Neuroanatomy Reflects Individual Variability in Impulsivity in Youth

Neuroanatomy Reflects Individual Variability in Impulsivity in Youth | 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 Neuroanatomy Reflects Individual Variability in Impulsivity in Youth Elvisha Dhamala, Erynn Christensen, jamie hanson, Jocelyn Ricard, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6520460/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Molecular Psychiatry → Version 1 posted 12 You are reading this latest preprint version Abstract Individual differences in neural circuits underlying emotional regulation, motivation, and decision-making are implicated in many psychiatric illnesses. Interindividual variability in these circuits may manifest, at least in part, as individual differences in impulsivity at both normative and clinically significant levels. Impulsivity reflects a tendency towards rapid, unplanned reactions to internal or external stimuli without considering potential negative consequences coupled with difficulty inhibiting responses. Here, we use multivariate brain-based predictive models to explore the neural bases of impulsivity across multiple behavioral scales, neuroanatomical features (cortical thickness, surface area, and gray matter volume), and sexes (females and males) in a large sample of youth from the Adolescent Brain Cognitive Development (ABCD) Study at baseline (n = 9,099) and two-year follow-up (n = 6,432). Impulsivity is significantly associated with neuroanatomical variability, and these associations vary across behavioral scales and neuroanatomical features. Impulsivity broadly maps onto cortical thickness in dispersed regions (e.g., inferior frontal, lateral occipital, superior frontal, entorhinal), as well as surface area and gray matter volume in specific medial (e.g., parahippocampal, cingulate) and polar (e.g., frontal and temporal) territories. Importantly, while many relationships are stable across sexes, others are sex-specific. These results highlight the complexity of the relationships between neuroanatomy and impulsivity across scales, features, sexes, and time points in youth. These findings suggest that neuroanatomy, in combination with other biological and environmental factors, reflects a key driver of individual differences in impulsivity in youth. As such, neuroanatomical markers may help identify youth at increased risk for developing impulsivity-related illnesses. Furthermore, this work emphasizes the importance of adopting a multidimensional and sex-specific approach in neuroimaging and behavioral research. Biological sciences/Neuroscience Biological sciences/Biological techniques BIS/BAS UPPS neuroanatomy machine learning sex differences development Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Impairments in emotional regulation, motivation, and decision-making are prevalent across a range of psychiatric illnesses 1 and often emerge during early adolescence 2 , 3 . These impairments contribute to the heterogeneity observed within psychiatric illnesses and may initially appear as more fundamental alterations in processes and behaviors such as impulsivity 4 , 5 . Impulsivity is a multifaceted construct that reflects “a predisposition toward rapid, unplanned reactions to internal or external stimuli without regard to the negative consequences of these reactions to the impulsive individual or to others. 6 ” Importantly, changes in impulsivity are a normal part of development. However, in some individuals, heightened levels of impulsivity may indicate increased risk for psychiatric illness 7 – 9 (see Supplemental Materials for specific examples). A better understanding of the neurobiological underpinnings of impulsivity, especially in youth, is crucial for developing more effective prevention and intervention strategies for these at-risk individuals. Neuroimaging techniques offer a powerful tool for investigating these neural substrates and identifying potential biomarkers for interventions. Given that adolescence is a period of significant brain plasticity, interventions targeting these impulsivity-related neural circuits in youth may be most effective. Although often treated as a single construct, the term impulsivity encompasses a variety of distinct but related functions that promote impulsive behavior 10 . These include an individual’s (in)ability to consider the consequences of a behavior (lack of premeditation), tendency to disengage from tasks due to boredom or difficulty before completion (lack of perseverance), responses to emotional states (positive and negative urgency), and motivation to experience rewarding sensations (sensation seeking) 9 . Impulsivity can also be considered a product of two systems that promote impaired self-regulation: the behavioral inhibition and approach systems (BIS/BAS) 11 . The BIS prevents actions that may lead to a negative outcome 12 , while the BAS encapsulates sensitivity to, and motivation for, reward/punishment, as well as escape from punishment, therefore encouraging incentive-motivated behavior 12 . These conceptualizations highlight the variability in how impulsivity is defined and measured, posing a challenge for research, and underscoring the need for greater conceptual clarity across different definitions of impusivity 6 , 10 . Consequently, a crucial consideration in exploring the neuroanatomical basis of impulsivity is the extent to which different, yet related, components of this construct may be subserved by shared versus distinct neurobiological substrates. Corticolimbic and corticostriatal circuitry regulate impulsivity 8 , 13 – 16 . The corticolimbic system contributes to processing emotional salience and regulating emotional responses 17 , while the corticostriatal system is involved in motivated behavior, reward processing, learning, and habit formation 18 . These systems mature throughout development 19 and this is accompanied by significant changes in synaptic connectivity and myelination 20 – 22 . Critically, this development is asynchronous. Limbic and striatal structures mature earlier than cortical structures, including the prefrontal cortex, resulting in heightened impulsivity during this period of developmental ‘mismatch’ 21 , 23 , 24 . Behaviorally, emotional regulation, motivation, and impulse control evolve throughout development with rapid changes in early life followed by gradual changes during adolescence 25 – 29 . This change is paralleled by changes in their neural substrates beyond corticolimbic and corticostriatal circuits, such as the insula and cingulate cortex 7 , 29 , 30 . However, previous studies examining these relationships have often focused on establishing univariate, cross-sectional associations between specific brain circuits and individual behavioral measures in small sample sizes. In contrast, brain-based predictive models use machine learning to analyze whole-brain multivariate relationships between brain features and behavioral measures 31 , which account for the interconnected nature of the brain, unlike traditional analyses focused on single regions. These models along with the large sample sizes provided by big data initiatives can provide insights into the whole-brain neuroanatomical basis of impulsivity 32 . In addition, longitudinal data can be used to examine whether these relationships are consistent throughout development. Here, we leverage these approaches to garner insights into the neural substrates of impulsivity in youth. Importantly, neurodevelopmental processes and behavioral expressions vary between males and females, raising questions about the extent to which sex-specific neuroanatomical patterns contribute to observed differences in impulsivity. There are significant sex differences in the developmental trajectories of corticolimbic and corticostriatal systems 33 , 34 , although findings have not always been consistent across studies. As an example, on average, females have greater relative volume in the prefrontal and orbitofrontal regions, while males have greater volume in ventral temporal and occipital regions 35 . Similarly, sex differences have been reported in impulsivity, but these results are also inconsistent 36 . Thus, it is plausible that sex differences exist across neuroanatomy, impulsivity, and their interrelationships, highlighting the importance of considering sex differences when studying impulsivity, particularly within a developmental framework. Furthermore, within the context of recently established large data initiatives, it remains to be determined whether sex differences in impulsivity are driven by unique neuroanatomical substrates. This can be addressed by establishing sex-specific relationships between neuroanatomy and impulsivity and examining the extent to which they overlap. Cortical thickness (CT), surface area (SA), and gray matter volume (GMV) reflect different aspects of neuroanatomy. CT (i.e., distance between the brain's outer surface and gray-white matter junction) reflects neuronal density and arrangement 37 , 38 . It increases rapidly during the prenatal period, continues growing after birth, peaks in early childhood (with regional variation), and then gradually thins 39 . Cortical SA (i.e., area of the pial surface) is linked to the organization and complexity of cortical columns 37 , 38 as well as neuronal proliferation 37 , 38 and gyrification 40 . SA expands prenatally and through childhood, peaking in late childhood/early adolescence, and then gradually declines 39 . GMV, encompassing thickness and SA, reflects the total amount of cells and synapses 37 , 38 , and generally follows the same developmental trajectory observed for SA 39 . Changes in these neuroanatomical features result from neurogenesis, synaptogenesis, synaptic pruning, cell death, and alterations in cell size and density, and are linked to various psychiatric conditions 37 , 38 . Importantly, these structural measures demonstrate high reliability and are considered more "trait-like" compared to functional measures, making them particularly valuable for investigation. Given these complexities, a multimodal analysis considering all three features of brain structure across all regions of the brain is warranter to reveal their unique and shared contributions to impulsivity, facilitating a more holistic understanding of these relationships. Here, we investigated the sex-specific neuroanatomical basis of impulsivity, across different neuroanatomical features and impulsivity measures, in a large sample of youth from the Adolescent Brain Cognitive Development (ABCD) Study at baseline and two-year follow-up. Using a data-driven, brain-based predictive modeling framework, we show that neuroanatomical features are associated with impulsivity and there are notable sex differences in these relationships. We also demonstrate that different domains of impulsivity are linked to shared and distinct neuroanatomical features. Some features vary across facets of impulsivity, others across sexes, and others across time points. These findings highlight substantial individual variability in the neural basis of impulsivity in youth. Understanding these distinct markers of impulsivity is crucial for establishing normative developmental patterns and paves the way for development of more effective early interventions grounded in neurobiological mechanism to prevent psychiatric illness. METHODS An overview of the methods is provided below. Details are in the Supplemental Materials. Dataset The ABCD Study is following a large community-based sample of children and adolescents throughout the course of development 41 . Participants are assessed on a comprehensive set of neuroimaging, behavioral, developmental, and psychiatric batteries. In this study, we used imaging and impulsivity data from 9,099 participants at baseline (ages 9–10 years) and 6,432 participants at the two-year follow-up (see Table S1 for demographic data). Details on data inclusion procedures (e.g., exclusion criteria for neuroimaging data) are provided in the Supplemental Materials (see Figures S1 -S2 for our participant inclusion pipeline). The research protocol for the dataset was reviewed and approved by a central Institutional Review Board (IRB) at the University of California, San Diego, and, in some cases, by individual site IRBs. Parents or guardians provided written informed consent, and children assented before participation. Neuroimaging The neuroimaging protocol and specific parameters for T1-weighted scans are detailed in previous publications 41 , 42 . We used measures of CT (mean), SA (total), and GMV (total) for 68 cortical regions (34 per hemisphere) from the Desikan-Killiany parcellation as provided on the NIMH Data Archive. Regional SA and GMV measures, but not CT, were proportionally corrected for individual differences in intracranial volume by dividing the raw values by intracranial volume, as recommended by prior work 43 (see Figure S3 for average measures and Figure S4 for sex differences in the measures). Impulsivity Impulsivity-related measures were derived from the Behavioral Inhibition/ Activation System (BIS/BAS) and the Modified Urgency, (lack of) Planning (or Premeditation), (lack of) Perseverance, Sensation-Seeking, and Positive Urgency (UPPS-P 44 ) Short Version scales, both of which have been validated in children 12 , 45 . We examined these measures separately, rather than combining them into summary or composite scores, as they assess distinct facets of impulsivity with potentially unique neural underpinnings, developmental trajectories, and sex differences. Differences in Impulsivity Across Development and Across Sexes We used non-parametric Mann-Whitney U rank tests to evaluate differences in the behavioral measures (4 BIS/BAS, 5 UPPS-P) between baseline and two-year follow-up as the scales were not normally distributed. We also used non-parametric Mann-Whitney U rank tests to evaluate sex differences in the behavioral measures at each time point. We corrected p-values for multiple comparisons within each behavioral scale using the Benjamini-Hochberg False Discovery Rate (q = 0.05) procedure 46 . We also computed sex-independent and sex-specific full correlations between the measures at each time point to evaluate their co-expression in youth. Predictive Modeling We used a cross-validated brain-based predictive modeling framework 31 which we have leveraged in prior work 43 , 47 – 51 . This framework avoids data leakage and minimizes overfitting to capture robust, reliable, and interpretable associations between imaging-derived measures and phenotypic data. For each pair of neuroanatomical features and behavioral measure, we developed separate sets of sex-independent (i.e., including the entire sample) and sex-specific (i.e., in either females or males) linear ridge regression models at each of the two time points to predict impulsivity based on neuroanatomy. We trained each model on neuroanatomical features (either CT, SA, or GMV) from 68 regions to predict a single impulsivity measure. We quantified model performance using prediction accuracy 51 – 54 and assessed significance in comparison to null distributions. We report test accuracies in our results to provide an unbiased estimate of model generalizability, as training accuracy can be inflated due to overfitting. We corrected p-values for multiple comparisons within each behavioral scale using the Benjamini-Hochberg procedure 46 . Feature Weights For models yielding reliable brain-behavior relationships in both sexes (as compared to null distributions), we conducted a series of analyses to determine feature importance and to guide mechanistic understanding. This conservative approach ensures that comparisons are only made where meaningful brain-behavior relationships are present in both sexes. We transformed the feature weights obtained from the models using the Haufe transformation 55 (to increase their interpretability and reliability 53 , 56 , 57 ) and then calculated a mean feature importance for each set of models. We computed cosine similarities between the mean feature importance values to evaluate overlap in the regional features associated with different impulsivity measures. RESULTS Expressions of impulsivity vary across youth. Analyses examining the distributions of the impulsivity measures and the correlations between them at baseline and two-year follow-up are presented in the Supplemental Materials (see Figure S5 for results across all participants and S6 for sex-specific results). These distributions are consistent with prior work examining impulsivity in the ABCD Study 58 , 59 , indicating that our sample is representative of the cohort. Broadly, these analyses indicated modest within-scale correlations of measures suggesting that they capture partially overlapping aspects of behavior, and significant but weak between-scale correlations indicating that the UPPS-P and BIS/BAS, while potentially related, measure somewhat independent constructs 60 . Neuroanatomy predicts impulsivity Brain-based predictive models were used to quantify associations between neuroanatomy and impulsivity (Fig. 1 , Table S2 ). CT : Models based on CT accurately predicted reward-responsiveness (prediction accuracy, r = 0.044, p FDR <0.001), drive (r = 0.093, p FDR <0.001), negative urgency (r = 0.055, p FDR =0.020), and positive urgency (r = 0.078, p FDR =0.015) at baseline; and inhibition (r = 0.059, p FDR =0.022), drive (r = 0.073, p FDR =0.004), fun-seeking (r = 0.029, p FDR =0.028), negative urgency (r = 