Distinctive neural substrates of low and high risky decision making: Evidence from the Balloon Analog Risk Task

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Despite numerous studies on risk-taking in decision-making, the neural mechanisms of decision-making regarding risk levels remains unclear. To investigate the neural correlates of individual differences in risk-taking under different risk-levels, we analyzed behavioral data of the Balloon Analogue Risk Task (BART) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) data of healthy participants (22–39 years, N = 93) from the University of California, Los Angeles Consortium for Neuropsychiatric Phenomics dataset. In the BART, the participants decided to pump for more points or stop pumping to avoid explosion of the balloons, where the risk level was manipulated by the explosion likelihood which was distinguished by the balloon color (blue for low-, red for high- risk condition). Compared with low-risk condition, the participants pumped less number, exploded more balloons, and showed more variability in pump numbers in high-risk condition, demonstrating the effective manipulation of the risky level. Next, resting state features and functional connectivity (rsFC) strength were associated with behavioral measures in low- and high-risk conditions. We found that the explosion number of balloons were correlated with the low frequency fluctuations (ALFF) in the left dorsolateral prefrontal cortex (L. DLPFC), the rsFC strength between L. DLPFC and the left anterior orbital gyrus in the low-risk condition. In the high-risk condition, we found variability in pump numbers was correlated with the ALFF in the left middle/superior frontal gyrus, the fractional ALFF (fALFF) in the medial segment of precentral gyrus (M. PrG), and the rsFC strength between the M. PrG and bilateral precentral gyrus. Our results highlighted significance of the L. DLPFC in lower risky decision making and the precentral gyrus in higher risky decision making, suggesting that distinctive neural correlates underlie the individual differences of decision-making under different risk level. BART risky decision making risky level ALFF fALFF functional connectivity Figures Figure 1 Figure 2 Figure 3 1 Introduction In daily life, people confront various situations requiring decision making. Decision making involves the ability to choose between competing behaviors associated with uncertain benefits and penalties (van Leijenhorst, Crone, & Bunge 2006). Each decision holds different risks and consequences. Risk refers to a condition that a certain benefit can be obtained along with the possibility of damage or danger (Leigh 1999). Risky decision making is a complex process that involves weighing different options in terms of their likelihood of potential rewards and risks (He, Xue, Chen, Dong, & Chen 2014). It has been proposed by a classical theoretical model that risky decision making relies on the net assets of the outcome (Machina 1982). Importantly, there are clear individual differences in risk-taking behaviors in various risk conditions (Bruine de Bruin, Parker, & Fischhoff 2007; Parker & Fischhoff 2005). Thus, studies considering individual difference may provide new insight on the neural bases of risky decision. Balloon Analogue Risk Task (BART), designed by C. W. Lejuez et al. (2002), is a widely used paradigm to investigate risky decision making. In the BART task, participants need to virtually pump balloons to earn as many points as they can. The balloon can either grow larger with the pump or explode. Every pump accumulates points, but the risk of balloon explosion increases as the number of pumps goes up. If the balloon explodes, the participant loses all the points acquired from the balloon. Thus, the participant needs to decide whether to pump for more points or to stop pumping to save the current point for that balloon. BART task performance has been shown to significantly correlate with risk-related variables such as impulsivity, substance abuse, gambling behavior, and risky behavior ( r = 0.20 ~ 0.44), establishing the reliability of the BART task (C. W. Lejuez et al. 2002). In line with these findings, other studies also reported correlation between the risk preferences measured by BART and scores on risks-related constructs (Hunt, Hopko, Bare, Lejuez, & Robinson 2005; C. Lejuez, Aklin, Zvolensky, & Pedulla 2003; C. W. Lejuez et al. 2003). Many BART experimental designs were based on a single risk condition. (Cazzell, Li, Lin, Patel, & Liu 2012; Gu, Zhang, Luo, Wang, & Broster 2018; C. W. Lejuez et al. 2002; Mata, Hau, Papassotiropoulos, & Hertwig 2012; Rao, Korczykowski, Pluta, Hoang, & Detre 2008; Juan Yang, Li, Zhang, Qiu, & Zhang 2007). Few studies examined the differences related to risk level (Hupen, Habel, Schneider, Kable, & Wagels 2019). J. Yang and Zhang (2011) measured event-related potential (ERP) in low- and high-risk conditions and found high-risk condition evoked a more negative N400 (time window of 300–500 ms) in the frontocentral areas than low-risk condition. Additionally, Juan Yang et al. (2007) found high-risk condition evoked greater N500 than low-risk condition, thus N500 was proposed to be related to responses in risky decision making (Juan Yang et al. 2007). Theses ERP studies indicated that high- and low- risky decision-making may have differential neural bases, but lacked information on which specific brain regions were related to such difference. Functional magnetic resonance imaging (fMRI) has been widely used to examine brain mechanism of cognitive functions. Schonberg et al. (2012) observed brain activation during the BART task and found that activities in bilateral anterior insula, anterior cingulate cortex, and right dorsolateral prefrontal cortex brain were correlated with the mean number of pumps, a measure for risk-taking tendency. These brain regions were commonly known to be linked to risk processing and risk-taking. Moreover, the same study found that ventromedial prefrontal cortex (vmPFC) and bilateral medial temporal lobe (MTL) decreased with the mean number of pumps (Schonberg et al., 2012). Tisdall et al. (2020) analyzed neuroimaging data from a subsample of the Basel–Berlin Risk Study which included two widely used risk-taking tasks, the BART and monetary gambles task. They found associations between the risky choice and activations in the nucleus accumbens (NAcc) and anterior cingulate cortex (ACC) in both tasks. Specifically, there were negative associations between the mean number of pumps in the BART and activation in ACC and NAcc, and negative associations between the proportion of accept decisions in monetary gambles task and activations in ACC, NAcc, and anterior insula cortex (AIC). Rao et al. (2008) compared active choice mode and passive no-choice mode brain activation during the BART using fMRI. The authors found that a wide network of regions such as midbrain, insula, dorsal lateral prefrontal cortex (DLPFC), striatum, and anterior cingulate/medial frontal cortex (ACC/MFC) were associated with the voluntary risk. Voluntary risk showed higher activation in insula, DLPFC, ACC/MFC, and striatum compared with the involuntary risk. These studies demonstrated that distributed brain regions, such as DLPFC, vmPFC, MTL, ACC, and AIC, are associated with risky decision making using the BART. In addition to task-related fMRI, several studies using resting-state fMRI have shown complementary and consistent findings of the neural correlates of decision making. For example, Gentili et al. (2022) found that resting state amplitude of low-frequency fluctuation (ALFF) in the right inferior parietal lobule and the left caudate lobe was positively correlated the brain activity evoked during BART execution. In addition, total earning of the BART was correlated with the ALFF in the ACC/MPFC, and the Hurst Exponent, a measure of efficient online information processing, in the IFG/insula was correlated with total earnings (Gentili et al. 2020). Moreover, the connectivity between vmPFC and dorsomedial prefrontal cortex (dmPFC) in the resting state was associated with the choice of high reward card in the Cambridge Gambling Task (CGT) and number of pumps in the BART across all the participants (including young and old participants) (Yu et al. 2017). However, to our knowledge, no studies have examined decision-making in the brain under both high- and low-risk situations. The present study aimed to investigate neural correlates contributing to individual differences in decision making with low- and high- risk level using resting state fMRI. To do it, we associated resting state brain activity features with behavioral performance of the BART, ALFF (defined as the total power within the frequency range between 0.01 and 0.1 Hz), fALFF (the ALFF of a given frequency band as a fraction of the sum amplitudes across the whole frequency range), and ReHo (the evaluation of the similarities or coherence of intra-regional spontaneous low-frequency (< 0.08 Hz) Blood Oxygen Level-Dependent (BOLD) signal fluctuations in voxel-wise analysis across the entire brain). Using the brain regions identified by the previous association analysis as seeds, we further checked the functional connectivity associated with the behavioral performance of the BART. In the BART, we picked four behavioral measurements to evaluate various aspects of behavioral performance in low- and high-risk situations: mean adjusted number of pumps, number of explosions, mean adjusted pumps following an explosion, and the coefficient of variation of adjusted pump number. Mean adjusted pump number is the mean number of pumps on trials where the balloon did not explode, this was preferred than absolute number of pumps because explosions artificially restrict the range of pumping behavior (Pleskac, Wallsten, Wang, & Lejuez 2008). Thus, mean adjusted pump is sensitive to risk-taking tendency. Number of explosions serves as a measure of the propensity for continued risk-taking behavior after experiencing a prior balloon explosion (Leslie, Leppanen, Paloyelis, Nazar, & Treasure 2019). Mean adjusted pumps following an explosion may reflect greater risk propensity because of their chronological association with a failure (DeMartini et al. 2014). Coefficient of variation of adjusted pump numbers reflects intra-individual variability of adjusted pumps (Blair, Moyett, Bato, DeRosse, & Karlsgodt 2018), and is a strong indicator of risky decision-making(Weber, Shafir, & Blais 2004). These behavioral measures can capture different aspects of individual variability in task performance and serve as indices to measure risk-taking propensity. (DeMartini et al. 2014; C. W. Lejuez et al. 2002). We expect brain regions related to decision making and risk-taking to show differences between low- and high-risk conditions. 2 Materials and methods 2.1 Participants Data for the present study were downloaded from the Openneuro database (Poldrack et al. 2016 https://openneuro.org/datasets/ds000030/ ). Only healthy participants were included. The participants were recruited from the community and had no history of psychiatric disorders as test results by SCID-IV (First, Spitzer, Gibbon, & Williams 1995) and substance abuse as tested by on the day Urinalysis. According to the database information, the data acquisition followed the Declaration of Helsinki, and written informed consent was given according to procedures approved by the UCLA Institutional Review Board. One hundred and thirty healthy participants’ data were included in the database. Participants’ data were excluded from the analysis if they met any of the following criteria: 1) incomplete behavioral data of the BART (10 participants); 2) behavioral performance beyond three standard deviations (9 participants); 3) missing MRI data (8 participants); 4) incorrect slide number in MRI data (1 participant); 5) excessive head movement ( > = 2.0 mm) (9 participants). Under these criteria, 93 healthy participants’ data (51 males; age range: 21–50; mean age = 30.2 ± 8.42) were left for further analyses. 