Individual Differences in the Effects of Neighborhood Socioeconomic Deprivation on Intertemporal Decision-Making and Psychotic-Like Experiences in Children

Individual Differences in the Effects of Neighborhood Socioeconomic Deprivation on Intertemporal Decision-Making and Psychotic-Like Experiences in Children | 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 Individual Differences in the Effects of Neighborhood Socioeconomic Deprivation on Intertemporal Decision-Making and Psychotic-Like Experiences in Children Jiook Cha, Junghoon Park, Minje Cho, Eunji Lee, Bo-Gyeom Kim, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4618474/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract This study elucidates the influence of socioeconomic environments on neurodevelopment and psychiatric vulnerability in children. Employing advanced machine learning-based causal inference (IV Forest), we analyzed the impact of neighborhood socioeconomic deprivation on delay discounting and psychotic-like experiences (PLEs) among 2,135 children. Our findings reveal that greater neighborhood deprivation correlates with increased future reward discounting and elevated PLEs, particularly hallucinational symptoms, over 1-year and 2-year follow-ups. Vulnerable children in these settings exhibited notable neuroanatomical changes, including reduced limbic volume, surface area, and white matter, and heightened BOLD reactivity in the prefrontal-limbic system during reward tasks. These findings highlight the complex interplay between environmental factors and brain reward mechanisms in shaping PLE risk, advocating for early, targeted interventions in socioeconomically disadvantaged communities. This research not only extends our understanding of environmental influences on child psychology but also guides personalized intervention strategies and prompts reflection on broader societal impacts. Biological sciences/Neuroscience/Social neuroscience Biological sciences/Neuroscience/Cognitive neuroscience/Decision Health sciences/Diseases/Psychiatric disorders/Psychosis Intertemporal reward valuation Psychotic-like experiences Causal machine learning Childhood socioeconomic environment Heterogeneous treatment effects Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In Critique of Practical Reason , Immanuel Kant champions the inherent power of human reason, suggesting that it is an a priori capacity independent of external factors, enabling individuals to engage in responsible actions 1 . Nevertheless, a wealth of scientific studies in recent decades stands in opposition to the Enlightenment philosopher's claims, highlighting the significant impact of environmental factors on the development of personal identity and behavior. Adverse childhood environments, such as low family income, malnutrition, physical or sexual abuse, and unsafe neighborhoods, are linked to an heightened risk of various mental or physical health issues, including psychosis 2–4 , impoverished cognitive ability 5–7 , anxiety, bipolar disorder, self-harm, depression 3,4,8 , substance abuse, and obesity 9,10 . Furthermore, these environments are associated with negative social outcomes, such as poor academic performance 11,12 , low income, unemployment 13–18 , higher rates of imprisonment, and increased likelihood of teen pregnancy 19 . Additionally, exposure to these adverse conditions in childhood is associated with a propensity for engaging in risky behaviors, including criminal activity 20 , excessive consumption of calorie-dense foods 21 , substance use 22,23 , deficient self-control 24 , and disrupted reward processing 25 . The intricate relationship between challenging childhood environments, irresponsible behavior, and adverse social and health outcomes raises important questions. We hypothesized that childhood adversity causes impairment in one’s valuation system, leading to negative life outcomes. Children who experienced social adversities such as poverty show steeper discounting of future rewards in adulthood and have higher psychotic-like experiences (PLEs) 2,3,26–28 . Lower socioeconomic status positively correlates with functional brain activity concordance and grey matter volume within reward-related areas (i.e., ventral striatum, putamen, caudate nucleus, orbital frontal cortex) and negatively with executive-related areas (i.e., frontal, medial frontal cortex) 29 . A recent study reported that neuroanatomical features including total cortical volume, surface area, and thickness mediates the association of environmental risk factors and PLEs in children 3 . In addition, individuals with steeper discounting of future rewards (i.e., value present rewards much higher than future rewards) tend to display a range of suboptimal behavior and outcomes. They are more likely to save less, invest less in their education, engage in criminal activities, exhibit lower academic performance, and accumulate less economic wealth 30–33 . Such impairments in intertemporal valuation are not only associated with financial and social disadvantages but are also linked to psychiatric disorders, including psychosis, attention deficit/hyperactivity disorder (ADHD), and addiction 34,35 . Psychosis, in particular, is associated with distinctive alterations in intertemporal decision-making, characterized by steeper discounting of future rewards 36–38 . This cognitive pattern in individuals with psychosis may be reflected in abnormal neural responses to non-relevant rewards, potentially driven by elevated levels of tonic dopamine 34,39–41 . Comparative studies have demonstrated that individuals with psychosis discount future rewards more steeply than healthy controls 36,42,43 , a pattern that is uniquely pronounced in psychosis compared to other psychopathologies such as primary mood disorders 42 , major depressive disorder 42 , and bipolar disorder 43 . The disruption in dopaminergic projections from the ventral tegmental area to the mesocorticolimbic regions is hypothesized to impair reward anticipation and perception processes 39,40 , potentially contributing to the phenomenology of delusions or hallucinations. In the present study, our primary objective was to investigate the impact of neighborhood socioeconomic deprivation on adolescents' delay discounting and PLEs. Delay discounting, which is evidenced by the extent to which individuals’ discount future rewards, pertains to their intertemporal decision-making and impulsive behavior. Exposure to adversities at the neighborhood level during childhood has been shown to negatively influence neurocognitive development 7,44,45 , subsequently resulting in psychiatric disorders 2,3,28 and unfavorable social outcomes, such as decreased income, reduced probability of college attendance, and limited employment opportunities 17 . This phenomenon is particularly pronounced in societies where discrimination based on family income or race/ethnicity restricts underprivileged families from selecting neighborhoods that present greater opportunities for upward social mobility, as observed in the United States 17 . It is crucial to note that PLEs, frequently reported in children, are considered as a clinically significant risk indicator for psychosis and general psychopathology 46,47 . Around 17% of 9–12 years old children report PLEs 48 , and individuals with PLEs at age 11 had greater risk of developing psychotic disorders in adulthood 49,50 . Prior studies revealed that PLEs are correlated to heightened vulnerability to other psychopathologies including suicidal behavior 2 , mood, anxiety, and substance disorders 46,48 , and exhibit the strongest association with environmental risk factors in comparison to other internalizing/externalizing symptoms during early adolescence 3 . The present study endeavors to explore the potential causal mechanisms underlying these associations. Our second aim was to test whether the potential causal effects of neighborhood deprivation on children’s PLEs are heterogeneous based on individual’s delay discounting and its genetic, neural correlates. The heterogeneous nature of psychopathology has long posed significant challenges for clinical diagnosis and treatment 51,52 . Given that the genetic and neural correlates of delay discounting substantially overlap with those of psychosis 40,41,53,54 , the shared biological foundations between reward valuation and psychosis may result in heterogeneous effects of environmental exposure on an individual's PLEs. By investigating these potential variations, this study seeks to enhance the understanding of the complex interplay between environmental factors and individual predispositions in the development of psychopathology. Identifying individual differences of treatment/exposure is crucial for the development of personalized health care. Delivering optimal health care for each patient necessitates the recognition of genetic markers, neurodevelopmental characteristics, and sociodemographic features associated with individual variations in treatment effects 55,56 . However, previous studies employing traditional methods of testing the individual differences in treatment effects have often been unsuccessful in discerning the intricate interplay between genetic and environmental factors 57,58 . Linear models with interaction terms of features selected a priori by the researcher may not fully reflect the complex and elusive gene-environment interplay, particularly in genetic and neuroscience research where the input features are usually high dimensional. Using instrumental variable (IV) random forests (henceforth IV Forest), an up-to-date causal machine learning approach 59,60 , we assessed the effects of neighborhood socioeconomic adversity on delay discounting and PLEs, and the potential individual differences within those effects. We leveraged multimodal magnetic resonance imaging (MRI) data from 11,876 preadolescent children aged 9 to 12 years old (the Adolescent Brain Cognitive Development (ABCD) Study). Integration of innovative analytical techniques and a large sample with diverse genetic and environmental backgrounds permits us to test the complex interactions between genetic and environmental factors, ultimately contributing to the development of more effective personalized health care strategies. Results The demographic characteristics of the final sample (N = 2,135) are presented in Table 1 . Within the sample, 46.14% were female, 76.63% of participants had married parents, the mean family income was $ 70,245, and 65.57% identified their race/ethnicity as white. To ensure the representativeness of the final sample, a supplementary table comparing the sample's demographic characteristics with those of the general United States population is provided in the Supplementary Information ( Supplementary Table 1 ). This comparison serves to reinforce the validity and generalizability of the study's findings. Table 1 Socioeconomic/demographic characteristics of the participants. Age is rounded to chronological month. Race/Ethnicity denote child’s self-reported racial / ethnic identity. Household Income is assessed as the total combined family income for the past 12 months. Parental Education is measured as the highest grade or level of school completed or highest degree received. Family History of Psychiatric Disorders represents the proportion of first-degree relatives who experienced mental illness. Demographic Characteristics N Ratio (%) Mean (SD) Age 2,135 120.1541 (7.4658) Sex Male 1,517 53.86% Female 985 46.14% Marital Status of the first caregiver Married 1,636 76.63% Widowed 12 0.56% Divorced 193 9.04% Separated 62 2.9% Never Married 142 6.65% Living with Partner 90 4.22% Race/Ethnicity White 1,400 65.57% Black 136 6.37% Hispanic 373 17.47% Asian 7 0.33% Other 219 10.26% Parent’s Identity Biological Mother 1,848 86.56% Biological Father 215 10.07% Adoptive Parent 39 1.83% Custodial Parent 12 0.56% Other 21 0.98% Household Income 2,135 $ 70,245 (1.937) Parental Education 2,135 17.2838 (2.3046) BMI 2,135 18.4298 (3.8572) Parental Age 2,135 40.8775 (6.3825) Family History of Psychiatric Disorders 2,135 0.0958 (0.1125) In our initial exploratory analysis, partial correlations were used to examine the relationship between psychopathological symptoms and delay discounting. Among the symptoms assessed (e.g., depression, anxiety, ADHD), only PLEs showed significant correlation with delay discounting (Spearman ρ= -0.067, p-FDR = 0.024 ~ ρ= -0.057, p-FDR = 0.035) ( Supplementary Table 2 ). This finding underscores the unique association between PLEs and delay discounting, laying the groundwork for subsequent investigations into how delay discounting—along with its genetic and neural correlates—may be associated with the heterogeneous effects of neighborhood socioeconomic adversity on PLEs. Given the non-randomized, observational nature of the ABCD Study, potential confounding factors, such as genetic, environmental variables, and their unobserved common causes, can lead to biased estimations 61 . A powerful and effective way to adjust such bias is the IV regression. Designed to address bias from unobserved confounders, IV regression is an effective method to conduct causal inference using non-randomized, observational data for research in various domains, including economics 62,63 , psychology 64 , neuroscience 65 , and psychiatry 66 . In this study, the instrumental variable used was the presence of state-level source of income (SOI) laws at baseline year assessment, which prevent income discrimination in housing. These laws ensure that landlords accept housing vouchers, aiding low-income families in securing quality housing. As such, SOI laws are critical in enabling better residential environments by mitigating neighborhood socioeconomic adversity, thereby serving as an effective instrument for assessing the potential causal effects of neighborhood socioeconomic adversity (measured with Area Deprivation Index , henceforth ADI), on delay discounting and PLEs (see Fig. 1 A). We used IV Forest 59,60 —a random forest-based IV regression 62 —to adjust for unobserved confounding bias in identifying the potential causal effects of ADI on delay discounting and PLEs. The IV Forest method enabled us to derive nonparametric, doubly robust estimates of the average (group-level) and heterogeneous (individual-level) treatment effects of ADI on these outcomes (see Fig. 1 B). This method is noted for delivering estimates with significantly lower mean-squared error compared to conventional k-nearest neighbor methods 59,60 . Furthermore, its use of independent subsamples for model construction and validation ensures honest, overfitting-resistant estimates of average and heterogeneous treatment effects 59,60,67 . Notably, this method is particularly useful for analyzing the complex, nonlinear interactions between genetic and environmental factors and their effects on neurocognitive development and PLEs, even within the confines of observational data 59 . Figure 2 presents the analytical framework of our study, examining the effects of neighborhood socioeconomic adversity on children's decision-making and mental health. ADI, recorded in the baseline year, serves as an indicator of this socioeconomic adversity. We assessed the impact of ADI on children’s intertemporal decision-making through delay discounting at a 1-year follow-up. PLEs, encompassing distress, delusional, and hallucinational symptoms, were evaluated at both 1-year and 2-year follow-ups. Our analysis spans multiple follow-up periods and PLE indicators to investigate the sustained influence of ADI over time and to explore differential effects on various PLE symptoms, particularly delusional versus hallucinational. Average Treatment Effects of Neighborhood Socioeconomic Adversity on Delay Discounting and PLEs IV Forest analyses revealed that a higher ADI has significant associations with a lower delay discounting (β= -1.73, p-FDR = 0.048) and a higher PLEs (distress score 1-year follow-up: β = 1.872, p-FDR = 0.048; distress score 2-year follow-up: β = 1.504, p-FDR = 0.039; delusional score 1-year follow-up: β = 5.97, p-FDR = 0.048; delusional score 2-year follow-up: β = 4.022, p-FDR = 0.048; hallucinational score 1-year follow-up: β = 3.761, p-FDR = 0.048; hallucinational score 2-year follow-up: β = 4.786, p-FDR = 0.039) (Table 2 ). Table 2 Potential causal effects of neighborhood socioeconomic adversity on intertemporal valuation and PLEs. Average treatment effects of ADI on delay discounting and PLEs in the IV Forest models are shown. All p-values were corrected for multiple comparison using false discovery rate. IV Forests: Average Treatment Effects Estimates Std. Error 95% Lower CI 95% Upper CI P-FDR Delay Discounting (1-year follow-up) -1.730 0.748 -3.195 -0.265 0.048 Distress Score PLEs (1-year follow-up) 1.872 0.612 0.673 3.071 0.048 Distress Score PLEs (2-year follow-up) 1.504 0.592 0.345 2.664 0.039 Delusional Score PLEs (1-year follow-up) 5.970 2.911 0.264 11.676 0.048 Delusional Score PLEs (2-year follow-up) 4.022 1.987 0.127 7.917 0.048 Hallucinational Score PLEs (1-year follow-up) 3.761 1.902 0.033 7.489 0.048 Hallucinational Score PLEs (2-year follow-up) 4.786 1.861 1.139 8.433 0.039 To evaluate the robustness of our findings from the IV Forest, we calculated the E-values for the average treatment effects from the IV Forest. The E-values quantify the minimum strength of association that unobserved confounders would need to possess with both ADI and the outcomes, conditional on the observed covariates in the IV Forest model, to nullify the observed relationships 68 . E-values indicated that unobserved confounders would need to have a relative risk greater than 9.13 for delay discounting and between 7.32 and 457.03 for PLEs to account entirely for the observed effects ( Supplementary Table 3 ). This suggests a high threshold for unobserved confounding effects, thereby strengthening the validity of the causal inferences drawn from our analyses. Supplementary analyses employing a conventional linear IV regression 62 and an alternative causal machine learning method, i.e., Double ML 69,70 , corroborated these findings. The conventional IV regression also showed that ADI has negative influence on childhood delay discounting (β= -0.468, p-FDR = 0.03) and positive PLEs (distress score 1-year follow-up: β = 0.609, p-FDR = 0.011; distress score 2-year follow-up: β = 0.78, p-FDR = 0.003; delusional score 1-year follow-up: β = 0.486, p-FDR = 0.028; delusional score 2-year follow-up: β = 0.578, p-FDR = 0.013; hallucinational score 1-year follow-up: β = 0.604, p-FDR = 0.011; hallucinational score 2-year follow-up: β = 0.827, p-FDR = 0.003). The partial-linear IV model of the Double ML algorithm