Abstract

Neurodevelopmental changes in mesolimbic regions are associated with adolescent risk-taking behavior. Numerous studies have shown exaggerated activation in the striatum in adolescents compared with children and adults during reward processing. However, striatal sensitivity to aversion remains elusive. Given the important role of the striatum in tracking both appetitive and aversive events, addressing this question is critical to understanding adolescent decision-making, as both positive and negative factors contribute to this behavior. In this study, human adult and adolescent participants performed a task in which they received squirts of appetitive or aversive liquid while undergoing fMRI, a novel approach in human adolescents. Compared with adults, adolescents showed greater behavioral and striatal sensitivity to both appetitive and aversive stimuli, an effect that was exaggerated in response to delivery of the aversive stimulus. Collectively, these findings contribute to understanding how neural responses to positive and negative outcomes differ between adolescents and adults and how they may influence adolescent behavior.

INTRODUCTION

Weighing the potential rewards and punishments of risky behavior is an essential component of optimal decision-making (Rangel, Camerer, & Montague, 2008). In general, rewards cause an increase, whereas punishments cause a decrease, in behavior (Domjan, 2010). Theories of adolescent behavior posit that adolescents engage in increased risky decision-making compared with other age groups because they place greater value on the potential positive, as opposed to negative, consequences of risk-taking (Steinberg, 2010; Casey, Getz, & Galván, 2008; Ernst, Pine, & Hardin, 2006). For instance, the Triadic Model (Ernst et al., 2006) of motivated behavior suggests that the adolescent brain is tilted toward reward-seeking behavior by enhanced input from the striatum, a region that receives dense dopaminergic innervations from the midbrain and is critically involved in reward and risk processing (Christopoulos, Tobler, Bossaerts, Dolan, & Schultz, 2009). This imbalance is in contrast to adults, in whom decision-making is presumably the product of equal and balanced integration of signals from the striatum, amygdala, and pFC to modulate approach, avoidance, and regulatory behaviors, respectively (Ernst et al., 2006). Similarly, dual-system theories suggest that protracted neurobiological maturation in the pFC is eclipsed by strong biases from the striatum toward reward-seeking behavior (Crone & Dahl, 2012; Galván, 2012; Somerville & Casey, 2010; Steinberg, 2008). Indeed, relative to children and adults, adolescents are more likely to approach rewarding cues in the lab (Somerville, Hare, & Casey, 2011) and in real life (Reyna & Rivers, 2008) and to show reward-related improvements in task performance (Geier & Luna, 2011; Padmanabhan, Geier, Ordaz, Teslovich, & Luna, 2011). fMRI studies show that the adolescent brain is uniquely sensitive to reward compared with other age groups (Cohen et al., 2010; van Leijenhorst, Zanolie, et al., 2010; Galván et al., 2006; Ernst et al., 2005; Bjork et al., 2004; Sowell, Thompson, Holmes, Jernigan, & Toga, 1999). Specifically, there is exaggerated striatal activation in adolescents compared with adults and children during reward processing (Cohen et al., 2010; van Leijenhorst, Zanolie, et al., 2010; Galván et al., 2006; Ernst et al., 2005; May et al., 2004); this enhanced activation is associated with increased risk-taking behavior (van Leijenhorst, Moor, et al., 2010; Galván, Hare, Voss, Glover, & Casey, 2007; but see Bjork, Smith, Chen, & Hommer, 2010; Bjork et al., 2004, for blunted activation in adolescents relative to older and younger individual).

Recent work has demonstrated that the striatum also shows robust activation in response to aversive outcomes (Budygin et al., 2012; Delgado, Li, Schiller, & Phelps, 2008; McNallly & Westbrook, 2006; Jensen et al., 2003; Horvitz, 2000), suggesting a bivalent role of this region in processing both reward and punishment (Levita et al., 2009; Delgado et al., 2008). The few studies that have examined neural responses to negative events in adolescents have yielded mixed results. Using monetary losses as a proxy for punishment, several studies have reported blunted striatal activation compared with adults in response to omission of monetary rewards (Ernst et al., 2005), monetary loss (Galván et al., 2006; Guyer et al., 2006), and relatively smaller monetary rewards (Galván et al., 2006). In contrast, there is also evidence that adolescents show exaggerated striatal responses in anticipation of potentially negative social feedback (Guyer, McClure-Tone, Shiffrin, Pine, & Nelson, 2009). These conflicting results may be due to confounding issues related to differences in stimuli and/or the use of secondary reinforcers (e.g., money and faces) to study negative events (Bjork, Smith, Chen, & Hommer, 2011; Helfinstein et al., 2011; Somerville et al., 2011; Geier, Terwilliger, Teslovich, Velanova, & Luna, 2010; van Leijenhorst, Zanolie, et al., 2010; Galván et al., 2006). Secondary reinforcers are commonly used in developmental studies, but this approach introduces a significant confound: Without equating subjective value or motivation, the same stimulus may elicit differential responses across development. For instance, a $5 gain/loss may be more meaningful to an adolescent than to an adult. Similarly, emotional faces are interpreted differently across development (Thomas et al., 2001), which elicit differences in behavioral responses across children, adolescents, and adults (Tottenham, Hare, & Casey, 2011). Another limitation of using monetary loss or threatening faces to address questions about value is that the former involves a delay of the actual experience (i.e., receiving or losing the money) until the end of the experiment, and the latter is a passive task in which the participant only experiences an “empty threat,” as he or she never actually receives a punishment. These methodological limitations may render the adolescent less motivated during the task and thus fail to elucidate how the adolescent brain responds to aversive outcomes “in-the-moment.” Nonetheless, this important question remains unresolved.