2.2 BART paradigm The Balloon Analogue Risk Task (BART) used in the database has been described in detail in Poldrack et al. (2016). Briefly, in the BART experiment, the participants pumped the red and blue balloons to earn as many points as possible. Each pump enabled the participants to earn 5 points. Importantly, the red balloons may explode randomly after 1–32 pumps, while blue balloons may explode randomly after 1-128 pumps. If the balloon exploded, no points can be earned for that balloon. Hence, the blue balloon trials represent lower risk situation, compared with the red balloon trials. During the task, the participants needed to evaluate the risk and to decide when to stop pumping to maximize their points. In total, 40 balloons (20 red balloons and 20 blue balloons) were presented to each participant. The number of points obtained in the task didn’t affects the actual money the participants received. Here, we used the BART data obtained outside of the scanner. 2.3 Data acquisition Brain imaging data were collected using a 3T Siemens Trio scanner. The resting state fMRI data were acquired using an echo-planar imaging (EPI) sequence with the following parameters: TR = 2 s, TE = 30 ms, flip angle = 90°, voxel size = 3 x 3 x 3 mm3, matrix 64×64, slice thickness = 4 mm, slices = 34, FOV = 192 mm. The parameters of T1-weighted high-resolution images were TR = 1.9 s, TE = 2.26 ms, slice thickness = 1 mm, number of slices = 176, voxel size = 1 x 1 x 1 mm3, matrix = 256×256, FOV = 250 mm (see details in Poldrack et al. (2016). The resting state fMRI scan lasted 304s and the participants were not stimulated or required to answer any questions, but were required to keep their eyes open. 2.4 Data analysis 2.4.1 Behavioral data analysis In order to study the individual differences related to decision making under low- and high- risk level, we picked mean adjusted pumps (MAP), total number of exploded balloons (ExplosionNums), mean adjusted pumps immediately after an explosion (MAP_afExplosion) and coefficient of variation of adjusted pumps (CV) to evaluate the behavior in the blue and red balloon trials. CV is calculated as the standard deviation of adjusted pumps divided by the mean adjusted pumps, representing the variability within the individual in decision-making. For these measurements, trials exceeded three standard deviations across all the red and blue balloon trials were discarded. For each measurement, we first checked normality using Shapiro–Wilk tests in both red and blue balloon trials and then compared the performance in the red and blue balloon trials using paired t-test if the data obey the normal distribution or Wilcoxon signed-rank test if the data didn’t obey the normal distribution. 2.4.2 Resting-state fMRI analysis Preprocessing Preprocessing of fMRI resting state data was conducted using Data Processing & Analysis for Resting State Brain Imaging DPARSF 5.3 (Data Processing Assistant for Resting-State fMRI (DPARSF) | The R-fMRI Network (rfmri.org)) and the Statistical Parametric Mapping toolbox (SPM12, https://www.fil.ion.ucl.ac.uk/spm/ ). The preprocessing steps included: ( 1 ) discarding the images of the first five time points to stabilize the magnetic field and obtain a better quality of the images, ( 2 ) slice timing and head movement correction, ( 3 ) coregistering T1 to functional and segmenting ( 4 ) regressing out interference covariates using Friston-24 motion parameters and three additional composite signals (white matter, cerebrospinal fluid, and global signals), ( 5 ) spatial normalization to the Montreal Neurological Institute space and resampling to 3×3×3 mm3, ( 6 ) spatial smoothing with 4mm FWHM (Full Width at Half Maximum) Gaussian kernel to get ALFF and fALFF values. Then ( 7 ) Spatial smoothing using time band-pass filtering (0.01–0.1 Hz) to get REHO values. Whole-brain voxel-wise analysis of rs-fMRI activities After preprocessing, we associated four behavioral measurements with resting-state fMRI activity measures using SPM 12 ( https://www.fil.ion.ucl.ac.uk/spm/ ). To do it, we computed ALFF, fALFF, and REHO values at the voxel level and then used multiple regression analysis to explore the brain areas whose activity measurements (ALFF, fALFF, and REHO) were correlated with behavioral data. For the regression analysis, age and gender were defined as unrelated covariates (Becker et al. 2019; Weiss et al. 2019). FWE corrected p < 0.05 with uncorrected p value 30 voxels were picked as significance level. Significant regions were used as ROIs to further examine how their resting-state functional connectivity relate to BART behavior (section 2.4.2.3), and tested in the prediction analysis (section 2.4.2.4). Resting state functional connectivity (rsFC) Subsequently, we analyzed seed-based voxel-wise using DPARSF 5.3 (Data Processing Assistant for Resting-State fMRI (DPARSF) | The R-fMRI Network (rfmri.org)) to compute resting state functional connectivity (rsFC), and the rsFC calculation without band-pass filter. The process included the following steps: First, picking the brain areas identified in the previous voxel-wise analysis as the seed for further rsFC analysis. Second, extracting and calculating time courses of all voxels for each seed region. Third, computing Pearson correlations between mean time course and all other voxels in the whole brain. At last, to promote the normality and strengthen the quality of the analysis of the statistics, using a transform Fisher from r to z to convert all rsFC maps into z-maps. Furthermore, multiple regression analyses were performed to assess the brain regions associated with high-risk and low-risk in rsFC individuals. Again, FWE corrected p < 0.05 with uncorrected p value 30 voxels were set as the significance level. Prediction analysis using Leave-One-Out Cross-Validation We further tested whether the obtained resting-state brain activity measures (ALFF, fALFF, REHO, rsFC) could reliably predict the individual behavioral data. To do it, we used leave-one-out cross-validation., Using resting activity indicators as features to train models to predict behavioral indicators. The predictive power of the established model was evaluated by computing the Pearson's correlation coefficients between the predicted and actual values. 3 Results 3.1 Behavioral results Behavioral data showed higher mean adjusted pumps (MAP) in the blue balloon trials (18.99 ± 11.21) than in the red balloon trials (10.72 ± 3.65) ( t (92) = 7.99, p < 0.001, see Fig. 1 a), demonstrating the participants generally pumps more in low-risk condition. The total number of exploded balloons (ExplosionNum) was lower in the blue balloon trials (3.25 ± 2.19) than in the red balloon trials (8.52 ± 2.85) ( t (92) = 19.44, p < 0.001, see Fig. 1 b), revealing that less balloons exploded in the low-risk condition than in the high-risk condition. In addition, the mean adjusted pumps immediately after an explosion (MAP_afExplosion) was higher in the blue balloon trials (16.92 ± 10.22) than that in the red balloon trials (9.88 ± 3.92) ( t (92) = 7.19, p < 0.001, see Fig. 1 c). Lastly, Wilcoxon signed-rank test was conducted to compare the CV in the red and blue balloon trials since the CV did not conform to a normal distribution. We found lower CV in the blue balloon trials than in the red balloon trials (blue: 0.38 ± 0.17, red: 0.42 ± 0.15, Z = 2.21, p = 0.027, see Fig. 1 d), which suggests that there is less variability within the individual in the low-risk condition. These results show that the participants pumped more, produced less explosion of balloons, pumped more immediately after the explosion, and produced more reliable pumps in the blue balloon trials (lower risk) than in the red balloon trials (high risk). Together, these results demonstrate that the blue and red balloons in the current study successfully mimicked relatively low and high risky situations. 3.2 Resting state results 3.2.1 Whole-brain voxel-wise analysis of rs-fMRI activities We found only one brain region where the resting state features were significantly correlated with the behaviors in the low-risk condition (Fig. 2 a). The ALFF in the left dorsolateral prefrontal cortex (L. DLPFC) (x, y, z = -39, 54, 24) was positively correlated with the ExplosionNum in the blue balloon trials ( r = 0.499, p < 0.001, 1000 bootstrap confidence interval [0.274, 0.658], Fig. 2 c) (see details in Table 1 ). In the high-risk condition, we found that resting state features in two brain regions were associated with behavior (Fig. 2 B). Specifically, the fALFF in the medial segment of the precentral gyrus (M. PrG) (x, y, z = 0, -18, 57) was positively correlated the CV In the red balloon trials ( r = 0.478, p < 0.001, 1000 bootstrap confidence interval [0.328, 0.619], Fig. 2 d). Additionally, the ALFF in the left middle/superior frontal gyrus (L. MFG/SFG) (x, y, z= -18, 39, 24) was negatively correlated with the MAP_afExplosion in the red balloon trials ( r = -0.376, p < 0.001, 1000 bootstrap confidence interval [-0.616, -0.361], Fig. 2 d). 3.2.2 Predictive analysis Leave-one-out cross-validation analysis (Fig. 2 e-f) revealed that resting state features could successfully predict low- and high-risk behaviors. The ALFF in the L. DLPFC could predict the ExplosionNum in the blue balloon trials ( r = 0.463, p < 0.001, Fig. 2 e), the fALFF in the M. PrG could predict the CV in the red balloon trials ( r = 0.444, p < 0.001, Fig. 2 f), and the ALFF in the L. MFG/SFG could predict the MAP_afExplosion in the red balloon trials ( r = 0.451, p < 0.001, Fig. 2 f). All these prediction results passed permutation test (iterations = 5000, p < 0.001). In contrast, the ALFF in the L. DLPFC couldn’t predict the Explosion Num in the red balloon trials ( r = 0.065, p = 0.535, Fig. 2 g), the fALFF in the M. PrG couldn’t predict the CV in the blue balloon trials ( r = 0.181, p = 0.083, Fig. 2 h), and the ALFF in the L. MFG/SFG couldn’t predict the MAP_afExplosion in the blue balloon trials ( r = 0.124, p = 0.237, Fig. 2 h). Together, these results demonstrated that resting state activity features may explain individual differences in the BART and further showed specificity of resting state features for respective behavioral performance in low or high risky situations. Table 1 Brain regions whose resting state features were associated with the behavioral measures in the BART. Behavioral Measure Resting State feature Brain Regions Cluster size Peak MNI coordinates r x y z Blue balloon trials ExplosionNum ALFF L. DLPFC 55 -36 54 24 0.499*** Red balloon trials CV fALFF M. PrG 33 0 -18 57 0.478*** MAP_afExplosion ALFF L. MFG/SFG 47 -18 39 24 -0.484*** Note: MNI, Montreal Neurological Institute; L: left; R: right. The significance level was set as FWE corrected p < 0.05; uncorrected p 30 voxels. r means the correlation coefficient between the brain measures and the behavioral data. L. DLPFC: left dorsolateral prefrontal cortex, M. PrG: medial segment of the precentral gyrus, L. MFG/SFG: left middle/superior frontal gyrus. *** represents significant correlation between resting state feature and behavioral performance at the significance level of p < 0.001 3.2.3 Seed-based functional connectivity In order to assess the association between functional connectivity and the BART performance in the low- and