Disentangling the Neural Underpinnings of Risk and Reward in Human Decision Making

Disentangling the Neural Underpinnings of Risk and Reward in Human Decision Making | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Disentangling the Neural Underpinnings of Risk and Reward in Human Decision Making Xinyi Deng, Minwoo Lee, Marlen Gonzalez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6837700/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Risk-taking is a fundamental human behavior subserved by separable cognitive processes. Understanding how these processes are represented in the brain offers critical insights into decision-making, development, and vulnerabilities to psychopathology. However, functional magnetic resonance imaging (fMRI) research often yokes risk and reward processes during risky decision-making and has limited sensitivity to deep subcortical regions, constraining our contributions. In this study, we present a modified Balloon Analogue Risk Task completed under a multi-echo fMRI protocol meant to enhance subcortical signal. Forty-eight participants inflated virtual balloons across three conditions: risky reward, guaranteed reward, and neutral. GLM analyses revealed increased signal in orbitofrontal cortex, anterior insula (AI), striatum and a brain stem nuclei, ventral tegmental area (VTA), during risky versus guaranteed rewards decision-making conditions. Multivariate analysis identified the AI as a key predictor of the risk condition, surpassing striatal and VTA contributions. These results suggest that neural response to reward-based decision-making is heightened under risk and illuminates putative neurobiological mechanisms which uniquely subserve risk separate from general reward processing. The study also provides a new tool to enhance the resolution of human neuroscience research on risk-taking across the lifespan and vulnerabilities to psychopathology. Biological sciences/Psychology/Human behaviour Biological sciences/Neuroscience/Reward Biological sciences/Physiology/Neurophysiology Biological sciences/Neuroscience/Cognitive neuroscience/Decision multi-echo fMRI risk tolerance reward sensitivity caudate NAcc putamen Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Risk-taking, or the willingness to pursue potential gains in the face of uncertain outcomes, represents a fundamental dimension of decision-making and plays a central role in everyday and life-altering choices. It involves the ability to evaluate and act on possible rewards and loss, an adaptive function evolved in organisms navigating uncertain environments towards maximizing survival and reproductive success (Mishra, 2014). Differences in risk-taking tendencies emerge across the lifespan, especially in adolescence, and alterations of risk-taking are core features of many clinical conditions, such as substance use, pathological gambling, and avoidant personality disorder (Chase et al., 2017; Rieser et al., 2019; Sanislow et al., 2012). Studying the proximate mechanisms that support risk-taking is therefore crucial, not only for advancing our understanding of human decision-making across the lifespan but also for designing more effective and targeted interventions aimed at addressing excessive or deficient risk-taking. Over the past two decades, functional magnetic resonance imaging (fMRI) has provided invaluable insights into the cognitive and affective processes underlying risk-related decisions. The Balloon Analogue Risk Task (BART) has especially emerged as one of the most widely used experimental paradigms (Lejuez et al., 2002). In its canonical implementation, BART allows participants to earn monetary rewards by sequentially inflating a virtual balloon, knowing that each pump increases both the potential reward and the risk of popping the balloon and losing all earnings for that trial. Importantly, the exact probability of explosion is not known in advance and participants must learn the risk structure gradually through repeated experience. This dynamic, uncertain, and cumulative decision-making context closely mirrors how people often face risk in everyday life (Buelow et al., 2024). Utilizing BART, prior neuroimaging studies have implicated a network of brain regions, such as the anterior cingulate cortex (ACC), anterior insula (AI), striatum, dorsolateral prefrontal cortex (dlPFC), and medial orbitofrontal cortex (mOFC) in risk-taking behavior, highlighting contributions from executive control, salience detection, and reward evaluation systems (Trutti et al., 2021; Wang et al., 2022). Despite its strengths, however, the traditional BART paradigm presents an important limitation: it confounds distinct psychological processes that can jointly contribute to the overt risk-taking behaviors, namely, sensitivity to reward with sensitivity to risk. For example, a person may highly value potential rewards but still avoid risky options due to heightened loss sensitivity, or conversely, may tolerate risk not because of high reward drive but due to insensitivity to possible negative outcomes. Behavioral studies suggest that motivation for obtaining more reward or avoiding negative outcomes (e.g., behavioral inhibition and activation system, BIS/BAS), while correlated, can indeed make independent contributions to decision-making (Demaree et al., 2008; Voigt et al., 2009). fMRI findings based on computational modeling approaches further support the idea that the brain tracks reward and risk as dissociable components. Rooted in neuroeconomics, these studies typically use behavioral paradigms that explicitly and parametrically vary gain and loss magnitudes and probabilities (e.g., mixed-gambling task; Schonberg et al., 2011), revealing that distinct brain regions encode separate dimensions of value and uncertainty (Jenni et al., 2022; Jia et al., 2023; O’Neill & Schultz, 2010; Schumacher et al., 2021; Sun et al., 2022; Tobler et al., 2007). For instance, the caudate nucleus has been shown to track expected reward magnitude, while the AI and mOFC are more sensitive to risk-related features such as variance or volatility (Jenni et al., 2022; Sun et al., 2022; Tobler et al., 2007). In corroboration, non-human animal computational models using juice as a reward showed that medial OFC are specifically sensitive to the risk component of reward cues (O’Neill & Schultz, 2010). A rat study further suggests that the circuit between medial OFC and dorsal striatum plays an important role in facilitating flexible reward seeking under risk (Jenni et al., 2022). Moreover, optogenetic inactivation of the caudate nucleus disrupts rats’ ability to choose between high vs. low reward choices, suggesting that the caudate engages in encoding reward magnitudes (Gore et al., 2023). These findings, although distinct from BART which focuses more on naturalistic modeling of risk and reward (Schonberg et al., 2011), point to the dissociability of reward-seeking from risk-tolerant processes in the brain. In the canonical BART paradigm, the only way participants can maximize earnings is by continuing to inflate the balloon, thereby also increasing the risk of explosion. This design makes it difficult for researchers to effectively disentangle whether an individual’s behavior reflects motivation to obtain reward or tolerance for the threat of loss. This limitation not only complicates interpretation of neural activation patterns but may also obscure subtler differences in how the brain encodes reward versus risk. The limitation further hinders our understanding of the multidimensional risk profile in the brain (Van Duijvenvoorde et al., 2022), such as problematic risk tolerance (e.g.,recklessness) or problematic risk intolerance (e.g., anxiety). To address this, the current study brings design improvements to the traditional BART framework. Specifically, we included a novel Guaranteed Reward (GR) condition, in which participants could inflate a balloon to accumulate reward without any risk of explosion. This condition retains the incremental reward structure of the standard BART but eliminates the threat of loss, allowing us to directly compare neural responses during decision-making under risky versus no-risk reward conditions. In addition, we employed multi-echo (ME) fMRI and denoising, an advanced acquisition and preprocessing method known to enhance sensitivity to BOLD signals in subcortical and midbrain regions (Kundu et al., 2017). Striatal and midbrain subregions are crucial for encoding reward and risk, but signals are often difficult to detect with conventional single-echo fMRI. With the design improvements, we will directly contrast brain responses to risky and guaranteed rewards using both univariate and multivariate techniques, thereby clarifying neural features contributing to decisions involving potential gains, losses, or both. We anticipate that this refined approach will offer better insights into individual differences in lifespan risk-related behavior and the neural underpinnings of vulnerabilities to psychopathology. Methods Participants Forty-nine healthy participants (30 females, all cis) were recruited from Cornell University and completed the current study. Their ages ranged from 18 to 22 years (M ± SD: 20.53 ± 1.92), 6 participants declined to report their age. Demographic characteristics were shown in Table 1. Of the participants, sex, race, college year, their parents’ income and highest level of education were included in Table 1. Measures Self-reported Surveys Self-reported surveys include the demographic questionnaire (i.e., age, sex, race, college year, their parents’ income and education, gender identity, etc.) as well as other psychological questionnaires not included in this analysis. Descriptions of the questionnaires related to risk and reward, as well as the corresponding results (see Table S1), are provided in the Supplementary Materials. Modified BART Paradigm The Balloon Risk Analogue Task (BART) paradigm is to measure the experience of risky reward decision-taking and receiving win or loss outcomes (Lejuez et al., 2002). Based on the adapted MRI version of BART (Kohno et al., 2015), we modified the paradigm to include three conditions: risky reward (RR), guaranteed reward (GR), and neutral (Neutral) condition as shown in Figure 1(B). Participants were instructed that there were three balloon colors (red, white, and gray) corresponding to three BART conditions (RR, GR, and Neutral respectively). Participants were told that they would receive all money earned at the end of the study and that they should try to make as much money as possible. This was not a deception. In the RR trials, participants could press buttons to inflate a red virtual balloon to earn money ($0.05 per pump), but losing all money if it popped. They could also choose to cash out in order to avoid risking the balloon popping. The more times they inflated, the greater the perceived reward and risk of the balloon bursting. In the GR trials, participants inflated white balloons for the same reward ($0.05 per pump) without the risk of losing money and balloon explosion. In the Neutral condition, participants inflated gray balloons without gaining or losing money, and no risk of popping existed. The maximal amount of inflation pumps for each balloon was randomly generated between 2 and 12 pumps for each participant for each run of the BART. Once the maximum was reached, red balloons popped, or gray and white balloons disappeared, and participants moved to the next trial. Following obtaining maximal pumps or cashout, pre-feedback interstimulus interval (ISI) was presented between 0.5 and 3 seconds. Then block feedback (e.g., negative, positive, and neutral feedback) was presented for 2 seconds. There was a randomly chosen jitter of 2, 6, or 8 seconds after the feedback to prepare for the next trial (Figure 1A). Procedure The Institutional Review Board for Human Participant Research (IRB) approval was obtained for all procedures (IRB protocol number: 1902008564). Participants were initially screened through a phone interview. Eligibility criteria included being right-handed, having no history of neurological conditions, no current or past episodes of psychosis, and not initiating psychotropic drugs close to the time of the study (e.g., Prozac, Lexapro, Xanax and Valium; 2 weeks for antidepressants, within 48 hours for fast acting drugs). According to safety standards for MRI scanning, participants were excluded if they had any metal in their body, experienced claustrophobia, or were pregnant. A written informed consent was obtained from participants after a detailed explanation of this study. Outside the scanner, participants practiced the modified BART paradigm on a screen to ensure they understood the color-coding rules of the balloon game. Participants remained still for a 6-minute anatomical scan and then performed two runs of the modified BART tasks during a multi-echo fMRI scan. The computer randomly generates 40 balloons of different colors and condition sequences in each run. The actual number of balloons participants pumped depended on how fast they completed the BART. After scanning, participants complete surveys including demographic questions as well as other questionnaires not included in this analysis. They received cash rewards based on their BART performance along with compensation for their time. They also completed an additional cognitive task (Stroop) during a scan, which was irrelevant to the goal of this study and not discussed in this manuscript. Data Acquisition Neuroimaging data were acquired using a General Electric (GE) Discovery MR750 3.0T MRI scanner at Cornell University. Task stimuli were projected on a screen at the back of the MRI’s bore, so participants viewed the stimuli with a 32-channel phased-array head