We find evidence for a gene-environment interplay for PTSD and show that genetic influences lose importance when environmental factors cause an extremely high trauma burden to an individual. In the future, it may be important to determine whether the effectiveness of therapeutic interventions in PTSD is also modulated by the SLC6A4 genotype.

Introduction Given rapid global population aging, developing interventions against age‐associated cognitive decline is an important medical and societal goal. We evaluated a cognitive training protocol combined with transcranial direct current stimulation (tDCS) on trained and non‐trained functions in non‐demented older adults. Methods Fifty‐six older adults (65–80 years) were randomly assigned to one of two interventional groups, using age and baseline performance as strata. Both groups performed a nine‐session cognitive training over 3 weeks with either concurrent anodal tDCS (atDCS, 1 mA, 20 minutes) over the left dorsolateral prefrontal cortex (target intervention) or sham stimulation (control intervention). Primary outcome was performance on the trained letter updating task immediately after training. Secondary outcomes included performance on other executive and memory (near and far transfer) tasks. All tasks were administered at baseline, post‐intervention, and at 1‐ and 7‐month follow‐up assessments. Prespecified analyses to investigate treatment effects were conducted using mixed‐model analyses. Results No between‐group differences emerged in the trained letter updating and Markov decision‐making tasks at post‐intervention and at follow‐up timepoints. Secondary analyses revealed group differences in one near‐transfer task: Superior n‐back task performance was observed in the tDCS group at post‐intervention and at follow‐up. No such effects were observed for the other transfer tasks. Improvements in working memory were associated with individually induced electric field strengths. Discussion Cognitive training with atDCS did not lead to superior improvement in trained task performance compared to cognitive training with sham stimulation. Thus, our results do not support the immediate benefit of tDCS‐assisted multi‐session cognitive training on the trained function. As the intervention enhanced performance in a near‐transfer working memory task, we provide exploratory evidence for effects on non‐trained working memory functions in non‐demented older adults that persist over a period of 1 month.

Forward planning is crucial to maximize outcome in complex sequential decision-making scenarios. In this cross-sectional study, we were particularly interested in age-related differences of forward planning. We presumed that especially older individuals would show a shorter planning depth to keep the costs of modelbased decision-making within limits. To test this hypothesis, we developed a sequential decision-making task to assess forward planning in younger (age < 40 years; n = 25) and older (age > 60 years; n = 27) adults. By using reinforcement learning modelling, we inferred planning depths from participants' choices. Our results showed significantly shorter planning depths and higher response noise for older adults. Age differences in planning depth were only partially explained by wellknown cognitive covariates such as working memory and processing speed. Consistent with previous findings, this indicates agerelated shifts away from modelbased behaviour in older adults. In addition to a shorter planning depth, our findings suggest that older adults also apply a variety of heuristical low-cost strategies.

Forward planning is crucial to maximize outcome in complex sequential decision-making scenarios. In this cross-sectional study, we were particularly interested in age-related differences of forward planning. We presumed that especially older individuals would show a shorter planning depth to keep the costs of model-based decision-making within limits. To test this hypothesis, we developed a sequential decision-making task to assess forward planning in younger (age < 40 years; n = 25) and older (age > 60 years; n = 27) adults. By using reinforcement learning modelling, we inferred planning depths from participants' choices. Our results showed significantly shorter planning depths and higher response noise for older adults. Age differences in planning depth were only partially explained by well-known cognitive covariates such as working memory and processing speed. Consistent with previous findings, this indicates age-related shifts away from model-based behaviour in older adults. In addition to a shorter planning depth, our findings suggest that older adults also apply a variety of heuristical low-cost strategies.

Spatial learning can be based on intramaze cues and environmental boundaries. These processes are predominantly subserved by striatal- and hippocampal-dependent circuitries, respectively. Maturation and aging processes in these brain regions may affect lifespan differences in their contributions to spatial learning. We independently manipulated an intramaze cue or the environment’s boundary in a navigation task in 27 younger children (6–8 years), 30 older children (10–13 years), 29 adolescents (15–17 years), 29 younger adults (20–35 years) and 26 older adults (65–80 years) to investigate lifespan age differences in the relative prioritization of either information. Whereas learning based on an intramaze cue showed earlier maturation during the progression from younger to later childhood and remained relatively stable across adulthood, maturation of boundary-based learning was more protracted towards peri-adolescence and showed strong aging-related decline. Furthermore, individual differences in prioritizing intramaze cue- over computationally more demanding boundary-based learning was positively associated with cognitive processing fluctuations and this association was partially mediated by spatial working memory capacity during adult, but not during child development. This evidence reveals different age gradients of two modes of spatial learning across the lifespan, which seem further influenced by individual differences in cognitive processing fluctuations and working memory, particularly during aging.