high- risky situations, we performed a seed-based functional connectivity analysis using the brain regions identified in the above analysis in 3.2.1 as seed regions. We first examined the connectivity of L. DLPFC, a brain region whose ALFF was correlated with the ExplosionNum in the blue balloon trials. Seed-based function connectivity analysis revealed that functional connectivity (FC) strength between the L. DLPFC and the left anterior orbital gyrus (L. AOrbG) (x, y, z = -21, 60, -15, Fig. 3 a) was positively correlated with ExplosionNum in the blue balloon trials ( r = 0.476, p < 0.001, 1000 bootstrap confidence interval [0.258, 0.635], Fig. 3 c) (see details in Table 2 ). Moreover, when the M. PrG, a brain region whose fALFF was correlated with the CV in the red balloon trials, served as the seed region, we found the functional connectivity strength between M. PrG and bilateral precentral gyrus (PrG) (L. PrG, x, y, z = -21, -21, -75; R. PrG, x, y, z = 24, -12, 75, Fig. 3 b) were positively correlated with CV in the red balloon trials (left precentral, r = 0.401, p < 0.001, 1000 bootstrap confidence interval [0.232, 0.580]; right precentral r = 0.375, p < 0.001, 1000 bootstrap confidence interval [0.167, 0.559], Fig. 3 d). Leave-one-out cross-validation analysis revealed these FC strengths could predict the corresponding behavioral performance (Fig. 3 E-F). The FC between the L. DLPFC and the L. AOrbG could successfully predict Explosion Num in the blue balloon trials ( r = 0.408, p < 0.001) (Fig. 3 e). Additionally, both the FC strengths between the M. PrG and the bilateral PrG successfully predicted CV in the red balloon trials (L. PrG, r = 0.308, p < 0.001; R. PrG, r = 0.324, p < 0.001; Fig. 3 e). Importantly, these FC strengths was not predictive of the corresponding behaviors in the other balloon trials (Fig. 3 g-h). In details, the FC strength between the L. DLPFC and the L. AorbG couldn’t predict the ExplosionNum in the red balloon trials ( r = -0.447, p < 0.001, Fig. 3 g), the FC strength between the M. PrG and the bilateral PrG couldn’t predict the CV in the blue balloon trials (L. PrG, r = 0.053, p = 0.617; R. PrG, r = 0.143, p = 0.172; Fig. 3 h). These results demonstrated that resting state FC contributes to individual differences in the BART, but with differential contribution to low and high risky situations. Table 2 Resting state functional connectivity results in brain regions associated with behavioral performance. Brain Regions Cluster size Peak MNI coordinates r x y z Blue balloon trials Seed Region: L. DLPFC L. AOrbG 42 -21 60 -15 0.476*** Red balloon trials Seed Region: M. PrG R. PrG 49 24 -12 75 0.401*** L. PrG 45 -21 -21 75 0.375*** Note: MNI, Montreal Neurological Institute; L: left; R: right. The significance level was set as FWE corrected p < 0.05; uncorrected p 30 voxels. r means the correlation coefficient between the connectivity strengths and behavioral data. L. DLPFC: left dorsolateral prefrontal cortex, L. AOrbG: Anterior Orbital Gyrus, M. PrG: medial segment of the precentral gyrus, L. PrG: left precentral gyrus, R. PrG: right precentral gyrus. 4. Discussions The present study investigated the neural correlates of decision making under different level of risky situation using the BART. We found that the blue and red balloon trials (low- and high-risk trials) in the BART reasonably simulated situations with lower and higher risks. The resting state fMRI analysis revealed an important role of the L. DLPFC in the low-risk condition, as shown by significant association between the explosion number of balloons and the ALFF of the L. DLPFC, the rsFC between the L. DLPFC and the L. AOrbG. No such association was found in the high-risk condition. Interestingly, we found noteworthy contribution of the precentral gyrus and the L. MFG/SFG to the high-risk condition, as shown by significant association between the variability in pump numbers of red balloons and the fALFF of the M. PrG, the ALFF of the L. MFG/SFG, the rsFC between the M. PrG and the bilateral precentral gyrus. Our findings revealed distinct behavior-brain relationship when healthy adult participants made decisions in low and high risky situations. We found the ALFF of the L. DLPFC not only was positively correlated with the explosion number, but also could successfully predict individual’s explosion number in low-risk trials. But it couldn’t predict individual’s explosion number in high-risk trials, showing specificity of the L. DLPFC related to the decision-making in the low-risk situation. Previous studies have consistently implicated the role of the left DLPFC in the decision-making process (Heekeren, Marrett, Ruff, Bandettini, & Ungerleider 2006; Rorie & Newsome 2005) and that DLPFC activation and risk taking measured by balloon explosion behavior was positive correlated (Claus & Hutchison 2012). Task-related fMRI studies have shown activation of the left DLPFC during the decision making (He, Xiao, et al. 2014; Li, Lu, D'Argembeau, Ng, & Bechara 2010; Lin et al. 2012). These studies emphasized importance of the left DLPFC in decision making, consistent with the finding that the L. DLPFC was associated with the behavioral performance in the BART. Yet, Bembich et al. (2014) applied multichannel near-infrared spectroscopy (NIRS) to test whether DLPFC is activated differently over time when the participants made low or high risk choices during the Iowa Gambling Task (IGT). The authors divided the IGT duration into four periods, each one contained 25 choices and found that the DLPFC was significantly activated with low-risk choices in the first period, whereas, but activated with high-risk choices in the second period. Such DLPFC activation disappeared in the following third and fourth periods. This finding suggests that the contribution of DLPFC to low- and high- risky decision makings may differ over time Huang et al. (2017) showed that anodal tDCS activation of the L. DLPFC leads to risk-averse decision making. This result suggests that, compared to high-risk conditions, the L. DLPFC may be more associated with low-risk conditions because of it closely related to risk aversion effects. These findings, together with our results, suggest the critical role of the DLPFC in low-risky decision-making. Moreover, the FC strength during the resting state between the L. DLPFC and the L. AOrbG was also correlated with the explosion number of the blue balloon trials, indicating that individuals with stronger L. DLPFC - L. AOrbG connection exploded more balloons in blue balloon trials. Orbitofrontal cortex (OFC) is widely recognized as a key region in reward processing, learning, and decision making (Zald & Rauch 2006). Kahnt, Chang, Park, Heinzle, and Haynes (2012) demonstrated that anterior-lateral region of the Orbitofrontal Cortex (OFC), particularly the anterior orbital gyrus (AOrbG), plays a crucial role in learning the value of stimuli and actions. Additionally, Kahnt's research revealed a positive functional connectivity between AOrbG and DLPFC. This finding suggests that sensory and modulatory inputs can influence reward representation, learning processes, and decision-making through the functional connectivity between the AOrbG and DLPFC. Together, our findings emphasized the role of the L. DLPFC in decision making and indicated individual’s decision-making behaviors under low-risky situation may be predicted by the neural activity and FC of the DLPFC. In the high-risk condition, we found the fALFF of the M. PrG was positively correlated with the CV in red balloon trials, indicating that the participants with bigger fALFF in the M. PrG varied their pumping responses more in high-risk condition. The CV (coefficient of variation of adjusted pumps) may reflect variation of choices across trials and the level of executive control that individuals can exert. It has been proposed that the CV can serve as an important predictor of risk selection and can be utilized as a risk propensity index (Weber et al. 2004), bigger CV represents higher propensity of risk-taking behavior. Therefore, CV can provide valuable insights into risk-taking behaviors and impulsive tendencies. Here, we found that individuals with higher fALFF in the M. PrG showed higher CV in the high-risk trials, indicating that the resting state activity of M. PrG was associated to individual’s propensity of risk-taking behavior. In addition, the FC strengths between the M. PrG and bilateral PrG were positively correlated with the CV in the high-risk trials. Importantly, the fALFF of the M. PrG and the FC strengths between M. PrG and bilateral PrG could successfully predict individual’s CV in the high-risk trials, but couldn’t predict individual’s CV in the low-risk trials. Our data suggested the importance of the M. PrG in identifying individual’s propensity of risk-taking behavior especially in the high-risky decision making. The PrG has been reported to be involved in various mental processes such as attention, motor readiness, and arousal (Litt, Plassmann, Shiv, & Rangel 2011). Additionally, the PrG is associated with the preparation and execution of motor responses (Bush et al. 2002; Rushworth, Walton, Kennerley, & Bannerman 2004), making it as an integral component of the incentive system. Specifically, Guo et al. (2013) found that the PrG activated more in high-risk condition where uncertainty about utility and expected outcomes is heightened compared with certain condition when participants made reward-based decisions (Guo et al. 2013). Here, we found not only the fALFF, but also the connection within PrG were associated with the response variation, which may suggest individuals' risk control and the stimulation of risk-taking tendencies under high-risk conditions. Additionally, the L. MFG/SFG showed its ALFF was negatively correlated with the mean adjusted pumps after an explosion in the red balloon trials, that the participants with bigger ALFF in the L. MFG/SFG pumped less after an explosion in the high-risk condition. The MFG/SFG is commonly recognized as a key brain region involved in processes related to emotion regulation (Frank et al. 2014). Voxel-based morphometric studies showed that the MFG/SFG is associated with the regulation of negative emotions in individuals(Ansell, Rando, Tuit, Guarnaccia, & Sinha 2012; Tyborowska et al. 2018). In the BART experiment, the explosion of a balloon may have induced different levels of negative emotion. Thus, we think the MFG/SFG may be related to the emotion regulation, which was more obvious in the high-risk condition. Additionally, the involvement of the Superior Frontal Gyrus (SFG) brain areas may not be necessary when selecting a single operation. However, they become crucial when there is a change in the set of rules guiding the operation selection process. Studies have shown that human activation in the SFG can be recorded when individuals are instructed to switch between two different sets of rules for selecting finger press responses to visual stimuli (Role of the human medial frontal cortex in task switching: a combined fMRI and TMS study). Taken together, our findings highlight the involvement of the M/SFG in emotion regulation and suggest that the SFG is particularly engaged in tasks requiring rule switching or changes in decision-making processes. Our study has found that differential brain regions contributed to individual differences in low- and high-risky decision-making. However, the correlation analysis to associate brain activities during the resting state and individual’s behavioral performance suggests that the identified brain regions are related to individual difference in risky decision making, which may be different from commonly