0.049, p FDR =0.008), and positive urgency (r = 0.062, p FDR <0.001) at two-year follow-up. SA : Models based on SA accurately predicted reward-responsiveness (r = 0.043, p FDR <0.001), drive (r = 0.064, p FDR =0.004), fun-seeking (r = 0.035, p FDR = < 0.001), positive urgency (r = 0.074, p FDR <0.001), lack of planning (r = 0.061, p FDR <0.001), and sensation-seeking (r = 0.055, p FDR =0.003) at baseline; and inhibition (r = 0.037, p FDR =0.044), reward-responsiveness (r = 0.019, p FDR =0.044), positive urgency (r = 0.055, p FDR =0.040), lack of planning (r = 0.050, p FDR =0.040), and sensation-seeking (r = 0.080, p FDR =0.025) at two-year follow-up. GMV : Models based on GMV accurately predicted inhibition (r = 0.024, p FDR =0.023), reward-responsiveness (r = 0.065, p FDR =0.002), drive (r = 0.088, p FDR =0.004), fun-seeking (r = 0.043, p FDR =0.002), positive urgency (r = 0.071 p FDR <0.001), lack of planning (r = 0.062, p FDR <0.001), and sensation-seeking (r = 0.036, p FDR =0.033) at baseline; and inhibition (r = 0.058, p FDR =0.010), drive (r = 0.077, p FDR <0.001), positive urgency (r = 0.059, p FDR <0.001), lack of planning (r = 0.056, p FDR =0.030), and sensation-seeking (r = 0.065, p FDR =0.005) at two-year follow-up. We next used the same framework to examine sex-specific associations (Fig. 2 , Tables S3-S4 ). CT : Sex-specific models based on CT accurately predicted reward-responsiveness (r female =0.065, p female =0.002; r male =0.046, p male =0.008) and drive (r female =0.133, p female <0.001; r male =0.060, p male =0.004) in both sexes, negative urgency (r = 0.048, p = 0.015) in males, and positive urgency (r = 0.119, p < 0.001) in females at baseline; and drive (r = 104, p = 0.028), negative urgency (r = 0.065, p = 0.005), positive urgency (r = 0.093, p < 0.001), and lack of perseverance (r = 0.051, p = 0.038) in females at two-year follow-up. SA : Sex-specific models based on SA accurately predicted positive urgency (r female =0.099, p female <0.001; r male =0.046, p male <0.005) in both sexes, drive (r = 0.106, p < 0.001) and fun-seeking (r = 0.045 p = 0.006) in females, and lack of planning (r = 0.031, p = 0.008) in males at baseline; and positive urgency in females (r = 0.075, p = 0.005) at two-year follow-up. GMV : Sex-specific models based on GMV accurately predicted reward-responsiveness (r female =0.052, p female =0.028; r male =0.059, p male =0.020) and drive (r female =0.105 p female <0.001; r male =0.072, p male <0.001) in both sexes, fun-seeking (r = 0.045, p = 0.006), and negative urgency (r = 0.034, p = 0.040) in females at baseline; and drive (r = 0.060, p < 0.001) in males and positive urgency in females (r = 0.094, p = 0.005) at two-year follow-up. Changes in impulsivity are part of normative development and, at their extremes, are linked to the development of mental illnesses later in life 61 , 62 . We show that individual variations in neuroanatomy can predict impulsivity in youth, and these relationships are generally stable across development. While certain measures can be predicted in both sexes, others yield significant results only in females, which may be due to the reliability of measures across sexes, reporting biases, and data quality, among other factors. Impulsivity maps onto shared and distinct brain regions We derived the feature importance maps from the models and computed cosine similarities to evaluate overlap, focusing on models that captured significant associations. These results, presented in their entirety in Fig. 3 A, indicated that models predicting measures from the same scale captured largely overlapping associations. As an example, CT features associated with positive and negative urgency were highly similar at both time points (cosine similarity, s baseline =0.69, s two−year =0.77). Further, while some models predicting measures from different scales were similar, others were orthogonal or opposite. For example, baseline CT features associated with reward-responsiveness and drive were dissimilar from those associated with lack of planning and sensation seeking (-0.60 ≤ s≤-0.29), but similar to those associated with positive urgency (0.62 ≤ s ≤ 0.64). These observed patterns were generally consistent across time points for CT and SA, but less so for GMV. We also evaluated the similarities in the neuroanatomical features associated with impulsivity measures across the sexes, focusing on the five pairs of models that yielded significant results in both sexes at baseline. GMV features associated with reward-responsiveness at baseline were quite similar across the sexes (s = 0.53), along with SA features associated with positive urgency (s = 0.53). However, other associations were considerably different across the sexes. Models based on CT to predict reward-responsiveness and drive captured distinct associations in males and females (s reward−responsiveness =0.24, s drive =0.37), as well as those based on GMV to predict drive (s = 0.36). These analyses reveal that impulsivity maps onto shared and distinct neuroanatomical features. Across the entire sample, reward sensitivity and urgency share a common neuroanatomical basis that is distinct from the neural substrates of lack of planning and sensation-seeking. These findings suggest that the BIS/BAS and UPPS-P scales, though separable constructs, share some neuroanatomical features while also exhibiting distinct features. Importantly, some of these associations differ across the sexes, suggesting the presence of sex-specific neuroanatomical substrates. Neuroanatomical basis of impulsivity varies across imaging modalities CT features associated with impulsivity were widespread, while SA and GMV features overlapped and were more localized (full results shown in Fig. 3 B). CT : Impulsivity was broadly negatively associated with CT in bilateral cuneus and lateral occipital regions (i.e., youth who were more impulsive had less CT in these areas relative to those who were less impulsive) and was positively associated with CT in the bilateral inferior frontal gyrus, particularly in the pars opercularis, superior frontal, and superior temporal regions (Fig. 4 A-D for 2 representative BIS/BAS and UPPS-P measures at each time point, Figure S7 for all other measures). Impulsivity was also negatively associated with CT in bilateral entorhinal, lingual, parahippocampal, and paracentral regions at baseline, but these associations were less consistent at two-year follow-up. Other regions exhibited measure- and hemisphere-specific relationships. For example, impulsivity was broadly negatively associated with CT in the left postcentral and precuneus regions, but BIS/BAS measures were positively associated with CT in the right postcentral region while UPPS-P measures were positively associated with CT in right precuneus at baseline. SA : Impulsivity was broadly negatively associated with SA in bilateral frontal poles at both time points (Fig. 4 E-H for 2 representative BIS/BAS and UPPS-P measures at each time point, Figure S8 for all other measures). Positive urgency was also positively associated with SA in bilateral temporal poles, but associations with temporal pole regions were more nuanced for other measures. Certain regions also exhibited opposite associations. For example, BIS/BAS measures were positively associated with SA in the transverse temporal and parahippocampal regions and negatively associated with SA in the rostral anterior cingulate and entorhinal regions, while the opposite was true for UPPS-P measures. GMV : Associations between GMV and impulsivity largely paralleled those with SA, although they were less pronounced and included a few exceptions ( Figure S9 for 2 representative BIS/BAS and UPPS-P measures at each time point, Figure S10 for all other measures). Impulsivity was broadly negatively associated with GMV in the pericalcarine region, and inhibition was positively associated with frontal pole volumes but negatively associated with isthmus cingulate volumes. These findings highlight the presence of multivariate relationships between neuroanatomy and impulsivity. While some associations are similar across the behavioral scales, others are less consistent. These results suggest a distributed network of brain regions encode individual differences in impulsive behaviors, and these relationships are dynamic. There are sex differences in the neuroanatomical basis of impulsivity We assessed sex differences in the associations between neuroanatomy and impulsivity, focusing on the models that were significant in both sexes. Across both sexes, BAS measures were negatively associated with CT in the entorhinal, lateral occipital, lateral orbitofrontal, and parahippocampal regions (Fig. 5 ). In females, they were also positively associated with CT in bilateral inferior frontal gyrus and, to a lesser extent, in the superior frontal gyrus, while in males, these relationships were present in the left hemisphere, but the opposite relationships were present in the right hemisphere. Further, although these measures were negatively associated with CT in the lingual, paracentral, and precuneus regions in both sexes, these relationships were stronger in the lingual regions in females and in the paracentral and precuneus regions in males. Relationships between GMV and reward-responsiveness at baseline were largely shared across the sexes, but those between GMV and drive were considerably different, although the differences were predominantly in strength rather than directionality ( Figure S11 ). As an example, GMV in the right posterior cingulate and right pars opercularis exhibited stronger relative positive associations with drive in females than in males. Results from all other sex-specific significant models generally resembled the results from the sex-independent models ( Figures S12-14) . These findings suggest that while many of these relationships are shared across the sexes, there are also important differences in the neuroanatomical underpinnings of impulsivity. These sex-specific relationships may, in part, explain observed sex differences in impulsive behaviors and vulnerability to impulsivity-related psychiatric illness. DISCUSSION Examining multiple facets of impulsivity and neuroanatomical features, we demonstrate that individual variability in neuroanatomy in a large cohort of youth is associated with impulsivity across facets and features, and these relationships, to some extent, differ between sexes. We show that impulsivity measures map onto shared and distinct brain regions across neuroanatomical features, and these relationships are largely stable across development. Some relationships overlap across the impulsivity measures and across neuroanatomical features, and are consistent across the sexes, while others vary across facets, features, and sexes. These results shed light on how individual differences in neuroanatomy throughout development contribute to the diverse expressions of impulsivity in youth. These findings also suggest that impulsivity maps onto specific patterns of neuroanatomy that, alongside other risk factors, could help identify youth at risk for impulsivity-related disorders. Impulsivity is, in part, driven by individual differences in corticolimbic, corticostriatal and motor-sensory circuits 8 , 13 – 16 , 63 . Recent advances in brain-based predictive modeling allow us to examine whole-brain multivariate relationships 31 , unlike traditional univariate analyses focused on individual regions. In recent years, a few studies have used this approach to investigate the neuroanatomical basis of impulsivity, although they have generally focused on specific neuroimaging features and impulsivity measures, ignored sex effects, and considered these relationships at a single time point 8 , 53 , 64 , 65 . Using a multivariate approach, we replicate univariate findings and demonstrate that impulsivity maps onto a dispersed set of cortical regions. While some relationships are shared across facets of impulsivity, others are distinct. One particularly stable relationship appears to be in the pars opercularis, where CT is positively linked to impulsivity measures from both scales at both time points. These results align with those from prior neuroanatomical, functional, and electrophysiological studies showing that the pars opercularis plays an important role in impulse control 66 across motor 67 – 69 and speech 70 , 71 domains. Here, we find that impulsivity is associated with reduced cortical thickness in the visual network and increased cortical thickness in heteromodal association networks (default, attention, and frontoparietal). Gray matter alterations in the visual network, responsible for the processing of visual stimuli, have been linked to impulsivity 72 . These associations may explain difficulties with visual attention and increased distractibility that are common in impulsive individuals 72 . In addition, functional network alterations in heteromodal association cortices are linked to broad psychopathology 53 , 68 , 73 – 77 . Structural alterations in these regions associated with impulsivity may, in part, explain the functional alterations that subsequently underlie various psychological disorders. Our analyses also revealed widespread associations between impulsivity and both surface area and gray matter volume within the limbic network, although the direction of these associations was dependent on the specific impulsivity measure and limbic region examined. The limbic network, which plays a crucial role in higher-order cognitive processes related to emotions, memory, and motivation, shows particularly interesting patterns in relation to impulsivity. Specifically, impulsivity is negatively linked to D2/3 receptor binding in limbic structures 78 , and the spatial density of these receptors has been linked to altered functional connectivity patterns in substance use disorder 79 . The structural alterations we observe here to be linked to impulsivity may, in part, be associated with these receptor density differences and related functional connectivity changes, potentially contributing to the vulnerability for impulsivity-related disorders. Collectively, these findings demonstrate that impulsivity is associated with a complex pattern of neurobiological alterations across multiple networks, providing important insights into the neurobiological mechanisms underlying impulsive behavior throughout development and its relationship to psychopathology. Impulsivity follows a non-linear trend, increasing during childhood and adolescence and decreasing throughout adulthood 80 – 82 . This trajectory mirrors developmental changes observed in neuroanatomy. During adolescence and early adulthood, significant maturation occurs in the prefrontal cortex, a region critical for emotional regulation and impulse control 20 – 22 . This maturation involves synaptic pruning and increased white matter connectivity to refine neural circuits, leading to improved cognitive control and decreased impulsivity 22 . Our results show that the neuroanatomical basis of impulsivity is not static, potentially reflecting these broader developmental changes. While some regions show consistent relationships, others demonstrate changes from baseline to two-year follow-up, suggesting that changes in impulsivity may be driven by shifts in the underlying neuroanatomical associations. Further, deviations from typical developmental trajectories in neuroanatomy may underlie impulsivity-related deficits. Research on sex differences in impulsivity has produced mixed findings 36 , 83 . The most consistent finding is that females exhibit greater inhibition and males exhibit greater sensation-seeking 36 . Activation-related impulsivity is comparable across the sexes 36 , though differences have been reported for specific rewards 84 . One study examining relationships between CT and a single global measure of impulsivity in the ABCD cohort reported significant associations in males but not the entire sample 85 . A separate study exploring the volumetric correlates of impulsivity in the same cohort found that lack of premeditation and sensation seeking were related to larger volumes in many cortical and subcortical regions, while positive urgency was related to smaller volumes in those same regions 86 . They also found that many of the relationships were stronger in females. These studies highlight the need for sex-specific investigations. Our analyses build on this work using a multivariate machine learning approach and show that while some brain-impulsivity associations are consistent across the sexes, others are not. In some cases, the same regions even exhibit opposite relationships across the sexes. Notably, we find that regions showing sex-specific associations with impulsivity are predominantly heteromodal association cortices. These same regions demonstrate established structural 35 and functional 87 sex differences, which are specifically coupled to regional expression of sex-chromosome genes and show enrichment for distinct cell-type signatures 35 , 88 , 89 . These findings suggest that sex differences in impulsivity may have a strong biological basis, rooted in sex-specific patterns of gene expression and cellular organization within key brain networks. There are several limitations to this work. First, we used a single dataset. Although participants reflect different demographic groups, income levels, and living environments, our findings may be limited in generalizability 90 , 91 . To maximize the robustness of our