Shared Neural Signatures of Socioeconomic Status, Scarcity, and Neighborhood Threat in Youth

Shared Neural Signatures of Socioeconomic Status, Scarcity, and Neighborhood Threat in Youth | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Shared Neural Signatures of Socioeconomic Status, Scarcity, and Neighborhood Threat in Youth View ORCID Profile K. Brosch , L. Wiersch , View ORCID Profile E. Christensen , View ORCID Profile D. Rakesh , View ORCID Profile J. Hanson , View ORCID Profile R.M. Schwartz , View ORCID Profile E. Dhamala doi: https://doi.org/10.1101/2025.06.11.659188 K. Brosch 1 Northwell Health , New Hyde Park, NY, USA 2 Institute of Behavioral Science, Feinstein Institutes for Medical Research , Manhasset, NY, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for K. Brosch For correspondence: kathi.brosch{at}gmail.com L. Wiersch 1 Northwell Health , New Hyde Park, NY, USA 2 Institute of Behavioral Science, Feinstein Institutes for Medical Research , Manhasset, NY, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site E. Christensen 1 Northwell Health , New Hyde Park, NY, USA 2 Institute of Behavioral Science, Feinstein Institutes for Medical Research , Manhasset, NY, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for E. Christensen D. Rakesh 3 Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King’s College London , London, UK PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for D. Rakesh J. Hanson 4 Department of Psychology, University of Pittsburgh , Pittsburgh PA USA 5 Learning Research and Development Center, University of Pittsburgh , Pittsburgh PA USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for J. Hanson R.M. Schwartz 1 Northwell Health , New Hyde Park, NY, USA 2 Institute of Behavioral Science, Feinstein Institutes for Medical Research , Manhasset, NY, USA 6 Department of Occupational Medicine, Epidemiology and Prevention, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell , Hempstead, NY, USA 7 Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell , Hempstead, NY, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for R.M. Schwartz E. Dhamala 1 Northwell Health , New Hyde Park, NY, USA 2 Institute of Behavioral Science, Feinstein Institutes for Medical Research , Manhasset, NY, USA 7 Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell , Hempstead, NY, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for E. Dhamala Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Early life adversity is a known risk factor for psychopathology, yet the neurodevelopmental impacts of distinct types of adversity remain unclear. Using machine learning, we examined how adversity (physical and sexual abuse, neighborhood threat, scarcity, household dysfunction, prenatal substance exposure, parental psychopathology) and socioeconomic status are associated with cortical thickness in youth. We used data from the Adolescent Brain Cognitive Development Study at three time points: baseline ( N =6,908, ages 9–10), two-year follow-up ( N =5,808), and four-year follow-up ( N =2,245). Cortical thickness was linked to socioeconomic status and neighborhood threat at all time points, and scarcity at baseline. The strongest negative associations were in medial temporal and occipital regions. Our analyses revealed neural effects of socioeconomic status, neighborhood threat, and scarcity, converging on regions involved in memory, visual processing, and higher-order cognition. These findings suggest consistent neural signatures linked to socioeconomic disadvantage, highlighting the importance of addressing inequality to promote neurodevelopmental health. Main Early life adversity encompasses a wide spectrum of pre- and postnatal stressors that occur during sensitive periods and can shape emotional, cognitive, physical and brain development 1 – 3 . Such early life stressors include prenatal substance exposure, sexual abuse, parental psychopathology, neglect, economic hardship, neighborhood violence, community violence, and housing instability 1 , 4 – 6 . Both chronic exposure to early life adversity and isolated acute incidents have been associated with changes in brain development and elevated risk for mental health problems 5 , 7 – 15 . Exposure to early life adversities can disrupt brain maturation, stress regulation, and socioemotional development, even in the absence of clinical symptoms 9 , 16 . Previous research on early life adversity has often relied on small samples, univariate models, or examined only one adversity factor at a time 10 , 17 – 19 . This fragmented approach has limited our understanding of how different adversity types may interact or share common neural pathways, making it difficult to capture the full complexity of early life adversity’s impact on development 20 . There is a growing need for large-scale, data-driven approaches that can capture both shared and distinct neural influences of different forms of early life adversity on adolescent development 21 , 22 . To better characterize adversity, dimensional models have proposed distinctions such as threat, deprivation, and unpredictability 23 , 24 . Threat-related adversity includes exposure to violence, sexual or physical abuse, and neighborhood violence, whereas deprivation encompasses reduced cognitive and emotional input, including neglect and resource scarcity 25 . In earlier studies, these dimensions show distinct neurodevelopmental signatures: threat was associated with smaller cortical surface area in the prefrontal cortex, while deprivation has been linked to larger cortical thickness in the occipital cortex, insula, and cingulate 26 , 27 . Cortical thickness refers to the width of gray matter of the human cortex and is a widely used marker of structural brain development 28 – 30 . During adolescence, cortical thickness decreases as part of normative brain development, driven by processes such as myelination, synaptic pruning, and pubertal maturation 31 , 32 . Notably, smaller cortical thickness has been consistently linked to adversity and psychopathology, underscoring its relevance when studying early life adversity 33 . While the underlying mechanisms are still being uncovered, evidence from both clinical and non-clinical samples suggests that early adversity is associated with a thinner cortex. Similar patterns across various psychiatric conditions further highlight its role as a transdiagnostic marker of neurodevelopmental risk 32 – 34 . Building on general patterns of adversity, emerging research has begun to characterize how different forms of early adversity may exert distinct effects on brain structure. When examining specific threat-related adversity types, childhood sexual abuse has been associated with enduring alterations, including reduced cortical thickness in the somatosensory cortex regions representing the genitalia, decreased gray matter volume in visual cortices, and increased cerebellar volume 10 , 11 , 18 . Physical abuse has similarly been linked to structural changes in frontolimbic circuits, although findings vary depending on the timing, chronicity, and presence of co-occurring exposures 35 , 36 . Structural brain alterations have also been reported in association with deprivation-related adversity. Neighborhood deprivation, which often includes limited access to resources in a child’s immediate environment has been linked to lower cortical thickness in multiple frontal, parietal and occipital regions, as well as lower overall subcortical volumes 25 , 37 , 38 . In contrast, institutional deprivation, marked by extreme, prolonged lack of caregiving and stimulation, such as in certain orphanage settings, has been associated with reduced gray matter volume in medial prefrontal, inferior frontal, and temporal cortices 39 . Most research has examined early adversity by focusing on one type of exposure or applying a priori theoretical models. These approaches may overlook the complexity of lived experience, as real-world adversity often cuts across boundaries. Not all forms of early life adversities map cleanly onto either threat, deprivation, or unpredictability, highlighting limitations of a priori theoretical models. For instance, household dysfunction includes experiences that may include all domains of threat, deprivation, and unpredictability 23 , 24 , 26 , 40 . Similarly, prenatal substance exposure, including exposure to alcohol, cannabis, or other specific substances in utero, represents a biologically embedded adversity that occurs prior to birth and exerts lasting effects despite its fixed timing 19 , 41 – 43 . These exposures are also often correlated with postnatal adversity, compounding developmental risk 42 , 44 . Prenatal substance exposure has been linked to widespread neurodevelopmental alterations, including reduced cortical thickness, disrupted gyrification, and atypical maturation patterns in regions critical for language, attention and cognition 45 . In children and adolescents, prenatal alcohol exposure was associated with smaller cortical thickness and less gyrification in the hippocampus, and smaller surface area in the middle temporal gyrus 41 , 43 . These brain alterations were found to mediate the relationship between prenatal alcohol exposure and executive functioning deficits 41 . Prenatal cannabis exposure has been associated with smaller global gray matter volume, higher risk for internalizing and externalizing symptoms, psychotic- like experiences, sleep problems, and poorer cognitive performance 19 , 43 . The associations between adversity and brain development are shaped by age, intensity, and type of exposure, yet these dimensions are rarely considered together. Different developmental stages involve distinct patterns of brain maturation, and adversity experienced during these windows can have varying impacts on neurodevelopment 4 , 31 . Moreover, adversity effects are dose-dependent, with each additional exposure during adolescence raising the likelihood of an adult psychiatric disorder by 52 %, illustrating how chronic or repeated adversity compounds risk 46 . Together, these findings underscore the need for models that can account for the complexity of adversity’s impact across development 9 , 17 , 47 , 48 . Large-scale datasets, such as The Adolescent Brain and Cognitive Development (ABCD) Study, provide an excellent resource to address these complexities. By following over 11,000 children from middle childhood into early adulthood, the study is collecting comprehensive data on mental health, brain development, early life adversity, and environmental influences 49 . Through its longitudinal assessment, the ABCD Study enables the investigation of how type and severity of adversity, age, and sex influence trajectories of brain development 