Neural and behavioral reward responsiveness in early adolescence: longitudinal associations with childhood adversity and resilience

Neural and behavioral reward responsiveness in early adolescence: longitudinal associations with childhood adversity and resilience | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Neural and behavioral reward responsiveness in early adolescence: longitudinal associations with childhood adversity and resilience Ryan Shepherd, Rebecca Elliott, Nils Muhlert, Mica Komarnyckyj, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8414265/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Blunted and maladapted reward responsiveness is a central component of several psychiatric disorders. Identifying how childhood adversity and resilience shape trajectories of reward responsiveness in adolescence is a key step towards understanding why transdiagnostic symptoms such as anhedonia, apathy and impulsivity emerge in young people. Using 3 timepoints from the Adolescent Brain Cognitive Development (ABCD) study (n=11868), we used longitudinal mixed effects models to examine which of 10 adversity factors at age 9/10 were associated with BIS/BAS Reward Responsiveness across ages 9-14, and whether potential resilience factors modify this relationship. To investigate correlations of neural reward responsiveness, we fit similar models on striatal activation measures from an fMRI monetary incentive delay task. We also examined whether the association of adversity with behavioral reward responsiveness was consistent over age. Behavioral reward responsiveness was negatively associated with emotional deprivation from primary (β=-0.076, p<0.001) and secondary caregiver (β=-0.205, p<0.001) and supervisory deprivation (β=-0.103, p<0.001); and positively associated with youth reported threat (β=0.167, p<0.001). Supervisory deprivation was also negatively associated with anticipatory striatal response (β=-0.010, p<0.05), and parent reported threat positively associated (β=0.008, p<0.01). There was a significant interaction between emotional deprivation and age (p<0.001), indicating an association that attenuates with increasing age, and with supervisory deprivation (p<0.001) indicating a negative association that strengthens with increasing age. Overall, early deprivation was associated with reduced reward responsiveness in early adolescence and threat with increased responsiveness. The relationship between deprivation and reward responsiveness varied across age and sex. Biological sciences/Psychology/Human behaviour Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Reward responsiveness, the ability to respond emotionally to the anticipation or receipt of rewards, forms one of three positive valence systems defined in the National Institute of Mental Health Research Domain Criteria 1,2 . It conceptually overlaps with anhedonia, the inability to experience pleasure 3 , and likely plays a central role in other transdiagnostic psychiatric symptoms, such as apathy 4 and impulsivity 5 . Identifying factors that influence early developmental trajectories of behavioral and neural reward responsiveness is a key step towards understanding why these transdiagnostic symptoms emerge in young people. Early life adversities (ELA) such as abuse and neglect cause some children to have poor mental health in later life 6–8 . While a growing body of literature characterizes the relationship between ELA and negative valence systems, such as threat processing 9,10 , there is a lack of research examining how ELA affects positive valence systems such as reward responsiveness. Also, many ELA studies focus on one specific type of adversity or consider multiple accumulating adversities. The first approach does not account for individuals who experience several adversities, and the second does not differentiate between types of adversity. The dimensional model of adversity and psychopathology assigns exposures to dimensions of threat and deprivation, using theory-driven expertise of shared mechanisms 11 . This framework has been supported by distinct neural differences observed in individuals with early experiences of threat and deprivation 12 . Adversities that involve experience of threatened harm (e.g., witnessing or experiencing abuse) are more likely to influence neurodevelopment via stress input, with implications for emotional processing 13 . By contrast, adversities that involve deprivation (e.g., emotional or cognitive neglect), may influence neurodevelopment via a lack of positive input, with implications for cognition 14 . Using data-driven, clustering approaches such as exploratory factor analysis allows researchers to identify clusters of highly correlated ELA, which can then be interpreted within a threat/deprivation theoretical framework. This approach has been applied previously to the Adolescent Brain Cognitive Development (ABCD) study to examine which aspects of ELA are associated with later mental health 15 and patterns of brain maturation in childhood 16 . These factors use items across multiple child and parent report surveys, capturing more subtle aspects of presently co-occurring ELA than other assessment tools, which rely primarily on adult retrospective reports. Importantly, not all children who are exposed to ELA exhibit poor mental health, potentially due to the presence of resilience ‘systems’ that buffer or negate the impact of adversities in some individuals 17 . Resilience systems may relate to external factors 18 such as a positive school environment 19 , or internal psychological processes such as prosocial behavior 20,21 . Identifying what helps children avoid the effects of ELA may provide crucial mechanistic evidence and additional preventative targets. In the present study, we examine associations between clusters of adversity and subsequent neural and behavioral reward responsiveness trajectories in early adolescence. We also explore the roles of two potential resilience factors: early prosocial behavior and school positivity. This is the first study to investigate how aspects of co-occurring ELA and resilience relate to adolescent trajectories of behavioral and neural reward responsiveness. We hypothesized that early deprivation would be associated with blunted reward responsiveness, informed by prior studies on the impact of early institutionalization 22,23 . We also expected early resilience factors to diminish the blunting effect of ELA on reward responsiveness. Methods and Materials Data source and ethical considerations The Adolescent Brain Cognitive Development (ABCD) study 24 is an ongoing longitudinal US community-based sample of 11,880 children, aged 9 or 10 at baseline (collected in 2018). They participated in yearly psychiatric and neurocognitive testing, with multimodal brain imaging measures every 2 years, harmonized across 21 sites. The ABCD study received ethical approval from an institutional review board at the University of California, San Diego 25 . ABCD data can be accessed freely via the NIH Brain Development Cohorts Data Hub. Scripts used to carry out data cleaning and analyses are available in the following repository https://github.com/r-j-shepherd/ELA_resilience_reward.git. Early life adversity Information on ELA was obtained from 60 items measured at baseline using caregiver and youth reported surveys (see Figure 1 for items used to create specific factors). 10 ELA dimensions were identified using the results of a prior exploratory factor analysis using the same dataset 15 . Each child’s continuous factor scores were derived using the prior study’s factor loading matrix. The factors include 4 ELA dimensions relating to threat: family aggression, family anger and arguments, trauma exposure and youth report of family conflict ; and 3 relating to deprivation: lack of supervision , primary caregiver lack of support , and secondary caregiver lack of support . 3 factors represent more complex exposures which do not map clearly to threat or deprivation, or feature elements of both: caregiver psychopathology and caregiver substance use and separation from biological parent, and socioeconomic disadvantage and neighborhood safety . [Insert Figure 1 here] Resilience factors Prosocial behavior and protective school environment were selected as potential resilience factors based on prior literature 19–21 , measured at baseline using the prosocial behavior scale from the Strengths and Difficulties Questionnaire 26 and the PhenX School Risk and Protective Factors Survey 27 respectively. Reward responsiveness Self-reported behavioral sensitivity to reward was measured repeatedly when children were aged 9-10, 11-12, and 13-14 using the 4-item reward responsiveness module of the revised Behavioral Inhibition and Behavioral Activation Scales (BIS/BAS) 28,29 . A sum score was calculated from 4-point Likert items (range 0-12). This module contains questions on how an individual feels in response to rewards, specifically the level of excitement around potential rewards and opportunity for rewards – see Table S1 for items used. Neural reward responsiveness was measured using data from an fMRI monetary incentive delay (MID) task, collected repeatedly at the same waves as BIS/BAS. In the ABCD MID task, participants are presented with cues which indicate potential reward, loss or neutral consequences. To successfully gain a reward or avoid loss, they must respond quickly via a remote when the target (black shape) appears. We used two contrasts from this task, large reward vs no reward during the anticipatory phase, and large reward vs no reward during the consummatory phase. We focused on activation in three a priori sub-regions of the striatum – nucleus accumbens, putamen and caudate nucleus. Following pre-processing and segmentation, average time series were extracted for each region, and task-based activation estimated using a general linear model framework. A detailed breakdown of ABCD’s MRI image preprocessing and task-based analysis pipeline has been published previously 30 . The stimuli and task phases used to derive these measures are visualized in Figure 2 . We excluded 1694 scans from 1542 individuals which failed to meet quality control standards due to poor quality images or MID task performance. [Insert Figure 2 here] Statistical Analysis Adversity and resilience scores were standardized (z-scores) to aid comparison of effect sizes. Associations between adversity factors and repeated behavioral and neural measures of reward responsiveness were estimated using linear mixed-effects models. For the behavioral measure models, two random effects were included to capture clustering within individuals and families. For the neural measure models, additional random effects were included for data collection site, to account for variability between MRI scanners. Additional individual-level random slopes were tested to model individual variation in change over time. For behavioral reward responsiveness, including these significantly improved model fit (p<0.001), whereas in neural models they were omitted because the models did not converge when present. Given prior evidence of sex differences in adolescent reward processing 31,32 and ethnicity differences in socioeconomic disadvantage 33 , sex and race-ethnicity were included as covariates to account for potential confounding of the relationship between adversity and reward responsiveness. Models included fixed effects for age (in years) since baseline (linear and squared term), sex (male, female), and race-ethnicity (Asian, Black, Hispanic, White, Other). To investigate whether potential resilience factors of prosocial behavior and protective school environment may buffer the association between adversity and reward responsiveness, we tested interactions between these variables and ELA factors that were negatively associated with reward responsiveness. In order to assess whether the impact of ELAs changed over age, we also examined the interaction between reward-blunting adversity factors with age and age squared. A total of 3,318 (28%) respondents were missing at least one adversity item at baseline, with missingness ranging from 0.04-8.57% for individual items ( Table S2 ). To address this, random forest multiple imputation models by chained equations were used to create 10 imputed datasets. Models were fitted to each imputed dataset, and estimates were pooled using Rubin’s rules 34 ( mice package in R). To validate the factor scores provided by Brieant et al. (2023), who used the baseline sample with complete data items and including one individual per family (n = 7115), in the imputed datasets, we repeated their Bayesian multilevel models in one of the imputed datasets. The results of this were visually and directionally similar to the smaller non-imputed sample (see Figures S1-2 for comparison of samples). Data cleaning and statistical analyses were carried out in R version 4.3.2. Supplementary analyses Four supplementary analyses were conducted. First, we explored the differential impact of pubertal development on reward processing by examining sex-specific models (ABCD utilizes a sex-specific self-report Pubertal Development Scale 35 ). Second, we examined sex-specific associations with adversity factors identified in the sex-stratified analyses, by including interaction terms in the mixed effects models and calculating adversity factor beta coefficients for each sex. Third, upon exploratory analyses of BIS/BAS distributions, we observed that the baseline scores for BAS: Reward Responsiveness were skewed. We explored whether findings were robust to non-normality by fitting a Poisson regression model for this timepoint. Fourth, we sought to determine if the relationship between race-ethnicity and reward processing outcomes is explained by perceived discrimination at age 11-12, measured using the Perceived Discrimination Scale 36 . For this, we used the same model as previously, with the addition of the Perceived Discrimination Scale score, in line with prior literature highlighting the role of racial discrimination in emotional regulation 37 and neural differences 38 . Results Sample characteristics Participants were identified by a parent or guardian as Asian (2%), Black (15%), Hispanic (20%), White (52%), or Other (11%). 