activated brain regions obtained from task-based fMRI studies. Brain activations investigated in task-related fMRI usually aimed to find commonly activated regions among participants, thus the brain regions identified in the present study, which captured individual differences, can provide complementary knowledge in the neural bases related to risky decision-making. Lastly, the current study cannot provide causal evidence of the identified brain regions in the decision-making under different levels of risk, thus future research using brain stimulation techniques may be used to explore causal contribution of brain regions. Together, the present study investigated whether the decision-making under low and high risky situations share the same neural substrates by associating resting-state brain activities with behavioral performances across different individuals. We found distinct brain activity measures and regions were related to low-risky or high-risky decision making. Specifically, the DLPFC was found to be selectively related to low-risky decision making, while the precentral gyrus was related to high-risky decision making. Declarations Conflict of interest statement The authors declare no competing conflicts of interest. Author Contribution J and X: Writing – review & editing, Writing – original draft, Supervision, Methodology, Funding acquisition, Conceptualization.S: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. C: Writing – review & editing, Visualization, Methodology, Investigation, Data curation. J and L: Writing – review & editing, Investigation, Conceptualization. Acknowledgements This research was supported by grants from NSFC (62176045), by Sichuan Science and Technology Program (2023YFS0191), 111 project (B12027), and the Fundamental Research Funds for the Central Universities (ZYGX2020FRJH014), NSERC DG, Brain Canada, Healthy Brain Healthy Lives (HBHL) and the Canada Research Chairs program. Data availability statement UCLA data are available at https://openneuro.org/datasets/ds000030/versions/1.0.0 . References Ansell EB, Rando K, Tuit K, Guarnaccia J, Sinha R (2012) Cumulative adversity and smaller gray matter volume in medial prefrontal, anterior cingulate, and insula regions. Biol Psychiatry 72(1):57–64. https://doi.org/10.1016/j.biopsych.2011.11.022 Becker KR, Keshishian AC, Liebman RE, Coniglio KA, Wang SB, Franko DL, Thomas JJ (2019) Impact of expanded diagnostic criteria for avoidant/restrictive food intake disorder on clinical comparisons with anorexia nervosa. 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Cite Share Download PDF Status: Published Journal Publication published 03 Dec, 2024 Read the published version in Brain Topography → Version 1 posted Editorial decision: Revision requested 01 Oct, 2024 Reviews received at journal 30 Sep, 2024 Reviewers agreed at journal 11 Sep, 2024 Reviews received at journal 03 Sep, 2024 Reviewers agreed at journal 10 Aug, 2024 Reviewers invited by journal 06 Aug, 2024 Editor assigned by journal 02 Mar, 2024 Submission checks completed at journal 27 Feb, 2024 First submitted to journal 27 Feb, 2024 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-3993983","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275310181,"identity":"7e8e3549-a1be-441d-af1e-f475eb8b40ca","order_by":0,"name":"Zhenlan Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACxmYg8aACxmUjVkvCGZhqYrSAQWIbKVqY23kPf0icd1hOfn7zA4YPZYcZ+Gc3EHIYX4JB4rbDxgbH2AwYZ5w7zCBx5wAhLTwGCUAtiRvYeBiYedsOMxhIJBDWciBxzuH6+W1ALX+J1GLYkNhwOIHhGFALI5FajBkSjqUbbjiWZnCw51w6j8QNAloM+88Yf/hQYy0v33z44YMfZdZy/DMIaWlA4hwAYh786oFAnqCKUTAKRsEoGAUAhko+5DihyiQAAAAASUVORK5CYII=","orcid":"","institution":"The Clinical Hospital of Chengdu Brain Science Institute, MOE KeyLab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China","correspondingAuthor":true,"prefix":"","firstName":"Zhenlan","middleName":"","lastName":"Jin","suffix":""},{"id":275310182,"identity":"f0062510-f025-4fce-8d03-272bec8c54d7","order_by":1,"name":"Simeng Li","email":"","orcid":"","institution":"The Clinical Hospital of Chengdu Brain Science Institute, MOE KeyLab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China","correspondingAuthor":false,"prefix":"","firstName":"Simeng","middleName":"","lastName":"Li","suffix":""},{"id":275310183,"identity":"be6c0162-3d81-47f3-b82b-dc02a9feb8a5","order_by":2,"name":"Changan Wang","email":"","orcid":"","institution":"The Clinical Hospital of Chengdu Brain Science Institute, MOE KeyLab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China","correspondingAuthor":false,"prefix":"","firstName":"Changan","middleName":"","lastName":"Wang","suffix":""},{"id":275310184,"identity":"c8f1ec99-0572-461f-8a4e-ab8ac94e8c7f","order_by":3,"name":"Xiaoqian Chai","email":"","orcid":"","institution":"Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 2B4, Montreal, Canada","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqian","middleName":"","lastName":"Chai","suffix":""},{"id":275310185,"identity":"fb077601-1216-4d1b-86a6-09dedf4e9815","order_by":4,"name":"Junjun Zhang","email":"","orcid":"","institution":"The Clinical Hospital of Chengdu Brain Science Institute, MOE KeyLab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China","correspondingAuthor":false,"prefix":"","firstName":"Junjun","middleName":"","lastName":"Zhang","suffix":""},{"id":275310186,"identity":"e7e24511-a18b-424c-9929-e58d9ef51815","order_by":5,"name":"Ling Li","email":"","orcid":"","institution":"The Clinical Hospital of Chengdu Brain Science Institute, MOE KeyLab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-02-27 13:16:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3993983/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3993983/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10548-024-01094-8","type":"published","date":"2024-12-03T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51822225,"identity":"7116e33d-7801-42aa-abd5-5ded4f7a0e9b","added_by":"auto","created_at":"2024-02-29 16:12:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52905,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of high-risk (red) and low-risk (blue) behavioral data. (a) MAP: Mean adjusted pumps, (b) ExplosionNum: Mean Explosion Num, (c) MAP_afExplosion: MAP after an explosion, (d) CV: Coefficient of variation of adjusted pumps, in the blue and red balloon trials. * \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3993983/v1/b39563725ebdf366eb705d4c.jpg"},{"id":51822741,"identity":"33ba39ab-3472-4cdb-938d-fa6d793dc04d","added_by":"auto","created_at":"2024-02-29 16:20:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":216504,"visible":true,"origin":"","legend":"\u003cp\u003eBART behavior-related brain regions and prediction results. (a) L. DLPFC was identified to be associated with behavioral performance in the blue balloon trials (low-risk condition). (b) M. PrG and L. MFG/SFG were identified to be associated with behavioral performance in the red balloon trials (high-risk condition). (c) ALFF in the L. DLPFC was positively correlated with ExplosionNum in the blue balloon trials. (d) left: fALFF in the M. PrG was positively correlated with CV in the red balloon trials, right: ALFF in the L. MFG/SFGwas negatively correlated with MAP_afExplosion in the red balloon trials. (e) Explosion Num in the blue balloon trials was successfully predicted by the ALFF in the L. DLPFC. (f) left: CV in the red balloon trials was successfully predicted by the fALFF in the M. PrG; right: MAP_afExplosion in the red balloon trials was successfully predicted by the ALFF in the left medial/superior frontal gyrus. (g) The ALFF in the L. DLPFCdid not predict Explosion Num in the red balloon trials. (f) left: the fALFF in M. PrGcouldn’t predict the CV in the blue balloon trials; right: the ALFF in the left medial/superior frontal gyrus did not predict MAP_afExplosion in the blue balloon trials. L. DLPFC: left dorsolateral prefrontal cortex, M. PrG: medial segment of the precentral gyrus, L. MFG/SFG: left middle/superior frontal gyrus.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3993983/v1/d700f3ddf777562aa4a6e493.jpg"},{"id":51822224,"identity":"1ccfc284-5772-42c6-9f26-6cb8016d1b2f","added_by":"auto","created_at":"2024-02-29 16:12:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194293,"visible":true,"origin":"","legend":"\u003cp\u003eResults of seed-based resting state FC analysis after controlling age and gender. (a) When the L. DLPFC serving as a seed, the FC strength between the seed and the L. AorbG was associated with the behavioral performance in the blue balloon trials. (b) When the M. PrG serving as a seed, the FC strengths between the seed and bilateral PrG were associated with the behavioral performance in the red balloon trials. \u0026nbsp;(c) L. DLPFC–L. AorbG FC strength was positively correlated with the ExplosionNum in the blue balloon trials. \u0026nbsp;(d) M. PrG – L. PrG and M. PrG – R. PrG FC strengths were positively correlated with the CV in the red balloon trials. (e) Explosion in the blue balloon trials could be well predicted by L. DLPFC–L. AorbG FC strength. (f) the CV in the red balloon trials could be well predicted by M. PrG – L. PrG and M. PrG – R. PrG FC strengths. (g) L. DLPFC – L. AorbG FC strength couldn’t predict Explosion in the red balloon trials. (h) M. PrG – L. PrG and M. PrG – R. PrG FC strengths couldn’t predict the CV in the blue balloon trials. L. DLPFC: left dorsolateral prefrontal cortex, M. PrG: medial segment of the precentral gyrus.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3993983/v1/7915e94372b7c69e7698a4a8.jpg"},{"id":70965465,"identity":"f4782952-aaef-447d-b1c4-0b5421829bba","added_by":"auto","created_at":"2024-12-09 16:20:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1027159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3993983/v1/1cb12683-cf5c-447f-8667-b06a32676cd7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinctive neural substrates of low and high risky decision making: Evidence from the Balloon Analog Risk Task","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn daily life, people confront various situations requiring decision making. Decision making involves the ability to choose between competing behaviors associated with uncertain benefits and penalties (van Leijenhorst, Crone, \u0026amp; Bunge 2006). Each decision holds different risks and consequences. Risk refers to a condition that a certain benefit can be obtained along with the possibility of damage or danger (Leigh 1999). Risky decision making is a complex process that involves weighing different options in terms of their likelihood of potential rewards and risks (He, Xue, Chen, Dong, \u0026amp; Chen 2014). It has been proposed by a classical theoretical model that risky decision making relies on the net assets of the outcome (Machina 1982). Importantly, there are clear individual differences in risk-taking behaviors in various risk conditions (Bruine de Bruin, Parker, \u0026amp; Fischhoff 2007; Parker \u0026amp; Fischhoff 2005). Thus, studies considering individual difference may provide new insight on the neural bases of risky decision.