Reduced Sensitivity to Immediate Reward during Decision-Making in Older than Younger Adults

We examined whether older adults differ from younger adults in the degree to which they favor immediate over delayed rewards during decision-making. To examine the neural correlates of age-related differences in delay discounting we acquired functional MR images while participants made decisions between smaller but sooner and larger but later monetary rewards. The behavioral results show age-related reductions in delay discounting. Less impulsive decision-making in older adults was associated with lower ventral striatal activations to immediate reward. Furthermore, older adults showed an overall higher percentage of delayed choices and reduced activity in the dorsal striatum than younger adults. This points to a reduced reward sensitivity of the dorsal striatum in older adults. Taken together, our findings indicate that less impulsive decision-making in older adults is due to a reduced sensitivity of striatal areas to reward. These age-related changes in reward sensitivity may result from transformations in dopaminergic neuromodulation with age

Computational neuroscience across the lifespan: Promises and pitfalls

In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination with experimental manipulations, these latent variables can help to test hypotheses about age-related changes in behavioral and neurobiological measures at a level of specificity that is not achievable with descriptive analysis approaches alone. This level of specificity can in turn be beneficial to establish the identity of the corresponding behavioral and neurobiological mechanisms. In this paper, we will illustrate applications of computational methods using examples of lifespan research on risk taking, strategy selection and reinforcement learning. We will elaborate on problems that can occur when computational neuroscience methods are applied to data of different age groups. Finally, we will discuss potential targets for future applications and outline general shortcomings of computational neuroscience methods for research on human lifespan development

Age differences in learning emerge from an insufficient representation of uncertainty in older adults

Healthy aging can lead to impairments in learning that affect many laboratory and real-life tasks. These tasks often involve the acquisition of dynamic contingencies, which requires adjusting the rate of learning to environmental statistics. For example, learning rate should increase when expectations are uncertain (uncertainty), outcomes are surprising (surprise) or contingencies are more likely to change (hazard rate). In this study, we combine computational modelling with an age-comparative behavioural study to test whether age-related learning deficits emerge from a failure to optimize learning according to the three factors mentioned above. Our results suggest that learning deficits observed in healthy older adults are driven by a diminished capacity to represent and use uncertainty to guide learning. These findings provide insight into age-related cognitive changes and demonstrate how learning deficits can emerge from a failure to accurately assess how much should be learned

Reduced Striatal Responses to Reward Prediction Errors in Older Compared with Younger Adults

We examined whether older adults differ from younger adults in how they learn from rewarding and aversive outcomes. Human participants were asked to either learn to choose actions that lead to monetary reward or learn to avoid actions that lead to monetary losses. To examine age differences in the neurophysiological mechanisms of learning, we applied a combination of computational modeling and fMRI. Behavioral results showed age-related impairments in learning from reward but not in learning from monetary losses. Consistent with these results, we observed age-related reductions in BOLD activity during learning from reward in the ventromedial PFC. Furthermore, the model-based fMRI analysis revealed a reduced responsivity of the ventral striatum to reward prediction errors during learning in older than younger adults. This age-related reduction in striatal sensitivity to reward prediction errors may result from a decline in phasic dopaminergic learning signals in the elderly

Repetitive transcranial magnetic stimulation over dorsolateral prefrontal cortex modulates value-based learning during sequential decision-making

Adaptive behavior in daily life often requires the ability to acquire and represent sequential contingencies between actions and the associated outcomes. Although accumulating evidence implicates the role of dorsolateral prefrontal cortex (dlPFC) in complex value-based learning and decision-making, direct evidence for involvements of this region in integrating information across sequential decision states is still scarce. Using a 3-stage deterministic Markov decision task, here we applied offline, inhibitory low-frequency 1-Hz repetitive transcranial magnetic stimulation (rTMS) over the left dlPFC in young male adults (n = 31, mean age = 23.8 years, SD = 2.5 years) in a within-subject cross-over design to study the roles of this region in influencing value-based sequential decision-making. In two separate sessions, each participant received 1-Hz rTMS stimulation either over the left dlPFC or over the vertex. The results showed that transiently inhibiting the left dlPFC impaired choice accuracy, particularly in situations in which the acquisition of sequential transitions between decision states and temporally lagged action-outcome contingencies played a greater role. Estimating parameters of a diffusion model from behavioral choices, we found that the diffusion drift rate, which reflects the efficiency of information integration, was attenuated by the stimulation. Moreover, the effects of rTMS interacted with session: individuals who could not efficiently integrate information across sequential states in the first session due to disrupted dlPFC function also could not catch up in performance during the second session with those individuals who could learn sequential transitions with intact dlPFC function in the first session. Taken together, our findings suggest that the left dlPFC is crucially involved in the acquisition of complex sequential relations and in the potential of such learning

