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Novelty seeking is related to individual risk preference and brain activation associated with risk prediction during decision making.

ABSTRACT: Novelty seeking (NS) is a personality trait reflecting excitement in response to novel stimuli. High NS is usually a predictor of risky behaviour such as drug abuse. However, the relationships between NS and risk-related cognitive processes, including individual risk preference and the brain activation associated with risk prediction, remain elusive. In this fMRI study, participants completed the Tridimensional Personality Questionnaire to measure NS and performed a probabilistic decision making task. Using a mathematical model, we estimated individual risk preference. Brain regions associated with risk prediction were determined via fMRI. The NS score showed a positive correlation with risk preference and a negative correlation with the activation elicited by risk prediction in the right posterior insula (r-PI), left anterior insula (l-AI), right striatum (r-striatum) and supplementary motor area (SMA). Within these brain regions, only the activation associated with risk prediction in the r-PI showed a correlation with NS after controlling for the effect of risk preference. Resting-state functional connectivity between the r-PI and r-striatum/l-AI was negatively correlated with NS. Our results suggest that high NS may be associated with less aversion to risk and that the r-PI plays an important role in relating risk prediction to NS.

SUBMITTER: Wang Y

PROVIDER: S-EPMC4464254 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Novelty seeking is related to individual risk preference and brain activation associated with risk prediction during decision making.

Wang Ying Y Liu Ying Y Yang Lizhuang L Gu Feng F Li Xiaoming X Zha Rujing R Wei Zhengde Z Pei Yakun Y Zhang Peng P Zhou Yifeng Y Zhang Xiaochu X

Scientific reports 20150611

Novelty seeking (NS) is a personality trait reflecting excitement in response to novel stimuli. High NS is usually a predictor of risky behaviour such as drug abuse. However, the relationships between NS and risk-related cognitive processes, including individual risk preference and the brain activation associated with risk prediction, remain elusive. In this fMRI study, participants completed the Tridimensional Personality Questionnaire to measure NS and performed a probabilistic decision making ...[more]

PMID: 26065910

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Project description:BackgroundShared decision-making is the gold standard for good clinical practice, and thus, psychometric instruments have been established to assess patients' generic preference for participation (e.g., the Autonomy Preference Index, API). However, patients' preferences may vary depending on the specific disease and with respect to the specific decision context. With a modified preference index (API-Uro), we assessed patients' specific participation preference in preference-sensitive decisions pertaining to urological cancer treatments and compared this with their generic participation preference.MethodsIn Study 1, we recruited (N = 469) urological outpatients (43.1% urooncological) at a large university hospital. Participation preference was assessed with generic measures (API and API case vignettes) and with the disease-specific API-Uro (urooncological case vignettes describing medical decisions of variable difficulty). A polychoric exploratory factor analysis was used to establish factorial validity and reduce items. In Study 2, we collected data from N = 204 bladder cancer patients in a multicenter study to validate the factorial structure with confirmatory factor analysis. Differences between the participation preference for different decision contexts were analyzed.ResultsStudy 1: Scores on the specific urooncological case vignettes (API-Uro) correlated with the generic measure (r = .44) but also provided incremental information. Among the disease-specific vignettes of the API-Uro, there were two factors with good internal consistency (α ≥ .8): treatment versus diagnostic decisions. Patients desired more participation for treatment decisions (77.8%) than for diagnostic decisions (22%), χ2(1) = 245.1, p ≤ .001. Study 2: Replicated the correlation of the API-Uro with the API (r = .39) and its factorial structure (SRMR = .08; CFI = .974). Bladder cancer patients also desired more participation for treatment decisions (57.4%) than for diagnostic decisions (13.3%), χ²(1) =84, p ≤ .001.ConclusionsThe desire to participate varies between treatment versus diagnostic decisions among urological patients. This underscores the importance of assessing participation preference for specific contexts. Overall, the new API-Uro has good psychometric properties and is well suited to assess patients' preferences. In routine care, measures of participation preference for specific decision contexts may provide incremental, allowing clinicians to better address their patients' individual needs.

Project description:BackgroundRisk prediction models are commonly used in practice to inform decisions on patients' treatment. Uncertainty around risk scores beyond the confidence interval is rarely explored. We conducted an uncertainty analysis of the QRISK prediction tool to evaluate the robustness of individual risk predictions with varying modelling decisions.MethodsWe derived a cohort of patients eligible for cardiovascular risk prediction from the Clinical Practice Research Datalink (CPRD) with linked hospitalisation and mortality records (N =?3,792,474). Risk prediction models were developed using the methods reported for QRISK2 and 3, before adjusting for additional risk factors, a secular trend, geographical variation in risk and the method for imputing missing data when generating a risk score (model A-model F). Ten-year risk scores were compared across the different models alongside model performance metrics.ResultsWe found substantial variation in risk on the individual level across the models. The 95 percentile range of risks in model F for patients with risks between 9 and 10% according to model A was 4.4-16.3% and 4.6-15.8% for females and males respectively. Despite this, the models were difficult to distinguish using common performance metrics (Harrell's C ranged from 0.86 to 0.87). The largest contributing factor to variation in risk was adjusting for a secular trend (HR per calendar year, 0.96 [0.95-0.96] and 0.96 [0.96-0.96]). When extrapolating to the UK population, we found that 3.8 million patients may be reclassified as eligible for statin prescription depending on the model used. A key limitation of this study was that we could not assess the variation in risk that may be caused by risk factors missing from the database (such as diet or physical activity).ConclusionsRisk prediction models that use routinely collected data provide estimates strongly dependent on modelling decisions. Despite this large variability in patient risk, the models appear to perform similarly according to standard performance metrics. Decision-making should be supplemented with clinical judgement and evidence of additional risk factors. The largest source of variability, a secular trend in CVD incidence, can be accounted for and should be explored in more detail.

Project description:ObjectiveAmong young children excessive externalizing behaviors often predict adolescent conduct and substance use disorders. Adolescents with those disorders show aberrant brain function when choosing between risky or cautious options. We therefore asked whether similarly aberrant brain function during risky decision-making accompanies excessive externalizing behaviors among children, hypothesizing an association between externalizing severity and regional intensity of brain activation during risky decision-making.MethodFifty-eight (58) 9-11 year-old children (both sexes), half community-recruited, half with substance-treated relatives, had parent-rated Child Behavior Checklist Externalizing scores. During fMRI, children repeatedly chose between doing a cautious behavior earning 1 point or a risky behavior that won 5 or lost 10 points. Conservative permutation-based whole-brain regression analyses sought brain regions where, during decision-making, activation significantly associated with externalizing score, with sex, and with their interaction.ResultsBefore risky responses higher externalizing scores were significantly, negatively associated with neural activation (t's: 2.91-4.76) in regions including medial prefrontal cortex (monitors environmental reward-punishment schedules), insula (monitors internal motivating states, e.g., hunger, anxiety), dopaminergic striatal and midbrain structures (anticipate and mediate reward), and cerebellum (where injuries actually induce externalizing behaviors). Before cautious responses there were no significant externalizing:activation associations (except in post hoc exploratory analyses), no significant sex differences in activation, and no significant sex-by-externalizing interactions.ConclusionsAmong children displaying more externalizing behaviors extensive decision-critical brain regions were hypoactive before risky behaviors. Such neural hypoactivity may contribute to the excessive real-life risky decisions that often produce externalizing behaviors. Substance exposure, minimal here, was a very unlikely cause.

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Novelty seeking is related to individual risk preference and brain activation associated with risk prediction during decision making.

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Novelty seeking is related to individual risk preference and brain activation associated with risk prediction during decision making.

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