48% reported their sex as female and 52% as male ( Table 1 ). The mean ages in years at baseline, follow-up 2 and follow-up 4 were 9.91 (SD=0.62), 12.03 (SD=0.67) and 14.08 (SD=0.68) respectively. [Insert Table 1 here] ELA factors and behavioral reward responsiveness There was a negative association between linear age and BAS reward responsiveness (β=-0.567, 95%CI [-0.618, -0.518], p <0.001) and a positive association with age squared, (β=0.067, 95%CI [0.053, 0.080], p <0.001) indicating the rate of decline decreased between ages 9-14 ( Figures S3-5) . Females had lower behavioral reward responsiveness compared to males (β=-0.173, 95%CI [-0.242, -0.105], p <0.001). Race-ethnicity was also associated with BAS Reward Responsiveness (p<0.001). In particular, a positive association with being Black (β=0.578, 95%CI [0.462, 0.693], p <0.001), Hispanic (β=0.279, 95%CI [0.184, 0.375], p <0.001), or Other (β=0.188, 95%CI [0.070, 0.306], p =0.02). Three deprivation-related ELA factors were associated with lower BAS reward responsiveness ( Figure 3 ) : lack of supervision (β=-0.104, 95% CI [-0.155, -0.053], p <0.001), lack of support from primary caregiver (β=-0.076, 95%CI [-0.118, -0.035], p <0.001), and lack of support from secondary caregiver (β=-0.205, 95%CI [-0.250, -0.160], p <0.001). One threat-related factor, youth report of family conflict , was associated with higher reward responsiveness (β=0.167, 95%CI [0.111, 0.223], p <0.001). Both resilience factors prosocial behavior (β=0.262, 95%CI [0.227, 0.298], p <0.001) and positive school environment (β=0.099, 95%CI [0.064, 0.135], p <0.001) were associated with increased reward responsiveness (see Tables S7-8 for unadjusted models) . There was marginal evidence for an interaction between age 9/10 prosocial behavior and lack of supervision (β=0.033, 95% CI [-0.00007, 0.067], p=0.05) such that the association was weaker when an adolescent scored higher on the prosocial scale. There was no other evidence of an interaction between a resilience factor and an ELA dimension. Supplementary analysis Self-reported discrimination at age 11-12 was positively associated with reward responsiveness (β=0.212, 95%CI [0.107, 0.317], p <0.001), though it did not substantially alter the association between race-ethnicity and reward responsiveness ( Figure S6 ). The results after fitting a Poisson model to non-normal data ( Table S5-6 ) were in line with our main findings. Our sex-stratified supplementary analysis revealed a significant positive association of pubertal development in the male sample (β= 0.054, 95%CI [0.007, 0.101], p =0.02), but not the female sample ( p= 0.78). However, the sex-specificity of the pubertal development scale prevented the fitting of an interaction term for sex and puberty in the full sample. Additionally, the association with lack of supervision remained in the male sample (β=-0.104, 95% CI [-0.171, -0.037], p =0.002), but not the female sample (p=0.19). We also observed a significant negative association between trauma exposure and reward responsiveness in the female sample (β=-0.123, 95% CI [-0.242, -0.004], p =0.04), but not the male sample (p=0.1). We observed evidence of a significant interaction for sex and lack of supervision in the full sample ( p =0.011), where the association with BAS: Reward Responsiveness was negative in males (β= -0.136, 95%CI [-0.193, -0.080], p <0.001) and non-significant in females ( p =0.19). However, we did not find evidence of an interaction between sex and trauma exposure in the full sample (p=0.65). [Insert Figure 3 here] ELA factors and neural reward responsiveness Several adversity factors were associated with neural reward responsiveness ( Figure 4 ). Similarly to the BAS Reward responsiveness models, the deprivation-related factor lack of supervision was associated with reduced anticipatory reward response in the nucleus accumbens (β=-0.010, 95%CI [-0.017, -0.003], p =0.005] and putamen (β=-0.005, 95%CI [-0.011, -0.0005], p =0.03). The threat-related factor family aggression was associated with increased anticipatory reward response in the nucleus accumbens (β=0.008, 95%CI [0.002, 0.015], p =0.01). The models of consummatory neural reward responsiveness suggest lack of support from secondary caregiver was associated with increased activation of the putamen during receipt of reward (β=0.008, 95%CI [0.002, 0.015], p =0.01). There was no evidence of an association between the resilience factors and striatal activation during anticipation or receipt of reward. Females had lower anticipatory putamen activation compared to males (β=-0.009, 95% CI [-0.016, -0.003], p =0.005). Females also had lower levels of consummatory activation in the nucleus accumbens (β=-0.017, 95% CI [-0.026, -0.007], p <0.001), putamen (β=-0.026, 95% CI [-0.033, -0.019], p <0.001), and caudate nucleus (β=-0.014, 95% CI [-0.021, -0.006], p <0.001). Race-ethnicity was associated with anticipatory and consummatory striatal activation during MID ( p <0.001). In particular, being Black was negatively associated with nucleus accumbens activation during anticipation (β=-0.019, 95% CI [-0.036, -0.002], p =0.02) and consumption (β=-0.022, 95% CI [-0.039, -0.004], p <0.01), and the caudate nucleus during consumption (β=-0.017, 95% CI [-0.021, -0.006], p =0.02). In our supplementary analysis, we did not find evidence of an association between perceived racial discrimination and anticipatory or consummatory reward activation in striatum ( Figures S7-8 ). Estimates for the age and age squared terms across models are included in Table S3 and S4 , respectively. Estimates for the unadjusted anticipatory and consummatory striatal models for adversity and resilience factors are included in Tables S9-20 . [Insert Figure 4 here] Interactions between early deprivation and age To examine whether reward-blunting from ELA factors was consistent over ages 9 to 14, separate models included interactions of lack of supervision and primary caregiver lack of support with age in years (and age squared). For both factors, we observed evidence of a significant interaction (p<0.001). The negative association between lack of supervision and BAS Reward Responsiveness became more pronounced over age such that no clear association was observed at age 9/10 ( p =0.83), there was a negative association at age 11/12 (β=-0.159, 95%CI [-0.212, -0.106], p <0.001), and a stronger negative association at 13/14 (β=-0.305, 95%CI [-0.417, 0.193], p <0.001; Figure 6). In contrast, the negative association with primary caregiver lack of support was only evident at ages 9/10 (β= -0.252, 95%CI [-0.337, -0.138], p <0.001), less so at age 11/12 (β= -0.051, 95%CI [-0.096, -0.007], p =0.02), and the relationship was positive at age 13/14 (β=0.149, 95%CI [-0.039, 0.259], p =0.007). [Insert Figure 5 here] Discussion We report the first large-scale evidence that early experiences of threat and deprivation have opposing developmental associations with adolescent reward responsiveness. Specifically, emotional deprivation was associated with lower behavioral reward responsiveness, and supervisory deprivation was associated with both behavioral and anticipatory neural reward responsiveness in the striatum. However, the association of supervisory deprivation with reward responsiveness appeared specific to males. In contrast, experiencing family conflict and aggression was associated with increased behavioral and anticipatory neural reward responsiveness in the striatum. Additionally, while prosocial behavior and positive school environment were positively associated with reward responsiveness, we found limited evidence to support the hypothesized buffering against the reward-blunting effects of early deprivation. Studies which investigate the specific effects of early deprivation on reward responsiveness are sparse, and report either a blunting effect 39 or do not find an association 40,41 . It is plausible that lack of cognitive and social input might have a blunting effect on neural and behavioral reward responsiveness across development, which may be caused by an absence of rewarding caregiver interactions in the early environment. It has previously been reported that reduced reward-related recruitment of the striatum mediates the relationship between emotional neglect and depressive symptoms in adolescence 42 . There is also good evidence indicating early institutionalization causes deficits of reward responsiveness and reward learning 23 . To our knowledge there are no studies which report a male-specific negative association of supervisory deprivation with reward responsiveness, although this finding aligns with literature reporting sex-specific effects of childhood neglect on mental health outcomes 43,44 . The relationship between early experiences of threat and reward processing is less studied than experiences of deprivation, and the evidence is conflicting. Some studies report a reward-blunted association with childhood threat 41,45 , whereas others report a sensitizing effect 46,47 . Disparate findings on the effects of threat and deprivation may reflect different timings of when adversity and reward responsiveness are measured. This is especially crucial given evidence that different types of adversity may have worse effects on mental health depending on the age of exposure 48,49 Sensitivity to aversive stimuli or loss following early exposure to threat is well-evidenced 50,51 , but threat-induced sensitivity to rewarding stimuli less so. It is understood that early threat exposure may impact development of top-down regulation of emotions by higher cognitive brain regions, and prime the brain for fear learning earlier in development than would be typical for a healthy child 52 . This maladapted balance between bottom-up affective response to the environment and top-down cognitive control of affective processes may explain increased reward responsiveness in adolescents exposed to threat. Additionally, life history theory, which examines developmental responses to adversity using an evolutionary lens, suggests that children exposed to early threat adopt specific traits as an adaptive response to optimize survival and reproduction during youth. Individuals with this adversity-adapted trajectory exhibit delay discounting, early maturation of specific neural processes and earlier sexual development 53,54 . Whilst vigilance and capacity to learn about threats in the environment is crucial for survival in an evolutionary context, a heightened response to immediate rewards in youth is also advantageous, as it would relate to gathering resources and reproductive success. We hypothesized that childhood prosocial behavior and school positivity would buffer against the blunting effects of deprivation, proposing that meaningful school and peer relationships might compensate for poor caregiver relationships. Overall, we found limited evidence to support this hypothesis. This could be due to a lack of individuals in the sample who experience deprivation and exhibit prosocial behavior, especially given prior evidence that early deprivation negatively impacts the development of prosocial behavior 55 . We also examined whether associations with deprivation adversity factors were consistent over age. The negative association with supervisory deprivation emerged later in adolescence, worsening with age, whereas the association with emotional deprivation was negative at the age of ELA exposure but attenuated. These two forms of early deprivation may have different effects on reward responsiveness over time, as social support networks change with age. During adolescence, peer relationships play an increasingly important role in providing support 56,57 , which could offset the impact of early emotional deprivation from parents. The age-dependent association with emotional deprivation on reward responsiveness might also explain why this relationship is not observed in cross-sectional samples of children at different ages 40,58 . In contrast, early supervisory deprivation is not easily supplemented by peer relationships. Further research is required to investigate how the effects of early deprivation vary across age and contribute to unique trajectories of reward responsiveness in adolescence. We observed significant associations between race-ethnicity and reward responsiveness but were unable to identify a potential mediator for these associations. Prior literature suggests this relationship may be mediated by socioeconomic disadvantage 59 or discrimination 37,38 , but the association remained after we controlled for these factors. Perceived racial discrimination, which may be considered a threat-related experience of adversity, was significantly associated with increased reward responsiveness, further supporting