\u003c/p\u003e \u003cp\u003eBalloon Analogue Risk Task (BART), designed by C. W. Lejuez et al. (2002), is a widely used paradigm to investigate risky decision making. In the BART task, participants need to virtually pump balloons to earn as many points as they can. The balloon can either grow larger with the pump or explode. Every pump accumulates points, but the risk of balloon explosion increases as the number of pumps goes up. If the balloon explodes, the participant loses all the points acquired from the balloon. Thus, the participant needs to decide whether to pump for more points or to stop pumping to save the current point for that balloon. BART task performance has been shown to significantly correlate with risk-related variables such as impulsivity, substance abuse, gambling behavior, and risky behavior (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20\u0026thinsp;~\u0026thinsp;0.44), establishing the reliability of the BART task (C. W. Lejuez et al. 2002). In line with these findings, other studies also reported correlation between the risk preferences measured by BART and scores on risks-related constructs (Hunt, Hopko, Bare, Lejuez, \u0026amp; Robinson 2005; C. Lejuez, Aklin, Zvolensky, \u0026amp; Pedulla 2003; C. W. Lejuez et al. 2003). Many BART experimental designs were based on a single risk condition. (Cazzell, Li, Lin, Patel, \u0026amp; Liu 2012; Gu, Zhang, Luo, Wang, \u0026amp; Broster 2018; C. W. Lejuez et al. 2002; Mata, Hau, Papassotiropoulos, \u0026amp; Hertwig 2012; Rao, Korczykowski, Pluta, Hoang, \u0026amp; Detre 2008; Juan Yang, Li, Zhang, Qiu, \u0026amp; Zhang 2007). Few studies examined the differences related to risk level (Hupen, Habel, Schneider, Kable, \u0026amp; Wagels 2019). J. Yang and Zhang (2011) measured event-related potential (ERP) in low- and high-risk conditions and found high-risk condition evoked a more negative N400 (time window of 300\u0026ndash;500 ms) in the frontocentral areas than low-risk condition. Additionally, Juan Yang et al. (2007) found high-risk condition evoked greater N500 than low-risk condition, thus N500 was proposed to be related to responses in risky decision making (Juan Yang et al. 2007). Theses ERP studies indicated that high- and low- risky decision-making may have differential neural bases, but lacked information on which specific brain regions were related to such difference.\u003c/p\u003e \u003cp\u003eFunctional magnetic resonance imaging (fMRI) has been widely used to examine brain mechanism of cognitive functions. Schonberg et al. (2012) observed brain activation during the BART task and found that activities in bilateral anterior insula, anterior cingulate cortex, and right dorsolateral prefrontal cortex brain were correlated with the mean number of pumps, a measure for risk-taking tendency. These brain regions were commonly known to be linked to risk processing and risk-taking. Moreover, the same study found that ventromedial prefrontal cortex (vmPFC) and bilateral medial temporal lobe (MTL) decreased with the mean number of pumps (Schonberg et al., 2012). Tisdall et al. (2020) analyzed neuroimaging data from a subsample of the Basel\u0026ndash;Berlin Risk Study which included two widely used risk-taking tasks, the BART and monetary gambles task. They found associations between the risky choice and activations in the nucleus accumbens (NAcc) and anterior cingulate cortex (ACC) in both tasks. Specifically, there were negative associations between the mean number of pumps in the BART and activation in ACC and NAcc, and negative associations between the proportion of accept decisions in monetary gambles task and activations in ACC, NAcc, and anterior insula cortex (AIC). Rao et al. (2008) compared active choice mode and passive no-choice mode brain activation during the BART using fMRI. The authors found that a wide network of regions such as midbrain, insula, dorsal lateral prefrontal cortex (DLPFC), striatum, and anterior cingulate/medial frontal cortex (ACC/MFC) were associated with the voluntary risk. Voluntary risk showed higher activation in insula, DLPFC, ACC/MFC, and striatum compared with the involuntary risk. These studies demonstrated that distributed brain regions, such as DLPFC, vmPFC, MTL, ACC, and AIC, are associated with risky decision making using the BART.\u003c/p\u003e \u003cp\u003eIn addition to task-related fMRI, several studies using resting-state fMRI have shown complementary and consistent findings of the neural correlates of decision making. For example, Gentili et al. (2022) found that resting state amplitude of low-frequency fluctuation (ALFF) in the right inferior parietal lobule and the left caudate lobe was positively correlated the brain activity evoked during BART execution. In addition, total earning of the BART was correlated with the ALFF in the ACC/MPFC, and the Hurst Exponent, a measure of efficient online information processing, in the IFG/insula was correlated with total earnings (Gentili et al. 2020). Moreover, the connectivity between vmPFC and dorsomedial prefrontal cortex (dmPFC) in the resting state was associated with the choice of high reward card in the Cambridge Gambling Task (CGT) and number of pumps in the BART across all the participants (including young and old participants) (Yu et al. 2017). However, to our knowledge, no studies have examined decision-making in the brain under both high- and low-risk situations.\u003c/p\u003e \u003cp\u003eThe present study aimed to investigate neural correlates contributing to individual differences in decision making with low- and high- risk level using resting state fMRI. To do it, we associated resting state brain activity features with behavioral performance of the BART, ALFF (defined as the total power within the frequency range between 0.01 and 0.1 Hz), fALFF (the ALFF of a given frequency band as a fraction of the sum amplitudes across the whole frequency range), and ReHo (the evaluation of the similarities or coherence of intra-regional spontaneous low-frequency (\u0026lt;\u0026thinsp;0.08 Hz) Blood Oxygen Level-Dependent (BOLD) signal fluctuations in voxel-wise analysis across the entire brain). Using the brain regions identified by the previous association analysis as seeds, we further checked the functional connectivity associated with the behavioral performance of the BART. In the BART, we picked four behavioral measurements to evaluate various aspects of behavioral performance in low- and high-risk situations: mean adjusted number of pumps, number of explosions, mean adjusted pumps following an explosion, and the coefficient of variation of adjusted pump number. Mean adjusted pump number is the mean number of pumps on trials where the balloon did not explode, this was preferred than absolute number of pumps because explosions artificially restrict the range of pumping behavior (Pleskac, Wallsten, Wang, \u0026amp; Lejuez 2008). Thus, mean adjusted pump is sensitive to risk-taking tendency. Number of explosions serves as a measure of the propensity for continued risk-taking behavior after experiencing a prior balloon explosion (Leslie, Leppanen, Paloyelis, Nazar, \u0026amp; Treasure 2019). Mean adjusted pumps following an explosion may reflect greater risk propensity because of their chronological association with a failure (DeMartini et al. 2014). Coefficient of variation of adjusted pump numbers reflects intra-individual variability of adjusted pumps (Blair, Moyett, Bato, DeRosse, \u0026amp; Karlsgodt 2018), and is a strong indicator of risky decision-making(Weber, Shafir, \u0026amp; Blais 2004). These behavioral measures can capture different aspects of individual variability in task performance and serve as indices to measure risk-taking propensity. (DeMartini et al. 2014; C. W. Lejuez et al. 2002). We expect brain regions related to decision making and risk-taking to show differences between low- and high-risk conditions.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eData for the present study were downloaded from the Openneuro database (Poldrack et al. 2016 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openneuro.org/datasets/ds000030/\u003c/span\u003e\u003cspan address=\"https://openneuro.org/datasets/ds000030/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Only healthy participants were included. The participants were recruited from the community and had no history of psychiatric disorders as test results by SCID-IV (First, Spitzer, Gibbon, \u0026amp; Williams 1995) and substance abuse as tested by on the day Urinalysis. According to the database information, the data acquisition followed the Declaration of Helsinki, and written informed consent was given according to procedures approved by the UCLA Institutional Review Board.\u003c/p\u003e \u003cp\u003eOne hundred and thirty healthy participants\u0026rsquo; data were included in the database. Participants\u0026rsquo; data were excluded from the analysis if they met any of the following criteria: 1) incomplete behavioral data of the BART (10 participants); 2) behavioral performance beyond three standard deviations (9 participants); 3) missing MRI data (8 participants); 4) incorrect slide number in MRI data (1 participant); 5) excessive head movement (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;2.0 mm) (9 participants). Under these criteria, 93 healthy participants\u0026rsquo; data (51 males; age range: 21\u0026ndash;50; mean age\u0026thinsp;=\u0026thinsp;30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.42) were left for further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 BART paradigm\u003c/h2\u003e \u003cp\u003eThe Balloon Analogue Risk Task (BART) used in the database has been described in detail in Poldrack et al. (2016). Briefly, in the BART experiment, the participants pumped the red and blue balloons to earn as many points as possible. Each pump enabled the participants to earn 5 points. Importantly, the red balloons may explode randomly after 1\u0026ndash;32 pumps, while blue balloons may explode randomly after 1-128 pumps. If the balloon exploded, no points can be earned for that balloon. Hence, the blue balloon trials represent lower risk situation, compared with the red balloon trials. During the task, the participants needed to evaluate the risk and to decide when to stop pumping to maximize their points. In total, 40 balloons (20 red balloons and 20 blue balloons) were presented to each participant. The number of points obtained in the task didn\u0026rsquo;t affects the actual money the participants received. Here, we used the BART data obtained outside of the scanner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data acquisition\u003c/h2\u003e \u003cp\u003eBrain imaging data were collected using a 3T Siemens Trio scanner. The resting state fMRI data were acquired using an echo-planar imaging (EPI) sequence with the following parameters: TR\u0026thinsp;=\u0026thinsp;2 s, TE\u0026thinsp;=\u0026thinsp;30 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, voxel size\u0026thinsp;=\u0026thinsp;3 x 3 x 3 mm3, matrix 64\u0026times;64, slice thickness\u0026thinsp;=\u0026thinsp;4 mm, slices\u0026thinsp;=\u0026thinsp;34, FOV\u0026thinsp;=\u0026thinsp;192 mm. The parameters of T1-weighted high-resolution images were TR\u0026thinsp;=\u0026thinsp;1.9 s, TE\u0026thinsp;=\u0026thinsp;2.26 ms, slice thickness\u0026thinsp;=\u0026thinsp;1 mm, number of slices\u0026thinsp;=\u0026thinsp;176, voxel size\u0026thinsp;=\u0026thinsp;1 x 1 x 1 mm3, matrix\u0026thinsp;=\u0026thinsp;256\u0026times;256, FOV\u0026thinsp;=\u0026thinsp;250 mm (see details in Poldrack et al. (2016). The resting state fMRI scan lasted 304s and the participants were not stimulated or required to answer any questions, but were required to keep their eyes open.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Behavioral data analysis\u003c/h2\u003e \u003cp\u003eIn order to study the individual differences related to decision making under low- and high- risk level, we picked mean adjusted pumps (MAP), total number of exploded balloons (ExplosionNums), mean adjusted pumps immediately after an explosion (MAP_afExplosion) and coefficient of variation of adjusted pumps (CV) to evaluate the behavior in the blue and red balloon trials. CV is calculated as the standard deviation of adjusted pumps divided by the mean adjusted pumps, representing the variability within the individual in decision-making.