showed significant effects of ADI on children’s delay discounting (β= -0.429, p-FDR = 0.044), distress score PLEs (1-year follow-up: β = 0.495, p-FDR = 0.023; 2-year follow-up: β = 0.609, p-FDR = 0.005), hallucinational score PLEs (1-year follow-up: β = 0.498, p-FDR = 0.018; 2-year follow-up: β = 0.683, p-FDR = 0.002), and 2-year follow-up delusional score PLEs (β = 0.417, p-FDR = 0.044). The negative effects of ADI on 1-year follow-up delusional score PLEs were marginally significant (β = 0.393, p-FDR = 0.051). These results of the conventional linear IV regression ( Supplementary Table 4 ) and Double ML partial-linear IV regression ( Supplementary Table 5 ) confirm the findings obtained from the IV Forest, further supporting the primary analyses and conclusions drawn from the study. Heterogeneous Treatment Effects of Neighborhood Socioeconomic Adversity on PLEs, conditioned on the Genetic and Neural Correlates of Delay Discounting Next, we tested whether the impact of ADI was heterogeneous across children, and, if so, whether the heterogeneity is linked to individual’s neurodevelopmental characteristics and the relevant genetic factors—assessed with genome-wide polygenic scores (GPS) and structural MRI and monetary incentive delay (MID) task fMRI data—correlated to intertemporal valuation. To identify the best subset of genetic and neural correlates of delay discounting, we first selected GPS and MRI brain regions of interest (ROIs) specifically related to delay discounting. To analyze the nonparametric correlations of multiple input variables, we used a random forest-based feature selection Boruta algorithm 71 . Its robustness and effectiveness in selecting relevant features in high dimensional, intercorrelated biomedical data (e.g., MRI) has been validated 71 and consistently applied in genetics and neuroscience research 72–74 . The variables significantly correlated with delay discounting (p-Bonferroni < 0.05) were GPS of cognitive performance, IQ, and education attainment; morphometric features (e.g., surface area, volume) in the limbic system (temporal pole, parahippocampal gyrus, caudate nucleus, rostral anterior cingulate, isthmus cingulate), inferior frontal gyrus (pars opercularis), and fusiform gyrus; mean beta activations of rewards/losses versus neutral feedback in the subcortical areas (thalamus proper, ventral diencephalon), precentral gyrus, supramarginal gyrus, temporal lobe (transverse temporal gyrus, superior temporal gyrus), and insula ( Supplementary Table 6 ). We then assessed the heterogeneous treatment effects of ADI on PLEs using three distinct IV Forest models: ( 1 ) the Delay Discounting model, incorporating sociodemographic features and delay discounting; ( 2 ) the Gene-Brain model, which included sociodemographic features and genetic and neural correlates of delay discounting (i.e., GPS and brain ROIs identified using the Boruta algorithm); and ( 3 ) the Integrated model which combined all the variables from the previous two models. All three models satisfied the overlap assumption (i.e., the estimated propensity scores are not close to one or zero), which is crucial for the validity of the estimated heterogeneous treatment effects ( Supplementary Fig. 1 ). In line with prior studies 75,76 , we obtained conditional average treatment effects, divided subjects into deciles (Q1: most vulnerable; Q10: most resilient) based on the conditional average treatment effects, and conducted three hypothesis tests 77 on each model to determine the most effective model for capturing the individual differences (heterogeneity) in the effects of ADI on PLEs: monotonicity, alternative hypothesis, and ANOVA. Among the three models, only the Integrated model successfully demonstrated significant individual differences in the ADI effects on PLEs. This was evident in the impact of ADI on 1-year follow-up distress score PLEs (monotonicity test: p-FDR = 0.011; alternative hypothesis test: p = 0.002; ANOVA test: p < 0.001) and 1-year follow-up hallucinational score PLEs (monotonicity test: p-FDR = 0.038; alternative hypothesis test: p = 0.004; ANOVA test: p < 0.001), as presented in Fig. 3 and Table 3 . In contrast, the Delay Discounting model and Gene-Brain model failed to pass the heterogeneity tests (monotonicity test: p-FDR ≥ 0.05; alternative hypothesis and ANOVA test: p ≥ 0.05). Table 3 Evaluation of individual differences in the potential causal effects of neighborhood socioeconomic adversity on children’s PLEs. We employed three distinct tests to evaluate the heterogeneous treatment effects of ADI on PLEs: monotonicity test, alternative hypothesis test, and ANOVA test. These tests were applied to three developed IV Forest models: Delay Discounting model, Gene-Brain model, and Integrated model. All p-values were corrected for multiple comparison using false discovery rate. A star (*) denotes overall significance, indicating that a model passed all three heterogeneity tests. Monotonicity Alternative Hypothesis ANOVA Overall Significance p-FDR Estimate Std. Error t-value p-FDR F-value p-FDR Delay Discounting Model Distress Score PLEs (1-year follow-up) 0.037 -22.261 40.164 -0.554 0.799 1.114 0.48 Distress Score PLEs (2-year follow-up) 0.094 -54.769 142.528 -0.384 0.799 1.081 0.48 Delusional Score PLEs (1-year follow-up) 0.019 55.047 188.400 0.292 0.799 0.965 0.495 Delusional Score PLEs (2-year follow-up) 0.303 23.289 91.550 0.254 0.799 0.852 0.568 Hallucinational Score PLEs (1-year follow-up) 0.362 -86.756 241.597 -0.359 0.799 0.995 0.495 Hallucinational Score PLEs (2-year follow-up) 0.098 -119.595 341.060 -0.351 0.799 1.010 0.495 Gene-Brain Model Distress Score PLEs (1-year follow-up) 0.058 -9.522 4.244 -2.244 0.05625 2.289 0.027 Distress Score PLEs (2-year follow-up) 0.099 -9.202 7.016 -1.312 0.285 1.608 0.161 Delusional Score PLEs (1-year follow-up) 0.086 -7.463 3.262 -2.288 0.05625 2.631 0.01 Delusional Score PLEs (2-year follow-up) 0.043 -9.512 4.886 -1.947 0.0936 3.493 0.001 Hallucinational Score PLEs (1-year follow-up) 0.022 -8.917 4.123 -2.163 0.062 1.878 0.083 Hallucinational Score PLEs (2-year follow-up) 0.146 -9.382 5.681 -1.651 0.162 2.695 0.009 Integrated Model Distress Score PLEs (1-year follow-up) 0.011 -8.922 2.922 -3.053 0.0252 8.388 < 0.001 * Distress Score PLEs (2-year follow-up) 0.074 -9.343 3.469 -2.693 0.0252 7.361 < 0.001 Delusional Score PLEs (1-year follow-up) 0.172 -6.946 2.460 -2.824 0.0252 6.816 < 0.001 Delusional Score PLEs (2-year follow-up) 0.136 -6.854 2.617 -2.619 0.027 7.525 < 0.001 Hallucinational Score PLEs (1-year follow-up) 0.038 -8.246 2.834 -2.910 0.0252 7.182 < 0.001 * Hallucinational Score PLEs (2-year follow-up) 0.089 -8.355 3.070 -2.721 0.0252 9.484 < 0.001 To elucidate the role of specific genetic and neural correlates within the heterogeneous effects of ADI on PLEs, we obtained Shapley additive explanation (SHAP) scores 78 . SHAP scores provide insights into how each variable contributes positively or negatively to the differential effects of ADI on 1-year follow-up observations of distress score and hallucinational score PLEs. These scores help differentiate the roles of these factors across subgroups, ranging from low to high conditional average treatment effects, thereby providing a nuanced understanding of how ADI influences PLEs through various genetic and neural pathways. In both distress score and hallucinational score PLEs, children who showed higher levels of ADI’s adverse effects on PLEs exhibited distinct neuroanatomical and functional brain patterns, particularly in the limbic system. These patterns included reduced neuroanatomical features such as smaller white matter and surface area in the right temporal pole, reduced area and volume in the right parahippocampal region, decreased left white surface area, smaller area in the right isthmus cingulate, reduced intracranial volume, smaller caudate nucleus volume, and lower total grey matter volume. Functionally, greater activation during MID tasks was observed in several areas including the posterior cingulate, right ventral diencephalon, right insula, left thalamus proper, and left precentral gyrus. Additionally, children more adversely affected by ADI, as indicated by higher conditional average treatment effects on PLEs, exhibited larger right fusiform volume, decreased activation in the left superior temporal gyrus, younger parental age, and lower BMI (Fig. 4 ). The analysis also revealed that higher conditional average treatment effects on distress score PLEs was associated with higher cognitive performance GPS and a lower likelihood of being Hispanic. In contrast, for hallucinational score PLEs, greater importance was attributed to increased activation in the left supramarginal gyrus during MID tasks and more pronounced discounting of future rewards. These nuanced associations are depicted in Fig. 4 . Lastly, we conducted a supplementary analysis to test whether the effects of delay discounting between the impact of ADI on PLEs are captured with a conventional linear mediation model 79 . This linear IV mediation model showed no significant mediation effects of delay discounting (β= -6.929E-6 [95% CI, -0.012 ~ 0.026] ~ 4.582E-6 [95% CI, -0.009 ~ 0.03]) ( Supplementary Table 7 ). Discussion In this study, we examined how neighborhood socioeconomic deprivation impacts children’s intertemporal choice behavior (delay discounting) and PLEs, considering the multifaceted effects of neighborhood adversity and its underlying biological, environmental, and behavioral drivers. Our findings can be distilled into two main points. Firstly, there was a notable link of living in socioeconomically disadvantaged neighborhoods to the propensity for children to prefer immediate rewards over larger, delayed ones—a behavior known as steep delay discounting (indicative of lower impulse control) and to a higher rate of PLEs. This association was significant even after adjusting for a range of confounding factors, both observed (e.g., familial socioeconomic status) and unobserved. Secondly, the influence of disadvantaged neighborhood environments on PLEs was found to be heterogeneous. This individual variability is influenced not just by delay discounting, but also by a confluence of factors including genetic predisposition for cognitive intelligence, and brain morphometry and functioning (task activation). Causal machine learning models utilized in our study have identified a spectrum of conditions that either exacerbate vulnerability or contribute to resilience, accounting for the diverse effects of neighborhood environments on children's PLEs. Our findings hold implications for social science. Using causal machine learning models, such as IV Forest and Double ML, we provide consistent and clear results that residential adversity during childhood leads to steeper discounting of future rewards. We propose three possible interpretations to explain the effects of neighborhood socioeconomic adversity on individual’s intertemporal decision-making, focusing on individual’s discount rate, resource scarcity, and social trust. The longstanding economic theory posits that an individual's rate of discounting future rewards (time preference) is an exogenous parameter of intertemporal choice, established a priori, and impervious to external influences 32 . Since the introduction of the discounted utility model 80 by Paul Samuelson in 1937, there has been limited exploration into whether environmental factors affect the development of an individual's parameter 32,81 . Our study challenges this notion by offering concrete evidence that the development of an individual's time preference is subject to environmental influences, and thereby opening new avenues for understanding the dynamics of intertemporal decision-making. Our second interpretation explores the cognitive impact of resource scarcity. Limited resources may overload cognitive capacity, diverting attention from long-term planning and precipitating poor financial decisions, such as impulsive purchasing and mismanagement of finances 82,83 . The third interpretation emphasizes the role of social trust. Lack of trust and reliability in receiving promised future rewards may logically drive individuals to prefer immediate gratification 84,85 . These three interpretations, though seemingly distinct, converge in real-world contexts where socioeconomically disadvantaged families often face both resource scarcity and reduced social trust 86 . This synthesis forms the basis of the ‘behavioral poverty trap’, wherein individuals raised in impoverished environments are prone to overvalue immediate rewards, leading to myopic behaviors such as overconsumption, inadequate savings 30,82 , and heightened risk of psychiatric disorders including psychosis and addiction 34,54 . These behaviors, in turn, perpetuate socioeconomic challenges and hinder escape from poverty 33,87 . We build on this behavioral poverty trap framework by identifying the potential causal influence of neighborhood environment on intertemporal choice, leveraging longitudinal observations of preadolescent children aged 9–12 years, a critical period for neurocognitive development. A plausible biological mechanism for this phenomenon is the effects of glucocorticoid on brain’s reward system. Prior studies indicate that adverse social environments induce chronic stress to children, elevating glucocorticoid hormones like cortisol 88–92 . In particular, neighborhood socioeconomic deprivation has a more pronounced association with cortisol increases in children compared to any other social environmental factors 93 . Long-term chronic stress from growing up in disadvantaged neighborhoods could result in epigenetic modifications affecting the mesocorticolimbic dopaminergic system, thereby altering the reward system 91,92 . This alteration may lead to a heightened preference for immediate rewards and impulsive behaviors, such as unhealthy eating and substance abuse 90–92,94−98 , further entrenching the cycle of socioeconomic disadvantage. Our second findings extend this understanding by linking the heterogeneous effects of ADI on children’s PLEs with the intricate relationship between childhood social adversity and the reward system. Our findings suggest that these differential effects of neighborhood socioeconomic adversity are modulated by genetic predispositions and neurodevelopmental traits associated with delay discounting. Children who experience residential deprivation and are at a higher PLEs demonstrate several distinct characteristics, including lower BMI, younger parental age, and altered brain structures and functions associated with delay discounting. Notably, these children showed reduced volume or white matter in specific brain regions (right temporal pole, right parahippocampal gyrus, right caudate nucleus, right isthmus cingulate), along with a smaller intracranial and total grey matter volume. Functionally, these children showed greater activation during MID tasks in regions including the right posterior cingulate, right ventral diencephalon, right insula, left precentral gyrus, left thalamus proper, and left superior temporal. This is particularly pronounced in children with a greater propensity for hallucinatory symptoms, who also show increased activity in the left supramarginal gyrus. It appears that variations in structural and functional aspects of the limbic system (the posterior cingulate, ventral diencephalon, insula, temporal pole, parahippocampal gyrus, and isthmus cingulate) play a crucial role in how socioeconomic hardship affects PLEs. This individual variability may be linked to individual differences in the glucocorticoid and reward system. The interaction between our genes and neurons, in response to chronic stress from poor socioeconomic conditions, may determine the differing impacts of such adversity on PLEs. Although direct testing of this association within the ABCD Study samples was not feasible due to lack of relevant data, extensive animal and human corroborate our hypotheses. These studies suggest that maladaptive valuation of intertemporal rewards, namely the excessive discounting of future rewards, is linked to dysfunction of the prefrontal-limbic system, associated with psychopathologies such as psychosis in adolescents and adults 34,35,38–41,99 . Animal models have demonstrated that adverse social environments trigger chronic dysregulation of glucocorticoid signaling in the hypothalamic-pituitary-adrenal axis and the dopaminergic mesocortical circuit 92 , through epigenetic control 91,92 . This dysregulation disrupts the adolescent reward circuit. In humans, childhood exposure to social adversity leads to changes in the hypothalamic-pituitary-adrenal axis and contributes to psychosis through abnormal neurodevelopment of the limbic regions, the temporal pole, cingulate cortices, parahippocampal gyrus, and caudate nucleus 100–103 . Young adults with a history of childhood social deprivation often show impaired reward processing, particularly in the cingulate and mesostriatal dopaminergic system 25,103–105 . The age of our study’s participants, 9–12 years old, is a critical period for development of the prefrontal-limbic system 103,106,107 . Children with psychotic disorders often exhibit greater reductions in grey matter compared to their healthy peers 108,109 . These neurodevelopmental alterations are associated with increased neuronal excitation, reduced inhibitory neural activities, and the resultant impulsive behaviors 110 . In line with our findings, previous research has shown a correlation between higher PLEs and neuroanatomical alterations in the right temporal fusiform, right temporal pole, and right parahippocampal gyrus 102 , as well as greater neural activations in limbic regions such as the insula and cingulate cortices during reward outcomes in MID task 111 . Overall, our findings on the heterogeneous effects of neighborhood deprivation contribute to the growing body of literature showing the role of glucocorticoid and reward systems in modulating the adverse effects of environmental deprivation on psychosis 101,103,105,112,113 . In our study, we discovered that children, when exposed to deprived neighborhoods and already facing the challenges of residential disadvantage, were more likely to experience PLEs. Surprisingly, these children also showed a higher GPS for cognitive performance. At first glance, this finding seems to contradict prior research, which has consistently identified a negative relationship between PLEs and cognitive performance 28,114 . To understand this complex relationship, we turned to the bioecological model and the Scarr-Rowe hypothesis on gene-environment interactions 115–117 . This theory proposes that the impact