invigoration, not effort maintenance and that the stimulation side affects generalization beyond food reward. Thus, taVNS may enhance the pursuit of prospective rewards which may pave avenues to treat motivational deficiencies. The vagus nerve transmits signals between the gut and the brain thereby tuning motivated behavior to physiological needs. Here, the authors show that acute non-invasive stimulation of the vagus nerve via the ear enhances the invigoration of effort for rewards.

•EEG microstate parameters are strongly related to vigilance levels and can predict them.•We find that vigilance Granger-causes changes in parameters of microstates.•Duration and occurrence of EEG microstates are differentially modulated by vigilance level. The momentary global functional state of the brain is reflected in its electric field configuration and cluster analytical approaches have consistently shown four configurations, referred to as EEG microstate classes A to D. Changes in microstate parameters are associated with a number of neuropsychiatric disorders, task performance, and mental state establishing their relevance for cognition. However, the common practice to use eye-closed resting state data to assess the temporal dynamics of microstate parameters might induce systematic confounds related to vigilance levels. Here, we studied the dynamics of microstate parameters in two independent data sets and showed that the parameters of microstates are strongly associated with vigilance level assessed both by EEG power analysis and fMRI global signal. We found that the duration and contribution of microstate class C, as well as transition probabilities towards microstate class C were positively associated with vigilance, whereas the sign was reversed for microstate classes A and B. Furthermore, in looking for the origins of the correspondence between microstates and vigilance level, we found Granger-causal effects of vigilance levels on microstate sequence parameters. Collectively, our findings suggest that duration and occurrence of microstates have a different origin and possibly reflect different physiological processes. Finally, our findings indicate the need for taking vigilance levels into consideration in resting-sate EEG investigations.

Metacognitive biases have been repeatedly associated with transdiagnostic psychiatric dimensions of ‘anxious-depression’ and ‘compulsivity and intrusive thought’, cross-sectionally. To progress our understanding of the underlying neurocognitive mechanisms, new methods are required to measure metacognition remotely, within individuals over time. We developed a gamified smartphone task designed to measure visuo-perceptual metacognitive (confidence) bias and investigated its psychometric properties across two studies (N = 3410 unpaid citizen scientists, N = 52 paid participants). We assessed convergent validity, split-half and test–retest reliability, and identified the minimum number of trials required to capture its clinical correlates. Convergent validity of metacognitive bias was moderate (r(50) = 0.64, p  < 0.001) and it demonstrated excellent split-half reliability (r(50) = 0.91, p  < 0.001). Anxious-depression was associated with decreased confidence (β =  − 0.23, SE = 0.02, p  < 0.001), while compulsivity and intrusive thought was associated with greater confidence (β = 0.07, SE = 0.02, p  < 0.001). The associations between metacognitive biases and transdiagnostic psychiatry dimensions are evident in as few as 40 trials. Metacognitive biases in decision-making are stable within and across sessions, exhibiting very high test–retest reliability for the 100-trial (ICC = 0.86, N = 110) and 40-trial (ICC = 0.86, N = 120) versions of Meta Mind. Hybrid ‘self-report cognition’ tasks may be one way to bridge the recently discussed reliability gap in computational psychiatry.

Open data offer the opportunity to economically combine data into large-scale datasets, fostering collaboration and re-use in the interest of treating researchers’ resources as well as study participants with care. Whereas advantages of utilising open data might be self-evident, the production of open datasets also challenges individual researchers. This is especially true for open data that include personal data, for which higher requirements have been legislated. Mainly building on our own experience as scholars from different research traditions (life sciences, social sciences and humanities), we describe best-practice approaches for opening up research data. We reflect on common barriers and strategies to overcome them, condensed into a step-by-step guide focused on actionable advice in order to mitigate the costs and promote the benefit of open data on three levels at once: society, the disciplines and individual researchers. Our contribution may prevent researchers and research units from re-inventing the wheel when opening data and enable them to learn from our experience.

Reinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has limited the number of measurements and the test-retest reliability of many decision-related parameters is therefore unknown. In this paper, we present an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) of current mental and physiological states for repeated assessment over weeks. In this task, players have to identify the most effective medication by integrating reward values with changing probabilities to win (according to random Gaussian walks). Participants can complete up to 31 runs with 150 trials each. To encourage replay, in-game screens provide feedback on the progress. Using an initial validation sample of 384 players (9729 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show poor to fair intra-class correlations (ICC: 0.22–0.53), indicating substantial within- and between-subject variance. Notably, items assessing the psychological state showed comparable ICCs as reinforcement learning parameters. To conclude, our innovative and openly customizable app framework provides a gamified task that optimizes repeated assessments of reward learning to better quantify intra- and inter-individual differences in value-based decision-making over time.

Reciprocal exchanges can be understood as the updating of an initial belief about a partner. This initial level of trust is essential when it comes to establishing cooperation with an unknown partner, as cooperation cannot arise without a minimum of trust not justified by previous successful exchanges with this partner. Here we demonstrate the existence of a representation of the initial trust level before an exchange with a partner has occurred. Specifically, we can predict the Investor’s initial investment—i.e. his initial level of trust toward the unknown trustee in Round 1 of a standard 10-round Trust Game—from resting-state functional connectivity data acquired several minutes before the start of the Trust Game. Resting-state functional connectivity is, however, not significantly associated with the level of trust in later rounds, potentially mirroring the updating of the initial belief about the partner. Our results shed light on how the initial level of trust is represented. In particular, we show that a person’s initial level of trust is, at least in part, determined by brain electrical activity acquired well before the beginning of an exchange.

