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. Author manuscript; available in PMC: 2022 Feb 1.
Published in final edited form as: Cogn Affect Behav Neurosci. 2021 Aug 3;22(1):123–133. doi: 10.3758/s13415-021-00937-2

Adolescent novelty seeking is associated with greater ventral striatal and prefrontal brain response during evaluation of risk and reward

Amanda C Del Giacco 1, Scott A Jones 1, Angelica M Morales 1, Dakota Kliamovich 2, Bonnie J Nagel 1,2
PMCID: PMC8792307  NIHMSID: NIHMS1762831  PMID: 34342865

Abstract

Adolescence is a period during which reward sensitivity is heightened. Studies suggest that there are individual differences in adolescent reward-seeking behavior, attributable to a variety of factors, including temperament. This study investigated the neurobiological underpinnings of risk and reward evaluation as they relate to self-reported pleasure derived from novel experiences on the revised Early Adolescent Temperament Questionnaire (EATQ-R). Healthy participants (N = 265, ~50% male), aged 12–17 years, underwent functional magnetic resonance imaging during a modified Wheel of Fortune task, where they evaluated choices with varying probability of winning different monetary rewards. Across all participants, there was increased brain response in salience, reward, and cognitive control circuitry when evaluating choices with larger (compared with moderate) difference in risk/reward. Whole brain and a priori region-of-interest regression analyses revealed that individuals reporting higher novelty seeking had greater activation in bilateral ventral striatum, left middle frontal gyrus, and bilateral posterior cingulate cortex when evaluating the choices for largest difference in risk/reward. These novelty seeking associations with brain response were seen in the absence of temperament-related differences in decision-making behavior. Thus, while heightened novelty seeking in adolescents might be associated with greater neural sensitivity to risk/reward, accompanying increased activation in cognitive control regions might regulate reward-driven risk-taking behavior. More research is needed to determine whether individual differences in brain activation associated with novelty seeking are related to decision making in more ecologically valid settings.

Keywords: Adolescence, Temperament, fMRI, Individual differences, Reward, Risk-taking

Introduction

Adolescence is a developmental period during which novelty seeking and reward sensitivity are heightened for many youth. Neurodevelopmental models partially explain adolescent propensity to seek out novel and rewarding experiences by the mismatch in developmental timing between early development of bottom-up, reward processing regions and the protracted development of top-down, regulatory control brain networks (Casey et al., 2008; Steinberg et al., 2008). According to the 2017 Youth Risk Behavior Survey, 74% of all deaths among persons aged 10–24 years resulted from motor vehicle crashes, unintentional injuries, suicide, or homicide, suggesting that risky behavior may play a role in mortality rates in this age range (Kann et al., 2018). That said, there is substantial individual variability in both behavioral and brain development during this time. Understanding neuromaturational changes during adolescence, as they relate to the processing and assessment of varying levels of risk and reward, may provide insight into which individuals may be more susceptible to engaging in health-risk behaviors (Bjork & Pardini, 2015).

Neuroimaging research has illustrated associations between adolescent risk-taking behaviors and activation of reward and cognitive control circuitry, including the ventral striatum (VS), insula, posterior cingulate cortex (PCC), and prefrontal cortex (PFC) (see meta-analysis, Silverman et al., 2015). While VS activation is typically greater in response to rewards during adolescence (Barkley-Levenson & Galván, 2014; Braams et al., 2015; Galvan, 2010), a recent study found no association between striatal response to reward receipt and risk-taking propensity (Demidenko et al., 2020). Furthermore, it remains unclear what role cognitive control regions of the brain (e.g., PFC) play in assessing risk and reward. Literature on the directionality of neural activation in these regions is inconsistent (see meta-analysis, Crone & Dahl, 2012), with both lesser (Blankenstein et al., 2018; Gianotti et al., 2009) and greater (Qu et al., 2015) prefrontal cognitive control activation reported as a function of self-reported risk-taking tendencies. Disparities in the association between brain response and adolescent risk taking might be due to the heterogeneous ways in which risk taking is measured and brain analyses are conducted (for review, Bjork, 2020; Sherman et al., 2018). As such, using whole-brain models to measure reward evaluation during a decision-making task (as opposed to reward anticipation or response) and understanding how brain response during decision making relates to individual differences, such as those related to temperament, might help clarify past contradictory findings and provide novel information regarding neurobiological markers associated with risk and reward assessment in adolescents.

