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BACKGROUND: Poor sleep and anxiety disorders are highly comorbid in youth, and each predicts altered ventral striatum (VS) response to rewards, which may impact mental health risk. Contrasting evidence suggests previously reported negative associations between sleep health and VS response may be stronger or weaker in youth with anxiety, indicating sensitivity to win/loss information or blunted reward processing, respectively. We cross-sectionally examined the role of sleep in VS response to rewards among youth with anxiety versus a no-psychiatric-diagnosis comparison (ND) group. We expected a group*sleep interaction on VS response to rewards but did not hypothesize directionality. METHODS: As part of the pretreatment battery for a randomized clinical trial, 74 youth with anxiety and 31 ND youth (ages 9-14 years; n = 55 female) completed a monetary reward task during fMRI. During the same pretreatment window, actigraphy and diary-estimated sleep were collected over 5 days, and participants and their parents each reported participants total sleep problems. We examined group*sleep interactions on VS response to monetary rewards versus losses via three mixed linear models corresponding to actigraphy, diary, and questionnaires, respectively. RESULTS: Each model indicated group*sleep interactions on VS response to rewards. Actigraphy and diary-estimated time awake after sleep onset predicted reduced VS response in youth with anxiety but not ND youth. Parent-reported sleep problems similarly interacted with group, but simple slopes were nonsignificant. CONCLUSIONS: Wake after sleep onset was associated with blunted reward response in youth with anxiety. These data suggest a potential pathway through which sleep could contribute to perturbed reward function and reward-related psychopathology (e.g., depression) in youth with anxiety.

Remembering sequences of events defines episodic memory, but retrieval can be driven by both ordinality and temporal contexts. Whether these modes of retrieval operate at the same time or not remains unclear. Theoretically, medial prefrontal cortex (mPFC) confers ordinality, while the hippocampus (HC) associates events in gradually changing temporal contexts. Here, we looked for evidence of each with BOLD fMRI in a sequence task that taxes both retrieval modes. To test ordinal modes, items were transferred between sequences but retained their position (e.g., AB3). Ordinal modes activated mPFC, but not HC. To test temporal contexts, we examined items that skipped ahead across lag distances (e.g., ABD). HC, but not mPFC, tracked temporal contexts. There was a mPFC and HC by retrieval mode interaction. These current results suggest that the mPFC and HC are concurrently engaged in different retrieval modes in support of remembering when an event occurred.

Mounting evidence suggests that the medial temporal lobe (MTL) and striatal learning systems support different forms of learning, which can be competitive or cooperative depending on task demands. We have previously shown how activity in these regions can be modulated in a conditional visuomotor associative learning task based on the consistency of response mappings or reward feedback (Mattfeld & Stark, 2015). Here, we examined the shift in learning towards the MTL and away from the striatum by placing strong demands on pattern separation, a process of orthogonalizing similar inputs into distinct representations. Mnemonically, pattern separation processes have been shown to rely heavily on processing in the hippocampus. Therefore, we predicted modulation of hippocampal activity by pattern separation demands, but no such modulation of striatal activity. Using a variant of the conditional visuomotor associative learning task that we have used previously, we presented participants with two blocked conditions: items with high and low perceptual overlap during functional magnetic resonance imaging (fMRI). As predicted, we observed learning-related activity in the hippocampus, which was greater in the high than the low overlap condition, particularly in the dentate gyrus. In contrast, the associative striatum also showed learning related activity, but it was not modulated by overlap condition. Using functional connectivity analyses, we showed that the correlation between the hippocampus and dentate gyrus with the associative striatum was differentially modulated by high vs. low overlap, suggesting that the coordination between these regions was affected when pattern separation demands were high. These findings contribute to a growing literature that suggests that the hippocampus and striatal network both contribute to the learning of arbitrary associations that are computationally distinct and can be altered by task demands.

This roundtable discussion brings together four prominent scholars from equine studies (Susanna Forrest, Richard Nash, Karen Raber, and Jeannette Vaught) to discuss how we have historically shaped the equine body (and mind) when we breed and train horses, and how taking this history into account might allow us to imagine new ways of living with horses. Three core concerns are addressed from multidisciplinary perspectives: How do human representations, desires, and fantasies inform our interactions with horses? How have equine bodies and minds been shaped by us over time? How do the stories we tell ourselves about equine bodies in turn shape how we relate to horses—and how might we do this differently?

The availability of various services for individual mobility is increasing, especially in urban areas. Dynamic ride-hailing services address these aspects and are gaining market share with providers such as MOIA, UberX Share, Sprinti or BerlKönig. To be able to offer competitive pricing for such a service and at the same time provide a high service quality (e.g. fast response times), effective capacity management is needed. In order to reach this goal, two challenges have to be met by the service provider. On the one hand, a proper demand control has to be installed, which optimizes the responses to transportation requests from customers. On the other hand, suitable fleet control needs to be set in place to optimize the route of the fleet so that the demand can be met. Papers in the literature do solve both but typically focus on one of these two challenges. As an example, value function approximation (VFA) can be used to learn a service offering decision while anticipating future incoming requests. A typical example of a routing-focused method is the multiple scenario approach (MSA) creating a routing which anticipates future requests using a sampling method. In this paper, we combine VFA and MSA to address the two challenges in an effective way. The resulting method is called anticipatory-routing-and-service-offering (ARS). We find that the combined method significantly outperforms the individual components, improving not only the total reward but also the accepted requests. It is found that this performance is particularly high with a heavy workload and thus resources are relatively scarce. We analyse how and under which conditions the components together or individually are particularly important.

Statistical models employed to test for group differences in quantized diffusion-weighted MRI white matter tracts often fail to account for the large number of data points per tract in addition to the distribution, type, and interdependence of the data. To address these issues, we propose the use of Generalized Additive Models (GAMs) and supply code and examples to aid in their implementation. Specifically, using diffusion data from 73 periadolescent clinically anxious and no-psychiatric-diagnosis control participants, we tested for group tract differences and show that a GAM allows for the identification of differences within a tract while accounting for the nature of the data as well as covariates and group factors. Further, we then used these tract differences to investigate their association with performance on a memory test. When comparing our high versus low anxiety groups, we observed a positive association between the left uncinate fasciculus and memory overgeneralization for negatively valenced stimuli. This same association was not evident in the right uncinate or anterior forceps. These findings illustrate that GAMs are well-suited for modeling diffusion data while accounting for various aspects of the data, and suggest that the adoption of GAMs will be a powerful investigatory tool for diffusion-weighted analyses.

The hippocampus and striatum are thought to have different functional roles in learning and memory. It is unknown under what experimental conditions their contributions are dissimilar or converge, and the extent to which they interact over the course of learning. In order to evaluate both the functional contributions of as well as the interactions between the human hippocampus and striatum, the present study used high-resolution functional magnetic resonance imaging (fMRI) and variations of a conditional visuomotor associative learning task that either taxed arbitrary associative learning (Experiment 1) or stimulus-response learning (Experiment 2). In the first experiment, we observed changes in activity in the hippocampus and anterior caudate that reflect differences between the two regions consistent with distinct computational principles. In the second experiment, we observed activity in the putamen that reflected content specific representations during the learning of arbitrary conditional visuomotor associations. In both experiments, the hippocampus and ventral striatum demonstrated dynamic functional coupling during the learning of new arbitrary associations, but not during retrieval of well-learned arbitrary associations using control variants of the tasks that did not preferentially tax one system versus the other. These findings suggest that both the hippocampus and subregions of the dorsal striatum contribute uniquely to the learning of arbitrary associations while the hippocampus and ventral striatum interact over the course of learning.