Risk tolerance, the degree to which an individual is willing to tolerate risk in order to achieve a greater expected return, influences a variety of financial choices and health behaviors. Here we identify intrinsic neural markers for risk tolerance in a large (n = 108) multimodal imaging dataset of healthy young adults, which includes anatomical and resting-state functional MRI and diffusion tensor imaging. Using a data-driven approach, we found that higher risk tolerance was most strongly associated with greater global functional connectivity (node strength) of and greater gray matter volume in bilateral amygdala. Further, risk tolerance was positively associated with functional connectivity between amygdala and medial prefrontal cortex and negatively associated with structural connectivity between these regions. These findings show how the intrinsic functional and structural architecture of the amygdala, and amygdala-medial prefrontal pathways, which have previously been implicated in anxiety, are linked to individual differences in risk tolerance during economic decision making.

We therefore examined how individual differences in risk tolerance are linked with individual variations in intrinsic functional and structural brain signatures, including resting-state functional connectivity (RSFC), GMV, and WM fiber tract strength, using multimodal, task-independent, brain imaging data in a large sample of healthy young adults (n = 108). We first identified associations between an individual’s risk tolerance and different brain region’s node strength, a graph-theoretical measure of the centrality of a region calculated from RS-fMRI (). This analysis highlighted the bilateral amygdalae, structures previously linked to aspects of risky decision making. To further characterize this association, we tested how RSFC to the amygdala was associated with risk tolerance using seed-based connectivity analysis, which found an association between amygdala-medial prefrontal connectivity and risk tolerance. We then identified further associations between amygdala structure (i.e., GMV) and amygdala-medial prefrontal structural connectivity (i.e., WM tract strength) and risk tolerance. Each of the identified features (structure, structural connectivity, and functional connectivity) explains unique variance in risk tolerance. This series of results identifies a coherent set of relationships between risk tolerances and multiple intrinsic functional and structural features of the amygdala and its connections with medial prefrontal cortex.

There is recent interest, though, in moving beyond predicting choice from simultaneously measured task-evoked brain activation, toward predicting behavior at greater remove, by testing whether task-independent measures of brain structure and function, from anatomical or resting-state functional MRI (RS-fMRI) or diffusion-tensor imaging (DTI), can predict decision-making tendencies (). For instance, increased gray matter volume (GMV) in the right posterior parietal cortex (rPPC) is associated with increased risk tolerance (). Examining multiple neuroimaging modalities together, however, may provide an even better understanding of the complex interplay among brain structure and function and behavior. In a recent example, the preference for positively skewed gambles (lotteries that yield large amounts with small chances) was associated with the coherence (fractional anisotropy [FA]) of the white matter (WM) tract connecting aINS and NAcc (), and activity in the NAcc during choice mediated the link between tract structure and choice behavior. To our knowledge, there have been no similar multimodal imaging investigations of neural markers for basic risk tolerances; identifying such markers is the goal of the present study.

Over the past decade, functional neuroimaging studies have identified multiple brain regions engaged when making decisions involving risk (). Activity in the parietal cortex reflects the probability of outcomes (); activity in medial prefrontal cortex (mPFC) and the ventral striatum (i.e., nucleus accumbens [NAcc]) reflects an integration of the magnitude and probability of rewards for given risky options (); and activity in the anterior insula (aINS), anterior cingulate cortex (ACC), and amygdala reflect the degree of risk or uncertainty (). Furthermore, neural activity, particularly in the NAcc, mPFC, and aINS, predicts the choice that the individual will make ().