\u003c/p\u003e \u003cp\u003eFor these measurements, trials exceeded three standard deviations across all the red and blue balloon trials were discarded. For each measurement, we first checked normality using Shapiro\u0026ndash;Wilk tests in both red and blue balloon trials and then compared the performance in the red and blue balloon trials using paired t-test if the data obey the normal distribution or Wilcoxon signed-rank test if the data didn\u0026rsquo;t obey the normal distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Resting-state fMRI analysis\u003c/h2\u003e \u003cp\u003ePreprocessing\u003c/p\u003e \u003cp\u003ePreprocessing of fMRI resting state data was conducted using Data Processing \u0026amp; Analysis for Resting State Brain Imaging DPARSF 5.3 (Data Processing Assistant for Resting-State fMRI (DPARSF) | The R-fMRI Network (rfmri.org)) and the Statistical Parametric Mapping toolbox (SPM12, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The preprocessing steps included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) discarding the images of the first five time points to stabilize the magnetic field and obtain a better quality of the images, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) slice timing and head movement correction, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) coregistering T1 to functional and segmenting (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) regressing out interference covariates using Friston-24 motion parameters and three additional composite signals (white matter, cerebrospinal fluid, and global signals), (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) spatial normalization to the Montreal Neurological Institute space and resampling to 3\u0026times;3\u0026times;3 mm3, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) spatial smoothing with 4mm FWHM (Full Width at Half Maximum) Gaussian kernel to get ALFF and fALFF values. Then (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Spatial smoothing using time band-pass filtering (0.01\u0026ndash;0.1 Hz) to get REHO values.\u003c/p\u003e \u003cp\u003eWhole-brain voxel-wise analysis of rs-fMRI activities\u003c/p\u003e \u003cp\u003eAfter preprocessing, we associated four behavioral measurements with resting-state fMRI activity measures using SPM 12 ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). To do it, we computed ALFF, fALFF, and REHO values at the voxel level and then used multiple regression analysis to explore the brain areas whose activity measurements (ALFF, fALFF, and REHO) were correlated with behavioral data. For the regression analysis, age and gender were defined as unrelated covariates (Becker et al. 2019; Weiss et al. 2019). FWE corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with uncorrected p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and cluster size\u0026thinsp;\u0026gt;\u0026thinsp;30 voxels were picked as significance level. Significant regions were used as ROIs to further examine how their resting-state functional connectivity relate to BART behavior (section 2.4.2.3), and tested in the prediction analysis (section 2.4.2.4).\u003c/p\u003e \u003cp\u003eResting state functional connectivity (rsFC)\u003c/p\u003e \u003cp\u003eSubsequently, we analyzed seed-based voxel-wise using DPARSF 5.3 (Data Processing Assistant for Resting-State fMRI (DPARSF) | The R-fMRI Network (rfmri.org)) to compute resting state functional connectivity (rsFC), and the rsFC calculation without band-pass filter. The process included the following steps: First, picking the brain areas identified in the previous voxel-wise analysis as the seed for further rsFC analysis. Second, extracting and calculating time courses of all voxels for each seed region. Third, computing Pearson correlations between mean time course and all other voxels in the whole brain. At last, to promote the normality and strengthen the quality of the analysis of the statistics, using a transform Fisher from r to z to convert all rsFC maps into z-maps.\u003c/p\u003e \u003cp\u003eFurthermore, multiple regression analyses were performed to assess the brain regions associated with high-risk and low-risk in rsFC individuals. Again, FWE corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with uncorrected p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and cluster size\u0026thinsp;\u0026gt;\u0026thinsp;30 voxels were set as the significance level.\u003c/p\u003e \u003cp\u003ePrediction analysis using Leave-One-Out Cross-Validation\u003c/p\u003e \u003cp\u003eWe further tested whether the obtained resting-state brain activity measures (ALFF, fALFF, REHO, rsFC) could reliably predict the individual behavioral data. To do it, we used leave-one-out cross-validation., Using resting activity indicators as features to train models to predict behavioral indicators. The predictive power of the established model was evaluated by computing the Pearson's correlation coefficients between the predicted and actual values.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Behavioral results\u003c/h2\u003e \u003cp\u003eBehavioral data showed higher mean adjusted pumps (MAP) in the blue balloon trials (18.99\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21) than in the red balloon trials (10.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65) (\u003cem\u003et\u003c/em\u003e \u003csub\u003e\u003cem\u003e(92)\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;7.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), demonstrating the participants generally pumps more in low-risk condition. The total number of exploded balloons (ExplosionNum) was lower in the blue balloon trials (3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19) than in the red balloon trials (8.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85) (\u003cem\u003et\u003c/em\u003e \u003csub\u003e\u003cem\u003e(92)\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;19.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), revealing that less balloons exploded in the low-risk condition than in the high-risk condition. In addition, the mean adjusted pumps immediately after an explosion (MAP_afExplosion) was higher in the blue balloon trials (16.92\u0026thinsp;\u0026plusmn;\u0026thinsp;10.22) than that in the red balloon trials (9.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92) (\u003cem\u003et\u003c/em\u003e \u003csub\u003e\u003cem\u003e(92)\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;7.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Lastly, Wilcoxon signed-rank test was conducted to compare the CV in the red and blue balloon trials since the CV did not conform to a normal distribution. We found lower CV in the blue balloon trials than in the red balloon trials (blue: 0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17, red: 0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15, \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), which suggests that there is less variability within the individual in the low-risk condition. These results show that the participants pumped more, produced less explosion of balloons, pumped more immediately after the explosion, and produced more reliable pumps in the blue balloon trials (lower risk) than in the red balloon trials (high risk). Together, these results demonstrate that the blue and red balloons in the current study successfully mimicked relatively low and high risky situations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Resting state results\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Whole-brain voxel-wise analysis of rs-fMRI activities\u003c/h2\u003e \u003cp\u003eWe found only one brain region where the resting state features were significantly correlated with the behaviors in the low-risk condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The ALFF in the left dorsolateral prefrontal cortex (L. DLPFC) (x, y, z = -39, 54, 24) was positively correlated with the ExplosionNum in the blue balloon trials (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.499, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 1000 bootstrap confidence interval [0.274, 0.658], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) (see details in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the high-risk condition, we found that resting state features in two brain regions were associated with behavior (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Specifically, the fALFF in the medial segment of the precentral gyrus (M. PrG) (x, y, z\u0026thinsp;=\u0026thinsp;0, -18, 57) was positively correlated the CV In the red balloon trials (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.478, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 1000 bootstrap confidence interval [0.328, 0.619], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Additionally, the ALFF in the left middle/superior frontal gyrus (L. MFG/SFG) (x, y, z= -18, 39, 24) was negatively correlated with the MAP_afExplosion in the red balloon trials (\u003cem\u003er\u003c/em\u003e = -0.376, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 1000 bootstrap confidence interval [-0.616, -0.361], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Predictive analysis\u003c/h2\u003e \u003cp\u003eLeave-one-out cross-validation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-f) revealed that resting state features could successfully predict low- and high-risk behaviors. The ALFF in the L. DLPFC could predict the ExplosionNum in the blue balloon trials (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.463, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee), the fALFF in the M. PrG could predict the CV in the red balloon trials (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.444, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef), and the ALFF in the L. MFG/SFG could predict the MAP_afExplosion in the red balloon trials (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.451, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). All these prediction results passed permutation test (iterations\u0026thinsp;=\u0026thinsp;5000, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the ALFF in the L. DLPFC couldn\u0026rsquo;t predict the Explosion Num in the red balloon trials (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.535, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg), the fALFF in the M. PrG couldn\u0026rsquo;t predict the CV in the blue balloon trials (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.181, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.083, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh), and the ALFF in the L. MFG/SFG couldn\u0026rsquo;t predict the MAP_afExplosion in the blue balloon trials (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.124, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.237, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). Together, these results demonstrated that resting state activity features may explain individual differences in the BART and further showed specificity of resting state features for respective behavioral performance in low or high risky situations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBrain regions whose resting state features were associated with the behavioral measures in the BART.