Abstract
Recent research indicates gender differences in the impact of stress on decision behavior, but little is known about the brain mechanisms involved in these gender-specific stress effects. The current study used functional magnetic resonance imaging (fMRI) to determine whether induced stress resulted in gender-specific patterns of brain activation during a decision task involving monetary reward. Specifically, we manipulated physiological stress levels using a cold pressor task, prior to a risky decision making task. Healthy men (n = 24, 12 stressed) and women (n = 23, 11 stressed) completed the decision task after either cold pressor stress or a control task during the period of cortisol response to the cold pressor. Gender differences in behavior were present in stressed participants but not controls, such that stress led to greater reward collection and faster decision speed in males but less reward collection and slower decision speed in females. A gender-by-stress interaction was observed for the dorsal striatum and anterior insula. With cold stress, activation in these regions was increased in males but decreased in females. The findings of this study indicate that the impact of stress on reward-related decision processing differs depending on gender.
INTRODUCTION
Recent experimental studies reveal stress-induced alterations to motivated decision making: stress alters reward learning (Cavanagh et al., 2011; Petzold et al., 2010), risk taking (Preston et al., 2007; Starcke et al., 2008; Lighthall et al., 2009; Porcelli and Delgado, 2009; van den Bos et al., 2009), reward responsivity (Bogdan and Pizzagalli, 2006; Ossewaarde et al., 2011) and decision-making speed (Porcelli and Delgado, 2009; van den Bos et al., 2009). Furthermore, several studies have observed gender-dependent effects of stress, including our previous work (Lighthall et al., 2009), which examined the impact of cold pressor stress (Lovallo, 1975) on subsequent decision behavior for the Balloon Analogue Risk Task (BART) (Lejuez et al., 2002). The BART is a risky decision task that involves pumping up a series of computerized balloons in order to earn reward. Balloons may be ‘cashed out’ to collect earnings at any time, with larger balloons yielding greater earnings. However, each additional pump increases the risk of an explosion that eliminates earnings for that balloon. Thus, earnings on the BART are optimized by balancing some risk taking to earn reward while avoiding too many explosions. Our previous study revealed that men and women had similar BART behavior and earnings under control conditions but diverged with stress (Lighthall et al., 2009). Specifically, stress increased earnings and risk taking under uncertainty in males, but decreased earnings and risk taking for females. Studies using psychological stressors and the Iowa Gambling Task report consistent findings (Preston et al., 2007; van den Bos et al., 2009). In men, psychological stress led to more high-risk disadvantageous choices. In women, increased stress led to more low-risk advantageous choices, with some decline in females’ performance at the highest levels of stress response (van den Bos et al., 2009). In a study of men alone, pharmacologically elevated stress hormone levels (cortisol) resulted in increased risk-taking behavior (Putman et al., 2010). Thus, at least in males, activation of the hypothalamic–pituitary–adrenal (HPA) axis appears to influence risk-related decision making. In addition, stress from threat of shock has been found to decrease women’s reward responsiveness (Bogdan and Pizzagalli, 2006), presenting the possibility that stress decreases women’s risk taking by diminishing the drive for larger rewards. Supporting this proposition, a recent study found that exposure to mild psychological stress (aversive movie clips) resulted in diminished reward-related activation of the medial prefrontal cortex (PFC) in women during a monetary incentive task (Ossewaarde et al., 2011). However, as males were not included in this study, it is unclear how gender may modulate stress effects on neural response to reward.
Little is known about the neural underpinnings of these gender-specific stress effects. To our knowledge, the present study is the first functional magnetic resonance imaging (fMRI) study to examine gender differences in response to decision making among individuals exposed to stress. We used the BART as our decision task as we have previously observed gender differences in stress effects with this task (Lighthall et al., 2009). Given the dearth of research on neural mechanisms of gender–stress interactions in decision processing, hypotheses for the brain regions mediating these interactions were derived by identifying brain regions that have been independently associated with (i) gender-related differences in response to stress and (ii) decision making on the BART.