work, we included all participants with complete imaging and impulsivity data and used a cross-validated predictive modeling framework known to yield reliable results 51 . Second, we only considered binary sex due to data availability and thus were not able to assess these relationships in intersex or other non-binary populations. We also did not consider the effects of gender, which influences neurobiology 47 and behavior 92 . Third, the brain continues to develop throughout adolescence, with females and males reaching developmental milestones at different times 93 . Here, we used data from two time points but did not include older ages due to data availability. As a result, the associations we report may continue to shift throughout development. Our work serves as an empirical baseline from which theses trajectories may be tracked in later waves of the ABCD study. Fourth, we analyzed relationships between cortical structures and impulsivity, but did not consider subcortical or cerebellar regions. A more complete understanding of neural substrates of impulsivity will require future research incorporating these subcortical and cerebellar regions. Finally, we examined the neural basis of impulsivity and explored sex differences in these relationships. However, we did not consider the effects of other biological or environmental factors (e.g., genetics, pubertal maturation, urbanicity) 94 – 97 . Future analyses within global open-access datasets that consider the effects of additional biological and environmental factors can address these limitations and provide additional evidence to confirm (or refute) these findings. Increases in impulsivity are a typical part of development, and, when significant, may be linked to risk for psychiatric illness. Understanding the neuroanatomical basis of impulsivity paves the way for development of more effective early interventions grounded in neurobiological mechanism to prevent psychiatric illness. These findings highlight how neuroanatomy underlies the diverse expressions of impulsivity throughout development and may represent potential markers of psychiatric risk. In addition, this work emphasizes the importance of conducting sex-disaggregated analyses when examining brain-behavior relationships. Declarations Data Availability Statement : Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development SM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. Code Availability Statement : Code used for the analyses are on GitHub: https://github.com/elvisha/neuroanat_impulsivity. Funding Sources : This work was supported by the following sources: Brain and Behavior Research Foundation (Young Investigator Grant to ED), Northwell Health Advancing Women in Science and Medicine (Career Development Award to ED and Educational Achievement Award to ED), Feinstein Institutes for Medical Research (Emerging Scientist Award to ED), NIAAA (R01AA027553 to SWY), NIDA (R01DA053301 to SWY), NIMH (2R01MH120080 to AJH and BTTY), Stanford University Knight-Hennessy Scholars Program (JAR) and the National Academies of Sciences, Engineering, and Medicine’s Ford Foundation Predoctoral Fellowship (JAR). SWY is also supported by the Canadian Institute of Health Research (CIHR) and by Women’s Healthy Research at Yale (WHRY). BTTY is also supported by the NUS Yong Loo Lin School of Medicine (NUHSRO/2020/124/TMR/LOA), the Singapore National Medical Research Council (NMRC) LCG (OFLCG19May-0035), NMRC CTG-IIT (CTGIIT23jan-0001), NMRC OF-IRG (OFIRG24jan-0006), NMRC STaR (STaR20nov-0003), Singapore Ministry of Health (MOH) Centre Grant (CG21APR1009). Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the funders. Disclosures : The authors have nothing to disclose. Author Contributions Conceptualization: ED; Methodology: ED; Software: ED; Validation: ED; Formal Analysis: ED; Investigation: ED; Resources: ED; Writing – Original Draft: ED, EC, JLH, JAR, NA, SB, SWY; Writing – Review & Editing: ED, EC, JLH, JAR, NA, SB, LW, KB, BTTY, AJH, SWY; Visualization: ED All authors reported no biomedical financial interests or potential conflicts of interest. References Sloan, E. et al. Emotion regulation as a transdiagnostic treatment construct across anxiety, depression, substance, eating and borderline personality disorders: A systematic review. Clinical psychology review 57 , 141-163 (2017). Cáceda, R., Nemeroff, C. B. & Harvey, P. D. Toward an understanding of decision making in severe mental illness. The Journal of neuropsychiatry and clinical neurosciences 26 , 196-213 (2014). Lincoln, T. M., Schulze, L. & Renneberg, B. The role of emotion regulation in the characterization, development and treatment of psychopathology. Nature Reviews Psychology 1 , 272-286 (2022). Sonmez, A. I., Garcia, J. Q., Thitiseranee, L., Blacker, C. J. & Lewis, C. P. Scoping review: Transdiagnostic measurement of impulsivity domains in youth using the UPPS impulsive behavior scales. Journal of the American Academy of Child & Adolescent Psychiatry (2024). Struijs, S. Y. et al. Approach and avoidance tendencies in depression and anxiety disorders. Psychiatry research 256 , 475-481 (2017). Moeller, F. G., Barratt, E. S., Dougherty, D. M., Schmitz, J. M. & Swann, A. C. Psychiatric aspects of impulsivity. American journal of psychiatry 158 , 1783-1793 (2001). Chambers, R. A., Taylor, J. R. & Potenza, M. N. Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. American journal of psychiatry 160 , 1041-1052 (2003). Kohler, R. et al. Identification of a composite latent dimension of reward and impulsivity across clinical, behavioral, and neurobiological domains among youth. Biological psychiatry: cognitive neuroscience and neuroimaging 9 , 407-416 (2024). Swann, A. C., Pazzaglia, P., Nicholls, A., Dougherty, D. M. & Moeller, F. G. Impulsivity and phase of illness in bipolar disorder. Journal of affective disorders 73 , 105-111 (2003). Strickland, J. C. & Johnson, M. W. Rejecting impulsivity as a psychological construct: A theoretical, empirical, and sociocultural argument. Psychological review 128 , 336 (2021). Braddock, K. H. et al. Impulsivity partially mediates the relationship between BIS/BAS and risky health behaviors. Journal of personality 79 , 793-810 (2011). Pagliaccio, D. et al. Revising the BIS/BAS Scale to study development: Measurement invariance and normative effects of age and sex from childhood through adulthood. Psychological assessment 28 , 429 (2016). Jean-Richard-Dit-Bressel, P., Killcross, S. & McNally, G. P. Behavioral and neurobiological mechanisms of punishment: implications for psychiatric disorders. Neuropsychopharmacology 43 , 1639-1650 (2018). Cardinal, R. N., Winstanley, C. A., Robbins, T. W. & Everitt, B. J. Limbic corticostriatal systems and delayed reinforcement. Annals of the New York Academy of Sciences 1021 , 33-50 (2004). Scheres, A., Milham, M. P., Knutson, B. & Castellanos, F. X. Ventral striatal hyporesponsiveness during reward anticipation in attention-deficit/hyperactivity disorder. Biol Psychiat 61 , 720-724 (2007). Piantadosi, P. T., Halladay, L. R., Radke, A. K. & Holmes, A. Advances in understanding meso‐cortico‐limbic‐striatal systems mediating risky reward seeking. Journal of neurochemistry 157 , 1547-1571 (2021). Kovner, R., Oler, J. A. & Kalin, N. H. Cortico-limbic interactions mediate adaptive and maladaptive responses relevant to psychopathology. American Journal of Psychiatry 176 , 987-999 (2019). O’Hare, J., Calakos, N. & Yin, H. H. Recent insights into corticostriatal circuit mechanisms underlying habits. Current opinion in behavioral sciences 20 , 40-46 (2018). Uhlhaas, P. J. et al. Towards a youth mental health paradigm: a perspective and roadmap. Molecular Psychiatry 28 , 3171-3181 (2023). Casey, B. J., Getz, S. & Galvan, A. The adolescent brain. Developmental review 28 , 62-77 (2008). Casey, B. J., Jones, R. M. & Hare, T. A. The adolescent brain. Ann N Y Acad Sci 1124 , 111-126, doi:10.1196/annals.1440.010 (2008). Luna, B. The relevance of immaturities in the juvenile brain to culpability and rehabilitation. The Hastings law journal 63 , 1469 (2012). Somerville, L. H., Jones, R. M. & Casey, B. A time of change: behavioral and neural correlates of adolescent sensitivity to appetitive and aversive environmental cues. Brain Cognition 72 , 124-133 (2010). Blakemore, S. J. The social brain in adolescence. Nat Rev Neurosci 9 , 267-277, doi:10.1038/nrn2353 (2008). Schweizer, S., Gotlib, I. H. & Blakemore, S.-J. The role of affective control in emotion regulation during adolescence. Emotion 20 , 80 (2020). Young, K. S., Sandman, C. F. & Craske, M. G. Positive and negative emotion regulation in adolescence: links to anxiety and depression. Brain sciences 9 , 76 (2019). Silvers, J. A. Adolescence as a pivotal period for emotion regulation development. Current opinion in psychology 44 , 258-263 (2022). Ripke, S. et al. Reward processing and intertemporal decision making in adults and adolescents: The role of impulsivity and decision consistency. Brain research 1478 , 36-47 (2012). Romer, D. Adolescent risk taking, impulsivity, and brain development: Implications for prevention. Developmental Psychobiology: The Journal of the International Society for Developmental Psychobiology 52 , 263-276 (2010). Casey, B., Heller, A. S., Gee, D. G. & Cohen, A. O. Development of the emotional brain. Neuroscience letters 693 , 29-34 (2019). Dhamala, E., Yeo, B. T. & Holmes, A. J. Methodological Considerations for Brain-Based Predictive Modelling in Psychiatry. Biol Psychiat (2022). Bzdok, D. & Yeo, B. T. Inference in the age of big data: Future perspectives on neuroscience. Neuroimage 155 , 549-564 (2017). Matte Bon, G., Kraft, D., Comasco, E., Derntl, B. & Kaufmann, T. Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders. Biology of Sex Differences 15 , 1-15 (2024). Laakso, A. et al. Sex differences in striatal presynaptic dopamine synthesis capacity in healthy subjects. Biol Psychiat 52 , 759-763 (2002). Liu, S., Seidlitz, J., Blumenthal, J. D., Clasen, L. S. & Raznahan, A. Integrative structural, functional, and transcriptomic analyses of sex-biased brain organization in humans. Proceedings of the National Academy of Sciences 117 , 18788-18798 (2020). Cross, C. P., Copping, L. T. & Campbell, A. Sex differences in impulsivity: a meta-analysis. Psychological bulletin 137 , 97 (2011). Rakic, P. Evolution of the neocortex: a perspective from developmental biology. Nature Reviews Neuroscience 10 , 724-735 (2009). Rakic, P., Ayoub, A. E., Breunig, J. J. & Dominguez, M. H. Decision by division: making cortical maps. Trends in neurosciences 32 , 291-301 (2009). Bethlehem, R. A. et al. Brain charts for the human lifespan. Nature 604 , 525-533 (2022). White, T., Su, S., Schmidt, M., Kao, C.-Y. & Sapiro, G. The development of gyrification in childhood and adolescence. Brain Cognition 72 , 36-45 (2010). Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental cognitive neuroscience 32 , 43-54, doi:10.1016/j.dcn.2018.03.001 (2018). Hagler, D. J., Jr. et al. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. Neuroimage 202 , 116091, doi:10.1016/j.neuroimage.2019.116091 (2019). Dhamala, E. et al. Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features and populations. NeuroImage , doi:10.1016/j.neuroimage.2022.119485 (2022). Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31 , 968-980, doi:10.1016/j.neuroimage.2006.01.021 (2006). Geurten, M., Catale, C., Gay, P., Deplus, S. & Billieux, J. Measuring impulsivity in children: adaptation and validation of a short version of the UPPS-P impulsive behaviors scale in children and investigation of its links with ADHD. Journal of attention disorders 25 , 105-114 (2021). Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. J R Stat Soc B 57 , 289-300 (1995). Dhamala, E., Bassett, D. S., Yeo, B. T. & Holmes, A. J. Functional brain networks are associated with both sex and gender in children. Science Advances 10 , eadn4202 (2024). Dhamala, E. et al. Sex differences in the functional network underpinnings of psychotic-like experiences in children. bioRxiv , 2024.2004. 2022.590660 (2024). Dhamala, E., Jamison, K. W., Jaywant, A., Dennis, S. & Kuceyeski, A. Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults. Human Brain Mapping 42 , 3102-3118, doi:10.1002/hbm.25420 (2021). 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. Human Brain Mapping , doi:10.1002/hbm.25709 (2022). Dhamala, E. et al. Brain-based predictions of psychiatric illness-linked behaviors across the sexes. Biol Psychiat (2023). Chen, J. et al. Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study. NeuroImage 274 , 120115 (2023). Chen, J. et al. Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Nature communications 13 , 2217, doi:10.1038/s41467-022-29766-8 (2022). Li, J. et al. Global signal regression strengthens association between resting-state functional connectivity and behavior. Neuroimage 196 , 126-141, doi:10.1016/j.neuroimage.2019.04.016 (2019). Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87 , 96-110 (2014). Tian, Y. & Zalesky, A. Machine learning prediction of cognition from functional connectivity: Are feature weights reliable? bioRxiv (2021). Chen, J. et al. There is no fundamental trade-off between prediction accuracy and feature importance reliability. bioRxiv (2022). Owens, M. M. et al. Neuroanatomical correlates of impulsive traits in children aged 9 to 10. Journal of abnormal psychology 129 , 831 (2020). Shao, I. Y. et al. From individual motivation to substance use initiation: A longitudinal cohort study assessing the associations between reward sensitivity and subsequent risk of substance use initiation among US adolescents. Addictive Behaviors 160 , 108162 (2025). Akoglu, H. User's guide to correlation coefficients. Turkish journal of emergency medicine 18 , 91-93 (2018). Cardoso Melo, R. D., Groen, R. N. & Hartman, C. A. Reward sensitivity at age 13 predicts the future course of psychopathology symptoms. Frontiers in Psychiatry 13 , 818047 (2022). Read, R. W. et al. A study of impulsivity and adverse childhood experiences in a population health setting. Frontiers in Public Health 12 , 1447008 (2024). Kebets, V. et al. Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biol Psychiatry 86 , 779-791, doi:10.1016/j.biopsych.2019.06.013 (2019). Ooi, L. Q. R. et al. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. NeuroImage , 119636 (2022). Ide, J. S. et al. Gray matter volumetric correlates of behavioral activation and inhibition system traits in children: An exploratory voxel-based morphometry study of the ABCD project data. Neuroimage 220 , 117085 (2020). Forstmann, B. U., van den Wildenberg, W. P. & Ridderinkhof, K. R. Neural mechanisms, temporal dynamics, and individual differences in interference control. Journal of Cognitive Neuroscience 20 , 1854-1865 (2008). Rizzolatti, G., Fogassi, L. & Gallese, V. Motor and cognitive functions of the ventral premotor cortex. Current opinion in neurobiology 12 , 149-154 (2002). Holmes, A. J., Hollinshead, M. O., Roffman, J. L., Smoller, J. W. & Buckner, R. L. Individual differences in cognitive control circuit anatomy link sensation seeking, impulsivity, and substance use. Journal of neuroscience 36 , 4038-4049 (2016). Levy, B. J. & Wagner, A. D. Cognitive control and right ventrolateral prefrontal cortex: reflexive reorienting, motor inhibition, and action updating. Annals of the New York academy of sciences 1224 , 40-62 (2011). Loh, K. K. et al. Cognitive control of orofacial motor and vocal responses in the ventrolateral and dorsomedial human frontal cortex. Proceedings of the National Academy of Sciences 117 , 4994-5005 (2020). Zhao, L., Silva, A. B., Kurteff, G. L. & Chang, E. F. Inhibitory control of speech production in the human premotor frontal cortex. Nature Human Behaviour , 1-16 (2025). Ide, J. S., Tung, H. C., Yang, C.-T., Tseng, Y.-C. & Li, C.-S. R. Barratt impulsivity in healthy adults is associated with higher gray matter concentration in the parietal occipital cortex that represents peripheral visual field. Frontiers in Human Neuroscience 11 , 222 (2017). Baker, J. T. et al. Functional connectomics of affective and psychotic pathology. Proceedings of the National Academy of Sciences 116 , 9050-9059 (2019). Chen, J. et al. Intrinsic Connectivity Patterns of Task-Defined Brain Networks Allow Individual Prediction of Cognitive Symptom Dimension of Schizophrenia and Are Linked to Molecular Architecture. Biol Psychiatry 89 , 308-319, doi:10.1016/j.biopsych.2020.09.024 (2021). Kong, R. et al. Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. Cerebral Cortex 29 , 2533-2551, doi:10.1093/cercor/bhy123 (2018). Cole, M. W., Repovš, G. & Anticevic, A. The frontoparietal control system: a central role in mental health. The Neuroscientist 20 , 652-664 (2014). Xia, C. H. et al. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat Commun 9 , 3003, doi:10.1038/s41467-018-05317-y (2018). Barlow, R. L. et al. Ventral striatal D2/3 receptor availability is associated with impulsive choice behavior as well as limbic corticostriatal connectivity. International Journal of Neuropsychopharmacology 21 , 705-715 (2018). Ricard, J. A. et al. A shared spatial topography links the functional connectome correlates of cocaine use disorder and dopamine D2/3 receptor densities. Communications Biology 7 , 1178 (2024). Betts, M. J. et al. Learning in anticipation of reward and punishment: perspectives across the human lifespan. Neurobiology of aging 96 , 49-57 (2020). Melo, R. D. C., Schreuder, M. J., Groen, R. N., Sarsembayeva, D. & Hartman, C. A. Reward sensitivity across the lifespan in males and females and its associations with psychopathology. Personality and Individual Differences 204 , 112041 (2023). Hammond, C. J., Potenza, M. N. & Mayes, L. C. Development of impulse control, inhibition, and self-regulatory behaviors in normative populations across the lifespan . Vol. 232 (Oxford University Press New York, NY, 2012). Weafer, J. & de Wit, H. Sex differences in impulsive action and impulsive choice. Addictive behaviors 39 , 1573-1579 (2014). Barendse, M. et al. Sex and pubertal variation in reward-related behavior and neural activation in early adolescents. Developmental Cognitive Neuroscience 66 , 101358 (2024). Assari, S. Sex Differences in the Association between Cortical Thickness and Children's Behavioral Inhibition. Journal of psychology & behavior research 2 , 49-64 (2020). Chen, Y. et al. Gray matter volumetric correlates of dimensional impulsivity traits in children: Sex differences and heritability. Human brain mapping 43 , 2634-2652 (2022). Shanmugan, S. et al. Sex differences in the functional topography of association networks in youth. Proceedings of the National Academy of Sciences 119 , e2110416119 (2022). DeCasien, A. R., Guma, E., Liu, S. & Raznahan, A. Sex differences in the human brain: a roadmap for more careful analysis and interpretation of a biological reality. Biology of sex differences 13 , 43 (2022). Zhang, X.-H. et al. The cell-type underpinnings of the human functional cortical connectome. Nature Neuroscience 28 , 150-160 (2025). Ricard, J. et al. Confronting racially exclusionary practices in the acquisition and analyses of neuroimaging data. Nature Neuroscience , 1-8 (2022). Dhamala, E. et al. Considering the interconnected nature of social identities in neuroimaging research. Nature Neuroscience , 1-12 (2024). Wierenga, L. M. et al. Recommendations for a better understanding of sex and gender in neuroscience of mental health. Biological Psychiatry Global Open Science , 100283 (2023). Marek, S., Tervo-Clemmens, B., Calabro, F., Nichols, T. & Dosenbach, N. Reproducible brain-wide association studies require thousands of individuals. Nature (2022). Bevilacqua, L. & Goldman, D. Genetics of impulsive behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences 368 , 20120380 (2013). Pauli, R. et al. Action initiation and punishment learning differ from childhood to adolescence while reward learning remains stable. Nature communications 14 , 5689 (2023). Bezdjian, S., Baker, L. A. & Tuvblad, C. Genetic and environmental influences on impulsivity: A meta-analysis of twin, family and adoption studies. Clinical psychology review 31 , 1209-1223 (2011). Meidenbauer, K. L. et al. Evidence for environmental influences on impulsivity and aggression. Urban Forestry & Urban Greening 103 , 128594 (2025). Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files neuroanatimpulsivitysupp250616.docx Supplemental Material Cite Share Download PDF Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Molecular Psychiatry → Version 1 posted Editorial decision: revise 14 Aug, 2025 Review # 3 received at journal 14 Jul, 2025 Review # 2 received at journal 10 Jul, 2025 Review # 1 received at journal 30 Jun, 2025 Reviewer # 3 agreed at journal 30 Jun, 2025 Reviewer # 2 agreed at journal 29 Jun, 2025 Reviewer # 1 agreed at journal 29 Jun, 2025 Reviewers invited by journal 29 Jun, 2025 Editor assigned by journal 23 Jun, 2025 Submission checks completed at journal 23 Jun, 2025 First submitted to journal 20 Jun, 2025 Unknown event 17 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Thomas Yeo","email":"","orcid":"https://orcid.org/0000-0002-0119-3276","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"B.T.","middleName":"Thomas","lastName":"Yeo","suffix":""},{"id":477993723,"identity":"33b89f29-6802-458d-9ee4-bda2e29e9c9a","order_by":9,"name":"Avram Holmes","email":"","orcid":"https://orcid.org/0000-0001-6583-803X","institution":"Rutgers University","correspondingAuthor":false,"prefix":"","firstName":"Avram","middleName":"","lastName":"Holmes","suffix":""},{"id":477993724,"identity":"b851ff8b-4727-473d-807f-76d57694bcc1","order_by":10,"name":"Sarah Yip","email":"","orcid":"https://orcid.org/0000-0003-2293-2864","institution":"Yale School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Yip","suffix":""}],"badges":[],"createdAt":"2025-04-24 11:40:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6520460/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6520460/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41380-026-03526-2","type":"published","date":"2026-03-24T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89972462,"identity":"063f4d4b-ea26-46bd-8790-37671013d56d","added_by":"auto","created_at":"2025-08-27 05:44:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":524284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeuroanatomy reflects individual differences in impulsivity.\u003cbr\u003e\n \u003c/strong\u003ePrediction accuracies (correlation between observed and predicted values) for models trained to predict behavioral measures from the BIS/BAS (A) and UPPS-P (B) scales. Results for models based on CT (left), SA (center), and GMV (right) at baseline (top) and two-year follow-up (bottom) are shown. The shape of the violins indicates the distribution of values, the dashed lines indicate the median, and the dotted lines indicate the interquartile range. Asterisks indicate the model captured significant associations.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6520460/v1/fe6305efa133fe479df77d4c.png"},{"id":89973892,"identity":"818b285e-6bd4-4b97-8dee-940831c89096","added_by":"auto","created_at":"2025-08-27 05:52:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":541782,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSex influences associations between neuroanatomy and impulsivity.\u003cbr\u003e\n \u003c/strong\u003ePrediction accuracies (correlation between observed and prediction values) for sex-specific models trained to predict behavioral measures from the BIS/BAS (A) and UPPS-P (B) scales. Results for female-specific (pink) and male-specific (blue) models based on CT (left), SA (center), and GMV (right) at baseline (top) and two-year follow-up (bottom) are shown. The shape of the violins indicates the distribution of values, the dashed lines indicate the median, and the dotted lines indicate the interquartile range. Asterisks indicate the model captured significant associations.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6520460/v1/929ec26d27bd673c54ec0d90.png"},{"id":89972461,"identity":"20e2db18-a143-45af-878b-2b70d6c31509","added_by":"auto","created_at":"2025-08-27 05:44:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":504942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShared and unique neuroanatomical features are linked to impulsivity.\u003cbr\u003e\n \u003c/strong\u003eCosine similarities between the Haufe-transformed regional feature weights from models trained to predict impulsivity (A). Results for models based on CT (left), SA (center), and GMV (right) that captured significant associations are shown. Warmer colors indicate greater similarity, cooler colors indicate a greater dissimilarity. Rows and columns corresponding to models that did not capture significant associations are left blank.\u003c/p\u003e\n\u003cp\u003eRelative regional associations (Haufe-transformed feature weights) between neuroanatomy and impulsivity derived from the models based on CT (left), SA (center), and GMV (right) (B). Left and right hemisphere are shown as denoted by the top, x-axis labels. Warmer colors indicate a stronger positive association, cooler colors indicate a stronger negative association. To facilitate visualization, association values for each set of models were divided by the maximum value for that model. Results are only shown for models that captured significant associations, as outlined below (ordered left to right), and measures from the scales are separated by vertical lines. \u003cbr\u003e\nCT, Baseline: Reward Responsiveness, Drive, Negative Urgency, Positive Urgency\u003cbr\u003e\nCT, Two-Year: Inhibition, Drive, Fun-Seeking, Negative Urgency, Positive Urgency\u003cbr\u003e\nSA, Baseline: Reward Responsiveness, Drive, Fun-Seeking, Positive Urgency, Lack of Planning, Sensation-Seeking\u003cbr\u003e\nSA, Two--Year: Inhibition, Reward Responsiveness, Positive Urgency, Lack of Planning, Sensation-Seeking\u003cbr\u003e\nGMV, Baseline: Inhibition, Reward Responsiveness, Drive, Fun-Seeking, Positive Urgency, Lack of Planning, Sensation-Seeking\u003cbr\u003e\nGMV, Two-Year: Inhibition, Drive, Fun-Seeking, Positive Urgency, Lack of Planning, Sensation-Seeking\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6520460/v1/5f90b8cb5d81821d40525e3d.png"},{"id":89972466,"identity":"1911af1c-76fa-4585-991d-c415e043271f","added_by":"auto","created_at":"2025-08-27 05:44:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2026293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between CT and impulsivity are dispersed throughout the cortex, while those between SA (and gray matter) and impulsivity are localized.\u003cbr\u003e\n \u003c/strong\u003eRelative regional associations (Haufe-transformed feature weights) from models trained on CT data to predict reward-responsiveness at baseline (A), drive at two-year follow-up (B), negative urgency at baseline (C), and positive urgency at two-year follow-up (D). Relative regional associations from models trained on SA data to predict fun-seeking at baseline (E), inhibition at two-year follow-up (F), positive urgency at baseline (G), lack of planning at two-year follow-up (H). Lateral (outer) and medial (inner) surfaces for left (left) and right (right) hemispheres are shown. Warmer colors indicate a stronger positive association, cooler colors indicate a stronger negative association. To facilitate visualization, association values for each set of models were divided by the maximum value for that model.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6520460/v1/dd98ad62ef55294e492e400a.png"},{"id":89973894,"identity":"11eca31a-1278-4673-892b-2d5db4713074","added_by":"auto","created_at":"2025-08-27 05:52:40","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":965298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThere are sex differences in the associations between CT and reward sensitivity.\u003cbr\u003e\n \u003c/strong\u003eRelative regional associations (Haufe-transformed feature weights) from models trained on CT data to predict reward-responsiveness at baseline in females (A) and males (B), and drive at baseline in females (C) and males (D). Lateral (outer) and medial (inner) surfaces for left (left) and right (right) hemispheres are shown. Warmer colors indicate a stronger positive association, cooler colors indicate a stronger negative association. To facilitate visualization, association values for each set of models were divided by the maximum value for that model.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6520460/v1/953a1d0b826019bee340a58b.jpeg"},{"id":105357973,"identity":"c1ce024c-41be-4d81-bc96-39fce0a592b8","added_by":"auto","created_at":"2026-03-25 07:13:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6285794,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6520460/v1/c86a7fef-8386-4b35-b3cc-994a543dfae2.pdf"},{"id":89972473,"identity":"777990d4-05a8-47c9-85ac-e6b24e71e0df","added_by":"auto","created_at":"2025-08-27 05:44:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14818802,"visible":true,"origin":"","legend":"Supplemental Material","description":"","filename":"neuroanatimpulsivitysupp250616.docx","url":"https://assets-eu.researchsquare.com/files/rs-6520460/v1/f28eccf68ab8b81e55b07c65.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Neuroanatomy Reflects Individual Variability in Impulsivity in Youth","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eImpairments in emotional regulation, motivation, and decision-making are prevalent across a range of psychiatric illnesses\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and often emerge during early adolescence\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These impairments contribute to the heterogeneity observed within psychiatric illnesses and may initially appear as more fundamental alterations in processes and behaviors such as impulsivity\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Impulsivity is a multifaceted construct that reflects \u0026ldquo;a predisposition toward rapid, unplanned reactions to internal or external stimuli without regard to the negative consequences of these reactions to the impulsive individual or to others.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u0026rdquo; Importantly, changes in impulsivity are a normal part of development. However, in some individuals, heightened levels of impulsivity may indicate increased risk for psychiatric illness\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e (see Supplemental Materials for specific examples). A better understanding of the neurobiological underpinnings of impulsivity, especially in youth, is crucial for developing more effective prevention and intervention strategies for these at-risk individuals. Neuroimaging techniques offer a powerful tool for investigating these neural substrates and identifying potential biomarkers for interventions. Given that adolescence is a period of significant brain plasticity, interventions targeting these impulsivity-related neural circuits in youth may be most effective.\u003c/p\u003e \u003cp\u003eAlthough often treated as a single construct, the term impulsivity encompasses a variety of distinct but related functions that promote impulsive behavior\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These include an individual\u0026rsquo;s (in)ability to consider the consequences of a behavior (lack of premeditation), tendency to disengage from tasks due to boredom or difficulty before completion (lack of perseverance), responses to emotional states (positive and negative urgency), and motivation to experience rewarding sensations (sensation seeking)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Impulsivity can also be considered a product of two systems that promote impaired self-regulation: the behavioral inhibition and approach systems (BIS/BAS)\u003csup\u003e11\u003c/sup\u003e. The BIS prevents actions that may lead to a negative outcome\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, while the BAS encapsulates sensitivity to, and motivation for, reward/punishment, as well as escape from punishment, therefore encouraging incentive-motivated behavior\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These conceptualizations highlight the variability in how impulsivity is defined and measured, posing a challenge for research, and underscoring the need for greater conceptual clarity across different definitions of impusivity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Consequently, a crucial consideration in exploring the neuroanatomical basis of impulsivity is the extent to which different, yet related, components of this construct may be subserved by shared versus distinct neurobiological substrates.\u003c/p\u003e \u003cp\u003eCorticolimbic and corticostriatal circuitry regulate impulsivity\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The corticolimbic system contributes to processing emotional salience and regulating emotional responses\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, while the corticostriatal system is involved in motivated behavior, reward processing, learning, and habit formation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. These systems mature throughout development\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and this is accompanied by significant changes in synaptic connectivity and myelination\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Critically, this development is asynchronous. Limbic and striatal structures mature earlier than cortical structures, including the prefrontal cortex, resulting in heightened impulsivity during this period of developmental \u0026lsquo;mismatch\u0026rsquo;\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Behaviorally, emotional regulation, motivation, and impulse control evolve throughout development with rapid changes in early life followed by gradual changes during adolescence\u003csup\u003e\u003cspan additionalcitationids=\"CR26 CR27 CR28\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This change is paralleled by changes in their neural substrates beyond corticolimbic and corticostriatal circuits, such as the insula and cingulate cortex\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. However, previous studies examining these relationships have often focused on establishing univariate, cross-sectional associations between specific brain circuits and individual behavioral measures in small sample sizes. In contrast, brain-based predictive models use machine learning to analyze whole-brain multivariate relationships between brain features and behavioral measures\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, which account for the interconnected nature of the brain, unlike traditional analyses focused on single regions. These models along with the large sample sizes provided by big data initiatives can provide insights into the whole-brain neuroanatomical basis of impulsivity\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In addition, longitudinal data can be used to examine whether these relationships are consistent throughout development. Here, we leverage these approaches to garner insights into the neural substrates of impulsivity in youth.