Predicting risk-taking behavior from prefrontal resting-state activity and personality.
Ontology highlight
ABSTRACT:
SUBMITTER: Studer B
PROVIDER: S-EPMC3792091 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC-personality relations should not be considered independently of gender.
| S-EPMC5997535 | biostudies-literature
Project description:Females demonstrate greater risk aversion than males on a variety of tasks, but the underlying neurobiological basis is still unclear. We studied how theta (4-7 Hz) oscillations at rest related to three different measures of risk taking. Thirty-five participants (15 females) completed the Bomb Risk Elicitation Task (BRET), which allowed us to measure risk taking during an economic game. The Domain-Specific Risk-Taking Scale (DOSPERT) was used to measure self-assessed risk attitudes as well as reward and punishment sensitivities. In addition, the Barratt Impulsiveness Scale (BIS11) was included to quantify impulsiveness. To obtain measures of frontal theta asymmetry and frontal theta power, we used magnetoencephalography (MEG) acquired prior to task completion, while participants were at rest. Frontal theta asymmetry correlated with average risk taking during the game but only in the female sample. By contrast, frontal theta power correlated with risk taking as well as with measures of reward and punishment sensitivity in the joint sample. Importantly, we showed that reward sensitivity mediated a correlation between risk taking and the power of theta oscillations localized to the anterior cingulate cortex. In addition, we observed significant sex differences in source- and sensor-space theta power, risk taking during the game, and reward sensitivity. Our findings suggest that sensitivity to rewards, associated with resting-state theta oscillations in the anterior cingulate cortex, is a trait that potentially contributes to sex differences in risk taking.
| S-EPMC8113640 | biostudies-literature
Project description:Altered activity in decision-making neural circuitry may underlie the maladaptive food choices found in obesity. Here, we aimed to identify the brain regions purportedly underpinning risk-taking behavior in individuals with obesity. Twenty-three adult women with obesity and twenty-three healthy weight controls completed the Risky Gains Task during functional magnetic resonance imaging (fMRI). This task allows participants to choose between a safe option for a small, guaranteed monetary reward and risky options with larger rewards. fMRI analyses comparing losing trials to winning trials found that participants with obesity presented decreased activity in the left anterior insula in comparison to controls (p < 0.05, AlphaSim corrected). Moreover, left insula activation during losses vs. wins was negatively correlated with UPPS-P questionnaire sensation seeking scores. During safe vs. risky trials following a loss, the control group exhibited increased activation in the ventromedial prefrontal cortex (vmPFC) (p < 0.05, AlphaSim corrected) in comparison to the OB group. Moreover, vmPFC response in the obesity group during post-loss trials was negatively correlated with risky choices on the task overall. As a whole, our findings support that diminished tuning of the insula towards interoceptive signals may lead to a lack of input to the vmPFC when weighing the costs and benefits of risky choices.
| S-EPMC6832276 | biostudies-literature
Project description:Who takes risks, and when? The relative state model proposes two non-independent selection pressures governing risk-taking: need-based and ability-based. The need-based account suggests that actors take risks when they cannot reach target states with low-risk options (consistent with risk-sensitivity theory). The ability-based account suggests that actors engage in risk-taking when they possess traits or abilities that increase the expected value of risk-taking (by increasing the probability of success, enhancing payoffs for success or buffering against failure). Adaptive risk-taking involves integrating both considerations. Risk-takers compute the expected value of risk-taking based on their state-the interaction of embodied capital relative to one's situation, to the same individual in other circumstances or to other individuals. We provide mathematical support for this dual pathway model, and show that it can predict who will take the most risks and when (e.g. when risk-taking will be performed by those in good, poor, intermediate or extreme state only). Results confirm and elaborate on the initial verbal model of state-dependent risk-taking: selection favours agents who calibrate risk-taking based on implicit computations of condition and/or competitive (dis)advantage, which in turn drives patterned individual differences in risk-taking behaviour.
| S-EPMC6030535 | biostudies-literature
Project description:Emotion regulation impacts the expected emotional responses to the outcomes of risky decisions via activation of cognitive control strategies. However, whether the regulation of emotional responses to preceding, incidental stimuli also impacts risk-taking in subsequent decisions is still poorly understood. In this study, we investigated the interplay between the regulation of incidentally induced emotional responses and subsequent choice behavior using a risky decision-making task in two independent samples (behavioral and functional magnetic resonance imaging experiment). We found that overall, emotion regulation was followed by less risky decisions, which was further reflected in an increase in activation in brain regions in dorsolateral and ventrolateral prefrontal cortex and cingulate cortex. These findings suggest that altering incidental emotions using reappraisal strategies impacts on subsequent risk-taking in decision-making.