Novelty seeking may be an important temperamental trait to measure, due to its potential relationship with adolescent engagement in risk-taking behaviors. A longitudinal study found that individual changes in sensation seeking (another temperamental trait very closely related to novelty seeking) from late childhood to adolescence were positively associated with individual variability in risk-taking behaviors (Harden et al., 2012). A recent study also found that reward-related VS activation predicted individual variability in sensation seeking at 3-year follow-up, and directionality of the relationship varied as a function of age (Hawes et al., 2017). Others have shown that individuals reporting greater sensation seeking showed greater reward-related brain activation than those with lower reported sensation seeking in the insula and PFC, suggesting the potential for greater neural sensitivity to reward in relation to such traits (Cservenka et al., 2013). Despite evidence suggesting that temperamental traits are associated with reward response and behaviors, understanding how individual differences relate to neural activity during evaluation of varying potential for risk and reward remains understudied.

This study examined the association between the temperamental trait of novelty seeking and brain activation while evaluating varying levels of risk and reward in youth using a modified Wheel of Fortune task (WOF). Previous studies investigating adolescent risk taking and brain function have focused primarily on neural activation associated with decision-making behavior (Jones et al., 2016; van Duijvenvoorde et al., 2014). Assessing brain activation during the evaluation of varying levels of risk and reward, irrespective of the decision made or the outcome achieved, is unhindered by participant behavior and may help to further disentangle individual differences (Blankenstein et al., 2018; Morales et al., 2018), particulary given that a previous study utilizing this task found no association between sensation seeking and risk-taking behavior (Cservenka et al., 2013). In light of evidence for adolescent hyper-responsivity to reward and associations with temperament (Cservenka et al., 2013; Hawes et al., 2017), we hypothesized that greater novelty seeking would be associated with greater activation during trials of higher reward potential in reward regions of the brain (e.g., VS). While we expected associations between novelty seeking and PFC response during risk/reward evaluation, considering the mixed findings on the role of the PFC in risk and reward tasks (Blankenstein et al., 2018; Qu et al., 2015; van Duijvenvoorde et al., 2015), we did not hypothesize directionality of the effect. The current study provides insight into how individual differences in neurobiology relate to risk/reward assessment and novelty seeking in adolescence which may help to identify phenotypes related to future health-risk or adaptive behaviors.

Methods

Participant recruitment, characteristics, and general procedures

Participants included 265 healthy adolescents, aged 12–17 years (n = 125 assigned females at birth), recruited from an ongoing community study on adolescent neurodevelopment (Jones et al., 2016; Morales et al., 2018) approved by the Oregon Health & Science University (OHSU) Institutional Review Board. Adolescents and their parents provided written assent and consent and completed comprehensive eligibility screening. Exclusionary criteria included probable diagnosis of a DSM-IV psychiatric disorder (i.e., exceeding diagnostic cutoffs on the National Institute of Mental Health Diagnostic Interview Schedule for Children Predictive Scales - DPS) (Lucas et al., 2001), serious medical or developmental problems (e.g. head trauma or intellectual disability), biological parent with schizophrenia or bipolar I, parent-reported prenatal exposure to drugs or alcohol, MRI contraindications (e.g., irremovable metal in the body), or left handedness (Oldfield, 1971). Adolescents also were excluded from the study if drug and alcohol use exceeded >10 lifetime alcoholic beverages, >2 drinks on one occasion, >10 lifetime uses of marijuana, or >4 lifetime uses of cigarettes (Brown et al., 1998). Intellectual functioning was measured using the two-subtest version of the Wechsler Abbreviated Scale of Intelligence (Wechsler, 2011), pubertal development was assessed using self-report on the Pubertal Development Scale (Petersen et al., 1988), and socioeconomic status was estimated using parent self-report on the Hollingshead Index of Social Position (Hollingshead, 1957).