In this study, our main goal was to examine how the adolescent brain responds to aversive outcomes. Instead of using secondary reinforcement such as money, we used primary reinforcement to address this question. While undergoing fMRI, hungry and thirsty participants received squirts of appetitive and aversive liquids. We used this liquid delivery approach because (a) it is an acute palpable experience (as opposed to an abstract notion of delayed monetary gains or losses), (b) the experience of each trial can be quantified for each subject (i.e., to assess how appetitive or aversive the outcome was to the participant and thus avoid confounds related to valuation/interpretation of the experience), and (c) this approach has been successfully used to study neural responses to appetitive and aversive events in adult humans (McClure, Berns, & Montague, 2003; O'Doherty, Deichmann, Critchley, & Rolan, 2002) and monkeys (Schultz, Dayan, & Montague, 1997), which provides a solid benchmark upon which to gauge our findings and helps us make predictions about underlying neural mechanisms. To the best of our knowledge, this is the first study using liquid delivery in human adolescents so a secondary, methodological, goal was to determine the utility of using this approach to study the developing brain.

METHODS

Subjects

Fifteen adolescents (eight girls; aged 13–17 years, M = 15.33) and 15 adults (eight women; aged 23–35 years, M = 28.5) participated in this study. Participants were recruited through advertisements on community Web sites (e.g., Craigslist) and through flyers posted throughout the community. Interested participants (or their parents if under 18) called the laboratory and provided responses to a detailed screening questionnaire administered by the study coordinator. Inclusion criteria included being English speaking, right-handed, and between the ages of 13–35. Exclusion criteria included a previous diagnosis of a psychiatric or developmental disorder, current pregnancy, alcohol or drug abuse, psychiatric medication use, presence of metal in the body (e.g., dental braces), or claustrophobia. After receiving an explanation of the study, participants who were 18 years or more provided written informed consent, as required by the University of California-Los Angeles Institutional Review Board; participants under age of 18 gave assent, and their parents gave consent. Adolescents were acclimated to the MRI scanner with a mock scanner before data collection. Participants were reimbursed for their participation.

Liquid Delivery in the Scanner

Liquid stimuli (1 ml) were delivered intraorally in the MRI via three Teflon tubes held between the lips using a computer-controlled delivery system (Infinity Controller J-KEM, Inc., St., Louis, MO). The appetitive liquid consisted of 0.5 M sucrose (sugar water). The aversive liquid consisted of 0.1 M NaCl (salt water), modified from work described previously (O'Doherty, Rolls, Francis, Bowtell, & McGlone, 2001).

Participants were scanned in the early evening (∼5 pm) and asked to refrain from eating or drinking anything for 3 hr before the scan, which was confirmed upon arrival to the lab through self-report via a hunger/thirst rating scale (1 = not thirsty/hungry, 7 = very thirsty hungry). There were no group differences on this scale (Madolescents = 6.2, Madults = 5.8). In the fMRI task, which was modeled after previous designs (Delgado et al., 2008; O'Doherty, Kringelbach, Rolls, Hornak, & Andrews, 2001), participants were presented with three cues (e.g., shapes), each of which was associated with 50% probability the delivery of water (neutral), sucrose (appetitive liquid), or sodium chloride (aversive liquid). A fourth cue indicated that no liquid would be delivered (null trials). Participants were explicitly told that each cue would predict a different outcome but were not trained specifically on which cue predicted which outcome. Each trial lasted 15 sec, during which the following events occurred: cue (3500 msec), liquid outcome (liquid delivery coterminated with cue at 500 msec), fixation (6000 msec), value rating scale (3000 msec), and jittered fixation (average 2000 msec; Figure 1). Participants were presented with a total of 88 trials (16 trials in which nothing was delivered, 24 neutral trials, 24 appetitive trials, and 24 aversive trials). The entire experiment consisted of four runs, each lasting 5.5 min. In two of the runs, participants were presented with the cues associated with water and the appetitive liquid. In the other two runs, participants were presented with the cues associated with water and the aversive liquid. Order of presentation was counterbalanced.

Figure 1. 

fMRI task. Participants were presented with one of four shapes that predicted (with 50% probability) the delivery of a neutral (water), appetitive (0.5 M sucrose), aversive (0.1 M NaCl) stimulus, or nothing.

Figure 1. 

fMRI task. Participants were presented with one of four shapes that predicted (with 50% probability) the delivery of a neutral (water), appetitive (0.5 M sucrose), aversive (0.1 M NaCl) stimulus, or nothing.

Liquid Rating Scale

Participants' subjective ratings of the pleasantness of the liquids were acquired on each trial using a two-part rating scale. The first scale asked, “What was your experience with the liquid you just tasted?” (1 = pleasant, 2 = unpleasant, 3 = neutral). If the participant indicated the liquid was pleasant, the second scale asked, “How pleasant was the liquid you just tasted?” (1 = slightly pleasant, 4 = extremely pleasant). If the participant indicated the liquid was unpleasant, the second scale asked, “How unpleasant was the liquid you just tasted?” (1 = slightly unpleasant, 4 = extremely unpleasant); scores were reverse-coded in the analysis for unpleasant ratings so that 1 = extremely unpleasant and 4 = slightly unpleasant.