the reward-sensitizing influence of early threat experiences. Overall, it is likely that other factors mediate the relationship between race-ethnicity and reward responsiveness, and that ABCD’s discrimination measure does not fully capture the experience of discrimination. Strengths and limitations A key strength of the present study is the operationalization of early life adversity using a rich set of baseline variables and factor analysis, allowing for specificity to investigate domain-specific associations. We also leveraged ABCD’s repeated measurement of neural and behavioral reward responsiveness measures, which allowed us to account for individual change in reward responsiveness with age. There were several key limitations of the present study. First, survey non-response may introduce selection bias. For example race-ethnicity and socioeconomic disadvantage are associated with missed ABCD study visits 60 , and those that were missing may have different results than those that responded. Second, although the probability sampling method used by ABCD supports broad generalizability to U.S. school‑enrolled children 61 , the cohort is not fully nationally representative. In particular, children from rural areas are underrepresented and those outside the school system not included. Third, we could not account for all potential confounds, particularly genetics. It is plausible that a genetic liability for low reward responsiveness may predispose a parent towards traits related to deprivation, confounding the association of deprivation with low reward responsiveness. Third, although we included a task-based measure of neural reward responsiveness in addition to BIS/BAS, self-report measures of reward responsiveness are limited in that they require individuals to reflect on how they respond to hypothetical rewards. Subjective reflection on hypothetical situations may not be accurate. Conclusions We provide evidence that early co-occurring deprivation adversities are associated with blunted neural and behavioral reward responsiveness in early adolescence, and that early co-occurring threat adversities are associated with increased neural and behavioral reward responsiveness. Early prosocial behavior and positive school environment were also associated with increased reward responsiveness, but we found limited evidence of an interaction between these resilience domains and early deprivation. Age-adversity interactions suggest the effects of different forms of early deprivation on reward responsiveness are not equal. Future work might distinguish between threat-related and deprivation-related developmental reward processing trajectories and examine whether they precede specific profiles of reward dysfunction and psychiatric risk in adulthood. Declarations Acknowledgements Funding RS is funded by the ESRC and BBSRC as part of the Soc-B Centre for Doctoral Training in Biosocial Research. MP is funded by a Henry Dale Fellowship from the Wellcome Trust and the Royal Society (224243/Z/21/Z). RE, MK and MP were supported by the NIHR Manchester Biomedical Research Centre (NIHR203308). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Author Contributions RS: Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review and editing, Investigation, Project administration, Data curation, Formal analysis. RE: Conceptualization, Writing – review and editing, Supervision. NM: Writing – review and editing, Supervision. MK: Visualization, Writing – review and editing. MP: Conceptualization, Methodology, Writing – review and editing, Supervision. Conflict of Interest The authors have no financial or non-financial interests to disclose. References Morris SE, Sanislow CA, Pacheco J, Vaidyanathan U, Gordon JA, Cuthbert BN. Revisiting the seven pillars of RDoC. BMC Med 2022; 20 : 220. Pacheco J, Garvey MA, Sarampote CS, Cohen ED, Murphy ER, Friedman-Hill SR. Annual Research Review: The contributions of the RDoC research framework on understanding the neurodevelopmental origins, progression and treatment of mental illnesses. J Child Psychol Psychiatry 2022; 63 : 360–376. Pizzagalli DA. Toward a Better Understanding of the Mechanisms and Pathophysiology of Anhedonia: Are We Ready for Translation? Am J Psychiatry 2022; 179 : 458–469. Steffens DC, Fahed M, Manning KJ, Wang L. The neurobiology of apathy in depression and neurocognitive impairment in older adults: a review of epidemiological, clinical, neuropsychological and biological research. Transl Psychiatry 2022; 12 : 525. Plichta MM, Scheres A. Ventral–striatal responsiveness during reward anticipation in ADHD and its relation to trait impulsivity in the healthy population: A meta-analytic review of the fMRI literature. Neurosci Biobehav Rev 2014; 38 : 125–134. Evans GW, Li D, Whipple SS. Cumulative risk and child development. Psychol Bull 2013; 139 : 1342–1396. Hashemi L, Fanslow J, Gulliver P, McIntosh T. Exploring the health burden of cumulative and specific adverse childhood experiences in New Zealand: Results from a population-based study. Child Abuse Negl 2021; 122 : 105372. Baldwin JR, Wang B, Karwatowska L, Schoeler T, Tsaligopoulou A, Munafò MR et al. Childhood maltreatment and mental health problems: A systematic review and meta-analysis of quasi-experimental studies. Am J Psychiatry 2023; 180 : 117–126. Furtado EJ, Camacho MC, Chin JH, Barch DM. Complex emotion processing and early life adversity in the Healthy Brain Network sample. Dev Cogn Neurosci 2024; 70 : 101469. Samaey C, Lecei A, Jackers M, Jennen L, Schruers K, Vervliet B et al. Childhood adversity is associated with reduced threat-safety discrimination and increased fear generalization in 12- to 16-year-olds. J Child Psychol Psychiatry 2025; 66 : 821–833. McLaughlin KA, Sheridan MA, Lambert HK. Childhood Adversity and Neural Development: Deprivation and Threat as Distinct Dimensions of Early Experience. Neurosci Biobehav Rev 2014; 47 : 578–591. McLaughlin KA, Weissman D, Bitrán D. Childhood Adversity and Neural Development: A Systematic Review. Annu Rev Dev Psychol 2019; 1 : 277–312. McLaughlin KA, Peverill M, Gold AL, Alves S, Sheridan MA. Child Maltreatment and Neural Systems Underlying Emotion Regulation. J Am Acad Child Adolesc Psychiatry 2015; 54 : 753–762. Sheridan MA, McLaughlin KA. Dimensions of Early Experience and Neural Development: Deprivation and Threat. Trends Cogn Sci 2014; 18 : 580–585. Brieant A, Vannucci A, Nakua H, Harris J, Lovell J, Brundavanam D et al. Characterizing the dimensional structure of early-life adversity in the Adolescent Brain Cognitive Development (ABCD) Study. Dev Cogn Neurosci 2023; 61 : 101256. Beck D, Whitmore L, MacSweeney N, Brieant A, Karl V, de Lange A-MG et al. Dimensions of Early-Life Adversity Are Differentially Associated With Patterns of Delayed and Accelerated Brain Maturation. Biol Psychiatry 2025; 97 : 64–72. Davydov DM, Stewart R, Ritchie K, Chaudieu I. Resilience and mental health. Clin Psychol Rev 2010; 30 : 479–495. Noltemeyer AL, Bush KR. Adversity and resilience: A synthesis of international research. Sch Psychol Int 2013; 34 : 474–487. Goetschius LG, McLoyd VC, Hein TC, Mitchell C, Hyde LW, Monk CS. School Connectedness as a Protective Factor Against Childhood Exposure to Violence and Social Deprivation: A Longitudinal Study of Adaptive and Maladaptive Outcomes. Dev Psychopathol 2023; 35 : 1219–1234. Haroz EE, Murray LK, Bolton P, Betancourt T, Bass JK. Adolescent Resilience in Northern Uganda: The Role of Social Support and Prosocial Behavior in Reducing Mental Health Problems. J Res Adolesc 2013; 23 : 138–148. Luo Y, Ma T, Deng Y. School climate and adolescents’ prosocial behavior: the mediating role of perceived social support and resilience. Front Psychol 2023; 14 . doi:10.3389/fpsyg.2023.1095566. Gunnar MR, Bowen M. What Was Learned From Studying the Effects of Early Institutional Deprivation. Pharmacol Biochem Behav 2021; 210 : 173272. Sheridan MA, McLaughlin KA, Winter W, Fox N, Zeanah C, Nelson CA. Early deprivation disruption of associative learning is a developmental pathway to depression and social problems. Nat Commun 2018; 9 : 2216. Casey BJ, Cannonier T, Conley MI, Cohen AO, Barch DM, Heitzeg MM et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev Cogn Neurosci 2018; 32 : 43–54. Auchter AM, Hernandez Mejia M, Heyser CJ, Shilling PD, Jernigan TL, Brown SA et al. A description of the ABCD organizational structure and communication framework. Dev Cogn Neurosci 2018; 32 : 8–15. Goodman R. The Strengths and Difficulties Questionnaire: a research note. J Child Psychol Psychiatry 1997; 38 : 581–586. Arthur MW, Briney JS, Hawkins JD, Abbott RD, Brooke-Weiss BL, Catalano RF. Measuring risk and protection in communities using the Communities That Care Youth Survey. Eval Program Plann 2007; 30 : 197–211. Carver CS, White TL. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS Scales. J Pers Soc Psychol 1994; 67 : 319–333. Pagliaccio D, Luking KR, Anokhin AP, Gotlib IH, Hayden EP, Olino TM et al. Revising the BIS/BAS to Study Development: Measurement Invariance and Normative Effects of Age and Sex from Childhood through Adulthood. Psychol Assess 2016; 28 : 429–442. Hagler DJ, Hatton S, Cornejo MD, Makowski C, Fair DA, Dick AS et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage 2019; 202 : 116091. Alarcón G, Cservenka A, Nagel BJ. Adolescent neural response to reward is related to participant sex and task motivation. Brain Cogn 2017; 111 : 51–62. Barendse MEA, Swartz JR, Taylor SL, Fine JR, Shirtcliff EA, Yoon L et al. Sex and pubertal variation in reward-related behavior and neural activation in early adolescents. Dev Cogn Neurosci 2024; 66 : 101358. Jorgensen NA, Muscatell KA, McCormick EM, Prinstein MJ, Lindquist KA, Telzer EH. Neighborhood disadvantage, race/ethnicity and neural sensitivity to social threat and reward among adolescents. Soc Cogn Affect Neurosci 2023; 18 : nsac053. Rubin DB. Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons Inc.: New York, NY, US, 1987. Petersen AC, Crockett L, Richards M, Boxer A. A self-report measure of pubertal status: Reliability, validity, and initial norms. J Youth Adolesc 1988; 17 : 117–133. Phinney JS, Madden T, Santos LJ. Psychological Variables as Predictors of Perceived Ethnic Discrimination Among Minority and Immigrant Adolescents. J Appl Soc Psychol 1998; 28 : 937–953. Vargas TG, Mittal VA. Testing whether implicit emotion regulation mediates the association between discrimination and symptoms of psychopathology in late childhood: An RDoC perspective. Dev Psychopathol 2021; 33 : 1634–1647. Wang H, Braun C, Enck P. How the brain reacts to social stress (exclusion) - A scoping review. Neurosci Biobehav Rev 2017; 80 : 80–88. Hanson JL, Hariri AR, Williamson DE. Blunted Ventral Striatum Development in Adolescence Reflects Emotional Neglect and Predicts Depressive Symptoms. Biol Psychiatry 2015; 78 : 598–605. Yang R, Yu Q, Owen CE, Ibarra Aspe G, Wiggins JL. Contributions of childhood abuse and neglect to reward neural substrates in adolescence. NeuroImage Clin 2021; 32 : 102832. Kasparek SW, Gastón-Panthaki A, Hanford LC, Lengua LJ, Sheridan MA, McLaughlin KA. Does reward processing moderate or mediate the link between childhood adversity and psychopathology: A longitudinal study. Dev Psychopathol 2023; 35 : 2338–2351. Hanson JL, Hariri AR, Williamson DE. Blunted Ventral Striatum Development in Adolescence Reflects Emotional Neglect and Predicts Depressive Symptoms. Biol Psychiatry 2015; 78 : 598–605. Dong C, Wang Z, Jia F, Tian H, Zhang Y, Liu H et al. Gender differences in the association between childhood maltreatment and the onset of major depressive disorder. J Affect Disord 2024; 351 : 111–119. Ernst M, Tibubos AN, Werner A, Beutel ME, Plener PL, Fegert JM et al. Sex-dependent associations of childhood neglect and bodyweight across the life span. Sci Rep 2019; 9 : 5080. Miu AC, Bîlc MI, Bunea I, Szentágotai-Tătar A. Childhood trauma and sensitivity to reward and punishment: Implications for depressive and anxiety symptoms. Personal Individ Differ 2017; 119 : 134–140. Letkiewicz AM, Suor JH, Glazer JE, Li LY, Bernat EM, Burkhouse KL et al. Severe Sexual Abuse in Childhood and Altered Neurophysiological Response to Reward in Female Adults. Child Abuse Negl 2024; 154 : 106945. Hendrikse CJ, du Plessis S, Luckhoff HK, Vink M, van den Heuvel LL, Scheffler F et al. Childhood trauma exposure and reward processing in healthy adults: A functional neuroimaging study. J Neurosci Res 2022; 100 : 1452–1462. Lee JO, Duan L, Constantino-Pettit A, Yoon Y, Oxford ML, Rose J et al. Does the timing matter? The association between childhood adversity and internalizing and externalizing problems from childhood to adolescence and its sex differences. Child Abuse Negl 2025; 163 : 107437. Hardi FA, Peckins MK, Mitchell C, McLoyd V, Brooks-Gunn J, Hyde LW et al. Childhood adversity and adolescent mental health: Examining cumulative and specificity effects across contexts and developmental timing. Dev Psychopathol 2025; 37 : 1954–1970. Birn RM, Roeber BJ, Pollak SD. Early childhood stress exposure, reward pathways, and adult decision making. Proc Natl Acad Sci 2017; 114 : 13549–13554. Krugers HJ, Arp JM, Xiong H, Kanatsou S, Lesuis SL, Korosi A et al. Early life adversity: Lasting consequences for emotional learning. Neurobiol Stress 2016; 6 : 14–21. Machlin L, Miller AB, Snyder J, McLaughlin KA, Sheridan MA. Differential Associations of Deprivation and Threat With Cognitive Control and Fear Conditioning in Early Childhood. Front Behav Neurosci 2019; 13 : 80. Ellis BJ, Figueredo AJ, Brumbach BH, Schlomer GL. Fundamental Dimensions of Environmental Risk : The Impact of Harsh versus Unpredictable Environments on the Evolution and Development of Life History Strategies. Hum Nat Hawthorne N 2009; 20 : 204–268. Yuan J, Yu Y, Liu D, Sun Y. Associations between distinct dimensions of early life adversity and accelerated reproductive strategy among middle-aged women in China. Am J Obstet Gynecol 2022; 226 : 104.e1-104.e14. Chen P, Zhang Q, Sun X, Ye X, Wang Y, Yang X. How do childhood abuse and neglect affect prosocial behavior? The mediating roles of different empathic components. Front Psychol 2023; 13 : 1051258. del Voile JF, Bravo A, López M. Parents and peers as providers of support in adolescents’ social network: A developmental perspective. J Community Psychol 2010; 38 : 16–27. Wentzel KR. Social relationships and motivation in middle school: The role of parents, teachers, and peers. J Educ Psychol 1998; 90 : 202–209. Dennison MJ, Rosen ML, Sambrook KA, Jenness JL, Sheridan MA, McLaughlin KA. Differential associations of distinct forms of childhood adversity with neurobehavioral measures of reward processing: A developmental pathway to depression. Child Dev 2019; 90 : e96–e113. White SF, Nusslock R, Miller GE. Low Socioeconomic Status Is Associated with a Greater Neural Response to Both Rewards and Losses. J Cogn Neurosci 2022; 34 : 1939–1951. Feldstein Ewing SW, Dash GF, Thompson WK, Reuter C, Diaz VG, Anokhin A et al. Measuring retention within the adolescent brain cognitive development (ABCD)SM study. Dev Cogn Neurosci 2022; 54 : 101081. Garavan H, Bartsch H, Conway K, Decastro A, Goldstein RZ, Heeringa S et al. Recruiting the ABCD sample: Design considerations and procedures. Dev Cogn Neurosci 2018; 32 : 16–22. Tables Table 1 is available in the Supplementary Files section. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files Table1.docx Table 1: Sample characteristics from total ABCD sample (n=11868). Study1supplementaryv7.0.docx Supplementary Material Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 28 Jan, 2026 Editor assigned by journal 04 Jan, 2026 Submission checks completed at journal 04 Jan, 2026 First submitted to journal 20 Dec, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8414265","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":582006954,"identity":"2f0e929d-c584-4600-b7f5-3561998bac47","order_by":0,"name":"Ryan Shepherd","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABNklEQVRIie2RMUvDQBTHXzhIlgtxPIjYr3AhkKNQ9atcOUiXYDdxECwInfwAFaKfIVOgW8tBXOoeOIcWoVMKFUFaKGhD66BppaPD/ZbHu8eP/zsegEbzfxkAkHXBV2WDf03+VkbfCj9UsbsHKKwnpmNYvZww9zabzx7TGrOeh5PlotEGS47RJvcHx3nIqNGd+vU4E/cPqfL6dxfCxzysd3BIEc4rCiE8IEZHNpM88pGdKiPJceAClxQgAoTnO5TWB4GVvEny9juyY3W+VthywT8pOMUeJQoImJLTPELI7qhmmQKYDyiQMmXHYri4JM2u9Pq90DfiTIlkFPkuDgU1yZQO4+r3idVKyXwla4yIVyiu1WnyNPLeFo0z6jhiMi6yigJHfHMEWpmYsOeQzva1qmg0Go1myxcbZ2447Ev/RQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0008-1592-9732","institution":"The University of Manchester","correspondingAuthor":true,"prefix":"","firstName":"Ryan","middleName":"","lastName":"Shepherd","suffix":""},{"id":582006955,"identity":"7fe43779-b5f0-44a6-9c92-6c83f17aa94a","order_by":1,"name":"Rebecca Elliott","email":"","orcid":"https://orcid.org/0000-0002-7602-010X","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Elliott","suffix":""},{"id":582006956,"identity":"7ad180a5-038f-4b27-8a5f-e2a450c8b079","order_by":2,"name":"Nils Muhlert","email":"","orcid":"","institution":"Division of Psychology, Communication and Human Neuroscience, The University of Manchester, Manchester","correspondingAuthor":false,"prefix":"","firstName":"Nils","middleName":"","lastName":"Muhlert","suffix":""},{"id":582006957,"identity":"bd1a0dd2-e871-4575-81ca-bebf680d11f7","order_by":3,"name":"Mica Komarnyckyj","email":"","orcid":"https://orcid.org/0000-0002-6749-3685","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Mica","middleName":"","lastName":"Komarnyckyj","suffix":""},{"id":582006958,"identity":"909be671-ae07-4e12-bcae-3cec24165d0d","order_by":4,"name":"Matthias Pierce","email":"","orcid":"https://orcid.org/0000-0003-2182-0369","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Pierce","suffix":""}],"badges":[],"createdAt":"2025-12-20 21:05:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8414265/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8414265/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101530116,"identity":"22a5b7fa-cc6a-4d6c-93f4-c07a26c4e8af","added_by":"auto","created_at":"2026-01-30 19:46:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1492021,"visible":true,"origin":"","legend":"\u003cp\u003eMap of items (observed variables) used to create latent adversity factors. Factor loadings were obtained from a previously conducted exploratory factor analysis in ABCD\u003csup\u003e15\u003c/sup\u003e. Reverse-coded variables are indicated by *. \u003cstrong\u003e(A)\u003c/strong\u003e Parent reported items. \u003cstrong\u003e(B)\u003c/strong\u003e Youth reported items.\u003c/p\u003e","description":"","filename":"Figure127.png","url":"https://assets-eu.researchsquare.com/files/rs-8414265/v1/6a3051cc05301378e32d0d4e.png"},{"id":101530114,"identity":"683a8ff0-03ef-4e6b-bee1-2307b1b80432","added_by":"auto","created_at":"2026-01-30 19:46:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":557784,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of ABCD’s monetary incentive delay paradigm stimuli timings, from which anticipatory and consummatory ROI-averaged BOLD contrasts were derived. Images are for illustrative purposes only.\u003c/p\u003e","description":"","filename":"Figure225.png","url":"https://assets-eu.researchsquare.com/files/rs-8414265/v1/150c11e5c8e38a7088733bbf.png"},{"id":101530120,"identity":"849a8f8b-c3ee-4d86-841b-8dcb7ad8e3d1","added_by":"auto","created_at":"2026-01-30 19:46:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":530731,"visible":true,"origin":"","legend":"\u003cp\u003eEarly adversity and self-reported BAS Reward Responsiveness. Forest plot showing the association between a standard deviation change in early life adversity factors and BAS Reward Responsiveness following linear mixed effects models. ELA factors related to deprivation are shown in blue, and threat factors in red.\u003c/p\u003e","description":"","filename":"Figure318.png","url":"https://assets-eu.researchsquare.com/files/rs-8414265/v1/a045d5c78db574cdd4b5cfc4.png"},{"id":101530118,"identity":"6d940ce2-68f2-409b-81eb-4ff644643031","added_by":"auto","created_at":"2026-01-30 19:46:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3068092,"visible":true,"origin":"","legend":"\u003cp\u003eEarly adversity and neural reward responsiveness. (\u003cstrong\u003eA\u003c/strong\u003e) Forest plot showing the association between a standard deviation change in early life adversity factors and striatal reward activation during the anticipation and consumption phases of reward. ELA factors related to deprivation are shown in blue, and threat factors in red. Striatal subregion is represented by shape. (\u003cstrong\u003eB\u003c/strong\u003e) 3-D representation of the three subcortical regions of interest selected for analysis. The nucleus accumbens is colored red, the caudate nucleus blue and the putamen green.\u003c/p\u003e","description":"","filename":"Figure416.png","url":"https://assets-eu.researchsquare.com/files/rs-8414265/v1/7d6c18780c7e535864bca9f0.png"},{"id":101530121,"identity":"300334df-1541-4107-abf9-8e53bb7d846f","added_by":"auto","created_at":"2026-01-30 19:46:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":550335,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of beta coefficients for age 9/10 adversity at each age from BAS: Reward responsiveness random slope interaction model – incorporating estimates for the interaction at each level of age and age squared. (\u003cstrong\u003eA\u003c/strong\u003e) Lack of supervision, (\u003cstrong\u003eB\u003c/strong\u003e) Primary caregiver lack of support.\u003c/p\u003e","description":"","filename":"Figure512.png","url":"https://assets-eu.researchsquare.com/files/rs-8414265/v1/45b5347aa944dc3f57e56f56.png"},{"id":101757282,"identity":"ca1604ae-5cf0-418e-a5b2-7394d43c4541","added_by":"auto","created_at":"2026-02-03 11:02:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6736693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8414265/v1/feb09b4d-b308-48e6-add9-a165f3e48ea9.pdf"},{"id":101752086,"identity":"072b446f-f1d6-4ddf-9bd8-534d98e85dfd","added_by":"auto","created_at":"2026-02-03 10:25:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1: \u003c/strong\u003eSample characteristics from total ABCD sample (n=11868).\u003c/p\u003e","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8414265/v1/40bb4d04e35124cf4975869d.docx"},{"id":101530117,"identity":"8a3dbd6f-d10a-4bd4-b666-50cbc0f9d1b3","added_by":"auto","created_at":"2026-01-30 19:46:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1686235,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material\u003c/p\u003e","description":"","filename":"Study1supplementaryv7.0.docx","url":"https://assets-eu.researchsquare.com/files/rs-8414265/v1/a00f86d4753a2f4a122881b6.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Neural and behavioral reward responsiveness in early adolescence: longitudinal associations with childhood adversity and resilience","fulltext":[{"header":"Introduction","content":"\u003cp\u003eReward responsiveness, the ability to respond emotionally to the anticipation or receipt of rewards, forms one of three positive valence systems defined in the National Institute of Mental Health Research Domain Criteria\u003csup\u003e1,2\u003c/sup\u003e. It conceptually overlaps with anhedonia, the inability to experience pleasure\u003csup\u003e3\u003c/sup\u003e, and likely plays a central role in other transdiagnostic psychiatric symptoms, such as apathy\u003csup\u003e4\u003c/sup\u003e and impulsivity\u003csup\u003e5\u003c/sup\u003e. Identifying factors that influence early developmental trajectories of behavioral and neural reward responsiveness is a key step towards understanding why these transdiagnostic symptoms emerge in young people.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEarly life adversities (ELA) such as abuse and neglect cause some children to have poor mental health in later life\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e. While a growing body of literature characterizes the relationship between ELA and negative valence systems, such as threat processing\u003csup\u003e9,10\u003c/sup\u003e, there is a lack of research examining how ELA affects positive valence systems such as reward responsiveness. Also, many ELA studies focus on one specific type of adversity or consider multiple accumulating adversities. The first approach does not account for individuals who experience several adversities, and the second does not differentiate between types of adversity. The dimensional model of adversity and psychopathology assigns exposures to dimensions of \u003cem\u003ethreat\u0026nbsp;\u003c/em\u003eand \u003cem\u003edeprivation,\u003c/em\u003e using theory-driven expertise of shared mechanisms\u003csup\u003e11\u003c/sup\u003e\u003cem\u003e.\u003c/em\u003e This framework has been supported by distinct neural differences observed in individuals with early experiences of threat and deprivation\u003csup\u003e12\u003c/sup\u003e. Adversities that involve experience of threatened harm (e.g., witnessing or experiencing abuse) are more likely to influence neurodevelopment via stress input, with implications for emotional processing\u003csup\u003e13\u003c/sup\u003e. By contrast, adversities that involve \u003cem\u003edeprivation\u003c/em\u003e (e.g., emotional or cognitive neglect), may influence neurodevelopment via a lack of positive input, with implications for cognition\u003csup\u003e14\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing data-driven, clustering approaches such as exploratory factor analysis allows researchers to identify clusters of highly correlated ELA, which can then be interpreted within a threat/deprivation theoretical framework. This approach has been applied previously to the Adolescent Brain Cognitive Development (ABCD) study to examine which aspects of ELA are associated with later mental health\u003csup\u003e15\u003c/sup\u003e and patterns of brain maturation in childhood\u003csup\u003e16\u003c/sup\u003e. These factors use items across multiple child and parent report surveys, capturing more subtle aspects of presently co-occurring ELA than other assessment tools, which rely primarily on adult retrospective reports.