coil. Before anatomical scanning, three-plane localizer images were acquired, and then ASSET (i.e., Array Spatial Sensitivity Encoding Technique) calibration was performed. One hundred and seventy-six high-resolution anatomical T1-weighted images were acquired in 6 minutes, using the sagittal plane of imaging and the magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) sequence (TR = 7 ms, TE = 3.42 ms, flip angle = 7°, field of view (FOV) = 256 mm, 256 × 256 matrix, 176 axial slices, voxel size = 1 × 1 × 1 mm 3 , 1-mm slice thickness). Thirty interleaved functional multi-echo (ME) Echo Planar images (EPIs) sensitive to BOLD (blood oxygenation level-dependent) contrast were obtained during two runs of the BART, each run lasting 10 min (TR = 2 , 600 ms; TE 1 = 13.4 ms, TE 2 = 35.8 ms, TE 3 = 58.5 ms; 80° flip angle; FOV = 192 mm; matrix = 96 × 96; 30 axial slices; 3 echoes; 2 × 2 × 1.5 mm 3 voxels; 240 volumes, slice thickness = 1.5 mm). The total number of voxels for functional images is 276,480. To focus on frontal-mesolimbic brain activity, the partial-brain functional images were selected from the edge between midbrain and pons to the top of the corpus callosum across 30 slices and were aligned with the AC-PC axis, as shown in Figure 2. All participants’ head positions were stabilized by foam pillows, and they used foam earplugs to diminish the scanning noise for data quality control. They were also informed of the importance of staying still during scanning, and all neuroimaging scans at the Cornell MRI Facility were performed by trained MR technologists working with a standardized protocol. Heart rate was recorded by an oximeter placed on the left index finger, and respiratory information was monitored by a sensor belt. Data Analysis Plan The study obtained behavioral and neuroimaging data from 49 participants. Univariate MRI analysis was performed on data from 43 participants. Five participants' MRI data were excluded for separate reasons: (1) one never received a loss outcome in the risky reward condition, resulting in no neural response to negative feedback; (2) one had MRI partial-brain images that couldn’t be preprocessed properly when using the pipeline; (3) three had incorrect field of view capturing partial-brain functional images. Multivariate pattern analysis was performed on data from 44 participants, including the one without negative feedback. Behavioral Measures Behavioral measures of BART include: (1) the number of trials (N Trials ) and pumps (N Pumps ) per condition, (2) reaction time between stimulus presentation and button pressing per pumps (RT per pump ) and per trials (RT per trial ), (3) money participants successfully saved into the bank for each condition (Money save in bank ), (4) the average number of pumps participants chose to cash out ( Averagepumps save ) and (5) the average number of pumps where the balloon exploded (Averagepumps explode ). A non-parametric test, related-samples Friedman test, was performed to test if those variables among three conditions are the same. Univariate Permutation Analysis Preprocessing . Imaging data were preprocessed using AFNI (Version 23.0.01; https://afni.nimh.nih.gov/; (Cox & Hyde, 1997). Using afni_proc.py, we remove spikes across the fMRI time series ( despike ) and slice-timing differences were adjusted ( tshift ). EPI images were aligned with the corresponding anatomical one ( align ), and then anatomical and functional data were normalized to the standard MNI brain template. Motion correction was applied using volreg . Then we created a binary brain mask from the aligned functional volumes (mask). There are 2,529,432 voxels in total for a preprocessed functional image. Moreover, using TE-dependent analysis (Tedana), preprocessed BOLD images with three echoes were denoised via principal component analysis (PCA) and independent component analysis (ICA), and non-brain materials were removed from denoised data (DuPre et al., 2021). Using FMRIB Software Library (FSL Version 6.0.6.2; www.fmrib.ox.ac.uk/fsl), signal-to-noise ratio was enhanced via a high-pass filter with a cutoff point of 100 second and we used a 2-mm FWHM Gaussian kernel, and grand-mean intensity normalization for spatial smoothing via the FEAT (Woolrich et al., 2009). The AFNI preprocessing pipeline did not successfully co-register the partial-brain functional images for four participants. To address this, FLIRT was applied to these four datasets to align the three echo images from each scan. The images were then combined into a single aligned image using Tedana. Finally, the aligned images were normalized with Advanced Normalization Tool (ANTs; Avants et al., 2009), preparing them for further analyses. Lower-level Analysis. After preprocessing, we used FSL’s FEAT to build a general linear model for reward and risk processing during decision-making and block feedback phase. Decision phase refers to the 1s duration from the onset of presenting the virtual balloon stimulus. In this phase, we included three regressors: RR decision, GR decision, and Neutral decision events. Block feedback was defined as a 2-second event for receiving feedback about a balloon. Four regressors were included: positive (gain) and negative (loss) feedback in the RR condition, positive feedback (gain) in the GR condition, and neutral feedback in the Neutral condition. The analysis process and results for block feedback were included in the supplemental materials. Additionally, the number of pumps was included as a parametric modulator for all regressors. To model risky reward decision-making, we contrasted BOLD signals between RR and Neutral decision events. To assess reward processing, we contrasted GR and Neutral decision events. For risk processing, we compared RR and GR decision events. To model the receipt of reward under risk, we contrasted RR positive feedback and Neutral feedback. We performed a fixed-effects model at the level two analysis in FEAT. For each participant, the average of the parameter estimates across two BART runs was calculated. This approach allowed us to enhance the reliability of the estimates by reducing the impact of noise. Higher-level analysis. The aggregated activations in seven contrasts were estimated using FSL’s non-parametric permutation testing and threshold-free cluster enhancement (TFCE) with 5000 permutations per contrast at the group-level analyses. Using TFCE, the cluster-like activations were enhanced by considering the magnitude and spatial extent of activations simultaneously, but these images remained fundamentally voxel-wise (S. Smith & Nichols, 2009). Controlling for false positives and multiplicity, the family-wise error (FWE) rate for the aggregated images’ contrast were corrected, and the individual voxel significance level was set at p<0.05. The locations of significant voxel-scanned activations in three contrasts were determined using the Harvard-Oxford Cortical and Subcortical Atlas. The VTA mask from the CIT168 atlas was used to identify the location of the significant voxels in the univariate results (Pauli et al., 2018). For each activation, the specific brain region and its corresponding coordinates were reported below. The statistically significant activation information was generated using FSL’s atlasquery. To illustrate the greater sensitivity to BOLD signals in multiecho fMRI,we also repeated our main analyses using single-echo EPI data. The relevant procedures and results are summarized in the Supplementary Materials (Table S3 and Figure S). Multivariate Pattern Analysis (MVPA) We use AFNI, nilearn (Nilearn contributors et al., 2024) and scikit-learn (Pedregosa et al., 2011) to build multivariate pattern classification between risky and guaranteed reward decision making trials. Our goal is to test if AI and striatum distinguish risk features of reward. Preprocessing was also performed using AFNI, mirroring the steps used in GLM except for normalization ( tlrc ). For regression analysis, voxel patterns of BOLD signals to RR and GR condition were extracted as beta estimates for block decision phase by using AFNI 3dDeconvolve and 3dTcat . The Block decision phase refers to the duration from the first appearance of a balloon until pre-feedback ISI. To ensure unbiased testing, the dataset is split into training and testing sets, with an 80/20 split. For the classification, a linear support vector machine (SVM) classifier was applied to train on the training set and tested on the testing voxels within different ROI masks. Each single ROI mask was generated according to definitions of the Harvard-Oxford cortical atlas (Desikan et al., 2006) while the ROI mask for VTA was based on the CIT168 atlas (Pauli et al., 2018). This process was conducted using Nilearn and scikit-learn and employed a five-fold cross-validation approach. ROIs included structural masks of AI, VTA, striatum, dorsal striatum, caudate, putamen, and NAcc, as we hypothesized (Pauli et al., 2018). We compared MVPA models to determine which pattern of neural activity best distinguishes the conditions of RR versus GR decision. The area under the curve (AUC) for the receiver operating characteristic (ROC) of the predicted values was calculated to examine how accurately the model can predict the risky reward and guaranteed reward decision (i.e., classification performance). The AUC values of two correlated ROC curves within each model were compared to each other and to random prediction using a one-tailed t-test. Results Behavioral Findings Participants cashed out in 15.73 trials (SD = 3.96) on average in the RR condition.The mean number of trials where the balloon exploded was 10.8 (SD = 3.37). Participants chose to cash out, on average, at the 4.64th pump during the RR trial. On average, balloons popped by the 3.27th pump when participants decided to keep inflating. As shown in Table 2, non-parametric analysis (N=49) revealed significant differences across three conditions. Participants completed significantly more trials in both RR and GR conditions than in the Neutral condition, p < .001. GR condition showed more pumps than both RR and Neutral conditions, p < .001. Money saved in the bank was significantly highest in the GR condition, followed by RR, and none in the Neutral condition, p < .001. Reaction time per trial was significantly longer in the Neutral condition than in GR, and in GR than in RR, p < .001. Reaction time per pump was longest in RR compared to GR and Neutral, p < .001. MRI Findings Univariate Permutation Results Decision Phase. The contrast of RR versus GR decision-making condition shows greater neural responses in bilateral frontal operculum cortex (FOC) extending to orbital frontal cortex (OFC) and AI, dorsal striatum including bilateral caudate and left putamen, left NAcc in very small clusters, right middle frontal gyrus (MFG), brainstem including right VTA (t = 5.54, TFCE-corrected p = 0.01, [X, Y ,Z] = [4.5, -13.5, -13.5]). However, compared to the GR decision context, we observed the greater responses to the RR decision in the posterior cingulate cortex (PCC) rather than ACC. Replicating previous BART MRI studies (Braams et al., 2015; Korucuoglu et al., 2020; Rao et al., 2008), the contrast between RR and Neutral during decision-making shows greater BOLD signal in bilateral FOC, OFC, AI, PCC, caudate, putamen and thalamus, right MFG, left VTA, cerebellum, and a very small cluster of left NAcc. The contrast between GR and Neutral decision showed greater activation in MFG, inferior frontal gyrus (IFG), FOC, OFC, AI, middle temporal gyrus (MTG), hippocampus, and amygdala in right hemisphere, and bilateral intra calcarine cortex. However, we did not observe greater responses to the GR decision in striatal regions compared with the Neutral decision as would be expected. The permutation results for the decision phase are shown in Figure 3 and Table 3. Consistent with previous fMRI findings, an analysis done with a single echo file revealed similar patterns, but it was less sensitive than our multi-echo analyses in mid-brain regions such as VTA and striatum (Gilmore et al., 2022; Kundu et al., 2017; Steel et al., 2022). The relevant results are provided in the Supplementary Materials (Table S3 and Figure S1). Furthermore, the univariate permutation results for block feedback are presented in the Supplementary Materials (Table S2). Multivariate Pattern Results To examine whether ROI’s voxel patterns could discern risky reward and guaranteed reward decision making, SVM classifier was performed and the area under the ROC curve (AUC) was calculated as an evaluation criterion for classification performance. As shown in Figure 4, Our multivariate results showed that AI had a significantly higher discriminative power than random choice (M ± SD: 0.61 ± 0.14, p < .001). In contrast, putamen and NAcc patterns showed significantly lower ability to discriminate between risky and guaranteed reward trials (M ± SD: 0.47 ± 0.10, p < .05; M ± SD: 0.46 ± 0.09, p < .05). Voxel patterns in striatum (M ± SD: 0.50 ± 0.10), dorsal striatum (M ± SD: 0.50 ± 0.11), caudate (M ± SD: 0.49 ± 0.12) and VTA (M ± SD: 0.50 ± 0.09), showed no significant discriminative ability with AUC of around 0.5. We also compared classification performance of different models within the corresponding ROIs, as shown in Table 4. The results indicated that the AUC values for voxel patterns within putamen were significantly lower compared to the striatum and dorsal striatum ( p s < .01). Similarly, NAcc showed significantly lower AUC values when compared to the striatum, dorsal striatum and caudate ( p < .05). VTA showed significantly higher AUC value than NAcc. AI had significantly higher AUC values compared to all other ROIs, (all p s < .001). Overall, these results highlight differential classification