49 , 50 . Data-driven approaches are especially useful in this context, as they allow for the detection of multivariate patterns in highly co-occurring exposures while minimizing reliance on a priori theoretical assumptions 21 . In a recent data-driven effort, Orendain and colleagues used exploratory factor analysis across 14 ABCD baseline questionnaires to derive six latent adversity factors, which they labeled “physical and sexual violence”, “prenatal substance exposure”, “scarcity”, “parental psychopathology”, “neighborhood threat”, and “household dysfunction” 6 . Each factor captured a specific domain of adversity. As an example, scarcity reflected material deprivation such as food insecurity and utility shutoffs, while neighborhood threat encompassed perceived danger and crime in the child’s community. These adversity factors varied in prevalence, with some form of parental psychopathology reported in over 80% of participants and physical and sexual violence reported in 7.3% 6 . All adversity dimensions, except scarcity, were significantly associated with increased internalizing (anxious, depressed, withdrawn) and externalizing (rule-breaking, aggression) symptoms. Moreover, adversity exposure was disproportionately reported among racially and ethnically minoritized youth and those from lower socioeconomic backgrounds, consistent with prior literature on structural inequality and developmental risk 6 , 51 . Together, these findings highlight that adversity rarely occurs in isolation and is often embedded within broader socioeconomic conditions. Further, socioeconomic status is closely tied to many forms of adversity and must be considered when studying early life adversity. It represents both an independent risk factor for altered neurodevelopment and often a proxy for exposure to other adversities 22 , 52 – 55 . This multifaceted construct can be captured using indicators such as household income, income-to-needs ratio, and parental education 5 , 25 , 56 . Children from lower socioeconomic backgrounds are disproportionately exposed to early life adversity, contributing to long-term developmental vulnerability 57 . Socioeconomic status itself has been linked to poorer cognitive and academic performance, greater mental health risk, and altered brain development 6 , 25 , 51 , 58 – 61 . These neural differences are especially evident in prefrontal regions and limbic regions involved in emotion and memory, including the amygdala and hippocampus 60 – 63 . Previous work has documented sex-specific differences in the prevalence of certain early life adversities, such as childhood sexual abuse 64 . In addition to differences in prevalence, adversity may also have sex-specific effects on neurodevelopment, underscoring the importance of sex-stratified analyses 10 , 65 , 66 . While some research has focused on specific subgroups, such as young women, large-scale studies directly comparing adversity-related brain effects across sexes remain scarce 10 , 18 , 20 . In this study, we use brain-based predictive models in the ABCD dataset to examine how six dimensions of early life adversity and socioeconomic status (measured using household income) relate to cortical thickness across three timepoints in adolescence (ages 9–14) 6 . We focus on cortical thickness, as it is a sensitive marker of brain development with robust links to early adversity 32 , 33 . We applied multivariate machine learning to capture distributed cortical patterns that univariate approaches may overlook, allowing us to investigate both shared and distinct effects of adversity across time and by sex 67 . Based on prior work, we expected predominantly negative associations with cortical thickness, distinct patterns for threat- and deprivation-related adversity, and sex-specific effects for physical and sexual abuse 4 , 10 , 18 , 25 , 32 , 33 , 63 , 68 . Our findings revealed consistent prediction patterns for neighborhood threat, scarcity, and socioeconomic status across timepoints, suggesting that these exposures may reflect enduring, brain-based signatures of broader socioeconomic disadvantage. By identifying adversity dimensions with stable cortical associations over development, our work highlights neurobiological pathways through which adversity may shape mental and behavioral health and underscores the need to address structural inequality early in life. Results We used separate brain-based predictive models to explore how patterns of cortical thickness are linked to different types of early life adversity and socioeconomic status at three time-points. To do this, we applied a multivariate approach that considers all brain regions simultaneously rather than one at a time. By using brain features to predict adversity exposure, we aimed to capture multivariate (i.e., high-dimensional) associations across the cortex. This allowed us to investigate which patterns across the brain were most strongly associated with each adversity factor. While we use the term “prediction”, these models reflect associations observed at the same point in time and do not imply causality. We first evaluated whether the models could reliably predict adversity scores and then identified the brain regions that contributed most to those predictions. Sample Characteristics and Measurement Overview To ensure data quality, we filtered participants based on data completeness and quality control criteria (see Supplementary Figures 1, 3, and 5). This filtering resulted in a final sample that included a higher proportion of White participants and youth from higher-income households, and a lower proportion of Black and Asian participants compared to those excluded (see Supplementary Table 1). Distributions of adversity measures are shown in Supplementary Figures 2A, 4A, and 6A. Adversity measures showed weak to moderate correlations (Spearman’s r = 0.03-0.35, p <.001; Supplementary Figures 2B, 4B, and 6B), indicating that each measure captured a distinct domain of early life adversity. We conducted cross-sectional analyses at three time-points to examine associations between adversity and cortical thickness. As a result, participants may have been included in one, two, or all three time points. Sample overlap across time-points is visualized in Supplementary Figure 7. A total of 1,275 participants were included at all three time points. At baseline, cortical thickness significantly predicts neighborhood threat, scarcity, and socioeconomic status At baseline, data were available for seven factors: the six adversity factors (physical abuse and sexual abuse, neighborhood threat, scarcity, household dysfunction, prenatal substance exposure, parental psychopathology), and socioeconomic status. Cortical thickness was significantly associated with neighborhood threat (prediction accuracy, r =0.141, p FDR <0.001), scarcity ( r =0.131, p FDR <0.001), and socioeconomic status ( r =0.274, p FDR <0.001). Cortical thickness was not significantly associated with physical and sexual abuse ( r =-0.004, p FDR =0.550), prenatal substance exposure ( r =0.028, p FDR =0.200), household dysfunction ( r =0.028, p FDR =0.327), or parental psychopathology ( r =0.046, p FDR =0.067). Prediction accuracies for all models and time-points are visualized in Figure 1 . Feature weights are visualized for the significant models in Figure 2 . Download figure Open in new tab Figure 1: Model prediction accuracies (correlation coefficient between observed and predicted scores) for models trained to predict cortical thickness at the three time-points, top panel: baseline, middle panel: two-year follow-up, bottom panel: four-year follow-up. Asterisks indicate significant predictive models after FDR-error correction. The shape of the violin plots indicates the distribution of values, the dashed lines indicate the median, and the dotted lines indicate the interquartile range. Download figure Open in new tab Figure 2: Relative associations between cortical thickness and significant factors at the three time-points. Deeper green colors indicate a stronger negative association, deeper yellow colors indicate a stronger positive association. To facilitate visualization, association values were divided by the maximum value for that model. Lateral (outer) and medial (inner) surfaces for left (left) and right (right) hemispheres are shown. At two-year follow-up, cortical thickness significantly predicts neighborhood threat and socioeconomic status At two-year follow-up, data were available for four factors: physical and sexual abuse, neighborhood threat, household dysfunction, and socioeconomic status. Cortical thickness was significantly associated with neighborhood threat ( r =0.115, p FDR =0.002) and socioeconomic status ( r =0.261, p FDR <0.001), but not with physical and sexual abuse ( r =0.017, p FDR =0.264) or household dysfunction ( r =0.021, p FDR =0.253). At four-year follow-up, cortical thickness significantly predicts neighborhood threat and socioeconomic status At four-year follow-up, data on neighborhood threat, household dysfunction and socioeconomic status were available. Cortical thickness was significantly associated with neighborhood threat ( r =0.085, p FDR =0.036) and socioeconomic status ( r =0.223, p FDR <0.001). There was no significant association with household dysfunction ( r =0.017, p FDR =0.636). Brain-Adversity Associations reveal few sex-specific findings We conducted separate sex-specific analyses for boys and girls, which revealed similar findings and feature weights across the three time points compared to the combined sample (see Supplementary Figures 8-10). Notably, at baseline, some regions showed descriptively higher feature weight scores in girls compared to boys, despite the overall similarity in patterns. While our results revealed few sex-specific differences, conducting sex-stratified analyses remains essential given known sex differences in the prevalence and impact of certain adversity types, such as childhood sexual abuse. Such analyses can uncover nuanced effects even when overall patterns appear similar. Overall, these findings suggest that, within the available data, socioeconomic status and neighborhood threat are consistently associated with cortical thickness across all three time-points. We then Haufe-transformed the feature weights to increase reliability and interpretability and examined the specific associations captured by each model 69 , 70 . Here, we focus our results on models that yielded significant results. Unlike traditional associations, feature weights reflect how much each region contributed to the model’s ability to predict adversity or socioeconomic status. The figures below visualize this relative importance of each brain region based on its contribution to the