of genetic factors is lessened in unfavorable environments. An easy way to visualize this is by comparing it to plant growth: in poor soil, a plant can't get the nutrients it needs, which limits its growth despite its genetic potential to grow tall 118 . But, when these children face residential disadvantages, this protective gene-psychosis link weakens. Their genetic resilience decreases, making them more vulnerable to the negative impacts of such disadvantages on PLEs. Essentially, those with higher cognitive ability GPS lose more of their potential genetic protection, making them more susceptible to the adverse effects of their environment on PLEs. Consistent with our findings, recent large-scale studies have demonstrated that the impact of genetics on brain structure, cognitive functions, and mental health disorders becomes less significant in harmful environments (e.g., abuse) 119,120 . Conversely, in more supportive and enriched settings, like those associated with higher socioeconomic status, genetic influences are more noticeable (e.g., high socioeconomic status) 116,121,122 . Together with these findings, our study contributes to a deeper understanding of how genetic and environmental factors interact to influence the development of psychopathology in children. In this study, we utilized innovative causal machine learning techniques to test the negative impacts of neighborhood deprivation on childhood psychopathology. Specifically, we employed the IV Forest method that allows us to discern how residential deprivation influences children’s PLEs in a manner dependent on a variety of genetic risk factors (e.g., GPS of cognitive performance, educational attainment, and IQ 114,123,124 ) and environmental risk factors (e.g., family income 3,125 ), as identified in existing literature. Our findings were adjusted to account for potential biases from both observed and unobserved variables. The machine learning algorithm we used was adept at modelling the complex interplay gene-environment interactions. Among the three IV Forest models we tested (i.e., Delay Discounting, Gene-Brain, Integrated), only the Integrated model—which included delay discounting, sociodemographic characteristics, and genetic and neural correlates of delay discounting—identified the significant heterogeneous effects of ADI on children’s PLEs. This suggests that the intricate interactions among environmental, genetic, neural factors, and delay discounting play a crucial role in how socioeconomic adversity impacts PLEs. In contrast, traditional linear mediation analysis, which relies on predefined interaction terms in a deductive statistical framework, failed to identify any significant mediation effect of delay discounting between neighborhood deprivation and PLEs. This underscores the effectiveness of our advanced causal machine learning approach over conventional methods in detecting the subtle effects of various interacting factors on childhood psychopathology. The IV Forest model represents a significant advancement over traditional analysis methods by enabling data-driven feature selection and the stratification of heterogeneous treatment effects 59,60 . Unlike methods that rely on patterns predetermined by researchers, the IV Forest model inductively identifies complex and nonlinear interactions, providing a deeper and more nuanced understanding of the data. Traditional deductive approaches often suffer from low statistical power and bias 61,126 , which inadequately capture the complexity of gene-environment interactions 57,58 . For instance, employing conventional linear regression to model interactions among the 45 covariates in our Integrated model would necessitate the inclusion of over 35 trillion interaction terms. This is not only impractical due to its complexity but also prone to issues like reduced statistical power, poor interpretability, and collinearity. Given these challenges, we believe that causal modeling approaches that assess heterogeneous treatment effects based on machine learning hold significant potential as powerful tools for advancing precision science in psychology and medicine. These approaches provide a more dynamic and accurate framework for understanding the multifaceted influences on psychopathology, demonstrating significant promise for future research in these fields. Several limitations of this study warrant consideration. Firstly, we used ABCD Study, a non-randomized, observational cohort. Despite employing IV methods, including IV Forest and DoubleML, to adjust for both observed and potential unobserved confounders, the inherent limitation of the exclusion restriction assumption persists. This assumption, critical to the validity of the IV methods, cannot be directly verified with data. Albeit we substantiated this assumption with extensive prior research discussed in the Methods section, its validity may still be subject to scrutiny, as might the overall efficacy of the IV method in fully adjusting for residual confounding bias. To mitigate this, we calculated E-values for the average treatment effects of neighborhood disadvantage on delay discounting and PLEs. The large E-values calculated indicate that it would require unobserved confounders with a significantly strong association with both the exposure and the outcomes to negate our findings. Given the magnitude of these E-values and our comprehensive adjustments for confounding, it is unlikely that unobserved confounding could fully account for the observed relationships, thereby supporting the potential causal interpretations, despite not providing absolute proof of causality. Secondly, since the majority of participants identified their race/ethnicity as white (63.76%, similar to the US population), the generalizability of our findings to other minor race/ethnicity might remain to be tested. Nonetheless, recent research suggests that temporal discounting measures are consistent across diverse populations worldwide (61 countries, N = 13,629) 127 , which may mitigate concerns regarding the representativeness of our findings. Thirdly, the relatively short follow-up periods in our study (1-year and 2-year follow-up) may not adequately capture the long-term neurodevelopmental processes underlying intertemporal valuation and related psychopathology. Notably, additional follow-up data from the ABCD Study became available after we finalized this manuscript. As the ABCD Study continues to collect more longitudinal observations, longer follow-up periods in future studies could yield deeper insights. Fourthly, despite efforts to ensure representativeness by recruiting from diverse school systems across 21 research sites in the United States, our sample does not fully mirror the entire US population 128 . To address this, we provide a supplementary table ( Supplementary Table 1 ) comparing the demographic characteristics of our final sample with the general United States population enhancing the relevance and generalizability of our results. Lastly, future research should examine the heterogeneous effects of additional environmental risk factors—such as parenting behavior 28 and early life stress 120 —as primary exposures to elucidate their potential causal effects on psychiatric disorders. Investigating how genetic and neural correlates interact with these risk factors will also advance our understanding of their unique contributions to individual differences in psychopathology. This study highlights the differential effects of neighborhood disadvantage on intertemporal economic decisions and PLEs during early childhood. It underscores the importance of identifying diverse treatment effects by integrating genetic and environmental factors to guide personalized healthcare approaches. Furthermore, we propose that enhancing the childhood environment could contribute to the reduction of economic and health inequality gaps. Economic policies promoting positive intertemporal choice (e.g., increased savings, healthy diet) have predominantly focused on paternalistic welfare policies in adulthood. These policies often assume that an individual’s tendency to discount future rewards is fixed (“exogenous”) 32 . However, our findings suggest that policies or interventions aimed at enhancing the socioeconomic environment during childhood may foster improved intertemporal choice behavior, thereby reducing economic 33 and health inequality 23,129 . By addressing the root of the problem, this indirect approach may assist individuals in developing the capacity to make more informed choices, ultimately promoting better outcomes. The insights gleaned from our novel analytical methods revive longstanding philosophical inquiries: do humans possess reason or free will independent of their environment? If our ability to act responsibly is indeed shaped by external circumstances, this challenges the traditional rationale for penalizing criminal and morally objectionable behavior based on the assumption of free will. This inquiry underscores the need for further interdisciplinary research, bridging insights from psychology, sociology, neuroscience, ethics, and law, to explore the nuanced relationship between individual agency and environmental influences. Such research is crucial for understanding how external factors impact decision-making and behavior, thereby informing more nuanced approaches to ethical and legal accountability. It invites a reevaluation of responsibility and justice, suggesting that effective interventions and policies must consider the complex interplay of individual predispositions and environmental conditions in shaping behavior. Methods Study Participants The ABCD Study recruited participants from 21 research sites across the nation, utilizing a stratified, probability sampling method to capture the sociodemographic variation of the US population 130 . We used the baseline, first year, and second year follow-up datasets included in ABCD Release 4.0, downloaded on February 10, 2022. Of the initial 11,876 ABCD samples, we removed participants without genotype data, MRI data, NIH Toolbox Cognitive Battery, delay discounting, residential address, ADI, and PLEs. Participants not meeting the ABCD Study’s MRI quality control standards were also excluded. As recommended by the ABCD team 131 , Johnson & Bickel’s two-part validity criterion 132 was used to exclude subjects with inconsistent responses (i.e., indifferent point for a given delay larger than that of an indifference point for a longer delay). Missing values of covariates were imputed using k-nearest neighbors. The final samples included 2,135 children from a variety of race/ethnic groups. Data Neighborhood Disadvantage Neighborhood disadvantage was measured with Residential History Derived Scores based on the Census tracts of each respondent’s primary addresses by the ABCD team. Consistent with prior research 3,44 , we chose national percentile scores of the Area Deprivation Index (ADI) in baseline year, calculated from the 2011 ~ 2015 American Community Survey 5-year summary. It has 17 sub-scores regarding various socioeconomic factors such as median household income, income disparity, percentage of population aged more than 25 years or more with at least a high school diploma, and percentage of single-parent households with children aged less than 18 years, etc. Higher values of the ADI indicate greater neighborhood disadvantage. Delay Discounting Delay discounting was measured by the adjusting delay discounting task in the 1-year follow-up ABCD data 131,133 . Each child was asked to make choices between a small immediate hypothetical reward or a larger hypothetical $ 100 delayed reward at multiple future time points (6h, one day, one week, one month, three months, one year, and five years). By increasing or decreasing the smaller immediate reward depending on the child’s response, the task records the indifference point (i.e., the small immediate amount deemed to have the same subjective value as the $ 100 delayed reward) at each of the seven delay intervals. Test-retest reliability of this delay discounting measure has been validated 134,135 . Studies show that preadolescent children are capable of comprehending the delay discounting task and show similar patterns of discounting as adults 136 . To avoid methodological problems regarding mathematical discounting models (hyperbolic vs. exponential) and positively skewed parameters of discounting functions 135,137 , we used the area under the curve, a model-free measure of delay discounting 137 . The area under the curve measure of delay discounting rates (henceforth discount rates ) ranges from 0 to 1, with lower values indicating steeper discounting and higher impulsivity. Psychotic-Like Experiences First and second-year follow-up observations of psychotic-like experiences (PLEs) were measured using the Prodromal Questionnaire-Brief Child Version (PQ-BC; child-reported). PQ-BC has a 21-item scale validated for use with a non-clinical population of children aged 9–10 years 138,139 . In line with the previous research 3,123,138,139 , we computed Total Score and Distress Score , each indicating the number of psychotic-like symptoms and levels of total distress. Total Score is the summary score of 21 questions ranging from 0 to 21, and Distress Score is the weighted sum of responses with the levels of distress, ranging from 0 to 126. Additionally, to test whether the heterogeneous treatment effects of neighborhood adversity differ among psychotic symptoms, Distress Score was divided into two separate scores: Delusional Score and Hallucinational Score 2,140 . A higher value indicates greater severity of PLEs. Genome-wide Polygenic Scores Children’s genetic predispositions were assessed with genome-wide polygenic scores (GPS). Summary statistics from genome-wide association studies were used to generate GPS of cognitive intelligence (cognitive performance 141 , education attainment 141 , IQ 142 ), psychiatric disorders (major depressive disorder 143 , post-traumatic stress disorder 144 , attention-deficit/hyperactivity disorder 145 , obsessive-compulsive disorder 146 , anxiety 147 , depression 148 , bipolar disorder 149 , autism spectrum disorder 150 , schizophrenia 151 , cross disorder 152 ), and health and behavioral traits (BMI 153 , neuroticism 154 , worrying 154 , risk tolerance 155 , automobile speeding propensity 155 , eating disorder 156 , drinking 155 , smoking 155 , cannabis use 157 , general happiness 158 , snoring 159 , insomnia 159 , alcohol dependence 160 ). PRS-CSx, a high-dimensional Bayesian regression framework that places continuous shrinkage prior on single nucleotide polymorphisms effect sizes 161 , was applied to enhance cross-population prediction. This method has consistently shown superior performance compared to other methods across a wide range of genetic architectures in simulation and real data analyses 161 . Hyperparameter optimization for the GPSs was conducted using a held-out validation set of 1,579 unrelated participants. Adjustments for population stratification were performed based on the first ten ancestrally informative principal components to account for potential confounding effects. Anatomical Brain Imaging: T1/T2, Freesurfer 6 Baseline year T1-weighted (T1w) 3D structural MRI acquired in the ABCD study were processed following established protocols 162,163 : To maximize geometric accuracy and image intensity reproducibility, gradient nonlinearity distortion was corrected 164 . After correcting intensity nonuniformity using tissue segmentation and spatial smoothing, images were resampled to 1 mm isotropic voxels. We used Freesurfer v6.0 ( https://surfer.nmr.mgh.harvard.edu ) for the following procedures: cortical surface followed by skull-stripping 165 , white matter segmentation, and mesh creation 166 , correction of topological defects, surface optimization 167 , and nonlinear registration to a spherical surface-based atlas 168,169 . Using Desikan–Killiany atlas 170 , a standard atlas for Freesurfer and ABCD study, we extracted 399 brain ROI measures, including volumes, surface area, thickness, mean curvature, sulcal depth, and gyrification. Functional MRI (fMRI): Monetary Incentive Delay (MID) task The MID task was used measure the neural activation during anticipation and receipt of monetary gains and losses. In each trial, participants were shown a graphical cue of the 5 possible incentive types: large reward ( $ 5), small reward ( $ 0.20), large loss (- $ 5), small loss (- $ 0.20), or neutral ( $ 0). The incentive cue is presented for 2,000 ms, followed by a jittered anticipatory delay (1,500–4,000 ms). Subsequently, a target to which participants respond to gain or avoid losing money was shown (150–500 ms), and feedback of their performance was provided (2,000 ms). A total of 40 reward, 40 loss, and 20 neutral trials were presented in pseudo-random order across the two task runs. Task parameters was dynamically manipulated for each subject to maintain 60% success rate 162 . We used baseline year observations of average beta weights of the MID task fMRI with Desikan-Killiany parcellations 170 . Covariates To adjust for the potential confounding effects, sociodemographic covariates were included. Consistent with existing research on psychiatric disorders in ABCD samples 3,123,138,171 , we controlled for the child’s sex, age, race/ethnicity, caregiver’s relationship to a child, BMI, parental education, marital status of the caregiver, household income, parent’s age, and family history of psychiatric disorders. The family history of psychiatric disorders, measured as the proportion of first-degree relatives who experienced psychosis, depression, mania, suicidality, previous hospitalization, or professional help for mental health issues 3 was included as a covariate. Given that delay discounting and PLEs are associated with an individual's neurocognitive capabilities 172–174 , NIH Toolbox total intelligence was used as a covariate. All covariates were from baseline year observations. Statistical Analyses Instrumental Variable Regression The IV method controls unobserved confounding bias by utilizing an instrumental variable Z which affects the treatment/exposure variable of interest X but has no direct effect on the outcome variable Y 62 . We tested the endogeneity of ADI (i.e., whether ADI as a treatment/exposure variable correlates with the error term), and found significant bias from unobserved confounding (all Hausman test 175 for differences, p ≤ 0.0158). This underscores the necessity of employing IV regression approach to control for the significant confounding effects and to test the potential causal relationship of neighborhood disadvantage with delay discounting and PLEs. The