\u003c/p\u003e\n\u003cp\u003eImportantly, not all children who are exposed to ELA exhibit poor mental health, potentially due to the presence of resilience \u0026lsquo;systems\u0026rsquo;\u003cem\u003e\u0026nbsp;\u003c/em\u003ethat buffer or negate the impact of adversities in some individuals\u003csup\u003e17\u003c/sup\u003e. Resilience systems may relate to external factors\u003csup\u003e18\u003c/sup\u003e such as a positive school environment\u003csup\u003e19\u003c/sup\u003e, or internal psychological processes such as prosocial behavior\u003csup\u003e20,21\u003c/sup\u003e. Identifying what helps children avoid the effects of ELA may provide crucial mechanistic evidence and additional preventative targets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study, we examine associations between clusters of adversity and subsequent neural and behavioral reward responsiveness trajectories in early adolescence. We also explore the roles of two potential resilience factors: early prosocial behavior and school positivity. This is the first study to investigate how aspects of co-occurring ELA and resilience relate to adolescent trajectories of behavioral and neural reward responsiveness. We hypothesized that early deprivation would be associated with blunted reward responsiveness, informed by prior studies on the impact of early institutionalization\u003csup\u003e22,23\u003c/sup\u003e. We also expected early resilience factors to diminish the blunting effect of ELA on reward responsiveness. \u0026nbsp;\u003c/p\u003e"},{"header":"Methods and Materials ","content":"\u003cp\u003e\u003cem\u003eData source and ethical considerations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Adolescent Brain Cognitive Development (ABCD) study\u003csup\u003e24\u003c/sup\u003e is an ongoing longitudinal US community-based sample of 11,880 children, aged 9 or 10 at baseline (collected in 2018). They participated in yearly psychiatric and neurocognitive testing, with multimodal brain imaging measures every 2 years, harmonized across 21 sites. The ABCD study received ethical approval from an institutional review board at the University of California, San Diego\u003csup\u003e25\u003c/sup\u003e. ABCD data can be accessed freely via the NIH Brain Development Cohorts Data Hub. Scripts used to carry out data cleaning and analyses are available in the following repository https://github.com/r-j-shepherd/ELA_resilience_reward.git.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEarly life adversity\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInformation on ELA was obtained from 60 items measured at baseline using caregiver and youth reported surveys (see \u003cstrong\u003e\u003cem\u003eFigure 1\u0026nbsp;\u003c/em\u003e\u003c/strong\u003efor items used to create specific factors). 10 ELA dimensions were identified using the results of a prior exploratory factor analysis using the same dataset\u003csup\u003e15\u003c/sup\u003e. Each child\u0026rsquo;s continuous factor scores were derived using the prior study\u0026rsquo;s factor loading matrix. The factors include 4 ELA dimensions relating to threat: \u003cem\u003efamily aggression, family anger and arguments, trauma exposure\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;youth report of family conflict\u003c/em\u003e; and 3 relating to deprivation: \u003cem\u003elack of supervision\u003c/em\u003e, \u003cem\u003eprimary caregiver lack of support\u003c/em\u003e, and \u003cem\u003esecondary caregiver lack of support\u003c/em\u003e. 3 factors represent more complex exposures which do not map clearly to threat or deprivation, or feature elements of both: \u003cem\u003ecaregiver psychopathology\u003c/em\u003e and \u003cem\u003ecaregiver substance use and separation from biological parent,\u0026nbsp;\u003c/em\u003eand \u003cem\u003esocioeconomic disadvantage and neighborhood safety\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[Insert \u003cstrong\u003e\u003cem\u003eFigure 1\u003c/em\u003e\u0026nbsp;\u003c/strong\u003ehere]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eResilience factors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eProsocial behavior and protective school environment were selected as potential resilience factors based on prior literature\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e, measured at baseline using the prosocial behavior scale from the Strengths and Difficulties Questionnaire \u003csup\u003e26\u003c/sup\u003e and the PhenX School Risk and Protective Factors Survey \u003csup\u003e27\u003c/sup\u003e respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eReward responsiveness\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSelf-reported behavioral sensitivity to reward was measured repeatedly when children were aged 9-10, 11-12, and 13-14 using the 4-item reward responsiveness module of the revised Behavioral Inhibition and Behavioral Activation Scales (BIS/BAS)\u003csup\u003e28,29\u003c/sup\u003e. A sum score was calculated from 4-point Likert items (range 0-12). This module contains questions on how an individual feels in response to rewards, specifically the level of excitement around potential rewards and opportunity for rewards \u0026ndash; see \u003cstrong\u003e\u003cem\u003eTable S1\u003c/em\u003e\u003c/strong\u003e for items used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNeural reward responsiveness was measured using data from an fMRI monetary incentive delay (MID) task, collected repeatedly at the same waves as BIS/BAS. \u0026nbsp; In the ABCD MID task, participants are presented with cues which indicate potential reward, loss or neutral consequences. To successfully gain a reward or avoid loss, they must respond quickly via a remote when the target (black shape) appears. We used two contrasts from this task, large reward vs no reward during the anticipatory phase, and large reward vs no reward during the consummatory phase.\u003c/p\u003e\n\u003cp\u003eWe focused on activation in three \u003cem\u003ea priori\u003c/em\u003e sub-regions of the striatum \u0026ndash; nucleus accumbens, putamen and caudate nucleus. Following pre-processing and segmentation, average time series were extracted for each region, and task-based activation estimated using a general linear model framework. A detailed breakdown of ABCD\u0026rsquo;s MRI image preprocessing and task-based analysis pipeline has been published previously\u003csup\u003e30\u003c/sup\u003e. The stimuli and task phases used to derive these measures are visualized in \u003cstrong\u003e\u003cem\u003eFigure 2\u003c/em\u003e\u003c/strong\u003e. We excluded 1694 scans from 1542 individuals which failed to meet quality control standards due to poor quality images or MID task performance.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[Insert \u003cstrong\u003e\u003cem\u003eFigure 2\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ehere]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdversity and resilience scores were standardized (z-scores) to aid comparison of effect sizes. Associations between adversity factors and repeated behavioral and neural measures of reward responsiveness were estimated using linear mixed-effects models. For the behavioral measure models, two random effects were included to capture clustering within individuals and families. For the neural measure models, additional random effects were included for data collection site, to account for variability between MRI scanners. Additional individual-level random slopes were tested to model individual variation in change over time. For behavioral reward responsiveness, including these significantly improved model fit (p\u0026lt;0.001), whereas in neural models they were omitted because the models did not converge when present.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven prior evidence of sex differences in adolescent reward processing\u003csup\u003e31,32\u003c/sup\u003e and ethnicity differences in socioeconomic disadvantage\u003csup\u003e33\u003c/sup\u003e, sex and race-ethnicity were included as covariates to account for potential confounding of the relationship between adversity and reward responsiveness. Models included fixed effects for age (in years) since baseline (linear and squared term), sex (male, female), and race-ethnicity (Asian, Black, Hispanic, White, Other).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate whether potential resilience factors of prosocial behavior and protective school environment may buffer the association between adversity and reward responsiveness, we tested interactions between these variables and ELA factors that were negatively associated with reward responsiveness. In order to assess whether the impact of ELAs changed over age, we also examined the interaction between reward-blunting adversity factors with age and age squared.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 3,318 (28%) respondents were missing at least one adversity item at baseline, with missingness ranging from 0.04-8.57% for individual items (\u003cstrong\u003e\u003cem\u003eTable S2\u003c/em\u003e\u003c/strong\u003e). To address this, random forest multiple imputation models by chained equations were used to create 10 imputed datasets. Models were fitted to each imputed dataset, and estimates were pooled using Rubin\u0026rsquo;s rules\u003csup\u003e34\u003c/sup\u003e (\u003cem\u003emice\u0026nbsp;\u003c/em\u003epackage in R). To validate the factor scores provided by Brieant et al. (2023), who used the baseline sample with complete data items and including one individual per family (n = 7115), in the imputed datasets, we repeated their Bayesian multilevel models in one of the imputed datasets. The results of this were visually and directionally similar to the smaller non-imputed sample (see \u003cstrong\u003e\u003cem\u003eFigures S1-2\u003c/em\u003e\u003c/strong\u003e for comparison of samples).\u003c/p\u003e\n\u003cp\u003eData cleaning and statistical analyses were carried out in R version 4.3.2.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eSupplementary analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFour supplementary analyses were conducted. First, we explored the differential impact of pubertal development on reward processing by examining sex-specific models (ABCD utilizes a sex-specific self-report Pubertal Development Scale\u003csup\u003e35\u003c/sup\u003e). Second, we examined sex-specific associations with adversity factors\u003cem\u003e\u0026nbsp;\u003c/em\u003eidentified in the sex-stratified analyses, by including interaction terms in the mixed effects models and calculating adversity factor beta coefficients for each sex. Third, upon exploratory analyses of BIS/BAS distributions, we observed that the baseline scores for BAS: Reward Responsiveness were skewed. We explored whether findings were robust to non-normality by fitting a Poisson regression model for this timepoint. Fourth, we sought to determine if the relationship between race-ethnicity and reward processing outcomes is explained by perceived discrimination at age 11-12, measured using the Perceived Discrimination Scale\u003csup\u003e36\u003c/sup\u003e. For this, we used the same model as previously, with the addition of the Perceived Discrimination Scale score, in line with prior literature highlighting the role of racial discrimination in emotional regulation\u003csup\u003e37\u003c/sup\u003e and neural differences\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eSample characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were identified by a parent or guardian as Asian (2%), Black (15%), Hispanic (20%), White (52%), or Other (11%). 48% reported their sex as female and 52% as male (\u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e). The mean ages in years at baseline, follow-up 2 and follow-up 4 were 9.91 (SD=0.62), 12.03 (SD=0.67) and 14.08 (SD=0.68) respectively.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[Insert \u003cstrong\u003e\u003cem\u003eTable 1\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ehere]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eELA factors and behavioral reward responsiveness\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThere was a negative association between linear age and BAS reward responsiveness (\u0026beta;=-0.567, 95%CI [-0.618, -0.518], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and a positive association with age squared, (\u0026beta;=0.067, 95%CI [0.053, 0.080], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) indicating the rate of decline decreased between ages 9-14 (\u003cstrong\u003e\u003cem\u003eFigures S3-5)\u003c/em\u003e\u003c/strong\u003e. Females had lower behavioral reward responsiveness compared to males (\u0026beta;=-0.173, 95%CI [-0.242, -0.105], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). Race-ethnicity was also associated with BAS Reward Responsiveness (p\u0026lt;0.001). In particular, a positive association with being Black (\u0026beta;=0.578, 95%CI [0.462, 0.693], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), Hispanic (\u0026beta;=0.279, 95%CI [0.184, 0.375], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), or Other (\u0026beta;=0.188, 95%CI [0.070, 0.306], \u003cem\u003ep\u003c/em\u003e=0.02).\u003c/p\u003e\n\u003cp\u003eThree deprivation-related ELA factors were associated with lower BAS reward responsiveness (\u003cstrong\u003e\u003cem\u003eFigure 3\u003c/em\u003e\u003c/strong\u003e)\u003cem\u003e: lack of supervision\u003c/em\u003e (\u0026beta;=-0.104, 95% CI [-0.155, -0.053], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), \u003cem\u003elack of support from primary caregiver\u003c/em\u003e (\u0026beta;=-0.076, 95%CI [-0.118, -0.035], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), and \u003cem\u003elack of support from secondary caregiver\u003c/em\u003e (\u0026beta;=-0.205, 95%CI [-0.250, -0.160], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). One threat-related factor, \u003cem\u003eyouth report of family conflict\u003c/em\u003e, was associated with higher reward responsiveness (\u0026beta;=0.167, 95%CI [0.111, 0.223], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eBoth resilience factors \u003cem\u003eprosocial behavior\u003c/em\u003e (\u0026beta;=0.262, 95%CI [0.227, 0.298], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and \u003cem\u003epositive school environment\u003c/em\u003e (\u0026beta;=0.099, 95%CI [0.064, 0.135], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) were associated with increased reward responsiveness (see \u003cstrong\u003e\u003cem\u003eTables S7-8\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003efor unadjusted models)\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThere was marginal evidence for an interaction between age 9/10 prosocial behavior and \u003cem\u003elack of supervision\u003c/em\u003e (\u0026beta;=0.033, 95% CI [-0.00007, 0.067], p=0.05) such that the association was weaker when an adolescent scored higher on the prosocial scale. There was no other evidence of an interaction between a resilience factor and an ELA dimension.