individual predictions. Neighborhood threat, scarcity, and socioeconomic status influence overlapping brain regions At baseline , neighborhood threat, scarcity, and socioeconomic status mapped onto overlapping regions, which were predominantly the bilateral medial temporal and occipital regions. We detected mostly negative associations, indicating smaller cortical regional thickness was associated with higher adversity exposure. Here, we detected the strongest associations for all three measures in the bilateral parahippocampal and entorhinal cortices, bilateral cunei, bilateral lingual gyri, bilateral pericalcarine and lateral occipital cortices, and bilateral temporal poles. We identified positive associations (indicating greater cortical thickness was associated with higher adversity exposure) for all three measures in the bilateral rostral and caudal anterior cingulate, and bilateral frontal poles ( Figure 2 ). At two-year follow-up, neighborhood threat and socioeconomic status were associated with cortical thickness in similar regions, which were in turn similar to the associations observed at baseline. The strongest negative associations were in the bilateral parahippocampal and entorhinal cortices, bilateral cunei, bilateral lingual gyri, bilateral pericalcarine and lateral occipital cortices, and bilateral temporal poles. Positive associations were observed in the bilateral rostral and caudal anterior cingulate, and bilateral frontal poles ( Figure 2 ). At four-year follow-up, associations between cortical thickness and socioeconomic status were very similar to those observed at both baseline and two-year follow-up. The strongest negative associations were in the bilateral parahippocampal cortices, bilateral temporal poles, bilateral lateral occipital and pericalcarine gyri, right cuneus and left transverse temporal. Positive associations were found in the right rostral and caudal anterior cingulate cortex and right frontal pole ( Figure 2 ). Distinct adversity factors and socioeconomic status map onto shared brain regions within and across time-points To assess whether significant models predicting different factors implicated similar brain regions, we calculated cosine similarities between feature weight vectors, allowing us to evaluate the spatial similarity of brain–exposure associations across models. Cosine similarity values closer to 1 indicate a greater overlap in the spatial distribution of feature weights, suggesting that the models rely on similar brain regions to predict different exposures. At baseline, neighborhood threat, scarcity, and socioeconomic status were associated with cortical thickness in overlapping brain regions (cosine similarity, s=0.98-0.99), indicating nearly perfect overlap. At two-year follow-up, socioeconomic status and neighborhood thread were associated with cortical thickness in overlapping brain regions (s=0.98). At four-year follow-up, socioeconomic status and neighborhood threat were again associated with cortical thickness in overlapping brain regions (s=0.98), see Supplementary Figure 8. To investigate if these associations were stable across different time-points, we also calculated cosine similarities for the models that captured significant associations across baseline, two-year, and four-year follow-up (see Supplement). Cosine similarities across time-points and models were consistently very high (s > 0.96), indicating that associations between cortical thickness and neighborhood threat, scarcity and socioeconomic status remained stable over those time points, see Supplementary Figure 11. Findings remain robust using income-to-needs ratio and non-overlapping samples To test whether our results for socioeconomic status were robust when using a more sensitive measure than household income, we re-ran the analyses using the income-to-needs ratio, which compares a family’s income to the poverty threshold, accounting for household size. At baseline, income-to-needs ratio was significantly associated with cortical thickness ( r =0.220, p FDR <0.001), showing similar feature weights as socioeconomic status, Supplementary Figure 12 This indicates that socioeconomic status is a relevant, stable factor and alternate measures support our findings. To assess whether participant overlap across time points influenced our findings, we re-ran the baseline and 2-year analyses using non-overlapping participant samples, while keeping the 4-year sample unchanged ( n = 2,245) to retain the largest possible sample at that time point. Specifically, we selected the largest available subsamples at baseline ( n = 2,413) and 2-year follow-up ( n = 4,086) with no participant overlap across time points Supplementary Figure 13. These analyses yielded results consistent with the main findings (Supplementary Figure 14), reinforcing the robustness of our results. Overall, these results indicate that socioeconomic status, neighborhood threat, and scarcity share an overlapping neural signature that is stable across three time-points. Discussion This study leveraged the scale and depth of the ABCD Study to investigate how early life adversity and socioeconomic status relate to cortical thickness across youth. Using predictive modeling in a large sample, we found that socioeconomic status, neighborhood threat, and (at baseline) scarcity were consistently associated with smaller cortical thickness. These three distinct factors showed highly overlapping associations, suggesting that together, they may reflect a common neural signature of broader structural disadvantage. Although fewer adversity factors were available at later time points, the spatial patterns of these associations remained stable, pointing to persistent cortical correlates of socioeconomic adversity across development. We observed mostly negative associations between adversity and cortical thickness, consistent with our hypotheses and with prior literature. These relationships were strongest in bilateral medial temporal, occipital, and temporal pole regions, with smaller clusters of positive associations in the bilateral rostral and anterior cingulate and frontal pole. These regions are broadly involved in memory, emotion regulation, language, and visual processing, highlighting the potential impact of socioeconomic adversity on multiple cognitive and affective systems 71 – 73 . In contrast, the small number of positive associations in frontal regions may reflect compensatory neural adaptations or individual variability in developmental timing. Together, these patterns point to a distributed and functionally meaningful neural signature of socioeconomic disadvantage that is detectable across multiple stages of adolescence. They also support the idea that early adversity is biologically embedded in brain development, shaping structural features that are foundational for cognition and emotion. Our findings of smaller cortical thickness associated with socioeconomic status, neighborhood threat, and scarcity align with prior work showing smaller cortical thickness in youth exposed to adversity or disadvantage 32 , 42 , 74 . However, these results require careful interpretation, as multiple mechanisms may underlie these associations. One prominent explanation is accelerated maturation, a process by which adversity exposure may speed up cortical development as an adaptation to challenging environments. A more mature cortex may offer short-term functional advantages by helping children navigate high-stress or resource-limited environments through enhanced cognitive and behavioral efficiency 9 , 75 . Supporting this interpretation, past research has documented accelerated cortical maturation and accelerated epigenetic aging in children from lower socioeconomic backgrounds 76 , 77 . However, smaller cortical thickness at the three time-points may not reflect faster versions of typical development, but rather indicate fundamentally different neurodevelopmental pathways. The detected patterns could reflect pre-existing structural differences that emerge early and persist over time, rather than altered developmental timing 25 . Our analyses cannot distinguish between these competing explanations. Disentangling them will require longitudinal designs that track individual-level developmental trajectories in relation to adversity exposure. Importantly, regardless of whether these structural differences reflect accelerated maturation or persistent alterations, they are consistently linked to adverse functional outcomes. A broad literature connects socioeconomic disadvantage to poorer mental health, lower academic performance, and greater functional impairments, suggesting that any neurodevelopmental “adaptations” reflected in cortical thickness are insufficient to mitigate the negative long-term impacts of socioeconomic disadvantage 59 – 61 , 78 – 80 . Further, children from lower socioeconomic backgrounds are more likely to be exposed to additional adversities, leading to greater risk for mental health problems, a pattern described as “double-disadvantage” 66 , 81 , 82 . While accelerated maturation may be adaptive in the short term, it may come at a cost, including reduced plasticity, earlier closure of sensitive periods, and long-term trade-offs in flexibility and resilience 9 . Certain limitations should be noted. In this study, we used caregiver-reported household income as a proxy for socioeconomic status. This measure complements other metrics used in the literature, such as income-to-needs ratio, parental education, or geocoded indices like the Area Deprivation Index, each capturing distinct aspects of socioeconomic context 5 , 68 , 83 , 84 . Despite its simplicity, household income aligned with more complex indices such as neighborhood threat and scarcity. Findings remained consistent when using income-to-needs ratio, highlighting the robustness of socioeconomic status as a predictor. Although socioeconomic status, neighborhood threat, and scarcity were only weakly to moderately correlated behaviorally, their neural correlates overlapped, suggesting shared neurobiological pathways. While not interchangeable with more comprehensive measures, household income may still offer a useful, albeit limited, proxy for adversity in settings where more detailed measures are unavailable. Interestingly, our multivariate results did not replicate previously described univariate associations between cortical thickness and certain adversity factors such as physical and sexual violence, prenatal substance exposure, parental psychopathology, and household dysfunction 10 , 13 , 17 – 19 , 41 , 43 . These discrepancies may be due to differences in analytic strategy, as our multivariate approach may capture different relationships than traditional univariate