To make adaptive choices, decision makers must integrate their beliefs about the possible outcomes of each action with their evaluation of those possible outcomes. However, one challenge that decision makers confront is that there is often uncertainty about what outcomes will result from a given action. A particular form of uncertainty, when information about the probability of each possible outcome is known, is referred to as “risk” (). Examples of risk include the outcomes of a fair coin toss, die roll, or roulette wheel. An individual’s risk tolerance (also referred to as “risk attitude” or “risk preference”), their willingness to accept risk in order to gain a greater expected return, can be measured by assessing preferences between small-but-certain and larger-but-risky rewards (). Understanding individual differences in risk tolerance is important, because risk tolerances are not only associated with financial decisions (e.g., investments, insurance) but also with smoking (), health behaviors (), migration (), self-employment status (), susceptibility to mental illness (), and patients’ attitude to treatment (). Here we examined neural predictors of individual differences in risk tolerance using a multimodal neuroimaging approach.

Associations between patients’ risk attitude and their adherence to statin treatment - a population based questionnaire and register study.

Preference parameters and behavioral heterogeneity: An experimental approach in the Health and Retirement Study.

A pilot examination of stress-related changes in impulsivity and risk taking as related to smoking status and cessation outcome in adolescents.

We also ran linear regressions with each of the measures individually to allow for a comparison of the variance explained by each measure. The amount of variance explained (R 2 ) was 0.070 for node strength alone, 0.188 for RSFC alone, 0.101 for GMV alone, and 0.020 for tract strength alone for left hemisphere (compared to 0.274 for the combined model); these values were 0.065, 0.183, 0.065, and 0.077 for the respective variables in the right hemisphere (compared to 0.273 for the combined model). Though RSFC appears to explain the most variance in risk tolerance, we caution against this interpretation as this measure is the only one that involves a degree of selection (the MPFC region was selected based on the peak RSFC correlation with risk tolerance) rather than using effects calculated in pre-determined ROIs. However, these results do further reinforce the conclusion that RSFC, GMV, and tract strength (in the right hemisphere) account for unique variance in risk tolerance, as the coefficients on these variables does not change dramatically when all of the variables are included in the regression and the total variance explained is near additive when considering these variables separately and together.

Finally, we examined whether these various measures of functional connectivity, structural connectivity, and structure each explain independent or overlapping variance in risk tolerance. We did this by performing a linear regression analysis including all of the functional and structural features of the amygdala for a given hemisphere to explain risk tolerance. Of note, significant features in such a model explain variance in risk tolerances over and above that explained by all other features. This is because the t-statistic for a given independent variable in a multiple regression is proportional to the correlation between the dependent variable and that portion of the independent variable that is uncorrelated with the remaining independent variables (). In a regression analysis for the left hemisphere, RSFC strength and relative GMV (i.e., absolute GMV divided by total intracranial volume) of the left amygdala were significant predictors of risk tolerance (p < 0.05; Table 2 ). In a regression analysis for the right hemisphere, RSFC strength, relative GMV, and tract strength of the right amygdala were significant predictors for risk tolerance (p < 0.05; Table 2 ). These regressions show that each of these different measures account for unique variance in risk tolerances.

Data are given as unstandardized coefficients, B (standard errors). Coefficients significantly different from zero indicated by asterisks: ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001. RSFC, resting-state functional connectivity; GMV, gray matter volume; R 2 , the amount of variance explained by the model.

Summary of Linear Regression Models with Each of the Measures Individually and Together

Table 2 Summary of Linear Regression Models with Each of the Measures Individually and Together

Next, we confirmed that none of our results depend on the specific functional form of risk tolerance that we used. We regressed each of the amygdala features above against (1) risk aversion parameters from two forms of risk-return/mean-variance model, an alternative to the expected utility model () and (2) the percentage of risky choices, as a model-free estimate of risk attitudes. There was a strong correspondence between the risk tolerances estimated from the expected utility model and both the risk aversion parameters estimated from the risk-return models (r = −0.93 and −0.92 for the classic and Weber formulations of risk-return) and the overall percentage of risky choices (r = 0.92). Not surprisingly given this correspondence, all of the amygdala features identified above were also significantly correlated with both the risk aversion parameters from the risk-return models and the overall percentage of risky choices ( Table 1 ).

Summary of Regression Analyses between Each of Brain Measures and Alternative Risk Estimates

Table 1 Summary of Regression Analyses between Each of Brain Measures and Alternative Risk Estimates

Predicting risk sensitivity in humans and lower animals: risk as variance or coefficient of variation.