IV method relies on two main assumptions: the exclusion restriction and the strong instrument. The exclusion restriction asserts that the instrumental variable impacts the outcome Y exclusively through the treatment/exposure X, conditioned on observed covariates. Although this assumption cannot be directly tested from data, its plausibility is typically drawn from prior research and theoretical underpinnings 62 . In our study, the instrument variable Z for the exposure of ADI was the presence of state-level source of income (SOI) laws at baseline assessment, which prohibit income discrimination in the housing market. SOI laws are designed to ensure that landlords cannot refuse housing vouchers, which are provided to low-income families to assist in securing quality housing. Such legislation is critical because, despite the intention behind vouchers, many landlords prefer direct cash payments and might otherwise decline voucher-based payments. Thus, families residing in states with SOI laws are more likely to have better residential environments (i.e., lower neighborhood socioeconomic adversity). Reports from the US Department of Housing and Urban Development indicate that SOI laws increase landlords’ acceptance of housing vouchers by 20.2%p to 59.3%p 176 . Research links SOI laws with significant reductions in neighborhood poverty 177 and improved health outcomes in children, including lower hospitalization rates, less impulsive consumption 178 , and substantially better mental health 179 . Taken together, the presence of SOI laws may affect cognitive and psychiatric outcomes—particularly delay discounting and PLEs—solely by enhancing neighborhood environment, conditional on the individual sociodemographic characteristics such as family income, parental education, and race/ethnicity. This relationship supports the plausibility of the exclusion restriction, crucial for the validity of our IV method. The second assumption requires the instrument variable to be strongly associated with the treatment/exposure X. F-statistic above ten is considered a strong instruments 180 . The F-statistic for each model was F = 34.031 (p < 0.0001), suggesting that the instrument SOI laws is strongly associated with the treatment ADI. In other words, our IV model is not likely to suffer from weak instrument bias. All continuous variables were standardized (z-scaled), and analyses were run using ivreg 181 in R version 4.1.2. For all analyses in our study, threshold for statistical significance was set at two-tailed p < 0.05, with multiple comparison correction based on false discovery rate. Causal Machine Learning for Treatment Effects IV Forest ( grf R package version 2.2.1) 59,60 is a novel causal machine learning approach extends from the conventional random forest framework 182 with recursive partitioning, subsampling, and random splitting to identify the average treatment effects and its individual differences. Initially, the IV Forest randomly splits the dataset into two independent subsets, S and T. Subset S is dedicated solely to the construction of individual trees within the forest, where each tree explores potential divisions—such as "Race/ethnicity = White"—to split S into groups \({S}_{1}\) and \({S}_{2}\) , based on the fulfillment of the specified criteria. The selection of these splits is strategically chosen to maximize the differences in conditional average treatment effects estimates between the groups \({S}_{1}\) and \({S}_{2}\) . Following the construction phase, subset T, independent of S, is employed for model validation. Fresh observations from T are introduced into groups with similar treatment responses by each tree. The aggregation of results from multiple trees is conducted through local weighting method, aimed at reducing overall estimate variance and improving accuracy. Using separate subsets for tree building and model validation ensures an honest estimation of conditional average treatment effects, systematically reducing the risk of overfitting 67 . Using IV Forests, we obtained augmented inverse propensity weighted estimates of average treatment effects, a doubly-robust estimator which can capture complex patterns of individual differences and do not rely on a priori model assumptions 59 such as linearity. This is particularly advantageous when the relationship between environmental variables and neurocognitive development is likely nonlinear 7,183,184 . To measure the average outcome between treated versus untreated subjects, ADI was binarized (i.e., mean split). In line with prior studies 75,76 , we evaluated heterogeneous treatment effects by testing whether the average treatment effects are significantly different among subgroups defined by their relative resilience/vulnerability 77 . These subgroups were defined across a decile spectrum, with Q1 representing the most vulnerable and Q10 the most resilient. We considered a model to have significant heterogeneous treatment effects only if it satisfied all three of the following criteria: The monotonicity test evaluates the existence of at least one inequality in the average treatment effects across the deciles. This is achieved by whether to accept or reject the null hypothesis ( \({\mathcal{H}}_{0})\) , which states that treatment effects are equal across all deciles. Essentially, the test determines whether there is a consistent, ordered relationship in the treatment effects from one decile to the next, indicating a monotonic trend. The alternative hypothesis test evaluates whether the average treatment effect in the highest decile exceeds the combined average treatment effects in the remaining deciles Q2 through Q10 \(.\) The ANOVA test determines whether the average treatment effects are statistically different across deciles. In this context, the group mean in the ANOVA corresponds to the average treatment effect of each decile. To ensure that the IV Forest estimations are robust across different random seeds, we developed 100-seed ensemble IV Forest model. Specifically, we used the following procedures: For each iteration, randomly split the data in half (i.e., train vs test sets) to build a forest model with the first half and perform estimation with the other half. We repeated this process 100 times using different seeds in each iteration to build 100 forest models. Combine the 100 forest models into one big IV Forest model and then rank the observations into deciles according to their estimated conditional average treatment effects. Obtain augmented inverse propensity weighted average treatment effects for each decile and perform monotonicity, alternative hypothesis, and ANOVA tests. Declarations Data availability The ABCD Study dataset is openly available to all eligible researchers upon the submission of an access request via the National Institutes of Mental Health Data Archive ( https://nda.nih.gov/abcd ). Comprehensive written informed consent was obtained from the parents of participants, with children providing assent. The study protocols were approved by the University of California, San Diego's Institutional Review Board (IRB), under approval number 160091, in addition to receiving approval from the IRBs of the 21 participating data collection sites 185 . Code availability All codes needed to replicate the results can be found at https://github.com/Transconnectome/DD-HTE . Author Contributions: J.P. and J.C. designed research; J.P. and M.C. performed research; J.P., M.C., E.L., B.-G.K., G.K., Y.Y.J. analyzed data; and J.P., M.C., and J.C. wrote the paper. Competing Interest Statement: Authors have no competing interests. References Kant, I. 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Experimental Analysis of Neighborhood Effects. Econometrica 75, 83–119 (2007). DOI: 10.1111/j.1468-0262.2007.00733.x Staiger, D. & Stock, J. H. Instrumental Variables Regression with Weak Instruments. Econometrica 65, 557 (1997). DOI: 10.2307/2171753 Fox, J., Kleiber, C. & Zeileis, A. ivreg: Instrumental-Variables Regression by '2SLS', '2SM', or '2SMM', with Diagnostics v. R package version 0.6-3 (2023). https://zeileis.github.io/ivreg Breiman, L. Random Forests. Machine Learning 45, 5–32 (2001). DOI: 10.1023/a:1010933404324 Berman, M. G., Stier, A. J. & Akcelik, G. N. Environmental neuroscience. Am Psychol 74, 1039–1052 (2019). DOI: 10.1037/amp0000583 Tooley, U. A., Bassett, D. S. & Mackey, A. P. Environmental influences on the pace of brain development. Nature Reviews Neuroscience 22, 372–384 (2021). DOI: 10.1038/s41583-021-00457-5 Auchter, A. M. et al. A description of the ABCD organizational structure and communication framework. Developmental Cognitive Neuroscience 32, 8–15 (2018). DOI: https://doi.org/10.1016/j.dcn.2018.04.003 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-4618474","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":319454809,"identity":"2ec4773f-338c-41f3-9aa6-7ce191109b8d","order_by":0,"name":"Jiook Cha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYHAD5gNQBhtRyg1AKhNI1sJjQJwW/vbeYw8+1PyR023v+fy5sI1Bnr+BLe0DPi0SZ86lG844ZmBsdubsNumZbQyGMw6wHZ6B10USOWbSvA0Gidtu5G5j5m1jYNzAwN6M3xMgLX9BWu6/efwZqMWeOC2MYFt4GKSBWhI3MLAdxqtF4swZM8meY8ZAv6SZSfOck0iecZgtGa8W/vYeM4kfNXJyZscPP/7MU2Zj29/eZoxXC4atwFRAkoZRMApGwSgYBdgAAA89QHqYputoAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Psychology, Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Jiook","middleName":"","lastName":"Cha","suffix":""},{"id":319454810,"identity":"4c3e7f46-4964-4c44-9d92-ce7a3d128d04","order_by":1,"name":"Junghoon Park","email":"","orcid":"https://orcid.org/0000-0001-8982-0387","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Junghoon","middleName":"","lastName":"Park","suffix":""},{"id":319454811,"identity":"97652628-9d22-47d6-b529-186a091677a0","order_by":2,"name":"Minje Cho","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Minje","middleName":"","lastName":"Cho","suffix":""},{"id":319454812,"identity":"c686adeb-8caa-4ff8-9a2c-4ecf69a06325","order_by":3,"name":"Eunji Lee","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Eunji","middleName":"","lastName":"Lee","suffix":""},{"id":319454813,"identity":"9e59050f-2c5c-48b3-a110-8ecaf88e59d3","order_by":4,"name":"Bo-Gyeom Kim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Bo-Gyeom","middleName":"","lastName":"Kim","suffix":""},{"id":319454814,"identity":"c6dc5f6c-e1e4-4fb9-932a-b71d6e296253","order_by":5,"name":"Gakyung Kim","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Gakyung","middleName":"","lastName":"Kim","suffix":""},{"id":319454815,"identity":"6f0317ca-a858-41b2-80b8-12ba8126770f","order_by":6,"name":"Yoonjung Joo","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Yoonjung","middleName":"","lastName":"Joo","suffix":""}],"badges":[],"createdAt":"2024-06-21 17:03:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4618474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4618474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60172889,"identity":"57065a86-ac35-4da4-a49a-e7538717487b","added_by":"auto","created_at":"2024-07-12 15:18:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":686081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustration of causal modeling using instrumental variables. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Graphical model of the IV method. This panel depicts how the IV method is applied to address the potential biases from unobserved confounders U when analyzing the effects of exposure/treatment (ADI) on outcomes (delay discounting and PLEs) within the observational context of the ABCD Study. The IV method uses the instrument (SOI laws) to effectively neutralize the influence of U on ADI, thus adjusting for potential confounding biases. (\u003cstrong\u003eB\u003c/strong\u003e) Graphical representation of the IV Forest algorithm. The IV Forest algorithm stratifies subjects in a manner that maximizes the individual differences in predicted conditional average treatment effects. This panel illustrates the use of IV Forest to assess the impact of ADI on delay discounting and PLEs, as well as to explore the heterogeneous effects of ADI on PLEs, factoring in individual variations in delay discounting and its associated genetic and neural correlates.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4618474/v1/c2e790bfdb6c0efe9de6d9cc.jpeg"},{"id":60172887,"identity":"6ebddd5d-f69f-4449-84ca-42f69c57f6e6","added_by":"auto","created_at":"2024-07-12 15:18:57","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":852908,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow diagram. \u003c/strong\u003eThis figure illustrates the participant selection and data processing in our study. We initially included 11,876 participants aged 9-12 years from the Adolescent Brain Cognitive Development (ABCD) Study, utilizing the release 4.0 dataset which encompasses baseline, 1-year follow-up, and 2-year follow-up observations. Sociodemographic features underwent kNN imputation. Subsequently, we excluded observations not meeting the ABCD Study’s MRI quality control standards and those failing the Johnson-Bickel validation criterion for delay discounting. This resulted in a final sample size of N=2,351. Using this cohort, our study first investigated the average treatment effects of neighborhood socioeconomic deprivation on children’s intertemporal valuation of rewards and PLEs. We then explored the individual differences of these effects in relation to children’s delay discounting behaviors and associated genetic and neural factors.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4618474/v1/9bbbe76ef60205c6948450c0.jpeg"},{"id":60174112,"identity":"5819fe80-64b2-4dd0-b3ae-bdf15103ef22","added_by":"auto","created_at":"2024-07-12 15:26:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":429657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDelineation of heterogeneous treatment effects by vulnerable/resilient subgroups. \u003c/strong\u003eHeterogeneity in the average treatment effects of the ADI on PLEs are shown as a bar plot, specifically focusing on 1-year follow-up distress score PLEs (\u003cstrong\u003eA\u003c/strong\u003e) and hallucinational score PLEs (\u003cstrong\u003eB\u003c/strong\u003e). These effects are plotted across ten deciles, which are organized based on the relative vulnerability or resilience of the participants, with Q1 denoting the most vulnerable and Q10 indicating the most resilient. Point estimates of the conditional average treatment effect of each decile were derived via a doubly-robust estimation method within the IV Forest algorithm. 95% confidence intervals are depicted using error bars.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4618474/v1/8c4d875f5ef7a49a6aeebcfc.jpeg"},{"id":60172890,"identity":"e2769dda-d4ba-47ef-8510-a8674a999353","added_by":"auto","created_at":"2024-07-12 15:18:57","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1485812,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBeeswarm summary plots of Shapley additive explanation (SHAP) values for Integrated model. \u003c/strong\u003eContributions of the top 20 variables of highest importance in the Integrated model for the heterogeneous treatment effects of neighborhood socioeconomic deprivation on 1-year follow-up distress score PLEs (\u003cstrong\u003eA\u003c/strong\u003e) and 1-year follow-up hallucinational PLEs (\u003cstrong\u003eB\u003c/strong\u003e) are shown. Variables are ordered by their relative importance in the model. Negative SHAP values indicate greater vulnerability (lower resilience) to the effects of ADI on PLEs; Positive values indicate lower vulnerability (greater resilience). Contrasts of average beta activations of the given brain ROIs during MID tasks are shown in parenthesis. GPS: genome-wide polygenic scores; Ventral dc: ventral diencephalon.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4618474/v1/06eec99aeb56606149ff75b9.jpeg"},{"id":60174649,"identity":"2486801d-a8b0-4f3b-aea2-46d2c4ee3197","added_by":"auto","created_at":"2024-07-12 15:34:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4737952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4618474/v1/adba2e83-2868-4674-9df1-78b49b4b7d41.pdf"},{"id":60174111,"identity":"f5295ed0-e110-4c06-9b04-ea7dde12e66e","added_by":"auto","created_at":"2024-07-12 15:26:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":333125,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4618474/v1/4cbb917a873e1945496d69e3.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Individual Differences in the Effects of Neighborhood Socioeconomic Deprivation on Intertemporal Decision-Making and Psychotic-Like Experiences in Children","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn \u003cem\u003eCritique of Practical Reason\u003c/em\u003e, Immanuel Kant champions the inherent power of human reason, suggesting that it is an \u003cem\u003ea priori\u003c/em\u003e capacity independent of external factors, enabling individuals to engage in responsible actions\u003csup\u003e1\u003c/sup\u003e. Nevertheless, a wealth of scientific studies in recent decades stands in opposition to the Enlightenment philosopher's claims, highlighting the significant impact of environmental factors on the development of personal identity and behavior.