Trial-by-Trial Analysis

Ratings for each trial were recorded for each participant. A trial-by-trial analysis was conducted in which an algorithm categorized each trial as having been positive (appetitive) or negative (aversive) for each condition type based on the participant's rating, such that trials rated as positive in the first rating scale were then analyzed on a scale of 1 (slightly pleasant) to 4 (extremely pleasant) whereas trials rated as negative were then analyzed on a scale of −1 (slightly unpleasant) to −4 (extremely unpleasant). This categorization yielded four condition types of interest: (1) appetitive-positive, (2) appetitive-negative, (3) aversive-positive, and (4) aversive-negative. This categorization procedure enabled us to examine brain activity based on the subjective value placed on each event, rather than on investigator-imposed classification of appetitive and aversive value. Statistical analyses of behavioral data were conducted using SPSS. There were no significant group differences on percentage of trials rated positively in the appetitive condition (adolescents: 89.88%; adults: 93.18%) or negatively in the aversive condition (adolescents: 91.07%; adults: 87.12%).

MRI Data Acquisition

Imaging data were collected on a 3T Siemens Trio MRI scanner. For each run, 168 functional T2*-weighted EPI were acquired (slice thickness = 4 mm, 34 slices, repetition time = 2 sec, echo time = 30 sec, flip angle = 90°, matrix = 64 × 64, field of view = 200 mm, voxel size = 3 × 3 × 4 mm3). Four volumes, collected at the beginning of each run to allow for T1 equilibrium effects, were discarded. A T2-weighted, matched-bandwidth (MBW), high-resolution, anatomical scan and magnetization-prepared rapid-acquisition gradient echo scan were acquired for each participant for registration purposes (repetition time = 2.3, echo time = 2.1, field of view = 256, matrix = 192 × 192, sagittal plane, slice thickness = 1 mm, 160 slices). The orientation for MBW and EPI scans was oblique axial to maximize brain coverage. E-Prime software (Psychology Software Tools, Inc., Sharpsburg, PA) was used for task programming and presentation to record timing of events and to communicate with the Infinity controller.

Image Preprocessing and Registration

Imaging data were analyzed using the FMRIB Software Library (FSL) 4.1.6 toolbox (www.fmrib.ox.ac.uk/fsl). The images were realigned to compensate for small head movements (Jenkinson, Bannister, Brady, & Smith, 2002). All data reported are from scans that exhibited ≤2 mm in translational movement. There were no group differences in motion (adults: M = 0.059 mm; adolescents: M = 0.08 mm), and none of the participants were excluded for excess motion. The data were smoothed using a 5-mm FWHM Gaussian kernel and filtered in the temporal domain using a nonlinear high-pass filter (100-sec cutoff). EPI images were first registered to the MBW scan, then to the magnetization-prepared rapid-acquisition gradient echo scan, and finally into standard Montreal Neurological Institute (MNI) space (MNI152, T1 2 mm) for group comparisons using linear transformation.

Analysis of Functional Neuroimaging Data

A general linear model analysis in FSL was used for fMRI analyses. Statistical modeling was first performed for each image, and variables of interest were compared between groups using two-sample t tests. Regressors of interest were created by convolving a delta function representing the onset time of each event with a canonical (double-gamma) hemodynamic response function. For each participant and each run, eight explanatory variables were modeled: (1) delivery of positively rated (appetitive or aversive, depending on run) liquid, (2) delivery of negatively rated (appetitive or aversive, depending on run) liquid, (3) delivery of water (all water trials within a particular run were collapsed as most were rated as neutral), (4) anticipatory water cue associated with water, (5) anticipatory no-liquid-delivery cue, (6) anticipatory sugar water cue (or salt water depending on run), (7) presentation of rating scale, and (8) junk (which included all appetitive or aversive trials rated as neutral and no-response trials). Six motion parameters were also modeled as events of no interest. Temporal derivatives were included as covariates of no interest to improve statistical sensitivity. Null events, consisting of the jittered intertrial interval and fixation points, were not explicitly modeled and therefore constituted an implicit baseline.

Eight primary contrasts of interest were generated at the first level for each run of each experimental condition. Six of these contrasts were used to examine neural response to liquid delivery: (1) appetitive-positive > nothing delivered, (2) appetitive-positive > water delivery during appetitive runs, (3) water delivery during appetitive runs > appetitive-positive, (4) appetitive-negative > nothing delivered, (5) appetitive-negative > water delivery during appetitive runs, and (6) water delivery during appetitive runs > appetitive-negative. Two contrasts were created to examine neural response to the anticipatory cues: (7) appetitive cue > water cue during appetitive cues and (8) appetitive cue > no-liquid-delivery cue. Any contrasts with water included only water trials from the appetitive runs. Eight respective contrasts were generated for the aversive condition; any contrasts with water included only water trials from the aversive runs. In the current study, we focused on activation during delivery of the appetitive-positive and aversive-negative liquids (relative to water delivery) for the purposes of addressing the main question. There were also not enough trials per condition to examine neural response to appetitive-negative and aversive-positive trials. These trials were simply included in the general linear model to preclude their inclusion in the implicit baseline. Because most ratings of water were neutral (71% of water trials in the appetitive runs and 73.2% of water trials in the aversive runs), there were not enough trials to examine pleasant (23% and 22.9% in the appetitive and aversive runs, respectively) and unpleasant ratings (5.1% and 3.6% in the appetitive and aversive runs, respectively) of water.

A second-level, fixed effects, voxel-wise analysis combined runs of each condition type for each participant (two appetitive runs and two aversive runs). A one-sample t test was performed at each voxel for each contrast. Z statistic images were generated to show clusters determined by a height threshold of Z > 2.3 and an extent threshold of p < .05, corrected using the theory of Gaussian random fields (Poline, Worsley, Evans, & Friston, 1997). A third-level analysis was performed to compare age groups using the FMRIB Local Analysis of Mixed Effects module in FSL (Beckmann, Jenkinson, & Smith, 2003), corrected at Z > 2.3 and p < .05. Outliers were automatically deweighted in the multisubject statistics using the outlier deweighting function in FSL (Woolrich, 2008), which uses the lower-level variance from each subject to effectively downweight subjects with high first-level variance. The approach is conservative and will default to nonoutlier behavior (i.e., assuming the error is purely Gaussian). A second third-level analysis was performed to compare activation to appetitive versus aversive conditions within each age group.

