Residence in Communities With Higher Violent Crimes Associated With Lower Reward-related Brain Function.

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Published in final edited form as: Biopsychosoc Sci Med. 2025 Apr 11;87(5):305–315. doi: 10.1097/PSY.0000000000001389 Residence in Communities with Higher Violent Crimes Associated with Lower Reward-Related Brain Function Zoe V Adogli 1 , Iris Ka-Yi Chat 1 , Andrew A Gepty 1, 2 , Ann L Carroll 3 , Katherine SF Damme 3 , Carly Flaig 1 , Robin Nusslock 3 , Lauren B Alloy 1, * Author information Article notes Copyright and License information 1 Department of Psychology and Neuroscience, Temple University 2 Seattle Children’s Research Institute, Seattle Children’s Hospital 3 Department of Psychology, Northwestern University * Corresponding author. Address: Department of Psychology and Neuroscience, 1701 N. 13th St. Philadelphia, PA 19122, lalloy@temple.edu Issue date 2025 Jun 1. PMC Copyright notice PMCID: PMC12173145  NIHMSID: NIHMS2084404  PMID: 40249134 The publisher's version of this article is available at Biopsychosoc Sci Med Abstract Objective: A growing body of research suggests that exposure to adversities shapes neural development and function that guide approach motivation and decision-making. Residing in communities with high violent crime is considered a form of adversity. However, its link to brain function is not adequately understood. Furthermore, most of the adversity literature examines individual-level exposure (e.g., crime victimization), whereas efforts to consider neighborhood-level factors (e.g., neighborhood safety) are sparse. This study examined the hypothesis that late adolescents and emerging adults who lived in a community with higher violent crime rates would exhibit altered reward-related neural activation. Methods: Adolescents (N = 101; 55% female) participated in the fMRI Monetary Incentive Delay task. Participants’ hometown violent crime statistics were extracted from the Federal Bureau of Investigation’s Uniform Crime Reports. Multiple regressions examined the association of crime rates with nucleus accumbens (NAc) and orbitofrontal cortex (OFC) activation to reward anticipation or outcome. Results: Living in high-crime communities was associated with lower NAc activation during reward anticipation B = −0.041, SE = 0.015, t = −2.695, p = 0.008, Adjusted p =0.032; ΔR 2 = 0.061, and not with OFC activation during anticipation, or NAc and OFC activation during outcome. Conclusions: Residence in neighborhoods with high levels of environmental threat, characteristic of high-crime communities, may be linked with blunted NAc reward anticipation. Although living in a high-crime community is a passive form of exposure to adversity, these findings indicate that it may be sufficient to observe distinct individual differences in reward-related brain function. Keywords: life adversity, neighborhood crime, brain, reward processes Research suggests that exposure to adversities shapes neural development and function that guide motivation and decision-making. Residing in communities with high violent crime is considered a form of adversity. Adogli et al. examined the hypothesis that late adolescents and emerging adults (n=101) who lived in a community with higher violent crime rates would exhibit altered reward-related neural activation. Findings showed that residence in neighborhoods with high levels of environmental threat, may be linked with blunted reward anticipation. Introduction Abundant research has demonstrated the impact of adversity exposure on neural function, neural development, and health outcomes ( 1 )( 2 )( 3 ). Most research examining the impact of adversities focuses on exposure at the individual-level (e.g., direct experience of physical and sexual assault, neglect, and living in poverty), whereas efforts focusing on adversities at the neighborhood-level (i.e., neighborhood disadvantages such as lack of neighborhood safety) are sparse. In parallel, distinct reward-related processing, a putative mechanism fundamental to approach motivation, decision-making, and daily functioning, has been commonly observed following exposure to adversities ( 4 )( 5 )( 6 )( 7 )( 8 )( 9 ), albeit the association between neighborhood-level adversity and reward-related brain function remains unexplored. Investigating the role of neighborhood characteristics in reward-related brain function can complement the adversity literature by assessing whether the effect of adversity also applies to more distal environmental exposure. This study examined whether residing in communities with high violent crime rates would be sufficient to observe distinct reward-related brain functions in late adolescence and emerging adulthood. Corticostriatal reward circuitry is widely examined in fMRI studies to assess neural reward function ( 10 ). Within this neural circuit, the orbitofrontal cortex (OFC) and nucleus accumbens (NAc) are two central regions. Beyond its various other roles, the OFC mediates goal-directed behaviors by processing and integrating emotional and cognitive information ( 11 ) ( 12 ). It also is known to be involved in the prediction and decision-making relevant to emotional and reward-related behaviors ( 13 )( 14 ). The NAc is a crucial component of the brain’s reward system, playing a key role in reward-related incentive learning ( 15 )( 16 ). It also governs the control of motivated actions ( 17 )( 18 ) and is involved in the processing of associative and motivational information ( 17 )( 19 ). Aberrant