Brain regions involved in decision making are also affected by acute stress (Dedovic et al., 2009 for review) and show gender differences in stress response. For example, Wang et al. (2007) exposed males and females to varying levels of a psychological stressor with mental arithmetic during a perfusion fMRI scan. Stress increased cerebral blood flow in the right PFC and decreased blood flow in the left orbitofrontal cortex in men, but increased blood flow responses in limbic structures including the insula, cingulate cortex, ventral striatum and dorsal striatum (putamen) in women. Further, cortisol reactivity predicted neural response to stress more in men than in women. Gender differences in neural response to visceral stress have also been observed, with greater stress responses in the ventromedial PFC, right anterior cingulate and left amygdala in women, but greater activation of the insula and right dorsolateral PFC in men (Naliboff et al., 2003). Presenting negative pictures to elicit stress in men and women revealed stronger amygdala and anterior cingulate responses in males when compared with females, with the magnitude of gender differences depending on menstrual cycle phase (Goldstein et al., 2010). Several brain regions showing gender-specific stress responses have also been associated with decision behavior on an fMRI-adapted BART (Rao et al., 2008). In this study, voluntary risk taking was found to rely on the striatum, anterior insula, midbrain, dorsolateral PFC and anterior cingulate/medial PFC. Thus, the common regions mediating BART behavior and gender–stress interactions appear to include the striatum, anterior insula and PFC, leading us to hypothesize that one or more of these regions would moderate gender differences in stress effects on decision making in our fMRI-adapted BART.
In the current study, healthy males and females were exposed to either the cold pressor stress task or a control task prior to playing an fMRI-adapted version of the BART, which included real monetary outcomes. Salivary cortisol was collected to confirm an elevated HPA axis response during decision processing among stressed participants. Based on our previous behavioral findings and those of others, we predicted different effects of stress on behavior and brain activation for males and females. More specifically, we predicted that stress would enhance risk taking in males but diminish risk taking in females. Further, we hypothesized that, depending on gender, stress would alter neural decision responses to the BART in one or more of our regions of interest. Given the lack of previous studies examining gender–stress interactions in neural response to decision making, we did not make directional hypotheses for effects on brain activation.
METHODS
Participants
Twenty-three females (in the age group of 18–31 years, Mage = 21.8 ± 3.6, 11 stressed) and 24 males (in the age group of 18–33 years, Mage = 23.0 ± 3.6, 12 stressed) participated in the study. All provided written informed consent approved by the University of Southern California (USC) Institutional Review Board. All were right-handed, nonsmokers free of hormone birth control, corticosteroid medications or β-adrenergic agonists. For further details on the sample, see ‘Methods’ in Supplementary Data.
Protocol
The study was conducted from 2 to 5 pm to reduce diurnal variations in cortisol levels. Participants completed psycho-social questionnaires and drank 8 oz of water (completed ≥10 min before the first saliva sample). Participants received scan- and task-related instructions and practiced two abbreviated trials of each task condition. Baseline saliva samples were then collected followed by either cold pressor stress or a control task outside the scanner. Next, participants entered the scanner and a brief structural scan was conducted. The decision task with fMRI followed, beginning ∼24 min after the start of the stress manipulation. To measure HPA axis response to the cold pressor or control task, saliva samples were taken immediately before and after the decision task, while participants were in the scanner. At the end of the session, participants provided postexperiment ratings of stress experienced during the hand immersion task and fMRI scan (7-point Likert scale; 1 = no stress, 7 = a great deal of stress) as well as ratings of effort put into the BART (7-point Likert scale; 1 = no effort, 7 = a great deal of effort).
Stress induction
The cold pressor task (Lovallo, 1975) was used to induce a stress response in one-half of the participants of each gender. The cold pressor involves holding one’s hand in ice water for as long as possible up to 3 min; the control task was identical except that the water was approximately body temperature. For more details, see Methods in Supplementary Data.
Salivary biomarkers
Before the manipulation, participants provided 1 ml of saliva (s1) for the assessment of baseline cortisol. Immediately before (s2) and after (s3) the decision task (21 and 35 min after the start of the manipulation, respectively), saliva samples for cortisol assay were collected while participants lay in the scanner bore. Based on prior research (Dickerson and Kemeny, 2004; see also Schwabe et al., 2008), cortisol responses to stress were expected to be at their peak during the decision task. Poststress samples were collected using sorbettes (Salimetrics, LLC, State College, PA, USA). The difference between s1 (baseline) cortisol levels and the average of s2 and s3 levels was used to measure cortisol change. Postexperiment, samples were stored in a laboratory freezer at −30°C and later transported frozen to a CLIA-certified laboratory (Salimetrics, LLC, State College, PA, USA) and stored frozen at −80°C until assayed. Samples were centrifuged on the day of assay at 3000 rpm for 15 min to remove mucins and samples were duplicate tested.