For visualization purposes, statistical maps of all analyses were projected onto a study-specific average brain of the participants. Anatomical localization within each cluster was determined by searching within maximum likelihood regions from the FSL Harvard-Oxford probabilistic atlas to obtain the maximum Z statistic and MNI coordinates within each anatomical region.

RESULTS

Behavioral Results

A 2 (Condition) × 2 (Age Group) ANOVA was performed to determine if there were developmental differences in ratings of the appetitive and aversive conditions. There was a significant main effect of Condition, F(1, 28) = 86.59, p < .00001, and significant interaction of Condition type × Age, F(1, 28) = 3.92, p < .05 (Figure 2). Post hoc tests revealed significantly higher positive ratings to the appetitive versus aversive liquid, an effect that was exaggerated in adolescents versus adults (appetitive liquid: adults [M = 1.33, SE = 0.53], adolescents [M = 2.18, SE = 0.56]; aversive liquid: adults [M = −1.81, SE = 0.32], adolescents [M = −2.67, SE = 0.23]).

Figure 2. 

Behavioral results. Both groups reported positive and negative value ratings for the appetitive and aversive conditions, respectively. Adolescents reported stronger positive and negative ratings to the appetitive and aversive conditions, respectively, than adults.

Figure 2. 

Behavioral results. Both groups reported positive and negative value ratings for the appetitive and aversive conditions, respectively. Adolescents reported stronger positive and negative ratings to the appetitive and aversive conditions, respectively, than adults.

A 4 (Condition) × 2 (Age Group) ANOVA was performed to determine the effect of Condition (water in aversive runs, water in appetitive runs, aversive condition, and appetitive condition) on RT during ratings that followed delivery of liquid. There was no significant main effect of Condition, Age, or interactions on RT.

fMRI Results

Anticipatory Cue

Whole-brain omnibus analyses to examine neural activation to cues associated with appetitive and aversive liquids were conducted. Appetitive cues (appetitive-positive cue > water cue and appetitive-positive cue > no-liquid-delivery cue) elicited activation in caudate, insula, inferior frontal gyrus, and OFC (Table 1). Aversive cues (aversive cue > water cue and aversive cue > no-liquid-delivery cue) elicited activation in inferior frontal gyrus, OFC, insula, frontal pole, caudate, and occipital cortex (Table 1). There were no significant group differences in activation to either contrast in any brain region.

Table 1. 

Significant Clusters of Activation from a Whole-brain Analysis during the Anticipatory Cue

Region

x
y
z
Max Z
Appetitive Cue > No-Liquid-Delivery Cue 
OFC 22 38 −18 3.23 
Insula 38 −4 −12 3.33 
 
Appetitive Cue > Water Cue 
Inferior frontal gyrus 52 22 14 3.02 
Caudate 10 14 3.23 
 
Aversive Cue > No-Liquid-Delivery Cue 
Insula −36 10 3.92 
Frontal pole 36 62 12 3.6 
 
Aversive Cue > Water Cue 
Insula R/L 36 14 −10 2.59 
−32 24 −2 3.18 
OFC R/L −46 42 −18 3.32 
34 46 −12 3.36 
Caudate −8 10 3.49 
Inferior frontal gyrus −50 20 3.81 
Occipital cortex 10 −94 3.21 
Region

x
y
z
Max Z
Appetitive Cue > No-Liquid-Delivery Cue 
OFC 22 38 −18 3.23 
Insula 38 −4 −12 3.33 
 
Appetitive Cue > Water Cue 
Inferior frontal gyrus 52 22 14 3.02 
Caudate 10 14 3.23 
 
Aversive Cue > No-Liquid-Delivery Cue 
Insula −36 10 3.92 
Frontal pole 36 62 12 3.6 
 
Aversive Cue > Water Cue 
Insula R/L 36 14 −10 2.59 
−32 24 −2 3.18 
OFC R/L −46 42 −18 3.32 
34 46 −12 3.36 
Caudate −8 10 3.49 
Inferior frontal gyrus −50 20 3.81 
Occipital cortex 10 −94 3.21 

Liquid Delivery

Appetitive condition

Whole-brain omnibus analyses to examine neural responses to the appetitive outcome (appetitive liquid > water delivery) revealed significant activation in left ventral striatum (VS; Figure 3A) and insular cortex, bilateral amygdala, caudate, inferior frontal gyrus, middle frontal gyrus, occipital cortex, and dorsolateral pFC, right superior frontal gyrus, cingulate gyrus, and precuneous (Table 2). Direct group comparisons revealed greater activation in adults compared with adolescents in temporal pole. Adolescents exhibited greater activation compared with adults in bilateral VS, insula, precentral gyrus, right operculum, middle frontal gyrus, and cingulate gyrus. Given the goal of probing developmental differences in the striatum, an ROI analysis was conducted by extracting parameter estimates from the VS and caudate. There was a trend toward significantly greater activation in the VS (x = −8, y = 8, z = −10) in adolescents (M = .36, SD = .51) than adults (M = −.12, SD = .27; F(1, 28) = 3.01, p = .055; Figure 3B) but no significant group differences in the caudate (M = .40, SD = .1 for adolescents and M = .28, SD = .22 for adults). There were no brain regions that showed greater activation to water versus appetitive liquid.

Figure 3. 

Neural activation to appetitive condition. (A) Neural activation during delivery of the appetitive liquid (versus water delivery) across groups. (B) Parameter estimates from the VS in adults and adolescents (circled in A).