activity of OFC and NAc has been linked to a variety of psychopathologies, including depression ( 20 )( 21 )( 22 )( 23 ), substance use, and bipolar disorders ( 24 )( 25 )( 26 ). Distinct reward processing commonly is observed in adolescents and emerging adults who have experienced adversities, with implications for psychopathology ( 27 ). However, results on the association between adversity exposure and reward processing are mixed. For example, children exhibited blunted approach motivation following exposure to institutional rearing, compared to those raised in a family environment ( 28 ). Similarly, another study showed that children exhibited less response to reward values following abuse and neglect ( 29 ). Several studies detected blunted reward anticipation following abuse as well ( 30 ). On the contrary, other studies either did not detect altered reward processing or found that reward-related function increased following adversities ( 31 ). Possible explanations for these discrepancies pertain to differences in aspects of reward processing (e.g., anticipation vs. consumption of reward), type of reward (e.g., monetary vs. social), and type of adversity exposure, given that theoretical models propose that different forms of adversity may yield different neurobiological consequences ( 32 )( 33 ). Furthermore, studies examining adversity exposure and reward processing largely focus on individual-level instead of neighborhood-level exposure. Emerging literature indicates that community-level disadvantages may serve as a distal form of adversity and impact development and health outcomes ( 34 )( 35 )( 36 )( 33 ); but very few studies examined the association between neighborhood-level adversity and reward-related brain functions. In one study ( 37 ), adolescents living in neighborhoods of lower quality displayed lower activation of NAc, but showed greater putative mesolimbic activation during reward anticipation. Another limiting factor characterizing this literature concerns the definition of neighborhood-level exposure to adversity. Specifically, neighborhood-level adversity tends to be defined based on the neighborhood’s socioeconomic status, disregarding other characteristics like neighborhood safety. To our knowledge, only one study specifically examined neighborhood crime rates and reward-related brain function ( 38 ). This study found distinct social reward-related neural activation among late adolescents who lived in higher-crime communities and who had higher interleukin-6, but did not detect significant results for non-social reward-related brain activation. To offer a more comprehensive conceptualization of the health impacts of adversity and reconcile the mixed findings, there is a benefit to broadening the scope of exposure to crime at the individual level to consider neighborhood-level crime exposure (i.e., neighborhood safety). From adolescence to emerging adulthood represents a developmental transition during which exposure to life events and stressors increases. During this period, developing reward-related neural functions are particularly sensitive to input from experiences and the environment ( 39 )( 40 ). Thus, studying reward-related neural correlates of systemic environmental factors during this developmental period informs a more nuanced conceptualization of how adversity exposure is linked to adolescent brain function and health ( 21 )( 22 )( 23 ). The current study examined the association between neighborhood-level adversity, as indicated by residence in communities with high violent crime rates, and reward-related brain function during the vulnerable developmental period of late adolescence and emerging adulthood. Participants completed an fMRI monetary incentive delay (MID) task and provided their home addresses, which allowed for the extraction of crime rates within their neighborhoods. We hypothesized that individuals residing in communities with higher violent crime rates would exhibit distinct activation of the NAc and OFC during reward processing compared to those living in communities with lower violent crime rates. Moreover, to test the specificity of neural reward anticipation and outcome in relation to community crime rates, we conducted additional exploratory analyses investigating loss anticipation and outcome in relation to community crime rates. Through characterizing reward-related neural mechanisms underlying neighborhood-level disadvantages, the findings from this study can inform the conceptualization of systemic environmental vulnerability in relation to brain function. Methods Participants Participants (55% female; M age = 20.99 years, SD= 2.04) were drawn from a longitudinal study on risks for mood disorders, Project TEAM ( 41 ). Out of the total number of participants from the longitudinal study, 136 participated in an additional fMRI substudy between 2014 and 2017. Twenty-nine of these participants were excluded from the present analyses due to excessive head motion, and six participants were excluded due to missing crime data. Thus, the analyses included 101 participants with complete data. The participants who were included did not differ from those who were not based on race (χ 2 [1] = 0.349, p = 0.555), gender (χ 2 [1] = 0.10, p = 0.757), mood disorder history (χ 2 [1] = 0.605, p = 0.437), age at scan ( F = 0.389, p = 0.534), and high vs. moderate reward sensitivity status (χ 2 [1] = 1.718, p = 0.190) based on the Behavioral Activation Scale (BAS) total score (BIS/BAS; ( 42 )( 43 )) and Sensitivity to Reward (SR) scale