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eSupplementary analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSelf-reported discrimination at age 11-12 was positively associated with reward responsiveness (\u0026beta;=0.212, 95%CI [0.107, 0.317], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), though it did not substantially alter the association between race-ethnicity and reward responsiveness (\u003cstrong\u003e\u003cem\u003eFigure S6\u003c/em\u003e\u003c/strong\u003e). The results after fitting a Poisson model to non-normal data (\u003cstrong\u003e\u003cem\u003eTable S5-6\u003c/em\u003e\u003c/strong\u003e) were in line with our main findings. Our sex-stratified supplementary analysis revealed a significant positive association of pubertal development in the male sample (\u0026beta;= 0.054, 95%CI [0.007, 0.101], \u003cem\u003ep\u003c/em\u003e=0.02), but not the female sample (\u003cem\u003ep=\u003c/em\u003e0.78). However, the sex-specificity of the pubertal development scale prevented the fitting of an interaction term for sex and puberty in the full sample. Additionally, the association with \u003cem\u003elack of supervision\u003c/em\u003e remained in the male sample (\u0026beta;=-0.104, 95% CI [-0.171, -0.037], \u003cem\u003ep\u003c/em\u003e=0.002), but not the female sample (p=0.19). We also observed a significant negative association between \u003cem\u003etrauma exposure\u003c/em\u003e and reward responsiveness in the female sample (\u0026beta;=-0.123, 95% CI [-0.242, -0.004], \u003cem\u003ep\u003c/em\u003e=0.04), but not the male sample (p=0.1). We observed evidence of a significant interaction for sex and \u003cem\u003elack of supervision\u0026nbsp;\u003c/em\u003ein the full sample (\u003cem\u003ep\u003c/em\u003e=0.011), where the association with BAS: Reward Responsiveness was negative in males (\u0026beta;= -0.136, 95%CI [-0.193, -0.080], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and non-significant in females (\u003cem\u003ep\u003c/em\u003e=0.19). However, we did not find evidence of an interaction between sex and \u003cem\u003etrauma exposure\u0026nbsp;\u003c/em\u003ein the full sample (p=0.65).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[Insert \u003cstrong\u003e\u003cem\u003eFigure 3\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ehere]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eELA factors and neural reward responsiveness\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSeveral adversity factors were associated with neural reward responsiveness (\u003cstrong\u003e\u003cem\u003eFigure 4\u003c/em\u003e\u003c/strong\u003e). Similarly to the BAS Reward responsiveness models, the deprivation-related factor \u003cem\u003elack of supervision\u003c/em\u003e was associated with reduced anticipatory reward response in the nucleus accumbens (\u0026beta;=-0.010, 95%CI [-0.017, -0.003], \u003cem\u003ep\u003c/em\u003e=0.005] and putamen (\u0026beta;=-0.005, 95%CI [-0.011, -0.0005], \u003cem\u003ep\u003c/em\u003e=0.03). The threat-related factor \u003cem\u003efamily aggression\u003c/em\u003e was associated with increased anticipatory reward response in the nucleus accumbens (\u0026beta;=0.008, 95%CI [0.002, 0.015], \u003cem\u003ep\u003c/em\u003e=0.01). The models of consummatory neural reward responsiveness suggest \u003cem\u003elack of support from secondary caregiver\u003c/em\u003e was associated with increased activation of the putamen during receipt of reward (\u0026beta;=0.008, 95%CI [0.002, 0.015], \u003cem\u003ep\u003c/em\u003e=0.01). There was no evidence of an association between the resilience factors and striatal activation during anticipation or receipt of reward.\u003c/p\u003e\n\u003cp\u003eFemales had lower anticipatory putamen activation compared to males (\u0026beta;=-0.009, 95% CI [-0.016, -0.003], \u003cem\u003ep\u003c/em\u003e=0.005). Females also had lower levels of consummatory activation in the nucleus accumbens (\u0026beta;=-0.017, 95% CI [-0.026, -0.007], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), putamen (\u0026beta;=-0.026, 95% CI [-0.033, -0.019], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), and caudate nucleus (\u0026beta;=-0.014, 95% CI [-0.021, -0.006], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). Race-ethnicity was associated with anticipatory and consummatory striatal activation during MID (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). In particular, being Black was negatively associated with nucleus accumbens activation during anticipation (\u0026beta;=-0.019, 95% CI [-0.036, -0.002], \u003cem\u003ep\u003c/em\u003e=0.02) and consumption (\u0026beta;=-0.022, 95% CI [-0.039, -0.004], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01), and the caudate nucleus during consumption (\u0026beta;=-0.017, 95% CI [-0.021, -0.006], \u003cem\u003ep\u003c/em\u003e=0.02). In our supplementary analysis, we did not find evidence of an association between perceived racial discrimination and anticipatory or consummatory reward activation in striatum (\u003cstrong\u003e\u003cem\u003eFigures S7-8\u003c/em\u003e\u003c/strong\u003e). Estimates for the age and age squared terms across models are included in \u003cstrong\u003e\u003cem\u003eTable S3\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003eS4\u003c/em\u003e\u003c/strong\u003e, respectively. Estimates for the unadjusted anticipatory and consummatory striatal models for adversity and resilience factors are included in \u003cstrong\u003e\u003cem\u003eTables S9-20\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[Insert \u003cstrong\u003e\u003cem\u003eFigure 4\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ehere]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eInteractions between early deprivation and age\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo examine whether reward-blunting from ELA factors was consistent over ages 9 to 14, separate models included interactions of \u003cem\u003elack of supervision\u003c/em\u003e and \u003cem\u003eprimary caregiver lack of support\u003c/em\u003e with age in years (and age squared). \u0026nbsp;For both factors, we observed evidence of a significant interaction (p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eThe negative association between \u003cem\u003elack of supervision\u003c/em\u003e and BAS Reward Responsiveness became more pronounced over age such that no clear association was observed at age 9/10 (\u003cem\u003ep\u003c/em\u003e=0.83), there was a negative association at age 11/12 (\u0026beta;=-0.159, 95%CI [-0.212, -0.106], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), and a stronger negative association at 13/14 (\u0026beta;=-0.305, 95%CI [-0.417, 0.193], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; Figure 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, the negative association with \u003cem\u003eprimary caregiver lack of support\u003c/em\u003e was only evident at ages 9/10 (\u0026beta;= -0.252, 95%CI [-0.337, -0.138], \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), less so at age 11/12 (\u0026beta;= -0.051, 95%CI [-0.096, -0.007], \u003cem\u003ep\u003c/em\u003e=0.02), and the relationship was positive at age 13/14 (\u0026beta;=0.149, 95%CI [-0.039, 0.259], \u003cem\u003ep\u003c/em\u003e=0.007).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[Insert \u003cstrong\u003e\u003cem\u003eFigure 5\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ehere]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe report the first large-scale evidence that early experiences of threat and deprivation have opposing developmental associations with adolescent reward responsiveness. Specifically, emotional deprivation was associated with lower behavioral reward responsiveness, and supervisory deprivation was associated with both behavioral and anticipatory neural reward responsiveness in the striatum. However, the association of supervisory deprivation with reward responsiveness appeared specific to males. In contrast, experiencing family conflict and aggression was associated with increased behavioral and anticipatory neural reward responsiveness in the striatum. Additionally, while prosocial behavior and positive school environment were positively associated with reward responsiveness, we found limited evidence to support the hypothesized buffering against the reward-blunting effects of early deprivation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies which investigate the specific effects of early deprivation on reward responsiveness are sparse, and report either a blunting effect\u003csup\u003e39\u003c/sup\u003e or do not find an association\u003csup\u003e40,41\u003c/sup\u003e. It is plausible that lack of cognitive and social input might have a blunting effect on neural and behavioral reward responsiveness across development, which may be caused by an absence of rewarding caregiver interactions in the early environment. It has previously been reported that reduced reward-related recruitment of the striatum mediates the relationship between emotional neglect and depressive symptoms in adolescence\u003csup\u003e42\u003c/sup\u003e. There is also good evidence indicating early institutionalization causes deficits of reward responsiveness and reward learning\u003csup\u003e23\u003c/sup\u003e. To our knowledge there are no studies which report a male-specific negative association of supervisory deprivation with reward responsiveness, although this finding aligns with literature reporting sex-specific effects of childhood neglect on mental health outcomes\u003csup\u003e43,44\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe relationship between early experiences of threat and reward processing is less studied than experiences of deprivation, and the evidence is conflicting. Some studies report a reward-blunted association with childhood threat\u003csup\u003e41,45\u003c/sup\u003e, whereas others report a sensitizing effect\u003csup\u003e46,47\u003c/sup\u003e. Disparate findings on the effects of threat and deprivation may reflect different timings of when adversity and reward responsiveness are measured. This is especially crucial given evidence that different types of adversity may have worse effects on mental health depending on the age of exposure\u003csup\u003e48,49\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity to aversive stimuli or loss following early exposure to threat is well-evidenced\u003csup\u003e50,51\u003c/sup\u003e, but threat-induced sensitivity to rewarding stimuli less so. It is understood that early threat exposure may impact development of top-down regulation of emotions by higher cognitive brain regions, and prime the brain for fear learning earlier in development than would be typical for a healthy child\u003csup\u003e52\u003c/sup\u003e. This maladapted balance between bottom-up affective response to the environment and top-down cognitive control of affective processes may explain increased reward responsiveness in adolescents exposed to threat. Additionally, life history theory, which examines developmental responses to adversity using an evolutionary lens, suggests that children exposed to early threat adopt specific traits as an adaptive response to optimize survival and reproduction during youth. Individuals with this adversity-adapted trajectory exhibit delay discounting, early maturation of specific neural processes and earlier sexual development\u003csup\u003e53,54\u003c/sup\u003e. Whilst vigilance and capacity to learn about threats in the environment is crucial for survival in an evolutionary context, a heightened response to immediate rewards in youth is also advantageous, as it would relate to gathering resources and reproductive success.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe hypothesized that childhood prosocial behavior and school positivity would buffer against the blunting effects of deprivation, proposing that meaningful school and peer relationships might compensate for poor caregiver relationships. Overall, we found limited evidence to support this hypothesis. This could be due to a lack of individuals in the sample who experience deprivation and exhibit prosocial behavior, especially given prior evidence that early deprivation negatively impacts the development of prosocial behavior\u003csup\u003e55\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also examined whether associations with deprivation adversity factors were consistent over age. The negative association with supervisory deprivation emerged later in adolescence, worsening with age, whereas the association with emotional deprivation was negative at the age of ELA exposure but attenuated. These two forms of early deprivation may have different effects on reward responsiveness over time, as social support networks change with age. During adolescence, peer relationships play an increasingly important role in providing support\u003csup\u003e56,57\u003c/sup\u003e, which could offset the impact of early emotional deprivation from parents. The age-dependent association with emotional deprivation on reward responsiveness might also explain why this relationship is not observed in cross-sectional samples of children at different ages\u003csup\u003e40,58\u003c/sup\u003e. In contrast, early supervisory deprivation is not easily supplemented by peer relationships. Further research is required to investigate how the effects of early deprivation vary across age and contribute to unique trajectories of reward responsiveness in adolescence.