Figure 3. 

Neural activation to appetitive condition. (A) Neural activation during delivery of the appetitive liquid (versus water delivery) across groups. (B) Parameter estimates from the VS in adults and adolescents (circled in A).

Table 2. 

Significant Clusters of Activation from a Whole-brain Analysis during Delivery of the Appetitive Outcome (Appetitive Liquid > Water)

Region

x
y
z
Max Z
VS −12 16 −4 3.90 
Caudate R/L 18 22 3.57 
−12 16 3.08 
Amygdala R/L 28 −8 −18 3.68 
−24 −8 −14 3.46 
Insular cortex −36 22 −4 2.77 
Inferior frontal gyrus R/L 54 14 26 2.86 
−50 14 22 2.83 
Superior frontal gyrus 24 14 54 3.7 
Middle frontal gyrus R/L 50 14 34 3.24 
−34 14 44 3.05 
Occipital cortex R/L 22 −80 46 3.14 
−20 −80 48 2.58 
Dorsolateral pFC R/L 36 44 28 3.61 
−32 44 22 3.18 
Cingulate gyrus  −10 46 2.85 
Precuneous  −8 −72 24 3.10 
 
Adults > Adolescents 
Temporal pole 52 12 −26 3.08 
 
Adolescents > Adults 
VS R/L −8 −10 3.04 
10 −12 2.71 
Precentral gyrus R/L 58 3.42 
−60 3.71 
Operculum 34 26 2.73 
Middle frontal gyrus 40 32 20 2.63 
Cingulate  28 2.52 
Insula R/L 38 −6 12 2.44 
−44 −6 3.14 
Region

x
y
z
Max Z
VS −12 16 −4 3.90 
Caudate R/L 18 22 3.57 
−12 16 3.08 
Amygdala R/L 28 −8 −18 3.68 
−24 −8 −14 3.46 
Insular cortex −36 22 −4 2.77 
Inferior frontal gyrus R/L 54 14 26 2.86 
−50 14 22 2.83 
Superior frontal gyrus 24 14 54 3.7 
Middle frontal gyrus R/L 50 14 34 3.24 
−34 14 44 3.05 
Occipital cortex R/L 22 −80 46 3.14 
−20 −80 48 2.58 
Dorsolateral pFC R/L 36 44 28 3.61 
−32 44 22 3.18 
Cingulate gyrus  −10 46 2.85 
Precuneous  −8 −72 24 3.10 
 
Adults > Adolescents 
Temporal pole 52 12 −26 3.08 
 
Adolescents > Adults 
VS R/L −8 −10 3.04 
10 −12 2.71 
Precentral gyrus R/L 58 3.42 
−60 3.71 
Operculum 34 26 2.73 
Middle frontal gyrus 40 32 20 2.63 
Cingulate  28 2.52 
Insula R/L 38 −6 12 2.44 
−44 −6 3.14 
Aversive condition

Whole-brain omnibus analyses to examine neural responses to the aversive liquid (aversive liquid > water delivery) revealed significant activation in bilateral amygdala, OFC, right VS, caudate, insula, opercular cortex and left occipital cortex, superior parietal cortex, cingulate, and cerebellum (Figure 4A; Table 3). Direct group comparisons revealed significantly greater activation in caudate (x = −14, y = 20, z = 10) in adolescents (M = 0.62 SD = 0.49) than adults (M = −0.49, SD = 0.80; F(1, 28) = 9.47, p = .005; Figure 4B). There was significantly greater activation in the right insula (x = 44, y = −10, z = −2; Figure 4C) in adults (M = 0.74, SD = 0.38) compared with adolescents (M = 0.31, SD = 0.43), F(1, 28) = 7.27, p = .01 (Figure 4D).

Figure 4. 

Neural activation to aversive condition. (A, C) Neural activation to aversive condition (vs. water) across groups. (B) Parameter estimates from caudate in adults and adolescents (circled in A). (D) Parameter estimates from insula in adults and adolescents (circled in C).

Figure 4. 

Neural activation to aversive condition. (A, C) Neural activation to aversive condition (vs. water) across groups. (B) Parameter estimates from caudate in adults and adolescents (circled in A). (D) Parameter estimates from insula in adults and adolescents (circled in C).

Table 3. 

Significant Clusters of Activation from a Whole-brain Analysis during Delivery of the Aversive Outcome (Aversive Liquid > Water)

Region

x
y
z
Max Z
VS −8 12 −6 3.42 
Amygdala R/L 20 −6 −10 3.69 
−26 −10 −14 3.41 
Frontal operculum 44 20 3.01 
Precentral gyrus 60 12 3.03 
Insular cortex 46 −10 3.83 
Superior parietal −28 −46 40 3.03 
OFC R/L 24 36 −16 3.43 
−22 36 −8 2.74 
Cingulate gyrus  −6 44 3.83 
Cerebellum  −40 −46 −34 2.76 
Occipital cortex −38 −72 −18 3.79 
 
Adults > Adolescents 
Insular cortex 44 −10 −2 3.77 
Lingual gyrus −82 2.77 
 
Adolescents > Adults 
Caudate −14 20 10 3.55 
Middle frontal gyrus −30 16 54 3.03 
Region

x
y
z
Max Z
VS −8 12 −6 3.42 
Amygdala R/L 20 −6 −10 3.69 
−26 −10 −14 3.41 
Frontal operculum 44 20 3.01 
Precentral gyrus 60 12 3.03 
Insular cortex 46 −10 3.83 
Superior parietal −28 −46 40 3.03 
OFC R/L 24 36 −16 3.43 
−22 36 −8 2.74 
Cingulate gyrus  −6 44 3.83 
Cerebellum  −40 −46 −34 2.76 
Occipital cortex −38 −72 −18 3.79 
 