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBehavioral Measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResting State feature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBrain Regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCluster size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePeak MNI coordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ey\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue balloon trials\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExplosionNum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL. DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.499***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRed balloon trials\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efALFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM. PrG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.478***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP_afExplosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL. MFG/SFG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.484***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: MNI, Montreal Neurological Institute; L: left; R: right. The significance level was set as FWE corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; uncorrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and cluster size\u0026thinsp;\u0026gt;\u0026thinsp;30 voxels. \u003cem\u003er\u003c/em\u003e means the correlation coefficient between the brain measures and the behavioral data. L. DLPFC: left dorsolateral prefrontal cortex, M. PrG: medial segment of the precentral gyrus, L. MFG/SFG: left middle/superior frontal gyrus.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*** represents significant correlation between resting state feature and behavioral performance at the significance level of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Seed-based functional connectivity\u003c/h2\u003e \u003cp\u003eIn order to assess the association between functional connectivity and the BART performance in the low- and high- risky situations, we performed a seed-based functional connectivity analysis using the brain regions identified in the above analysis in 3.2.1 as seed regions.\u003c/p\u003e \u003cp\u003eWe first examined the connectivity of L. DLPFC, a brain region whose ALFF was correlated with the ExplosionNum in the blue balloon trials. Seed-based function connectivity analysis revealed that functional connectivity (FC) strength between the L. DLPFC and the left anterior orbital gyrus (L. AOrbG) (x, y, z = -21, 60, -15, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) was positively correlated with ExplosionNum in the blue balloon trials (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.476, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 1000 bootstrap confidence interval [0.258, 0.635], Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) (see details in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, when the M. PrG, a brain region whose fALFF was correlated with the CV in the red balloon trials, served as the seed region, we found the functional connectivity strength between M. PrG and bilateral precentral gyrus (PrG) (L. PrG, x, y, z = -21, -21, -75; R. PrG, x, y, z\u0026thinsp;=\u0026thinsp;24, -12, 75, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) were positively correlated with CV in the red balloon trials (left precentral, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.401, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 1000 bootstrap confidence interval [0.232, 0.580]; right precentral \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.375, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 1000 bootstrap confidence interval [0.167, 0.559], Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eLeave-one-out cross-validation analysis revealed these FC strengths could predict the corresponding behavioral performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F). The FC between the L. DLPFC and the L. AOrbG could successfully predict Explosion Num in the blue balloon trials (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.408, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Additionally, both the FC strengths between the M. PrG and the bilateral PrG successfully predicted CV in the red balloon trials (L. PrG, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.308, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R. PrG, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.324, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Importantly, these FC strengths was not predictive of the corresponding behaviors in the other balloon trials (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg-h). In details, the FC strength between the L. DLPFC and the L. AorbG couldn\u0026rsquo;t predict the ExplosionNum in the red balloon trials (\u003cem\u003er\u003c/em\u003e = -0.447, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg), the FC strength between the M. PrG and the bilateral PrG couldn\u0026rsquo;t predict the CV in the blue balloon trials (L. PrG, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.053, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.617; R. PrG, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.143, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.172; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh). These results demonstrated that resting state FC contributes to individual differences in the BART, but with differential contribution to low and high risky situations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResting state functional connectivity results in brain regions associated with behavioral performance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eBrain Regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCluster size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePeak MNI coordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ey\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue balloon trials\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSeed Region: L. DLPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL. AOrbG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.476***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRed balloon trials\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed Region: M. PrG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR. PrG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.401***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL. PrG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.375***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: MNI, Montreal Neurological Institute; L: left; R: right. The significance level was set as FWE corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; uncorrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and cluster size\u0026thinsp;\u0026gt;\u0026thinsp;30 voxels. \u003cem\u003er\u003c/em\u003e means the correlation coefficient between the connectivity strengths and behavioral data. L. DLPFC: left dorsolateral prefrontal cortex, L. AOrbG: Anterior Orbital Gyrus, M. PrG: medial segment of the precentral gyrus, L. PrG: left precentral gyrus, R. PrG: right precentral gyrus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eThe present study investigated the neural correlates of decision making under different level of risky situation using the BART. We found that the blue and red balloon trials (low- and high-risk trials) in the BART reasonably simulated situations with lower and higher risks. The resting state fMRI analysis revealed an important role of the L. DLPFC in the low-risk condition, as shown by significant association between the explosion number of balloons and the ALFF of the L. DLPFC, the rsFC between the L. DLPFC and the L. AOrbG. No such association was found in the high-risk condition. Interestingly, we found noteworthy contribution of the precentral gyrus and the L. MFG/SFG to the high-risk condition, as shown by significant association between the variability in pump numbers of red balloons and the fALFF of the M. PrG, the ALFF of the L. MFG/SFG, the rsFC between the M. PrG and the bilateral precentral gyrus. Our findings revealed distinct behavior-brain relationship when healthy adult participants made decisions in low and high risky situations.\u003c/p\u003e \u003cp\u003eWe found the ALFF of the L. DLPFC not only was positively correlated with the explosion number, but also could successfully predict individual\u0026rsquo;s explosion number in low-risk trials. But it couldn\u0026rsquo;t predict individual\u0026rsquo;s explosion number in high-risk trials, showing specificity of the L. DLPFC related to the decision-making in the low-risk situation. Previous studies have consistently implicated the role of the left DLPFC in the decision-making process (Heekeren, Marrett, Ruff, Bandettini, \u0026amp; Ungerleider 2006; Rorie \u0026amp; Newsome 2005) and that DLPFC activation and risk taking measured by balloon explosion behavior was positive correlated (Claus \u0026amp; Hutchison 2012). Task-related fMRI studies have shown activation of the left DLPFC during the decision making (He, Xiao, et al. 2014; Li, Lu, D'Argembeau, Ng, \u0026amp; Bechara 2010; Lin et al. 2012). These studies emphasized importance of the left DLPFC in decision making, consistent with the finding that the L. DLPFC was associated with the behavioral performance in the BART. Yet, Bembich et al. (2014) applied multichannel near-infrared spectroscopy (NIRS) to test whether DLPFC is activated differently over time when the participants made low or high risk choices during the Iowa Gambling Task (IGT). The authors divided the IGT duration into four periods, each one contained 25 choices and found that the DLPFC was significantly activated with low-risk choices in the first period, whereas, but activated with high-risk choices in the second period. Such DLPFC activation disappeared in the following third and fourth periods. This finding suggests that the contribution of DLPFC to low- and high- risky decision makings may differ over time Huang et al. (2017) showed that anodal tDCS activation of the L. DLPFC leads to risk-averse decision making. This result suggests that, compared to high-risk conditions, the L. DLPFC may be more associated with low-risk conditions because of it closely related to risk aversion effects. These findings, together with our results, suggest the critical role of the DLPFC in low-risky decision-making.\u003c/p\u003e \u003cp\u003eMoreover, the FC strength during the resting state between the L. DLPFC and the L. AOrbG was also correlated with the explosion number of the blue balloon trials, indicating that individuals with stronger L. DLPFC - L. AOrbG connection exploded more balloons in blue balloon trials. Orbitofrontal cortex (OFC) is widely recognized as a key region in reward processing, learning, and decision making (Zald \u0026amp; Rauch 2006). Kahnt, Chang, Park, Heinzle, and Haynes (2012) demonstrated that anterior-lateral region of the Orbitofrontal Cortex (OFC), particularly the anterior orbital gyrus (AOrbG), plays a crucial role in learning the value of stimuli and actions. Additionally, Kahnt's research revealed a positive functional connectivity between AOrbG and DLPFC. This finding suggests that sensory and modulatory inputs can influence reward representation, learning processes, and decision-making through the functional connectivity between the AOrbG and DLPFC. Together, our findings emphasized the role of the L. DLPFC in decision making and indicated individual\u0026rsquo;s decision-making behaviors under low-risky situation may be predicted by the neural activity and FC of the DLPFC.