Decision task
The BART (Lejuez et al., 2002) was adapted to allow for a blocked design and programmed using MATLAB software (The Mathworks, Inc., Natick, MA, USA). The task included four ‘active’ blocks (Figure 1A) and four ‘passive’ blocks (Figure 1B). For a full description of the decision task, see Methods in Supplementary Data.
Two versions of the adapted BART included (A) an active task (PLAY GAME screen) in which participants decided how large to inflate a series of balloons in order to earn money which accumulated with each ‘pump’ while trying to avoid ‘explosions’ which resulted in a loss of earnings for the current balloon and a balloon popping sound effect. At any point, participants could decision to ‘cash out’ their earnings for the current balloon, which transferred current balloon earnings to the permanent bank and resulted in a cash register sound effect. In (B) the passive task (KEEP CLICKING screen) participants could not earn or lose money but were instructed to press the pump balloon button throughout the block. In the passive task, the ‘Cash out $$$’ button was disabled and balloons appeared at random sizes while cash out and explosion sounds occurred randomly. Fixation periods (10 s) appeared in between alternating active and passive task blocks (four blocks per condition).
Imaging data acquisition
Imaging was done using a 3 T Siemens MAGNETOM Trio scanner with a 12-channel matrix head coil at the USC Dornsife Cognitive Neuroscience Imaging Center. Functional scans were acquired in a single 9.5 min run, with a repetition time of 2000 ms in a T2*-sensitive echo-planar imaging sequence (echo time, 25 ms; flip angle, 90°). Volumes included 31 slices at 3.5-mm thickness (in-plane resolution, 3 × 3 mm; no gap; matrix size = 64 × 64) extended axially from the temporal lobe to the top of the skull. Prior to the functional scan, high-resolution structural scans were acquired using a T1-weighted MPRAGE sequence (resolution, 1 × 1 × 1 mm; repetition time, 1950 ms; echo time, 2.26 ms; flip angle, 7°).
Whole-brain analysis
Whole-brain analyses were conducted with FMRIB's Software Library (FSL; www.fmrib.ox.ac.uk/fsl) using FSL FEAT v. 5.98. Preprocessing included: motion correction with MCFLIRT, spatial smoothing with a Gaussian kernel of full-width half-maximum 5 mm, high-pass temporal filtering equivalent to 140 s and skull stripping of structural images with BET. MELODIC ICA (Beckmann and Smith, 2004) was used to remove noise components (see Methods in Supplementary Data for further details). Registration was performed with FLIRT; each functional image was registered to both the participant’s high-resolution brain-extracted structural image and the standard Montreal Neurological Institute (MNI) average of 152 brains (with 2-mm voxel resolution) using an affine transformation with 12 degrees of freedom.
The individual time series statistical analysis was carried out using FILM (Woolrich et al., 2001) with local autocorrelation correction. Both explanatory variable regressors (active, passive), convolved with a double-gamma hemodynamic response function and their temporal derivates were used to model data. The primary lower level contrasts were conducted for active–passive and its inverse. Higher level mixed-effects analysis was carried out using FMRIB’s local analysis of mixed effects (FLAME 1+2; Beckmann et al., 2003). The general linear model (GLM) included two between-subject conditions, each with two levels: gender (male, female) and stress (cold pressor, control task). Unequal variance among the four gender/stress groups was assumed. The GLM was used to test for main effects of stress and gender and their interaction for the two lower-level contrasts. In these lower level and higher level analyses, Z (Gaussianized T/F) statistic images were corrected for multiple comparisons with clusters determined by Z > 2.3 voxel-wise thresholding and a family-wise error-corrected cluster significance threshold of P < 0.05 (Worsley, 2001). To facilitate communication of results including clusters spanning several brain regions (revealed by the whole-brain analysis at Z > 2.3, cluster threshold P < 0.05), more stringent thresholds were applied in some cases (see Supplementary Tables S1 and S2).
Region-of-interest analysis
The whole-brain analyses described above test for gender-by-stress interactions, but do not indicate which group showed more task-related activation. As post hoc tests to characterize the direction and relative magnitude of gender-specific stress effects, regions-of-interest (ROIs) were created. These were based on the significant clusters of activation revealed in the whole-brain gender-by-stress interactions that were hypothesized to mediate gender–stress interactions. For significant clusters, spanning multiple brain regions, anatomical borders for ROIs were structurally defined using masks from FSL’s MNI structural atlas (based on probabilistic map; P = 0.5). Average percent signal change values were determined for each ROI. To determine the relationship between individual differences in ROI activation and other outcome measures, Pearson’s correlations were conducted across and within groups.