\u003c/p\u003e \u003cp\u003eImportantly, neurodevelopmental processes and behavioral expressions vary between males and females, raising questions about the extent to which sex-specific neuroanatomical patterns contribute to observed differences in impulsivity. There are significant sex differences in the developmental trajectories of corticolimbic and corticostriatal systems\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, although findings have not always been consistent across studies. As an example, on average, females have greater relative volume in the prefrontal and orbitofrontal regions, while males have greater volume in ventral temporal and occipital regions\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Similarly, sex differences have been reported in impulsivity, but these results are also inconsistent\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Thus, it is plausible that sex differences exist across neuroanatomy, impulsivity, and their interrelationships, highlighting the importance of considering sex differences when studying impulsivity, particularly within a developmental framework. Furthermore, within the context of recently established large data initiatives, it remains to be determined whether sex differences in impulsivity are driven by unique neuroanatomical substrates. This can be addressed by establishing sex-specific relationships between neuroanatomy and impulsivity and examining the extent to which they overlap.\u003c/p\u003e \u003cp\u003eCortical thickness (CT), surface area (SA), and gray matter volume (GMV) reflect different aspects of neuroanatomy. CT (i.e., distance between the brain's outer surface and gray-white matter junction) reflects neuronal density and arrangement\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. It increases rapidly during the prenatal period, continues growing after birth, peaks in early childhood (with regional variation), and then gradually thins\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Cortical SA (i.e., area of the pial surface) is linked to the organization and complexity of cortical columns \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e as well as neuronal proliferation\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and gyrification\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. SA expands prenatally and through childhood, peaking in late childhood/early adolescence, and then gradually declines\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. GMV, encompassing thickness and SA, reflects the total amount of cells and synapses\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and generally follows the same developmental trajectory observed for SA\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Changes in these neuroanatomical features result from neurogenesis, synaptogenesis, synaptic pruning, cell death, and alterations in cell size and density, and are linked to various psychiatric conditions\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Importantly, these structural measures demonstrate high reliability and are considered more \"trait-like\" compared to functional measures, making them particularly valuable for investigation. Given these complexities, a multimodal analysis considering all three features of brain structure across all regions of the brain is warranter to reveal their unique and shared contributions to impulsivity, facilitating a more holistic understanding of these relationships.\u003c/p\u003e \u003cp\u003eHere, we investigated the sex-specific neuroanatomical basis of impulsivity, across different neuroanatomical features and impulsivity measures, in a large sample of youth from the Adolescent Brain Cognitive Development (ABCD) Study at baseline and two-year follow-up. Using a data-driven, brain-based predictive modeling framework, we show that neuroanatomical features are associated with impulsivity and there are notable sex differences in these relationships. We also demonstrate that different domains of impulsivity are linked to shared and distinct neuroanatomical features. Some features vary across facets of impulsivity, others across sexes, and others across time points. These findings highlight substantial individual variability in the neural basis of impulsivity in youth. Understanding these distinct markers of impulsivity is crucial for establishing normative developmental patterns and paves the way for development of more effective early interventions grounded in neurobiological mechanism to prevent psychiatric illness.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eAn overview of the methods is provided below. Details are in the Supplemental Materials.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDataset\u003c/h2\u003e \u003cp\u003eThe ABCD Study is following a large community-based sample of children and adolescents throughout the course of development\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Participants are assessed on a comprehensive set of neuroimaging, behavioral, developmental, and psychiatric batteries. In this study, we used imaging and impulsivity data from 9,099 participants at baseline (ages 9\u0026ndash;10 years) and 6,432 participants at the two-year follow-up (see \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e for demographic data). Details on data inclusion procedures (e.g., exclusion criteria for neuroimaging data) are provided in the Supplemental Materials (see \u003cb\u003eFigures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S2\u003c/b\u003e for our participant inclusion pipeline). The research protocol for the dataset was reviewed and approved by a central Institutional Review Board (IRB) at the University of California, San Diego, and, in some cases, by individual site IRBs. Parents or guardians provided written informed consent, and children assented before participation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNeuroimaging\u003c/h3\u003e\n\u003cp\u003eThe neuroimaging protocol and specific parameters for T1-weighted scans are detailed in previous publications\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. We used measures of CT (mean), SA (total), and GMV (total) for 68 cortical regions (34 per hemisphere) from the Desikan-Killiany parcellation as provided on the NIMH Data Archive. Regional SA and GMV measures, but not CT, were proportionally corrected for individual differences in intracranial volume by dividing the raw values by intracranial volume, as recommended by prior work\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e (see \u003cb\u003eFigure S3\u003c/b\u003e for average measures and \u003cb\u003eFigure S4\u003c/b\u003e for sex differences in the measures).\u003c/p\u003e\n\u003ch3\u003eImpulsivity\u003c/h3\u003e\n\u003cp\u003eImpulsivity-related measures were derived from the Behavioral Inhibition/ Activation System (BIS/BAS) and the Modified Urgency, (lack of) Planning (or Premeditation), (lack of) Perseverance, Sensation-Seeking, and Positive Urgency (UPPS-P\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e) Short Version scales, both of which have been validated in children\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. We examined these measures separately, rather than combining them into summary or composite scores, as they assess distinct facets of impulsivity with potentially unique neural underpinnings, developmental trajectories, and sex differences.\u003c/p\u003e\n\u003ch3\u003eDifferences in Impulsivity Across Development and Across Sexes\u003c/h3\u003e\n\u003cp\u003eWe used non-parametric Mann-Whitney U rank tests to evaluate differences in the behavioral measures (4 BIS/BAS, 5 UPPS-P) between baseline and two-year follow-up as the scales were not normally distributed. We also used non-parametric Mann-Whitney U rank tests to evaluate sex differences in the behavioral measures at each time point. We corrected p-values for multiple comparisons within each behavioral scale using the Benjamini-Hochberg False Discovery Rate (q\u0026thinsp;=\u0026thinsp;0.05) procedure\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. We also computed sex-independent and sex-specific full correlations between the measures at each time point to evaluate their co-expression in youth.\u003c/p\u003e\n\u003ch3\u003ePredictive Modeling\u003c/h3\u003e\n\u003cp\u003eWe used a cross-validated brain-based predictive modeling framework\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e which we have leveraged in prior work\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan additionalcitationids=\"CR48 CR49 CR50\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. This framework avoids data leakage and minimizes overfitting to capture robust, reliable, and interpretable associations between imaging-derived measures and phenotypic data. For each pair of neuroanatomical features and behavioral measure, we developed separate sets of sex-independent (i.e., including the entire sample) and sex-specific (i.e., in either females or males) linear ridge regression models at each of the two time points to predict impulsivity based on neuroanatomy. We trained each model on neuroanatomical features (either CT, SA, or GMV) from 68 regions to predict a single impulsivity measure. We quantified model performance using prediction accuracy\u003csup\u003e\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and assessed significance in comparison to null distributions. We report test accuracies in our results to provide an unbiased estimate of model generalizability, as training accuracy can be inflated due to overfitting. We corrected p-values for multiple comparisons within each behavioral scale using the Benjamini-Hochberg procedure\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFeature Weights\u003c/h2\u003e \u003cp\u003eFor models yielding reliable brain-behavior relationships in both sexes (as compared to null distributions), we conducted a series of analyses to determine feature importance and to guide mechanistic understanding. This conservative approach ensures that comparisons are only made where meaningful brain-behavior relationships are present in both sexes. We transformed the feature weights obtained from the models using the Haufe transformation\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e (to increase their interpretability and reliability\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e) and then calculated a mean feature importance for each set of models. We computed cosine similarities between the mean feature importance values to evaluate overlap in the regional features associated with different impulsivity measures.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eExpressions of impulsivity vary across youth.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnalyses examining the distributions of the impulsivity measures and the correlations between them at baseline and two-year follow-up are presented in the Supplemental Materials (see \u003cb\u003eFigure S5\u003c/b\u003e for results across all participants and \u003cb\u003eS6\u003c/b\u003e for sex-specific results). These distributions are consistent with prior work examining impulsivity in the ABCD Study\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, indicating that our sample is representative of the cohort. Broadly, these analyses indicated modest within-scale correlations of measures suggesting that they capture partially overlapping aspects of behavior, and significant but weak between-scale correlations indicating that the UPPS-P and BIS/BAS, while potentially related, measure somewhat independent constructs\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eNeuroanatomy predicts impulsivity\u003c/h3\u003e\n\u003cp\u003eBrain-based predictive models were used to quantify associations between neuroanatomy and impulsivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCT\u003c/span\u003e: Models based on CT accurately predicted reward-responsiveness (prediction accuracy, r = 0.044, p\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001), drive (r = 0.093, p\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001), negative urgency (r = 0.055, p\u003csub\u003eFDR\u003c/sub\u003e=0.020), and positive urgency (r = 0.078, p\u003csub\u003eFDR\u003c/sub\u003e=0.015) at baseline; and inhibition (r = 0.059, p\u003csub\u003eFDR\u003c/sub\u003e=0.022), drive (r = 0.073, p\u003csub\u003eFDR\u003c/sub\u003e=0.004), fun-seeking (r = 0.029, p\u003csub\u003eFDR\u003c/sub\u003e=0.028), negative urgency (r = 0.049, p\u003csub\u003eFDR\u003c/sub\u003e=0.008), and positive urgency (r = 0.062, p\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001) at two-year follow-up.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSA\u003c/span\u003e: Models based on SA accurately predicted reward-responsiveness (r = 0.043, p\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001), drive (r = 0.064, p\u003csub\u003eFDR\u003c/sub\u003e=0.004), fun-seeking (r = 0.035, p\u003csub\u003eFDR\u003c/sub\u003e = \u0026lt; 0.001), positive urgency (r = 0.074, p\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001), lack of planning (r = 0.061, p\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001), and sensation-seeking (r = 0.055, p\u003csub\u003eFDR\u003c/sub\u003e=0.003) at baseline; and inhibition (r = 0.037, p\u003csub\u003eFDR\u003c/sub\u003e=0.044), reward-responsiveness (r = 0.019, p\u003csub\u003eFDR\u003c/sub\u003e=0.044), positive urgency (r = 0.055, p\u003csub\u003eFDR\u003c/sub\u003e=0.040), lack of planning (r = 0.050, p\u003csub\u003eFDR\u003c/sub\u003e=0.040), and sensation-seeking (r = 0.080, p\u003csub\u003eFDR\u003c/sub\u003e=0.025) at two-year follow-up.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGMV\u003c/span\u003e: Models based on GMV accurately predicted inhibition (r = 0.024, p\u003csub\u003eFDR\u003c/sub\u003e=0.023), reward-responsiveness (r = 0.065, p\u003csub\u003eFDR\u003c/sub\u003e=0.002), drive (r = 0.088, p\u003csub\u003eFDR\u003c/sub\u003e=0.004), fun-seeking (r = 0.043, p\u003csub\u003eFDR\u003c/sub\u003e=0.002), positive urgency (r = 0.071 p\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001), lack of planning (r = 0.062, p\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001), and sensation-seeking (r = 0.036, p\u003csub\u003eFDR\u003c/sub\u003e=0.033) at baseline; and inhibition (r = 0.058, p\u003csub\u003eFDR\u003c/sub\u003e=0.010), drive (r = 0.077, p\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001), positive urgency (r = 0.059, p\u003csub\u003eFDR\u003c/sub\u003e\u0026lt;0.001), lack of planning (r = 0.056, p\u003csub\u003eFDR\u003c/sub\u003e=0.030), and sensation-seeking (r = 0.065, p\u003csub\u003eFDR\u003c/sub\u003e=0.005) at two-year follow-up.\u003c/p\u003e \u003cp\u003eWe next used the same framework to examine sex-specific associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eTables S3-S4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCT\u003c/span\u003e: Sex-specific models based on CT accurately predicted reward-responsiveness (r\u003csub\u003efemale\u003c/sub\u003e=0.065, p\u003csub\u003efemale\u003c/sub\u003e=0.002; r\u003csub\u003emale\u003c/sub\u003e=0.046, p\u003csub\u003emale\u003c/sub\u003e=0.008) and drive (r\u003csub\u003efemale\u003c/sub\u003e=0.133, p\u003csub\u003efemale\u003c/sub\u003e\u0026lt;0.001; r\u003csub\u003emale\u003c/sub\u003e=0.060, p\u003csub\u003emale\u003c/sub\u003e=0.004) in both sexes, negative urgency (r = 0.048, p = 0.015) in males, and positive urgency (r = 0.119, p \u0026lt; 0.001) in females at baseline; and drive (r = 104, p = 0.028), negative urgency (r = 0.065, p = 0.005), positive urgency (r = 0.093, p \u0026lt; 0.001), and lack of perseverance (r = 0.051, p = 0.038) in females at two-year follow-up.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSA\u003c/span\u003e: Sex-specific models based on SA accurately predicted positive urgency (r\u003csub\u003efemale\u003c/sub\u003e=0.099, p\u003csub\u003efemale\u003c/sub\u003e\u0026lt;0.001; r\u003csub\u003emale\u003c/sub\u003e=0.046, p\u003csub\u003emale\u003c/sub\u003e\u0026lt;0.005) in both sexes, drive (r = 0.106, p \u0026lt; 0.001) and fun-seeking (r = 0.045 p = 0.006) in females, and lack of planning (r = 0.031, p = 0.008) in males at baseline; and positive urgency in females (r = 0.075, p = 0.005) at two-year follow-up.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGMV\u003c/span\u003e: Sex-specific models based on GMV accurately predicted reward-responsiveness (r\u003csub\u003efemale\u003c/sub\u003e=0.052, p\u003csub\u003efemale\u003c/sub\u003e=0.028; r\u003csub\u003emale\u003c/sub\u003e=0.059, p\u003csub\u003emale\u003c/sub\u003e=0.020) and drive (r\u003csub\u003efemale\u003c/sub\u003e=0.105 p\u003csub\u003efemale\u003c/sub\u003e\u0026lt;0.001; r\u003csub\u003emale\u003c/sub\u003e=0.072, p\u003csub\u003emale\u003c/sub\u003e\u0026lt;0.001) in both sexes, fun-seeking (r = 0.045, p = 0.006), and negative urgency (r = 0.034, p = 0.040) in females at baseline; and drive (r = 0.060, p \u0026lt; 0.001) in males and positive urgency in females (r = 0.094, p = 0.005) at two-year follow-up.