Assessment of novelty seeking

Novelty seeking was assessed using the high-intensity pleasure subscale from the revised version of the Early Adolescent Temperament Questionnaire (EATQ-R) (Capaldi & Rothbart, 1992; Rothbart, 2001). Each item of this scale used a 5-point Likert-type rating (1 = “almost never true”; 5 = “almost always true”). Higher scores reflect greater pleasure derived from novel experiences (e.g., “I enjoy going to places where there are big crowds and a lot of excitement”), otherwise referred to as novelty seeking (Capaldi & Rothbart, 1992; Muris & Meesters, 2009). The reliability index for this scale has a Cronbach’s alpha of 0.71 (Muris & Meesters, 2009).

Risk and reward evaluation

To assess the neurobiological underpinnings of risk and reward evaluation, participants completed a modified version of the WOF task during functional MRI (Cservenka et al., 2012; Ernst et al., 2004). The task consisted of three phases: evaluation/selection, anticipation, and feedback (Supplemental Figure 1). In the evaluation/selection phase, participants viewed a wheel that provided a visual illustration of the percentage associated with each of the two options for monetary reward available on that trial. More specifically, the three conditions in the evaluation/selection phase included: a wheel with a large difference in potential risk and reward between competing options (probability of winning $7 is 10% vs. $1 is 90%); a wheel with a moderate difference in potential risk and reward between competing options (probability of winning $2 is 30% vs. $1 is 70%); and a wheel with no difference in potential risk and reward between competing options (probability of winning $2 is 50% vs. $2 is 50%). Participants were asked to select the portion of the wheel they believed would win them the most money, because at the end of the task they would receive a portion of their earnings. The anticipation phase required participants to answer how sure they were in winning the preceding trial on a 1–3 scale. Although the evaluation/selection phase and anticipation phase both visually depict the same proportions of the wheel associated with winning and losing, only the evaluation/selection phase displayed the reward amounts associated with the shaded portions of the wheel. That is, the evaluation/selection phase was the first and only time all information required for choice selection was presented. The feedback phase indicated on the screen whether they had won that trial or not and displayed the participant’s cumulative winning dollar amount up through that trial. Participants won the monetary reward if their choice for a given trial matched that of a predetermined probability.

A total of 72 task trials were presented over two 10-minute runs. Each of the runs consisted of 12 trials with large differences between risk/reward, 14 trials with moderate differences between risk/reward and 10 trials with equal risk/reward. In total, trials were 10.5 s long, and consisted of an evaluation/selection phase (3 s), anticipation phase (3.5 s), and feedback phase (4 s), with intertrial fixation jitter between 1 and 11 s. In an effort to match the brain analysis, a behavioral contrast was generated by taking the difference between the percentage of $7 (risky) choices on the 10/90 wheel (number of $7 selections/total number of 10/90 trials) minus the percentage of $2 (risky) choices on the 30/70 (number of $2 selections/total number of 30/70 trials).

