Brown, C. A. | Search bioRxiv

bioRxiv2019

Skeletal and Dental Development Preserve Evidence of Energetic Stress in the Moose of Isle Royale

Food shortages can leave diagnostic, and in the case of the dentition, irreversible changes in mineralized tissue that persist into historical and fossil records. Consequently, developmental defects of tooth enamel might be used to track ungulate population irruption but dental tissues capacity for preserving historical population density changes has yet to be investigated in wild populations. We test the ability of enamel defects, mandible and metapodial lengths to track changes in the well-known fluctuating moose population of Isle Royale National Park. Our study demonstrates that 1) a moose density threshold exists on the island above which there is a significant decrease in mandible and metatarsus length and a concomitant increase in enamel hypoplasias, 2) food limitation has a more pronounced effect on male than female skeletal growth, and 3) combined data from tooth enamel hypoplasias and bone lengths reflect the relative density of this ungulate population, and should be broadly applicable to other ungulate osteological samples. Developmental defects in dental enamel were among the highest recorded in a wild population, and even during low-density intervals the population density of Isle Royale moose has been high enough to negatively impact skeletal and dental growth, indicating the comparatively poor health of this century-old ecosystem

ecology↗

bioRxiv2019

Sub-Optimal Learning of Tactile-Spatial Predictions in Patients with Complex Regional Pain Syndrome

In Complex Regional Pain Syndrome (CRPS), tactile sensory deficits have motivated the therapeutic use of sensory discrimination training. However, the hierarchical organisation of the brain is such that low-level sensory processing can be dynamically influenced by higher-level knowledge, e.g. knowledge learnt from statistical regularities in the environment. It is unknown whether the learning of such statistical regularities is impaired in CRPS. Here, we employed a hierarchical Bayesian model of predictive coding to investigate statistical learning of tactile-spatial predictions in CRPS. Using a sensory change-detection task, we manipulated bottom-up (spatial displacement of a tactile stimulus) and top-down (probabilistic structure of occurrence) factors to estimate hierarchies of prediction and prediction error signals, as well as their respective precisions or reliability. Behavioural responses to spatial changes were influenced by both the magnitude of spatial displacement (bottom-up) and learnt probabilities of change (top-down). The Bayesian model revealed that patients predictions (of spatial displacements) were found to be less precise, deviating further from the ideal (statistical optimality) compared to healthy controls. This imprecision was less context-dependent, i.e. more enduring across changes in probabilistic context and less finely-tuned to statistics of the environment. This caused greater precision on prediction errors, resulting in predictions that were driven more by momentary spatial changes and less by the history of spatial changes. These results suggest inefficiencies in higher-order statistical learning in CRPS. This may have implications for therapies based on sensory re-training whose effects may be more short-lived if success depends on higher-order learning.

neuroscience↗