\u003c/p\u003e \u003cp\u003eAdverse childhood environments, such as low family income, malnutrition, physical or sexual abuse, and unsafe neighborhoods, are linked to an heightened risk of various mental or physical health issues, including psychosis\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e, impoverished cognitive ability\u003csup\u003e5\u0026ndash;7\u003c/sup\u003e, anxiety, bipolar disorder, self-harm, depression\u003csup\u003e3,4,8\u003c/sup\u003e, substance abuse, and obesity\u003csup\u003e9,10\u003c/sup\u003e. Furthermore, these environments are associated with negative social outcomes, such as poor academic performance\u003csup\u003e11,12\u003c/sup\u003e, low income, unemployment\u003csup\u003e13\u0026ndash;18\u003c/sup\u003e, higher rates of imprisonment, and increased likelihood of teen pregnancy\u003csup\u003e19\u003c/sup\u003e. Additionally, exposure to these adverse conditions in childhood is associated with a propensity for engaging in risky behaviors, including criminal activity\u003csup\u003e20\u003c/sup\u003e, excessive consumption of calorie-dense foods\u003csup\u003e21\u003c/sup\u003e, substance use\u003csup\u003e22,23\u003c/sup\u003e, deficient self-control\u003csup\u003e24\u003c/sup\u003e, and disrupted reward processing\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe intricate relationship between challenging childhood environments, irresponsible behavior, and adverse social and health outcomes raises important questions. We hypothesized that childhood adversity causes impairment in one\u0026rsquo;s valuation system, leading to negative life outcomes. Children who experienced social adversities such as poverty show steeper discounting of future rewards in adulthood and have higher psychotic-like experiences (PLEs)\u003csup\u003e2,3,26\u0026ndash;28\u003c/sup\u003e. Lower socioeconomic status positively correlates with functional brain activity concordance and grey matter volume within reward-related areas (i.e., ventral striatum, putamen, caudate nucleus, orbital frontal cortex) and negatively with executive-related areas (i.e., frontal, medial frontal cortex)\u003csup\u003e29\u003c/sup\u003e. A recent study reported that neuroanatomical features including total cortical volume, surface area, and thickness mediates the association of environmental risk factors and PLEs in children\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, individuals with steeper discounting of future rewards (i.e., value present rewards much higher than future rewards) tend to display a range of suboptimal behavior and outcomes. They are more likely to save less, invest less in their education, engage in criminal activities, exhibit lower academic performance, and accumulate less economic wealth\u003csup\u003e30\u0026ndash;33\u003c/sup\u003e. Such impairments in intertemporal valuation are not only associated with financial and social disadvantages but are also linked to psychiatric disorders, including psychosis, attention deficit/hyperactivity disorder (ADHD), and addiction\u003csup\u003e34,35\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePsychosis, in particular, is associated with distinctive alterations in intertemporal decision-making, characterized by steeper discounting of future rewards\u003csup\u003e36\u0026ndash;38\u003c/sup\u003e. This cognitive pattern in individuals with psychosis may be reflected in abnormal neural responses to non-relevant rewards, potentially driven by elevated levels of tonic dopamine\u003csup\u003e34,39\u0026ndash;41\u003c/sup\u003e. Comparative studies have demonstrated that individuals with psychosis discount future rewards more steeply than healthy controls\u003csup\u003e36,42,43\u003c/sup\u003e, a pattern that is uniquely pronounced in psychosis compared to other psychopathologies such as primary mood disorders\u003csup\u003e42\u003c/sup\u003e, major depressive disorder\u003csup\u003e42\u003c/sup\u003e, and bipolar disorder\u003csup\u003e43\u003c/sup\u003e. The disruption in dopaminergic projections from the ventral tegmental area to the mesocorticolimbic regions is hypothesized to impair reward anticipation and perception processes\u003csup\u003e39,40\u003c/sup\u003e, potentially contributing to the phenomenology of delusions or hallucinations.\u003c/p\u003e \u003cp\u003eIn the present study, our primary objective was to investigate the impact of neighborhood socioeconomic deprivation on adolescents' delay discounting and PLEs. Delay discounting, which is evidenced by the extent to which individuals\u0026rsquo; discount future rewards, pertains to their intertemporal decision-making and impulsive behavior. Exposure to adversities at the neighborhood level during childhood has been shown to negatively influence neurocognitive development\u003csup\u003e7,44,45\u003c/sup\u003e, subsequently resulting in psychiatric disorders\u003csup\u003e2,3,28\u003c/sup\u003e and unfavorable social outcomes, such as decreased income, reduced probability of college attendance, and limited employment opportunities\u003csup\u003e17\u003c/sup\u003e. This phenomenon is particularly pronounced in societies where discrimination based on family income or race/ethnicity restricts underprivileged families from selecting neighborhoods that present greater opportunities for upward social mobility, as observed in the United States\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is crucial to note that PLEs, frequently reported in children, are considered as a clinically significant risk indicator for psychosis and general psychopathology\u003csup\u003e46,47\u003c/sup\u003e. Around 17% of 9\u0026ndash;12 years old children report PLEs\u003csup\u003e48\u003c/sup\u003e, and individuals with PLEs at age 11 had greater risk of developing psychotic disorders in adulthood\u003csup\u003e49,50\u003c/sup\u003e. Prior studies revealed that PLEs are correlated to heightened vulnerability to other psychopathologies including suicidal behavior\u003csup\u003e2\u003c/sup\u003e, mood, anxiety, and substance disorders\u003csup\u003e46,48\u003c/sup\u003e, and exhibit the strongest association with environmental risk factors in comparison to other internalizing/externalizing symptoms during early adolescence\u003csup\u003e3\u003c/sup\u003e. The present study endeavors to explore the potential causal mechanisms underlying these associations.\u003c/p\u003e \u003cp\u003eOur second aim was to test whether the potential causal effects of neighborhood deprivation on children\u0026rsquo;s PLEs are heterogeneous based on individual\u0026rsquo;s delay discounting and its genetic, neural correlates. The heterogeneous nature of psychopathology has long posed significant challenges for clinical diagnosis and treatment\u003csup\u003e51,52\u003c/sup\u003e. Given that the genetic and neural correlates of delay discounting substantially overlap with those of psychosis\u003csup\u003e40,41,53,54\u003c/sup\u003e, the shared biological foundations between reward valuation and psychosis may result in heterogeneous effects of environmental exposure on an individual's PLEs. By investigating these potential variations, this study seeks to enhance the understanding of the complex interplay between environmental factors and individual predispositions in the development of psychopathology.\u003c/p\u003e \u003cp\u003eIdentifying individual differences of treatment/exposure is crucial for the development of personalized health care. Delivering optimal health care for each patient necessitates the recognition of genetic markers, neurodevelopmental characteristics, and sociodemographic features associated with individual variations in treatment effects\u003csup\u003e55,56\u003c/sup\u003e. However, previous studies employing traditional methods of testing the individual differences in treatment effects have often been unsuccessful in discerning the intricate interplay between genetic and environmental factors\u003csup\u003e57,58\u003c/sup\u003e. Linear models with interaction terms of features selected a priori by the researcher may not fully reflect the complex and elusive gene-environment interplay, particularly in genetic and neuroscience research where the input features are usually high dimensional.\u003c/p\u003e \u003cp\u003eUsing instrumental variable (IV) random forests (henceforth IV Forest), an up-to-date causal machine learning approach\u003csup\u003e59,60\u003c/sup\u003e, we assessed the effects of neighborhood socioeconomic adversity on delay discounting and PLEs, and the potential individual differences within those effects. We leveraged multimodal magnetic resonance imaging (MRI) data from 11,876 preadolescent children aged 9 to 12 years old (the Adolescent Brain Cognitive Development (ABCD) Study). Integration of innovative analytical techniques and a large sample with diverse genetic and environmental backgrounds permits us to test the complex interactions between genetic and environmental factors, ultimately contributing to the development of more effective personalized health care strategies.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe demographic characteristics of the final sample (N\u0026thinsp;=\u0026thinsp;2,135) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Within the sample, 46.14% were female, 76.63% of participants had married parents, the mean family income was \u003cspan\u003e$\u003c/span\u003e70,245, and 65.57% identified their race/ethnicity as white. To ensure the representativeness of the final sample, a supplementary table comparing the sample's demographic characteristics with those of the general United States population is provided in the \u003cb\u003eSupplementary Information\u003c/b\u003e (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). This comparison serves to reinforce the validity and generalizability of the study's findings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic/demographic characteristics of the participants.\u003c/b\u003e \u003cem\u003eAge\u003c/em\u003e is rounded to chronological month. \u003cem\u003eRace/Ethnicity\u003c/em\u003e denote child\u0026rsquo;s self-reported racial / ethnic identity. \u003cem\u003eHousehold Income\u003c/em\u003e is assessed as the total combined family income for the past 12 months. \u003cem\u003eParental Education\u003c/em\u003e is measured as the highest grade or level of school completed or highest degree received. \u003cem\u003eFamily History of Psychiatric Disorders\u003c/em\u003e represents the proportion of first-degree relatives who experienced mental illness.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120.1541 (7.4658)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eMarital Status of the first caregiver\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiving with Partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eRace/Ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eParent\u0026rsquo;s Identity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological Mother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological Father\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdoptive Parent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCustodial Parent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e70,245 (1.937)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParental Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.2838 (2.3046)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.4298 (3.8572)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParental Age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.8775 (6.3825)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily History of Psychiatric Disorders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0958 (0.1125)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn our initial exploratory analysis, partial correlations were used to examine the relationship between psychopathological symptoms and delay discounting. Among the symptoms assessed (e.g., depression, anxiety, ADHD), only PLEs showed significant correlation with delay discounting (Spearman ρ= -0.067, p-FDR\u0026thinsp;=\u0026thinsp;0.024\u0026thinsp;~\u0026thinsp;ρ= -0.057, p-FDR\u0026thinsp;=\u0026thinsp;0.035) (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). This finding underscores the unique association between PLEs and delay discounting, laying the groundwork for subsequent investigations into how delay discounting\u0026mdash;along with its genetic and neural correlates\u0026mdash;may be associated with the heterogeneous effects of neighborhood socioeconomic adversity on PLEs.\u003c/p\u003e \u003cp\u003eGiven the non-randomized, observational nature of the ABCD Study, potential confounding factors, such as genetic, environmental variables, and their unobserved common causes, can lead to biased estimations\u003csup\u003e61\u003c/sup\u003e. A powerful and effective way to adjust such bias is the IV regression. Designed to address bias from unobserved confounders, IV regression is an effective method to conduct causal inference using non-randomized, observational data for research in various domains, including economics\u003csup\u003e62,63\u003c/sup\u003e, psychology\u003csup\u003e64\u003c/sup\u003e, neuroscience\u003csup\u003e65\u003c/sup\u003e, and psychiatry\u003csup\u003e66\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, the instrumental variable used was the presence of state-level source of income (SOI) laws at baseline year assessment, which prevent income discrimination in housing. These laws ensure that landlords accept housing vouchers, aiding low-income families in securing quality housing. As such, SOI laws are critical in enabling better residential environments by mitigating neighborhood socioeconomic adversity, thereby serving as an effective instrument for assessing the potential causal effects of neighborhood socioeconomic adversity (measured with \u003cem\u003eArea Deprivation Index\u003c/em\u003e, henceforth ADI), on delay discounting and PLEs (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe used IV Forest\u003csup\u003e59,60\u003c/sup\u003e\u0026mdash;a random forest-based IV regression\u003csup\u003e62\u003c/sup\u003e\u0026mdash;to adjust for unobserved confounding bias in identifying the potential causal effects of ADI on delay discounting and PLEs. The IV Forest method enabled us to derive nonparametric, doubly robust estimates of the average (group-level) and heterogeneous (individual-level) treatment effects of ADI on these outcomes (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). This method is noted for delivering estimates with significantly lower mean-squared error compared to conventional k-nearest neighbor methods\u003csup\u003e59,60\u003c/sup\u003e. Furthermore, its use of independent subsamples for model construction and validation ensures honest, overfitting-resistant estimates of average and heterogeneous treatment effects\u003csup\u003e59,60,67\u003c/sup\u003e. Notably, this method is particularly useful for analyzing the complex, nonlinear interactions between genetic and environmental factors and their effects on neurocognitive development and PLEs, even within the confines of observational data\u003csup\u003e59\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the analytical framework of our study, examining the effects of neighborhood socioeconomic adversity on children's decision-making and mental health. ADI, recorded in the baseline year, serves as an indicator of this socioeconomic adversity. We assessed the impact of ADI on children\u0026rsquo;s intertemporal decision-making through delay discounting at a 1-year follow-up. PLEs, encompassing distress, delusional, and hallucinational symptoms, were evaluated at both 1-year and 2-year follow-ups. Our analysis spans multiple follow-up periods and PLE indicators to investigate the sustained influence of ADI over time and to explore differential effects on various PLE symptoms, particularly delusional versus hallucinational.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAverage Treatment Effects of Neighborhood Socioeconomic Adversity on Delay Discounting and PLEs\u003c/h2\u003e \u003cp\u003eIV Forest analyses revealed that a higher ADI has significant associations with a lower delay discounting (β= -1.73, p-FDR\u0026thinsp;=\u0026thinsp;0.048) and a higher PLEs (distress score 1-year follow-up: β\u0026thinsp;=\u0026thinsp;1.872, p-FDR\u0026thinsp;=\u0026thinsp;0.048; distress score 2-year follow-up: β\u0026thinsp;=\u0026thinsp;1.504, p-FDR\u0026thinsp;=\u0026thinsp;0.039; delusional score 1-year follow-up: β\u0026thinsp;=\u0026thinsp;5.97, p-FDR\u0026thinsp;=\u0026thinsp;0.048; delusional score 2-year follow-up: β\u0026thinsp;=\u0026thinsp;4.022, p-FDR\u0026thinsp;=\u0026thinsp;0.048; hallucinational score 1-year follow-up: β\u0026thinsp;=\u0026thinsp;3.761, p-FDR\u0026thinsp;=\u0026thinsp;0.048; hallucinational score 2-year follow-up: β\u0026thinsp;=\u0026thinsp;4.786, p-FDR\u0026thinsp;=\u0026thinsp;0.039) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePotential causal effects of neighborhood socioeconomic adversity on intertemporal valuation and PLEs.\u003c/b\u003e Average treatment effects of ADI on delay discounting and PLEs in the IV Forest models are shown. All p-values were corrected for multiple comparison using false discovery rate.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eIV Forests: Average Treatment Effects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% Lower CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% Upper CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-FDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay Discounting\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistress Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistress Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelusional Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelusional Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHallucinational Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHallucinational Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the robustness of our findings from the IV Forest, we calculated the E-values for the average treatment effects from the IV Forest. The E-values quantify the minimum strength of association that unobserved confounders would need to possess with both ADI and the outcomes, conditional on the observed covariates in the IV Forest model, to nullify the observed relationships\u003csup\u003e68\u003c/sup\u003e. E-values indicated that unobserved confounders would need to have a relative risk greater than 9.13 for delay discounting and between 7.32 and 457.03 for PLEs to account entirely for the observed effects (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). This suggests a high threshold for unobserved confounding effects, thereby strengthening the validity of the causal inferences drawn from our analyses.