Given previous reports of a positive relationship between risk tolerance and GMV in the right posterior parietal cortex (rPPC) (), we also conducted an ROI analysis to test whether this previously reported association replicates in the present sample. Consistent with previous studies, this ROI analysis revealed a positive association between rPPC GMV and risk tolerance (r = 0.174, p = 0.078), though this relationship was not significant in our data.

One potential concern is that we may have pursued a different set of connectivity analyses, leading to a different set of findings, had we examined the different imaging modalities in a different order. Reassuringly, though, a whole-brain analysis of GMV also highlights the amygdala, further justifying our focus above on amygdala structural and functional connectivity. A whole-brain analysis revealed that GMV in bilateral amygdalae had the strongest associations with risk tolerance, in the same way that the node strengths of bilateral amygdalae had the strongest associations with risk tolerance ( Figure S1 ). Therefore, regardless of data modality we initially use for data-driven analyses, individuals’ risk tolerance has a stronger association with the functional and structural features of amygdala than with any other brain region.

We then examined the relationship between risk tolerance and amygdala structure. Amygdala GMV in each hemisphere was significantly positively correlated with risk tolerance (left amygdala, r = 0.343, p = 0.0004; right amygdala, r = 0.270, p = 0.0057; Figure 5 ). Greater amygdala GMV was associated with higher risk tolerance.

For illustration purposes, this scatterplot was generated by performing Pearson correlation analysis between residuals after regressing out age, sex, IQ, and total intracranial volume.

We confirmed that the laterality of the structural connectivity effect was not due to using different target ROIs for each hemisphere. The reconstructed WM tract between amygdala and frontal cortex is particularly strong to the ventral part of mPFC (i.e., medial orbitofrontal cortex [OFC]), and the target ROI (mPFC/rectus) defined by RSFC for right amygdala is more ventrally located than that (mPFC/ACC) for left amygdala. To address this issue, we conducted additional analyses using symmetric anatomical ROIs for ventral mPFC, defined according to Automated Anatomical Labeling (AAL) template labels (), including both medial OFC and rectus. This analysis confirmed that risk tolerance had a significant association with only the tract strength between right amygdala and ventral mPFC (r = −0.242, p = 0.013 for right hemisphere; r = −0.132, p = 0.179 for left hemisphere).

Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

There was an inverse relationship between WM tract strength and RSFC. The tract strength between right amygdala and mPFC/rectus exhibited a non-significant negative correlation with RSFC strength between these two regions (r = −0.177, p = 0.070), while the tract strength between left amygdala and mPFC/ACC exhibited a significant negative correlation with RSFC strength between these two regions (r = −0.292, p = 0.002).

Next, based on our RSFC results described above, we examined whether risk tolerance was also associated with structural connectivity between the amygdala and mPFC. We defined mPFC targets from the above seed-based RSFC results and performed probabilistic tractography using diffusion imaging data ( Figure 4 A). The tracts between the amygdala and the mPFC coursed medially through ventral striatal regions, consistent with findings from previous probabilistic tractography studies (; see Figure 4 B for a single subject map identified using deterministic tractography for illustration purposes only). The tract strength between right amygdala and mPFC/rectus was significantly negatively correlated with risk tolerance (r = −0.279, p = 0.004; Figure 4 C). The tract strength between left amygdala and mPFC/ACC was also negatively correlated with risk tolerance (r = −0.140, p = 0.155; Figure 4 D), though this relationship was not significant. Thus, greater right amygdala-mPFC tract strength was associated with lower risk tolerance.

(D) Partial correlation scatterplot between risk tolerance and tract strength from left amygdala to mPFC/ACC. For illustration purposes, (C) and (D) were generated by performing Pearson correlation analysis between residuals after regressing out age, sex, and IQ.

(C) Partial correlation scatterplot between risk tolerance and tract strength from right amygdala to mPFC/rectus.