analyses. Further, differences in sample age, exposure intensity, and how adversity factors were defined and captured across studies may impact findings 17 . For instance, some studies rely on dichotomous classifications of adversity, which may limit sensitivity to variation in exposure and reduce comparability across analytic frameworks 17 . In contrast, we treated adversity dimensions as continuous to capture gradations in exposure and increase model sensitivity. Another consideration for the interpretation of our findings is that we did not observe meaningful changes in the neural correlates of adversity over time. Associations were largely stable across time points, and adversity exposures themselves showed little change across the study period (Supplementary Figure 15). This may reflect limited variability or intensity of exposure in this age range or specific sample. Additionally, some adversity factors were not reassessed at follow-up, limiting our ability to examine change. Selective attrition may also have influenced results, as higher-risk participants or those with lower socioeconomic status may have been more likely to drop out or be excluded due to poor imaging data 85 . Supporting this, we found lower prevalences of adversity exposure in our sample compared to Orendain et al.’s ABCD baseline sample (e.g., 3.5% vs. 7% for sexual abuse, 81.4% vs. 50.3% for parental psychopathology) 6 . Our exclusion of siblings and those with missing or poor-quality MRI data may have further reduced the representation of higher-risk groups, which remains a broader challenge in imaging research 86 – 88 . Our sample also skewed toward higher-income families (see Supplement Table 1). Finally, some forms of adversity may be underreported, especially interpersonal trauma, which caregivers may be less aware of or less likely to disclose. Although this study focused specifically on cortical thickness as one marker of brain development, early life adversity is known to affect a broader range of neural systems. Prior work has demonstrated associations between adversity and functional connectivity, particularly in networks supporting emotion regulation and executive functioning 22 , 89 – 94 . These findings suggest that adversity may exert diffuse effects across structural and functional domains 58 , 89 . In this study, we did not include race as a covariate, as it often functions as a proxy for socioeconomic status and structural inequality 12 , 95 and because previous research has shown that race-related differences in brain structure can be attributed to differences in exposure to adversity 12 . Despite these considerations, our findings were robust as we replicated our findings using completely non-overlapping participant samples at the three time-points. This suggests that our results are not driven by repeated measurements of the same individuals. In addition, we replicated our findings for household income (socioeconomic status) using the income-to-needs ratio, a more sensitive measure of socioeconomic status 38 . The convergence of findings across different samples and socioeconomic status measures underscores the reliability of these associations. Our findings demonstrate the significant and pervasive negative impact of socioeconomic disadvantage on adolescent brain development. Although certain protective factors may buffer some of these effects, the overall burden of disadvantage remains profound 96 – 99 . The effects of socioeconomic disadvantage permeate nearly every aspect of a child’s lived experience, including reduced access to high-quality healthcare, nutritious food, and stable, safe housing, as well as increased exposure to environmental hazards, overcrowded living conditions, and neighborhood violence. These disadvantages often extend to diminished educational resources, limited extracurricular opportunities, and lower availability of community support systems 98 , 100 , 101 . Financial strain may also reduce caregiver availability and increase family stress, further compounding risk. Together, these structural and contextual burdens create a chronic, multifaceted environment that may undermine healthy brain development 53 , 54 . While it is essential to acknowledge these risks, strength-based approaches that highlight resilience and adaptive potential in the face of adversity are equally important for fostering a more comprehensive understanding of developmental outcomes 66 , 99 . We provide a valuable foundation for future work by using multivariate brain-based predictive modeling in a large dataset. By examining six dimensions of early adversity and socioeconomic status across three timepoints, we identified robust, shared cortical thickness patterns, particularly for exposures to neighborhood threat, scarcity, and low socioeconomic status. These findings suggest that some adversity-related alterations may reflect stable neurodevelopmental signatures. While we cannot determine whether these reflect accelerated or blunted maturation, our work offers a critical step toward clarifying these mechanisms. With this multi-timepoint, data-driven framework in place, future studies can investigate whether cortical differences represent altered developmental timing or persistent structural differences 25 . Moving forward, future work should apply longitudinal analyses and multimodal data, including structural, epigenetic, and environmental measures. Such approaches will determine whether adversity-related cortical changes reflect altered developmental timing or permanent structural shifts. This knowledge will guide when, how, and for whom to intervene to mitigate early-adversity impacts and inform programs that build resilience, particularly among socioeconomically disadvantaged youth. Methods Participants Data from the Adolescent Brain Cognitive Development (ABCD) study (ABCD 5.1 release) were used to establish associations between adverse childhood experiences and cortical thickness at three time points. The ABCD study assesses the development of over 11,000 children (aged 9-10 at baseline) from 21 sites across the United States. Participants complete extensive assessments, including neuroimaging scans, cognitive assessments, and mental health questionnaires, among others 49 , 102 . Inclusion criteria for the ABCD study can be found here 49 . In our study, we included individuals with complete and usable structural imaging data, socioeconomic status data, and complete data for all items necessary to calculate the adversity factors at the specific time point. We excluded intersex individuals, individuals with clinically relevant brain structural variations, individuals with T1 data not recommended for inclusion. We also excluded one or more members from sibling pairs to ensure no siblings were included in the sample, and excluded sites with fewer than 20 participants. At baseline, we included 6,908 individuals (3,282 females), at two-year follow-up we included 5,808 individuals (2,686 females), and at four-year follow-up we included 2,245 individuals (1,044 females). Measures Early life adversity We use the term early life adversity to refer to a range of stressors experienced prenatally and during youth. We considered six adversity factors, previously identified using factor analysis in the ABCD study by Orendain and colleagues 6 : 1) Physical and Sexual Violence, 2) Neighborhood Threat, 3) Prenatal Substance Exposure, 4) Household Dysfunction, 5) Scarcity, and 6) Parental Psychopathology. We calculated continuous scores for each factor from 47 items using 14 different questionnaires assessed in the ABCD study, as described by Orendain et al., see Supplement. All items were rated by the parent or caregiver, except for the household dysfunction items, which were rated by the youth. We additionally included socioeconomic status, operationalized as household income, as we expected neighborhood threat and scarcity to correlate with income. Not all questionnaires necessary to calculate the specific adversity factors were collected at all timepoints. At baseline, data for all six adversity factors were available. At two-year follow-up, only physical and sexual abuse, neighborhood threat, and household dysfunction data were available. At four-year follow-up, only neighborhood threat and household dysfunction data were available. Socioeconomic status was assessed at all three time-points. The available scores for each of the time-points were calculated as described by Orendain 6 ; a detailed description can be found in the Supplemental Material. Socioeconomic status was assessed on a 10-point Likert scale capturing income brackets from $200,000. We reverse-coded the scale so that higher scores reflected greater disadvantage, allowing for direct comparison with the adversity factors (e.g., 1 = >$200,000, 2 = $100,000 - $199,999). We used Spearman correlations to investigate associations between adversity factors. To explore the robustness of our findings, we additionally conducted brain-based predictive analyses using the income-to-needs ratio, a more sensitive socioeconomic measure that accounts for household size and individual poverty thresholds. Specifically, we calculated this ratio by taking the median value of each income bracket and dividing it by the federal poverty threshold corresponding to the respective household size 38 . Neuroimaging Data T1-weighted images were collected using 3T MRIs. The ABCD neuroimaging and (pre)processing protocols can be found here 102 , 103 . We extracted mean cortical thickness values from 34 bilateral cortical regions defined by the Desikan-Killiany atlas, as part of the ABCD study’s standard FreeSurfer preprocessing pipeline. Statistical Analyses Predictive Modeling We applied a cross-validated brain-based predictive modeling framework using linear ridge regression algorithms 104 – 109 . This framework minimizes overfitting, prevents data leakage, and captures robust, interpretable associations between brain measures and behavioral variables. In this study, we used these models to predict adversity factors and socioeconomic status from regional cortical thickness across three time-points (7 factors at baseline, four factors at two-year follow-up, and three factors at four-year follow-up). For each time point and each factor, we trained models using 68 cortical thickness features from the Desikan-Killiany atlas. We first randomly split the dataset into 100 distinct training and test sets, ensuring that participants from the same data collection site were not split across sets. Within each training set, we optimized the regularization hyperparameter using three-fold cross-validation, keeping data from sites intact within folds. After optimization, we trained the model on the full