of the Sensitivity to Punishment Sensitivity to Reward Questionnaire (SPSRQ; ( 44 )). Participants provided informed written consent, and the Institutional Review Board at Temple University approved all study protocols. The sample characteristics are summarized in Table 1A . Table 1A. Sample Demographic and Clinical Characteristics (N=101) Variable M (SD) % (n) Gender Male 45.5 (46) Female 54.5 (55) Race White 66.3 (67) Asian 7.9 (8) Black 20.8 (21) Biracial 4 (4) Other 1 (1) Age (at scan) 20.99 (2.04) Time Elapsed 25.25 (27.38) Household income $115,448 (65,503) History of psychotropic medication use No use 93.1 (94) History of mood disorder diagnosis No history 71.0 (70.3) Reward measures BAS 43.54 (4.57) SR 15.86 (3.98) Open in a new tab Note. BAS = Behavioral Activation System scale from Behavioral Inhibition System/Behavioral Activation System Scales; SR = Sensitivity to Reward subscale from Sensitivity to Punishment Sensitivity to Reward Questionnaire Measures Community Violent Crime Violent crime incidence rates data were extracted from the Federal Bureau of Investigation Crime Data Explorer website ( https://cde.ucr.cjis.gov/LATEST/webapp/#/pages/explorer/crime/crime-trend ). The data included in the analyses were aggregated and retrieved from individual police reports that published the violent crime incidences of participants’ communities based on their self-reported home addresses from 2008 to 2015, starting when participants were ages 14-19, which reflects the time frame during which participants began participation in the larger Project TEAM. Thus, the crime rate variable was extracted as the number of violent crimes that occurred between 2008 and 2015 ( 38 )( 45 ). We then adjusted the crime variable by the constant 1000 for interpretational and analytical purposes. For graphical purposes, we used the crime rate variable that was not divided by a constant. Behavioral Activation System (BAS) Scale and Sensitivity to Reward (SR) Scale The Behavioral Activation System Scale ( 42 ) and Sensitivity to Reward Scale ( 44 ) are self-report measures of trait sensitivity to reward and were used to assess reward sensitivity status in the sample screening protocol. Participants were recruited based on BAS and SR scores falling in the 40 th to 60 th percentile (moderate reward sensitivity) and the top 15 th percentile (high reward sensitivity): BAS cut point ≥ 43 for High Reward and ≥ 37 and ≤ 39 for Moderate Reward and SR cut point ≥ 16 for High Reward and ≥ 10.4 and ≤ 12.6 for Moderate Reward ( 41 ). Both measures showed sufficient internal consistency: BAS (α = 0.80) and SR (α = 0.76), which is consistent with previous research ( 42 )( 42 )( 46 ) and correlated r = .40 with each other in the Project TEAM screening sample. These two reward measures were not used as part of the current study or included in the current analyses. fMRI Monetary Incentive Delay (MID) Task The MID task is a widely used measure of neural reward function and is designed to measure the neural activation of mesolimbic regions during a monetary incentive task ( 47 ). Participants completed this task in fMRI scans that took place between 2014 and 2017. During the pre-scan protocol, participants were informed of the procedure of the task and became familiar with the task by performing a practice computer simulation of the monetary game. During the MID, a series of events were visually cued and displayed for about 2 seconds each on the screen. The participants were instructed by a first cue that they had the opportunity to win or lose a sum of $0.00, $1.50, or $5.00. The next screen displayed a cross in the center, reminding participants to look attentively at the screen and was followed by the target cue (a solid white square), during which participants had about 2 seconds to press a button. By pressing the button fast enough, the participants either won or avoided losing the sum of money that was first displayed. Another cross then was displayed on the screen until the screen displayed the feedback cue informing the participants whether they won, did not win, lost, or did not lose the sum of money. The final cue displayed a cross again. The trial structure of the MID task and the reward/loss cues are illustrated in Figure 1 . These separated cues/feedback allow for the measurements of neural activation during anticipation and outcome of winning/losing a monetary reward. The participant’s reaction time is calculated during the pre-scan procedure to determine the initial target duration, which is done so that the hit rate is kept at approximately 66%. The participants completed two MID runs with a total of 96 trials. Figure 1. Open in a new tab The (A) trial structure of the Monetary Incentive Delay Task and (B) the task’s possible reward and loss cues (adapted from: Young & Nusslock, Positive mood enhances reward-related neural activity, Social Cognitive and Affective Neuroscience , 2016, 11(6), 934–44, by permission of Oxford University Press) fMRI Data Acquisition and Analysis A 3.0 Tesla Siemens Verio wide-bore MRI scanner with a 12-channel head coil was used to collect neuroimaging data at Temple University Medical Center. Blood oxygenation level dependent (BOLD) functional images were acquired with a gradient echo-planar imaging sequence and covered 36 axial slices 4 mm thick (TR/TE= 2000/25 ms, FOV= 236 mm, matrix= 64 x 64, voxel size= 3.7 x 3.7 x 4.0 mm, flip angle= 70°, acquisition volume= 292). Structural 3D MPRAGE scans in the sagittal plane were acquired with the following parameters: TR/TE= 