performance of the models across various ROIs. AI may better discern risk features of reward from no-risk contexts than other ROIs, whereas NAcc and putamen showed lower discriminative power. Discussion This study addressed key limitations in dissociating the neural underpinnings of risk and reward processing in human research using multi-echo imaging and adding a guaranteed reward condition to the popular BART paradigm. Univariate permutation tests largely replicated and extended previous work indicating that decision-making in the risky reward condition coincided with increased BOLD response in striatum, VTA, OFC, and AI, compared to neutral and GR conditions. Single-echo analyses yielded similar, but more restricted results, suggesting that multi-echo enhanced sensitivity. Multivariate analyses indicated that AIN discerned risky rewards from guaranteed rewards slightly above chance (61%) and much better than striatal regions and VTA. Interestingly, we saw no NAcc response in the GR > Neutral contrast and NAcc was below chance at discerning risk from guaranteed reward conditions. Below, we interpret the contributions of these findings to disentangling the neural bases of risk vs reward processing under decision-making. Based on these findings we contend that our methodological changes to traditional BART studies can further research into lifespan and clinical inquiries into the neural underpinnings of risk and reward processing. Decision-making under risk coincides with broad activation even accounting for reward Our study suggests that rather than part of some more general reward-related processing, canonical reward neural features may be doing something distinct during risky decision-making. Unlike in the previous iterations of the BART, we were able to directly compare risky versus guaranteed reward conditions, presumably subtracting out reward-specific related activation. OFC activation was lateralized when comparing risky vs guaranteed reward contexts, in line with previous research suggesting lOFC is distinctly involved in stimulus-reward contingencies especially as they relate to non-rewards and punishments, unlike medial subsections (Rolls et al., 2020). Further, connections between lateral OFC, ACC, AI and striatum, all significant during the risky condition, have long been associated with reinforcement learning, decision-making, and risky choice (Braams et al., 2015; Groman et al., 2019; Rao et al., 2008). We found PCC but not ACC BOLD increases in any contrast, possibly due to our restricted field of view meant to enhance mid-brain resolution. High temporal and spatial resolution of striatum and brainstem clarify NAcc and VTA Our higher temporal and spatial resolution in our scanning parameters found greater NAcc and VTA response to risky versus neutral and guaranteed reward conditions, as shown in Figure S1 . Although NAcc is often discussed in BART studies, especially as a putative endophenotype of sensitivity to rewards, only 1 out of 9 studies reported NAcc activation specifically (Korucuoglu et al., 2020). Our findings here suggest that better temporal and spatial resolution may resolve some of this conflict. In support, our VTA response aligned with previously recorded coordinates and probability map created with a 7-tesla MRI (Pauli et al., 2018). Like many brainstem regions, VTA is difficult to image and study in humans, but plays a large role in non-human animal research (Forstmann et al., 2017). Neurons in VTA produce dopamine and project this neurotransmitter to NAcc and dorsal striatum and other brain regions (Stopper & Floresco, 2015). Non-human animal studies support a role for VTA in differentiating reward behaviors with and without risk and its involvement in appetitive reinforcement learning (Park & Moghaddam, 2017). The ability to image these small features is important to greater translation across non-human and human research and highlights the utility of multi-echo imaging in investigating subcortical nuclei as others have argued (e.g., Reddy et al., 2024). Multivariate analyses suggest that AI, not striatum decodes risk Our findings highlight a prominent role for AI in differentiating risky from guaranteed reward decision-making above more canonical reward regions. AI BOLD coincided with risk conditions in both univariate and multivariate analyses. Using MVPA, we found that AI patterns differentiated risk from reward conditions better than chance and much better than either dorsal or ventral striatum, or VTA patterns. A previous BART study similarly found that the neural patterns of AI, lateral OFCs and ACC before risky choice can better distinguish risky (pump) choice from safe (cashout) choice in risky reward condition, but they could not compare it with a non-risk reward condition (Helfinstein et al., 2014). AI is involved in signaling the probability of aversive outcomes, which could only occur in a risky condition (Clark et al., 2008). Non-human animal research suggests a causal selective role for AI in behaviors under risky but not no-risk reward contexts (Ishii et al., 2012). Computational human neuroimaging also suggests that AI tracks variance in expected value (Sun et al., 2022). Since the risky condition involves a wider range of outcomes than the no-risk condition, it is still possible that AI is specifically tracking the ambiguity inherent in risk, instead of the probability of loss per-se . AI is important to affective processing, emotion regulation, and mental health, with a recent review touting it as a possible “gatekeeper” in switching from salience and executive network states important to decision-making (Molnar-Szakacs & Uddin, 2022). How this feature tracks variance in both negative and positive outcomes is therefore important for future research and this paradigm could help this work. Despite the univariate findings and relatively greater temporal and spatial resolution of our scan our multivariate analyses showed that dorsal striatum, including caudate and putamen, were at chance and NAcc significantly worse than putamen in differentiating risky vs guaranteed reward conditions. Previous computational neuroimaging studies showed that dorsal striatum tracked expected reward magnitude, whereas the AI and mOFC primarily encoded risk-related information, such as variance (Sun et al., 2022). Others suggest that connectivity between striatal regions and OFC and not mere amplitude differences between conditions might better distinguish risky decision making (Groman et al., 2019). Similarly, VTA was not meaningfully able to decode risk and guaranteed reward conditions. However, some studies indicated that VTA has neurons sensitive to both aversive and appetitive cues via projections to different mid and frontal regions (De Jong et al., 2019) and may therefore not be a good candidate for differentiation with any achievable fMRI resolution. Taken together, future work may need to probe more complex patterns of connectivity to understand the role of striatum in differentiating risk and reward processing and sensitivity in decision-making. Limitations and Future Directions Despite our approaches’ relative utility on balance, several limitations will need to be addressed in future work. First, the guaranteed reward condition, though innovative, did lack a performance contingency. Even the most widely used monetary reward paradigm, the monetary incentive delay task (MID), includes a performance component, albeit a nominal one, and hit rates are person-matched. Further, while the guaranteed reward condition did coincide with greater BOLD signals in right MFG (Elliott et al., 2004), right MTG (Wilson et al., 2018), and bilateral occipital cortex (A. B. Smith et al., 2011) as expected for the anticipation of a monetary reward, we saw no significantly greater activation in striatum and mOFC when compared to the neutral condition. These two factors paint a provocative picture. Unlike in voluntary risk-taking, involuntary risk-taking does not coincide with canonical mesolimbic responses in BART (Rao et al., 2008). While the guaranteed reward paradigm had a behavioral contingency (participant must press the button), there was no performance contingency and no possible behavioral modification that would impact the receipt of the monetary reward. Thus, it might not require major recruitment from NAcc and OFC. Future research should probe this further as it may suggest that these neural features are important to agency and behavioral modification, and not just rewards per se . Second, in its attempt to capture psychological risk, the study does not capture the neuroeconomics definition of risk as variability in possible rewards. Future research will need to model these two related, but separable operationalizations/ phenomena to better understand the functions of AI and subregions of OFC in risky decision making. Finally, though the standard BART has evidence of good test-retest reliability using fMRI (Korucuoglu et al., 2020; Li et al., 2020), this modified task needs to be evaluated further for its psychometric properties. Future research will need to increase and diversify participant samples to do this properly. Finally, we reduced our field of view to hone in on mesolimbic regions, but future research will need to expand to whole-brain imaging as regions like the medial dACC have also been shown to be important in risky decision-making, but were not imaged here. Conclusion Risk-taking behavior arises out of a number of dissociable cognitive processes such as sensitivity to potential loss (risk tolerance) and desire for maximize rewards (reward sensitivity). However, relatively little human neuroscience separates these processes, potentially hiding the heterogeneous mechanisms by which individuals may arrive at the same risk-taking behavior. Despite limitations, this research illuminates putative neurobiological mechanisms which uniquely subserve risk vs guaranteed reward contexts and provides a new tool from which to probe risk tolerance separately from reward sensitivity. Future research may further expand the utility of this tool by adding different reward levels, adding a performance contingency to the guaranteed reward condition, constructing computational models, and expanding sample size and diversity. This method provides a simple manipulation to a well-known tool to expand the scope of what we can learn about the neural underpinnings of risk-taking behaviors and its associated cognitive processes. We suspect it will be useful for neural inquiries into decision-making, lifespan changes in risk and reward processing, and vulnerability to psychopathology. Abbreviations Anterior cingulate cortex, ACC Anterior insula, AI Dorsal anterior cingulate cortex, dACC Dorsal striatum, DS Posterior cingulate cortex, PCC Dorsolateral prefrontal cortex, dlPFC Ventromedial prefrontal cortex, vmPFC Ventral tegmental area, VTA Orbitofrontal cortex, OFC Nucleus accumbens, NAcc Middle temporal gyrus, MTG Middle frontal gyrus, MFG Inferior frontal gyrus, IFG Regions of Interest, ROI Threshold-free cluster enhancement, TFCE General linear model, GLM Blood-Oxygen-Level Dependence, BOLD Multivariate Pattern Analysis, MVPA Declarations Author Contributions . M.Z.G. designed and performed experiments, analyzed data and wrote the paper; X.D. developed analytical tools, analyzed data and wrote the paper. M.L. helped develop analytical tools and wrote the paper. ORCID . X.D: https://orcid.org/0000-0002-0520-0808; M.L: https://orcid.org/0000-0002-3369-2790; M.Z.G: https://orcid.org/0000-0002-1290-4483; Acknowledgements. We sincerely thank Dr. Elizabeth B. Riley for her valuable support with the multi-echo fMRI analysis pipeline as well as Mirely Garcia and Melody Xu, who were essential to data collection and data quality check. Funding. This work was supported by startup funds from Cornell University . Ethics declarations. The authors declare no competing interests. Supplementary information . The self-reported survey related to risk and reward, along with the results of the neural contrasts during the block feedback phase (using multi-echo imaging) and the decision-making phase (using single-echo imaging), are included in the Supplementary Materials. Data availability . The data that support the findings of this study are available from the corresponding author upon reasonable request. References Avants, B., Tustison, N. J., & Song, G. (2009). Advanced Normalization Tools: V1.0. The Insight Journal . https://doi.org/10.54294/uvnhin Braams, B. R., van Duijvenvoorde, A. C. K., Peper, J. S., & Crone, E. A. (2015). 