training set and evaluated performance on the corresponding test set. Model performance was evaluated based on prediction accuracy, defined as the correlation between predicted and observed values, in line with prior work 70 , 86 , 104 , 110 . We assessed within-factor prediction accuracy (how accurately each factor was predicted from cortical thickness at a given time point). We further tested whether model performance exceeded chance using permutation-based null models (generated using 1000 repetitions). P-values were calculated as the proportion of null models with prediction accuracy equal to or greater than the observed value. To correct for multiple comparisons, we applied the Benjamini-Hochberg procedure to control the false discovery rate (FDR). At baseline, data to calculate all six adversity factors and socioeconomic status were available. At two-year follow-up, data to calculate physical and sexual abuse, neighborhood threat, household dysfunction, and data on socioeconomic status were available. At four-year follow-up, data to calculate neighborhood threat, household dysfunction, and data on socioeconomic status were available. We corrected p-values for 7 tests at baseline, 4 tests at two-year follow-up, and 3 tests at four-year follow-up based on the number of adversity factors available at each time point. Results were considered statistically significant at a threshold of p FDR = 0.050. Finally, we conducted sex-specific analyses (see Supplement), examining associations between adversity factors and cortical thickness separately for boys and girls at each time point. Covariates In line with methodological considerations in our previous work, we did not include covariates such as age and sex, as these may mask important findings in the brain-based models 104 , 111 , 112 . Covariate adjustment assumes a linear and independent association between outcome and covariate, which may not accurately reflect complex biology. Excluding covariates enabled us to detect more robust brain-behavior associations as the models can reflect a larger range of variation in the neuroimaging data. Importantly, the ABCD sample varies only narrowly in age at each time point, with a standard deviation of only 7 months at each assessment, which reduces the likelihood that age-related differences drive our findings. Additionally, we conducted sex-specific analyses to explore potential differences in associations by sex. We did not use intracranial volume as a covariate, in line with recommendations for cortical thickness analyses 109 , 113 . Feature Weights To increase interpretability and reliability, we used the Haufe transformation to transform the obtained feature weights from the predictive models 69 . We then calculated mean feature importance for each set of the models. To evaluate the similarity between regional features associated with the adversity factors, we calculated cosine similarities, which is calculated using the dot product of two vectors divided by the product of their magnitudes. Values range from −1 to 1, with 1 indicating complete similarity, −1 indicating complete dissimilarity, and 0 indicating orthogonality. Software and Code Availability All analyses were conducted in Python (version 3.12.3) using Jupyter Notebook and RStudio (4.3.3). Feature weights were visualized using RStudio, and prediction accuracy plots were generated using the Seaborn package in Python. All code used to generate the results will be made available on GitHub. Funding This work was supported by the following awards to ED : Northwell Health Advancing Women in Science and Medicine (Career Development Award and Educational Advancement Award), Feinstein Institutes for Medical Research (Emerging Scientist Award), and the Brain and Behavior Research Foundation (Young Investigator Grant); and KB : Northwell Health Advancing Women in Science and Medicine (Educational Advancement Award). Declaration of competing interest All authors declare no conflicts of interest. CRediT authorship contribution statement KB : Conceptualization, Writing - original draft, Software, Formal Analysis, Visualization; LW : Software, Formal Analysis, Writing - review & editing ; EC : Writing - review & editing; DR: Writing - review & editing, JH: Writing - review & editing, RS: Writing - review & editing , ED : Methodology, Software, Writing - review & editing, Supervision, Funding acquisition. Acknowledgements During the preparation of this work the authors used LLM to improve readability and language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Funder Information Declared Northwell Health Advancing Women in Science and Medicine , Career Development Award , Educational Advancement Award Feinstein Institutes for Medical Research , Emerging Scientist Award Brain & Behavior Research Foundation , Young Investigator Grant References 1. ↵ Teicher , M. H. & Samson , J. A . Childhood maltreatment and psychopathology: A case for ecophenotypic variants as clinically and neurobiologically distinct subtypes . American Journal of Psychiatry 170 , 1114 – 1133 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 2. Baumeister , D. , Akhtar , R. , Ciufolini , S. , Pariante , C. M. & Mondelli , V . Childhood trauma and adulthood inflammation: A meta-analysis of peripheral C-reactive protein, interleukin-6 and tumour necrosis factor-α . Mol Psychiatry 21 , 642 – 649 ( 2016 ). OpenUrl CrossRef PubMed 3. ↵ Hanson , J. L. , van den Bos , W ., Roeber , B. J. , Rudolph , K. D. , Davidson , R. J. & Pollak , S. D. Early adversity and learning: implications for typical and atypical behavioral development . Journal of Child Psychology and Psychiatry 58 , 770 – 778 ( 2017 ). OpenUrl CrossRef PubMed 4. ↵ Pollok , T. M. , Kaiser , A. , Kraaijenvanger , E. J. , Monninger , M. , Brandeis , D. , Banaschewski , T. , Eickhoff , S. B. & Holz , N. E . Neurostructural traces of early life adversities: A meta-analysis exploring age- and adversity-specific effects . Neurosci Biobehav Rev 135 , 104589 ( 2022 ). 5. ↵ Webb , E. K. , Ely , T. D. , Rowland , G. E. , Lebois , L. A. M. , van Rooij , S. J. H. , Bruce , S. E. , Jovanovic , T. , House , S. L. , Beaudoin , F. L. , An , X. , Neylan , T. C. , Clifford , G. D. , Linnstaedt , S. D. , Germine , L. T. , Bollen , K. A. , Rauch , S. L. , Haran , J. P. , Storrow , A. B. , Lewandowski , C. , Musey , P. I. , Hendry , P. L. , Sheikh , S. , Jones , C. W. , Punches , B. E. , Swor , R. A. , Pascual , J. L. , Seamon , M. J. , Datner , E. M. , Pearson , C. , Peak , D. A. , Merchant , R. C. , Domeier , R. M. , Rathlev , N. K. , Sergot , P. , Sanchez , L. D. , Kessler , R. C. , Koenen , K. C. , McLean , S. A. , Stevens , J. S. , Ressler , K. J. & Harnett , N. G. Neighborhood Disadvantage and Neural Correlates of Threat and Reward Processing in Survivors of Recent Trauma . JAMA Netw Open 6 , e2334483 – e2334483 ( 2023 ). OpenUrl CrossRef 6. ↵ Orendain , N. , Anderson , A. , Galván , A. , Bookheimer , S. & Chung , P. J . A data-driven approach to categorizing early life adversity exposure in the ABCD Study . BMC Med Res Methodol 23 , 1 – 12 ( 2023 ). OpenUrl CrossRef PubMed 7. ↵ Malave , L. , van Dijk , M. T. & Anacker , C . Early life adversity shapes neural circuit function during sensitive postnatal developmental periods . Translational Psychiatry 2022 12:1 12 , 1 – 14 ( 2022 ). OpenUrl CrossRef PubMed 8. Teicher , M. H. , Gordon , J. B. & Nemeroff , C. B . Recognizing the importance of childhood maltreatment as a critical factor in psychiatric diagnoses, treatment, research, prevention, and education . Molecular Psychiatry 2021 27:3 27 , 1331 – 1338 ( 2021 ). OpenUrl PubMed 9. ↵ Teicher , M. H. , Samson , J. A. , Anderson , C. M. & Ohashi , K . The effects of childhood maltreatment on brain structure, function and connectivity . Nat Rev Neurosci 17 , 652 – 666 ( 2016 ). OpenUrl CrossRef PubMed 10. ↵ Tomoda , A. , Navalta , C. P. , Polcari , A. , Sadato , N. & Teicher , M. H . Childhood Sexual Abuse Is Associated with Reduced Gray Matter Volume in Visual Cortex of Young Women . Biol Psychiatry 66 , 642 – 648 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 11. ↵ Brosch , K. , Hammes , V. , Usemann , P. , Stein , F. , Zika , S. , Schrott , F. , Thomas-Odenthal , F. , Teutenberg , L. , Meinert , S. , Thiel , K. , Flinkenflügel , K. , Schürmeyer , N. , Goltermann , J. , Leehr , E. J. , Bonnekoh , L. M. , Grotegerd , D. , Winter , N. , Hahn , T. , Straube , B. , Jamalabadi , H. , Jansen , A. , Krug , A. , Dannlowski , U. , Nenadić , I. , Dhamala , E. , Kircher , T. & Alexander , N . Unique neural signatures of childhood sexual abuse revisited . medRxiv 2025.02.10.25322009 ( 2025 ). doi: 10.1101/2025.02.10.25322009 OpenUrl Abstract / FREE Full Text 12. ↵ Dumornay , N. M. , Lebois , L. A. M. , Ressler , K. J. & Harnett , N. G . Racial Disparities in Adversity During Childhood and the False Appearance of Race-Related Differences in Brain Structure . American Journal of Psychiatry 180 , 127 – 138 ( 2023 ). OpenUrl CrossRef PubMed 13. ↵ Negriff , S . ACEs are not equal: Examining the relative impact of household dysfunction versus childhood maltreatment on mental health in adolescence . Soc Sci Med 245 , 112696 ( 2020 ). OpenUrl CrossRef PubMed 14. Lopez , M. , Ruiz , M. O. , Rovnaghi , C. R. , Tam , G. K. Y. , Hiscox , J. , Gotlib , I. H. , Barr , D. A. , Carrion , V. G. & Anand , K. J. S . The social ecology of childhood and early life adversity . Pediatr Res 89 , 353 – 367 ( 2021 ). OpenUrl CrossRef PubMed 15. ↵ Hanson , J. L. & Nacewicz , B. M . Amygdala Allostasis and Early Life Adversity: Considering Excitotoxicity and Inescapability in the Sequelae of Stress . Front Hum Neurosci 15 , 624705 ( 2021 ). OpenUrl CrossRef PubMed 16. ↵ Duffy , K. A. , McLaughlin , K. A. & Green , P. A . Early life adversity and health-risk behaviors: proposed psychological and neural mechanisms . Ann N Y Acad Sci 1428 , 151 – 169 ( 2018 ). OpenUrl CrossRef PubMed 17. ↵ Paquola , C. , Bennett , M. R. & Lagopoulos , J . Understanding heterogeneity in grey matter research of adults with childhood maltreatment—A meta-analysis and review . Neurosci Biobehav Rev 69 , 299 – 312 ( 2016 ). OpenUrl CrossRef PubMed 18. ↵ Heim , C. M. , Mayberg , H. S. , Mletzko , T. , Nemeroff , C. B. & Pruessner , J. C . Decreased cortical representation of genital somatosensory field after childhood sexual abuse . American Journal of Psychiatry 170 , 616 – 623 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 19. ↵ Paul , S. E. , Hatoum , A. S. , Fine , J. D. , Johnson , E. C. , Hansen , I. , Karcher , N. R. , Moreau , A. L. , Bondy , E. , Qu , Y. , Carter , E. B. , Rogers , C. E. , Agrawal , A. , Barch , D. M. & Bogdan , R . Associations Between Prenatal Cannabis Exposure and Childhood Outcomes: Results From the ABCD Study . JAMA Psychiatry 78 , 64 – 76 ( 2021 ). OpenUrl CrossRef PubMed 20. ↵ Henry , L. M. , Gracey , K. , Shaffer , A. , Ebert , J. , Kuhn , T. , Watson , K. H. , Gruhn , M. , Vreeland , A. , Siciliano , R. , Dickey , L. , Lawson , V. , Broll , C. , Cole , D. A. & Compas , B. E. Comparison of Three Models of Adverse Childhood Experiences: Associations With Child and Adolescent Internalizing and Externalizing Symptoms . J Abnorm Psychol 130 , 9 ( 2020 ). 