Adults > Adolescents 
Insular cortex 44 −10 −2 3.77 
Lingual gyrus −82 2.77 
 
Adolescents > Adults 
Caudate −14 20 10 3.55 
Middle frontal gyrus −30 16 54 3.03 

Relationship between Neural Activation and Value Ratings

A robust regression analysis revealed that parametric increases in value ratings of the delivery of the appetitive liquid were associated with neural activation. In adolescents, increasingly more positive ratings of the appetitive liquid were positively correlated with activation in the bilateral VS and putamen, right caudate, bilateral amygdala and hippocampus, bilateral OFC, and primary gustatory cortex (insula/operculum) and right middle temporal gyrus (Figure 5A; Table 4). For illustrative purposes only, Figure 5B illustrates the association between left VS and appetitive ratings in adolescents (r = .72, p = .005). In adults, positive ratings only correlated with activation in the occipital cortex. There were no negative correlations in either group. There were no significant correlations between value ratings to the aversive liquid and brain activation.

Figure 5. 

Relationship between neural activation and value ratings. (A) Neural activation delivery of appetitive liquid. The scatterplots illustrate the correlation between value ratings of the appetitive liquid and parameter estimates extracted from the entire cluster in left VS in adolescents (B) and adults (C). These scatterplots are circular (because they was created using a biased ROI) and are presented only for illustration purposes (circled in A).

Figure 5. 

Relationship between neural activation and value ratings. (A) Neural activation delivery of appetitive liquid. The scatterplots illustrate the correlation between value ratings of the appetitive liquid and parameter estimates extracted from the entire cluster in left VS in adolescents (B) and adults (C). These scatterplots are circular (because they was created using a biased ROI) and are presented only for illustration purposes (circled in A).

Table 4. 

Positive Association between Value Rating and Neural Activation

Region

x
y
z
Max Z
Adults 
Occipital cortex 30 −76 48 3.24 
 
Adolescents 
Ventral striaum R/L 10 16 −6 2.77 
−12 10 −6 2.75 
Amygdala R/L 16 −4 −22 3.20 
−24 −22 2.55 
Caudate 16 10 18 3.18 
Insula/Gustatory cortex R/L 38 20 −4 2.75 
−38 20 −8 3.58 
Putamen 16 10 −12 2.55 
Middle temporal gyrus 46 −26 −8 3.31 
Hippocampus R/L 28 −16 −18 2.78 
−18 −16 −18 3.24 
Medial pFC  −2 42 2.99 
Region

x
y
z
Max Z
Adults 
Occipital cortex 30 −76 48 3.24 
 
Adolescents 
Ventral striaum R/L 10 16 −6 2.77 
−12 10 −6 2.75 
Amygdala R/L 16 −4 −22 3.20 
−24 −22 2.55 
Caudate 16 10 18 3.18 
Insula/Gustatory cortex R/L 38 20 −4 2.75 
−38 20 −8 3.58 
Putamen 16 10 −12 2.55 
Middle temporal gyrus 46 −26 −8 3.31 
Hippocampus R/L 28 −16 −18 2.78 
−18 −16 −18 3.24 
Medial pFC  −2 42 2.99 
Neural activation to appetitive versus aversive stimulus delivery

There were no significant differences in activation to appetitive versus aversive conditions in adults or adolescents at corrected thresholds. However, at an uncorrected threshold of p < .005, adults showed greater activation in the middle frontal gyrus (x = −34, y = 24, z = 44), hippocampus (x = 28, y = −6, z = −22), putamen (x = 20, y = 4, z = 6), and caudate (x = −8, y = 10, z = 8) in a contrast comparing delivery of the appetitive versus aversive liquid. The opposite contrast, comparing delivery of the aversive versus appetitive liquid yielded activation in the insula (x = 42, y = −10, z = −4). At an uncorrected threshold, adolescents showed greater activation in lateral occipital cortex (x = −40, y = −70, z = 38), precuneous (x = −6, y = −70, z = 22), amygdala (x = 22, y = −6, z = −22), parahippocampal gyrus (x = 32, y = −24, z = −22), and middle frontal gyrus (x = −44, y = 6, z = 34) in a contrast comparing delivery of the appetitive versus aversive liquid. They showed greater activation in the superior parietal cortex (x = −22, y = −46, z = 48) to the aversive versus appetitive liquid.

DISCUSSION

To understand factors that contribute to adolescent risk-taking, many studies have focused on neural sensitivity to reward, with less attention on the equally important question of how the adolescent brain processes aversive outcomes. Work in adult humans has suggested that frontostriatal circuitry processes both appetitive and aversive events in overlapping (Plassman, O'Doherty, & Rangel, 2010; Delgado et al., 2008) but distinct regions (Liu et al., 2007; Rolls, Kringelbach, & de Araujo, 2003; O'Doherty, Rolls, et al., 2001), but this question remained elusive in adolescent. We tested it using a liquid delivery paradigm and fMRI. This report provides insight into two unresolved questions in developmental cognitive neuroscience. First, it provides evidence that in addition to enhanced neural sensitivity to appetitive outcomes, adolescents show exaggerated striatal sensitivity to aversive events relative to adults, an effect that is paralleled by more negative ratings of aversive events in adolescents. By examining both positive and negative outcomes in one study, our data suggest that adolescents are generally more reactive to valenced stimuli (both positive and negative) than adults. Second, we demonstrate the utility of using primary reward (nonmonetary) stimuli to study questions regarding value in adolescent humans; this approach allowed us to circumvent potentially confounding issues related to value and sensitivity to secondary reinforcers, such as money and faces, across development.