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NeuroImage , 45 (1), S173–S186. https://doi.org/10.1016/j.neuroimage.2008.10.055 Tables Table 1 Demographic Characteristics (N=49) Factor Category N Race Asian 17 Black 3 Hispanic 10 Middle Eastern 1 White 13 Mixed Race 5 College Year 1st year undergrad 8 2nd year undergrad 7 3rd year undergrad 14 4th year undergrad 12 Graduate student 8 Transfer Student No 42 Yes 7 Parents' Income Below $40,000 8 $40,000 - $59,999 7 $60,000 - $99,999 5 $100,000 - $174,999 13 $175,000 - $299,999 10 $500,000 - $749,999 2 More than $750,000 2 Parents' Education Doctoral degree 8 Advanced professional degree 3 Master's degree 14 Bachelor's degree 7 Some college 4 High school diploma 11 Less than high school 2 Sex Female 30 Male 19 Table 2 BART Conditions Impact Behavioral Performance Variables M ± SD p Post-hoc RR GR Neutral N Trials 26.53 ± 2.96 26.29 ± 2.97 17.29 ± 2.08 *** RR>Neutral, GR>Neutral N pumps 106.84 ± 21.55 172.8 ± 22 112.57 ± 15.88 *** GR>RR, Neutral>GR Money save in bank 3.52 ± 0.87 8.64 ± 1.1 0 ± 0 *** GR>RR>Neutral RT per balloon 3.15 ± 1.84 3.27 ± 1.66 3.29 ± 1.65 *** GR>RR, Neutral>GR RT per pump 0.79 ± 0.46 0.49 ± 0.24 0.51 ± 0.25 *** RR>GR, RR>Neutral Average pumps save 4.64 ± 1.11 Average pumps explode 3.27 ± 0.79 Notes . Related-samples Friedman test was performed. RT=reaction time, RR=risky reward, GR=Guaranteed reward. The reaction time was presented in seconds. N=49. p GR Decision R Medial Occipital Cortex 4319 5.58 16.5 -93 0 R Frontal Operculum Cortex 2123 7.57 31.5 27 1.5 R Thalamus 1898 8.15 10.5 6 9 R Middle Frontal Gyrus 1500 7.43 39 58.5 10.5 L Frontal Operculum Cortex 1091 7.59 -42 15 0 R VTA 248 5.54 4.5 -13.5 -13.5 L Caudate 80 5.55 -9 3 9 L Caudate 62 5.25 -13.5 -3 16.5 L Thalamus 60 4.98 -16.5 -24 16.5 R Caudate 39 5.92 18 10.5 18 L Caudate 23 4.56 -15 18 -1.5 L Substantia nigra 22 4.38 -4.5 -33 -15 R Precentral Gyrus 22 3.34 42 4.5 25.5 R Middle Frontal Gyrus 16 4.9 45 46.5 -1.5 R Thalamus 11 3.31 9 -10.5 3 RR>Neutral Decision R Lingual Gyrus 17051 7.25 10.5 -3 19.5 L Brain Stem 6.77 -3 -28.5 -3 Corpus callosum 6.73 0 -30 9 L Dorsal Occipital Cortex 6.41 7.5 6 4.5 R Occipital Pole 6.18 16.5 -94.5 1.5 R Caudate 6.12 7.5 9 3 R Frontal Operculum Cortex R Anterior insula 3233 8.24 39 22.5 0 R Anterior Insula 7.87 31.5 27 1.5 R Frontal Operculum cortex 7.7 36 19.5 7.5 R Anterior insula 6.69 33 21 9 R Frontal Operculum cortex 6.34 46.5 19.5 -4.5 R Frontal Orbital Cortex 6.12 37.5 22.5 -6 R Middle Frontal Gyrus 2350 7.37 40.5 57 13.5 L Anterior Insula 1267 6.73 -42 15 0 L Cingulate Gyrus, posterior division 234 5.84 -1.5 -24 27 L Precentral Gyrus 53 5.4 -57 7.5 19.5 GR>Neutral Decision Intracalcarine Cortex 11700 6.36 0 -87 12 R Middle Frontal Gyrus 1345 5.07 43.5 54 -1.5 R Middle Temporal Gyrus, temporooccipital part 969 5.2 66 -39 -1.5 R Frontal Operculum Cortex 626 4.9 36 22.5 -3 R Inferior Frontal Gyrus, pars opercularis 349 4.36 57 15 21 L Lateral Occipital Cortex, superior division 317 5.05 -27 -67.5 34.5 L Lingual Gyrus 168 4.1 -13.5 -45 0 R Parahippocampal Gyrus, posterior division 81 3.94 16.5 -31.5 -7.5 R Lateral Occipital Cortex, inferior division 72 4.73 49.5 -73.5 0 R Cingulate Gyrus, posterior division 34 3.85 12 -43.5 0 R Inferior Temporal Gyrus, temporo occipital part 34 4.32 37.5 -57 -1.5 R Frontal Operculum Cortex 22 3.85 34.5 16.5 15 R Lingual Gyrus 22 3.76 16.5 -43.5 -6 L Lateral Occipital Cortex, superior division 22 2.83 -24 -79.5 48 R Inferior Frontal Gyrus, pars opercularis 14 3.68 52.5 16.5 9 L Lateral Occipital Cortex, inferior division 13 4.14 -55.5 -67.5 -6 L Lateral Occipital Cortex, superior division 12 2.91 -18 -79.5 40.5 R Amygdala 11 3.48 18 -12 -10.5 Notes. Whole-brain analyses for partial-brain images were conducted at a voxel threshold of p FWE 10, parametric modulator = the number of pumps. The local maxima coordinates were included in RR > Neutral Decision contrast. The voxel size is 1.5*1.5*1.5 mm 3 and the total number of voxels is 2,529,432. Abbreviation . TFCE, threshold-free clustering enhancement; L, left; R, right; k, voxel size. Table 4 AUC Comparisons among Multivariate Models of BART Conditions Striatum DS Caudate Putamen NAcc AI DS .00 Caudate -.01 -.01 Putamen .00** .00** .01 NAcc -.03* -.03* -.03 -.03 AI .11* .11* .12* .11* .14* VTA .00 .00 .01 .00* .04* -.11* Notes . The mean differences in Area Under the Curve (AUC) values between pairs of models. AI outperforms the multivariate models of BART condition. Each cell displays the AUC difference, calculated as the ROI on the vertical axis minus the ROI on the horizontal axis. A positive value means the vertical-axis ROI distinguishes risky from guaranteed reward decisions better than the horizontal-axis ROI, while a negative value indicates the opposite. DS, dorsal striatum; NAcc, nucleus accumbens; AI, anterior insula; VTA, ventral tegmental area. N=44. One-tailed t-test were conducted, p <.05*, p <.01**. Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterial.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6837700","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":478844750,"identity":"348e4470-297d-4ad9-9b5c-fa7342dcb690","order_by":0,"name":"Xinyi Deng","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Deng","suffix":""},{"id":478844751,"identity":"cbc850bb-81c2-455b-a5cb-6eed36e44553","order_by":1,"name":"Minwoo Lee","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Minwoo","middleName":"","lastName":"Lee","suffix":""},{"id":478844752,"identity":"08b62e8d-c222-450c-88cf-ee04cdf39452","order_by":2,"name":"Marlen Gonzalez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYHAC9g8fwDRz4wEgQZQWNsYZYJqxgXgtzDwkadHtP37tsU2NTb45eyNQS4V1YgMhLWYHzpQb5xxLs9zZcxCo5Uw6EVoO9iRI5zYcNjC4kdhwgLHtMBFaDvMkSFuCtNx/CNTyjxgtx9iPSTOCbQF6H8ggQssZHmbDnmNpBgZngA5LOJZuTFjL+eMPH/yosTEwOH744IMPNdayBLUwMPAYINgJhJWDAPsD4tSNglEwCkbByAUAaShGQTlio4AAAAAASUVORK5CYII=","orcid":"","institution":"Cornell University","correspondingAuthor":true,"prefix":"","firstName":"Marlen","middleName":"","lastName":"Gonzalez","suffix":""}],"badges":[],"createdAt":"2025-06-06 14:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6837700/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6837700/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87042161,"identity":"ae6e8c60-9f70-4f8d-a81d-53782dac1663","added_by":"auto","created_at":"2025-07-18 14:09:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120221,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e(A) Flow Diagram of Modified BART Paradigm\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(B) Experimental Design of Modified BART\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes\u003c/em\u003e. ISI, interstimulus interval; Red, white, and gray balloons refer to risky reward (RR), guaranteed reward (GR), and neutral (Neutral) condition, separately.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6837700/v1/7ff77bb65beb5556fd108f98.png"},{"id":87042168,"identity":"0d9c4ed3-cdd8-4a4e-95be-cfaf0de7225d","added_by":"auto","created_at":"2025-07-18 14:09:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":385031,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eField of View for Partial Brain Scanning\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6837700/v1/a9cf9286924cce5cfe7b8934.png"},{"id":87042163,"identity":"c84631ad-8d0c-4726-b2a3-fadcd14f9e4f","added_by":"auto","created_at":"2025-07-18 14:09:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":448783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTFCE-corrected t-value Map\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes\u003c/em\u003e. TFCE-corrected t-value map from the group comparison between RR, GR, and Neutral decision-making phases with a threshold set at \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFWE\u003c/sub\u003e \u0026lt; 0.05 obtained with TFCE. Color bar: t-value. \u003cem\u003eAbbreviations\u003c/em\u003e. NAcc, nucleus accumbens; FOC, frontal operculum cortex; VTA, ventral tegmental area; MFG, middle frontal gyrus; TFCE, threshold-free clustering enhancement. FWE, family-wise error.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6837700/v1/3d6992a971d3c2e7dbba8baa.png"},{"id":87042164,"identity":"697ebdb1-0806-406e-a3e5-0a1467a279d4","added_by":"auto","created_at":"2025-07-18 14:09:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eArea Under the Curve Values for ROIs: Discerning Risky vs. Guaranteed Reward Decision\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes\u003c/em\u003e. A summary of classification performance of seven ROIs for discerning risky reward and guaranteed reward conditions (N=44). Especially, AI decodes risky versus guaranteed reward conditions above chance. AUC, the area under the ROC curve was calculated as an evaluation criterion. ROI, region of interest; DS, dorsal striatum; NAcc, nucleus accumbens; AI, anterior insula; VTA, ventral tegmental area. \u003cem\u003ep\u003c/em\u003e\u0026lt;.05*, \u003cem\u003ep\u003c/em\u003e\u0026lt;.01**, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6837700/v1/6203682df3c9a48d2397d40e.png"},{"id":100949389,"identity":"12a0d40e-e9fa-4d34-a152-370073945c00","added_by":"auto","created_at":"2026-01-23 07:01:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2455527,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6837700/v1/eca7fe96-e489-4f8f-bf70-08118cc46f30.pdf"},{"id":87043543,"identity":"0ae4ad1f-3c4d-44a8-8739-67b958b73414","added_by":"auto","created_at":"2025-07-18 14:17:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1511708,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6837700/v1/452324b80cc4e8634b32e539.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disentangling the Neural Underpinnings of Risk and Reward in Human Decision Making","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRisk-taking, or the willingness to pursue potential gains in the face of uncertain outcomes, represents a fundamental dimension of decision-making and plays a central role in everyday and life-altering choices. It involves the ability to evaluate and act on possible rewards and loss, an adaptive function evolved in organisms navigating uncertain environments towards maximizing survival and reproductive success (Mishra, 2014). Differences in risk-taking tendencies emerge across the lifespan, especially in adolescence, and alterations of risk-taking are core features of many clinical conditions, such as substance use, pathological gambling, and avoidant personality disorder (Chase et al., 2017; Rieser et al., 2019; Sanislow et al., 2012). Studying the proximate mechanisms that support risk-taking is therefore crucial, not only for advancing our understanding of human decision-making across the lifespan but also for designing more effective and targeted interventions aimed at addressing excessive or deficient risk-taking.\u003c/p\u003e \u003cp\u003eOver the past two decades, functional magnetic resonance imaging (fMRI) has provided invaluable insights into the cognitive and affective processes underlying risk-related decisions. The Balloon Analogue Risk Task (BART) has especially emerged as one of the most widely used experimental paradigms (Lejuez et al., 2002). In its canonical implementation, BART allows participants to earn monetary rewards by sequentially inflating a virtual balloon, knowing that each pump increases both the potential reward and the risk of popping the balloon and losing all earnings for that trial. Importantly, the exact probability of explosion is not known in advance and participants must learn the risk structure gradually through repeated experience. This dynamic, uncertain, and cumulative decision-making context closely mirrors how people often face risk in everyday life (Buelow et al., 2024). Utilizing BART, prior neuroimaging studies have implicated a network of brain regions, such as the anterior cingulate cortex (ACC), anterior insula (AI), striatum, dorsolateral prefrontal cortex (dlPFC), and medial orbitofrontal cortex (mOFC) in risk-taking behavior, highlighting contributions from executive control, salience detection, and reward evaluation systems (Trutti et al., 2021; Wang et al., 2022).\u003c/p\u003e \u003cp\u003eDespite its strengths, however, the traditional BART paradigm presents an important limitation: it confounds distinct psychological processes that can jointly contribute to the overt risk-taking behaviors, namely, sensitivity to reward with sensitivity to risk. For example, a person may highly value potential rewards but still avoid risky options due to heightened loss sensitivity, or conversely, may tolerate risk not because of high reward drive but due to insensitivity to possible negative outcomes. Behavioral studies suggest that motivation for obtaining more reward or avoiding negative outcomes (e.g., behavioral inhibition and activation system, BIS/BAS), while correlated, can indeed make independent contributions to decision-making (Demaree et al., 2008; Voigt et al., 2009).\u003c/p\u003e \u003cp\u003efMRI findings based on computational modeling approaches further support the idea that the brain tracks reward and risk as dissociable components. Rooted in neuroeconomics, these studies typically use behavioral paradigms that explicitly and parametrically vary gain and loss magnitudes and probabilities (e.g., mixed-gambling task; Schonberg et al., 2011), revealing that distinct brain regions encode separate dimensions of value and uncertainty (Jenni et al., 2022; Jia et al., 2023; O\u0026rsquo;Neill \u0026amp; Schultz, 2010; Schumacher et al., 2021; Sun et al., 2022; Tobler et al., 2007). For instance, the caudate nucleus has been shown to track expected reward magnitude, while the AI and mOFC are more sensitive to risk-related features such as variance or volatility (Jenni et al., 2022; Sun et al., 2022; Tobler et al., 2007). In corroboration, non-human animal computational models using juice as a reward showed that medial OFC are specifically sensitive to the risk component of reward cues (O\u0026rsquo;Neill \u0026amp; Schultz, 2010). A rat study further suggests that the circuit between medial OFC and dorsal striatum plays an important role in facilitating flexible reward seeking under risk (Jenni et al., 2022). Moreover, optogenetic inactivation of the caudate nucleus disrupts rats\u0026rsquo; ability to choose between high vs. low reward choices, suggesting that the caudate engages in encoding reward magnitudes (Gore et al., 2023). These findings, although distinct from BART which focuses more on naturalistic modeling of risk and reward (Schonberg et al., 2011), point to the dissociability of reward-seeking from risk-tolerant processes in the brain.