Scan acquisition

Participants were scanned on a 3.0 Tesla Siemens Magnetom Tim Trio scanner at OHSU’s Advanced Imaging Research Center. Each subject underwent a high-resolution, T1-weighted anatomical MPRAGE structural scan (160 slices, time to repetition [TR] = 2,300 ms, time to echo [TE] = 3.58 ms, inversion time [TI] = 900 ms, flip angle = 10°, voxel size 1×1× 1.1 mm, acquisition time = 9:14) acquired in the sagittal plane. Blood-oxygen-level-dependent (BOLD) signal during the WOF task was assessed using a functional T2*-weighted gradient echo-planar imaging (EPI) sequence collected in axial slices parallel to the anterior-posterior commissure line (33 slices, TR = 2,000 ms, TE = 30 ms, flip angel = 90°, field of view [FOV] = 240 mm2, voxel size = 3.75 × 3.75 × 3.8 mm, acquisition time = 10 min per run).

fMRI preprocessing

Analysis of Function NeuroImages (AFNI, 19.0.26) software was implemented for preprocessing (Cox, 1996), via methods described previously for this task (Morales et al., 2018). Functional data preprocessing included visual inspection for scanner-related artifacts, slice time correction, motion-correction via alignment of all of the TRs to the volume requiring the least amount of adjustment, co-registration of the BOLD image to the high-resolution structural image, and linear and nonlinear registration to standard Talairach space. In an effort to limit interpolation error, all spatial transformations, including motion correction, co-registration, and registration to standard space were applied to the BOLD images via a one-step registration process. Data were then smoothed using a 6-mm Gaussian kernel, percent signal change was calculated for each individual run, and the two WOF runs were concatenated prior to running the first-level model. Average percent signal change from the contrast images (described below) were resampled to 3 mm3 voxels before group-level analyses.

First-level modeling

To model brain response during the WOF task, all phases of the task (evaluation/selection, anticipation, and feedback phases) were included as regressors in the first-level model. Event onset times for each phase of the task were defined by the designated stimulus onset times. The duration of the event was coded as the length of each phase convolved with a gamma-variate hemodynamic response function (Cohen, 1997). For the evaluation/selection phase, separate regressors of interest were created for each wheel type (10/90, 30/70, and 50/50), regardless of participants’ choices. To measure brain response during evaluation of greater versus moderate differences in potential for risk and reward, the primary contrast of interest for this study was BOLD response during 10/90 trials (largest difference/potential for reward) versus 30/70 trials (moderate difference/potential for reward). The individual-level model also included linear drift and six motion parameters. Furthermore, to remove potential motion-related artifacts, if framewise displacement exceeded 0.7 mm, those volumes were censored, as were any uncensored segments of the data with fewer than five contiguous frames (Siegel et al., 2014).

Analytic strategy

Demographic variables were analyzed using R (version 4.0.2). Demographic data were visually inspected for normality, and non-parametric tests were performed as needed. Correlation analyses were used to assess the relationships between key covariates, including age, IQ, socioeconomic status, and pubertal development and novelty seeking scores. The relationship between sex assigned at birth and novelty seeking was examined with an independent-samples t-test. The association between novelty seeking and decision making was evaluated using the behavioral contrast between the percentage of risky selections on the 10/90 wheel minus the percentage of risky selections on the 30/70 wheel (as described above). Then, linear regression was used to examine the association between novelty seeking and decision making, while controlling for age, sex assigned at birth, and IQ.

To investigate the association between novelty seeking and VS activity in the large versus moderate risk/reward condition, we generated an a priori bilateral VS standard-space mask from the Oxford-GSK-Imanova structural and connectivity striatal atlas (Tziortzi et al., 2013). Then, brain response for the 10/90 vs. 30/70 trials was extracted for every subject, and a regression analysis was performed to assess the relationship between novelty seeking scores and brain response in this region, controlling for participant age, sex assigned at birth, and IQ.

