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Distinct encoding of risk and value in economic choice between multiple risky options.

ABSTRACT: Neural encoding of value-based stimuli is suggested to involve representations of summary statistics, including risk and expected value (EV). A more complex, but ecologically more common, context is when multiple risky options are evaluated together. However, it is unknown whether encoding related to option evaluation in these situations involves similar principles. Here we employed fMRI during a task that parametrically manipulated EV and risk in two simultaneously presented lotteries, both of which contained either gains or losses. We found representations of EV in medial prefrontal cortex and anterior insula, an encoding that was dependent on which option was chosen (i.e. chosen and unchosen EV) and whether the choice was over gains or losses. Parietal activity reflected whether the riskier or surer option was selected, whilst activity in a network of regions that also included parietal cortex reflected both combined risk and difference in risk for the two options. Our findings provide support for the idea that summary statistics underpin a representation of value-based stimuli, and further that these summary statistics undergo distinct forms of encoding.

SUBMITTER: Wright ND

PROVIDER: S-EPMC3734351 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Distinct encoding of risk and value in economic choice between multiple risky options.

Wright Nicholas D ND Symmonds Mkael M Dolan Raymond J RJ

NeuroImage 20130516

Neural encoding of value-based stimuli is suggested to involve representations of summary statistics, including risk and expected value (EV). A more complex, but ecologically more common, context is when multiple risky options are evaluated together. However, it is unknown whether encoding related to option evaluation in these situations involves similar principles. Here we employed fMRI during a task that parametrically manipulated EV and risk in two simultaneously presented lotteries, both of ...[more]

PMID: 23684860

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Project description:BackgroundDecision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial.ObjectivesOur main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA).MethodsWe describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as "treat none" or "treat all patients with a specific treatment" strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo.ResultsWe illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the "treat patients according to the prediction model" strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness.ConclusionsThis novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making.HighlightsDecision curve analysis is extended into a (network) meta-analysis framework.Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials.Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined.This extension of decision curve analysis can be applied to (network) meta-analysis-based prediction models to evaluate their use to aid treatment decision making.

Dataset Information

Distinct encoding of risk and value in economic choice between multiple risky options.

Publications

Distinct encoding of risk and value in economic choice between multiple risky options.

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