Neural mechanisms regulating different forms of risk-related decision-making: Insights from animal models

Over the past 20 years there has been a growing interest in the neural underpinnings of cost/benefit decision-making. Recent studies with animal models have made considerable advances in our understanding of how different prefrontal, striatal, limbic and monoaminergic circuits interact to promote efficient risk/reward decision-making, and how dysfunction in these circuits underlies aberrant decision-making observed in numerous psychiatric disorders. This review will highlight recent findings from studies exploring these questions using a variety of behavioral assays, as well as molecular, pharmacological, neurophysiological, and translational approaches. We begin with a discussion of how neural systems related to decision subcomponents may interact to generate more complex decisions involving risk and uncertainty. This is followed by an overview of interactions between prefrontal-amygdala-dopamine and habenular circuits in regulating choice between certain and uncertain rewards and how different modes of dopamine transmission may contribute to these processes. These data will be compared with results from other studies investigating the contribution of some of these systems to guiding decision-making related to rewards vs. punishment. Lastly, we provide a brief summary of impairments in risk-related decision-making associated with psychiatric disorders, highlighting recent translational studies in laboratory animals.     

Parameter Identification in Uncertain Scalar Conservation Laws Discretized with the Discontinuous Stochastic Galerkin Scheme

We study an identification problem which estimates the parameters of the underlying random distribution for uncertain scalar conservation laws. The hyperbolic equations are discretized with the so-called discontinuous stochastic Galerkin method, i.e., using a spatial discontinuous Galerkin scheme and a Multielement stochastic Galerkin ansatz in the random space. We assume an uncertain flux or uncertain initial conditions and that a data set of an observed solution is given. The uncertainty is assumed to be uniformly distributed on an unknown interval and we focus on identifying the correct endpoints of this interval. The first-order optimality conditions from the discontinuous stochastic Galerkin discretization are computed on the time-continuous level. Then, we solve the resulting semi-discrete forward and backward schemes with the Runge-Kutta method. To illustrate the feasibility of the approach, we apply the method to a stochastic advection and a stochastic equation of Burgers' type. The results show that the method is able to identify the distribution parameters of the random variable in the uncertain differential equation even if discontinuities are present.     

The communication of uncertainty in health: A scoping review

ObjectiveTo conduct a scoping review of existing studies that examine communication strategies that address uncertainty in health and categorize them using the taxonomy of uncertainty.MethodsRelevant articles retrieved from ten databases were categorized according to the dimensions of the taxonomy of uncertainty, and study characteristics were extracted from each article.ResultsAll articles (n = 63) explored uncertainty in the context of probabilistic risk and related to scientific issues (n = 63; 100%). The majority focused on complexity (n = 24; 38.1%) and uncertainty experienced by patients (n = 52; 82.5%). Most utilized quantitative methods (n = 46; 73.0%), hypothetical scenarios (n = 49; 77.8%), and focused on cancer (n = 20; 31.7%). Theory guided messages and study design in fewer than half (n = 27; 42.9%).ConclusionsHeterogeneity in terminology used to refer to different types of uncertainties preclude a unified research agenda on uncertainty communication. Research predominately focuses on probability as the source of uncertainty, uncertainties related to scientific issues, and uncertainty experienced by patients.Practice implicationsAdditional efforts are needed to understand providers’ experience of uncertainty, and to identify strategies to address ambiguity. Future studies should use consistent terminology to allow for coherence and advancement of uncertainty communication scholarship. Continued efforts to refine the existing taxonomy should be undertaken.     

Ethics of uncertainty

Uncertainty is inherent in clinical medicine. However, just because absolute certainty is unachievable does not mean that rational and optimal decisions cannot be made. It is argued that we need to distinguish legitimate from illegitimate scientific uncertainties that are generated by manufacturing doubts aiming to create mis- and disinformation. The attempt to create doubts implies that actions under uncertainties are impossible. Such a belief ultimately harms public, which requires reasoned actions within a context of genuine scientific and medical uncertainties. The latter indicates that rational decisions, even in the absence of guaranteed absolute certainty, are not only possible but, on average, beneficial both for society and individuals.     