RESULTS
Salivary cortisol
The cold pressor nearly doubled mean cortisol levels in stress subjects, while cortisol levels did not change in controls, F1,43 = 25.56, P = 0.000002 (Figure 2). Although mean cortisol levels were higher in males, overall, F1,43 = 5.15, P = 0.03, there were no main effects of gender on cortisol change, F1,43 < 1, nor was there a gender-by-stress condition interaction, F1,43 < 1. The effect of the stressor on cortisol elevation remained highly significant after excluding participants from the analysis who did not complete the full 3-min cold pressor challenge (see Results in Supplementary Data). These results indicate that the cold pressor reliably elevated cortisol levels without significant gender-specific effects.
Cortisol levels increased significantly in the stress group (cold pressor) but not in the control group (warm water). Cortisol responses to stress were not dependent on gender. Poststress cortisol is the average of cortisol at 21 and 35 min after the start of the hand immersion task (s2, s3, respectively); immediately before and just after the decision task. Error bars represent s.e.m.
Subjective stress: hand immersion task
Postexperiment ratings of stress resulting from the hand immersion task indicated that subjective stress experienced by participants in the cold pressor group was greater than that experienced by the control group, F1,43 = 231.75, P < 0.000001 (Mstress = 5.11 ± 1.41; Mcontrol = 1.04 ± 0.21). Subjective responses to the cold pressor were greater in women than in men, F1,43 = 4.57, P = 0.04 (Mwomen = 5.73 ± 0.91; Mmen = 4.50 ± 1.57), with no gender differences in the control group (Mwomen = 1.0 ± 0.00; Mmen = 1.08 ± 0.29), resulting in a gender-by-stress interaction, F1,43 = 6.00, P = 0.02. Additional analyses indicated that gender differences in stress-modulated decision processing were not simply the result of greater subjective stress response in females compared with males (see Results in Supplementary Data).
Behavioral data
Consistent with our previous behavioral findings (Lighthall et al., 2009), BART behavior and earnings were similar for males and females under control conditions, but diverged with stress, leading to a gender-by-stress interaction. As displayed in Figure 3, stress increased gender differences in reward collection rate (mean number of balloons ‘cashed out’), F1,43 = 4.82, P = 0.03, decision speed (button pressing intervals), F1,43 = 4.79, P = 0.03 and total earnings, F1,43 = 7.93, P = 0.007. Examination of confidence intervals indicated no gender differences in these outcome measures in the control condition, only in the stress condition. While gender-by-stress interactions were observed for these measures, individual cortisol change values were not correlated with any of the behavioral outcomes across conditions or within any groups (P’s > 0.05). Regarding our measure of risk taking (mean number of pumps per balloon for nonexploding balloons), we did not find any differences by stress condition, F1,43 < 1, gender, F1,43 < 1, nor was there a gender-by-stress interaction, F1,43 < 1. Relative to the number of pumps possible per balloon (maximum = 90), the average number of pumps was fairly low across groups (M = 19.39 ± 2.38), indicating low risk taking overall. As the likelihood of losses (‘explosions’) during the BART increased with the number of pumps per balloon, the average number of explosions experienced per block was also low across conditions (M = 2.59 ± 0.31). There were no significant group differences or interactions in the number of explosions per block (P’s > 0.05); as may be expected given the low number of pumps per balloon on average.
Gender-by-stress effects on behavior and earnings in the BART. Group means for number of balloons completed per active block (A), decision speed (B) and total earnings in US dollars across all four active blocks (C). Error bars represent s.e.m.
While risk taking was not modulated by gender or stress in this study, our fMRI-adapted BART differed from the original task (Lejuez et al., 2002) in that the number of balloons played was only limited by the duration of active blocks. This introduced a potentially successful strategy—not present in the original BART—of playing as many balloons as quickly as possible in order to earn more money. Notably, Pearson’s correlations confirmed that balloon count and decision speed were related to earnings (R47 = 0.33, P = 0.03; R47 = −0.76, P < 0.000001, respectively). Direction of stress effects by gender indicated that, from an earnings standpoint, stress led to more profitable decision behavior in males but less profitable behavior in females
See Supplementary Data in the Results section for ‘Subjective stress: Scan session and BART effort’.