\u003c/p\u003e\n\u003cp\u003eWe observed significant associations between race-ethnicity and reward responsiveness but were unable to identify a potential mediator for these associations. Prior literature suggests this relationship may be mediated by socioeconomic disadvantage\u003csup\u003e59\u003c/sup\u003e or discrimination\u003csup\u003e37,38\u003c/sup\u003e, but the association remained after we controlled for these factors. Perceived racial discrimination, which may be considered a threat-related experience of adversity, was significantly associated with increased reward responsiveness, further supporting the reward-sensitizing influence of early threat experiences. Overall, it is likely that other factors mediate the relationship between race-ethnicity and reward responsiveness, and that ABCD\u0026rsquo;s discrimination measure does not fully capture the experience of discrimination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eStrengths and limitations\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA key strength of the present study is the operationalization of early life adversity using a rich set of baseline variables and factor analysis, allowing for specificity to investigate domain-specific associations. We also leveraged ABCD\u0026rsquo;s repeated measurement of neural and behavioral reward responsiveness measures, which allowed us to account for individual change in reward responsiveness with age.\u003c/p\u003e\n\u003cp\u003eThere were several key limitations of the present study. First, survey non-response may introduce selection bias. For example race-ethnicity and socioeconomic disadvantage are associated with missed ABCD study visits\u003csup\u003e60\u003c/sup\u003e, and those that were missing may have different results than those that responded. \u0026nbsp;Second, although the probability sampling method used by ABCD supports broad generalizability to U.S. school‑enrolled children\u003csup\u003e61\u003c/sup\u003e, the cohort is not fully nationally representative. In particular, children from rural areas are underrepresented and those outside the school system not included. Third, we could not account for all potential confounds, particularly genetics. It is plausible that a genetic liability for low reward responsiveness may predispose a parent towards traits related to deprivation, confounding the association of deprivation with low reward responsiveness. Third, although we included a task-based measure of neural reward responsiveness in addition to BIS/BAS, self-report measures of reward responsiveness are limited in that they require individuals to reflect on how they respond to hypothetical rewards. Subjective reflection on hypothetical situations may not be accurate.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe provide evidence that early co-occurring deprivation adversities are associated with blunted neural and behavioral reward responsiveness in early adolescence, and that early co-occurring threat adversities are associated with increased neural and behavioral reward responsiveness. Early prosocial behavior and positive school environment were also associated with increased reward responsiveness, but we found limited evidence of an interaction between these resilience domains and early deprivation. Age-adversity interactions suggest the effects of different forms of early deprivation on reward responsiveness are not equal. Future work might distinguish between threat-related and deprivation-related developmental reward processing trajectories and examine whether they precede specific profiles of reward dysfunction and psychiatric risk in adulthood. \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRS is funded by the ESRC and BBSRC as part of the Soc-B Centre for Doctoral Training in Biosocial Research. MP is funded by a Henry Dale Fellowship from the Wellcome Trust and the Royal Society (224243/Z/21/Z).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRE, MK and MP were supported by the NIHR Manchester Biomedical Research Centre (NIHR203308). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRS:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review and editing, Investigation, Project administration, Data curation, Formal analysis. RE: Conceptualization, Writing \u0026ndash; review and editing, Supervision. NM: Writing \u0026ndash; review and editing, Supervision. MK:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eVisualization, Writing \u0026ndash; review and editing. MP: Conceptualization, Methodology, Writing \u0026ndash; review and editing, Supervision.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMorris SE, Sanislow CA, Pacheco J, Vaidyanathan U, Gordon JA, Cuthbert BN. Revisiting the seven pillars of RDoC. \u003cem\u003eBMC Med\u003c/em\u003e 2022; \u003cstrong\u003e20\u003c/strong\u003e: 220.\u003c/li\u003e\n \u003cli\u003ePacheco J, Garvey MA, Sarampote CS, Cohen ED, Murphy ER, Friedman-Hill SR. Annual Research Review: The contributions of the RDoC research framework on understanding the neurodevelopmental origins, progression and treatment of mental illnesses. \u003cem\u003eJ Child Psychol Psychiatry\u003c/em\u003e 2022; \u003cstrong\u003e63\u003c/strong\u003e: 360\u0026ndash;376.\u003c/li\u003e\n \u003cli\u003ePizzagalli DA. Toward a Better Understanding of the Mechanisms and Pathophysiology of Anhedonia: Are We Ready for Translation? \u003cem\u003eAm J Psychiatry\u003c/em\u003e 2022; \u003cstrong\u003e179\u003c/strong\u003e: 458\u0026ndash;469.\u003c/li\u003e\n \u003cli\u003eSteffens DC, Fahed M, Manning KJ, Wang L. The neurobiology of apathy in depression and neurocognitive impairment in older adults: a review of epidemiological, clinical, neuropsychological and biological research. \u003cem\u003eTransl Psychiatry\u003c/em\u003e 2022; \u003cstrong\u003e12\u003c/strong\u003e: 525.\u003c/li\u003e\n \u003cli\u003ePlichta MM, Scheres A. Ventral\u0026ndash;striatal responsiveness during reward anticipation in ADHD and its relation to trait impulsivity in the healthy population: A meta-analytic review of the fMRI literature. \u003cem\u003eNeurosci Biobehav Rev\u003c/em\u003e 2014; \u003cstrong\u003e38\u003c/strong\u003e: 125\u0026ndash;134.\u003c/li\u003e\n \u003cli\u003eEvans GW, Li D, Whipple SS. Cumulative risk and child development. \u003cem\u003ePsychol Bull\u003c/em\u003e 2013; \u003cstrong\u003e139\u003c/strong\u003e: 1342\u0026ndash;1396.\u003c/li\u003e\n \u003cli\u003eHashemi L, Fanslow J, Gulliver P, McIntosh T. Exploring the health burden of cumulative and specific adverse childhood experiences in New Zealand: Results from a population-based study. \u003cem\u003eChild Abuse Negl\u003c/em\u003e 2021; \u003cstrong\u003e122\u003c/strong\u003e: 105372.\u003c/li\u003e\n \u003cli\u003eBaldwin JR, Wang B, Karwatowska L, Schoeler T, Tsaligopoulou A, Munaf\u0026ograve; MR \u003cem\u003eet al.\u003c/em\u003e Childhood maltreatment and mental health problems: A systematic review and meta-analysis of quasi-experimental studies.\u0026nbsp;\u003cem\u003eAm J Psychiatry\u003c/em\u003e 2023; \u003cstrong\u003e180\u003c/strong\u003e: 117\u0026ndash;126.\u003c/li\u003e\n \u003cli\u003eFurtado EJ, Camacho MC, Chin JH, Barch DM. Complex emotion processing and early life adversity in the Healthy Brain Network sample. \u003cem\u003eDev Cogn Neurosci\u003c/em\u003e 2024; \u003cstrong\u003e70\u003c/strong\u003e: 101469.\u003c/li\u003e\n \u003cli\u003eSamaey C, Lecei A, Jackers M, Jennen L, Schruers K, Vervliet B \u003cem\u003eet al.\u003c/em\u003e Childhood adversity is associated with reduced threat-safety discrimination and increased fear generalization in 12- to 16-year-olds. \u003cem\u003eJ Child Psychol Psychiatry\u003c/em\u003e 2025; \u003cstrong\u003e66\u003c/strong\u003e: 821\u0026ndash;833.\u003c/li\u003e\n \u003cli\u003eMcLaughlin KA, Sheridan MA, Lambert HK. Childhood Adversity and Neural Development: Deprivation and Threat as Distinct Dimensions of Early Experience. \u003cem\u003eNeurosci Biobehav Rev\u003c/em\u003e 2014; \u003cstrong\u003e47\u003c/strong\u003e: 578\u0026ndash;591.\u003c/li\u003e\n \u003cli\u003eMcLaughlin KA, Weissman D, Bitr\u0026aacute;n D. Childhood Adversity and Neural Development: A Systematic Review. \u003cem\u003eAnnu Rev Dev Psychol\u003c/em\u003e 2019; \u003cstrong\u003e1\u003c/strong\u003e: 277\u0026ndash;312.\u003c/li\u003e\n \u003cli\u003eMcLaughlin KA, Peverill M, Gold AL, Alves S, Sheridan MA. Child Maltreatment and Neural Systems Underlying Emotion Regulation. \u003cem\u003eJ Am Acad Child Adolesc Psychiatry\u003c/em\u003e 2015; \u003cstrong\u003e54\u003c/strong\u003e: 753\u0026ndash;762.\u003c/li\u003e\n \u003cli\u003eSheridan MA, McLaughlin KA. Dimensions of Early Experience and Neural Development: Deprivation and Threat. \u003cem\u003eTrends Cogn Sci\u003c/em\u003e 2014; \u003cstrong\u003e18\u003c/strong\u003e: 580\u0026ndash;585.\u003c/li\u003e\n \u003cli\u003eBrieant A, Vannucci A, Nakua H, Harris J, Lovell J, Brundavanam D \u003cem\u003eet al.\u003c/em\u003e Characterizing the dimensional structure of early-life adversity in the Adolescent Brain Cognitive Development (ABCD) Study. \u003cem\u003eDev Cogn Neurosci\u003c/em\u003e 2023; \u003cstrong\u003e61\u003c/strong\u003e: 101256.\u003c/li\u003e\n \u003cli\u003eBeck D, Whitmore L, MacSweeney N, Brieant A, Karl V, de Lange A-MG \u003cem\u003eet al.\u003c/em\u003e Dimensions of Early-Life Adversity Are Differentially Associated With Patterns of Delayed and Accelerated Brain Maturation. \u003cem\u003eBiol Psychiatry\u003c/em\u003e 2025; \u003cstrong\u003e97\u003c/strong\u003e: 64\u0026ndash;72.\u003c/li\u003e\n \u003cli\u003eDavydov DM, Stewart R, Ritchie K, Chaudieu I. Resilience and mental health. \u003cem\u003eClin Psychol Rev\u003c/em\u003e 2010; \u003cstrong\u003e30\u003c/strong\u003e: 479\u0026ndash;495.\u003c/li\u003e\n \u003cli\u003eNoltemeyer AL, Bush KR. Adversity and resilience: A synthesis of international research. \u003cem\u003eSch Psychol Int\u003c/em\u003e 2013; \u003cstrong\u003e34\u003c/strong\u003e: 474\u0026ndash;487.\u003c/li\u003e\n \u003cli\u003eGoetschius LG, McLoyd VC, Hein TC, Mitchell C, Hyde LW, Monk CS. School Connectedness as a Protective Factor Against Childhood Exposure to Violence and Social Deprivation: A Longitudinal Study of Adaptive and Maladaptive Outcomes. \u003cem\u003eDev Psychopathol\u003c/em\u003e 2023; \u003cstrong\u003e35\u003c/strong\u003e: 1219\u0026ndash;1234.\u003c/li\u003e\n \u003cli\u003eHaroz EE, Murray LK, Bolton P, Betancourt T, Bass JK. Adolescent Resilience in Northern Uganda: The Role of Social Support and Prosocial Behavior in Reducing Mental Health Problems. \u003cem\u003eJ Res Adolesc\u003c/em\u003e 2013; \u003cstrong\u003e23\u003c/strong\u003e: 138\u0026ndash;148.\u003c/li\u003e\n \u003cli\u003eLuo Y, Ma T, Deng Y. School climate and adolescents\u0026rsquo; prosocial behavior: the mediating role of perceived social support and resilience. \u003cem\u003eFront Psychol\u003c/em\u003e 2023; \u003cstrong\u003e14\u003c/strong\u003e. doi:10.3389/fpsyg.2023.1095566.\u003c/li\u003e\n \u003cli\u003eGunnar MR, Bowen M. What Was Learned From Studying the Effects of Early Institutional Deprivation. \u003cem\u003ePharmacol Biochem Behav\u003c/em\u003e 2021; \u003cstrong\u003e210\u003c/strong\u003e: 173272.\u003c/li\u003e\n \u003cli\u003eSheridan MA, McLaughlin KA, Winter W, Fox N, Zeanah C, Nelson CA. Early deprivation disruption of associative learning is a developmental pathway to depression and social problems. \u003cem\u003eNat Commun\u003c/em\u003e 2018; \u003cstrong\u003e9\u003c/strong\u003e: 2216.\u003c/li\u003e\n \u003cli\u003eCasey BJ, Cannonier T, Conley MI, Cohen AO, Barch DM, Heitzeg MM \u003cem\u003eet al.\u003c/em\u003e The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. \u003cem\u003eDev Cogn Neurosci\u003c/em\u003e 2018; \u003cstrong\u003e32\u003c/strong\u003e: 43\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003eAuchter AM, Hernandez Mejia M, Heyser CJ, Shilling PD, Jernigan TL, Brown SA \u003cem\u003eet al.\u003c/em\u003e A description of the ABCD organizational structure and communication framework. \u003cem\u003eDev Cogn Neurosci\u003c/em\u003e 2018; \u003cstrong\u003e32\u003c/strong\u003e: 8\u0026ndash;15.\u003c/li\u003e\n \u003cli\u003eGoodman R. The Strengths and Difficulties Questionnaire: a research note. \u003cem\u003eJ Child Psychol Psychiatry\u003c/em\u003e 1997; \u003cstrong\u003e38\u003c/strong\u003e: 581\u0026ndash;586.\u003c/li\u003e\n \u003cli\u003eArthur MW, Briney JS, Hawkins JD, Abbott RD, Brooke-Weiss BL, Catalano RF. Measuring risk and protection in communities using the Communities That Care Youth Survey. \u003cem\u003eEval Program Plann\u003c/em\u003e 2007; \u003cstrong\u003e30\u003c/strong\u003e: 197\u0026ndash;211.\u003c/li\u003e\n \u003cli\u003eCarver CS, White TL. