Comparison of neural correlates of risk decision making between genders: An exploratory fNIRS study of the Balloon Analogue Risk Task (BART)

Cazzell, Mary; Li, Lin; Lin, Zi Jing; Patel, Sonal; Liu, Hanli

doi:10.1016/j.neuroimage.2012.05.030

Cited by 107 publications

(82 citation statements)

References 90 publications

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“…(BART) is a computerized measure of risk-taking behavior, adapted from an original version by Lejuez et al, 2002. The BART has well-established psychometric properties (White et al, 2008) and predictive validity for real-world risk taking (Cazzell et al, 2012). The task includes a series of 20 balloons displayed on the computer screen.…”

Section: Performance-based Risk Takingmentioning

confidence: 99%

Impulsivity and Risk Taking in Bipolar Disorder and Schizophrenia

Reddy

Lee

Davis

et al. 2013

Neuropsychopharmacol

161

112

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Impulsive risk taking contributes to deleterious outcomes among clinical populations. Indeed, pathological impulsivity and risk taking are common in patients with serious mental illness, and have severe clinical repercussions including novelty seeking, response disinhibition, aggression, and substance abuse. Thus, the current study seeks to examine self-reported impulsivity (Barratt Impulsivity Scale) and performance-based behavioral risk taking (Balloon Analogue Risk Task) in bipolar disorder and schizophrenia. Participants included 68 individuals with bipolar disorder, 38 with schizophrenia, and 36 healthy controls. Self-reported impulsivity was elevated in the bipolar group compared with schizophrenia patients and healthy controls, who did not differ from each other. On the risk-taking task, schizophrenia patients were significantly more risk averse than the bipolar patients and controls. Aside from the diagnostic group differences, there was a significant effect of antipsychotic (AP) medication within the bipolar group: bipolar patients taking AP medications were more risk averse than those not taking AP medications. This difference in risk taking because of AP medications was not explained by history of psychosis. Similarly, the differences in risk taking between schizophrenia and bipolar disorder were not fully explained by AP effects. Implications for clinical practice and future research are discussed.

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Section: Performance-based Risk Takingmentioning

confidence: 99%

Impulsivity and Risk Taking in Bipolar Disorder and Schizophrenia

Reddy

Lee

Davis

et al. 2013

Neuropsychopharmacol

161

112

View full text Add to dashboard Cite

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“…Our experiment and data also go beyond previous applications of fNIRS to monetary reward processing (Cazzell et al, 2012) by focusing on the processing of a formally defined risk parameter in steady-state situations rather than wins and losses in ambiguous learning situations (with risk, probabilities are known, with ambiguity they are not). Here we capture risk as variance, in agreement with the standard mean-variance approach used by finance theory but higher-level risk terms, such as skewness, also affect behavior and neural activity (e.g.…”

Section: Comparison Of Fnirs and Fmrimentioning

confidence: 99%

“…So far, the method has not been widely applied in decision-making research within the laboratory. The few studies that used fNIRS in decision-related task paradigms did either not differentiate cortical signals between typical decision parameters, but reported results over whole task durations (Suhr and Hammers, 2010); or focused on non-risk-related decision aspects such as wins and losses (Cazzell et al, 2012), which do not provide insight into individual risk processing. In the present study, regarding the alternatives stated above, we expected that the hemodynamic activity of the lateral prefrontal cortex to high versus low risk measured with fNIRS shows a similar negative linear relation with risk aversion as the activity previously measured with fMRI.…”

Section: Introductionmentioning

confidence: 99%

Comparison of functional near-infrared spectroscopy and electrodermal activity in assessing objective versus subjective risk during risky financial decisions

Holper¹,

Wolf²,

Tobler

2014

NeuroImage

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Risk is an important factor impacting financial decisions. Risk can be processed objectively, e.g. as variance across possible outcomes of a choice option or subjectively, e.g. as value of that variance to a given individual. The aim of the present study was to test the potential of functional near-infrared spectroscopy (fNIRS) in assessing these different ways of processing risk while subjects decided between either high or low risk financial options or a safe (risk-free) option. For comparison we simultaneously measured electrodermal activity (EDA), a well-established method in decision-making research and a core measure of affective processes. FNIRS showed that lateral prefrontal cortex responses to high risk were enhanced relative to low risk only in risk-seeking individuals but reduced relative to low risk in risk-averse individuals. This is in-line with individual-specific risk processing reflecting the subjective value of risk. By contrast, EDA showed enhanced responses to high risk, independent of individual risk attitude, in-line with the notion of objective risk processing. The dissociation between the two measures arose even though they overall were equally sensitive to detect individual risk-related differences and even though there was an increased, risk attitude-independent, temporal coherence between the two measures during high-risk conditions. Our results suggest that hemodynamic responses in lateral prefrontal cortex as measured by fNIRS reflect the subjective value of risk, whereas EDA may index the objective amount of risk people are presented with. The findings suggest that fNIRS could be a useful method for studying risk behavior in financial decisions. AbstractRisk is an important factor impacting financial decisions. Risk can be processed objectively, e.g. as variance across possible outcomes of a choice option or subjectively, e.g. as value of that variance to a given individual. The aim of the present study was to test the potential of functional near-infrared spectroscopy (fNIRS) in assessing these different ways of processing risk while subjects decided between either high or low risk financial options or a safe (risk-free) option. For comparison we simultaneously measured electrodermal activity (EDA), a well-established method in decision-making research and a core measure of affective processes. FNIRS showed that lateral prefrontal cortex responses to high risk were enhanced relative to low risk only in risk-seeking individuals but reduced relative to low risk in risk-averse individuals. This is in-line with individual-specific risk processing reflecting the subjective value of risk. By contrast, EDA showed enhanced responses to high risk, independent of individual risk attitude, in-line with the notion of objective risk processing. The dissociation between the two measures arose even though they overall were equally sensitive to detect individual risk-related differences and even though there was increased, risk attitude-independent, temporal coherence between the two me...

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“…However, the neural data show significantly higher activation in the left and right medial prefrontal cortices when approaching a yellow light when the participants drove with a passenger. Studies have shown these areas to be associated with active risky decision-making (Rao et al, 2008), voluntary decision-making (Cazzell et al, 2012), and response inhibition (Rubia et al, 2003). Thus, despite no outward (simulator-measured) behavioral evidence of specific driving decisions, the data suggest active differences in neural processing during these scenarios.…”

Section: Resultsmentioning

confidence: 83%

“…The regions of interest (ROI) of the brain were determined based on previous studies of functional activation. Since fNIRS measurements is limited to the cortical surface and the limited size of the probes and headgear have additional constraints, in this study the ROI were chosen to encompass the prefrontal cortex (PFC) including the ventrolateral prefrontal cortex (VLPFC), the dorsolateral prefrontal cortex (DLPFC), and the orbitofrontal cortex (OFC), based on their roles in response inhibition (Herrmann et al, 2005), incentive processing and cognitive control (Chein et al, 2011), and risky decision making (Cazzell et al, 2012;Chen et al, 2015). The final headgear included 7 emitters and 7 detectors spaced 3 cm apart, yielding 19 data channels sampled at 50 Hz.…”

Section: Functional Near-infrared Spectroscopy (Fnirs)mentioning

confidence: 99%