Whole-brain analyses
As expected, decision-related activation (active–passive contrast) across groups was observed in regions associated with motivation and decision making. In particular, the decision task resulted in robust activation of the thalamus, putamen, caudate, anterior cingulate, dorsolateral and ventrolateral PFC, insula, inferior parietal lobe and inferior frontal gyrus. Significant clusters were also apparent in sensorimotor and visual structures (Supplementary Table S1). Further details on passive task-related activation and group differences by gender and stress can be found in Results in Supplementary Data. Of primary interest, group level analysis of decision-related activation revealed gender-by-stress interactions in motivation and decision regions; most notably in the left dorsal striatum (putamen) and left anterior insula (Figure 4 and Table 1). Gender–stress interactions were also apparent in sensorimotor and visual regions.
Whole-brain analysis revealed significant gender–stress interactions for the active vs passive contrast in the left putamen and left anterior insula (A) with participant as a random factor (Z > 2.3; FWE-corrected cluster significance threshold of P < 0.05) in the mixed-effects analysis. To ensure we extracted signal change values separately for the putamen and insula, we used structurally defined regions of the putamen and insula from FSL’s MNI structural atlas (B) to mask these significant clusters from the mixed-effects analysis (C). Mean percent signal change was greater in the stress condition for males and diminished in the stress condition in females in the left putamen (D) and left anterior insula (E).
Regions with activation differences by gender and stress conditions (for lower level active– passive contrast).
| Brain Region | BA | H | MNI Peak Coordinates | Zmax value | Voxels | ||
|---|---|---|---|---|---|---|---|
| x | y | z | |||||
| Precuneus | 7 | L | −28 | −52 | 56 | 4.6 | 3276 |
| Inferior parietal lobule | 40 | L | −48 | −30 | 40 | 4.5 | |
| Postcentral gyrus | 40 | L | −62 | −28 | 24 | 4.5 | |
| Precentral gyrus | 6 | L | −56 | −4 | 32 | 4.4 | |
| Inferior frontal gyrus | 47 | L | −38 | 28 | −4 | 4.2 | 1247 |
| Putamen | L | −22 | 20 | −2 | 4.1 | ||
| Superior temporal gyrus | 22 | L | −48 | 0 | −6 | 3.7 | |
| Middle occipital gyrus | 37 | R | 52 | −70 | −6 | 3.7 | 611 |
| Inferior occipital gyrus | 18 | R | 38 | −86 | 0 | 3.6 | |
| Middle occipital gyrus | 18 | R | 40 | −82 | −8 | 3.6 | |
| Inferior temporal gyrus | 37 | R | 48 | −70 | 2 | 3.5 | |
| Brain Region | BA | H | MNI Peak Coordinates | Zmax value | Voxels | ||
|---|---|---|---|---|---|---|---|
| x | y | z | |||||
| Precuneus | 7 | L | −28 | −52 | 56 | 4.6 | 3276 |
| Inferior parietal lobule | 40 | L | −48 | −30 | 40 | 4.5 | |
| Postcentral gyrus | 40 | L | −62 | −28 | 24 | 4.5 | |
| Precentral gyrus | 6 | L | −56 | −4 | 32 | 4.4 | |
| Inferior frontal gyrus | 47 | L | −38 | 28 | −4 | 4.2 | 1247 |
| Putamen | L | −22 | 20 | −2 | 4.1 | ||
| Superior temporal gyrus | 22 | L | −48 | 0 | −6 | 3.7 | |
| Middle occipital gyrus | 37 | R | 52 | −70 | −6 | 3.7 | 611 |
| Inferior occipital gyrus | 18 | R | 38 | −86 | 0 | 3.6 | |
| Middle occipital gyrus | 18 | R | 40 | −82 | −8 | 3.6 | |
| Inferior temporal gyrus | 37 | R | 48 | −70 | 2 | 3.5 | |
Threshold set to Z = 2.3 and a FWE-corrected cluster significance threshold of P = 0.05.
Clusters > 50 voxels displayed here.
BA: Brodmann area; H: hemisphere.