\u003c/p\u003e \u003cp\u003eSupplementary analyses employing a conventional linear IV regression\u003csup\u003e62\u003c/sup\u003e and an alternative causal machine learning method, i.e., \u003cem\u003eDouble ML\u003c/em\u003e\u003csup\u003e69,70\u003c/sup\u003e, corroborated these findings. The conventional IV regression also showed that ADI has negative influence on childhood delay discounting (β= -0.468, p-FDR\u0026thinsp;=\u0026thinsp;0.03) and positive PLEs (distress score 1-year follow-up: β\u0026thinsp;=\u0026thinsp;0.609, p-FDR\u0026thinsp;=\u0026thinsp;0.011; distress score 2-year follow-up: β\u0026thinsp;=\u0026thinsp;0.78, p-FDR\u0026thinsp;=\u0026thinsp;0.003; delusional score 1-year follow-up: β\u0026thinsp;=\u0026thinsp;0.486, p-FDR\u0026thinsp;=\u0026thinsp;0.028; delusional score 2-year follow-up: β\u0026thinsp;=\u0026thinsp;0.578, p-FDR\u0026thinsp;=\u0026thinsp;0.013; hallucinational score 1-year follow-up: β\u0026thinsp;=\u0026thinsp;0.604, p-FDR\u0026thinsp;=\u0026thinsp;0.011; hallucinational score 2-year follow-up: β\u0026thinsp;=\u0026thinsp;0.827, p-FDR\u0026thinsp;=\u0026thinsp;0.003). The partial-linear IV model of the Double ML algorithm showed significant effects of ADI on children\u0026rsquo;s delay discounting (β= -0.429, p-FDR\u0026thinsp;=\u0026thinsp;0.044), distress score PLEs (1-year follow-up: β\u0026thinsp;=\u0026thinsp;0.495, p-FDR\u0026thinsp;=\u0026thinsp;0.023; 2-year follow-up: β\u0026thinsp;=\u0026thinsp;0.609, p-FDR\u0026thinsp;=\u0026thinsp;0.005), hallucinational score PLEs (1-year follow-up: β\u0026thinsp;=\u0026thinsp;0.498, p-FDR\u0026thinsp;=\u0026thinsp;0.018; 2-year follow-up: β\u0026thinsp;=\u0026thinsp;0.683, p-FDR\u0026thinsp;=\u0026thinsp;0.002), and 2-year follow-up delusional score PLEs (β\u0026thinsp;=\u0026thinsp;0.417, p-FDR\u0026thinsp;=\u0026thinsp;0.044). The negative effects of ADI on 1-year follow-up delusional score PLEs were marginally significant (β\u0026thinsp;=\u0026thinsp;0.393, p-FDR\u0026thinsp;=\u0026thinsp;0.051). These results of the conventional linear IV regression (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e) and Double ML partial-linear IV regression (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e) confirm the findings obtained from the IV Forest, further supporting the primary analyses and conclusions drawn from the study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHeterogeneous Treatment Effects of Neighborhood Socioeconomic Adversity on PLEs, conditioned on the Genetic and Neural Correlates of Delay Discounting\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNext, we tested whether the impact of ADI was heterogeneous across children, and, if so, whether the heterogeneity is linked to individual\u0026rsquo;s neurodevelopmental characteristics and the relevant genetic factors\u0026mdash;assessed with genome-wide polygenic scores (GPS) and structural MRI and monetary incentive delay (MID) task fMRI data\u0026mdash;correlated to intertemporal valuation. To identify the best subset of genetic and neural correlates of delay discounting, we first selected GPS and MRI brain regions of interest (ROIs) specifically related to delay discounting. To analyze the nonparametric correlations of multiple input variables, we used a random forest-based feature selection \u003cem\u003eBoruta\u003c/em\u003e algorithm\u003csup\u003e71\u003c/sup\u003e. Its robustness and effectiveness in selecting relevant features in high dimensional, intercorrelated biomedical data (e.g., MRI) has been validated\u003csup\u003e71\u003c/sup\u003e and consistently applied in genetics and neuroscience research\u003csup\u003e72\u0026ndash;74\u003c/sup\u003e. The variables significantly correlated with delay discounting (p-Bonferroni\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were GPS of cognitive performance, IQ, and education attainment; morphometric features (e.g., surface area, volume) in the limbic system (temporal pole, parahippocampal gyrus, caudate nucleus, rostral anterior cingulate, isthmus cingulate), inferior frontal gyrus (pars opercularis), and fusiform gyrus; mean beta activations of rewards/losses versus neutral feedback in the subcortical areas (thalamus proper, ventral diencephalon), precentral gyrus, supramarginal gyrus, temporal lobe (transverse temporal gyrus, superior temporal gyrus), and insula (\u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe then assessed the heterogeneous treatment effects of ADI on PLEs using three distinct IV Forest models: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the Delay Discounting model, incorporating sociodemographic features and delay discounting; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the Gene-Brain model, which included sociodemographic features and genetic and neural correlates of delay discounting (i.e., GPS and brain ROIs identified using the Boruta algorithm); and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the Integrated model which combined all the variables from the previous two models. All three models satisfied the overlap assumption (i.e., the estimated propensity scores are not close to one or zero), which is crucial for the validity of the estimated heterogeneous treatment effects (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). In line with prior studies\u003csup\u003e75,76\u003c/sup\u003e, we obtained conditional average treatment effects, divided subjects into deciles (Q1: most vulnerable; Q10: most resilient) based on the conditional average treatment effects, and conducted three hypothesis tests\u003csup\u003e77\u003c/sup\u003e on each model to determine the most effective model for capturing the individual differences (heterogeneity) in the effects of ADI on PLEs: monotonicity, alternative hypothesis, and ANOVA.\u003c/p\u003e \u003cp\u003eAmong the three models, only the Integrated model successfully demonstrated significant individual differences in the ADI effects on PLEs. This was evident in the impact of ADI on 1-year follow-up distress score PLEs (monotonicity test: p-FDR\u0026thinsp;=\u0026thinsp;0.011; alternative hypothesis test: p\u0026thinsp;=\u0026thinsp;0.002; ANOVA test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1-year follow-up hallucinational score PLEs (monotonicity test: p-FDR\u0026thinsp;=\u0026thinsp;0.038; alternative hypothesis test: p\u0026thinsp;=\u0026thinsp;0.004; ANOVA test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In contrast, the Delay Discounting model and Gene-Brain model failed to pass the heterogeneity tests (monotonicity test: p-FDR\u0026thinsp;\u0026ge;\u0026thinsp;0.05; alternative hypothesis and ANOVA test: p\u0026thinsp;\u0026ge;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eEvaluation of individual differences in the potential causal effects of neighborhood socioeconomic adversity on children\u0026rsquo;s PLEs.\u003c/b\u003e We employed three distinct tests to evaluate the heterogeneous treatment effects of ADI on PLEs: monotonicity test, alternative hypothesis test, and ANOVA test. These tests were applied to three developed IV Forest models: Delay Discounting model, Gene-Brain model, and Integrated model. All p-values were corrected for multiple comparison using false discovery rate. A star (*) denotes overall significance, indicating that a model passed all three heterogeneity tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonotonicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eAlternative Hypothesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall Significance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-FDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-FDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-FDR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eDelay Discounting Model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistress Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-22.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistress Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-54.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelusional Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e188.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelusional Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHallucinational Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-86.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHallucinational Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-119.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e341.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eGene-Brain Model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistress Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistress Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelusional Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelusional Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHallucinational Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHallucinational Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eIntegrated Model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistress Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistress Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelusional Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelusional Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHallucinational Score PLEs\u003c/p\u003e \u003cp\u003e(1-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHallucinational Score PLEs\u003c/p\u003e \u003cp\u003e(2-year follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo elucidate the role of specific genetic and neural correlates within the heterogeneous effects of ADI on PLEs, we obtained Shapley additive explanation (SHAP) scores\u003csup\u003e78\u003c/sup\u003e. SHAP scores provide insights into how each variable contributes positively or negatively to the differential effects of ADI on 1-year follow-up observations of distress score and hallucinational score PLEs. These scores help differentiate the roles of these factors across subgroups, ranging from low to high conditional average treatment effects, thereby providing a nuanced understanding of how ADI influences PLEs through various genetic and neural pathways.\u003c/p\u003e \u003cp\u003eIn both distress score and hallucinational score PLEs, children who showed higher levels of ADI\u0026rsquo;s adverse effects on PLEs exhibited distinct neuroanatomical and functional brain patterns, particularly in the limbic system. These patterns included reduced neuroanatomical features such as smaller white matter and surface area in the right temporal pole, reduced area and volume in the right parahippocampal region, decreased left white surface area, smaller area in the right isthmus cingulate, reduced intracranial volume, smaller caudate nucleus volume, and lower total grey matter volume. Functionally, greater activation during MID tasks was observed in several areas including the posterior cingulate, right ventral diencephalon, right insula, left thalamus proper, and left precentral gyrus. Additionally, children more adversely affected by ADI, as indicated by higher conditional average treatment effects on PLEs, exhibited larger right fusiform volume, decreased activation in the left superior temporal gyrus, younger parental age, and lower BMI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe analysis also revealed that higher conditional average treatment effects on distress score PLEs was associated with higher cognitive performance GPS and a lower likelihood of being Hispanic. In contrast, for hallucinational score PLEs, greater importance was attributed to increased activation in the left supramarginal gyrus during MID tasks and more pronounced discounting of future rewards. These nuanced associations are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eLastly, we conducted a supplementary analysis to test whether the effects of delay discounting between the impact of ADI on PLEs are captured with a conventional linear mediation model\u003csup\u003e79\u003c/sup\u003e. This linear IV mediation model showed no significant mediation effects of delay discounting (β= -6.929E-6 [95% CI, -0.012\u0026thinsp;~\u0026thinsp;0.026]\u0026thinsp;~\u0026thinsp;4.582E-6 [95% CI, -0.009\u0026thinsp;~\u0026thinsp;0.03]) (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we examined how neighborhood socioeconomic deprivation impacts children\u0026rsquo;s intertemporal choice behavior (delay discounting) and PLEs, considering the multifaceted effects of neighborhood adversity and its underlying biological, environmental, and behavioral drivers. Our findings can be distilled into two main points. Firstly, there was a notable link of living in socioeconomically disadvantaged neighborhoods to the propensity for children to prefer immediate rewards over larger, delayed ones\u0026mdash;a behavior known as steep delay discounting (indicative of lower impulse control) and to a higher rate of PLEs. This association was significant even after adjusting for a range of confounding factors, both observed (e.g., familial socioeconomic status) and unobserved. Secondly, the influence of disadvantaged neighborhood environments on PLEs was found to be heterogeneous. This individual variability is influenced not just by delay discounting, but also by a confluence of factors including genetic predisposition for cognitive intelligence, and brain morphometry and functioning (task activation). Causal machine learning models utilized in our study have identified a spectrum of conditions that either exacerbate vulnerability or contribute to resilience, accounting for the diverse effects of neighborhood environments on children's PLEs.\u003c/p\u003e \u003cp\u003eOur findings hold implications for social science. Using causal machine learning models, such as IV Forest and Double ML, we provide consistent and clear results that residential adversity during childhood leads to steeper discounting of future rewards. We propose three possible interpretations to explain the effects of neighborhood socioeconomic adversity on individual\u0026rsquo;s intertemporal decision-making, focusing on individual\u0026rsquo;s discount rate, resource scarcity, and social trust.\u003c/p\u003e \u003cp\u003eThe longstanding economic theory posits that an individual's rate of discounting future rewards (time preference) is an exogenous parameter of intertemporal choice, established a priori, and impervious to external influences\u003csup\u003e32\u003c/sup\u003e. Since the introduction of the discounted utility model\u003csup\u003e80\u003c/sup\u003e by Paul Samuelson in 1937, there has been limited exploration into whether environmental factors affect the development of an individual's parameter\u003csup\u003e32,81\u003c/sup\u003e. Our study challenges this notion by offering concrete evidence that the development of an individual's time preference is subject to environmental influences, and thereby opening new avenues for understanding the dynamics of intertemporal decision-making.\u003c/p\u003e \u003cp\u003eOur second interpretation explores the cognitive impact of resource scarcity. Limited resources may overload cognitive capacity, diverting attention from long-term planning and precipitating poor financial decisions, such as impulsive purchasing and mismanagement of finances\u003csup\u003e82,83\u003c/sup\u003e. The third interpretation emphasizes the role of social trust. Lack of trust and reliability in receiving promised future rewards may logically drive individuals to prefer immediate gratification\u003csup\u003e84,85\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese three interpretations, though seemingly distinct, converge in real-world contexts where socioeconomically disadvantaged families often face both resource scarcity and reduced social trust\u003csup\u003e86\u003c/sup\u003e. This synthesis forms the basis of the \u0026lsquo;behavioral poverty trap\u0026rsquo;, wherein individuals raised in impoverished environments are prone to overvalue immediate rewards, leading to myopic behaviors such as overconsumption, inadequate savings\u003csup\u003e30,82\u003c/sup\u003e, and heightened risk of psychiatric disorders including psychosis and addiction\u003csup\u003e34,54\u003c/sup\u003e. These behaviors, in turn, perpetuate socioeconomic challenges and hinder escape from poverty\u003csup\u003e33,87\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe build on this behavioral poverty trap framework by identifying the potential causal influence of neighborhood environment on intertemporal choice, leveraging longitudinal observations of preadolescent children aged 9\u0026ndash;12 years, a critical period for neurocognitive development. A plausible biological mechanism for this phenomenon is the effects of glucocorticoid on brain\u0026rsquo;s reward system. Prior studies indicate that adverse social environments induce chronic stress to children, elevating glucocorticoid hormones like cortisol\u003csup\u003e88\u0026ndash;92\u003c/sup\u003e. In particular, neighborhood socioeconomic deprivation has a more pronounced association with cortisol increases in children compared to any other social environmental factors\u003csup\u003e93\u003c/sup\u003e. Long-term chronic stress from growing up in disadvantaged neighborhoods could result in epigenetic modifications affecting the mesocorticolimbic dopaminergic system, thereby altering the reward system\u003csup\u003e91,92\u003c/sup\u003e. This alteration may lead to a heightened preference for immediate rewards and impulsive behaviors, such as unhealthy eating and substance abuse\u003csup\u003e90\u0026ndash;92,94\u0026minus;98\u003c/sup\u003e, further entrenching the cycle of socioeconomic disadvantage.\u003c/p\u003e \u003cp\u003eOur second findings extend this understanding by linking the heterogeneous effects of ADI on children\u0026rsquo;s PLEs with the intricate relationship between childhood social adversity and the reward system. Our findings suggest that these differential effects of neighborhood socioeconomic adversity are modulated by genetic predispositions and neurodevelopmental traits associated with delay discounting. Children who experience residential deprivation and are at a higher PLEs demonstrate several distinct characteristics, including lower BMI, younger parental age, and altered brain structures and functions associated with delay discounting. Notably, these children showed reduced volume or white matter in specific brain regions (right temporal pole, right parahippocampal gyrus, right caudate nucleus, right isthmus cingulate), along with a smaller intracranial and total grey matter volume. Functionally, these children showed greater activation during MID tasks in regions including the right posterior cingulate, right ventral diencephalon, right insula, left precentral gyrus, left thalamus proper, and left superior temporal. This is particularly pronounced in children with a greater propensity for hallucinatory symptoms, who also show increased activity in the left supramarginal gyrus.\u003c/p\u003e \u003cp\u003eIt appears that variations in structural and functional aspects of the limbic system (the posterior cingulate, ventral diencephalon, insula, temporal pole, parahippocampal gyrus, and isthmus cingulate) play a crucial role in how socioeconomic hardship affects PLEs. This individual variability may be linked to individual differences in the glucocorticoid and reward system. The interaction between our genes and neurons, in response to chronic stress from poor socioeconomic conditions, may determine the differing impacts of such adversity on PLEs.