Given the heterogeneity of the spatial localization of clusters and function of the PFC associated with risk and reward assessment (Crone & Dahl, 2012; Romer et al., 2017), a whole-brain analysis was conducted to identify regions where there were differences in larger versus moderate potential for risk/reward conditions across the entire sample, and then to identify regions where novelty seeking was associated with differential brain response during the larger versus moderate potential for risk/reward conditions. Whole-brain analysis was performed in a standardized gray matter mask to remove the potential for false findings in the white matter and ventricles and to reduce the number of voxel-wise comparisons. Analyses were run using AFNI’s 3dttest++ to identify regions where there was significant activation/deactivation in the 10/90 vs. 30/70 evaluation/selection phase contrast and to assess the association between novelty seeking scores and brain response during 10/90 vs. 30/70 evaluation/selection phase, controlling for age, sex assigned at birth, and IQ with a voxel-wise threshold of p < 0.001. Nonparametric permutation testing (via the-Clustsim flag) determined the number of voxels needed for cluster-level significance (α < 0.05) using a two-sided threshold (cluster size > 39 voxels) (Cox et al., 2017). For all regions with a significant association between novelty seeking and 10/90 vs. 30/70 brain response, post-hoc regression analyses of the simple effects (10/90 vs. baseline & 30/70 vs. baseline) were used to investigate the relationship of novelty seeking and brain response by trial type.

Results

Group characteristics and risk-taking behavior

Participant characteristics are illustrated in Table 1. This sample was predominately white (83% white, 10% multiracial, 2% Hispanic/Latinx, 2% black/African American, 1% Asian, 1% Indigenous/Alaskan native). The EATQ-R scores on the novelty seeking subscale were normally distributed (mean = 3.33; range 1.55–4.82). Participants who were older (r = 0.14, p < 0.05) and who had lower IQ scores (r = −0.14, p < 0. 05) reported greater novelty seeking. Novelty seeking was not associated with socioeconomic status, pubertal development, or sex assigned at birth. On average adolescents reported 4% (SD = 22%) less risky decisions on the 10/90 than the 30/70 wheel; however, the difference in risky decision making between the two conditions (10/90 vs. 30/70) was not related to novelty seeking scores (β = 0.10, p = 0.12, controlling for age, sex assigned at birth, and IQ). Despite no association between novelty seeking and decision making on the task, to ensure brain response was not driven by decision-making behavior, all analyses were rerun with the behavioral contrast representing percent risky selections as a covariate, and all voxel-wise iand ROI findings remained significant.

Table 1.

Demographic characteristics

Variable Mean (SD) or N Relation to EATQ-R HIP
Age (yr) 14.39 (1.34) Pearson, r = 0.14, p < 0.05*
IQa 110.314 (11.32) Pearson, r = −0.14, p < 0.05*
SESb 29.70 (13.93) Spearman, r = −0.01, p > 0.05
PDSc 2.89 (0.69) Spearman, r = 0.09, p > 0.05
Sex (male/female) 140/125 t-test, t = −1.64, p > 0.05
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Analyses testing the associations with the outcome variable of interest, EATQ-R, high intensity pleasure (HIP).

a

Wechsler Abbreviated Scale of Intelligence;

b

Hollingshead Index of Social Position;

c

Pubertal Development Scale.

Differences in brain activation between 10/90 vs. 30/70 conditions

Across the entire sample, adolescents showed greater activation in regions implicated in reward, salience detection, visual perception, working memory, decision making, and cognitive control during the greater risk/reward trials of the WOF task (Fig. 1). Specifically, greater activation during the larger difference in risk/reward trials (10/90 trials) compared to the moderate difference in risk/reward trials (30/70 trials) was seen in the bilateral middle temporal gyrus, insula, striatum, calcarine gyrus, inferior frontal gyrus, anterior, middle and posterior cingulate, thalamus, and middle frontal gyrus. Less activation was shown in the bilateral superior temporal gyrus during the larger risk/reward versus the moderate risk/reward trial conditions.

Fig. 1.