(B) A single subject map of amygdala to mPFC tract identified using deterministic tractography (for illustration purposes only).

(A) Group probability maps of amygdala to mPFC white matter tracts (blue for left hemisphere; red for right hemisphere) are illustrated in coronal, sagittal, and axial views. For illustration purposes, individual subject’s probabilistic tractography results were transformed into standard space, binarized, and summed across all subjects. Finally, the summed tract images were thresholded to show only overlapping pathways in at least 54 of 108 participants.

Quantitative investigation of connections of the prefrontal cortex in the human and macaque using probabilistic diffusion tractography.

Though node strength gives an overall measure of connectivity, it does not provide anatomic specificity regarding the most important connections driving the association with risk tolerance. We next performed whole-brain seed-based (i.e., seed-to-voxel-based) connectivity analysis to further examine the relationship between amygdala RSFC and risk tolerance. There was significant positive correlation between risk tolerance and RSFC between the left amygdala and mPFC dorsally in the ACC (mPFC/ACC; peak MNI x, y, z coordinate = 3, 9, 27; peak z value = 3.68) and between risk tolerance and RSFC between the right amygdala and mPFC ventrally along the gyrus rectus (mPFC/rectus; x, y, z = −6, 9, −15; z value = 3.73; cluster-forming height threshold of p < 0.001, uncorrected, corrected for multiple comparisons using a cluster extent threshold of p < 0.05; Figure 3 ). The areas of mPFC identified by their connectivity to the left and right amygdala seeds overlapped at a height threshold of p < 0.005 (uncorrected) and cluster size correction to p < 0.05 for multiple comparisons across the whole brain ( Figure 3 ). Greater amygdala-mPFC functional connectivity was associated with higher risk tolerance.

Risk tolerance was significantly positively correlated with RSFC between left amygdala and mPFC/ACC (at a height p < 0.001 [sky] or 0.005 [blue]) and RSFC between right amygdala and mPFC/rectus (at a height p < 0.001 [yellow] or 0.005 [red]). These clusters, identified at a height p < 0.005 and an extent corrected p < 0.05, partially overlapped (orange).

We first tested whether risk tolerance was associated with node strength, a graph theoretic measure of the importance or centrality of a region in the resting-state functional connectivity network. Using a standard whole-brain parcellation, we calculated node strength of each parcel in the RS-fMRI dataset. The node strengths of left and right amygdalae had the highest correlation with risk tolerance (left amygdala, r = 0.265, p = 0.007; right amygdala, r = 0.261, p = 0.007; Figures 2 A, 2B, and 2C ). Greater amygdala node strength was associated with higher risk tolerance. Though these effects survived a false-positive adjustment used in previous studies of network measures (), they did not survive Bonferonni correction. Nonetheless, the fact that the strongest relationship between node strength and risk tolerance was in the amygdala led us to focus on the amygdala in subsequent analyses.

(C) Partial correlation scatterplot between risk tolerance and left (right) amygdala node strength. For illustration purposes, this scatterplot was generated by performing Pearson correlation analysis between residuals after regressing out age, sex, IQ, and mean framewise displacement (motion) in the resting state scan. L, left; R, right; Sup, superior; Inf, inferior; Orb, orbito; Oper, opercular; AMY, amygdala.

(B) Brain regions with the highest correlation coefficients (top 10%) between risk tolerance and node strength, calculated using graph theoretical analysis of resting-state fMRI.

Risk tolerances were assessed using a well-validated task ( Figure 1 A;). One hundred and eight participants (age [mean ± SD], 24.36 ± 4.69 years old; 44 females; risk tolerance α, 0.69 ± 0.31) made 120 binary choices between a certain gain of $20 and a larger-risky reward that varied from trial to trial ( Figure 1 B). We modeled the subjective value (SV) for each option using the functional form for expected utility. In our case, SV = p × Aα, where p and A are the reward probability and amount of winning, respectively, since there is always a 1−p chance of receiving nothing. α is the risk tolerance parameter; larger α values mean increased risk tolerance (see STAR Methods for a detailed explanation).