\u003c/p\u003e \u003cp\u003eIn the canonical BART paradigm, the only way participants can maximize earnings is by continuing to inflate the balloon, thereby also increasing the risk of explosion. This design makes it difficult for researchers to effectively disentangle whether an individual\u0026rsquo;s behavior reflects motivation to obtain reward or tolerance for the threat of loss. This limitation not only complicates interpretation of neural activation patterns but may also obscure subtler differences in how the brain encodes reward versus risk. The limitation further hinders our understanding of the multidimensional risk profile in the brain (Van Duijvenvoorde et al., 2022), such as problematic risk tolerance (e.g.,recklessness) or problematic risk intolerance (e.g., anxiety).\u003c/p\u003e \u003cp\u003eTo address this, the current study brings design improvements to the traditional BART framework. Specifically, we included a novel \u003cem\u003eGuaranteed Reward\u003c/em\u003e (GR) condition, in which participants could inflate a balloon to accumulate reward without any risk of explosion. This condition retains the incremental reward structure of the standard BART but eliminates the threat of loss, allowing us to directly compare neural responses during decision-making under risky versus no-risk reward conditions. In addition, we employed multi-echo (ME) fMRI and denoising, an advanced acquisition and preprocessing method known to enhance sensitivity to BOLD signals in subcortical and midbrain regions (Kundu et al., 2017). Striatal and midbrain subregions are crucial for encoding reward and risk, but signals are often difficult to detect with conventional single-echo fMRI.\u003c/p\u003e \u003cp\u003eWith the design improvements, we will directly contrast brain responses to risky and guaranteed rewards using both univariate and multivariate techniques, thereby clarifying neural features contributing to decisions involving potential gains, losses, or both. We anticipate that this refined approach will offer better insights into individual differences in lifespan risk-related behavior and the neural underpinnings of vulnerabilities to psychopathology.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eForty-nine healthy participants (30 females, all cis) were recruited from Cornell University and completed the current study. Their ages ranged from 18 to 22 years (M \u0026plusmn; SD: 20.53 \u0026plusmn; 1.92), 6 participants declined to report their age. Demographic characteristics were shown in Table 1. Of the participants, sex, race, college year, their parents\u0026rsquo; income and highest level of education were included in Table 1. \u003c/p\u003e\n\n\u003ch2\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSelf-reported Surveys\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSelf-reported surveys include the demographic questionnaire (i.e., age, sex, race, college year, their parents\u0026rsquo; income and education, gender identity, etc.) as well as other psychological questionnaires not included in this analysis. Descriptions of the questionnaires related to risk and reward, as well as the corresponding results (see Table S1), are provided in the Supplementary Materials.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eModified BART Paradigm \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Balloon Risk Analogue Task (BART) paradigm is to measure the experience of risky reward decision-taking and receiving win or loss outcomes (Lejuez et al., 2002). Based on the adapted MRI version of BART (Kohno et al., 2015), we modified the paradigm to include three conditions: risky reward (RR), guaranteed reward (GR), and neutral (Neutral) condition as shown in Figure 1(B). Participants were instructed that there were three balloon colors (red, white, and gray) corresponding to three BART conditions (RR, GR, and Neutral respectively). Participants were told that they would receive all money earned at the end of the study and that they should try to make as much money as possible. This was not a deception. \u003c/p\u003e\n\n\u003cp\u003eIn the RR trials, participants could press buttons to inflate a red virtual balloon to earn money ($0.05 per pump), but losing all money if it popped. They could also choose to cash out in order to avoid risking the balloon popping. The more times they inflated, the greater the perceived reward and risk of the balloon bursting. In the GR trials, participants inflated white balloons for the same reward ($0.05 per pump) without the risk of losing money and balloon explosion. In the Neutral condition, participants inflated gray balloons without gaining or losing money, and no risk of popping existed. \u003c/p\u003e\n\n\u003cp\u003eThe maximal amount of inflation pumps for each balloon was randomly generated between 2 and 12 pumps for each participant for each run of the BART. Once the maximum was reached, red balloons popped, or gray and white balloons disappeared, and participants moved to the next trial. Following obtaining maximal pumps or cashout, pre-feedback interstimulus interval (ISI) was presented between 0.5 and 3 seconds. Then block feedback (e.g., negative, positive, and neutral feedback) was presented for 2 seconds. There was a randomly chosen jitter of 2, 6, or 8 seconds after the feedback to prepare for the next trial (Figure 1A).\u003c/p\u003e\n\n\u003ch2\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe Institutional Review Board for Human Participant Research (IRB) approval was obtained for all procedures (IRB protocol number: 1902008564). Participants were initially screened through a phone interview. Eligibility criteria included being right-handed, having no history of neurological conditions, no current or past episodes of psychosis, and not initiating psychotropic drugs close to the time of the study (e.g., Prozac, Lexapro, Xanax and Valium; 2 weeks for antidepressants, within 48 hours for fast acting drugs). According to safety standards for MRI scanning, participants were excluded if they had any metal in their body, experienced claustrophobia, or were pregnant. A written informed consent was obtained from participants after a detailed explanation of this study. Outside the scanner, participants practiced the modified BART paradigm on a screen to ensure they understood the color-coding rules of the balloon game. Participants remained still for a 6-minute anatomical scan and then performed two runs of the modified BART tasks during a multi-echo fMRI scan. The computer randomly generates 40 balloons of different colors and condition sequences in each run. The actual number of balloons participants pumped depended on how fast they completed the BART. After scanning, participants complete surveys including demographic questions as well as other questionnaires not included in this analysis. They received cash rewards based on their BART performance along with compensation for their time. They also completed an additional cognitive task (Stroop) during a scan, which was irrelevant to the goal of this study and not discussed in this manuscript.\u003c/p\u003e\n\n\u003ch2\u003e\u003cstrong\u003eData Acquisition\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNeuroimaging data were acquired using a General Electric (GE) Discovery MR750 3.0T MRI scanner at Cornell University. Task stimuli were projected on a screen at the back of the MRI\u0026rsquo;s bore, so participants viewed the stimuli with a 32-channel phased-array head coil. Before anatomical scanning, three-plane localizer images were acquired, and then ASSET (i.e., Array Spatial Sensitivity Encoding Technique) calibration was performed. One hundred and seventy-six high-resolution anatomical T1-weighted images were acquired in 6 minutes, using the sagittal plane of imaging and the magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) sequence (TR = 7 ms, TE = 3.42 ms, flip angle = 7\u0026deg;, field of view (FOV) = 256 mm, 256 \u0026times; 256 matrix, 176 axial slices, voxel size = 1 \u0026times; 1 \u0026times; 1 mm\u003csup\u003e3\u003c/sup\u003e, 1-mm slice thickness).\u003c/p\u003e\n\n\u003cp\u003eThirty interleaved functional multi-echo (ME) Echo Planar images (EPIs) sensitive to BOLD (blood oxygenation level-dependent) contrast were obtained during two runs of the BART, each run lasting 10 min (TR\u0026thinsp;=\u0026thinsp;2\u003cstrong\u003e,\u003c/strong\u003e600\u0026thinsp;ms; TE\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;13.4 ms, TE\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;35.8\u0026thinsp;ms, TE\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;58.5\u0026thinsp;ms; 80\u0026deg; flip angle; FOV\u0026thinsp;=\u0026thinsp;192\u0026thinsp;mm; matrix\u0026thinsp;=\u0026thinsp;96\u0026thinsp;\u0026times;\u0026thinsp;96; 30 axial slices; 3 echoes; 2\u0026thinsp;\u0026times;\u0026thinsp;2\u0026thinsp;\u0026times;\u0026thinsp;1.5 mm\u003csup\u003e3\u003c/sup\u003e voxels; 240 volumes, slice thickness = 1.5 mm). The total number of voxels for functional images is 276,480. To focus on frontal-mesolimbic brain activity, the partial-brain functional images were selected from the edge between midbrain and pons to the top of the corpus callosum across 30 slices and were aligned with the AC-PC axis, as shown in Figure 2. All participants\u0026rsquo; head positions were stabilized by foam pillows, and they used foam earplugs to diminish the scanning noise for data quality control. They were also informed of the importance of staying still during scanning, and all neuroimaging scans at the Cornell MRI Facility were performed by trained MR technologists working with a standardized protocol. Heart rate was recorded by an oximeter placed on the left index finger, and respiratory information was monitored by a sensor belt.\u003c/p\u003e\n\n\u003ch2\u003e\u003cstrong\u003eData Analysis Plan\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe study obtained behavioral and neuroimaging data from 49 participants. Univariate MRI analysis was performed on data from 43 participants. Five participants\u0026apos; MRI data were excluded for separate reasons: (1) one never received a loss outcome in the risky reward condition, resulting in no neural response to negative feedback; (2) one had MRI partial-brain images that couldn\u0026rsquo;t be preprocessed properly when using the pipeline; (3) three had incorrect field of view capturing partial-brain functional images. Multivariate pattern analysis was performed on data from 44 participants, including the one without negative feedback.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eBehavioral Measures\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eBehavioral measures of BART include: (1) the number of trials (N \u003csub\u003eTrials\u003c/sub\u003e) and pumps (N \u003csub\u003ePumps\u003c/sub\u003e) per condition, (2) reaction time between stimulus presentation and button pressing per pumps (RT \u003csub\u003eper pump\u003c/sub\u003e) and per trials (RT \u003csub\u003eper trial\u003c/sub\u003e), (3) money participants successfully saved into the bank for each condition (Money \u003csub\u003esave in bank\u003c/sub\u003e), (4) the average number of pumps participants chose to cash out ( Averagepumps \u003csub\u003esave\u003c/sub\u003e) and (5) the average number of pumps where the balloon exploded (Averagepumps \u003csub\u003eexplode\u003c/sub\u003e). A non-parametric test, related-samples Friedman test, was performed to test if those variables among three conditions are the same. \u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eUnivariate Permutation Analysis \u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreprocessing\u003c/strong\u003e. Imaging data were preprocessed using AFNI (Version 23.0.01; https://afni.nimh.nih.gov/; (Cox \u0026amp; Hyde, 1997). Using afni_proc.py, we remove spikes across the fMRI time series (\u003cem\u003edespike\u003c/em\u003e) and slice-timing differences were adjusted (\u003cem\u003etshift\u003c/em\u003e). EPI images were aligned with the corresponding anatomical one (\u003cem\u003ealign\u003c/em\u003e), and then anatomical and functional data were normalized to the standard MNI brain template. Motion correction was applied using \u003cem\u003evolreg\u003c/em\u003e. Then we created a binary brain mask from the aligned functional volumes (mask). There are 2,529,432 voxels in total for a preprocessed functional image. Moreover, using TE-dependent analysis (Tedana), preprocessed BOLD images with three echoes were denoised via principal component analysis (PCA) and independent component analysis (ICA), and non-brain materials were removed from denoised data (DuPre et al., 2021). Using FMRIB Software Library (FSL Version 6.0.6.2; www.fmrib.ox.ac.uk/fsl), signal-to-noise ratio was enhanced via a high-pass filter with a cutoff point of 100 second and we used a 2-mm FWHM Gaussian kernel, and grand-mean intensity normalization for spatial smoothing via the FEAT (Woolrich et al., 2009). The AFNI preprocessing pipeline did not successfully co-register the partial-brain functional images for four participants. To address this, FLIRT was applied to these four datasets to align the three echo images from each scan. The images were then combined into a single aligned image using Tedana. Finally, the aligned images were normalized with Advanced Normalization Tool (ANTs; Avants et al., 2009), preparing them for further analyses.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eLower-level Analysis.\u003c/strong\u003e After preprocessing, we used FSL\u0026rsquo;s FEAT to build a general linear model for reward and risk processing during decision-making and block feedback phase. Decision phase refers to the 1s duration from the onset of presenting the virtual balloon stimulus. In this phase, we included three regressors: RR decision, GR decision, and Neutral decision events. Block feedback was defined as a 2-second event for receiving feedback about a balloon. Four regressors were included: positive (gain) and negative (loss) feedback in the RR condition, positive feedback (gain) in the GR condition, and neutral feedback in the Neutral condition. The analysis process and results for block feedback were included in the supplemental materials. Additionally, the number of pumps was included as a parametric modulator for all regressors.