Fig. 1

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Difference in brain activation between the 10/90 vs. 30/70 trials of the evaluation/selection phase of the task. Significantly greater activation in the 10/90 trials (red/yellow colors) was observed in the bilateral middle temporal gyrus, insula, striatum, calcarine gyrus, inferior frontal gyrus, anterior, middle and posterior cingulate, thalamus, and middle frontal gyrus. Significantly less activation in the 10/90 trials (blue color) was observed in the bilateral superior temporal gyrus. (p < 0.001, corrected for multiple comparisons)

Association between novelty seeking and VS activation

Using a region-of-interest approach, there was a positive association between novelty seeking and VS activation in the contrast comparing activation in large (10/90) versus moderate (30/70) risk/reward conditions (β = 0.16, p = 0.01), controlling for age, sex assigned at birth, and IQ (Fig. 2B). Age, sex assigned at birth, and IQ were not associated with 10/90 vs. 30/70 VS activation. Simple effects within the VS showed a significant positive association between novelty seeking and VS activation in the 10/90 vs. baseline contrast (β = 0.13, p < 0.05); however, the 30/70 vs. baseline contrast did not show a similar significant effect (β = −0.02, p = 0.73).

Fig. 2.

Fig. 2

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A. Ventral striatal mask from the Oxford-GSK-Imanova atlas used to test a priori region-of-interest (ROI) association with novelty seeking. B. ROI analysis investigating the relationship between brain response in the ventral striatum and novelty seeking scores. Individuals reporting greater novelty seeking had greater activation in the ventral striatum during the evaluation of choices with a larger versus moderate discrepancy between risk and reward (β = 0.16, p = 0.01).

Association between novelty seeking and whole-brain activity

Whole-brain analysis revealed two additional regions, the left middle frontal gyrus (MFG; voxel size = 75, x = 23, y = −5, z = 43) and bilateral posterior cingulate cortex (PCC; voxel size = 52, x = −5, y = 41, z = 16), where greater activation during 10/90 vs. 30/70 trials was associated with greater youth-reported novelty seeking (Fig. 3, voxel-wise threshold of p < 0.001 and a cluster-forming threshold of α < 0.05). Age and IQ were not associated with the 10/90 vs. 30/70 activation in either of these clusters. Male adolescents had less 10/90 vs. 30/70 activation than female adolescents in the MFG but showed no differences in the PCC. Investigation of the simple contrasts revealed that in the 10/90 vs. baseline contrast, greater reported novelty seeking was associated with greater brain response in both regions (MFG: β = 0.17, p = 0.005; PCC: β = 0.15, p = 0.02), controlling for age, sex assigned at birth, and IQ. In the 30/70 vs. baseline contrast greater novelty seeking was associated with less activation in the MFG (β = −0.13, p = 0.05) but not the PCC (β = −0.09, p = 0.13).

Fig. 3.

Fig. 3

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A. Whole-brain activation during the evaluation of 10/90 versus 30/70 trials and the association with novelty seeking (voxel-wise threshold of p < 0.001 and cluster-forming threshold of α < 0.05). Adolescents with greater reported novelty seeking showed greater neural activation in the middle frontal gyrus (MFG) and posterior cingulate cortex (PCC) during evaluation of larger potential for risk/reward compared to moderate potential for risk/reward trials. B. Whole-brain regression analyses illustrated that greater novelty seeking scores were associated with greater than average brain activation in the left MFG (top) and PCC (bottom). Notably, post-hoc regression showed that after removal of the outlier in the MFG cluster, brain activation in this region was still significantly associated with youth-reported novelty seeking (β = 0.31, p < 0.001)

Discussion

This cross-sectional study examined the relationship between youth-reported novelty seeking and neural response during assessment of varying levels of risk and reward. Adolescent novelty seeking was not associated with decision making during the task; however, our neuroimaging findings supported our hypotheses, in that adolescents with greater novelty seeking showed greater VS sensitivity to the larger potential for risk and reward. In addition, whole-brain analysis demonstrated that adolescents with greater novelty seeking displayed greater activation in the left MFG and bilateral PCC when presented with the potential for larger risk and reward, also supporting our hypothesis and providing directionality of this association. These findings suggest that the temperament trait of novelty seeking may be associated with enhanced reward response in the brain when there is potential for greater risk and reward, but the associated engagement of the PFC in choice evaluation may have prevented increased risky behavior in our laboratory paradigm.