(B) Risky options used in the task. Each point represents the risky option offered on a single trial. The x axis indicates the reward amount ($) and the y axis indicates the reward probability (%).

(A) A depiction of one trial of the task. Participants chose between a smaller-certain reward (100% chance of $20) and a larger-riskier reward (e.g., 48% chance of $80) for each of 120 trials. The smaller-certain reward was fixed at 100% chance of $20 and the larger-risky reward was varied from trial to trial. Each trial began with the presentation of the risky option; the standard certain option was not shown to simplify the display. When subjects made their choice, a marker indicating that choice (“✔” if the risky option was chosen, “✗” if the certain option was chosen) appeared for 1 s. Subjects had 4 s to make their choice.

Discussion

To identify neural markers of risk tolerance, we examined the relationship between an individual’s risk tolerance and multimodal, context-independent, brain measures, including functional connectivity from RS-fMRI, structural connectivity from DTI, and structural features (specifically, GMV) from T1 anatomical imaging, in a large sample of healthy young adults. We found several intrinsic functional and structural brain markers of risk tolerances. Individuals who were more tolerant of risk showed greater overall connectivity (node strength) of the amygdala, specific increases in RSFC between the amygdala and mPFC, reduced WM tract strength between the amygdala and mPFC, and larger GMV in the amygdala. Of these measures, amygdala-mPFC RSFC and amygdala GMV for each hemisphere made the largest contributions to predicting risk tolerance. These results identify multiple intrinsic structural and functional features of the amygdala that are associated with individual differences in risk tolerance.

Ford and Kensinger, 2014 Ford J.H.

Kensinger E.A. The relation between structural and functional connectivity depends on age and on task goals. Although previously noted in young adults (), the inverse relationship we observed between functional and structural amygdala-mPFC connectivity is not immediately intuitive. One possibility is that more effective communication between the amygdala and mPFC (as indexed by higher RSFC) may depend on pruning the structural connections between the two regions (as indexed by lower probabilistic tract strength). Another possibility is that the two measures may differentially weight amygdala-to-mPFC versus mPFC-to-amygdala projections, and projections of different directionality may play opposite roles in promoting risk tolerance. An important aim for future research should be to understand the undoubtedly complex relationship between RSFC and probabilistic tractography measures.

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Whalen P.J. The inverse relationship between the microstructural variability of amygdala-prefrontal pathways and trait anxiety is moderated by sex. Though most of our brain markers, including node strength, RSFC, and GMV, were significant for both the right and left amygdala, the association between risk tolerance and amygdala-mPFC tract strength was significant only in right hemisphere. Though we did not have hypotheses about hemispheric lateralization, much previous work has suggested potential hemispheric specialization of the human amygdala. Previous studies have argued that the right amygdala is more involved in avoidance behavior and the left in approach, the right is more involved in negative emotions and the left in positive (), the right in formation of emotional memory and the left in retrieval (), and the right in rapid emotional processing and the left in more elaborative (). The lateralization of functional or structural connections between the amygdala and mPFC is less well studied, though two recent studies have found that amygdala-mPFC WM tract strength in the right hemisphere is more strongly associated with anxiety than the left ().

To our knowledge, this is the highest-powered study to date to investigate the multimodal (context-independent) neural markers of risk tolerance. We found that structural features of the amygdala, and functional and structural connectivity between the amygdala and mPFC, predicted risk tolerance. The direction of these associations with risk aversion matches that observed previously between these same markers and anxiety. These results further reinforce a key role for interactions between amygdala and mPFC in value-based decision making, particularly in the context of risk. Based on these results, to the extent that experimental manipulations (such as lesions or brain stimulation) altered functional or structural connectivity between amygdala and mPFC, we would expect that these manipulations also change risk tolerance in the according direction. In addition, if further refined and validated, the biomarkers observed here might someday prove useful in predicting individual differences in risk tolerance and risk-taking behavior.