\u003c/p\u003e \u003cp\u003eAlthough direct testing of this association within the ABCD Study samples was not feasible due to lack of relevant data, extensive animal and human corroborate our hypotheses. These studies suggest that maladaptive valuation of intertemporal rewards, namely the excessive discounting of future rewards, is linked to dysfunction of the prefrontal-limbic system, associated with psychopathologies such as psychosis in adolescents and adults\u003csup\u003e34,35,38\u0026ndash;41,99\u003c/sup\u003e. Animal models have demonstrated that adverse social environments trigger chronic dysregulation of glucocorticoid signaling in the hypothalamic-pituitary-adrenal axis and the dopaminergic mesocortical circuit\u003csup\u003e92\u003c/sup\u003e, through epigenetic control\u003csup\u003e91,92\u003c/sup\u003e. This dysregulation disrupts the adolescent reward circuit. In humans, childhood exposure to social adversity leads to changes in the hypothalamic-pituitary-adrenal axis and contributes to psychosis through abnormal neurodevelopment of the limbic regions, the temporal pole, cingulate cortices, parahippocampal gyrus, and caudate nucleus\u003csup\u003e100\u0026ndash;103\u003c/sup\u003e. Young adults with a history of childhood social deprivation often show impaired reward processing, particularly in the cingulate and mesostriatal dopaminergic system\u003csup\u003e25,103\u0026ndash;105\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe age of our study\u0026rsquo;s participants, 9\u0026ndash;12 years old, is a critical period for development of the prefrontal-limbic system\u003csup\u003e103,106,107\u003c/sup\u003e. Children with psychotic disorders often exhibit greater reductions in grey matter compared to their healthy peers\u003csup\u003e108,109\u003c/sup\u003e. These neurodevelopmental alterations are associated with increased neuronal excitation, reduced inhibitory neural activities, and the resultant impulsive behaviors\u003csup\u003e110\u003c/sup\u003e. In line with our findings, previous research has shown a correlation between higher PLEs and neuroanatomical alterations in the right temporal fusiform, right temporal pole, and right parahippocampal gyrus\u003csup\u003e102\u003c/sup\u003e, as well as greater neural activations in limbic regions such as the insula and cingulate cortices during reward outcomes in MID task\u003csup\u003e111\u003c/sup\u003e. Overall, our findings on the heterogeneous effects of neighborhood deprivation contribute to the growing body of literature showing the role of glucocorticoid and reward systems in modulating the adverse effects of environmental deprivation on psychosis\u003csup\u003e101,103,105,112,113\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, we discovered that children, when exposed to deprived neighborhoods and already facing the challenges of residential disadvantage, were more likely to experience PLEs. Surprisingly, these children also showed a higher GPS for cognitive performance. At first glance, this finding seems to contradict prior research, which has consistently identified a negative relationship between PLEs and cognitive performance\u003csup\u003e28,114\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo understand this complex relationship, we turned to the bioecological model and the Scarr-Rowe hypothesis on gene-environment interactions\u003csup\u003e115\u0026ndash;117\u003c/sup\u003e. This theory proposes that the impact of genetic factors is lessened in unfavorable environments. An easy way to visualize this is by comparing it to plant growth: in poor soil, a plant can't get the nutrients it needs, which limits its growth despite its genetic potential to grow tall\u003csup\u003e118\u003c/sup\u003e. But, when these children face residential disadvantages, this protective gene-psychosis link weakens. Their genetic resilience decreases, making them more vulnerable to the negative impacts of such disadvantages on PLEs. Essentially, those with higher cognitive ability GPS lose more of their potential genetic protection, making them more susceptible to the adverse effects of their environment on PLEs.\u003c/p\u003e \u003cp\u003eConsistent with our findings, recent large-scale studies have demonstrated that the impact of genetics on brain structure, cognitive functions, and mental health disorders becomes less significant in harmful environments (e.g., abuse)\u003csup\u003e119,120\u003c/sup\u003e. Conversely, in more supportive and enriched settings, like those associated with higher socioeconomic status, genetic influences are more noticeable (e.g., high socioeconomic status)\u003csup\u003e116,121,122\u003c/sup\u003e. Together with these findings, our study contributes to a deeper understanding of how genetic and environmental factors interact to influence the development of psychopathology in children.\u003c/p\u003e \u003cp\u003eIn this study, we utilized innovative causal machine learning techniques to test the negative impacts of neighborhood deprivation on childhood psychopathology. Specifically, we employed the IV Forest method that allows us to discern how residential deprivation influences children\u0026rsquo;s PLEs in a manner dependent on a variety of genetic risk factors (e.g., GPS of cognitive performance, educational attainment, and IQ\u003csup\u003e114,123,124\u003c/sup\u003e) and environmental risk factors (e.g., family income\u003csup\u003e3,125\u003c/sup\u003e), as identified in existing literature. Our findings were adjusted to account for potential biases from both observed and unobserved variables.\u003c/p\u003e \u003cp\u003eThe machine learning algorithm we used was adept at modelling the complex interplay gene-environment interactions. Among the three IV Forest models we tested (i.e., Delay Discounting, Gene-Brain, Integrated), only the Integrated model\u0026mdash;which included delay discounting, sociodemographic characteristics, and genetic and neural correlates of delay discounting\u0026mdash;identified the significant heterogeneous effects of ADI on children\u0026rsquo;s PLEs. This suggests that the intricate interactions among environmental, genetic, neural factors, and delay discounting play a crucial role in how socioeconomic adversity impacts PLEs.\u003c/p\u003e \u003cp\u003eIn contrast, traditional linear mediation analysis, which relies on predefined interaction terms in a deductive statistical framework, failed to identify any significant mediation effect of delay discounting between neighborhood deprivation and PLEs. This underscores the effectiveness of our advanced causal machine learning approach over conventional methods in detecting the subtle effects of various interacting factors on childhood psychopathology.\u003c/p\u003e \u003cp\u003eThe IV Forest model represents a significant advancement over traditional analysis methods by enabling data-driven feature selection and the stratification of heterogeneous treatment effects\u003csup\u003e59,60\u003c/sup\u003e. Unlike methods that rely on patterns predetermined by researchers, the IV Forest model inductively identifies complex and nonlinear interactions, providing a deeper and more nuanced understanding of the data. Traditional deductive approaches often suffer from low statistical power and bias\u003csup\u003e61,126\u003c/sup\u003e, which inadequately capture the complexity of gene-environment interactions\u003csup\u003e57,58\u003c/sup\u003e. For instance, employing conventional linear regression to model interactions among the 45 covariates in our Integrated model would necessitate the inclusion of over 35 trillion interaction terms. This is not only impractical due to its complexity but also prone to issues like reduced statistical power, poor interpretability, and collinearity.\u003c/p\u003e \u003cp\u003eGiven these challenges, we believe that causal modeling approaches that assess heterogeneous treatment effects based on machine learning hold significant potential as powerful tools for advancing precision science in psychology and medicine. These approaches provide a more dynamic and accurate framework for understanding the multifaceted influences on psychopathology, demonstrating significant promise for future research in these fields.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study warrant consideration. Firstly, we used ABCD Study, a non-randomized, observational cohort. Despite employing IV methods, including IV Forest and DoubleML, to adjust for both observed and potential unobserved confounders, the inherent limitation of the exclusion restriction assumption persists. This assumption, critical to the validity of the IV methods, cannot be directly verified with data. Albeit we substantiated this assumption with extensive prior research discussed in the \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/span\u003e section, its validity may still be subject to scrutiny, as might the overall efficacy of the IV method in fully adjusting for residual confounding bias. To mitigate this, we calculated E-values for the average treatment effects of neighborhood disadvantage on delay discounting and PLEs. The large E-values calculated indicate that it would require unobserved confounders with a significantly strong association with both the exposure and the outcomes to negate our findings. Given the magnitude of these E-values and our comprehensive adjustments for confounding, it is unlikely that unobserved confounding could fully account for the observed relationships, thereby supporting the potential causal interpretations, despite not providing absolute proof of causality.\u003c/p\u003e \u003cp\u003eSecondly, since the majority of participants identified their race/ethnicity as white (63.76%, similar to the US population), the generalizability of our findings to other minor race/ethnicity might remain to be tested. Nonetheless, recent research suggests that temporal discounting measures are consistent across diverse populations worldwide (61 countries, N\u0026thinsp;=\u0026thinsp;13,629)\u003csup\u003e127\u003c/sup\u003e, which may mitigate concerns regarding the representativeness of our findings. Thirdly, the relatively short follow-up periods in our study (1-year and 2-year follow-up) may not adequately capture the long-term neurodevelopmental processes underlying intertemporal valuation and related psychopathology. Notably, additional follow-up data from the ABCD Study became available after we finalized this manuscript. As the ABCD Study continues to collect more longitudinal observations, longer follow-up periods in future studies could yield deeper insights. Fourthly, despite efforts to ensure representativeness by recruiting from diverse school systems across 21 research sites in the United States, our sample does not fully mirror the entire US population\u003csup\u003e128\u003c/sup\u003e. To address this, we provide a supplementary table (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e) comparing the demographic characteristics of our final sample with the general United States population enhancing the relevance and generalizability of our results. Lastly, future research should examine the heterogeneous effects of additional environmental risk factors\u0026mdash;such as parenting behavior\u003csup\u003e28\u003c/sup\u003e and early life stress\u003csup\u003e120\u003c/sup\u003e\u0026mdash;as primary exposures to elucidate their potential causal effects on psychiatric disorders. Investigating how genetic and neural correlates interact with these risk factors will also advance our understanding of their unique contributions to individual differences in psychopathology.\u003c/p\u003e \u003cp\u003eThis study highlights the differential effects of neighborhood disadvantage on intertemporal economic decisions and PLEs during early childhood. It underscores the importance of identifying diverse treatment effects by integrating genetic and environmental factors to guide personalized healthcare approaches. Furthermore, we propose that enhancing the childhood environment could contribute to the reduction of economic and health inequality gaps. Economic policies promoting positive intertemporal choice (e.g., increased savings, healthy diet) have predominantly focused on paternalistic welfare policies in adulthood. These policies often assume that an individual\u0026rsquo;s tendency to discount future rewards is fixed (\u0026ldquo;exogenous\u0026rdquo;)\u003csup\u003e32\u003c/sup\u003e. However, our findings suggest that policies or interventions aimed at enhancing the socioeconomic environment during childhood may foster improved intertemporal choice behavior, thereby reducing economic\u003csup\u003e33\u003c/sup\u003e and health inequality\u003csup\u003e23,129\u003c/sup\u003e. By addressing the root of the problem, this indirect approach may assist individuals in developing the capacity to make more informed choices, ultimately promoting better outcomes.\u003c/p\u003e \u003cp\u003eThe insights gleaned from our novel analytical methods revive longstanding philosophical inquiries: do humans possess reason or free will independent of their environment? If our ability to act responsibly is indeed shaped by external circumstances, this challenges the traditional rationale for penalizing criminal and morally objectionable behavior based on the assumption of free will. This inquiry underscores the need for further interdisciplinary research, bridging insights from psychology, sociology, neuroscience, ethics, and law, to explore the nuanced relationship between individual agency and environmental influences. Such research is crucial for understanding how external factors impact decision-making and behavior, thereby informing more nuanced approaches to ethical and legal accountability. It invites a reevaluation of responsibility and justice, suggesting that effective interventions and policies must consider the complex interplay of individual predispositions and environmental conditions in shaping behavior.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eThe ABCD Study recruited participants from 21 research sites across the nation, utilizing a stratified, probability sampling method to capture the sociodemographic variation of the US population\u003csup\u003e130\u003c/sup\u003e. We used the baseline, first year, and second year follow-up datasets included in ABCD Release 4.0, downloaded on February 10, 2022.\u003c/p\u003e \u003cp\u003eOf the initial 11,876 ABCD samples, we removed participants without genotype data, MRI data, NIH Toolbox Cognitive Battery, delay discounting, residential address, ADI, and PLEs. Participants not meeting the ABCD Study\u0026rsquo;s MRI quality control standards were also excluded. As recommended by the ABCD team\u003csup\u003e131\u003c/sup\u003e, Johnson \u0026amp; Bickel\u0026rsquo;s two-part validity criterion\u003csup\u003e132\u003c/sup\u003e was used to exclude subjects with inconsistent responses (i.e., indifferent point for a given delay larger than that of an indifference point for a longer delay). Missing values of covariates were imputed using k-nearest neighbors. The final samples included 2,135 children from a variety of race/ethnic groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eNeighborhood Disadvantage\u003c/h2\u003e \u003cp\u003eNeighborhood disadvantage was measured with Residential History Derived Scores based on the Census tracts of each respondent\u0026rsquo;s primary addresses by the ABCD team. Consistent with prior research\u003csup\u003e3,44\u003c/sup\u003e, we chose national percentile scores of the Area Deprivation Index (ADI) in baseline year, calculated from the 2011\u0026thinsp;~\u0026thinsp;2015 American Community Survey 5-year summary. It has 17 sub-scores regarding various socioeconomic factors such as median household income, income disparity, percentage of population aged more than 25 years or more with at least a high school diploma, and percentage of single-parent households with children aged less than 18 years, etc. Higher values of the ADI indicate greater neighborhood disadvantage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eDelay Discounting\u003c/h2\u003e \u003cp\u003eDelay discounting was measured by the adjusting delay discounting task in the 1-year follow-up ABCD data\u003csup\u003e131,133\u003c/sup\u003e. Each child was asked to make choices between a small immediate hypothetical reward or a larger hypothetical \u003cspan\u003e$\u003c/span\u003e100 delayed reward at multiple future time points (6h, one day, one week, one month, three months, one year, and five years). By increasing or decreasing the smaller immediate reward depending on the child\u0026rsquo;s response, the task records the indifference point (i.e., the small immediate amount deemed to have the same subjective value as the \u003cspan\u003e$\u003c/span\u003e100 delayed reward) at each of the seven delay intervals. Test-retest reliability of this delay discounting measure has been validated\u003csup\u003e134,135\u003c/sup\u003e. Studies show that preadolescent children are capable of comprehending the delay discounting task and show similar patterns of discounting as adults\u003csup\u003e136\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo avoid methodological problems regarding mathematical discounting models (hyperbolic vs. exponential) and positively skewed parameters of discounting functions\u003csup\u003e135,137\u003c/sup\u003e, we used the area under the curve, a model-free measure of delay discounting\u003csup\u003e137\u003c/sup\u003e. The area under the curve measure of delay discounting rates (henceforth \u003cem\u003ediscount rates\u003c/em\u003e) ranges from 0 to 1, with lower values indicating steeper discounting and higher impulsivity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003ePsychotic-Like Experiences\u003c/h3\u003e\n\u003cp\u003eFirst and second-year follow-up observations of psychotic-like experiences (PLEs) were measured using the Prodromal Questionnaire-Brief Child Version (PQ-BC; child-reported). PQ-BC has a 21-item scale validated for use with a non-clinical population of children aged 9\u0026ndash;10 years\u003csup\u003e138,139\u003c/sup\u003e. In line with the previous research\u003csup\u003e3,123,138,139\u003c/sup\u003e, we computed \u003cem\u003eTotal Score\u003c/em\u003e and \u003cem\u003eDistress Score\u003c/em\u003e, each indicating the number of psychotic-like symptoms and levels of total distress. Total Score is the summary score of 21 questions ranging from 0 to 21, and Distress Score is the weighted sum of responses with the levels of distress, ranging from 0 to 126. Additionally, to test whether the heterogeneous treatment effects of neighborhood adversity differ among psychotic symptoms, Distress Score was divided into two separate scores: \u003cem\u003eDelusional Score\u003c/em\u003e and \u003cem\u003eHallucinational Score\u003c/em\u003e\u003csup\u003e2,140\u003c/sup\u003e. A higher value indicates greater severity of PLEs.