Regions with activation differences by gender and stress conditions (for lower level active– passive contrast).
| Brain Region | BA | H | MNI Peak Coordinates | Zmax value | Voxels | ||
|---|---|---|---|---|---|---|---|
| x | y | z | |||||
| Precuneus | 7 | L | −28 | −52 | 56 | 4.6 | 3276 |
| Inferior parietal lobule | 40 | L | −48 | −30 | 40 | 4.5 | |
| Postcentral gyrus | 40 | L | −62 | −28 | 24 | 4.5 | |
| Precentral gyrus | 6 | L | −56 | −4 | 32 | 4.4 | |
| Inferior frontal gyrus | 47 | L | −38 | 28 | −4 | 4.2 | 1247 |
| Putamen | L | −22 | 20 | −2 | 4.1 | ||
| Superior temporal gyrus | 22 | L | −48 | 0 | −6 | 3.7 | |
| Middle occipital gyrus | 37 | R | 52 | −70 | −6 | 3.7 | 611 |
| Inferior occipital gyrus | 18 | R | 38 | −86 | 0 | 3.6 | |
| Middle occipital gyrus | 18 | R | 40 | −82 | −8 | 3.6 | |
| Inferior temporal gyrus | 37 | R | 48 | −70 | 2 | 3.5 | |
| Brain Region | BA | H | MNI Peak Coordinates | Zmax value | Voxels | ||
|---|---|---|---|---|---|---|---|
| x | y | z | |||||
| Precuneus | 7 | L | −28 | −52 | 56 | 4.6 | 3276 |
| Inferior parietal lobule | 40 | L | −48 | −30 | 40 | 4.5 | |
| Postcentral gyrus | 40 | L | −62 | −28 | 24 | 4.5 | |
| Precentral gyrus | 6 | L | −56 | −4 | 32 | 4.4 | |
| Inferior frontal gyrus | 47 | L | −38 | 28 | −4 | 4.2 | 1247 |
| Putamen | L | −22 | 20 | −2 | 4.1 | ||
| Superior temporal gyrus | 22 | L | −48 | 0 | −6 | 3.7 | |
| Middle occipital gyrus | 37 | R | 52 | −70 | −6 | 3.7 | 611 |
| Inferior occipital gyrus | 18 | R | 38 | −86 | 0 | 3.6 | |
| Middle occipital gyrus | 18 | R | 40 | −82 | −8 | 3.6 | |
| Inferior temporal gyrus | 37 | R | 48 | −70 | 2 | 3.5 | |
Threshold set to Z = 2.3 and a FWE-corrected cluster significance threshold of P = 0.05.
Clusters > 50 voxels displayed here.
BA: Brodmann area; H: hemisphere.
ROI analyses
We anticipated gender–stress interactions for brain activation response to reward-related decision processing in the insula, PFC and striatum. The whole-brain analysis revealed gender-by-stress interactions in the dorsal striatum (putamen) and anterior insula (Figure 4A–C). We examined the direction of effects in these ROIs by extracting mean percent signal change values by group for the lower level contrast of active–passive. An ANOVA was performed on signal change values with gender and stress condition as between-subject factors and significant interactions were found for both the left dorsal striatum, F1,43 = 22.51, P < 0.0001 and the left anterior insula, F1,43 = 6.88, P < 0.05. Examination of group means revealed that stress increased activation in the dorsal striatum and anterior insula for males during decision making but decreased activation in these regions for females (Figure 4D and E). Dorsal striatum activation did not appear to be the result of differences in motor movements alone between the active and passive conditions (see Results in Supplementary Data). Further, an Independent Component Analysis (ICA) (Calhoun et al., 2001) was conducted to identify functional networks in the brain that were differentially involved in the active BART depending on one’s gender and stress status (see Methods and Results in Supplementary Data). The ICA results largely confirm results from the whole-brain GLM and ROI analyses, suggesting that males and females generally relied on the same network of brain regions to complete the BART; but under stress, there were gender differences in the involvement of the putamen and insula in this network.
Correlations were determined for cortisol change and activation in dorsal striatum and insula ROIs across and within conditions. For males only, change in cortisol predicted activation in the dorsal striatum ROI during decision processing (R24 = 0.55, P = 0.005; all other P’s > 0.05); change in cortisol did not predict insula activation for any group (Ps > 0.05). Thus, in males, higher levels of physiological stress response were associated with enhanced dorsal striatum response to reward-related decision making.
Behavior and ROI correlations
A full description of correlation analysis for behavior and ROI activation can be found in the Results in Supplementary Data. Of particular interest, there was a relationship between anterior insula ROI activation and number of balloons cashed (R23 = 0.60, P = 0.002) for the stress group. In addition, for stress males alone, analyses revealed a positive correlation for dorsal striatum ROI activation and number of balloons cashed (R12 = 0.90, P < 0.000001) and a negative correlation between dorsal striatum ROI activation and risk taking (R12 = −0.75, P = 0.005). No other significant relationships were observed within gender–stress groups.