1600/2.46 ms, FOV= 252 mm, voxel size= 0.5 x 0.5 x 1.0 mm, flip angle= 9 °, acquisition volumes= 176. Data were analyzed using a general linear model in SPM8 (Wellcome Trust Centre for Neuroimaging, London, UK). Functional images were realigned and corrected for errors in slice-timing. Images then were spatially normalized to the Montreal Neurological Institute (MNI) stereotactic space using a 12-parameter affine model and then smoothed to reduce noise and corrected for the residual difference in gyral anatomy with a Gaussian filter operationalized at 6 mm full width at half maximum. We computed translational movement in millimeters (x, y, z) and rotational motion in degrees (pitch, roll, yaw) based on SPM8 parameters to correct motion for the functional images in each participant. The final sample had less than 3 mm of head movement. Using a general linear model, the hemodynamic signal was deconvolved, identifying the six different trial types (Win or Lose $0.00, $1.50, $5.00) during the outcome and anticipation phases of the MID task. The anticipation phase of the MID task was defined as the period after the presentation of the cue indicating the possibility of winning or losing money and before the presentation of the solid white square target (2-2.5s). The outcome phase was defined as the period during which the feedback was presented (2s). Six variables of no interest for motion were included. First-level voxel-wise t -statistics were computed for each participant, contrasting reward (i.e., Win $1.50, Win $5.00) vs. non-reward (i.e., Win $0.00) trials to calculate reward anticipation and outcome, and loss (i.e., Lose $1.50, Lose $5.00) vs. non-loss (i.e., Lose $0.00) trials to calculate loss anticipation and outcome. Consistent with previous research (e.g., ( 47 )), $1.50 and $5.00 trials were combined to facilitate interpretation and to increase reliability (given more trials in the combined score). Parameter estimates (beta-weights) were extracted from predefined regions-of-interest (ROIs; Fig. 2A and Fig. 2B ) for the NAc and OFC during reward and loss anticipation and outcome, and these parameter estimates were exported into R for analyses. Bilateral NAc and OFC masks were anatomically defined using the Harvard Oxford Atlas. Figure 2. Open in a new tab Region of interest for A) the bilateral orbitofrontal cortex and B) bilateral nucleus accumbens as defined by the Harvard Oxford Atlas. Statistical Analysis Plan Power analysis We conducted a power analysis using G*Power to determine the sample size required for our regression models to be sufficiently powered. With a power of 0.80 and a significance level of 0.05, the analysis indicated that a minimum of 93 participants is needed to detect an expected small effect size ( ΔR 2 ≈ .08; ( 38 )). Preliminary analyses We included the following covariates, which have been associated with neural reward functions: sex ( 48 ), race, age, history of mood disorder ( 49 ), and psychotropic medication use ( 50 ). We included neural anticipation and outcome of loss as an additional covariate to test the specificity of reward-related neural activation beyond loss-related neural activation. Furthermore, ROI activation during loss anticipation and outcome were significantly correlated with the dependent variables. Correlations among the variables mentioned above is summarized in Table 1B . Table 1B. Bivariate Correlations among the Continuous Study Variables (N=101) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1. NAc anticipation of reward 1 *** 2. NAc outcome of reward −.466 ** 1 *** 3. OFC anticipation of reward .649 ** −0.147 1 *** 4. OFC outcome of reward −.220 * .670 ** −0.075 1 *** 5. NAc anticipation of loss .353 ** −.231 * .224 * −0.114 1 *** 6. NAc outcome of loss −.401 ** .648 ** −0.133 .443 ** −.373 ** 1 *** 7. OFC anticipation of loss 0.149 −0.117 .277 ** −0.148 .679 ** −0.172 1 *** 8. OFC outcome of loss −.278 ** .377 ** −0.027 .351 ** −.210 * .689 ** −0.082 1 *** 9. Neighborhood SES −0.095 0.005 −0.021 −0.011 −0.119 −0.011 −0.045 −0.139 1 *** 10. Age −0.113 .232 * 0.010 0.179 −0.051 .247 * −0.091 .309 ** −0.034 1 *** 11. Neighborhood crime −.250 * 0.100 −0.152 −0.008 0.040 0.026 0.061 −0.035 .370 ** 0.143 1 *** 12. Time elapsed between Crime and Scan −0.142 .276 ** −0.154 0.142 0.012 .257 * −0.026 .304 ** −0.014 .837 ** .240 * 1 *** 13. Individual Exposure to crime −0.044 0.196 0.054 0.201 0.071 .253 * −0.011 0.169 0.075 0.211 0.014 0.214 1 *** 14. Household Income −0.045 0.101 −0.017 −0.012 0.005 0.181 0.016 0.164 0.026 0.036 −0.149 0.096 0.054 1 *** Open in a new tab Note: * : p-value < 0.05; ** : p-value < 0.01; *** : p-value < 0.001 Primary analyses Multiple regressions were conducted to examine the relationship between residing in communities with high violent crime rates and neural activation in the NAc and OFC during the MID task. In separate models, we regressed neural activation in the NAc and OFC during reward anticipation and outcome on the community violent crime rates and identified covariates. In a different set of models, activation during loss anticipation or outcome in the corresponding neural regions was included as an additional covariate to evaluate the specificity of the association of residence in communities with high violent crime with neural activation during reward anticipation and outcome above and beyond neural activation during loss anticipation and outcome. In addition, we regressed neural activation in the NAc and OFC during loss anticipation and outcome on the independent variable