\u003c/p\u003e \u003cp\u003eChanges in impulsivity are part of normative development and, at their extremes, are linked to the development of mental illnesses later in life\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. We show that individual variations in neuroanatomy can predict impulsivity in youth, and these relationships are generally stable across development. While certain measures can be predicted in both sexes, others yield significant results only in females, which may be due to the reliability of measures across sexes, reporting biases, and data quality, among other factors.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImpulsivity maps onto shared and distinct brain regions\u003c/h2\u003e \u003cp\u003eWe derived the feature importance maps from the models and computed cosine similarities to evaluate overlap, focusing on models that captured significant associations. These results, presented in their entirety in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, indicated that models predicting measures from the same scale captured largely overlapping associations. As an example, CT features associated with positive and negative urgency were highly similar at both time points (cosine similarity, s\u003csub\u003ebaseline\u003c/sub\u003e=0.69, s\u003csub\u003etwo−year\u003c/sub\u003e=0.77). Further, while some models predicting measures from different scales were similar, others were orthogonal or opposite. For example, baseline CT features associated with reward-responsiveness and drive were dissimilar from those associated with lack of planning and sensation seeking (-0.60 ≤ s≤-0.29), but similar to those associated with positive urgency (0.62 ≤ s ≤ 0.64). These observed patterns were generally consistent across time points for CT and SA, but less so for GMV.\u003c/p\u003e \u003cp\u003eWe also evaluated the similarities in the neuroanatomical features associated with impulsivity measures across the sexes, focusing on the five pairs of models that yielded significant results in both sexes at baseline. GMV features associated with reward-responsiveness at baseline were quite similar across the sexes (s = 0.53), along with SA features associated with positive urgency (s = 0.53). However, other associations were considerably different across the sexes. Models based on CT to predict reward-responsiveness and drive captured distinct associations in males and females (s\u003csub\u003ereward−responsiveness\u003c/sub\u003e=0.24, s\u003csub\u003edrive\u003c/sub\u003e=0.37), as well as those based on GMV to predict drive (s = 0.36).\u003c/p\u003e \u003cp\u003eThese analyses reveal that impulsivity maps onto shared and distinct neuroanatomical features. Across the entire sample, reward sensitivity and urgency share a common neuroanatomical basis that is distinct from the neural substrates of lack of planning and sensation-seeking. These findings suggest that the BIS/BAS and UPPS-P scales, though separable constructs, share some neuroanatomical features while also exhibiting distinct features. Importantly, some of these associations differ across the sexes, suggesting the presence of sex-specific neuroanatomical substrates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNeuroanatomical basis of impulsivity varies across imaging modalities\u003c/h2\u003e \u003cp\u003eCT features associated with impulsivity were widespread, while SA and GMV features overlapped and were more localized (full results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCT\u003c/span\u003e: Impulsivity was broadly negatively associated with CT in bilateral cuneus and lateral occipital regions (i.e., youth who were more impulsive had less CT in these areas relative to those who were less impulsive) and was positively associated with CT in the bilateral inferior frontal gyrus, particularly in the pars opercularis, superior frontal, and superior temporal regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D for 2 representative BIS/BAS and UPPS-P measures at each time point, \u003cb\u003eFigure S7\u003c/b\u003e for all other measures). Impulsivity was also negatively associated with CT in bilateral entorhinal, lingual, parahippocampal, and paracentral regions at baseline, but these associations were less consistent at two-year follow-up. Other regions exhibited measure- and hemisphere-specific relationships. For example, impulsivity was broadly negatively associated with CT in the left postcentral and precuneus regions, but BIS/BAS measures were positively associated with CT in the right postcentral region while UPPS-P measures were positively associated with CT in right precuneus at baseline.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSA\u003c/span\u003e: Impulsivity was broadly negatively associated with SA in bilateral frontal poles at both time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-H for 2 representative BIS/BAS and UPPS-P measures at each time point, \u003cb\u003eFigure S8\u003c/b\u003e for all other measures). Positive urgency was also positively associated with SA in bilateral temporal poles, but associations with temporal pole regions were more nuanced for other measures. Certain regions also exhibited opposite associations. For example, BIS/BAS measures were positively associated with SA in the transverse temporal and parahippocampal regions and negatively associated with SA in the rostral anterior cingulate and entorhinal regions, while the opposite was true for UPPS-P measures.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGMV\u003c/span\u003e: Associations between GMV and impulsivity largely paralleled those with SA, although they were less pronounced and included a few exceptions (\u003cb\u003eFigure S9\u003c/b\u003e for 2 representative BIS/BAS and UPPS-P measures at each time point, \u003cb\u003eFigure S10\u003c/b\u003e for all other measures). Impulsivity was broadly negatively associated with GMV in the pericalcarine region, and inhibition was positively associated with frontal pole volumes but negatively associated with isthmus cingulate volumes.\u003c/p\u003e \u003cp\u003eThese findings highlight the presence of multivariate relationships between neuroanatomy and impulsivity. While some associations are similar across the behavioral scales, others are less consistent. These results suggest a distributed network of brain regions encode individual differences in impulsive behaviors, and these relationships are dynamic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThere are sex differences in the neuroanatomical basis of impulsivity\u003c/h2\u003e \u003cp\u003eWe assessed sex differences in the associations between neuroanatomy and impulsivity, focusing on the models that were significant in both sexes. Across both sexes, BAS measures were negatively associated with CT in the entorhinal, lateral occipital, lateral orbitofrontal, and parahippocampal regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In females, they were also positively associated with CT in bilateral inferior frontal gyrus and, to a lesser extent, in the superior frontal gyrus, while in males, these relationships were present in the left hemisphere, but the opposite relationships were present in the right hemisphere. Further, although these measures were negatively associated with CT in the lingual, paracentral, and precuneus regions in both sexes, these relationships were stronger in the lingual regions in females and in the paracentral and precuneus regions in males.\u003c/p\u003e \u003cp\u003eRelationships between GMV and reward-responsiveness at baseline were largely shared across the sexes, but those between GMV and drive were considerably different, although the differences were predominantly in strength rather than directionality (\u003cb\u003eFigure S11\u003c/b\u003e). As an example, GMV in the right posterior cingulate and right pars opercularis exhibited stronger relative positive associations with drive in females than in males. Results from all other sex-specific significant models generally resembled the results from the sex-independent models (\u003cb\u003eFigures S12-14)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThese findings suggest that while many of these relationships are shared across the sexes, there are also important differences in the neuroanatomical underpinnings of impulsivity. These sex-specific relationships may, in part, explain observed sex differences in impulsive behaviors and vulnerability to impulsivity-related psychiatric illness.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eExamining multiple facets of impulsivity and neuroanatomical features, we demonstrate that individual variability in neuroanatomy in a large cohort of youth is associated with impulsivity across facets and features, and these relationships, to some extent, differ between sexes. We show that impulsivity measures map onto shared and distinct brain regions across neuroanatomical features, and these relationships are largely stable across development. Some relationships overlap across the impulsivity measures and across neuroanatomical features, and are consistent across the sexes, while others vary across facets, features, and sexes. These results shed light on how individual differences in neuroanatomy throughout development contribute to the diverse expressions of impulsivity in youth. These findings also suggest that impulsivity maps onto specific patterns of neuroanatomy that, alongside other risk factors, could help identify youth at risk for impulsivity-related disorders.\u003c/p\u003e\u003cp\u003eImpulsivity is, in part, driven by individual differences in corticolimbic, corticostriatal and motor-sensory circuits\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Recent advances in brain-based predictive modeling allow us to examine whole-brain multivariate relationships\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, unlike traditional univariate analyses focused on individual regions. In recent years, a few studies have used this approach to investigate the neuroanatomical basis of impulsivity, although they have generally focused on specific neuroimaging features and impulsivity measures, ignored sex effects, and considered these relationships at a single time point\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Using a multivariate approach, we replicate univariate findings and demonstrate that impulsivity maps onto a dispersed set of cortical regions. While some relationships are shared across facets of impulsivity, others are distinct. One particularly stable relationship appears to be in the pars opercularis, where CT is positively linked to impulsivity measures from both scales at both time points. These results align with those from prior neuroanatomical, functional, and electrophysiological studies showing that the pars opercularis plays an important role in impulse control\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e across motor\u003csup\u003e\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e–\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and speech\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e domains.\u003c/p\u003e\u003cp\u003eHere, we find that impulsivity is associated with reduced cortical thickness in the visual network and increased cortical thickness in heteromodal association networks (default, attention, and frontoparietal). Gray matter alterations in the visual network, responsible for the processing of visual stimuli, have been linked to impulsivity\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. These associations may explain difficulties with visual attention and increased distractibility that are common in impulsive individuals\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. In addition, functional network alterations in heteromodal association cortices are linked to broad psychopathology\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan additionalcitationids=\"CR74 CR75 CR76\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e–\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Structural alterations in these regions associated with impulsivity may, in part, explain the functional alterations that subsequently underlie various psychological disorders. Our analyses also revealed widespread associations between impulsivity and both surface area and gray matter volume within the limbic network, although the direction of these associations was dependent on the specific impulsivity measure and limbic region examined. The limbic network, which plays a crucial role in higher-order cognitive processes related to emotions, memory, and motivation, shows particularly interesting patterns in relation to impulsivity. Specifically, impulsivity is negatively linked to D2/3 receptor binding in limbic structures\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, and the spatial density of these receptors has been linked to altered functional connectivity patterns in substance use disorder\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. The structural alterations we observe here to be linked to impulsivity may, in part, be associated with these receptor density differences and related functional connectivity changes, potentially contributing to the vulnerability for impulsivity-related disorders. Collectively, these findings demonstrate that impulsivity is associated with a complex pattern of neurobiological alterations across multiple networks, providing important insights into the neurobiological mechanisms underlying impulsive behavior throughout development and its relationship to psychopathology.\u003c/p\u003e\u003cp\u003eImpulsivity follows a non-linear trend, increasing during childhood and adolescence and decreasing throughout adulthood\u003csup\u003e\u003cspan additionalcitationids=\"CR81\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e–\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. This trajectory mirrors developmental changes observed in neuroanatomy. During adolescence and early adulthood, significant maturation occurs in the prefrontal cortex, a region critical for emotional regulation and impulse control\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This maturation involves synaptic pruning and increased white matter connectivity to refine neural circuits, leading to improved cognitive control and decreased impulsivity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Our results show that the neuroanatomical basis of impulsivity is not static, potentially reflecting these broader developmental changes. While some regions show consistent relationships, others demonstrate changes from baseline to two-year follow-up, suggesting that changes in impulsivity may be driven by shifts in the underlying neuroanatomical associations. Further, deviations from typical developmental trajectories in neuroanatomy may underlie impulsivity-related deficits.\u003c/p\u003e\u003cp\u003eResearch on sex differences in impulsivity has produced mixed findings\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. The most consistent finding is that females exhibit greater inhibition and males exhibit greater sensation-seeking\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Activation-related impulsivity is comparable across the sexes\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, though differences have been reported for specific rewards\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. One study examining relationships between CT and a single global measure of impulsivity in the ABCD cohort reported significant associations in males but not the entire sample\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. A separate study exploring the volumetric correlates of impulsivity in the same cohort found that lack of premeditation and sensation seeking were related to larger volumes in many cortical and subcortical regions, while positive urgency was related to smaller volumes in those same regions\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. They also found that many of the relationships were stronger in females. These studies highlight the need for sex-specific investigations. Our analyses build on this work using a multivariate machine learning approach and show that while some brain-impulsivity associations are consistent across the sexes, others are not. In some cases, the same regions even exhibit opposite relationships across the sexes. Notably, we find that regions showing sex-specific associations with impulsivity are predominantly heteromodal association cortices. These same regions demonstrate established structural\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and functional\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e sex differences, which are specifically coupled to regional expression of sex-chromosome genes and show enrichment for distinct cell-type signatures\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. These findings suggest that sex differences in impulsivity may have a strong biological basis, rooted in sex-specific patterns of gene expression and cellular organization within key brain networks.