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide Polygenic Scores\u003c/h2\u003e \u003cp\u003eChildren\u0026rsquo;s genetic predispositions were assessed with genome-wide polygenic scores (GPS). Summary statistics from genome-wide association studies were used to generate GPS of cognitive intelligence (cognitive performance\u003csup\u003e141\u003c/sup\u003e, education attainment\u003csup\u003e141\u003c/sup\u003e, IQ\u003csup\u003e142\u003c/sup\u003e), psychiatric disorders (major depressive disorder\u003csup\u003e143\u003c/sup\u003e, post-traumatic stress disorder\u003csup\u003e144\u003c/sup\u003e, attention-deficit/hyperactivity disorder\u003csup\u003e145\u003c/sup\u003e, obsessive-compulsive disorder\u003csup\u003e146\u003c/sup\u003e, anxiety\u003csup\u003e147\u003c/sup\u003e, depression\u003csup\u003e148\u003c/sup\u003e, bipolar disorder\u003csup\u003e149\u003c/sup\u003e, autism spectrum disorder\u003csup\u003e150\u003c/sup\u003e, schizophrenia\u003csup\u003e151\u003c/sup\u003e, cross disorder\u003csup\u003e152\u003c/sup\u003e), and health and behavioral traits (BMI\u003csup\u003e153\u003c/sup\u003e, neuroticism\u003csup\u003e154\u003c/sup\u003e, worrying\u003csup\u003e154\u003c/sup\u003e, risk tolerance\u003csup\u003e155\u003c/sup\u003e, automobile speeding propensity\u003csup\u003e155\u003c/sup\u003e, eating disorder\u003csup\u003e156\u003c/sup\u003e, drinking\u003csup\u003e155\u003c/sup\u003e, smoking\u003csup\u003e155\u003c/sup\u003e, cannabis use\u003csup\u003e157\u003c/sup\u003e, general happiness\u003csup\u003e158\u003c/sup\u003e, snoring\u003csup\u003e159\u003c/sup\u003e, insomnia\u003csup\u003e159\u003c/sup\u003e, alcohol dependence\u003csup\u003e160\u003c/sup\u003e). PRS-CSx, a high-dimensional Bayesian regression framework that places continuous shrinkage prior on single nucleotide polymorphisms effect sizes\u003csup\u003e161\u003c/sup\u003e, was applied to enhance cross-population prediction. This method has consistently shown superior performance compared to other methods across a wide range of genetic architectures in simulation and real data analyses\u003csup\u003e161\u003c/sup\u003e. Hyperparameter optimization for the GPSs was conducted using a held-out validation set of 1,579 unrelated participants. Adjustments for population stratification were performed based on the first ten ancestrally informative principal components to account for potential confounding effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnatomical Brain Imaging: T1/T2, Freesurfer 6\u003c/h2\u003e \u003cp\u003eBaseline year T1-weighted (T1w) 3D structural MRI acquired in the ABCD study were processed following established protocols\u003csup\u003e162,163\u003c/sup\u003e: To maximize geometric accuracy and image intensity reproducibility, gradient nonlinearity distortion was corrected\u003csup\u003e164\u003c/sup\u003e. After correcting intensity nonuniformity using tissue segmentation and spatial smoothing, images were resampled to 1 mm isotropic voxels. We used Freesurfer v6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the following procedures: cortical surface followed by skull-stripping\u003csup\u003e165\u003c/sup\u003e, white matter segmentation, and mesh creation\u003csup\u003e166\u003c/sup\u003e, correction of topological defects, surface optimization\u003csup\u003e167\u003c/sup\u003e, and nonlinear registration to a spherical surface-based atlas\u003csup\u003e168,169\u003c/sup\u003e. Using Desikan\u0026ndash;Killiany atlas\u003csup\u003e170\u003c/sup\u003e, a standard atlas for Freesurfer and ABCD study, we extracted 399 brain ROI measures, including volumes, surface area, thickness, mean curvature, sulcal depth, and gyrification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFunctional MRI (fMRI): Monetary Incentive Delay (MID) task\u003c/h2\u003e \u003cp\u003eThe MID task was used measure the neural activation during anticipation and receipt of monetary gains and losses. In each trial, participants were shown a graphical cue of the 5 possible incentive types: large reward (\u003cspan\u003e$\u003c/span\u003e5), small reward (\u003cspan\u003e$\u003c/span\u003e0.20), large loss (-\u003cspan\u003e$\u003c/span\u003e5), small loss (-\u003cspan\u003e$\u003c/span\u003e0.20), or neutral (\u003cspan\u003e$\u003c/span\u003e0). The incentive cue is presented for 2,000 ms, followed by a jittered anticipatory delay (1,500\u0026ndash;4,000 ms). Subsequently, a target to which participants respond to gain or avoid losing money was shown (150\u0026ndash;500 ms), and feedback of their performance was provided (2,000 ms). A total of 40 reward, 40 loss, and 20 neutral trials were presented in pseudo-random order across the two task runs. Task parameters was dynamically manipulated for each subject to maintain 60% success rate\u003csup\u003e162\u003c/sup\u003e. We used baseline year observations of average beta weights of the MID task fMRI with Desikan-Killiany parcellations\u003csup\u003e170\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eTo adjust for the potential confounding effects, sociodemographic covariates were included. Consistent with existing research on psychiatric disorders in ABCD samples\u003csup\u003e3,123,138,171\u003c/sup\u003e, we controlled for the child\u0026rsquo;s sex, age, race/ethnicity, caregiver\u0026rsquo;s relationship to a child, BMI, parental education, marital status of the caregiver, household income, parent\u0026rsquo;s age, and family history of psychiatric disorders. The family history of psychiatric disorders, measured as the proportion of first-degree relatives who experienced psychosis, depression, mania, suicidality, previous hospitalization, or professional help for mental health issues\u003csup\u003e3\u003c/sup\u003e was included as a covariate. Given that delay discounting and PLEs are associated with an individual's neurocognitive capabilities\u003csup\u003e172\u0026ndash;174\u003c/sup\u003e, NIH Toolbox total intelligence was used as a covariate. All covariates were from baseline year observations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eInstrumental Variable Regression\u003c/h2\u003e \u003cp\u003eThe IV method controls unobserved confounding bias by utilizing an instrumental variable Z which affects the treatment/exposure variable of interest X but has no direct effect on the outcome variable Y\u003csup\u003e62\u003c/sup\u003e. We tested the endogeneity of ADI (i.e., whether ADI as a treatment/exposure variable correlates with the error term), and found significant bias from unobserved confounding (all Hausman test\u003csup\u003e175\u003c/sup\u003e for differences, p\u0026thinsp;\u0026le;\u0026thinsp;0.0158). This underscores the necessity of employing IV regression approach to control for the significant confounding effects and to test the potential causal relationship of neighborhood disadvantage with delay discounting and PLEs.\u003c/p\u003e \u003cp\u003eThe IV method relies on two main assumptions: the exclusion restriction and the strong instrument. The exclusion restriction asserts that the instrumental variable impacts the outcome Y exclusively through the treatment/exposure X, conditioned on observed covariates. Although this assumption cannot be directly tested from data, its plausibility is typically drawn from prior research and theoretical underpinnings\u003csup\u003e62\u003c/sup\u003e. In our study, the instrument variable Z for the exposure of ADI was the presence of state-level source of income (SOI) laws at baseline assessment, which prohibit income discrimination in the housing market. SOI laws are designed to ensure that landlords cannot refuse housing vouchers, which are provided to low-income families to assist in securing quality housing. Such legislation is critical because, despite the intention behind vouchers, many landlords prefer direct cash payments and might otherwise decline voucher-based payments. Thus, families residing in states with SOI laws are more likely to have better residential environments (i.e., lower neighborhood socioeconomic adversity).\u003c/p\u003e \u003cp\u003eReports from the US Department of Housing and Urban Development indicate that SOI laws increase landlords\u0026rsquo; acceptance of housing vouchers by 20.2%p to 59.3%p\u003csup\u003e176\u003c/sup\u003e. Research links SOI laws with significant reductions in neighborhood poverty\u003csup\u003e177\u003c/sup\u003e and improved health outcomes in children, including lower hospitalization rates, less impulsive consumption\u003csup\u003e178\u003c/sup\u003e, and substantially better mental health\u003csup\u003e179\u003c/sup\u003e. Taken together, the presence of SOI laws may affect cognitive and psychiatric outcomes\u0026mdash;particularly delay discounting and PLEs\u0026mdash;solely by enhancing neighborhood environment, conditional on the individual sociodemographic characteristics such as family income, parental education, and race/ethnicity. This relationship supports the plausibility of the exclusion restriction, crucial for the validity of our IV method.\u003c/p\u003e \u003cp\u003eThe second assumption requires the instrument variable to be strongly associated with the treatment/exposure X. F-statistic above ten is considered a strong instruments\u003csup\u003e180\u003c/sup\u003e. The F-statistic for each model was F\u0026thinsp;=\u0026thinsp;34.031 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), suggesting that the instrument SOI laws is strongly associated with the treatment ADI. In other words, our IV model is not likely to suffer from weak instrument bias.\u003c/p\u003e \u003cp\u003eAll continuous variables were standardized (z-scaled), and analyses were run using \u003cem\u003eivreg\u003c/em\u003e\u003csup\u003e181\u003c/sup\u003e in R version 4.1.2. For all analyses in our study, threshold for statistical significance was set at two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with multiple comparison correction based on false discovery rate.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCausal Machine Learning for Treatment Effects\u003c/h2\u003e \u003cp\u003eIV Forest (\u003cem\u003egrf\u003c/em\u003e R package version 2.2.1)\u003csup\u003e59,60\u003c/sup\u003e is a novel causal machine learning approach extends from the conventional random forest framework\u003csup\u003e182\u003c/sup\u003e with recursive partitioning, subsampling, and random splitting to identify the average treatment effects and its individual differences.\u003c/p\u003e \u003cp\u003eInitially, the IV Forest randomly splits the dataset into two independent subsets, S and T. Subset S is dedicated solely to the construction of individual trees within the forest, where each tree explores potential divisions\u0026mdash;such as \"Race/ethnicity\u0026thinsp;=\u0026thinsp;White\"\u0026mdash;to split S into groups \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{2}\\)\u003c/span\u003e\u003c/span\u003e, based on the fulfillment of the specified criteria. The selection of these splits is strategically chosen to maximize the differences in conditional average treatment effects estimates between the groups \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{2}\\)\u003c/span\u003e\u003c/span\u003e. Following the construction phase, subset T, independent of S, is employed for model validation. Fresh observations from T are introduced into groups with similar treatment responses by each tree. The aggregation of results from multiple trees is conducted through local weighting method, aimed at reducing overall estimate variance and improving accuracy. Using separate subsets for tree building and model validation ensures an honest estimation of conditional average treatment effects, systematically reducing the risk of overfitting\u003csup\u003e67\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing IV Forests, we obtained augmented inverse propensity weighted estimates of average treatment effects, a doubly-robust estimator which can capture complex patterns of individual differences and do not rely on a priori model assumptions\u003csup\u003e59\u003c/sup\u003e such as linearity. This is particularly advantageous when the relationship between environmental variables and neurocognitive development is likely nonlinear\u003csup\u003e7,183,184\u003c/sup\u003e. To measure the average outcome between treated versus untreated subjects, ADI was binarized (i.e., mean split).\u003c/p\u003e \u003cp\u003eIn line with prior studies\u003csup\u003e75,76\u003c/sup\u003e, we evaluated heterogeneous treatment effects by testing whether the average treatment effects are significantly different among subgroups defined by their relative resilience/vulnerability\u003csup\u003e77\u003c/sup\u003e. These subgroups were defined across a decile spectrum, with Q1 representing the most vulnerable and Q10 the most resilient. We considered a model to have significant heterogeneous treatment effects only if it satisfied all three of the following criteria:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe monotonicity test evaluates the existence of at least one inequality in the average treatment effects across the deciles. This is achieved by whether to accept or reject the null hypothesis (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mathcal{H}}_{0})\\)\u003c/span\u003e\u003c/span\u003e, which states that treatment effects are equal across all deciles. Essentially, the test determines whether there is a consistent, ordered relationship in the treatment effects from one decile to the next, indicating a monotonic trend.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe alternative hypothesis test evaluates whether the average treatment effect in the highest decile exceeds the combined average treatment effects in the remaining deciles Q2 through Q10\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe ANOVA test determines whether the average treatment effects are statistically different across deciles. In this context, the group mean in the ANOVA corresponds to the average treatment effect of each decile.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo ensure that the IV Forest estimations are robust across different random seeds, we developed 100-seed ensemble IV Forest model. Specifically, we used the following procedures:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor each iteration, randomly split the data in half (i.e., train vs test sets) to build a forest model with the first half and perform estimation with the other half. We repeated this process 100 times using different seeds in each iteration to build 100 forest models.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCombine the 100 forest models into one big IV Forest model and then rank the observations into deciles according to their estimated conditional average treatment effects.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eObtain augmented inverse propensity weighted average treatment effects for each decile and perform monotonicity, alternative hypothesis, and ANOVA tests.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe ABCD Study dataset is openly available to all eligible researchers upon the submission of an access request via the National Institutes of Mental Health Data Archive (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nda.nih.gov/abcd\u003c/span\u003e\u003c/span\u003e). Comprehensive written informed consent was obtained from the parents of participants, with children providing assent. The study protocols were approved by the University of California, San Diego\u0026apos;s Institutional Review Board (IRB), under approval number 160091, in addition to receiving approval from the IRBs of the 21 participating data collection sites\u003csup\u003e185\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAll codes needed to replicate the results can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Transconnectome/DD-HTE\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.P. and J.C. designed research; J.P. and M.C. performed research; J.P., M.C., E.L., B.-G.K., G.K., Y.Y.J. analyzed data; and J.P., M.C., and J.C. wrote the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKant, I. \u003cem\u003eCritique of Practical Reason\u003c/em\u003e. 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DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dcn.2018.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.dcn.2018.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Intertemporal reward valuation, Psychotic-like experiences, Causal machine learning, Childhood socioeconomic environment, Heterogeneous treatment effects","lastPublishedDoi":"10.21203/rs.3.rs-4618474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4618474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study elucidates the influence of socioeconomic environments on neurodevelopment and psychiatric vulnerability in children. Employing advanced machine learning-based causal inference (IV Forest), we analyzed the impact of neighborhood socioeconomic deprivation on delay discounting and psychotic-like experiences (PLEs) among 2,135 children. Our findings reveal that greater neighborhood deprivation correlates with increased future reward discounting and elevated PLEs, particularly hallucinational symptoms, over 1-year and 2-year follow-ups. Vulnerable children in these settings exhibited notable neuroanatomical changes, including reduced limbic volume, surface area, and white matter, and heightened BOLD reactivity in the prefrontal-limbic system during reward tasks. These findings highlight the complex interplay between environmental factors and brain reward mechanisms in shaping PLE risk, advocating for early, targeted interventions in socioeconomically disadvantaged communities. This research not only extends our understanding of environmental influences on child psychology but also guides personalized intervention strategies and prompts reflection on broader societal impacts.\u003c/p\u003e","manuscriptTitle":"Individual Differences in the Effects of Neighborhood Socioeconomic Deprivation on Intertemporal Decision-Making and Psychotic-Like Experiences in Children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 15:18:52","doi":"10.21203/rs.3.rs-4618474/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f62c93e1-6d8f-48d7-9e14-2ce8103f9fc6","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":33777960,"name":"Biological sciences/Neuroscience/Social neuroscience"},{"id":33777961,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Decision"},{"id":33777962,"name":"Health sciences/Diseases/Psychiatric disorders/Psychosis"}],"tags":[],"updatedAt":"2024-07-12T15:18:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-12 15:18:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4618474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4618474","identity":"rs-4618474","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}