\u003c/p\u003e\n\n\u003cp\u003eTo model risky reward decision-making, we contrasted BOLD signals between RR and Neutral decision events. To assess reward processing, we contrasted GR and Neutral decision events. For risk processing, we compared RR and GR decision events. To model the receipt of reward under risk, we contrasted RR positive feedback and Neutral feedback. We performed a fixed-effects model at the level two analysis in FEAT. For each participant, the average of the parameter estimates across two BART runs was calculated. This approach allowed us to enhance the reliability of the estimates by reducing the impact of noise. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eHigher-level analysis. \u003c/strong\u003eThe aggregated activations in seven contrasts were estimated using FSL\u0026rsquo;s non-parametric permutation testing and threshold-free cluster enhancement (TFCE) with 5000 permutations per contrast at the group-level analyses. Using TFCE, the cluster-like activations were enhanced by considering the magnitude and spatial extent of activations simultaneously, but these images remained fundamentally voxel-wise (S. Smith \u0026amp; Nichols, 2009). Controlling for false positives and multiplicity, the family-wise error (FWE) rate for the aggregated images\u0026rsquo; contrast were corrected, and the individual voxel significance level was set at p\u0026lt;0.05. The locations of significant voxel-scanned activations in three contrasts were determined using the Harvard-Oxford Cortical and Subcortical Atlas. The VTA mask from the CIT168 atlas was used to identify the location of the significant voxels in the univariate results (Pauli et al., 2018). For each activation, the specific brain region and its corresponding coordinates were reported below. The statistically significant activation information was generated using FSL\u0026rsquo;s atlasquery. To illustrate the greater sensitivity to BOLD signals in multiecho fMRI,we also repeated our main analyses using single-echo EPI data. The relevant procedures and results are summarized in the Supplementary Materials (Table S3 and Figure S).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eMultivariate Pattern Analysis (MVPA)\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe use AFNI, nilearn (Nilearn contributors et al., 2024) and scikit-learn (Pedregosa et al., 2011) to build multivariate pattern classification between risky and guaranteed reward decision making trials. Our goal is to test if AI and striatum distinguish risk features of reward. Preprocessing was also performed using AFNI, mirroring the steps used in GLM except for normalization (\u003cem\u003etlrc\u003c/em\u003e). For regression analysis, voxel patterns of BOLD signals to RR and GR condition were extracted as beta estimates for block decision phase by using AFNI \u003cem\u003e3dDeconvolve\u003c/em\u003e and \u003cem\u003e3dTcat\u003c/em\u003e. The Block decision phase refers to the duration from the first appearance of a balloon until pre-feedback ISI. To ensure unbiased testing, the dataset is split into training and testing sets, with an 80/20 split. For the classification, a linear support vector machine (SVM) classifier was applied to train on the training set and tested on the testing voxels within different ROI masks. Each single ROI mask was generated according to definitions of the Harvard-Oxford cortical atlas (Desikan et al., 2006) while the ROI mask for VTA was based on the CIT168 atlas (Pauli et al., 2018). This process was conducted using Nilearn and scikit-learn and employed a five-fold cross-validation approach. ROIs included structural masks of AI, VTA, striatum, dorsal striatum, caudate, putamen, and NAcc, as we hypothesized (Pauli et al., 2018). We compared MVPA models to determine which pattern of neural activity best distinguishes the conditions of RR versus GR decision. The area under the curve (AUC) for the receiver operating characteristic (ROC) of the predicted values was calculated to examine how accurately the model can predict the risky reward and guaranteed reward decision (i.e., classification performance). The AUC values of two correlated ROC curves within each model were compared to each other and to random prediction using a one-tailed t-test. \u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eBehavioral Findings\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eParticipants cashed out in 15.73 trials (SD = 3.96) on average in the RR condition.The mean number of trials where the balloon exploded was 10.8 (SD = 3.37). Participants chose to cash out, on average, at the 4.64th pump during the RR trial. On average, balloons popped by the 3.27th pump when participants decided to keep inflating. As shown in Table 2, non-parametric analysis (N=49) revealed significant differences across three conditions. Participants completed significantly more trials in both RR and GR conditions than in the Neutral condition,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; .001. GR condition showed more pumps than both RR and Neutral conditions, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. Money saved in the bank was significantly highest in the GR condition, followed by RR, and none in the Neutral condition,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; .001. Reaction time per trial was significantly longer in the Neutral condition than in GR, and in GR than in RR, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. Reaction time per pump was longest in RR compared to GR and Neutral, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eMRI Findings\u003c/strong\u003e\u003c/h2\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eUnivariate Permutation Results\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eDecision Phase.\u003c/strong\u003e The contrast of RR versus GR decision-making condition shows greater neural responses in bilateral frontal operculum cortex (FOC) extending to orbital frontal cortex (OFC) and AI, dorsal striatum including bilateral caudate and left putamen, left NAcc in very small clusters, right middle frontal gyrus (MFG), brainstem including right VTA (t = 5.54, TFCE-corrected \u003cem\u003ep\u003c/em\u003e = 0.01, [X, Y ,Z] = [4.5, -13.5, -13.5]). However, compared to the GR decision context, we observed the greater responses to the RR decision in the posterior cingulate cortex (PCC) rather than ACC. Replicating previous BART MRI studies (Braams et al., 2015; Korucuoglu et al., 2020; Rao et al., 2008), the contrast between RR and Neutral during decision-making shows greater BOLD signal in bilateral FOC, OFC, AI, PCC, caudate, putamen and thalamus, right MFG, left VTA, cerebellum, and a very small cluster of left NAcc. The contrast between GR and Neutral decision showed greater activation in MFG, inferior frontal gyrus (IFG), FOC, OFC, AI, middle temporal gyrus (MTG), hippocampus, and amygdala in right hemisphere, and bilateral intra calcarine cortex. However, we did not observe greater responses to the GR decision in striatal regions compared with the Neutral decision as would be expected. The permutation results for the decision phase are shown in Figure 3 and Table 3.\u003c/p\u003e\n\u003cp\u003eConsistent with previous fMRI findings, an analysis done with a single echo file revealed similar patterns, but it was less sensitive than our multi-echo analyses in mid-brain regions such as VTA and striatum (Gilmore et al., 2022; Kundu et al., 2017; Steel et al., 2022). The relevant results are provided in the Supplementary Materials (Table S3 and Figure S1). Furthermore, the univariate permutation results for block feedback are presented in the Supplementary Materials (Table S2).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eMultivariate Pattern Results\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo examine whether ROI\u0026rsquo;s voxel patterns could discern risky reward and guaranteed reward decision making, SVM classifier was performed and the area under the ROC curve (AUC) was calculated as an evaluation criterion for classification performance. As shown in Figure 4, Our multivariate results showed that AI had a significantly higher discriminative power than random choice (M \u0026plusmn; SD: 0.61 \u0026plusmn; 0.14,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; .001). In contrast, putamen and NAcc patterns showed significantly lower ability to discriminate between risky and guaranteed reward trials (M \u0026plusmn; SD: 0.47 \u0026plusmn; 0.10, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05; M \u0026plusmn; SD: 0.46 \u0026plusmn; 0.09, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). Voxel patterns in striatum (M \u0026plusmn; SD: 0.50 \u0026plusmn; 0.10), dorsal striatum (M \u0026plusmn; SD: 0.50 \u0026plusmn; 0.11), caudate (M \u0026plusmn; SD: 0.49 \u0026plusmn; 0.12) and VTA (M \u0026plusmn; SD: 0.50 \u0026plusmn; 0.09), showed no significant discriminative ability with AUC of around 0.5. We also compared classification performance of different models within the corresponding ROIs, as shown in Table 4. The results indicated that the AUC values for voxel patterns within putamen were significantly lower compared to the striatum and dorsal striatum (\u003cem\u003ep\u003c/em\u003es \u0026lt; .01). Similarly, NAcc showed significantly lower AUC values when compared to the striatum, dorsal striatum and caudate (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05). VTA showed significantly higher AUC value than NAcc. AI had significantly higher AUC values compared to all other ROIs, (all \u003cem\u003ep\u003c/em\u003es \u0026lt; .001). Overall, these results highlight differential classification performance of the models across various ROIs. AI may better discern risk features of reward from no-risk contexts than other ROIs, whereas NAcc and putamen showed lower discriminative power.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study addressed key limitations in dissociating the neural underpinnings of risk and reward processing in human research using multi-echo imaging and adding a guaranteed reward condition to the popular BART paradigm. Univariate permutation tests largely replicated and extended previous work indicating that decision-making in the risky reward condition coincided with increased BOLD response in striatum, VTA, OFC, and AI, compared to neutral and GR conditions. Single-echo analyses yielded similar, but more restricted results, suggesting that multi-echo enhanced sensitivity. Multivariate analyses indicated that AIN discerned risky rewards from guaranteed rewards slightly above chance (61%) and much better than striatal regions and VTA. Interestingly, we saw no NAcc response in the GR\u0026thinsp;\u0026gt;\u0026thinsp;Neutral contrast and NAcc was below chance at discerning risk from guaranteed reward conditions. Below, we interpret the contributions of these findings to disentangling the neural bases of risk vs reward processing under decision-making. Based on these findings we contend that our methodological changes to traditional BART studies can further research into lifespan and clinical inquiries into the neural underpinnings of risk and reward processing.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDecision-making under risk coincides with broad activation even accounting for reward\u003c/h2\u003e \u003cp\u003eOur study suggests that rather than part of some more general reward-related processing, canonical reward neural features may be doing something distinct during risky decision-making. Unlike in the previous iterations of the BART, we were able to directly compare risky versus guaranteed reward conditions, presumably subtracting out reward-specific related activation. OFC activation was lateralized when comparing risky vs guaranteed reward contexts, in line with previous research suggesting lOFC is distinctly involved in stimulus-reward contingencies especially as they relate to non-rewards and punishments, unlike medial subsections (Rolls et al., 2020). Further, connections between lateral OFC, ACC, AI and striatum, all significant during the risky condition, have long been associated with reinforcement learning, decision-making, and risky choice (Braams et al., 2015; Groman et al., 2019; Rao et al., 2008). We found PCC but not ACC BOLD increases in any contrast, possibly due to our restricted field of view meant to enhance mid-brain resolution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eHigh temporal and spatial resolution of striatum and brainstem clarify NAcc and VTA\u003c/h2\u003e \u003cp\u003eOur higher temporal and spatial resolution in our scanning parameters found greater NAcc and VTA response to risky versus neutral and guaranteed reward conditions, as shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Although NAcc is often discussed in BART studies, especially as a putative endophenotype of sensitivity to rewards, only 1 out of 9 studies reported NAcc activation specifically (Korucuoglu et al., 2020). Our findings here suggest that better temporal and spatial resolution may resolve some of this conflict. In support, our VTA response aligned with previously recorded coordinates and probability map created with a 7-tesla MRI (Pauli et al., 2018). Like many brainstem regions, VTA is difficult to image and study in humans, but plays a large role in non-human animal research (Forstmann et al., 2017). Neurons in VTA produce dopamine and project this neurotransmitter to NAcc and dorsal striatum and