Uncertainty management for individuals with Lynch Syndrome: Identifying and responding to healthcare barriers

ObjectiveExamine the uncertainty management process of individuals with Lynch syndrome (LS).Methods19 phone interviews were conducted with individuals with LS. The interview guide included questions on family communication, risk perceptions, and uncertainty management. Data were analyzed using the constant comparison method to code for emergent themes.ResultsQualitative analysis found individuals with LS tried to manage their uncertainty through preventive care, but were often confounded by healthcare barriers. Healthcare barriers included cost and insurance issues, absence of coordinated care, insufficient provider knowledge, and lack of patient-centered communication. Participants reported increased uncertainty and anxiety due to these barriers and used alternative uncertainty management strategies such as advocating for themselves with providers, seeking information online, and communicating with family for emotional support.ConclusionHealthcare barriers identified in this study exacerbated uncertainty and anxiety for individuals with LS and challenged their ability to engage in preventive care. In response, participants used alternative uncertainty management strategies to reduce their uncertainty, which may have unintended negative consequences.Practice implicationsFindings support the need for providers to partner with specialists in genetics and/or LS to better care for individuals with LS. Findings highlight opportunities for interventions in healthcare to better support individuals with LS.     

“Eh – What type of cells are these – flourishing in the liver?” Cancer patients’ disclosure of existential concerns in routine hospital consultations

ObjectiveAdvanced cancer poses a threat to all aspects of being, potentially causing existential suffering. We explore what kind of existential concerns patients with advanced cancer disclose during a routine hospital consultation, and how they communicate such concerns.MethodsWe analyzed thirteen video-recorded hospital consultations involving adult patients with advanced cancer. The study has a qualitative and exploratory design, using procedures from microanalysis of face-to-face-dialogue.ResultsNearly all patients disclosed how the illness experience included losses and threats of loss that are strongly associated with existential suffering, displaying uncertainty about future and insecurity about self and coping. Patients usually disclosed existential concerns uninvited, but they did so indirectly and subtly, typically hiding concerns in biomedical terms or conveying them with hesitation and very little emotion.ConclusionsPatients may have existential concerns they want to address, but they may be uncertain whether these are issues they can discuss with the physician.Practice implicationsHealth professionals should be attentive to underlying existential messages embedded in the patient’s questions and concerns. Acknowledging these existential concerns provides an opportunity to briefly explore the patient’s needs and may direct how the physician tailors information and support to promote coping, autonomy, and existential health.     

Antilogic,a new supervised machine learning software for the automatic interpretation of antibiotic susceptibility testing in clinical microbiology: proof-of-concept on three frequently isolated bacterial species

ObjectiveAntibiotic susceptibility testing (AST) is necessary in order to adjust empirical antibiotic treatment, but the interpretation of results requires experience and knowledge. We have developed a machine learning software that is capable of reading AST images without any human intervention and that automatically interprets the AST, based on a database of antibiograms that have been clinically validated with European Committee on Antimicrobial Susceptibility Testing rules.MethodsWe built a database of antibiograms that were labelled by senior microbiologists for three species: Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. We then developed Antilogic, a Python software based on an original image segmentation module and supervised learning models that we trained against the database. Finally, we blind tested Antilogic against a validation set of 5100 photos of antibiograms.ResultsWe trained Antilogic against a database of 18072 pictures of antibiograms. Overall agreement against the validation set reached 97% (16 855/17 281) regarding phenotypes. The severity rate of errors was also evaluated: 1.66% (287/17 281) were major errors and 0.80% (136/17 281) were very major errors. After implementation of uncertainty quantifications, the rate of errors decreased to 0.80% (114/13 451) and 0.42% (51/13 451) for major and very major errors respectively.DiscussionAntilogic is the first machine learning software that has been developed for AST interpretation. It is based on a novel approach that differs from the typical diameter measurement and expert system approach. Antilogic is a proof of concept that artificial intelligence can contribute to faster and easier diagnostic methods in the field of clinical microbiology.