An Architecture for Multi-User Software Development Environments

We present an architecture for multi-user software development environments, covering general, process-centered and rule-based MUSDEs. Our architecture is founded on componentization, with particular concern for the capability to replace the synchronization component - to allow experimentation with novel concurrency control mechanisms - with minimal effects on other components while still supporting integration. The architecture has been implemented in the MARVEL SD

Regulation of Oxidative Stress Response by CosR, an Essential Response Regulator in Campylobacter jejuni

CosR (Campylobacter oxidative stress regulator; Cj0355c) is an OmpR-type response regulator essential for the viability of Campylobacter jejuni, a leading foodborne pathogen causing human gastroenteritis worldwide. Despite importance, the function of CosR remains completely unknown mainly because of cell death caused by its knockout mutation. To overcome this technical limitation, in this study, antisense technology was used to investigate the regulatory function of CosR by modulating the level of CosR expression. Two-dimensional gel electrophoresis (2DGE) was performed to identify the CosR regulon either by suppressing CosR expression with antisense peptide nucleic acid (PNA) or by overexpressing CosR in C. jejuni. According to the results of 2DGE, CosR regulated 32 proteins involved in various cellular processes. Notably, CosR negatively regulated a few key proteins of the oxidative stress response of C. jejuni, such as SodB, Dps, Rrc and LuxS, whereas CosR positively controlled AhpC. Electrophoretic mobility shift assay showed that CosR directly bound to the promoter region of the oxidative stress genes. DNase I footprinting assays identified 21-bp CosR binding sequences in the sodB and ahpC promoters, suggesting CosR specifically recognizes and binds to the regulated genes. Interestingly, the level of CosR protein was significantly reduced by paraquat (a superoxide generator) but not by hydrogen peroxide. Consistent with the overall negative regulation of oxidative stress defense proteins by CosR, the CosR knockdown by antisense rendered C. jejuni more resistant to oxidative stress compared to the wild type. Overall, this study reveals the important role played by the essential response regulator CosR in the oxidative stress defense of C. jejuni

Impact of Cerebral Microbleeds in Stroke Patients with Atrial Fibrillation

OBJECTIVES: Cerebral microbleeds are associated with the risks of ischemic stroke and intracranial hemorrhage, causing clinical dilemmas for antithrombotic treatment decisions. We aimed to evaluate the risks of intracranial hemorrhage and ischemic stroke associated with microbleeds in patients with atrial fibrillation treated with Vitamin K antagonists, direct oral anticoagulants, antiplatelets, and combination therapy (i.e. concurrent oral anticoagulant and antiplatelet) METHODS: We included patients with documented atrial fibrillation from the pooled individual patient data analysis by the Microbleeds International Collaborative Network. Risks of subsequent intracranial hemorrhage and ischemic stroke were compared between patients with and without microbleeds, stratified by antithrombotic use. RESULTS: A total of 7,839 patients were included. The presence of microbleeds was associated with an increased relative risk of intracranial hemorrhage (aHR 2.74, 95% confidence interval 1.76 - 4.26) and ischemic stroke (aHR 1.29, 95% confidence interval 1.04 - 1.59). For the entire cohort, the absolute incidence of ischemic stroke was higher than intracranial hemorrhage regardless of microbleeds burden. However, for the subgroup of patients taking combination of anticoagulant and antiplatelet therapy, the absolute risk of intracranial hemorrhage exceeded that of ischemic stroke in those with 2-4 microbleeds (25 vs 12 per 1,000 patient-years) and ≥11 microbleeds (94 vs 48 per 1,000 patient-years). INTERPRETATION: Patients with atrial fibrillation and high burden of microbleeds receiving combination therapy have a tendency of higher rate of intracranial hemorrhage than ischemic stroke, with potential for net harm. Further studies are needed to help optimize stroke preventive strategies in this high-risk group. This article is protected by copyright. All rights reserved

Developmental Changes in Learning: Computational Mechanisms and Social Influences

Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development

Valence bias in metacontrol of decision making in adolescents and young adults

Metacontrol refers to the human ability to dynamically adapt decision-making strategies to changes in internal and external demands. In this study, we investigated the development of metacontrol from adolescence into young adulthood as well as developmental differences in the sensitivity of metacontrol to framing effects. Adolescents and young adults were assessed with a decision-making task that dissociates model-free and model-based decision-making strategies. In this task, we manipulated outcome magnitude and outcome valence, i.e. the framing of outcomes. With increasing age, we found a greater adaptation of model-based decision making to outcome magnitudes. Model-based decision making was more pronounced for loss compared to gain frames but this framing effect did not differ with age. Our findings suggest that metacontrol continues to develop into young adulthood. While losses generally increase the motivation to invest cognitive resources into an effortful decision-making strategy, the development of metacontrol is not sensitive to framing effects