Association between novelty seeking and VS activation when evaluating risk/reward

Previous findings suggest that adolescence is a time defined by heightened sensitivity to reward. As shown in our study, across the whole sample, youth showed greater VS activation during the condition with the largest potential for reward, and this effect was even more pronounced in youth with higher self-reported novelty seeking. Our findings are broadly consistent with prior work indicating that novelty seeking is related to reward-related VS activation in adolescence (Hawes et al., 2017, van Duijvenvoorde et al., 2014); however, we extend prior literature by demonstrating that these associations can be detected even during the evaluation of risk and reward, particularly when reward, but also risk potential, is highest. This may have relevance for understanding the types of environmental situations that youth find most rewarding. Certainly, more work is needed to investigate the mechanisms contributing to the association between VS activation and novelty seeking and to determine how these findings relate to real-world behaviors, because the relationships between mesolimbic responsiveness and real-world, risk-taking behavior has been more difficult to replicate (Bjork, 2020).

Associations between VS activation and novelty-seeking scores may be related to individual differences in the dopamine system, as studies in adults have shown that novelty seeking is related to dopamine synthesis capacity (Lawrence & Brooks, 2014) and D2-like receptor availability (Gjedde et al., 2010; Zald et al., 2008). Given dopamine’s potential to modulate risk-taking behavior, as well as findings linking structure and functional connectivity of the VS, sensation seeking, and alcohol use in adolescents and young adults (Kohno et al., 2015; Morales et al., 2019; Weiland et al., 2013), more research into how these associations impact behavior outside the laboratory is warranted.

Dorsolateral prefrontal cortex

Most studies examining neural correlates related to temperament traits, such as sensation or novelty seeking, have focused on brain regions involved in reward processing (Hawes et al., 2017, van Duijvenvoorde et al., 2014); however, a growing literature suggests that novelty seeking is also related to the structure (Holmes et al., 2016) and function (Qu et al., 2015) of regions implicated in cognitive control, such as the dorsolateral PFC (dlPFC). This study showed that greater reported novelty seeking among adolescents was associated with greater dlPFC activation, but not greater risk taking, in situations involving larger discrepancies between risk and reward. This finding suggests that adolescents with higher reported novelty seeking may have required greater engagement of cognitive control circuitry to regulate reward-driven risky choices on our laboratory-based assessment, and this requirement of a higher threshold of dlPFC activation to regulate decision-making behavior in the lab setting may translate to more risk taking in real-world situations. Indeed, one longitudinal study of adolescents showed an association between dlPFC activation during a laboratory-based risky decision-making task and real-world risky behaviors, such as substance use and delinquency (Qu et al., 2015) that have been associated with novelty seeking in other studies (Büchel et al., 2017; Wills et al., 1994). Future studies are needed to fully uncover the mechanistic links between novelty seeking, real-word behavior, and dlPFC function.

Posterior cingulate cortex

Lastly, the observed greater brain activation in the PCC during the 10/90 vs. 30/70 contrast (reflecting a larger potential for reward, but smaller likelihood of winning that reward) and association with novelty seeking may be attributable to the role the PCC plays in reward processing (Dixon & Christoff, 2014; Silverman et al., 2015). Using a similar WOF task, a study found that PCC activation was modulated by reward magnitude (Smith et al., 2009). More specifically, a meta-analysis demonstrated the activation in the PCC is positively correlated with the subjective value of available alternatives when presented with choices (Bartra et al., 2013). To better understand the observed association between novelty seeking and PCC activation, future studies may benefit from collecting behavioral data aimed at teasing apart choice preference by using behavioral tasks where value and other decision-making parameters (i.e., risk) are orthogonal (Litt et al., 2010).