\u003c/p\u003e \u003cp\u003eIn the high-risk condition, we found the fALFF of the M. PrG was positively correlated with the CV in red balloon trials, indicating that the participants with bigger fALFF in the M. PrG varied their pumping responses more in high-risk condition. The CV (coefficient of variation of adjusted pumps) may reflect variation of choices across trials and the level of executive control that individuals can exert. It has been proposed that the CV can serve as an important predictor of risk selection and can be utilized as a risk propensity index (Weber et al. 2004), bigger CV represents higher propensity of risk-taking behavior. Therefore, CV can provide valuable insights into risk-taking behaviors and impulsive tendencies. Here, we found that individuals with higher fALFF in the M. PrG showed higher CV in the high-risk trials, indicating that the resting state activity of M. PrG was associated to individual\u0026rsquo;s propensity of risk-taking behavior. In addition, the FC strengths between the M. PrG and bilateral PrG were positively correlated with the CV in the high-risk trials. Importantly, the fALFF of the M. PrG and the FC strengths between M. PrG and bilateral PrG could successfully predict individual\u0026rsquo;s CV in the high-risk trials, but couldn\u0026rsquo;t predict individual\u0026rsquo;s CV in the low-risk trials. Our data suggested the importance of the M. PrG in identifying individual\u0026rsquo;s propensity of risk-taking behavior especially in the high-risky decision making. The PrG has been reported to be involved in various mental processes such as attention, motor readiness, and arousal (Litt, Plassmann, Shiv, \u0026amp; Rangel 2011). Additionally, the PrG is associated with the preparation and execution of motor responses (Bush et al. 2002; Rushworth, Walton, Kennerley, \u0026amp; Bannerman 2004), making it as an integral component of the incentive system. Specifically, Guo et al. (2013) found that the PrG activated more in high-risk condition where uncertainty about utility and expected outcomes is heightened compared with certain condition when participants made reward-based decisions (Guo et al. 2013). Here, we found not only the fALFF, but also the connection within PrG were associated with the response variation, which may suggest individuals' risk control and the stimulation of risk-taking tendencies under high-risk conditions.\u003c/p\u003e \u003cp\u003eAdditionally, the L. MFG/SFG showed its ALFF was negatively correlated with the mean adjusted pumps after an explosion in the red balloon trials, that the participants with bigger ALFF in the L. MFG/SFG pumped less after an explosion in the high-risk condition. The MFG/SFG is commonly recognized as a key brain region involved in processes related to emotion regulation (Frank et al. 2014). Voxel-based morphometric studies showed that the MFG/SFG is associated with the regulation of negative emotions in individuals(Ansell, Rando, Tuit, Guarnaccia, \u0026amp; Sinha 2012; Tyborowska et al. 2018). In the BART experiment, the explosion of a balloon may have induced different levels of negative emotion. Thus, we think the MFG/SFG may be related to the emotion regulation, which was more obvious in the high-risk condition. Additionally, the involvement of the Superior Frontal Gyrus (SFG) brain areas may not be necessary when selecting a single operation. However, they become crucial when there is a change in the set of rules guiding the operation selection process. Studies have shown that human activation in the SFG can be recorded when individuals are instructed to switch between two different sets of rules for selecting finger press responses to visual stimuli (Role of the human medial frontal cortex in task switching: a combined fMRI and TMS study). Taken together, our findings highlight the involvement of the M/SFG in emotion regulation and suggest that the SFG is particularly engaged in tasks requiring rule switching or changes in decision-making processes.\u003c/p\u003e \u003cp\u003eOur study has found that differential brain regions contributed to individual differences in low- and high-risky decision-making. However, the correlation analysis to associate brain activities during the resting state and individual\u0026rsquo;s behavioral performance suggests that the identified brain regions are related to individual difference in risky decision making, which may be different from commonly activated brain regions obtained from task-based fMRI studies. Brain activations investigated in task-related fMRI usually aimed to find commonly activated regions among participants, thus the brain regions identified in the present study, which captured individual differences, can provide complementary knowledge in the neural bases related to risky decision-making. Lastly, the current study cannot provide causal evidence of the identified brain regions in the decision-making under different levels of risk, thus future research using brain stimulation techniques may be used to explore causal contribution of brain regions.\u003c/p\u003e \u003cp\u003eTogether, the present study investigated whether the decision-making under low and high risky situations share the same neural substrates by associating resting-state brain activities with behavioral performances across different individuals. We found distinct brain activity measures and regions were related to low-risky or high-risky decision making. Specifically, the DLPFC was found to be selectively related to low-risky decision making, while the precentral gyrus was related to high-risky decision making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest statement\u003c/h2\u003e \u003cp\u003eThe authors declare no competing conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ and X: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Supervision, Methodology, Funding acquisition, Conceptualization.S: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. C: Writing \u0026ndash; review \u0026amp; editing, Visualization, Methodology, Investigation, Data curation. J and L: Writing \u0026ndash; review \u0026amp; editing, Investigation, Conceptualization.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis research was supported by grants from NSFC (62176045), by Sichuan Science and Technology Program (2023YFS0191), 111 project (B12027), and the Fundamental Research Funds for the Central Universities (ZYGX2020FRJH014), NSERC DG, Brain Canada, Healthy Brain Healthy Lives (HBHL) and the Canada Research Chairs program.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eUCLA data are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openneuro.org/datasets/ds000030/versions/1.0.0\u003c/span\u003e\u003cspan address=\"https://openneuro.org/datasets/ds000030/versions/1.0.0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnsell EB, Rando K, Tuit K, Guarnaccia J, Sinha R (2012) Cumulative adversity and smaller gray matter volume in medial prefrontal, anterior cingulate, and insula regions. 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Oxford University Press, USA\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"brain-topography","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"btop","sideBox":"Learn more about [Brain Topography](http://link.springer.com/journal/10548)","snPcode":"10548","submissionUrl":"https://submission.nature.com/new-submission/10548/3","title":"Brain Topography","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"BART, risky decision making, risky level, ALFF, fALFF, functional connectivity","lastPublishedDoi":"10.21203/rs.3.rs-3993983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3993983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman beings exhibit varying risk-taking behaviors in response to different risk levels. Despite numerous studies on risk-taking in decision-making, the neural mechanisms of decision-making regarding risk levels remains unclear. To investigate the neural correlates of individual differences in risk-taking under different risk-levels, we analyzed behavioral data of the Balloon Analogue Risk Task (BART) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) data of healthy participants (22\u0026ndash;39 years, N\u0026thinsp;=\u0026thinsp;93) from the University of California, Los Angeles Consortium for Neuropsychiatric Phenomics dataset. In the BART, the participants decided to pump for more points or stop pumping to avoid explosion of the balloons, where the risk level was manipulated by the explosion likelihood which was distinguished by the balloon color (blue for low-, red for high- risk condition). Compared with low-risk condition, the participants pumped less number, exploded more balloons, and showed more variability in pump numbers in high-risk condition, demonstrating the effective manipulation of the risky level. Next, resting state features and functional connectivity (rsFC) strength were associated with behavioral measures in low- and high-risk conditions. We found that the explosion number of balloons were correlated with the low frequency fluctuations (ALFF) in the left dorsolateral prefrontal cortex (L. DLPFC), the rsFC strength between L. DLPFC and the left anterior orbital gyrus in the low-risk condition. In the high-risk condition, we found variability in pump numbers was correlated with the ALFF in the left middle/superior frontal gyrus, the fractional ALFF (fALFF) in the medial segment of precentral gyrus (M. PrG), and the rsFC strength between the M. PrG and bilateral precentral gyrus. Our results highlighted significance of the L. DLPFC in lower risky decision making and the precentral gyrus in higher risky decision making, suggesting that distinctive neural correlates underlie the individual differences of decision-making under different risk level.\u003c/p\u003e","manuscriptTitle":"Distinctive neural substrates of low and high risky decision making: Evidence from the Balloon Analog Risk Task","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-29 16:12:42","doi":"10.21203/rs.3.rs-3993983/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-01T06:14:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-30T21:06:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45975323583639366163479256992713005176","date":"2024-09-11T14:50:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-03T15:12:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243465984791936839122565750005704883248","date":"2024-08-11T01:08:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-07T01:26:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-02T14:08:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-28T04:32:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Topography","date":"2024-02-27T13:11:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-topography","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"btop","sideBox":"Learn more about [Brain Topography](http://link.springer.com/journal/10548)","snPcode":"10548","submissionUrl":"https://submission.nature.com/new-submission/10548/3","title":"Brain Topography","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4de7d651-803b-4971-bcfa-be0b0ee600d8","owner":[],"postedDate":"February 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:14:33+00:00","versionOfRecord":{"articleIdentity":"rs-3993983","link":"https://doi.org/10.1007/s10548-024-01094-8","journal":{"identity":"brain-topography","isVorOnly":false,"title":"Brain Topography"},"publishedOn":"2024-12-03 15:57:26","publishedOnDateReadable":"December 3rd, 2024"},"versionCreatedAt":"2024-02-29 16:12:42","video":"","vorDoi":"10.1007/s10548-024-01094-8","vorDoiUrl":"https://doi.org/10.1007/s10548-024-01094-8","workflowStages":[]},"version":"v1","identity":"rs-3993983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3993983","identity":"rs-3993983","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}