| S-EPMC6970147 | biostudies-literature
Project description:Antisocial personality disorder (ASPD) is closely connected to criminal behavior. A better understanding of functional connectivity in the brains of ASPD patients will help to explain abnormal behavioral syndromes and to perform objective diagnoses of ASPD. In this study we designed an exploratory data-driven classifier based on machine learning to investigate changes in functional connectivity in the brains of patients with ASPD using resting state functional magnetic resonance imaging (fMRI) data in 32 subjects with ASPD and 35 controls. The results showed that the classifier achieved satisfactory performance (86.57% accuracy, 77.14% sensitivity and 96.88% specificity) and could extract stabile information regarding functional connectivity that could be used to discriminate ASPD individuals from normal controls. More importantly, we found that the greatest change in the ASPD subjects was uncoupling between the default mode network and the attention network. Moreover, the precuneus, superior parietal gyrus and cerebellum exhibited high discriminative power in classification. A voxel-based morphometry analysis was performed and showed that the gray matter volumes in the parietal lobule and white matter volumes in the precuneus were abnormal in ASPD compared to controls. To our knowledge, this study was the first to use resting-state fMRI to identify abnormal functional connectivity in ASPD patients. These results not only demonstrated good performance of the proposed classifier, which can be used to improve the diagnosis of ASPD, but also elucidate the pathological mechanism of ASPD from a resting-state functional integration viewpoint.
| S-EPMC3625191 | biostudies-literature
Project description:BackgroundPrefrontal subregions, including the ventromedial prefrontal cortex (PFC), dorsomedial PFC, and dorsolateral PFC (DLPFC), are differentially implicated in the pathophysiology of posttraumatic stress disorder (PTSD), though few existing studies have examined subregional differences in resting-state functional connectivity (rsFC). We hypothesized that PTSD would involve weaker positive rsFC between ventromedial PFC, dorsomedial PFC, and other default mode network regions and increased negative rsFC between DLPFC and posterior default mode network regions. Additionally, we hypothesized that prefrontal regions exhibiting group differences in rsFC would be characterized by alterations in cortical thickness.MethodsParticipants included 36 healthy control subjects, 30 trauma-exposed control subjects, and 21 individuals with current DSM-IV PTSD resulting from community-acquired trauma. Participants completed the Clinician Administered PTSD Scale, questionnaires (Childhood Trauma Questionnaire, Adverse Childhood Events, Life Events Checklist, Beck Depression Inventory), structural neuroimaging, and resting-state functional magnetic resonance imaging. rsFC of DLPFC, ventromedial PFC, and dorsomedial PFC seeds was evaluated in SPM12 and CONN. Cortical thickness for regions with significant rsFC findings was assessed using FreeSurfer.ResultsRelative to both healthy control and trauma-exposed control subjects, individuals with PTSD showed increased negative rsFC between the DLPFC and a region of precuneus. This finding was associated with increased overall symptom severity but not with trauma load or childhood trauma exposure. Greater negative DLPFC-precuneus connectivity was associated with greater bilateral precuneus thickness.ConclusionsGiven participation of precuneus subregions in the central executive network, increased anticorrelation between right DLPFC and precuneus in this sample may reflect increased opposition between anterior and posterior central executive network hubs in PTSD.
| S-EPMC6447465 | biostudies-literature
Project description:Dispositional greed, characterized by the insatiable hunger for more and the dissatisfaction for not having enough, has often been associated with heightened impulsivity and excessive risk-taking. Despite its far-reaching implications in social sciences and economics, however, the exact neural mechanisms of how greed personality influences risk-taking are still ill understood. In the present study, we showed the correlation between subject's greed personality trait (GPT) score and risk-taking was selectively mediated by individual's loss aversion, but not risk attitude. In addition, our neuroimaging results indicated that gain and loss prospects were jointly represented in the activities of the ventral striatum and medial orbitofrontal cortex (mOFC). Furthermore, mOFC responses also encoded the neural loss aversion signal and mediated the association between individual differences in GPT scores and behavioral loss aversion. Our findings provide a basis for understanding the specific neural mechanisms that mediate the effect of greed personality trait on risk-taking behavior.
| S-EPMC6506209 | biostudies-literature
Project description:Smartphones enjoy high adoption rates around the globe. Rarely more than an arm's length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals' Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain ([Formula: see text] = 0.37) and narrow facet levels ([Formula: see text] = 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals' private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.
| S-EPMC7395458 | biostudies-literature
Project description:The DOSPERT, developed by Weber, Blais and Betz, can be used to measure risk behaviors in a variety of domains. We investigated the use of this scale in China. The participants were 1144 undergraduate students. After we removed some items that were not homogeneous, a principal component analysis extracted six components that accounted for 44.48% of the variance, a value similar to that obtained in the analysis conducted by Weber et al. Chinese undergraduates scored higher on the investment subscale compared with the results of Weber's study. The analysis of individual differences indicated that there was a significant gender difference in the ethical, investment and health/safety subscales, where males scored significantly higher than females. The type of home location was also significant on the ethical and health/safety subscales, where undergraduates from the countryside scored lower than undergraduates from cities and towns on the ethical subscale, and undergraduates from towns scored higher than those from other two areas on the health/safety subscale. Male undergraduates from towns scored higher than male undergraduates from other areas on the gambling subscale.
| S-EPMC4024034 | biostudies-other