To cast valid predictions of future behavior or diagnose disorders, the reliable measurement of a “biomarker” such as the brain activation to prospective reward is a prerequisite. Surprisingly, only a small fraction of functional magnetic resonance imaging (fMRI) studies report or cite the reliability of brain activation maps involved in group analyses. Here, using simulations and exemplary longitudinal data of 126 healthy adolescents performing an intertemporal choice task, we demonstrate that reproducing a group activation map over time is not a sufficient indication of reliable measurements at the individual level. Instead, selecting regions based on significant main effects at the group level may yield estimates that fail to reliably capture individual variance in the subjective evaluation of an offer. Collectively, our results call for more attention on the reliability of supposed biomarkers at the level of the individual. Thus, caution is warranted in employing brain activation patterns prematurely for clinical applications such as diagnosis or tailored interventions before their reliability has been conclusively established by large-scale studies. To facilitate assessing and reporting of the reliability of fMRI contrasts in future studies, we provide a toolbox that incorporates common measures of global and local reliability. •To classify and predict, we need reliable brain-response patterns.•Extensive evaluation of reliability in longitudinal fMRI study on discounting.•Congruent group effects arise despite unreliable individual estimates.•Reliability is not universal for task, but depends on contrast.•Toolbox fmreli bundles common reliability indices.

Mood plays an important role in our life which is illustrated by the disruptive impact of aberrant mood states in depression. Although vagus nerve stimulation (VNS) has been shown to improve symptoms of depression, the exact mechanism is still elusive, and it is an open question whether non-invasive VNS could be used to swiftly and robustly improve mood. Here, we investigated the effect of left- and right-sided transcutaneous auricular VNS (taVNS) a sham control condition on mood after the exertion of physical and cognitive effort in 82 healthy participants (randomized cross-over design) using linear mixed-effects and hierarchical Bayesian analyses of mood ratings. We found that 90 min of either left-sided or right-sided taVNS improved positive mood [ = 5.11, 95% credible interval, CI (1.39-9.01), 9.6% improvement relative to the mood intercept, BF = 7.69, = 0.017], yet only during the post-stimulation phase. Moreover, lower baseline scores of positive mood were associated with greater taVNS-induced improvements in motivation [ = -0.42, 95% CI (-0.58 to -0.21), BF = 249]. We conclude that taVNS boosts mood after a prolonged period of effort exertion with concurrent stimulation and that acute motivational effects of taVNS are partly dependent on initial mood states. Collectively, our results show that taVNS may help quickly improve affect after a mood challenge, potentially by modulating interoceptive signals contributing to the reappraisal of effortful behavior. This suggests that taVNS could be a useful add-on to current behavioral therapies.

Background Loneliness is associated with lower cognitive function and may increase dementia risk. However, it is unclear if this effect is mediated by depression. Resolving this issue is important to design effective interventions to promote healthy aging. We adopted a complementary between‐ and within‐person approach, which allowed us to study cross‐sectional relationships as well as the dynamic interactions between loneliness, mood, and cognition in natural environments over time. Method A total of 3,416 participants between 18 and 84 years (1,149 male; M age = 45.89±14.55) completed cross‐sectional self‐reported questionnaires of loneliness and depression alongside gamified assessments of memory, processing speed, cognitive flexibility, and planning through a smartphone app, Neureka. A subsample of 286 participants between 18 and 82 years (89 male; M age = 50.14±13.16) also underwent 8‐week ecological momentary assessment (EMA) reporting every 12 hours how lonely and down they felt. We measured cognition at these same timepoints using a recently validated passive measure of cognitive processing speed (digital questionnaire response time, DQRT). Multiple regressions and network analysis were performed to analyze cross‐sectional and EMA data, respectively. Result Loneliness and lower cognitive function were associated cross‐sectionally (all ps<.001) except for planning (p = .08). Significant effects did not survive after controlling for depression (all ps>.06). Turning to EMA data, a contemporaneous network analysis showed that within‐person 12‐hour fluctuations in loneliness were related to fluctuations in mood (r = .35, p<.001). However, fluctuations in mood (but not loneliness) were linked to changes in DQRT (r = .13, p<.001). To understand the causal path, we conducted temporal network analysis, which revealed a bi‐directional relationship between loneliness and low mood (β = .08 and β = .06, respectively, both p<.001). In contrast, lower mood predicted slower DQRT 12‐hours later (β = .02, p = 0.03) and not the other way around (β = .005, p = 0.43). In older adults, loneliness and low mood were less related to one‐another, but the relationship between low mood and slower DQRT was stronger. Conclusion Depression symptoms mediate the effect of loneliness on cognition, both cross‐sectionally and when assessed within‐person. Older participants show less coupling between loneliness and low mood, but stronger coupling between low mood and slower cognition. Results have implications for differential interventions across the lifespan.