Greater Striatal Sensitivity to Delivery, But Not Anticipation, of Aversive Stimuli in Adolescents than Adults

Our goal in the current study was to focus on neural differences in the striatum, given its role in processing both positive and negative events (Levita et al., 2009; Jensen et al., 2003). Physiological studies have shown that different neurons in the nucleus accumbens respond to both aversive and appetitive stimuli (Roitman, Wheeler, & Carelli, 2005; Wilson & Bowman, 2005; Setlow, Schoenbaum, & Gallagher, 2003; Yangimoto & Maeda, 2003). Specifically, elevated dopamine levels are observed in the accumbens in response to various aversive outcomes, including electric shocks, restraint stress, and anxiogenic drugs (Young, 2004; Kalivas & Duffy, 1995; Robinson, Becker, Young, Akil, & Castaneda, 1987). This region plays an important role in aversive conditioning and avoidance behavior in nonhuman animals (Hoebel, Avena, & Rada, 2007; Schwienbacher, Fendt, Richardson, & Schnitzler, 2004). This effect has also been shown in human fMRI studies in response to both conditioned and unconditioned aversive stimuli (Jensen et al., 2003; Gottfried, O'Doherty, & Dolan, 2002; Bechara, Damasio, & Damasio, 2000), but whether this effect emerges only in adulthood or if the striatum shows a similar role in adolescence remained unclear.

Most neurodevelopmental investigations of the striatum have focused on reward and appetitive processing (Galván, 2010), perhaps because studies in adults and animals have strongly implicated this region as a key node in motivational, learning and salience pathways (Shohamy, 2011; Smith, Berridge, & Aldridge, 2011) and because reward sensitivity is tightly linked to adolescent risk-taking (Steinberg, 2008). This focus may have had the unintentional consequence of limiting interest in probing whether this region also shows unique developmental activation patterns to aversion. Across participants and consistent with previous reports (Delgado, Jou, & Phelps, 2011; Small et al., 2003; Gottfried et al., 2002; Salamone, 1994), participants in our study recruited the striatum, amygdala, insula, and sensory regions upon delivery of aversive stimuli. Of particular interest, two regions showed opposite developmental patterns: although there was greater striatal activation in adolescents versus adults, adults exhibited greater insular activation compared with adolescents. This finding is quite intriguing because it suggests that neural signatures of sensitivity to aversive events might emerge gradually across the transition from adolescence into adulthood. Specifically, although adolescents recruit the same regions as adults (e.g., the striatum and insula), the degree to which they do so changes with development. In adolescence, individuals are hyperresponsive in the striatum, which has been associated with both reward (McClure et al., 2003) and punishment (Robinson, Frank, Sahakian, & Cools, 2010; Delgado et al., 2008) in adults, as noted above. We offer two possible explanations, both of which focus on dopamine fluctuations across the transition from adolescence to adulthood. Although fMRI does not afford us the resolution to examine dopamine activity to test either of these hypotheses, greater activation in striatum, a dopamine-rich region, may be an index of underlying neuronal activity (Logothetis, 2002). First, this exaggerated striatal response may simply be a reflection of greater dopamine activity in adolescents versus adults in general to both positive and negative events. We base this speculation on animal work suggesting that the dopamine system undergoes extensive remodeling across development, such that the striatum has a larger dopamine storage pool (Stamford, 1989) and releases greater dopamine levels (Teicher et al., 2003; Andersen & Gazzara, 1993) if stimulated by the environment (Laviola, Pascucci, & Pieretti, 2001) during adolescence relative to adulthood. Second, we speculate that heightened striatal sensitivity to aversion in the adolescent group may be based on the conditioning component of the task. Adolescence is a developmental period of heightened learning and plasticity (Spear, 2000). In rodents, aversive learning (pairing a cue with an aversive outcome) leads to greater dopamine release in the striatum (Pezze, Heidbreder, Feldon, & Murphy, 2001; Murphy, Pezze, Feldon, & Heidbreder, 2000). More recent rodent work suggests that engagement of the striatum in response to both positive and negative feedback facilitates and enhances learning during the transitional developmental period of adolescence (Robinson, Zitzman, Smith, & Spear, 2011; Brenhouse, Dumais, & Andersen, 2010). Interestingly, a recent article demonstrates that, relative to adults, adolescents showed better punishment- than reward-related learning (van der Schaaf, Warmerdam, Crone, & Cools, 2011). The same group also showed that, whereas adults and 11- to 13-year-olds learn from and recruit pre-SMA/ACC during negative feedback, 8- to 9-year-olds did not, which the authors interpret as an indication that early adolescence is a transition period toward an increased influence of negative feedback on learning and performance (van Duijvenvoorde, Zanolie, Rombouts, Raijmakers, & Crone, 2008). The possibility that exaggerated engagement of the striatum in adolescents relative to other age groups enhances learning and flexibility is intriguing and worth exploring further (Crone & Dahl, 2012).

Greater activation in the insula in the adults versus adolescents was a surprising, but intriguing, finding. The insula has been implicated in the affective component of visceral aversive stimuli (Lamm & Singer, 2010; Singer, Critchley, & Preuschoff, 2009) and in the subjective intensity of both one's own and others' emotional experiences (Wicker et al., 2003). Greater engagement of this region by adults perhaps suggests that they derive greater emotional significance from aversive outcomes than adolescents. This response may subsequently facilitate risk avoidance to a greater extent than it does during adolescence. The current data cannot directly address this speculation but future experiments should test if insula sensitivity to aversive outcomes influences subsequent risk taking differentially in adolescents versus adults.