\u003c/p\u003e\u003cp\u003eThere are several limitations to this work. First, we used a single dataset. Although participants reflect different demographic groups, income levels, and living environments, our findings may be limited in generalizability\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. To maximize the robustness of our work, we included all participants with complete imaging and impulsivity data and used a cross-validated predictive modeling framework known to yield reliable results\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Second, we only considered binary sex due to data availability and thus were not able to assess these relationships in intersex or other non-binary populations. We also did not consider the effects of gender, which influences neurobiology\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e and behavior\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. Third, the brain continues to develop throughout adolescence, with females and males reaching developmental milestones at different times\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. Here, we used data from two time points but did not include older ages due to data availability. As a result, the associations we report may continue to shift throughout development. Our work serves as an empirical baseline from which theses trajectories may be tracked in later waves of the ABCD study. Fourth, we analyzed relationships between cortical structures and impulsivity, but did not consider subcortical or cerebellar regions. A more complete understanding of neural substrates of impulsivity will require future research incorporating these subcortical and cerebellar regions. Finally, we examined the neural basis of impulsivity and explored sex differences in these relationships. However, we did not consider the effects of other biological or environmental factors (e.g., genetics, pubertal maturation, urbanicity)\u003csup\u003e\u003cspan additionalcitationids=\"CR95 CR96\" citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e–\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. Future analyses within global open-access datasets that consider the effects of additional biological and environmental factors can address these limitations and provide additional evidence to confirm (or refute) these findings.\u003c/p\u003e\u003cp\u003eIncreases in impulsivity are a typical part of development, and, when significant, may be linked to risk for psychiatric illness. Understanding the neuroanatomical basis of impulsivity paves the way for development of more effective early interventions grounded in neurobiological mechanism to prevent psychiatric illness. These findings highlight how neuroanatomy underlies the diverse expressions of impulsivity throughout development and may represent potential markers of psychiatric risk. In addition, this work emphasizes the importance of conducting sex-disaggregated analyses when examining brain-behavior relationships.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development\u003csup\u003eSM\u003c/sup\u003e (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability Statement\u003c/strong\u003e: Code used for the analyses are on GitHub: https://github.com/elvisha/neuroanat_impulsivity. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e: This work was supported by the following sources: Brain and Behavior Research Foundation (Young Investigator Grant to ED), Northwell Health Advancing Women in Science and Medicine (Career Development Award to ED and Educational Achievement Award to ED), Feinstein Institutes for Medical Research (Emerging Scientist Award to ED), NIAAA (R01AA027553 to SWY), NIDA (R01DA053301 to SWY), NIMH (2R01MH120080 to AJH and BTTY), Stanford University Knight-Hennessy Scholars Program (JAR) and the National Academies of Sciences, Engineering, and Medicine’s Ford Foundation Predoctoral Fellowship (JAR). SWY is also supported by the Canadian Institute of Health Research (CIHR) and by Women’s Healthy Research at Yale (WHRY). BTTY is also supported by the NUS Yong Loo Lin School of Medicine (NUHSRO/2020/124/TMR/LOA), the Singapore National Medical Research Council (NMRC) LCG (OFLCG19May-0035), NMRC CTG-IIT (CTGIIT23jan-0001), NMRC OF-IRG (OFIRG24jan-0006), NMRC STaR (STaR20nov-0003), Singapore Ministry of Health (MOH) Centre Grant (CG21APR1009). Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the funders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e: The authors have nothing to disclose. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: ED; Methodology: ED; Software: ED; Validation: ED; Formal Analysis: ED; Investigation: ED; Resources: ED; Writing – Original Draft: ED, EC, JLH, JAR, NA, SB, SWY; Writing – Review \u0026amp; Editing: ED, EC, JLH, JAR, NA, SB, LW, KB, BTTY, AJH, SWY; Visualization: ED\u003c/p\u003e\u003cp\u003eAll authors reported no biomedical financial interests or potential conflicts of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSloan, E.\u003cem\u003e et al.\u003c/em\u003e Emotion regulation as a transdiagnostic treatment construct across anxiety, depression, substance, eating and borderline personality disorders: A systematic review. \u003cem\u003eClinical psychology review\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 141-163 (2017).\u003c/li\u003e\n\u003cli\u003eC\u0026aacute;ceda, R., Nemeroff, C. B. \u0026amp; Harvey, P. D. Toward an understanding of decision making in severe mental illness. \u003cem\u003eThe Journal of neuropsychiatry and clinical neurosciences\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 196-213 (2014).\u003c/li\u003e\n\u003cli\u003eLincoln, T. M., Schulze, L. \u0026amp; Renneberg, B. The role of emotion regulation in the characterization, development and treatment of psychopathology. \u003cem\u003eNature Reviews Psychology\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 272-286 (2022).\u003c/li\u003e\n\u003cli\u003eSonmez, A. I., Garcia, J. Q., Thitiseranee, L., Blacker, C. J. \u0026amp; Lewis, C. P. Scoping review: Transdiagnostic measurement of impulsivity domains in youth using the UPPS impulsive behavior scales. \u003cem\u003eJournal of the American Academy of Child \u0026amp; Adolescent Psychiatry\u003c/em\u003e (2024).\u003c/li\u003e\n\u003cli\u003eStruijs, S. Y.\u003cem\u003e et al.\u003c/em\u003e Approach and avoidance tendencies in depression and anxiety disorders. \u003cem\u003ePsychiatry research\u003c/em\u003e \u003cstrong\u003e256\u003c/strong\u003e, 475-481 (2017).\u003c/li\u003e\n\u003cli\u003eMoeller, F. G., Barratt, E. S., Dougherty, D. M., Schmitz, J. M. \u0026amp; Swann, A. C. Psychiatric aspects of impulsivity. \u003cem\u003eAmerican journal of psychiatry\u003c/em\u003e \u003cstrong\u003e158\u003c/strong\u003e, 1783-1793 (2001).\u003c/li\u003e\n\u003cli\u003eChambers, R. A., Taylor, J. R. \u0026amp; Potenza, M. N. Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. \u003cem\u003eAmerican journal of psychiatry\u003c/em\u003e \u003cstrong\u003e160\u003c/strong\u003e, 1041-1052 (2003).\u003c/li\u003e\n\u003cli\u003eKohler, R.\u003cem\u003e et al.\u003c/em\u003e Identification of a composite latent dimension of reward and impulsivity across clinical, behavioral, and neurobiological domains among youth. \u003cem\u003eBiological psychiatry: cognitive neuroscience and neuroimaging\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 407-416 (2024).\u003c/li\u003e\n\u003cli\u003eSwann, A. C., Pazzaglia, P., Nicholls, A., Dougherty, D. M. \u0026amp; Moeller, F. G. Impulsivity and phase of illness in bipolar disorder. \u003cem\u003eJournal of affective disorders\u003c/em\u003e \u003cstrong\u003e73\u003c/strong\u003e, 105-111 (2003).\u003c/li\u003e\n\u003cli\u003eStrickland, J. C. \u0026amp; Johnson, M. W. Rejecting impulsivity as a psychological construct: A theoretical, empirical, and sociocultural argument. \u003cem\u003ePsychological review\u003c/em\u003e \u003cstrong\u003e128\u003c/strong\u003e, 336 (2021).\u003c/li\u003e\n\u003cli\u003eBraddock, K. H.\u003cem\u003e et al.\u003c/em\u003e Impulsivity partially mediates the relationship between BIS/BAS and risky health behaviors. \u003cem\u003eJournal of personality\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 793-810 (2011).\u003c/li\u003e\n\u003cli\u003ePagliaccio, D.\u003cem\u003e et al.\u003c/em\u003e Revising the BIS/BAS Scale to study development: Measurement invariance and normative effects of age and sex from childhood through adulthood. \u003cem\u003ePsychological assessment\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 429 (2016).\u003c/li\u003e\n\u003cli\u003eJean-Richard-Dit-Bressel, P., Killcross, S. \u0026amp; McNally, G. P. Behavioral and neurobiological mechanisms of punishment: implications for psychiatric disorders. \u003cem\u003eNeuropsychopharmacology\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 1639-1650 (2018).\u003c/li\u003e\n\u003cli\u003eCardinal, R. N., Winstanley, C. A., Robbins, T. W. \u0026amp; Everitt, B. J. Limbic corticostriatal systems and delayed reinforcement. \u003cem\u003eAnnals of the New York Academy of Sciences\u003c/em\u003e \u003cstrong\u003e1021\u003c/strong\u003e, 33-50 (2004).\u003c/li\u003e\n\u003cli\u003eScheres, A., Milham, M. P., Knutson, B. \u0026amp; Castellanos, F. X. Ventral striatal hyporesponsiveness during reward anticipation in attention-deficit/hyperactivity disorder. \u003cem\u003eBiol Psychiat\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, 720-724 (2007).\u003c/li\u003e\n\u003cli\u003ePiantadosi, P. T., Halladay, L. R., Radke, A. K. \u0026amp; Holmes, A. Advances in understanding meso‐cortico‐limbic‐striatal systems mediating risky reward seeking. \u003cem\u003eJournal of neurochemistry\u003c/em\u003e \u003cstrong\u003e157\u003c/strong\u003e, 1547-1571 (2021).\u003c/li\u003e\n\u003cli\u003eKovner, R., Oler, J. A. \u0026amp; Kalin, N. H. Cortico-limbic interactions mediate adaptive and maladaptive responses relevant to psychopathology. \u003cem\u003eAmerican Journal of Psychiatry\u003c/em\u003e \u003cstrong\u003e176\u003c/strong\u003e, 987-999 (2019).\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Hare, J., Calakos, N. \u0026amp; Yin, H. H. Recent insights into corticostriatal circuit mechanisms underlying habits. \u003cem\u003eCurrent opinion in behavioral sciences\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 40-46 (2018).\u003c/li\u003e\n\u003cli\u003eUhlhaas, P. J.\u003cem\u003e et al.\u003c/em\u003e Towards a youth mental health paradigm: a perspective and roadmap. \u003cem\u003eMolecular Psychiatry\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 3171-3181 (2023).\u003c/li\u003e\n\u003cli\u003eCasey, B. J., Getz, S. \u0026amp; Galvan, A. The adolescent brain. \u003cem\u003eDevelopmental review\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 62-77 (2008).\u003c/li\u003e\n\u003cli\u003eCasey, B. J., Jones, R. M. \u0026amp; Hare, T. A. The adolescent brain. \u003cem\u003eAnn N Y Acad Sci\u003c/em\u003e \u003cstrong\u003e1124\u003c/strong\u003e, 111-126, doi:10.1196/annals.1440.010 (2008).\u003c/li\u003e\n\u003cli\u003eLuna, B. The relevance of immaturities in the juvenile brain to culpability and rehabilitation. \u003cem\u003eThe Hastings law journal\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e, 1469 (2012).\u003c/li\u003e\n\u003cli\u003eSomerville, L. H., Jones, R. M. \u0026amp; Casey, B. A time of change: behavioral and neural correlates of adolescent sensitivity to appetitive and aversive environmental cues. \u003cem\u003eBrain Cognition\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 124-133 (2010).\u003c/li\u003e\n\u003cli\u003eBlakemore, S. J. The social brain in adolescence. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 267-277, doi:10.1038/nrn2353 (2008).\u003c/li\u003e\n\u003cli\u003eSchweizer, S., Gotlib, I. H. \u0026amp; Blakemore, S.-J. The role of affective control in emotion regulation during adolescence. \u003cem\u003eEmotion\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 80 (2020).\u003c/li\u003e\n\u003cli\u003eYoung, K. S., Sandman, C. F. \u0026amp; Craske, M. G. Positive and negative emotion regulation in adolescence: links to anxiety and depression. \u003cem\u003eBrain sciences\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 76 (2019).\u003c/li\u003e\n\u003cli\u003eSilvers, J. A. Adolescence as a pivotal period for emotion regulation development. \u003cem\u003eCurrent opinion in psychology\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 258-263 (2022).\u003c/li\u003e\n\u003cli\u003eRipke, S.\u003cem\u003e et al.\u003c/em\u003e Reward processing and intertemporal decision making in adults and adolescents: The role of impulsivity and decision consistency. \u003cem\u003eBrain research\u003c/em\u003e \u003cstrong\u003e1478\u003c/strong\u003e, 36-47 (2012).\u003c/li\u003e\n\u003cli\u003eRomer, D. Adolescent risk taking, impulsivity, and brain development: Implications for prevention. \u003cem\u003eDevelopmental Psychobiology: The Journal of the International Society for Developmental Psychobiology\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 263-276 (2010).\u003c/li\u003e\n\u003cli\u003eCasey, B., Heller, A. S., Gee, D. G. \u0026amp; Cohen, A. O. Development of the emotional brain. \u003cem\u003eNeuroscience letters\u003c/em\u003e \u003cstrong\u003e693\u003c/strong\u003e, 29-34 (2019).\u003c/li\u003e\n\u003cli\u003eDhamala, E., Yeo, B. T. \u0026amp; Holmes, A. J. Methodological Considerations for Brain-Based Predictive Modelling in Psychiatry. \u003cem\u003eBiol Psychiat\u003c/em\u003e (2022).\u003c/li\u003e\n\u003cli\u003eBzdok, D. \u0026amp; Yeo, B. T. Inference in the age of big data: Future perspectives on neuroscience. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e155\u003c/strong\u003e, 549-564 (2017).\u003c/li\u003e\n\u003cli\u003eMatte Bon, G., Kraft, D., Comasco, E., Derntl, B. \u0026amp; Kaufmann, T. Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders. \u003cem\u003eBiology of Sex Differences\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1-15 (2024).\u003c/li\u003e\n\u003cli\u003eLaakso, A.\u003cem\u003e et al.\u003c/em\u003e Sex differences in striatal presynaptic dopamine synthesis capacity in healthy subjects. \u003cem\u003eBiol Psychiat\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 759-763 (2002).\u003c/li\u003e\n\u003cli\u003eLiu, S., Seidlitz, J., Blumenthal, J. D., Clasen, L. S. \u0026amp; Raznahan, A. Integrative structural, functional, and transcriptomic analyses of sex-biased brain organization in humans. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 18788-18798 (2020).\u003c/li\u003e\n\u003cli\u003eCross, C. P., Copping, L. T. \u0026amp; Campbell, A. Sex differences in impulsivity: a meta-analysis. \u003cem\u003ePsychological bulletin\u003c/em\u003e \u003cstrong\u003e137\u003c/strong\u003e, 97 (2011).\u003c/li\u003e\n\u003cli\u003eRakic, P. Evolution of the neocortex: a perspective from developmental biology. \u003cem\u003eNature Reviews Neuroscience\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 724-735 (2009).\u003c/li\u003e\n\u003cli\u003eRakic, P., Ayoub, A. E., Breunig, J. J. \u0026amp; Dominguez, M. H. Decision by division: making cortical maps. \u003cem\u003eTrends in neurosciences\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 291-301 (2009).\u003c/li\u003e\n\u003cli\u003eBethlehem, R. A.\u003cem\u003e et al.\u003c/em\u003e Brain charts for the human lifespan. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e604\u003c/strong\u003e, 525-533 (2022).\u003c/li\u003e\n\u003cli\u003eWhite, T., Su, S., Schmidt, M., Kao, C.-Y. \u0026amp; Sapiro, G. The development of gyrification in childhood and adolescence. \u003cem\u003eBrain Cognition\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 36-45 (2010).\u003c/li\u003e\n\u003cli\u003eCasey, B. J.\u003cem\u003e et al.\u003c/em\u003e The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. \u003cem\u003eDevelopmental cognitive neuroscience\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 43-54, doi:10.1016/j.dcn.2018.03.001 (2018).\u003c/li\u003e\n\u003cli\u003eHagler, D. J., Jr.\u003cem\u003e et al.\u003c/em\u003e Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e202\u003c/strong\u003e, 116091, doi:10.1016/j.neuroimage.2019.116091 (2019).\u003c/li\u003e\n\u003cli\u003eDhamala, E.\u003cem\u003e et al.\u003c/em\u003e Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features and populations. \u003cem\u003eNeuroImage\u003c/em\u003e, doi:10.1016/j.neuroimage.2022.119485 (2022).\u003c/li\u003e\n\u003cli\u003eDesikan, R. S.\u003cem\u003e et al.\u003c/em\u003e An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 968-980, doi:10.1016/j.neuroimage.2006.01.021 (2006).\u003c/li\u003e\n\u003cli\u003eGeurten, M., Catale, C., Gay, P., Deplus, S. \u0026amp; Billieux, J. Measuring impulsivity in children: adaptation and validation of a short version of the UPPS-P impulsive behaviors scale in children and investigation of its links with ADHD. \u003cem\u003eJournal of attention disorders\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 105-114 (2021).\u003c/li\u003e\n\u003cli\u003eBenjamini, Y. \u0026amp; Hochberg, Y. Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. \u003cem\u003eJ R Stat Soc B\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 289-300 (1995).\u003c/li\u003e\n\u003cli\u003eDhamala, E., Bassett, D. S., Yeo, B. T. \u0026amp; Holmes, A. J. Functional brain networks are associated with both sex and gender in children. \u003cem\u003eScience Advances\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, eadn4202 (2024).\u003c/li\u003e\n\u003cli\u003eDhamala, E.\u003cem\u003e et al.\u003c/em\u003e Sex differences in the functional network underpinnings of psychotic-like experiences in children. \u003cem\u003ebioRxiv\u003c/em\u003e, 2024.2004. 2022.590660 (2024).\u003c/li\u003e\n\u003cli\u003eDhamala, E., Jamison, K. W., Jaywant, A., Dennis, S. \u0026amp; Kuceyeski, A. Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults. \u003cem\u003eHuman Brain Mapping\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 3102-3118, doi:10.1002/hbm.25420 (2021).