other brain regions (Stopper \u0026amp; Floresco, 2015). Non-human animal studies support a role for VTA in differentiating reward behaviors with and without risk and its involvement in appetitive reinforcement learning (Park \u0026amp; Moghaddam, 2017). The ability to image these small features is important to greater translation across non-human and human research and highlights the utility of multi-echo imaging in investigating subcortical nuclei as others have argued (e.g., Reddy et al., 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate analyses suggest that AI, not striatum decodes risk\u003c/h2\u003e \u003cp\u003eOur findings highlight a prominent role for AI in differentiating risky from guaranteed reward decision-making above more canonical reward regions. AI BOLD coincided with risk conditions in both univariate and multivariate analyses. Using MVPA, we found that AI patterns differentiated risk from reward conditions better than chance and much better than either dorsal or ventral striatum, or VTA patterns. A previous BART study similarly found that the neural patterns of AI, lateral OFCs and ACC before risky choice can better distinguish risky (pump) choice from safe (cashout) choice in risky reward condition, but they could not compare it with a non-risk reward condition (Helfinstein et al., 2014). AI is involved in signaling the probability of aversive outcomes, which could only occur in a risky condition (Clark et al., 2008). Non-human animal research suggests a causal selective role for AI in behaviors under risky but not no-risk reward contexts (Ishii et al., 2012). Computational human neuroimaging also suggests that AI tracks variance in expected value (Sun et al., 2022). Since the risky condition involves a wider range of outcomes than the no-risk condition, it is still possible that AI is specifically tracking the ambiguity inherent in risk, instead of the probability of loss \u003cem\u003eper-se\u003c/em\u003e. AI is important to affective processing, emotion regulation, and mental health, with a recent review touting it as a possible \u0026ldquo;gatekeeper\u0026rdquo; in switching from salience and executive network states important to decision-making (Molnar-Szakacs \u0026amp; Uddin, 2022). How this feature tracks variance in both negative and positive outcomes is therefore important for future research and this paradigm could help this work.\u003c/p\u003e \u003cp\u003eDespite the univariate findings and relatively greater temporal and spatial resolution of our scan our multivariate analyses showed that dorsal striatum, including caudate and putamen, were at chance and NAcc significantly worse than putamen in differentiating risky vs guaranteed reward conditions. Previous computational neuroimaging studies showed that dorsal striatum tracked expected reward magnitude, whereas the AI and mOFC primarily encoded risk-related information, such as variance (Sun et al., 2022). Others suggest that connectivity between striatal regions and OFC and not mere amplitude differences between conditions might better distinguish risky decision making (Groman et al., 2019). Similarly, VTA was not meaningfully able to decode risk and guaranteed reward conditions. However, some studies indicated that VTA has neurons sensitive to both aversive and appetitive cues via projections to different mid and frontal regions (De Jong et al., 2019) and may therefore not be a good candidate for differentiation with any achievable fMRI resolution. Taken together, future work may need to probe more complex patterns of connectivity to understand the role of striatum in differentiating risk and reward processing and sensitivity in decision-making.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eDespite our approaches\u0026rsquo; relative utility on balance, several limitations will need to be addressed in future work. First, the guaranteed reward condition, though innovative, did lack a performance contingency. Even the most widely used monetary reward paradigm, the monetary incentive delay task (MID), includes a performance component, albeit a nominal one, and hit rates are person-matched. Further, while the guaranteed reward condition did coincide with greater BOLD signals in right MFG (Elliott et al., 2004), right MTG (Wilson et al., 2018), and bilateral occipital cortex (A. B. Smith et al., 2011) as expected for the anticipation of a monetary reward, we saw no significantly greater activation in striatum and mOFC when compared to the neutral condition. These two factors paint a provocative picture. Unlike in voluntary risk-taking, involuntary risk-taking does not coincide with canonical mesolimbic responses in BART (Rao et al., 2008). While the guaranteed reward paradigm had a behavioral contingency (participant must press the button), there was no performance contingency and no possible behavioral modification that would impact the receipt of the monetary reward. Thus, it might not require major recruitment from NAcc and OFC. Future research should probe this further as it may suggest that these neural features are important to agency and behavioral modification, and not just rewards \u003cem\u003eper se\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eSecond, in its attempt to capture psychological risk, the study does not capture the neuroeconomics definition of risk as variability in possible rewards. Future research will need to model these two related, but separable operationalizations/ phenomena to better understand the functions of AI and subregions of OFC in risky decision making. Finally, though the standard BART has evidence of good test-retest reliability using fMRI (Korucuoglu et al., 2020; Li et al., 2020), this modified task needs to be evaluated further for its psychometric properties. Future research will need to increase and diversify participant samples to do this properly. Finally, we reduced our field of view to hone in on mesolimbic regions, but future research will need to expand to whole-brain imaging as regions like the medial dACC have also been shown to be important in risky decision-making, but were not imaged here.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eRisk-taking behavior arises out of a number of dissociable cognitive processes such as sensitivity to potential loss (risk tolerance) and desire for maximize rewards (reward sensitivity). However, relatively little human neuroscience separates these processes, potentially hiding the heterogeneous mechanisms by which individuals may arrive at the same risk-taking behavior. Despite limitations, this research illuminates putative neurobiological mechanisms which uniquely subserve risk vs guaranteed reward contexts and provides a new tool from which to probe risk tolerance separately from reward sensitivity. Future research may further expand the utility of this tool by adding different reward levels, adding a performance contingency to the guaranteed reward condition, constructing computational models, and expanding sample size and diversity. This method provides a simple manipulation to a well-known tool to expand the scope of what we can learn about the neural underpinnings of risk-taking behaviors and its associated cognitive processes. We suspect it will be useful for neural inquiries into decision-making, lifespan changes in risk and reward processing, and vulnerability to psychopathology.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAnterior cingulate cortex, ACC\u003c/p\u003e\n\u003cp\u003eAnterior insula, AI\u003c/p\u003e\n\u003cp\u003eDorsal anterior cingulate cortex, dACC\u003c/p\u003e\n\u003cp\u003eDorsal striatum, DS\u003c/p\u003e\n\u003cp\u003ePosterior cingulate cortex, PCC\u003c/p\u003e\n\u003cp\u003eDorsolateral prefrontal cortex, dlPFC\u003c/p\u003e\n\u003cp\u003eVentromedial prefrontal cortex, vmPFC\u003c/p\u003e\n\u003cp\u003eVentral tegmental area, VTA\u003c/p\u003e\n\u003cp\u003eOrbitofrontal cortex, OFC\u003c/p\u003e\n\u003cp\u003eNucleus accumbens, NAcc\u003c/p\u003e\n\u003cp\u003eMiddle temporal gyrus, MTG\u003c/p\u003e\n\u003cp\u003eMiddle frontal gyrus, MFG\u003c/p\u003e\n\u003cp\u003eInferior frontal gyrus, IFG\u003c/p\u003e\n\u003cp\u003eRegions of Interest, ROI\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThreshold-free cluster enhancement, TFCE\u003c/p\u003e\n\u003cp\u003eGeneral linear model, GLM\u003c/p\u003e\n\u003cp\u003eBlood-Oxygen-Level Dependence, BOLD\u003c/p\u003e\n\u003cp\u003eMultivariate Pattern Analysis, MVPA\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e. M.Z.G. designed and performed experiments, analyzed data and wrote the paper; X.D. developed analytical tools, analyzed data and wrote the paper. M.L. helped develop analytical tools and wrote the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e. X.D: https://orcid.org/0000-0002-0520-0808; M.L: https://orcid.org/0000-0002-3369-2790; M.Z.G: https://orcid.org/0000-0002-1290-4483;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003eWe sincerely thank Dr. Elizabeth B. Riley for her valuable support with the multi-echo fMRI analysis pipeline as well as Mirely Garcia and Melody Xu, who were essential to data collection and data quality check.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003eThis work was supported by startup funds from Cornell University . \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations.\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e. The self-reported survey related to risk and reward, along with the results of the neural contrasts during the block feedback phase (using multi-echo imaging) and the decision-making phase (using single-echo imaging), are included in the Supplementary Materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e. The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAvants, B., Tustison, N. J., \u0026amp; Song, G. (2009). Advanced Normalization Tools: V1.0. \u003cem\u003eThe Insight Journal\u003c/em\u003e. https://doi.org/10.54294/uvnhin\u003c/li\u003e\n\u003cli\u003eBraams, B. R., van Duijvenvoorde, A. C. K., Peper, J. S., \u0026amp; Crone, E. A. (2015). Longitudinal changes in adolescent risk-taking: A comprehensive study of neural responses to rewards, pubertal development, and risk-taking behavior. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(18), 7226\u0026ndash;7238. https://doi.org/10.1523/JNEUROSCI.4764-14.2015\u003c/li\u003e\n\u003cli\u003eBuelow, M. T., Okdie, B. M., \u0026amp; Kowalsky, J. M. (2024). 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Bayesian analysis of neuroimaging data in FSL. \u003cem\u003eNeuroImage\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(1), S173\u0026ndash;S186. https://doi.org/10.1016/j.neuroimage.2008.10.055\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"467\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 99.7859%;\"\u003e\n \u003ch5\u003eTable 1 \u003cem\u003eDemographic Characteristics (N=49)\u003c/em\u003e\u0026nbsp;\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003eFactor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eMiddle Eastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eMixed Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003eCollege Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e1st year undergrad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e2nd year undergrad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e3rd year undergrad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e4th year undergrad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eGraduate student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003eTransfer Student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;Parents\u0026apos; Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eBelow $40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e$40,000 - $59,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e$60,000 - $99,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e$100,000 - $174,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e$175,000 - \u0026nbsp;$299,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e$500,000 - $749,999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eMore than $750,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003eParents\u0026apos; Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eDoctoral degree\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eAdvanced professional degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eBachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eSome college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eHigh school diploma\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 214px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"665\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100%;\" colspan=\"6\"\u003e\n \u003ch5\u003eTable 2 \u003cem\u003eBART Conditions Impact Behavioral Performance\u0026nbsp;\u003c/em\u003e\u003c/h5\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 144px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 324px;\"\u003e\n \u003cp\u003e\u0026nbsp;M \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePost-hoc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003eN \u003csub\u003eTrials\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e26.53 \u0026plusmn; 2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e26.29 \u0026plusmn; 2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e17.29 \u0026plusmn; 2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eRR\u0026gt;Neutral, GR\u0026gt;Neutral\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003eN \u003csub\u003epumps\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e106.84 \u0026plusmn; 21.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e172.8 \u0026plusmn; 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e112.57 \u0026plusmn; 15.