We speculate that these novel findings have important implications for adolescent decision-making. Current theories of adolescent risk taking posit that adolescents are more reward-sensitive, but less punishment-sensitive, than adults. In other words, sensitivity to punishment shows a linear increase with age whereas reward sensitivity, sensation seeking, and risk proclivity show a curvilinear trajectory from childhood, adolescence, and into adulthood (Steinberg, 2008). Our current findings suggest that increased sensitivity to punishment from adolescence to adulthood is subserved by greater neural recruitment of the insula, given its important role in signaling potentially aversive outcomes (Christopoulos et al., 2009). Furthermore, our work challenges the claim that adolescents are less sensitive to negative outcomes. Instead, these data suggest that, although adolescents may not neurobiologically differ from adults during the anticipation of punishment (responses to aversive cues), regions that process aversive outcomes are actually more engaged in the consummatory component (delivery of aversive liquid). This adds to the growing literature suggesting that a more nuanced interpretation regarding developmental responses to reward is warranted (Bjork et al., 2011; Forbes et al., 2010; Geier et al., 2010). Geier and Luna (2011) report that temporally distinct stages of reward processing show unique developmental profiles in which the VS showed attenuated and heightened activation patterns during cue assessment and reward anticipation, respectively, in adolescents versus adults (Geier et al., 2010). Together, these reports underscore the need to consider seemingly subtle differences in the design, difficulty, and components of the task that influence the results and interpretations (Galván, 2010; Luna, Velanova, & Geier, 2010).

It should be noted that previous developmental studies have, indeed, examined the adolescent neural response to negative cues but most tended to focus on the amygdala in the context of social or emotional information (e.g., faces; Forbes, Phillips, Silk, Ryan, & Dahl, 2011; Guyer et al., 2008; Hare et al., 2008; Yang et al., 2007) or monetary loss (Ernst et al., 2005). These studies have generally found enhanced amygdala activation in adolescents compared with adults in response to negative information and during threat discrimination learning (Lau et al., 2011). These findings are consistent with the notion that limbic regions are generally more responsive to emotional, salient, or novel events during adolescence versus other developmental periods (Somerville, Jones, & Casey, 2010; Casey et al., 2008), which may render regulatory regions such as the pFC more vulnerable to incentive- or emotion-based modulation (Somerville & Casey, 2010). Our study contributes to this working hypothesis by showing that magnified limbic sensitivity is not specific to social and emotional processing (Blakemore, 2008) but that even the most basic stimuli (e.g., aversive liquid), even after controlling for the perceived value of the stimulus, trigger a heightened response in adolescents.

Reward Sensitivity across Development Is Influenced by Value

During reward processing, we found that both groups showed activation in frontostriatal and frontolimbic circuitry, replicating earlier work (Knutson & Cooper, 2005; O'Doherty et al., 2002). However, we were surprised that, developmentally, there was only a statistical trend toward greater VS activation in adolescents versus adults. This finding is in contrast to previous studies showing robust striatal “hypersensitivity” to rewards during adolescence (van Leijenhorst, Zanolie, et al., 2010; Geier & Luna, 2009; Galván et al., 2006; Ernst et al., 2005). A plausible explanation for these differences is that the use of money in the previous studies may have exaggerated developmental differences in VS hypersensitivity because of differences in how adolescents and adults value monetary rewards. In other words, if adolescents place greater value than adults on a particular amount of money, what may appear to be developmental differences may simply be valuation differences. Here, we were able to control for this confound by asking participants to provide a value rating of each trial, which ensured that stimulus value was equated across groups before fMRI analyses. Recent work (Geier & Luna, 2011) shows similar findings: when motivation for an incentive is equated between adolescents and adults, there are no developmental differences in behavioral responses.

Limitations

Although the study has notable strengths, there are limitations worth noting. First, the study draws on a relatively small sample size and further replication of findings is needed. Second, the relatively long trial length precluded inclusion of more trials. Third, the 3-hr fast before the scan could only be confirmed by self-report, and we did not collect ratings of hunger or thirst after the scan.

Conclusion

These data suggest that, during adolescence, the striatum exhibits heightened sensitivity to both appetitive and aversive outcomes whereas the insula shows dampened activation to aversion compared with adults. Given that sensitivity to punishment renders an individual less likely to engage in a behavior of interest, such as risk-taking, these data may have implications for tailoring appropriate consequences for adolescent risky decision-making. Furthermore, the current study suggests that the exaggerated activation in the striatum during adolescence goes above and beyond simply tracking reward.

On a broader scale, these findings suggest a general sensitivity to both positive and negative events in adolescents. The effects of positive reinforcement on academic and social behavior have been documented previously (Knox, 2010; Stipek, 1993), but there is still an ongoing conversation regarding youth discipline and punishment. For instance, it may contribute to research showing that harsh punishment (e.g., spanking) is not only ineffective but can lead to internalizing and externalizing behaviors (Maguire-Jack, Gromoske, & Berger, 2012; Knox, 2010); our findings suggest that this may be because youth have a more neurobiologically sensitive response to aversive events than adults. It may also inform juvenile justice policy in terms of the impact of punitive sanctions on adolescent development and behavior (i.e., the effectiveness of punishment; Steinberg, 2009). The current study is not designed to address these broader issues but does suggest that the general sensitivity to valenced stimuli in adolescents has the potential to have far-reaching implications on adolescent behavior.

Acknowledgments

This work was funded in part by the National Science Foundation (grant BCS-0963750) and University of California–Los Angeles Faculty Awards to A. G. We gratefully acknowledge assistance with data collection from E. B. L. and E. R.

Reprint requests should be sent to Adriana Galván, 1285 Franz Hall, Box 951563, Department of Psychology, University of California-Los Angeles, Los Angeles, CA 90095, or via e-mail: agalvan@ucla.edu.

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