of residence in communities with high violent crime rates. The false discovery rate (FDR) at 5% correction was applied to the primary models (i.e., controlling for four tests: reward anticipation in NAc, reward anticipation in OFC, reward outcome in NAc, and reward outcome in OFC) using the Benjamini-Hochberg procedure ( 51 ). Corrected q-values were reported for any significant results. All preliminary and primary analyses were conducted in R (Version 2023.03.1+446; ( 52 )). Data and analysis code are available upon request from the first and corresponding authors. Secondary Analyses Three sets of secondary analyses were conducted. First, to address the bimodal distribution of violent crime rates, the regression models were repeated by treating the neighborhood crime rates variable as categorical ( Table S1 in Supplementary Material ). To form the categorical variable, we categorized the neighborhood crime rates into two groups: “Low Violent Crime Rate” for values below the median and “High Violent Crime Rate” for values above the median. Second, to test the specificity of residence in communities with high violent crimes beyond the effects of other neighborhood or individual factors, we repeated the analyses by including neighborhood SES, individual SES (i.e., annual household income), or individual exposure to crime as additional covariates for any significant results obtained from the primary analyses. The measures for neighborhood SES, individual SES, and individual-level exposure to crime, as well as the results, are described in the Supplemental Material . Finally, as fMRI scans occurred from 2014 to 2017 and violent neighborhood crime exposure was extracted from 2008 to 2015, the amount of time between the collection of each measure may vary for each participant. As such, we conducted additional analyses with the number of months that elapsed between each time point as a covariate to account for this variability. We decided not to include this variable in the primary model due to its high correlation, and thus, redundancy, with age and due to potential issues of multicollinearity ( 53 ). Results The full statistical results for the primary analyses are displayed in Table 1C . Residence in neighborhoods with higher violent crime rates was significantly associated with lower NAc activation during reward anticipation after adjusting for the corresponding loss anticipation and after applying the FDR correction method to account for multiple comparisons ( B = −0.041, SE = 0.015, t = −2.695, p = 0.008, Adjusted p =0.032; ΔR 2 = 0.061; Figure 3 ). Our secondary analyses showed that the association also was significant when including the neighborhood SES as an additional covariate ( B = −0.049, SE = 0.016, t = −3.020, p = 0.003; ΔR 2 = 0.076; Table S2 ), when including individual-level SES as an additional covariate ( B = −0.043, SE = 0.016, t = −2.664, p = 0.009; ΔR 2 = 0.073), when including the time elapsed between the collection of neighborhood crime and scan ( B = −0.038, SE = 0.016, t = − 2.332, p = 0.022; ΔR 2 = 0.05), and finally when treating residence in communities with high violent crime rates as a categorical variable ( B = −0.732, SE = 0.255, t =−2.869, p = 0.005, ΔR 2 = 0.069; see Table S1 ). The result was no longer significant when including individual-level exposure to crime as an additional covariate ( B = −0.037, SE = 0.021, t = −1.807, p = 0.076, ΔR 2 = 0.046; Table S2 ). However, we are hesitant to interpret this result due to the plausible bias attributable to the lack of data ( n = 54) and the very little variability in the individual exposure to crime variable. Table 1C. Regression Models of the Associations Between Community Violent Crime and NAc and OFC Activation (N=101) Model 1 Model 2 Model 3 B SE t p B SE t p B SE t P NAc anticipation Crime −0.039 0.015 −2.574 0.012 −0.038 0.016 −2.359 0.02 −0.041 0.015 −2.695 0.008 0.032 * Med - - - - −0.384 0.525 −0.732 0.466 −0.288 0.489 −0.588 0.558 Mood - - - - −0.182 0.297 −0.612 0.542 −0.087 0.278 −0.312 0.756 Gender - - - - 0.349 0.291 1.197 0.234 0.396 0.272 1.460 0.148 Race - - - - −0.199 0.301 −0.651 0.516 −0.026 0.289 −0.091 0.927 Age - - - - −0.059 0.07 −0.874 0.385 −0.059 0.063 −0.797 0.427 NAc loss - - - - - - - - 0.449 0.116 3.867 <0.001 NAc outcome Crime 0.017 0.017 0.995 0.322 0.015 0.017 0.904 0.368 0.012 0.014 0.902 0.369 Med - - - - 0.915 0.5457 1.633 0.106 0.558 0.442 1.263 0.21 Mood - - - - 0.617 0.316 1.954 0.054 0.243 0.254 0.958 0.341 Gender - - - - −0.182 0.309 −0.589 0.558 −0.129 0.244 −0.531 0.596 Race - - - 0.338 0.325 1.039 0.302 0.472 0.257 1.836 0.069 Age - - - - 0.153 0.071 2.148 0.034 0.044 0.058 0.758 0.450 NAc loss - - - - - - - - 0.629 0.083 7.583 <0.001 OFC anticipation Crime −0.013 0.009 −1.528 0.130 −0.012 0.009 −1.390 0.168 −0.014 0.009 −1.656 0.101 Med - - - - 0.205 0.294 0.697 0.487 0.233 0.282 0.828 0.41 Mood - - - - 0.149 0.166 0.896 0.373 0.134 0.159 0.836 0.406 Gender - - - - 0.005 0.163 0.303 0.763 0.072 0.157 0.456 0.649 Race - - - - 0.012 0.172 0.059 0.953 0.018 0.165 0.114 0.91 Age - - - - 0.009 0.038 0.227 0.821 0.018 0.036 0.507 0.613 OFC loss - - - - - - - - 0.313 0.105 2.983 0.003 OFC outcome Crime −0.002 0.009 −0.081 0.936 −0.001 0.009 −0.128 0.899 −0.001 0.009 −0.050 0.961 Med - - - - 0.526 0.300 1.731 0.087 0.4 0.291 1.375 0.172 Mood - - - - 0.254 0.169 1.504 0.136 0.149 0.168 0.889 0.376 Gender - - - - −0.112 0.165 −0.679 0.499 −0.112 0.159 −0.699 0.486 Race - - - - −0.004 0.174 −0.021 0.283 0.53 0.160 0.314 0.754 Age - - - - 0.069 0.038 1.832 0.070 0.036 