Limitations

While our study provides novel evidence about how individual differences in temperament play a role in choice evaluation, some limitations are worth noting. First, we used a cross-sectional design; therefore, we are unable to say whether altered neural response during reward evaluation leads to greater novelty seeking, whether greater novelty seeking alters how the brain responds to reward, or whether the two share a bidirectional relationship. Ultimately, future research should examine large longitudinal datasets to better understand the developmental nature of these relationships (Lydon-Staley & Bassett, 2018; Sherman et al., 2018). Additionally, this study utilized a broad age range, 12 to 17 years, during which there is significant development in terms of brain anatomy and function, and engagement in different forms of risk-taking behaviors. However, age was a covariate in all of our models, and we found that age was not significantly associated with brain response in any region where there was a significant effect of novelty seeking. It is worth noting that our sample was predominantly from high income status families and white, and the potential for generalizability among more diverse populations is limited. Moreover, the WOF task used in this study has some design limitations. While this study defined the initial phase of the task as the evaluation/selection phase, we cannot definitively state that this phase is solely evaluative and does not include early anticipation of an upcoming reward. However, we believe the contemplation of overall outcome values, probabilities of risk/reward, and expected values are all involved in the evaluative process of decision making and are present during the selection phase of our task. By controlling for decision-making behavior on the task, we believe we may have at least partially controlled for individual differences in brain response associated with reward anticipation. Finally, while we found no association between novelty seeking and decision making on the WOF task, experimental risk-taking paradigms often lack real-world environmental influences (e.g., peer relationships or parent involvement) and often do not reflect real-world risk taking (Sherman et al., 2018; Telzer et al., 2015). That said, contrary to the view that increased novelty seeking may act as a potential marker for future maladaptive choices, it also may be argued that novelty seeking during adolescence can be seen as adaptive (e.g., seeking out social relationships) (Crone & Dahl, 2012; Crone et al., 2016; van Duijvenvoorde et al., 2016; Willoughby et al., 2013). It would be beneficial for future studies to examine more ecologically valid assessments of adolescent risk/reward-related behavior and to incorporate both assessment of adaptive and maladaptive behavior linked to novelty seeking.

Conclusions

In this empirical study, we aimed to identify how individual differences related to novelty seeking were associated with decision-making behavior and neurobiology during adolescence. Our study used a novel approach to examine brain activation while evaluating varying levels of risk/reward, thus providing a wider understanding of behavioral ch oices and neural r esponse during decision-making assessment. We found that greater activation in brain regions implicated in salience detection, reward, and cognitive control were all positively associated with greater self-reported novelty seeking when evaluating the potential for large risk and reward, which may have implications for individual differences in reward-motivated behavior. These findings may contribute to identifying neurobiological markers associated with adaptive and maladaptive risk and reward assessment in adolescents in order to understand future health-risk behaviors.

Supplementary Material

supplemental

Acknowledgments

The authors acknowledge the participants and their families. AD acknowledges the OHSU Developmental Brain Imaging Lab research staff and laboratory, for their support. AD acknowledges the Advanced Imaging Research Center and their funding through the Oregon opportunity partnership for advancing biomedical research.

Funding

Dr. Nagel received funding from the National Institute on Alcohol Abuse and Alcoholism (R01 AA017664) and Dr. Morales received funding from the National Institute on Drug Abuse (K01 DA046649). This work was also supported by for the High Performance Computing Cluster, housed in OHSU’s Advanced Imaging Research Center (NIH S10OD018224).

Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.3758/s13415–021-00937–2.

Declarations

Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethics Approval The research and methodology for this study was approved by the Oregon Health & Science University Institutional Review Board.

Consent to participate Informed consent and assent were obtained from all individual participants and their parents included in the study.

Consent for publication Not applicable

Data and Code availability Data for this project are available upon reasonable request to the corresponding author.

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