DISCUSSION
Exposure to cold pressor stress resulted in differential reward-related decision processing on a risky decision task in males and females. Specifically, stress exposure affected behavior and brain activity during the decision task in opposite ways for men and women. Across conditions the risky decision task elicited robust responses in reward- and decision-associated regions including the thalamus, striatum, anterior cingulate, insula, as well as prefrontal and parietal regions (for reviews, see Ernst and Paulus, 2005; Taylor et al., 2007; Clark, 2010; Haber and Knutson, 2010). The decision task was timed to occur during HPA axis response to cold stress (∼24 min poststress). Salivary hormone measures confirmed a significant elevation in cortisol for the stress group during decision making with no gender differences in cortisol response. We also observed no gender differences in decision behavior or neural response to decision making under control conditions. With stress, however, decision behavior diverged for males and females for several outcome measures; but not risk taking, which was low across groups. This finding runs counter to our prediction that risk taking would increase in males and decrease in females with stress, but task design limitations may explain this finding as discussed below.
We did observe gender-dependent stress effects for number of balloons ‘cashed out’ (reward collection rate), decision speed and total money earned. Relative to controls, males exposed to cold stress exhibited more profitable decision behavior which included faster decision responses and more cashed balloons, while stressed females had slower decision responses and fewer cashed balloons resulting in diminished earnings. Notably, these behavioral differences were associated with group and individual differences in brain activation. The fMRI results presented here are the first we know of to demonstrate that exposure to an acute stressor affects brain activity during motivated decision making differently for healthy men and women. Consistent with our predictions, the striatum and insula were associated with gender-dependent stress effects on decision processing. Specifically, exposure to cold stress increased neural response to the risky decision task in the dorsal striatum (putamen) and anterior insula among men, but decreased response in these regions among women.
Differences in correlations between activation of these ROIs and behavior by group shed light on the underlying mechanisms of gender-dependent stress effects in this decision task. For participants exposed to cold stress, activation of the anterior insula ROI was associated with reward collection rate. Relevant to this finding, the anterior insula has been associated with riskless choices and behavioral switching from risky to safe choices (Kuhnen and Knutson, 2005). Risk taking was low overall but with each additional pump of the balloon, the chance of losses (‘explosions’) increased. Thus, making the decision to stop inflating a balloon and collect its earnings, reflects a switch from taking risk to making a ‘safe’ decision. The relationship between insula ROI activation and reward collection rate was similar across stressed males and females, suggesting that the anterior insula mediated cash-out choices under stress for both genders.
Stress did not alter risk taking in men or women as previously observed (Preston et al., 2007; Lighthall et al., 2009; van den Bos et al., 2009). In fact, participants displayed low risk-taking behavior (number of pumps per balloon) across groups. The absence of group differences in risk taking is likely due to changes made to the BART for compatibility with fMRI analysis. In particular, the original BART (Lejuez et al., 2002) limited the number of balloons, while our version of this risky decision task did not limit the balloon number but instead limited the total time available to play the game. Thus, in our task, participants could increase earnings by increasing decision speed and keeping balloons relatively small to reduce risk of losses. While this design choice may have limited our ability to observe differences in risk processing, a notable strength of our design was that participants could respond flexibly in a way that captured the impact of stress on motivated decision processes.
Indeed, because multiple strategies could be used to increase profits in our decision task, the present study provides new insight into the conditions under which men and women may become more risk seeking or risk averse. That is, this version of the BART presented two potentially profitable strategies: (i) fast decision speed with greater risk taking and longer reward delays (large balloons, cashed intermittently) or (ii) fast decision speed with less risk taking and shorter reward delays (small balloons, cashed frequently). Our results revealed that when an alternative low-risk option was present that provided rapid delivery of small rewards during the entire active block, males were biased toward this option under stress. This stress-related shift in behavior appeared to be related to activation of the dorsal striatum as, in stressed males alone, activation in this ROI was significantly associated with an increased reward collection rate and less risk taking. Further, stress-related effects in males included increased decision speed, consistent with more automatic processing (Porcelli and Delgado, 2009). In addition, among males only, cortisol change from baseline to the decision task was positively correlated with decision-related activation in the dorsal striatum. This finding is consistent with previous reports of stronger relationships between cortisol and neural response to stress in males (Wang et al., 2007) and further suggests that males and females differ in the degree to which acute fluctuations in cortisol predict neural response to motivated decision making. Relatedly, some recent evidence suggests that male traders’ cortisol responses to volatile financial markets may result in exaggerated market movements (Coates and Herbert, 2008). An important avenue for future research will be to determine whether real-life financial decisions, including stock trading, are differentially affected by physiological stress responses in men and women.