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eGR\u0026gt;RR, Neutral\u0026gt;GR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003eMoney\u003csub\u003e\u0026nbsp;save in bank\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3.52 \u0026plusmn; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e8.64 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0 \u0026plusmn; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eGR\u0026gt;RR\u0026gt;Neutral\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003eRT \u003csub\u003eper balloon\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3.15 \u0026plusmn; 1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3.27 \u0026plusmn; 1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3.29 \u0026plusmn; 1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eGR\u0026gt;RR, Neutral\u0026gt;GR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003eRT \u003csub\u003eper pump\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.79 \u0026plusmn; 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.49 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.51 \u0026plusmn; 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eRR\u0026gt;GR, RR\u0026gt;Neutral\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAverage\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003epumps \u003csub\u003esave\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4.64 \u0026plusmn; 1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAverage pumps \u003csub\u003eexplode\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3.27 \u0026plusmn; 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 665px;\"\u003e\n \u003cp\u003e\u003cem\u003eNotes\u003c/em\u003e. Related-samples Friedman test was performed. RT=reaction time, RR=risky reward, GR=Guaranteed reward. The reaction time was presented in seconds. N=49. \u003cem\u003ep\u003c/em\u003e \u0026lt; .001 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 625px;\"\u003e\n \u003cp\u003e\u003cem\u003eTable 3 Peak Coordinates for Significant Voxels Identified by TFCE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003eContrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eBrain Areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003ek\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eX\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eY\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 625px;\"\u003e\n \u003cp\u003eRR\u0026gt;GR Decision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Medial Occipital Cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Frontal Operculum Cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR \u0026nbsp; \u0026nbsp; Middle Frontal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL \u0026nbsp; \u0026nbsp; Frontal Operculum Cortex \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR VTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Caudate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Caudate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Caudate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Caudate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Substantia nigra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR \u0026nbsp; \u0026nbsp; Precentral Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Middle Frontal Gyrus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 353px;\"\u003e\n \u003cp\u003eRR\u0026gt;Neutral Decision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Lingual Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;L Brain Stem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Corpus callosum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;L Dorsal Occipital Cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;R Occipital Pole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;R Caudate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Frontal Operculum Cortex \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; R Anterior insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;R Anterior Insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;R Frontal Operculum cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;R Anterior insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;R Frontal Operculum cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;R Frontal Orbital Cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Middle Frontal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Anterior Insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Cingulate Gyrus, posterior division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Precentral Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 353px;\"\u003e\n \u003cp\u003eGR\u0026gt;Neutral Decision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eIntracalcarine Cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e11700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Middle Frontal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Middle Temporal Gyrus, temporooccipital part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Frontal Operculum Cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Inferior Frontal Gyrus, pars opercularis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Lateral Occipital Cortex, superior division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Lingual Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Parahippocampal Gyrus, posterior division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Lateral Occipital Cortex, inferior division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e49.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-73.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Cingulate Gyrus, posterior division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Inferior Temporal Gyrus, temporo occipital part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Frontal Operculum Cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Lingual Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Lateral Occipital Cortex, superior division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Inferior Frontal Gyrus, pars opercularis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e52.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Lateral Occipital Cortex, inferior division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eL Lateral Occipital Cortex, superior division\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003eR Amygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 625px;\"\u003e\n \u003cp\u003eNotes. Whole-brain analyses for partial-brain images were conducted at a voxel threshold of \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFWE\u003c/sub\u003e \u0026lt; 0.05 by TFCE. k\u0026gt;10, parametric modulator = the number of pumps. The local maxima coordinates were included in RR \u0026gt; Neutral Decision contrast. The voxel size is 1.5*1.5*1.5 mm\u003csup\u003e3\u003c/sup\u003e and the total number of voxels is 2,529,432. \u003cem\u003eAbbreviation\u003c/em\u003e. TFCE, threshold-free clustering enhancement; L, left; R, right; k, voxel size.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch5 style='margin-top:12.0pt;margin-right:0in;margin-bottom:4.0pt;margin-left:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;color:#666666;font-weight:normal;'\u003e\u003cspan style=\"color:black;\"\u003eTable 4\u0026nbsp;\u003c/span\u003e\u003cem\u003eAUC Comparisons among Multivariate Models of BART Conditions\u003c/em\u003e\u003c/h5\u003e\n\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse;border: none;width: 514px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.5pt;border: none;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:right;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.5pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid black;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003eStriatum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.75pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid black;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003eDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.5pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid black;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003eCaudate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.5pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid black;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003ePutamen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.75pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid black;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003eNAcc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.75in;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid black;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.5pt;border-top: none;border-bottom: none;border-left: none;border-image: initial;border-right: 1pt solid black;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u0026nbsp;DS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.5pt;border: none;background: rgb(255, 195, 109);padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"color:black;\"\u003e.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.75pt;border: none;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.5pt;border: none;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.5pt;border: none;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.75pt;border: none;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.75in;border: none;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.5pt;border-top: none;border-bottom: none;border-left: none;border-image: initial;border-right: 1pt solid black;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003eCaudate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.5pt;border: none;background: rgb(255, 199, 114);padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"color:black;\"\u003e-.01\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.75pt;border: none;background: rgb(255, 198, 112);padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"color:black;\"\u003e-.01\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55.5pt;border: none;padding: 1pt 1pt 5pt;height: 25.25pt;vertical-align: bottom;\"\u003e\n \u003cp 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\u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cem\u003eNotes\u003c/em\u003e. The mean differences in Area Under the Curve (AUC) values between pairs of models. AI outperforms the multivariate models of BART condition. Each cell displays the AUC difference, calculated as the ROI on the vertical axis minus the ROI on the horizontal axis. A positive value means the vertical-axis ROI distinguishes risky from guaranteed reward decisions better than the horizontal-axis ROI, while a negative value indicates the opposite. DS, dorsal striatum; NAcc, nucleus accumbens; AI, anterior insula; VTA, ventral tegmental area. N=44. One-tailed t-test were conducted, \u003cem\u003ep\u003c/em\u003e\u0026lt;.05*, \u003cem\u003ep\u003c/em\u003e\u0026lt;.01**.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"multi-echo fMRI, risk tolerance, reward sensitivity, caudate, NAcc, putamen","lastPublishedDoi":"10.21203/rs.3.rs-6837700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6837700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRisk-taking is a fundamental human behavior subserved by separable cognitive processes. Understanding how these processes are represented in the brain offers critical insights into decision-making, development, and vulnerabilities to psychopathology. However, functional magnetic resonance imaging (fMRI) research often yokes risk and reward processes during risky decision-making and has limited sensitivity to deep subcortical regions, constraining our contributions. In this study, we present a modified Balloon Analogue Risk Task completed under a multi-echo fMRI protocol meant to enhance subcortical signal. Forty-eight participants inflated virtual balloons across three conditions: risky reward, guaranteed reward, and neutral. GLM analyses revealed increased signal in orbitofrontal cortex, anterior insula (AI), striatum and a brain stem nuclei, ventral tegmental area (VTA), during risky versus guaranteed rewards decision-making conditions. Multivariate analysis identified the AI as a key predictor of the risk condition, surpassing striatal and VTA contributions. These results suggest that neural response to reward-based decision-making is heightened under risk and illuminates putative neurobiological mechanisms which uniquely subserve risk separate from general reward processing. The study also provides a new tool to enhance the resolution of human neuroscience research on risk-taking across the lifespan and vulnerabilities to psychopathology.\u003c/p\u003e","manuscriptTitle":"Disentangling the Neural Underpinnings of Risk and Reward in Human Decision Making","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 14:09:18","doi":"10.21203/rs.3.rs-6837700/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4212b34b-e029-41d5-ba2a-3b8aa2669e31","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50840197,"name":"Biological sciences/Psychology/Human behaviour"},{"id":50840198,"name":"Biological sciences/Neuroscience/Reward"},{"id":50840199,"name":"Biological sciences/Physiology/Neurophysiology"},{"id":50840200,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Decision"}],"tags":[],"updatedAt":"2026-01-21T20:24:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 14:09:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6837700","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6837700","identity":"rs-6837700","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}