0.039 0.911 0.365 OFC loss - - - - - - - - 0274 0.099 2.732 0.008 Open in a new tab Notes . Model 1 = Without any covariates. Model 2 = With medication use, mood disorder history, gender, race, and age included as covariates. Model 3 = With anticipation/outcome of loss included as a covariate in addition to the ones included in Model 2. An asterisk (*) denotes significant results after FDR correction using the Benjamini-Hochberg procedure (corrected p-value, q < 0.05). Figure 3. Open in a new tab Community Violent Crime Rate and NAc Activation during Reward Anticipation Residence in communities with higher violent crime rates was not significantly associated with OFC activation during reward anticipation ( B = −0.014, SE 0.009, t = −1.656, p = 0.101, ΔR 2 = 0.026, Table S1 ). Residence in communities with higher violent crime rates also was not significantly associated with NAc activation during reward outcome ( B = 0.012, SE = 0.014, t = 0.902, p = 0.369, ΔR 2 = 0.005) or with OFC activation during reward outcome ( B = −0.001, SE = 0.009, t = −0.050, p = 0.961, ΔR 2 < 0.001). Residence in communities with higher violent crime rates was not significantly associated with NAc activation during loss anticipation ( B = 0.017, SE = 0.013, t = 1.355, p = 0.178, ΔR 2 = 0.016) or with OFC activation during anticipation of loss ( B = 0.009, SE = 0.008, t = 1.136, p = 0.300, ΔR 2 = 0.013). Finally, residence in communities with higher violent crime rates was not significantly associated with NAc activation during loss outcome ( B = −0.004, SE = 0.013, t = −0.316, p = 0.752, ΔR 2 <0.001) or with OFC activation during loss outcome ( B = −0.002, SE = 0.009, t = −0.265, p = 0.792, ΔR 2 = <0.001). Discussion In examining the relationships of adversity exposure with neural function and development, many studies considered direct, individual-level adversities (e.g., poverty, discrimination, direct experience of traumas, etc.) ( 54 )( 55 )( 56 ), whereas few studies focused on adversities at the neighborhood-level (e.g., crime rates in one’s residential neighborhood). Research linking reward processing, a putative mechanism central to a range of behaviors and functioning, and neighborhood-level adversities is particularly limited. To complement the adversity exposure literature, we sought to characterize neural activation in corticostriatal regions during the processing of monetary reward in late adolescents and emerging adults whose residential communities varied in violent crime rates. Results show that residence in communities with higher violent crime rates was associated with lower NAc activation during reward anticipation, and we did not find significant associations for NAc activation during reward outcome, OFC activation during reward anticipation or outcome, or NAc/OFC activation during loss anticipation or outcome. Although living in an unsafe community itself is a distal form of exposure to crime, it may be sufficient to exhibit associations with reward-related brain functions. Thus, our findings preliminarily support the idea of broadening the scope of assessing the impact of adversity from individual-levels to also considering community-levels. The current findings may be interpreted by applying the dimensional model of adversity, which proposes that different forms of adversity can be divided into theoretically meaningful constructs, namely, “threat” and “deprivation” dimensions. In theory, each of these dimensions is associated with distinct impacts on neural development and function ( 57 )( 58 ). In this approach, residing in communities with high crime rates would be conceptualized as falling under the umbrella of “threat” adversity ( 58 ). Also integrating reinforcement learning theories to understand the current results, exposure to such threat adversity might be associated with hypervigilance to danger cues in order to prioritize personal safety, but this may come at the cost of less attention or approach towards reward that is irrelevant to personal safety. Prior studies demonstrated altered reward function associated with “threat” adversity. For example, individuals exhibited blunted reward function following threat adversity (e.g., history of abuse and maltreatment; ( 29 )( 59 ). Several other studies showed the opposite results. Dennison et al. ( 31 ) found that “threat” adversity, as indexed by child maltreatment, was associated with greater striatal activation during a positive social images task. Gonzalez et al. ( 37 ) reported that lower neighborhood quality, as indexed by neighborhood crime rate and other indicators such as neighborhood risk, connectedness, and deterioration, was associated with heightened anticipation of monetary reward and loss during an MID task in an adult sample. These seemingly mixed results may be reconciled by the type of reward and whether an increase vs. decrease in reward processing might be an adaptive or maladaptive response depending on the context. For example, individuals living in a high-crime neighborhood may adapt by being more motivated to approach social reward (e.g., care and social support; ( 38 )) and other rewards if personal safety is not necessarily threatened. Future research should consider the nature of adversity and reward, which may allow for better understanding of whether the adversity will be associated with increased or decreased reward processing. Our results are consistent with the premise that life adversity is related to lower motivation to pursue reward, but not the ability to experience pleasure ( 39 )( 