Compared with female controls, stressed females in our study exhibited decreased dorsal striatum activation, slower decision speeds and fewer reward collections. Our findings are consistent with other reports of decreased reward responsiveness in stress-exposed females (Bogdan and Pizzagalli, 2006; Ossewaarde et al., 2011); as, in our study, stressed females tended to collect their earnings less frequently (i.e. decreased drive for small rewards) while stress did not affect risk taking. Furthermore, in contrast to males, exposure to cold stress led to slower decision speed in females, perhaps indicative of more deliberative processing under stress in females. These stress effects in women are in line with a previously observed trend toward greater explicit knowledge about game contingencies in females with increasing stress response, but opposite patterns in males (Preston et al., 2007). Together, with stress effects, we observed in males, these findings support the conclusion that the dorsal striatum mediated gender–stress interactions in level of automatic and reward-driven processing for the BART. The dorsal striatum is thought to integrate sensorimotor, cognitive and motivational, as well as emotional signals (Balleine et al., 2007). In decision making, this region appears to play a role in obtaining predictable rewards (Doya, 2008). For example, single-cell recordings with monkeys show activation of the dorsal striatum during execution of well-learned behaviors resulting in a juice reward (Miyachi et al., 2002). In our study, dorsal striatum activation was associated with reward-motivated behavior that carried little risk. That is, cashing out many smaller balloons quickly to accumulate small—but predictable—rewards. This behavior is also consistent with the proposed role of the dorsal striatum as the ‘instrumental actor’ that maintains information action–reward associations (O’Doherty et al., 2004).
While this study provides new information about gender–stress interactions in motivated decision processing, further research is needed to better understand interaction mechanisms. In particular, future studies may use more controlled fMRI tasks to examine stress effects on specific decision components (e.g. Bolla et al., 2004; Rao et al., 2008; Xue et al., 2010). For example, studies may test stress effects on level of automatic processing or reward responsiveness among men and women. Implementation of different stressors may also help to determine the precise mechanisms of gender differences in decision making under stress. We chose to use the cold pressor stress task, which resulted in equivalent and sustained cortisol responses in men and women. Some gender differences in subjective stress response to the cold pressor were observed, which may have reflected real differences in psychological stress and/or other factors, such as gender-related social norms about expressing pain-related stress. With respect to social factors, our study included a female experimenter at each session, which has been related to under-rerported pain, unpleasantness and arousal in males exposed to a thermal stressors; even when their physiological response is similar to female subjects (Levine and De Simone, 1991; Aslaksen et al., 2007). Although our behavioral and fMRI findings were largely unaffected after we controlled for gender differences in subjective stress response, important insights can be gained from studies that specifically examine the relationship between levels of psychological distress and decision processing. In particular, we did not find gender–stress interactions in PFC response to decision making as hypothesized, but it is possible that our choice of stressor impacted our ability to observe group differences in this region. This proposal is supported by research indicating that exposure to a psychological stressor (aversive movie clips) altered PFC response to reward processing among females (Ossewaarde et al., 2011). Finally, from an earnings perspective, more deliberative processing among stressed females was not beneficial in our risky decision task due to time constraints. A full understanding of gender–stress interactions in decision making requires consideration of decision tasks in which optimal behavior is associated with thoughtful and rational processing. It may be in these situations that women perform best under stress.
In sum, the current study found that cold pressor stress altered motivated decision making on a risky decision-making task and did so in a gender-specific manner. Behavioral results indicated that risk taking was not altered by stress when an alternative option was present that offered rapid delivery of small rewards under a time constraint. In addition, neural substrates of reward-motivated decision making for this task, including the dorsal striatum and anterior insula, were differentially altered by stress exposure in males and females. The present study also found differences in decision speed between men and women only after stress exposure, which raises the possibility that stress leads to gender differences in levels of processing. While the current study contributes to our understanding of the neural mechanisms of these gender–stress interactions, it also begs the larger question about why such interactions exist. Addressing this question is likely to require consideration of individual effects of social environment, genetics, sex hormones, development and their interactions.
Conflict of Interest
None declared.
We would like to thank Andrej Schoeke for his assistance in data analysis. This work was supported by the National Institute on Aging (R21AG030758, R01AG038043, K02AG032309 and 5T32AG000037).