21. ↵ Brieant , A. , Sisk , L. M. , Keding , T. J. , Cohodes , E. M. & Gee , D. G . Leveraging multivariate approaches to advance the science of early-life adversity . Child Abuse Negl 106754 ( 2024 ). doi: 10.1016/J.CHIABU.2024.106754 OpenUrl CrossRef 22. ↵ Beck , D. , Whitmore , L. , MacSweeney , N. , Brieant , A. , Karl , V. , de Lange , A.-M. G. , Westlye , L. T. , Mills , K. L. & Tamnes , C. K . Dimensions of early life adversity are differentially associated with patterns of delayed and accelerated brain maturation . Biol Psychiatry 97 , 64 – 72 ( 2024 ). OpenUrl PubMed 23. ↵ McLaughlin , K. A. , Sheridan , M. A. & Lambert , H. K . Childhood Adversity and Neural Development: Deprivation and Threat as Distinct Dimensions of Early Experience NIH Public Access . Neurosci Biobehav Rev 47 , 578 – 591 ( 2014 ). OpenUrl CrossRef PubMed 24. ↵ Ellis , B. J. , Sheridan , M. A. , Belsky , J. & McLaughlin , K. A . Why and how does early adversity influence development? Toward an integrated model of dimensions of environmental experience . Dev Psychopathol 34 , 447 – 471 ( 2022 ). OpenUrl CrossRef PubMed 25. ↵ Rakesh , D. , Whittle , S. , Sheridan , M. A. & McLaughlin , K. A . Childhood socioeconomic status and the pace of structural neurodevelopment: accelerated, delayed, or simply different? Trends Cogn Sci 27 , 833 – 851 ( 2023 ). OpenUrl CrossRef PubMed 26. ↵ Machlin , L. , Egger , H. L. , Stein , C. R. , Navarro , E. , Carpenter , K. L. H. , Goel , S. , Patel , K. K. , Copeland , W. E. & Sheridan , M. A . Distinct Associations of Deprivation and Threat With Alterations in Brain Structure in Early Childhood . J Am Acad Child Adolesc Psychiatry 62 , 885 – 894.e3 ( 2023 ). OpenUrl CrossRef PubMed 27. ↵ McLaughlin , K. A. , Weissman , D. & Bitrán , D . Childhood Adversity and Neural Development: A Systematic Review . Annu Rev Dev Psychol 1 , 277 – 312 ( 2019 ). OpenUrl CrossRef PubMed 28. ↵ Winkler , A. M. , Kochunov , P. , Blangero , J. , Almasy , L. , Zilles , K. , Fox , P. T. , Duggirala , R. & Glahn , D. C . Cortical Thickness or Grey Matter Volume? The Importance of Selecting the Phenotype for Imaging Genetics Studies . Neuroimage 53 , 1135 ( 2009 ). OpenUrl PubMed Web of Science 29. Tahedl , M . Towards individualized cortical thickness assessment for clinical routine . J Transl Med 18 , 1 – 12 ( 2020 ). OpenUrl CrossRef PubMed 30. ↵ Gale , S. D. & Huff , T. Cortical Thickness . Encyclopedia of Clinical Neuropsychology 1–2 ( 2017 ). doi: 10.1007/978-3-319-56782-2_9063-2 OpenUrl CrossRef 31. ↵ Bethlehem , R. A. I. , Seidlitz , J. , White , S. R. , Vogel , J. W. , Anderson , K. M. , Adamson , C. , Adler , S. , Alexopoulos , G. S. , Anagnostou , E. , Areces-Gonzalez , A. , Astle , D. E. , Auyeung , B. , Ayub , M. , Bae , J. , Ball , G. , Baron-Cohen , S. , Beare , R. , Bedford , S. A. , Benegal , V. , Beyer , F. , Blangero , J. , Blesa Cábez , M. , Boardman , J. P. , Borzage , M. , Bosch-Bayard , J. F. , Bourke , N. , Calhoun , V. D. , Chakravarty , M. M. , Chen , C. , Chertavian , C. , Chetelat , G. , Chong , Y. S. , Cole , J. H. , Corvin , A. , Costantino , M. , Courchesne , E. , Crivello , F. , Cropley , V. L. , Crosbie , J. , Crossley , N. , Delarue , M. , Delorme , R. , Desrivieres , S. , Devenyi , G. A. , Di Biase , M. A. , Dolan , R. , Donald , K. A. , Donohoe , G. , Dunlop , K. , Edwards , A. D. , Elison , J. T. , Ellis , C. T. , Elman , J. A. , Eyler , L. , Fair , D. A. , Feczko , E. , Fletcher , P. C. , Fonagy , P. , Franz , C. E. , Galan-Garcia , L. , Gholipour , A. , Giedd , J. , Gilmore , J. H. , Glahn , D. C. , Goodyer , I. M. , Grant , P. E. , Groenewold , N. A. , Gunning , F. M. , Gur , R. E. , Gur , R. C. , Hammill , C. F. , Hansson , O. , Hedden , T. , Heinz , A. , Henson , R. N. , Heuer , K. , Hoare , J. , Holla , B. , Holmes , A. J. , Holt , R. , Huang , H. , Im , K. , Ipser , J. , Jack , C. R. , Jackowski , A. P. , Jia , T. , Johnson , K. A. , Jones , P. B. , Jones , D. T. , Kahn , R. S. , Karlsson , H. , Karlsson , L. , Kawashima , R. , Kelley , E. A. , Kern , S. , Kim , K. W. , Kitzbichler , M. G. , Kremen , W. S. , Lalonde , F. , Landeau , B. , Lee , S. , Lerch , J. , Lewis , J. D. , Li , J. , Liao , W. , Liston , C. , Lombardo , M. V. , Lv , J. , Lynch , C. , Mallard , T. T. , Marcelis , M. , Markello , R. D. , Mathias , S. R. , Mazoyer , B. , McGuire , P. , Meaney , M. J. , Mechelli , A. , Medic , N. , Misic , B. , Morgan , S. E. , Mothersill , D. , Nigg , J. , Ong , M. Q. W. , Ortinau , C. , Ossenkoppele , R. , Ouyang , M. , Palaniyappan , L. , Paly , L. , Pan , P. M. , Pantelis , C. , Park , M. M. , Paus , T. , Pausova , Z. , Paz-Linares , D. , Pichet Binette , A. , Pierce , K. , Qian , X. , Qiu , J. , Qiu , A. , Raznahan , A. , Rittman , T. , Rodrigue , A. , Rollins , C. K. , Romero-Garcia , R. , Ronan , L. , Rosenberg , M. D. , Rowitch , D. H. , Salum , G. A. , Satterthwaite , T. D. , Schaare , H. L. , Schachar , R. J. , Schultz , A. P. , Schumann , G. , Schöll , M. , Sharp , D. , Shinohara , R. T. , Skoog , I. , Smyser , C. D. , Sperling , R. A. , Stein , D. J. , Stolicyn , A. , Suckling , J. , Sullivan , G. , Taki , Y. , Thyreau , B. , Toro , R. , Traut , N. , Tsvetanov , K. A. , Turk-Browne , N. B. , Tuulari , J. J. , Tzourio , C. , Vachon-Presseau , Valdes-Sosa , M. J. , Valdes-Sosa , P. A. , Valk , S. L. , van Amelsvoort , T. , Vandekar , S. N. , Vasung , L. , Victoria , L. W. , Villeneuve , S. , Villringer , A. , Vértes , P. E. , Wagstyl , K. , Wang , Y. S. , Warfield , S. K. , Warrier , V. , Westman , E. , Westwater , M. L. , Whalley , H. C. , Witte , A. V. , Yang , N. , Yeo , B. , Yun , H. , Zalesky , A. , Zar , H. J. , Zettergren , A. , Zhou , J. H. , Ziauddeen , H. , Zugman , A. , Zuo , X. N. , Rowe , C. , Frisoni , G. B. , Binette , A. P. , Bullmore , E. T. & Alexander-Bloch , A. F. Brain charts for the human lifespan . Nature 604 , 525 – 533 ( 2022 ). OpenUrl CrossRef PubMed 32. ↵ Aberizk , K. , Collins , M. A. , Addington , J. , Bearden , C. E. , Cadenhead , K. S. , Cornblatt , B. A. , Mathalon , D. H. , McGlashan , T. H. , Perkins , D. O. , Tsuang , M. T. , Woods , S. W. , Cannon , T. D. & Walker , E. F . Life Event Stress and Reduced Cortical Thickness in Youth at Clinical High Risk for Psychosis and Healthy Control Subjects . Biol Psychiatry Cogn Neurosci Neuroimaging 7 , 171 – 179 ( 2022 ). OpenUrl PubMed 33. ↵ Luking , K. R. , Jirsaraie , R. J. , Tillman , R. , Luby , J. L. , Barch , D. M. & Sotiras , A . Timing and Type of Early Psychopathology Symptoms Predict Longitudinal Change in Cortical Thickness From Middle Childhood Into Early Adolescence . Biol Psychiatry Cogn Neurosci Neuroimaging 7 , 397 – 405 ( 2022 ). OpenUrl PubMed 34. ↵ Whittle , S. , Rakesh , D. , Simmons , J. G. , Schwartz , O. , Vijayakumar , N. & Allen , N. B . Prospective Associations Between Structural Brain Development and Onset of Depressive Disorder During Adolescence and Emerging Adulthood . Am J Psychiatry ( 2025 ). doi: 10.1176/APPI.AJP.20240588 OpenUrl CrossRef 35. ↵ Hanson , J. L. , Chung , M. K. , Avants , B. B. , Shirtcliff , E. A. , Gee , J. C. , Davidson , R. J. & Pollak , S. D . Early Stress Is Associated with Alterations in the Orbitofrontal Cortex: A Tensor-Based Morphometry Investigation of Brain Structure and Behavioral Risk . Journal of Neuroscience 30 , 7466 – 7472 ( 2010 ). OpenUrl Abstract / FREE Full Text 36. ↵ Yuan , M. , Rubin-Falcone , H. , Lin , X. , Rizk , M. M. , Miller , J. M. , Sublette , M. E. , Oquendo , M. A. , Burke , A. , Ogden , R. T. & Mann , J. J . Smaller left hippocampal subfield CA1 volume is associated with reported childhood physical and/or sexual abuse in major depression: A pilot study . J Affect Disord 272 , 348 – 354 ( 2020 ). OpenUrl CrossRef PubMed 37. ↵ Hackman , D. A. , Cserbik , D. , Chen , J. C. , Berhane , K. , Minaravesh , B. , McConnell , R. & Herting , M. M . Association of Local Variation in Neighborhood Disadvantage in Metropolitan Areas With Youth Neurocognition and Brain Structure . JAMA Pediatr 175 , e210426 – e210426 ( 2021 ). OpenUrl CrossRef 38. ↵ Rakesh , D. , Zalesky , A. & Whittle , S . Assessment of Parent Income and Education , Neighborhood Disadvantage, and Child Brain Structure. JAMA Netw Open 5 , e2226208 – e2226208 ( 2022 ). OpenUrl PubMed 39. ↵ Mackes , N. K. , Golm , D. , Sarkar , S. , Kumsta , R. , Rutter , M. , Fairchild , G. , Mehta , M. A. & Sonuga-Barke , E. J. S . Early childhood deprivation is associated with alterations in adult brain structure despite subsequent environmental enrichment . Proc Natl Acad Sci U S A 117 , 641 – 649 ( 2020 ). OpenUrl Abstract / FREE Full Text 40. ↵ Schäfer , J. L. , McLaughlin , K. A. , Manfro , G. G. , Pan , P. , Rohde , L. A. , Miguel , E. C. , Simioni , A. , Hoffmann , M. S. & Salum , G. A . Threat and deprivation are associated with distinct aspects of cognition, emotional processing, and psychopathology in children and adolescents . Dev Sci 26 , e13267 ( 2023 ). OpenUrl CrossRef PubMed 41. ↵ Gimbel , B. A. , Wozniak , J. R. , Mueller , B. A. , Tuominen , K. A. , Ernst , A. M. , Anthony , M. E. , Water , E. de , CIFASD , the & Roediger , D. J. Regional hippocampal thinning and gyrification abnormalities and associated cognition in children with prenatal alcohol exposure . J Neurodev Disord 17 , 5 ( 2025 ). OpenUrl CrossRef PubMed 42. ↵ Lebel , C. A. , McMorris , C. A. , Kar , P. , Ritter , C. , Andre , Q. , Tortorelli , C. & Gibbard , W. Ben . Characterizing adverse prenatal and postnatal experiences in children . Birth Defects Res 111 , 848 – 858 ( 2019 ). OpenUrl CrossRef PubMed 43. ↵ Hendrikse , C. J. , Joshi , S. H. , Ringshaw , J. E. , Bradford , L. , Roos , A. , Wedderburn , C. J. , Hoffman , N. , Burd , T. , Narr , K. L. , Woods , R. P. , Zar , H. J. , Stein , D. J. & Donald , K. A . Prenatal alcohol exposure alters brain structure and neurocognitive outcomes for 6- to 7- year-old children in a South African birth cohort . Alcohol, clinical & experimental research ( 2025 ). doi: 10.1111/ACER.70048 OpenUrl CrossRef 44. ↵ Eiden , R. D. , Perry , K. J. , Ivanova , M. Y. & Marcus , R. C. Prenatal Substance Exposure . Annu Rev Dev Psychol 5 , 19 – 44 ( 2023 ). OpenUrl CrossRef 45. ↵ Irner , T. B . Substance exposure in utero and developmental consequences in adolescence: A systematic review . Child Neuropsychology 18 , 521 – 549 ( 2012 ). OpenUrl 46. ↵ Daníelsdóttir , H. B. , Aspelund , T. , Shen , Q. , Halldorsdottir , T. , Jakobsdóttir , J. , Song , H. , Lu , D. , Kuja-Halkola , R. , Larsson , H. , Fall , K. , Magnusson , P. K. E. , Fang , F. , Bergstedt , J. & Valdimarsdóttir , U. A . Adverse Childhood Experiences and Adult Mental Health Outcomes . JAMA Psychiatry 81 , 586 ( 2024 ). 