60 )( 61 ). This result also was observed above and beyond the effect of activation in the same neural regions during loss-related processing, and in separate analyses, neighborhood violent crime rates were not significantly associated with loss-related activation, offering some insight that the higher neighborhood crime rates may be specifically linked to reward motivation. One study similarly demonstrated specificity in that adults who experienced childhood maltreatment showed lower levels of reward anticipation in the left basal ganglia but not during the outcome phase of reward ( 30 ). In addition, a few studies found that depressed patients are more prone to experiencing blunted motivation for reward rather than experiencing the inability to enjoy the appetitive phase of a reward ( 62 )( 63 )( 64 )( 65 )( 66 ). Drawing on findings from these studies, individuals living in high-crime communities who experience a lack of motivation to pursue rewards may be at risk of developing various psychiatric disorders. Whereas altered reward motivation is widely accepted as a risk for developing depression, the health impact seems to apply to other psychiatric disorders as well. For example, blunted reward anticipation also has been linked to substance use disorders ( 24 )( 25 )( 26 ) and schizophrenia spectrum disorders ( 67 )( 68 ). Thus, the altered neural reward motivation documented in this study may have implications for a broad range of psychopathology commonly observed following exposure to systemic environmental disadvantage ( 34 )( 69 )( 36 )( 70 ). However, we cannot confidently establish clinical implications from the present results, given that our analyses did not include psychopathology outcomes. Future research should replicate the findings and also investigate whether lower levels of reward anticipation are necessarily maladaptive or indeed predict psychopathology among individuals living in high-crime communities. It is important to note that some similar studies to ours found contradictory findings. For instance, one study by Westerman et al. ( 71 ) investigated the relationship between neural response to reward in relation to exposure to community violence and found greater activation of the ventral striatum in individuals exposed to greater community violence. One potential explanation for this difference in findings is that our sample comprises individuals at risk for depressive symptoms. Thus, future studies may investigate whether the association between lower NAc activation during reward anticipation and exposure to community violent crime persists in a sample that is not at risk for psychopathologies. Moreover, another study examining the relationship between SES and neural reward activation found that lower SES was associated with greater response in regions involved in attention to reward and loss cues and feedback ( 72 ). As we specifically focused on exposure to violent crime beyond the effects of individual and neighborhood SES, it is possible that individuals experiencing threat are more prone to decreased reward function as opposed to those experiencing deprivation as found in Sadikova et al. ( 73 ). This association may highlight the need to differentiate between experiencing low-income versus living in high-crime communities in future research. Another possible explanation of contradictory findings may be due to the issues with the unreliability of the MID task as well as methodological challenges such as statistical power and sample size, ultimately creating imprecise and inconsistent estimates ( 74 )( 75 ). This study has several strengths. First, the sample comprises approximately equal proportions of males and females and individuals from diverse racial and socioeconomic backgrounds. Second, this study provides additional insight into the relationship between environmental stressors and distinct forms of reward processes. Relatedly, to our knowledge, this is one of the few studies examining indirect exposure to community violence and the neurobiology of reward processing. Finally, controlling individually for both neighborhood SES and individual-level SES in the secondary analyses allows us to distinguish the specific role of residing in high-crime communities from neighborhood-level and individual poverty, other adversity factors that tend to co-occur. However, results should be interpreted with consideration of the limitations of this study. First, although neighborhood crime rate information always preceded the fMRI visit, the cross-sectional design of this study prevents us from establishing causality between residing in high-crime communities and reward-related brain function. Second, we did not adjust for some other forms of adversity that tend to co-occur with living in high-crime communities ( 76 ), such as negative life events or past traumatic experiences ( 77 ), limiting our ability to attribute the observed results for reward-related brain activation specifically to community violent crime rates. Third, we could not systematically test the plausible claim that individual exposure to violent crimes accounted for the observed results. Although the larger Project TEAM administered a measure that includes the assessment of individual exposure to crime, there was a lack of available data. Furthermore, our study examined an objective measure of neighborhood safety but did not explicitly capture the subjective experience of living in unsafe neighborhoods that may be relevant. Including measures of the perception and personal experience of