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS Scales. \u003cem\u003eJ Pers Soc Psychol\u003c/em\u003e 1994; \u003cstrong\u003e67\u003c/strong\u003e: 319\u0026ndash;333.\u003c/li\u003e\n \u003cli\u003ePagliaccio D, Luking KR, Anokhin AP, Gotlib IH, Hayden EP, Olino TM \u003cem\u003eet al.\u003c/em\u003e Revising the BIS/BAS to Study Development: Measurement Invariance and Normative Effects of Age and Sex from Childhood through Adulthood. \u003cem\u003ePsychol Assess\u003c/em\u003e 2016; \u003cstrong\u003e28\u003c/strong\u003e: 429\u0026ndash;442.\u003c/li\u003e\n \u003cli\u003eHagler DJ, Hatton S, Cornejo MD, Makowski C, Fair DA, Dick AS \u003cem\u003eet al.\u003c/em\u003e Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. \u003cem\u003eNeuroImage\u003c/em\u003e 2019; \u003cstrong\u003e202\u003c/strong\u003e: 116091.\u003c/li\u003e\n \u003cli\u003eAlarc\u0026oacute;n G, Cservenka A, Nagel BJ. Adolescent neural response to reward is related to participant sex and task motivation. \u003cem\u003eBrain Cogn\u003c/em\u003e 2017; \u003cstrong\u003e111\u003c/strong\u003e: 51\u0026ndash;62.\u003c/li\u003e\n \u003cli\u003eBarendse MEA, Swartz JR, Taylor SL, Fine JR, Shirtcliff EA, Yoon L \u003cem\u003eet al.\u003c/em\u003e Sex and pubertal variation in reward-related behavior and neural activation in early adolescents. \u003cem\u003eDev Cogn Neurosci\u003c/em\u003e 2024; \u003cstrong\u003e66\u003c/strong\u003e: 101358.\u003c/li\u003e\n \u003cli\u003eJorgensen NA, Muscatell KA, McCormick EM, Prinstein MJ, Lindquist KA, Telzer EH. Neighborhood disadvantage, race/ethnicity and neural sensitivity to social threat and reward among adolescents. \u003cem\u003eSoc Cogn Affect Neurosci\u003c/em\u003e 2023; \u003cstrong\u003e18\u003c/strong\u003e: nsac053.\u003c/li\u003e\n \u003cli\u003eRubin DB. \u003cem\u003eMultiple Imputation for Nonresponse in Surveys.\u003c/em\u003e John Wiley \u0026amp; Sons Inc.: New York, NY, US, 1987.\u003c/li\u003e\n \u003cli\u003ePetersen AC, Crockett L, Richards M, Boxer A. A self-report measure of pubertal status: Reliability, validity, and initial norms. \u003cem\u003eJ Youth Adolesc\u003c/em\u003e 1988; \u003cstrong\u003e17\u003c/strong\u003e: 117\u0026ndash;133.\u003c/li\u003e\n \u003cli\u003ePhinney JS, Madden T, Santos LJ. Psychological Variables as Predictors of Perceived Ethnic Discrimination Among Minority and Immigrant Adolescents. \u003cem\u003eJ Appl Soc Psychol\u003c/em\u003e 1998; \u003cstrong\u003e28\u003c/strong\u003e: 937\u0026ndash;953.\u003c/li\u003e\n \u003cli\u003eVargas TG, Mittal VA. Testing whether implicit emotion regulation mediates the association between discrimination and symptoms of psychopathology in late childhood: An RDoC perspective. \u003cem\u003eDev Psychopathol\u003c/em\u003e 2021; \u003cstrong\u003e33\u003c/strong\u003e: 1634\u0026ndash;1647.\u003c/li\u003e\n \u003cli\u003eWang H, Braun C, Enck P. How the brain reacts to social stress (exclusion) - A scoping review. \u003cem\u003eNeurosci Biobehav Rev\u003c/em\u003e 2017; \u003cstrong\u003e80\u003c/strong\u003e: 80\u0026ndash;88.\u003c/li\u003e\n \u003cli\u003eHanson JL, Hariri AR, Williamson DE. Blunted Ventral Striatum Development in Adolescence Reflects Emotional Neglect and Predicts Depressive Symptoms. \u003cem\u003eBiol Psychiatry\u003c/em\u003e 2015; \u003cstrong\u003e78\u003c/strong\u003e: 598\u0026ndash;605.\u003c/li\u003e\n \u003cli\u003eYang R, Yu Q, Owen CE, Ibarra Aspe G, Wiggins JL. Contributions of childhood abuse and neglect to reward neural substrates in adolescence. \u003cem\u003eNeuroImage Clin\u003c/em\u003e 2021; \u003cstrong\u003e32\u003c/strong\u003e: 102832.\u003c/li\u003e\n \u003cli\u003eKasparek SW, Gast\u0026oacute;n-Panthaki A, Hanford LC, Lengua LJ, Sheridan MA, McLaughlin KA. Does reward processing moderate or mediate the link between childhood adversity and psychopathology: A longitudinal study. \u003cem\u003eDev Psychopathol\u003c/em\u003e 2023; \u003cstrong\u003e35\u003c/strong\u003e: 2338\u0026ndash;2351.\u003c/li\u003e\n \u003cli\u003eHanson JL, Hariri AR, Williamson DE. Blunted Ventral Striatum Development in Adolescence Reflects Emotional Neglect and Predicts Depressive Symptoms. \u003cem\u003eBiol Psychiatry\u003c/em\u003e 2015; \u003cstrong\u003e78\u003c/strong\u003e: 598\u0026ndash;605.\u003c/li\u003e\n \u003cli\u003eDong C, Wang Z, Jia F, Tian H, Zhang Y, Liu H \u003cem\u003eet al.\u003c/em\u003e Gender differences in the association between childhood maltreatment and the onset of major depressive disorder. \u003cem\u003eJ Affect Disord\u003c/em\u003e 2024; \u003cstrong\u003e351\u003c/strong\u003e: 111\u0026ndash;119.\u003c/li\u003e\n \u003cli\u003eErnst M, Tibubos AN, Werner A, Beutel ME, Plener PL, Fegert JM \u003cem\u003eet al.\u003c/em\u003e Sex-dependent associations of childhood neglect and bodyweight across the life span. \u003cem\u003eSci Rep\u003c/em\u003e 2019; \u003cstrong\u003e9\u003c/strong\u003e: 5080.\u003c/li\u003e\n \u003cli\u003eMiu AC, B\u0026icirc;lc MI, Bunea I, Szent\u0026aacute;gotai-Tătar A. Childhood trauma and sensitivity to reward and punishment: Implications for depressive and anxiety symptoms. \u003cem\u003ePersonal Individ Differ\u003c/em\u003e 2017; \u003cstrong\u003e119\u003c/strong\u003e: 134\u0026ndash;140.\u003c/li\u003e\n \u003cli\u003eLetkiewicz AM, Suor JH, Glazer JE, Li LY, Bernat EM, Burkhouse KL \u003cem\u003eet al.\u003c/em\u003e Severe Sexual Abuse in Childhood and Altered Neurophysiological Response to Reward in Female Adults. \u003cem\u003eChild Abuse Negl\u003c/em\u003e 2024; \u003cstrong\u003e154\u003c/strong\u003e: 106945.\u003c/li\u003e\n \u003cli\u003eHendrikse CJ, du Plessis S, Luckhoff HK, Vink M, van den Heuvel LL, Scheffler F \u003cem\u003eet al.\u003c/em\u003e Childhood trauma exposure and reward processing in healthy adults: A functional neuroimaging study.\u0026nbsp;\u003cem\u003eJ Neurosci Res\u003c/em\u003e 2022; \u003cstrong\u003e100\u003c/strong\u003e: 1452\u0026ndash;1462.\u003c/li\u003e\n \u003cli\u003eLee JO, Duan L, Constantino-Pettit A, Yoon Y, Oxford ML, Rose J \u003cem\u003eet al.\u003c/em\u003e Does the timing matter? The association between childhood adversity and internalizing and externalizing problems from childhood to adolescence and its sex differences. \u003cem\u003eChild Abuse Negl\u003c/em\u003e 2025; \u003cstrong\u003e163\u003c/strong\u003e: 107437.\u003c/li\u003e\n \u003cli\u003eHardi FA, Peckins MK, Mitchell C, McLoyd V, Brooks-Gunn J, Hyde LW \u003cem\u003eet al.\u003c/em\u003e Childhood adversity and adolescent mental health: Examining cumulative and specificity effects across contexts and developmental timing. \u003cem\u003eDev Psychopathol\u003c/em\u003e 2025; \u003cstrong\u003e37\u003c/strong\u003e: 1954\u0026ndash;1970.\u003c/li\u003e\n \u003cli\u003eBirn RM, Roeber BJ, Pollak SD. Early childhood stress exposure, reward pathways, and adult decision making. \u003cem\u003eProc Natl Acad Sci\u003c/em\u003e 2017; \u003cstrong\u003e114\u003c/strong\u003e: 13549\u0026ndash;13554.\u003c/li\u003e\n \u003cli\u003eKrugers HJ, Arp JM, Xiong H, Kanatsou S, Lesuis SL, Korosi A \u003cem\u003eet al.\u003c/em\u003e Early life adversity: Lasting consequences for emotional learning. \u003cem\u003eNeurobiol Stress\u003c/em\u003e 2016; \u003cstrong\u003e6\u003c/strong\u003e: 14\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eMachlin L, Miller AB, Snyder J, McLaughlin KA, Sheridan MA. Differential Associations of Deprivation and Threat With Cognitive Control and Fear Conditioning in Early Childhood. \u003cem\u003eFront Behav Neurosci\u003c/em\u003e 2019; \u003cstrong\u003e13\u003c/strong\u003e: 80.\u003c/li\u003e\n \u003cli\u003eEllis BJ, Figueredo AJ, Brumbach BH, Schlomer GL. Fundamental Dimensions of Environmental Risk : The Impact of Harsh versus Unpredictable Environments on the Evolution and Development of Life History Strategies. \u003cem\u003eHum Nat Hawthorne N\u003c/em\u003e 2009; \u003cstrong\u003e20\u003c/strong\u003e: 204\u0026ndash;268.\u003c/li\u003e\n \u003cli\u003eYuan J, Yu Y, Liu D, Sun Y. Associations between distinct dimensions of early life adversity and accelerated reproductive strategy among middle-aged women in China. \u003cem\u003eAm J Obstet Gynecol\u003c/em\u003e 2022; \u003cstrong\u003e226\u003c/strong\u003e: 104.e1-104.e14.\u003c/li\u003e\n \u003cli\u003eChen P, Zhang Q, Sun X, Ye X, Wang Y, Yang X. How do childhood abuse and neglect affect prosocial behavior? The mediating roles of different empathic components. \u003cem\u003eFront Psychol\u003c/em\u003e 2023; \u003cstrong\u003e13\u003c/strong\u003e: 1051258.\u003c/li\u003e\n \u003cli\u003edel Voile JF, Bravo A, L\u0026oacute;pez M. Parents and peers as providers of support in adolescents\u0026rsquo; social network: A developmental perspective. \u003cem\u003eJ Community Psychol\u003c/em\u003e 2010; \u003cstrong\u003e38\u003c/strong\u003e: 16\u0026ndash;27.\u003c/li\u003e\n \u003cli\u003eWentzel KR. Social relationships and motivation in middle school: The role of parents, teachers, and peers. \u003cem\u003eJ Educ Psychol\u003c/em\u003e 1998; \u003cstrong\u003e90\u003c/strong\u003e: 202\u0026ndash;209.\u003c/li\u003e\n \u003cli\u003eDennison MJ, Rosen ML, Sambrook KA, Jenness JL, Sheridan MA, McLaughlin KA. Differential associations of distinct forms of childhood adversity with neurobehavioral measures of reward processing: A developmental pathway to depression. \u003cem\u003eChild Dev\u003c/em\u003e 2019; \u003cstrong\u003e90\u003c/strong\u003e: e96\u0026ndash;e113.\u003c/li\u003e\n \u003cli\u003eWhite SF, Nusslock R, Miller GE. Low Socioeconomic Status Is Associated with a Greater Neural Response to Both Rewards and Losses. \u003cem\u003eJ Cogn Neurosci\u003c/em\u003e 2022; \u003cstrong\u003e34\u003c/strong\u003e: 1939\u0026ndash;1951.\u003c/li\u003e\n \u003cli\u003eFeldstein Ewing SW, Dash GF, Thompson WK, Reuter C, Diaz VG, Anokhin A \u003cem\u003eet al.\u003c/em\u003e Measuring retention within the adolescent brain cognitive development (ABCD)SM study. \u003cem\u003eDev Cogn Neurosci\u003c/em\u003e 2022; \u003cstrong\u003e54\u003c/strong\u003e: 101081.\u003c/li\u003e\n \u003cli\u003eGaravan H, Bartsch H, Conway K, Decastro A, Goldstein RZ, Heeringa S \u003cem\u003eet al.\u003c/em\u003e Recruiting the ABCD sample: Design considerations and procedures. \u003cem\u003eDev Cogn Neurosci\u003c/em\u003e 2018; \u003cstrong\u003e32\u003c/strong\u003e: 16\u0026ndash;22.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8414265/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8414265/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Blunted and maladapted reward responsiveness is a central component of several psychiatric disorders. Identifying how childhood adversity and resilience shape trajectories of reward responsiveness in adolescence is a key step towards understanding why transdiagnostic symptoms such as anhedonia, apathy and impulsivity emerge in young people. Using 3 timepoints from the Adolescent Brain Cognitive Development (ABCD) study (n=11868), we used longitudinal mixed effects models to examine which of 10 adversity factors at age 9/10 were associated with BIS/BAS Reward Responsiveness across ages 9-14, and whether potential resilience factors modify this relationship. To investigate correlations of neural reward responsiveness, we fit similar models on striatal activation measures from an fMRI monetary incentive delay task. We also examined whether the association of adversity with behavioral reward responsiveness was consistent over age. Behavioral reward responsiveness was negatively associated with emotional deprivation from primary (β=-0.076, p\u003c0.001) and secondary caregiver (β=-0.205, p\u003c0.001) and supervisory deprivation (β=-0.103, p\u003c0.001); and positively associated with youth reported threat (β=0.167, p\u003c0.001). Supervisory deprivation was also negatively associated with anticipatory striatal response (β=-0.010, p\u003c0.05), and parent reported threat positively associated (β=0.008, p\u003c0.01). There was a significant interaction between emotional deprivation and age (p\u003c0.001), indicating an association that attenuates with increasing age, and with supervisory deprivation (p\u003c0.001) indicating a negative association that strengthens with increasing age. Overall, early deprivation was associated with reduced reward responsiveness in early adolescence and threat with increased responsiveness. The relationship between deprivation and reward responsiveness varied across age and sex.","manuscriptTitle":"Neural and behavioral reward responsiveness in early adolescence: longitudinal associations with childhood adversity and resilience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 19:46:03","doi":"10.21203/rs.3.rs-8414265/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-28T19:01:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-04T15:47:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-04T15:41:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2025-12-20T21:03:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3385aada-de8e-4b0c-be50-9fc1b14ff63d","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61921948,"name":"Biological sciences/Psychology/Human behaviour"},{"id":61921949,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-01-30T19:46:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 19:46:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8414265","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8414265","identity":"rs-8414265","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}