47. ↵ Farooq , B. , Russell , A. E. , Howe , L. D. , Herbert , A. , Smith , A. D. A. C. , Fisher , H. L. , Baldwin , J. R. , Arseneault , L. , Danese , A. & Mars , B . The relationship between type, timing and duration of exposure to adverse childhood experiences and adolescent self-harm and depression: findings from three UK prospective population-based cohorts . J Child Psychol Psychiatry 65 , 1369 ( 2024 ). OpenUrl CrossRef PubMed 48. ↵ Gabard-Durnam , L. & McLaughlin , K. A . Sensitive periods in human development: charting a course for the future . Curr Opin Behav Sci 36 , 120 – 128 ( 2020 ). OpenUrl CrossRef 49. ↵ Garavan , H. , Bartsch , H. , Conway , K. , Decastro , A. , Goldstein , R. Z. , Heeringa , S. , Jernigan , T. , Potter , A. , Thompson , W. & Zahs , D . Recruiting the ABCD sample: Design considerations and procedures . Dev Cogn Neurosci 32 , 16 – 22 ( 2018 ). OpenUrl CrossRef PubMed 50. ↵ Hoffman , E. A. , Clark , D. B. , Orendain , N. , Hudziak , J. , Squeglia , L. M. & Dowling , G. J . Stress exposures, neurodevelopment and health measures in the ABCD study . Neurobiol Stress 10 , ( 2019 ). 51. ↵ Madigan , S. , Deneault , A. A. , Racine , N. , Park , J. , Thiemann , R. , Zhu , J. , Dimitropoulos , G. , Williamson , T. , Fearon , P. , Cénat , J. M. , McDonald , S. , Devereux , C. & Neville , R. D . Adverse childhood experiences: a meta-analysis of prevalence and moderators among half a million adults in 206 studies . World Psychiatry 22 , 463 – 471 ( 2023 ). OpenUrl CrossRef PubMed 52. ↵ Rakesh , D. , Flournoy , J. C. & McLaughlin , K. A . Associations between socioeconomic status and mental health trajectories during early adolescence: Findings from the Adolescent Brain Cognitive Development study . JCPP Advances e70001 ( 2025 ). doi: 10.1002/JCV2.70001 OpenUrl CrossRef 53. ↵ Palacios-Barrios , E. E. & Hanson , J. L . Poverty and self-regulation: Connecting psychosocial processes, neurobiology, and the risk for psychopathology . Compr Psychiatry 90 , 52 – 64 ( 2019 ). OpenUrl CrossRef PubMed 54. ↵ Rakesh , D. , Lee , P. A. , Gaikwad , A. & McLaughlin , K. A . Annual Research Review: Associations of socioeconomic status with cognitive function, language ability, and academic achievement in youth: a systematic review of mechanisms and protective factors . Journal of Child Psychology and Psychiatry 66 , 417 – 439 ( 2025 ). OpenUrl CrossRef PubMed 55. ↵ Miller , J. G. , López , V. , Buthmann , J. L. , Garcia , J. M. & Gotlib , I. H . A Social Gradient of Cortical Thickness in Adolescence: Relationships With Neighborhood Socioeconomic Disadvantage , Family Socioeconomic Status, and Depressive Symptoms. Biological Psychiatry Global Open Science 2 , 253 ( 2022 ). OpenUrl PubMed 56. ↵ Long , K. & Renbarger , R . Persistence of Poverty: How Measures of Socioeconomic Status Have Changed Over Time . Educational Researcher 52 , 144 – 154 ( 2023 ). OpenUrl CrossRef 57. ↵ Villanueva , K. , Turrell , G. , Alderton , A. , Davern , M. , Brinkman , S. , Gauvin , L. , Goldfeld , S. & Badland , H . Neighborhood ‘double disadvantage’ and child development in inner city and growth areas. Wellbeing , Space and Society 7 , 100231 ( 2024 ). OpenUrl 58. ↵ Rakesh , D. , Zalesky , A. & Whittle , S . Similar but distinct – Effects of different socioeconomic indicators on resting state functional connectivity: Findings from the Adolescent Brain Cognitive Development (ABCD) Study® . Dev Cogn Neurosci 51 , 101005 ( 2021 ). OpenUrl CrossRef PubMed 59. ↵ Hair , N. L. , Hanson , J. L. , Wolfe , B. L. & Pollak , S. D . Association of Child Poverty, Brain Development, and Academic Achievement . JAMA Pediatr 169 , 822 – 829 ( 2015 ). OpenUrl CrossRef PubMed 60. ↵ Hanson , J. L. , Nacewicz , B. M. , Sutterer , M. J. , Cayo , A. A. , Schaefer , S. M. , Rudolph , K. D. , Shirtcliff , E. A. , Pollak , S. D. & Davidson , R. J . Behavioral Problems After Early Life Stress: Contributions of the Hippocampus and Amygdala . Biol Psychiatry 77 , 314 – 323 ( 2015 ). OpenUrl CrossRef PubMed 61. ↵ Rakesh , D. & Whittle , S . Socioeconomic status and the developing brain – A systematic review of neuroimaging findings in youth . Neurosci Biobehav Rev 130 , 379 – 407 ( 2021 ). OpenUrl CrossRef PubMed 62. Hanson , J. L. , Chandra , A. , Wolfe , B. L. & Pollak , S. D . Association between Income and the Hippocampus . PLoS One 6 , e18712 ( 2011 ). OpenUrl CrossRef PubMed 63. ↵ Hair , N. L. , Hanson , J. L. , Wolfe , B. L. & Pollak , S. D . Association of Child Poverty, Brain Development, and Academic Achievement . JAMA Pediatr 169 , 822 – 829 ( 2015 ). OpenUrl CrossRef PubMed 64. ↵ World Health Organisation . Child Maltreatment . https://www.who.int/news-room/fact-sheets/detail/child-maltreatment ( 2022 ). 65. ↵ Millman , Z. B. , Hwang , M. , Sydnor , V. J. , Reid , B. E. , Goldenberg , J. E. , Talero , J. N. , Bouix , S. , Shenton , M. E. , Öngür , D. & Shinn , A. K . Auditory hallucinations, childhood sexual abuse, and limbic gray matter volume in a transdiagnostic sample of people with psychosis . Schizophrenia 2022 8:1 8 , 1 – 9 ( 2022 ). OpenUrl CrossRef 66. ↵ Brosch , K. & Dhamala , E . Influences of sex and gender on the associations between risk and protective factors, brain, and behavior . Biol Sex Differ 15 , 1 – 17 ( 2024 ). OpenUrl CrossRef PubMed 67. ↵ Zhao , S. , Su , H. , Cong , J. , Wen , X. , Yang , H. , Chen , P. , Wu , G. , Fan , Q. , Ma , Y. , Xu , X. , Hu , C. , Li , H. , Keller , A. , Pines , A. , Chen , R. & Cui , Z . Hierarchical individual variation and socioeconomic impact on personalized functional network topography in children . BMC Med 22 , 1 – 18 ( 2024 ). OpenUrl CrossRef PubMed 68. ↵ Dennis , E. , Manza , P. & Volkow , N. D. Socioeconomic status, BMI, and brain development in children . Transl Psychiatry 12 , ( 2022 ). 69. ↵ Haufe , S. , Meinecke , F. , Görgen , K. , Dähne , S. , Haynes , J. D. , Blankertz , B. & Bießmann , F . On the interpretation of weight vectors of linear models in multivariate neuroimaging . Neuroimage 87 , 96 – 110 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 70. ↵ Chen , J. , Ooi , L. Q. R. , Tan , T. W. K. , Zhang , S. , Li , J. , Asplund , C. L. , Eickhoff , S. B. , Bzdok , D. , Holmes , A. J. & Yeo , B. T. T . Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study . Neuroimage 274 , 120115 ( 2023 ). OpenUrl CrossRef PubMed 71. ↵ Olson , I. R. , Plotzker , A. & Ezzyat , Y . The Enigmatic temporal pole: A review of findings on social and emotional processing . Brain 130 , 1718 – 1731 ( 2007 ). OpenUrl CrossRef PubMed Web of Science 72. Grill-Spector , K. & Malach , R . The human visual cortex . Annu Rev Neurosci 27 , 649 – 677 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 73. ↵ Ranganath , C. & Ritchey , M . Two cortical systems for memory-guided behaviour . Nature Reviews Neuroscience 2012 13:10 13 , 713 – 726 ( 2012 ). OpenUrl CrossRef PubMed 74. ↵ Lockhart , S. , Sawa , A. & Niwa , M . Developmental trajectories of brain maturation and behavior: Relevance to major mental illnesses . J Pharmacol Sci 137 , 1 – 4 ( 2018 ). OpenUrl CrossRef 75. ↵ Squeglia , L. M. , Jacobus , J. , Sorg , S. F. , Jernigan , T. L. & Tapert , S. F . Early Adolescent Cortical Thinning Is Related to Better Neuropsychological Performance . J Int Neuropsychol Soc 19 , 962 ( 2013 ). OpenUrl CrossRef PubMed 76. ↵ Willems , Y. E. , Rezaki , A. D. , Aikins , M. , Bahl , A. , Wu , Q. , Belsky , D. W. & Raffington , L . Social determinants of health and epigenetic clocks: Meta-analysis of 140 studies . medRxiv 2025.05.08.25327207 ( 2025 ). doi: 10.1101/2025.05.08.25327207 OpenUrl Abstract / FREE Full Text 77. ↵ Piccolo , L. R. , Merz , E. C. , He , X. , Sowell , E. R. & Noble , K. G . Age-Related Differences in Cortical Thickness Vary by Socioeconomic Status . PLoS One 11 , e0162511 ( 2016 ). OpenUrl CrossRef PubMed 78. ↵ Langensee , L. , Rumetshofer , T. & Mårtensson , J . Interplay of socioeconomic status, cognition, and school performance in the ABCD sample . NPJ Sci Learn 9 , 1 – 8 ( 2024 ). OpenUrl CrossRef PubMed 79. Weissman , D. G. , Hatzenbuehler , M. L. , Cikara , M. , Barch , D. M. & McLaughlin , K. A . State-level macro-economic factors moderate the association of low income with brain structure and mental health in U.S. children . Nature Communications 2023 14:1 14 , 1 – 13 ( 2023 ). OpenUrl CrossRef PubMed 80. ↵ Tomasi , D. & Volkow , N. D . Associations of family income with cognition and brain structure in USA children: prevention implications . Mol Psychiatry 26 , 6619 – 6629 ( 2021 ). OpenUrl CrossRef PubMed 81. ↵ Evans-Lacko , S. , Font Gilabert , P. & Knapp , M . The double disadvantage faced by adolescents from low socioeconomic backgrounds with mental health problems affects earnings up to mid-life . Soc Sci Med 362 , 117385 ( 2024 ). OpenUrl CrossRef PubMed 82. ↵ Grollman , E. A . Multiple forms of perceived discrimination and health among adolescents and young adults . J Health Soc Behav 53 , 199 – 214 ( 2012 ). OpenUrl CrossRef PubMed 83. ↵ Ku , B. S. , Aberizk , K. , Feurer , C. , Yuan , Q. , Druss , B. G. , Jeste , D. V. & Walker , E. F . Aspects of Area Deprivation Index in Relation to Hippocampal Volume Among Children . JAMA Netw Open 7 , e2416484 – e2416484 ( 2024 ). OpenUrl CrossRef PubMed 84. ↵ Fan , C. C. , Marshall , A. , Smolker , H. , Gonzalez , M. R. , Tapert , S. F. , Barch , D. M. , Sowell , E. , Dowling , G. J. , Cardenas-Iniguez , C. , Ross , J. , Thompson , W. K. & Herting , M. M. Adolescent Brain Cognitive Development (ABCD) study Linked External Data (LED): Protocol and practices for geocoding and assignment of environmental data . Dev Cogn Neurosci 52 , 101030 ( 2021 ). OpenUrl CrossRef PubMed 85. ↵ Rakesh , D. , Flournoy , J. C. & McLaughlin , K. A . Associations between socioeconomic status and mental health trajectories during early adolescence: Findings from the Adolescent Brain Cognitive Development study . JCPP Advances e70001 ( 2025 ). doi: 10.1002/JCV2.70001 OpenUrl CrossRef 86. ↵ Chen , J. , Tam , A. , Kebets , V. , Orban , C. , Ooi , L. Q. R. , Asplund , C. L. , Marek , S. , Dosenbach , N. U. F. , Eickhoff , S. B. , Bzdok , D. , Holmes , A. J. & Yeo , B. T. T . Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study . Nature Communications 2022 13:1 13 , 1 – 17 ( 2022 ). OpenUrl CrossRef PubMed 87. Ramduny , J. , Uddin , L. Q. , Vanderwal , T. , Feczko , E. , Fair , D. A. , Kelly , C. & Baskin-Sommers , A. Increasing the representation of minoritized youth for inclusive and reproducible brain-behavior associations . bioRxiv 2024.06.22.600221 ( 2024 ). doi: 10.1101/2024.06.22.600221 OpenUrl Abstract / FREE Full Text 88. ↵ Cosgrove , K. T. , McDermott , T. J. , White , E. J. , Mosconi , M. W. , Thompson , W. K. , Paulus , M. P. , Cardenas-Iniguez , C. & Aupperle , R. L . Limits to the generalizability of resting-state functional magnetic resonance imaging studies of youth: An examination of ABCD Study® baseline data . Brain Imaging Behav 16 , 1919 – 1925 ( 2022 ). OpenUrl CrossRef PubMed 89. ↵ Cheng , T. W. , Mills , K. L. , Miranda Dominguez , O. , Zeithamova , D. , Perrone , A. , Sturgeon , D. , Feldstein Ewing , S. W. , Fisher , P. A. , Pfeifer , J. H. , Fair , D. A. & Mackiewicz Seghete , K. L . Characterizing the impact of adversity, abuse, and neglect on adolescent amygdala resting-state functional connectivity . Dev Cogn Neurosci 47 , 100894 ( 2021 ). OpenUrl CrossRef PubMed 90. Goltermann , J. , Winter , N. R. , Meinert , S. , Sindermann , L. , Lemke , H. , Leehr , E. J. , Grotegerd , D. , Winter , A. , Thiel , K. , Waltemate , L. , Breuer , F. , Repple , J. , Gruber , M. , Richter , M. , Teckentrup , V. , Kroemer , N. B. , Brosch , K. , Meller , T. , Pfarr , J. K. , Ringwald , K. G. , Stein , F. , Heindel , W. , Jansen , A. , Kircher , T. , Nenadić , I. , Dannlowski , U. , Opel , N. & Hahn , T . Resting-state functional connectivity patterns associated with childhood maltreatment in a large bicentric cohort of adults with and without major depression . Psychol Med 53 , 4720 – 4731 ( 2023 ). OpenUrl CrossRef PubMed 91. Hosseini-Kamkar , N. , Varvani Farahani , M. , Nikolic , M. , Stewart , K. , Goldsmith , S. , Soltaninejad , M. , Rajabli , R. , Lowe , C. , Nicholson , A. A. , Morton , J. B. & Leyton , M . Adverse Life Experiences and Brain Function: A Meta-Analysis of Functional Magnetic Resonance Imaging Findings . JAMA Netw Open 6 , e2340018 – e2340018 ( 2023 ). OpenUrl CrossRef PubMed 92. Vedechkina , M. , Astle , D. E. & Holmes , J . Dimensions of early life adversity and their associations with functional brain organisation . Imaging Neuroscience 2 , 1 – 25 ( 2024 ). OpenUrl CrossRef 93. Chahal , R. , Miller , J. G. , Yuan , J. P. , Buthmann , J. L. & Gotlib , I. H . An exploration of dimensions of early adversity and the development of functional brain network connectivity during adolescence: Implications for trajectories of internalizing symptoms . Dev Psychopathol 34 , 557 – 571 ( 2022 ). OpenUrl CrossRef PubMed 94. ↵ Chan , S. Y. , Ngoh , Z. M. , Ong , Z. Y. , Teh , A. L. , Kee , M. Z. L. , Zhou , J. H. , Fortier , M. V. , Yap , F. , MacIsaac , J. L. , Kobor , M. S. , Silveira , P. P. , Meaney , M. J. & Tan , A. P . The influence of early-life adversity on the coupling of structural and functional brain connectivity across childhood . Nature Mental Health 2 , 52 – 62 ( 2024 ). OpenUrl CrossRef 95. ↵ Cardenas-Iniguez , C. & Gonzalez , M. R . Recommendations for the responsible use and communication of race and ethnicity in neuroimaging research . Nature Neuroscience 2024 1 – 14 ( 2024 ). doi: 10.1038/s41593-024-01608-4 OpenUrl CrossRef 96. ↵ Rakesh , D. , Sadikova , E. & McLaughlin , K. A . Beyond the income-achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low-income youth . ( 2024 ). doi: 10.31234/OSF.IO/QEVTU OpenUrl CrossRef 97. Maurer , J. , Meyrose , A. K. , Kaman , A. , Mauz , E. , Ravens-Sieberer , U. & Reiss , F. Socioeconomic Status, Protective Factors, and Mental Health Problems in Transition from Adolescence to Emerging Adulthood: Results of the Longitudinal BELLA Study . Child Psychiatry Hum Dev 1–12 ( 2023 ). doi: 10.1007/S10578-023-01582-1/TABLES/3 OpenUrl CrossRef 98. ↵ Levine , C. S . Psychological buffers against poor health: the role of the socioeconomic environment . Curr Opin Psychol 18 , 137 – 140 ( 2017 ). OpenUrl CrossRef PubMed 99. ↵ DeJoseph , M. L. , Ellwood-Lowe , M. E. , Miller-Cotto , D. , Silverman , D. , Shannon , K. A. , Reyes , G. , Rakesh , D. & Frankenhuis , W. E . The promise and pitfalls of a strength-based approach to child poverty and neurocognitive development: Implications for policy . Dev Cogn Neurosci 66 , 101375 ( 2024 ). OpenUrl CrossRef PubMed 100. ↵ Whittle , S. , Zhang , L. & Rakesh , D . Environmental and neurodevelopmental contributors to youth mental illness . Neuropsychopharmacology 50 , 201 – 210 ( 2024 ). OpenUrl PubMed 101. ↵ Zaneva , M. , Guzman-Holst , C. , Reeves , A. & Bowes , L . The Impact of Monetary Poverty Alleviation Programs on Children’s and Adolescents’ Mental Health: A Systematic Review and Meta-Analysis Across Low-, Middle-, and High-Income Countries . Journal of Adolescent Health 71 , 147 – 156 ( 2022 ). OpenUrl CrossRef PubMed 102. ↵ Casey , B. J. , Cannonier , T. , Conley , M. I. , Cohen , A. O. , Barch , D. M. , Heitzeg , M. M. , Soules , M. E. , Teslovich , T. , Dellarco , D. V. , Garavan , H. , Orr , C. A. , Wager , T. D. , Banich , M. T. , Speer , N. K. , Sutherland , M. T. , Riedel , M. C. , Dick , A. S. , Bjork , J. M. , Thomas , K. M. , Chaarani , B. , Mejia , M. H. , Hagler , D. J. , Daniela Cornejo , M. , Sicat , C. S. , Harms , M. P. , Dosenbach , N. U. F. , Rosenberg , M. , Earl , E. , Bartsch , H. , Watts , R. , Polimeni , J. R. , Kuperman , J. M. , Fair , D. A. & Dale , A. M . The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites . Dev Cogn Neurosci 32 , 43 – 54 ( 2018 ). OpenUrl CrossRef PubMed 103. ↵ Hagler , D. J. , Hatton , S. N. , Cornejo , M. D. , Makowski , C. , Fair , D. A. , Dick , A. S. , Sutherland , M. T. , Casey , B. J. , Barch , D. M. , Harms , M. P. , Watts , R. , Bjork , J. M. , Garavan , H. P. , Hilmer , L. , Pung , C. J. , Sicat , C. S. , Kuperman , J. , Bartsch , H. , Xue , F. , Heitzeg , M. M. , Laird , A. R. , Trinh , T. T. , Gonzalez , R. , Tapert , S. F. , Riedel , M. C. , Squeglia , L. M. , Hyde , L. W. , Rosenberg , M. D. , Earl , E. A. , Howlett , K. D. , Baker , F. C. , Soules , M. , Diaz , J. , de Leon , O. R. , Thompson , W. K. , Neale , M. C. , Herting , M. , Sowell , E. R. , Alvarez , R. P. , Hawes , S. W. , Sanchez , M. , Bodurka , J. , Breslin , F. J. , Morris , A. S. , Paulus , M. P. , Simmons , W. K. , Polimeni , J. R. , van der Kouwe , A. , Nencka , A. S. , Gray , K. M. , Pierpaoli , C. , Matochik , J. A. , Noronha , A. , Aklin , W. M. , Conway , K. , Glantz , M. , Hoffman , E. , Little , R. , Lopez , M. , Pariyadath , V. , Weiss , S. R. , Wolff-Hughes , D. L. , DelCarmen-Wiggins , R. , Feldstein Ewing , S. W. , Miranda-Dominguez , O. , Nagel , B. J. , Perrone , A. J. , Sturgeon , D. T. , Goldstone , A. , Pfefferbaum , A. , Pohl , K. M. , Prouty , D. , Uban , K. , Bookheimer , S. Y. , Dapretto , M. , Galvan , A. , Bagot , K. , Giedd , J. , Infante , M. A. , Jacobus , J. , Patrick , K. , Shilling , P. D. , Desikan , R. , Li , Y. , Sugrue , L. , Banich , M. T. , Friedman , N. , Hewitt , J. K. , Hopfer , C. , Sakai , J. , Tanabe , J. , Cottler , L. B. , Nixon , S. J. , Chang , L. , Cloak , C. , Ernst , T. , Reeves , G. , Kennedy , D. N. , Heeringa , S. , Peltier , S. , Schulenberg , J. , Sripada , C. , Zucker , R. A. , Iacono , W. G. , Luciana , M. , Calabro , F. J. , Clark , D. B. , Lewis , D. A. , Luna , B. , Schirda , C. , Brima , T. , Foxe , J. J. , Freedman , E. G. , Mruzek , D. W. , Mason , M. J. , Huber , R. , McGlade , E. , Prescot , A. , Renshaw , P. F. , Yurgelun-Todd , D. A. , Allgaier , N. A. , Dumas , J. A. , Ivanova , M. , Potter , A. , Florsheim , P. , Larson , C. , Lisdahl , K. , Charness , M. E. , Fuemmeler , B. , Hettema , J. M. , Maes , H. H. , Steinberg , J. , Anokhin , A. P. , Glaser , P. , Heath , A. C. , Madden , P. A. , Baskin-Sommers , A. , Constable , R. T. , Grant , S. J. , Dowling , G. J. , Brown , S. A. , Jernigan , T. L. & Dale , A. M . Image processing and analysis methods for the Adolescent Brain Cognitive Development Study . Neuroimage 202 , 116091 ( 2019 ). OpenUrl CrossRef PubMed 104. ↵ Dhamala , E. , Rong Ooi , L. Q. , Chen , J. , Ricard , J. A. , Berkeley , E. , Chopra , S. , Qu , Y. , Zhang , X. H. , Lawhead , C. , Yeo , B. T. T. & Holmes , A. J . Brain-Based Predictions of Psychiatric Illness–Linked Behaviors Across the Sexes . Biol Psychiatry 94 , 479 – 491 ( 2023 ). OpenUrl CrossRef PubMed 105. Dhamala , E. , Jamison , K. W. , Jaywant , A. & Kuceyeski , A . Shared functional connections within and between cortical networks predict cognitive abilities in adult males and females . Hum Brain Mapp 43 , 1087 – 1102 ( 2022 ). OpenUrl CrossRef PubMed 106. Dhamala , E. , Jamison , K. W. , Jaywant , A. , Dennis , S. & Kuceyeski , A . Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults . Hum Brain Mapp 42 , 3102 – 3118 ( 2021 ). OpenUrl CrossRef PubMed 107. Dhamala , E. , Chopra , S. , Ooi , L. Q. R. , Rubio , J. M. , Yeo , B. T. T. , Malhotra , A. K. & Holmes , A. J. Sex differences in the functional network underpinnings of psychotic-like experiences in children . bioRxiv 2024.04.22.590660 ( 2024 ). doi: 10.1101/2024.04.22.590660 OpenUrl Abstract / FREE Full Text 108. Dhamala , E. , Bassett , D. S. , Yeo , B. T. T. & Holmes , A. J . Functional brain networks are associated with both sex and gender in children . Sci Adv 10 , 4202 ( 2024 ). OpenUrl CrossRef 109. ↵ Dhamala , E. , Ooi , L. Q. R. , Chen , J. , Kong , R. , Anderson , K. M. , Chin , R. , Yeo , B. T. T. & Holmes , A. J. J . Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development . Neuroimage 260 , 119485 ( 2022 ). OpenUrl CrossRef PubMed 110. ↵ Li , J. , Kong , R. , Liégeois , R. , Orban , C. , Tan , Y. , Sun , N. , Holmes , A. J. , Sabuncu , M. R. , Ge , T. & Yeo , B. T. T . Global signal regression strengthens association between resting-state functional connectivity and behavior . Neuroimage 196 , 126 – 141 ( 2019 ). OpenUrl CrossRef PubMed 111. ↵ Elvisha Dhamala , A. , Christensen , E. , Hanson , J. L. , Ricard , J. A. , Arcaro , N. , Bhola , S. , Wiersch , L. , Brosch , K. , Thomas Yeo , B. , Holmes , A. J. & Yip , S. W. Neuroanatomy Reflects Individual Variability in Impulsivity in Youth . bioRxiv 2025.04.23.650222 ( 2025 ). doi: 10.1101/2025.04.23.650222 OpenUrl Abstract / FREE Full Text 112. ↵ Dhamala , E. , Yeo , B. T. T. & Holmes , A. J . One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry . Biol Psychiatry 93 , 717 – 728 ( 2023 ). OpenUrl CrossRef PubMed 113. ↵ Vijayakumar , N. , Mills , K. L. , Alexander-Bloch , A. , Tamnes , C. K. & Whittle , S . Structural brain development: A review of methodological approaches and best practices . Dev Cogn Neurosci 33 , 129 – 148 ( 2018 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted June 15, 2025. 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