community safety may provide more comprehensive knowledge about the effects of residing in high-crime communities beyond direct exposure to violent crime and perceptions of community threat and safety. Fourth, the cross-sectional design of this study restricts our ability to determine how long participants resided at their address if they relocated. Longitudinal research tracking changes in crime rates in an individual’s residing neighborhood over time can offer a more comprehensive understanding of the current findings. Fifth, this study did not directly test whether the observed link between NAc activation and residence in high-crime communities contributes to the development of mental or physical health issues. However, a large body of research shows that blunted reward activation is associated with various risks and psychiatric disorders, including depression ( 21 )( 9 ), substance use ( 24 )( 25 )( 26 ), and bipolar disorder ( 49 ), suggesting the need to examine the clinical implications of the observed findings in future research. Finally, as noted above, the results of the current study need to be interpreted with caution as there are growing concerns about the reliability and reproducibility of task-based fMRI, including with the MID task ( 74 ), and the recognition that univariate neuroimaging effect sizes such as ours ( N =101) tend to be small. Consequently, the estimated associations presented here are likely imprecise, and the findings should be approached with caution unless replicated in larger samples with demonstrated reliability of the measures ( 75 )( 78 ). As such, future research across different paradigms, with larger sample sizes and including multivariate fMRI analyses that are found to mitigate these concerns ( 78 )( 74 ), is needed to replicate the current findings. Conclusion This study showed that residing in communities with higher violent crime rates was associated with less responsivity to reward anticipation in the nucleus accumbens, suggesting a link between distal forms of adversity exposure and distinct neural function. The correlational nature of these findings prevents us from establishing causality; however, the present results offer a starting point, highlighting the need to investigate the health implications of neural function linked to distal forms of adversity. Does the observed link between living in a high-crime community and lower levels of reward function represent adaptive processes or a mechanism linked to a greater risk for developing poor health outcomes? This line of work may advance the understanding of social determinants of health and inform early interventions tailored to individuals at risk for health issues due to neighborhood-related disadvantages. Supplementary Material Supplemental Digital Content Table S1. Regression Models of the Associations Between Community Violent Crime as a Categorical Variable and NAc and OFC Activation (N=101). Table S2. Regression Models of the Associations Between Community Violent Crime and NAc Activation during Anticipation (including the Covariate Individual Exposure to Crime, Neighborhood SES or Individual SES) (N=101) Table S3. Regression Models of the Associations Between Community Violent Crime as a Categorical Variable and NAc Activation during Anticipation (including the Covariate Individual Exposure to Crime, Neighborhood SES, or Individual SES) (N=101) NIHMS2084404-supplement-Supplemental_Digital_Content.docx (40.9KB, docx) Figure 2C. Boxplots with Means and Medians of Neural Activation During the Anticipation Phases of Reward. Open in a new tab Notes. The black lines inside the boxes represent the mean, the red dots inside the box plots represent the medians, and the red dots outside the box plots represent outliers. Acknowledgments This work was supported by National Institute of Mental Health grants (R01MH77908, R01MH123473, and R01MH126911) to Lauren B. Alloy. Iris Ka-Yi Chat was supported by the National Research Service Award (F31MH132230) and Peter and Malina James & Dr. Louis P. James Legacy Scholarship from the American Psychological Foundation. List of Abbreviations: fMRI Functional Magnetic Resonance Imaging NAc Nucleus Accumbens OFC Orbitofrontal Cortex MID Monetary Incentive Delay BIS/BAS Behavioral Inhibition Scale / Behavioral Activation Scale SPSRQ Sensitivity to Punishment Sensitivity to Reward Questionnaire SR Sensitivity to Reward BOLD Blood Oxygenation Level Dependent ROIs Regions of Interest TR Repetition Time TE Echo Time FOV Field of View MNI Montreal Neurological Institute FDR False Discovery Rate SES Socio-Economic Status LES Life Event Scale LEI Life Event Interview NaNDA National Neighborhood Data Archive Footnotes This study was not preregistered in an independent, institutional registry. Data and analysis code are available upon request from the first and corresponding authors. References 1). Lupien SJ, Juster R-P, Raymond C, & Marin M-F (2018). The effects of chronic stress on the human brain: From neurotoxicity, to vulnerability, to opportunity. 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Regression Models of the Associations Between Community Violent Crime as a Categorical Variable and NAc Activation during Anticipation (including the Covariate Individual Exposure to Crime, Neighborhood SES, or Individual SES) (N=101) NIHMS2084404-supplement-Supplemental_Digital_Content.docx (40.9KB, docx) ACTIONS View on publisher site PDF (915.4 KB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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