\u003c/li\u003e\n\u003cli\u003eDhamala, E., Jamison, K. W., Jaywant, A. \u0026amp; Kuceyeski, A. Shared functional connections within and between cortical networks predict cognitive abilities in adult males and females. \u003cem\u003eHuman Brain Mapping\u003c/em\u003e, doi:10.1002/hbm.25709 (2022).\u003c/li\u003e\n\u003cli\u003eDhamala, E.\u003cem\u003e et al.\u003c/em\u003e Brain-based predictions of psychiatric illness-linked behaviors across the sexes. \u003cem\u003eBiol Psychiat\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eChen, J.\u003cem\u003e et al.\u003c/em\u003e Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e274\u003c/strong\u003e, 120115 (2023).\u003c/li\u003e\n\u003cli\u003eChen, J.\u003cem\u003e et al.\u003c/em\u003e Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. \u003cem\u003eNature communications\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 2217, doi:10.1038/s41467-022-29766-8 (2022).\u003c/li\u003e\n\u003cli\u003eLi, J.\u003cem\u003e et al.\u003c/em\u003e Global signal regression strengthens association between resting-state functional connectivity and behavior. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e196\u003c/strong\u003e, 126-141, doi:10.1016/j.neuroimage.2019.04.016 (2019).\u003c/li\u003e\n\u003cli\u003eHaufe, S.\u003cem\u003e et al.\u003c/em\u003e On the interpretation of weight vectors of linear models in multivariate neuroimaging. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 96-110 (2014).\u003c/li\u003e\n\u003cli\u003eTian, Y. \u0026amp; Zalesky, A. Machine learning prediction of cognition from functional connectivity: Are feature weights reliable? \u003cem\u003ebioRxiv\u003c/em\u003e (2021).\u003c/li\u003e\n\u003cli\u003eChen, J.\u003cem\u003e et al.\u003c/em\u003e There is no fundamental trade-off between prediction accuracy and feature importance reliability. \u003cem\u003ebioRxiv\u003c/em\u003e (2022).\u003c/li\u003e\n\u003cli\u003eOwens, M. M.\u003cem\u003e et al.\u003c/em\u003e Neuroanatomical correlates of impulsive traits in children aged 9 to 10. \u003cem\u003eJournal of abnormal psychology\u003c/em\u003e \u003cstrong\u003e129\u003c/strong\u003e, 831 (2020).\u003c/li\u003e\n\u003cli\u003eShao, I. Y.\u003cem\u003e et al.\u003c/em\u003e From individual motivation to substance use initiation: A longitudinal cohort study assessing the associations between reward sensitivity and subsequent risk of substance use initiation among US adolescents. \u003cem\u003eAddictive Behaviors\u003c/em\u003e \u003cstrong\u003e160\u003c/strong\u003e, 108162 (2025).\u003c/li\u003e\n\u003cli\u003eAkoglu, H. User\u0026apos;s guide to correlation coefficients. \u003cem\u003eTurkish journal of emergency medicine\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 91-93 (2018).\u003c/li\u003e\n\u003cli\u003eCardoso Melo, R. D., Groen, R. N. \u0026amp; Hartman, C. A. Reward sensitivity at age 13 predicts the future course of psychopathology symptoms. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 818047 (2022).\u003c/li\u003e\n\u003cli\u003eRead, R. W.\u003cem\u003e et al.\u003c/em\u003e A study of impulsivity and adverse childhood experiences in a population health setting. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1447008 (2024).\u003c/li\u003e\n\u003cli\u003eKebets, V.\u003cem\u003e et al.\u003c/em\u003e Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. \u003cem\u003eBiol Psychiatry\u003c/em\u003e \u003cstrong\u003e86\u003c/strong\u003e, 779-791, doi:10.1016/j.biopsych.2019.06.013 (2019).\u003c/li\u003e\n\u003cli\u003eOoi, L. Q. R.\u003cem\u003e et al.\u003c/em\u003e Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. \u003cem\u003eNeuroImage\u003c/em\u003e, 119636 (2022).\u003c/li\u003e\n\u003cli\u003eIde, J. S.\u003cem\u003e et al.\u003c/em\u003e Gray matter volumetric correlates of behavioral activation and inhibition system traits in children: An exploratory voxel-based morphometry study of the ABCD project data. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e220\u003c/strong\u003e, 117085 (2020).\u003c/li\u003e\n\u003cli\u003eForstmann, B. U., van den Wildenberg, W. P. \u0026amp; Ridderinkhof, K. R. Neural mechanisms, temporal dynamics, and individual differences in interference control. \u003cem\u003eJournal of Cognitive Neuroscience\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1854-1865 (2008).\u003c/li\u003e\n\u003cli\u003eRizzolatti, G., Fogassi, L. \u0026amp; Gallese, V. Motor and cognitive functions of the ventral premotor cortex. \u003cem\u003eCurrent opinion in neurobiology\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 149-154 (2002).\u003c/li\u003e\n\u003cli\u003eHolmes, A. J., Hollinshead, M. O., Roffman, J. L., Smoller, J. W. \u0026amp; Buckner, R. L. Individual differences in cognitive control circuit anatomy link sensation seeking, impulsivity, and substance use. \u003cem\u003eJournal of neuroscience\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 4038-4049 (2016).\u003c/li\u003e\n\u003cli\u003eLevy, B. J. \u0026amp; Wagner, A. D. Cognitive control and right ventrolateral prefrontal cortex: reflexive reorienting, motor inhibition, and action updating. \u003cem\u003eAnnals of the New York academy of sciences\u003c/em\u003e \u003cstrong\u003e1224\u003c/strong\u003e, 40-62 (2011).\u003c/li\u003e\n\u003cli\u003eLoh, K. K.\u003cem\u003e et al.\u003c/em\u003e Cognitive control of orofacial motor and vocal responses in the ventrolateral and dorsomedial human frontal cortex. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 4994-5005 (2020).\u003c/li\u003e\n\u003cli\u003eZhao, L., Silva, A. B., Kurteff, G. L. \u0026amp; Chang, E. F. Inhibitory control of speech production in the human premotor frontal cortex. \u003cem\u003eNature Human Behaviour\u003c/em\u003e, 1-16 (2025).\u003c/li\u003e\n\u003cli\u003eIde, J. S., Tung, H. C., Yang, C.-T., Tseng, Y.-C. \u0026amp; Li, C.-S. R. Barratt impulsivity in healthy adults is associated with higher gray matter concentration in the parietal occipital cortex that represents peripheral visual field. \u003cem\u003eFrontiers in Human Neuroscience\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 222 (2017).\u003c/li\u003e\n\u003cli\u003eBaker, J. T.\u003cem\u003e et al.\u003c/em\u003e Functional connectomics of affective and psychotic pathology. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 9050-9059 (2019).\u003c/li\u003e\n\u003cli\u003eChen, J.\u003cem\u003e et al.\u003c/em\u003e Intrinsic Connectivity Patterns of Task-Defined Brain Networks Allow Individual Prediction of Cognitive Symptom Dimension of Schizophrenia and Are Linked to Molecular Architecture. \u003cem\u003eBiol Psychiatry\u003c/em\u003e \u003cstrong\u003e89\u003c/strong\u003e, 308-319, doi:10.1016/j.biopsych.2020.09.024 (2021).\u003c/li\u003e\n\u003cli\u003eKong, R.\u003cem\u003e et al.\u003c/em\u003e Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. \u003cem\u003eCerebral Cortex\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 2533-2551, doi:10.1093/cercor/bhy123 (2018).\u003c/li\u003e\n\u003cli\u003eCole, M. W., Repov\u0026scaron;, G. \u0026amp; Anticevic, A. The frontoparietal control system: a central role in mental health. \u003cem\u003eThe Neuroscientist\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 652-664 (2014).\u003c/li\u003e\n\u003cli\u003eXia, C. H.\u003cem\u003e et al.\u003c/em\u003e Linked dimensions of psychopathology and connectivity in functional brain networks. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 3003, doi:10.1038/s41467-018-05317-y (2018).\u003c/li\u003e\n\u003cli\u003eBarlow, R. L.\u003cem\u003e et al.\u003c/em\u003e Ventral striatal D2/3 receptor availability is associated with impulsive choice behavior as well as limbic corticostriatal connectivity. \u003cem\u003eInternational Journal of Neuropsychopharmacology\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 705-715 (2018).\u003c/li\u003e\n\u003cli\u003eRicard, J. A.\u003cem\u003e et al.\u003c/em\u003e A shared spatial topography links the functional connectome correlates of cocaine use disorder and dopamine D2/3 receptor densities. \u003cem\u003eCommunications Biology\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 1178 (2024).\u003c/li\u003e\n\u003cli\u003eBetts, M. J.\u003cem\u003e et al.\u003c/em\u003e Learning in anticipation of reward and punishment: perspectives across the human lifespan. \u003cem\u003eNeurobiology of aging\u003c/em\u003e \u003cstrong\u003e96\u003c/strong\u003e, 49-57 (2020).\u003c/li\u003e\n\u003cli\u003eMelo, R. D. C., Schreuder, M. J., Groen, R. N., Sarsembayeva, D. \u0026amp; Hartman, C. A. Reward sensitivity across the lifespan in males and females and its associations with psychopathology. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e \u003cstrong\u003e204\u003c/strong\u003e, 112041 (2023).\u003c/li\u003e\n\u003cli\u003eHammond, C. J., Potenza, M. N. \u0026amp; Mayes, L. C. \u003cem\u003eDevelopment of impulse control, inhibition, and self-regulatory behaviors in normative populations across the lifespan\u003c/em\u003e. Vol. 232 (Oxford University Press New York, NY, 2012).\u003c/li\u003e\n\u003cli\u003eWeafer, J. \u0026amp; de Wit, H. Sex differences in impulsive action and impulsive choice. \u003cem\u003eAddictive behaviors\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 1573-1579 (2014).\u003c/li\u003e\n\u003cli\u003eBarendse, M.\u003cem\u003e et al.\u003c/em\u003e Sex and pubertal variation in reward-related behavior and neural activation in early adolescents. \u003cem\u003eDevelopmental Cognitive Neuroscience\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 101358 (2024).\u003c/li\u003e\n\u003cli\u003eAssari, S. Sex Differences in the Association between Cortical Thickness and Children\u0026apos;s Behavioral Inhibition. \u003cem\u003eJournal of psychology \u0026amp; behavior research\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 49-64 (2020).\u003c/li\u003e\n\u003cli\u003eChen, Y.\u003cem\u003e et al.\u003c/em\u003e Gray matter volumetric correlates of dimensional impulsivity traits in children: Sex differences and heritability. \u003cem\u003eHuman brain mapping\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 2634-2652 (2022).\u003c/li\u003e\n\u003cli\u003eShanmugan, S.\u003cem\u003e et al.\u003c/em\u003e Sex differences in the functional topography of association networks in youth. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e119\u003c/strong\u003e, e2110416119 (2022).\u003c/li\u003e\n\u003cli\u003eDeCasien, A. R., Guma, E., Liu, S. \u0026amp; Raznahan, A. Sex differences in the human brain: a roadmap for more careful analysis and interpretation of a biological reality. \u003cem\u003eBiology of sex differences\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 43 (2022).\u003c/li\u003e\n\u003cli\u003eZhang, X.-H.\u003cem\u003e et al.\u003c/em\u003e The cell-type underpinnings of the human functional cortical connectome. \u003cem\u003eNature Neuroscience\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 150-160 (2025).\u003c/li\u003e\n\u003cli\u003eRicard, J.\u003cem\u003e et al.\u003c/em\u003e Confronting racially exclusionary practices in the acquisition and analyses of neuroimaging data. \u003cem\u003eNature Neuroscience\u003c/em\u003e, 1-8 (2022).\u003c/li\u003e\n\u003cli\u003eDhamala, E.\u003cem\u003e et al.\u003c/em\u003e Considering the interconnected nature of social identities in neuroimaging research. \u003cem\u003eNature Neuroscience\u003c/em\u003e, 1-12 (2024).\u003c/li\u003e\n\u003cli\u003eWierenga, L. M.\u003cem\u003e et al.\u003c/em\u003e Recommendations for a better understanding of sex and gender in neuroscience of mental health. \u003cem\u003eBiological Psychiatry Global Open Science\u003c/em\u003e, 100283 (2023).\u003c/li\u003e\n\u003cli\u003eMarek, S., Tervo-Clemmens, B., Calabro, F., Nichols, T. \u0026amp; Dosenbach, N. Reproducible brain-wide association studies require thousands of individuals. \u003cem\u003eNature\u003c/em\u003e (2022).\u003c/li\u003e\n\u003cli\u003eBevilacqua, L. \u0026amp; Goldman, D. Genetics of impulsive behaviour. \u003cem\u003ePhilosophical Transactions of the Royal Society B: Biological Sciences\u003c/em\u003e \u003cstrong\u003e368\u003c/strong\u003e, 20120380 (2013).\u003c/li\u003e\n\u003cli\u003ePauli, R.\u003cem\u003e et al.\u003c/em\u003e Action initiation and punishment learning differ from childhood to adolescence while reward learning remains stable. \u003cem\u003eNature communications\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 5689 (2023).\u003c/li\u003e\n\u003cli\u003eBezdjian, S., Baker, L. A. \u0026amp; Tuvblad, C. Genetic and environmental influences on impulsivity: A meta-analysis of twin, family and adoption studies. \u003cem\u003eClinical psychology review\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1209-1223 (2011).\u003c/li\u003e\n\u003cli\u003eMeidenbauer, K. L.\u003cem\u003e et al.\u003c/em\u003e Evidence for environmental influences on impulsivity and aggression. \u003cem\u003eUrban Forestry \u0026amp; Urban Greening\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 128594 (2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"BIS/BAS, UPPS, neuroanatomy, machine learning, sex differences, development ","lastPublishedDoi":"10.21203/rs.3.rs-6520460/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6520460/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndividual differences in neural circuits underlying emotional regulation, motivation, and decision-making are implicated in many psychiatric illnesses. Interindividual variability in these circuits may manifest, at least in part, as individual differences in impulsivity at both normative and clinically significant levels. Impulsivity reflects a tendency towards rapid, unplanned reactions to internal or external stimuli without considering potential negative consequences coupled with difficulty inhibiting responses.\u003c/p\u003e \u003cp\u003eHere, we use multivariate brain-based predictive models to explore the neural bases of impulsivity across multiple behavioral scales, neuroanatomical features (cortical thickness, surface area, and gray matter volume), and sexes (females and males) in a large sample of youth from the Adolescent Brain Cognitive Development (ABCD) Study at baseline (n\u0026thinsp;=\u0026thinsp;9,099) and two-year follow-up (n\u0026thinsp;=\u0026thinsp;6,432).\u003c/p\u003e \u003cp\u003eImpulsivity is significantly associated with neuroanatomical variability, and these associations vary across behavioral scales and neuroanatomical features. Impulsivity broadly maps onto cortical thickness in dispersed regions (e.g., inferior frontal, lateral occipital, superior frontal, entorhinal), as well as surface area and gray matter volume in specific medial (e.g., parahippocampal, cingulate) and polar (e.g., frontal and temporal) territories. Importantly, while many relationships are stable across sexes, others are sex-specific.\u003c/p\u003e \u003cp\u003eThese results highlight the complexity of the relationships between neuroanatomy and impulsivity across scales, features, sexes, and time points in youth. These findings suggest that neuroanatomy, in combination with other biological and environmental factors, reflects a key driver of individual differences in impulsivity in youth. As such, neuroanatomical markers may help identify youth at increased risk for developing impulsivity-related illnesses. Furthermore, this work emphasizes the importance of adopting a multidimensional and sex-specific approach in neuroimaging and behavioral research.\u003c/p\u003e","manuscriptTitle":"Neuroanatomy Reflects Individual Variability in Impulsivity in Youth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 05:44:35","doi":"10.21203/rs.3.rs-6520460/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-08-14T07:08:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-14T15:39:22+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-10T15:55:00+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-06-30T20:06:43+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-30T15:54:21+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-29T20:18:16+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-29T16:38:54+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-06-29T15:55:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-23T12:12:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-23T12:07:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2025-06-20T20:08:02+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-06-17T10:06:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c9e630fb-58c1-4e92-8b51-d2ef5b68f6f0","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50749935,"name":"Biological sciences/Neuroscience"},{"id":50749936,"name":"Biological sciences/Biological techniques"}],"tags":[],"updatedAt":"2026-03-25T07:12:46+00:00","versionOfRecord":{"articleIdentity":"rs-6520460","link":"https://doi.org/10.1038/s41380-026-03526-2","journal":{"identity":"molecular-psychiatry","isVorOnly":false,"title":"Molecular Psychiatry"},"publishedOn":"2026-03-24 04:00:00","publishedOnDateReadable":"March 24th, 2026"},"versionCreatedAt":"2025-08-27 05:44:35","video":"","vorDoi":"10.1038/s41380-026-03526-2","vorDoiUrl":"https://doi.org/10.1038/s41380-026-03526-2","workflowStages":[]},"version":"v1","identity":"rs-6520460","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6520460","identity":"rs-6520460","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}