Hundreds of published studies have advanced understanding of the hypothesized general factor of psychological problems, but confusion still surrounds the hypothesis. This partly results from critics conflating our hypotheses with those of other authors, but we have created confusion ourselves by stating two different general factor hypotheses, which we differentiate here. In the psychometric general factor hypothesis, the general factor is the term in bifactor models that quantifies the variance shared by all measured psychological problems, whereas two or more specific factors are defined by orthogonal pools of variance shared only by items loading on each specific factor. Although the psychometric bifactor model is sometimes viewed as an alternative to taxonomic models based on correlated factor models, it is not. Correlated factors models properly describe the overlapping dimensions of psychological problems experienced in everyday life. The separate hierarchical causal hypothesis is that correlations among the problems that define the general factor result from some of their causes and mechanisms being directly or indirectly shared, whereas the specific factors are the result of other orthogonal causes being shared by subsets of problems. There is growing evidence that some genetic and environmental causes-and their attendant psychobiological mechanisms-are shared to varying degrees with essentially all psychological problems. Other independent causes and mechanisms influence only subgroups of psychological problems (e.g., internalizing problems). (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The effects of serial order of presentation on parent ratings of items referring to the behavior of their children were examined using two scales measuring dispositions and psychopathology that were administered to a representative sample of 1,358 4–17 year olds. Items within each scale were pseudo-randomized instead of being grouped according to the constructs they were written to measure. Each scale was presented in either forward or reverse serial order, with parents who were unaware of all hypotheses being randomly selected for forward or reverse order of administration. In both the disposition and psychopathology scales, the same items were rated significantly differently depending on the order of presentation to the raters; ratings declined in magnitude as the parents rated increasing numbers of items. Although parent ratings presumably partly reflect the behavior of their children, they are biased by robust order effects. The unbiased counter-balancing of items in scale construction and the use of randomized forward versus reverse orders of scale administrations can be used to minimize extraneous order effects.

Traditional mental health diagnoses rely on symptom-based classifications. Yet this approach can oversimplify clinical presentations as diagnoses often do not adequately map onto neurobiological features. Alternatively, our study used structural imaging data and a semisupervised machine learning technique, heterogeneity through discriminative analysis, to identify neurobiological subtypes in 9- to 10-year-olds with high psychopathology endorsements (n = 9,027). Our model revealed two stable neurobiological subtypes (adjusted Rand index = 0.38). Subtype 1 showed smaller structural properties, elevated conduct problems and attention-deficit/hyperactivity disorder symptoms, and impaired cognitive performance compared to Subtype 2 and typically developing youth. Subtype 2 had larger structural properties, cognitive abilities comparable to typically developing youth, and elevated internalizing symptoms relative to Subtype 1 and typically developing youth. These subtypes remained stable in their neurobiological characteristics, cognitive ability, and associated psychopathology traits over time. Taken together, our data-driven approach uncovered evidence of neural heterogeneity as demonstrated by structural patterns that map onto divergent profiles of psychopathology symptoms and cognitive performance in youth.

IntroductionAlthough psychotic behaviors can be difficult to assess in children, early identification of children at high risk for the emergence of psychotic symptoms may facilitate the prevention of related disorders. Psychotic-like experiences (PLEs), or subthreshold thought and perceptual disturbances, could be early manifestations of psychosis that may predict a future diagnosis of a psychosis-related disorder or nonspecific correlates of a wide range of psychological problems. Additional research is needed regarding how PLEs map onto dimensions of psychopathology in children.MethodsIn the present study, we examined the association between PLEs and general and specific dimensions of psychological problems in a sample of 10,692 children from the Adolescent Brain Cognitive Development Study (ABCD Study).ResultsThe results of this study showed that self-reported PLEs were associated with a general psychopathology factor and an ADHD factor, which were defined in hierarchical models of parent-rated psychological problems.DiscussionThese findings suggest that PLEs are broadly associated with a wide range of psychological problems through the general psychopathology factor even before psychotic disorders typically manifest. This study supports the need for longitudinal analyses of future waves of the ABCD Study to determine if PLEs can detect children at high risk for serious psychological problems in adulthood.

Background We used a polygenic score for externalizing behavior (extPGS) and structural MRI to examine potential pathways from genetic liability to conduct problems via the brain across the adolescent transition. Methods Three annual assessments of child conduct problems, attention‐deficit/hyperactivity problems, and internalizing problems were conducted across across 9–13 years of age among 4,475 children of European ancestry in the Adolescent Brain Cognitive Development SM Study (ABCD Study®). Results The extPGS predicted conduct problems in each wave ( R 2 = 2.0%–2.9%). Bifactor models revealed that the extPRS predicted variance specific to conduct problems ( R 2 = 1.7%–2.1%), but also variance that conduct problems shared with other measured problems ( R 2 = .8%–1.4%). Longitudinally, extPGS predicted levels of specific conduct problems ( R 2 = 2.0%), but not their slope of change across age. The extPGS was associated with total gray matter volume (TGMV; R 2 = .4%) and lower TGMV predicted both specific conduct problems ( R 2 = 1.7%–2.1%) and the variance common to all problems in each wave ( R 2 = 1.6%–3.1%). A modest proportion of the polygenic liability specific to conduct problems in each wave was statistically mediated by TGMV. Conclusions Across the adolescent transition, the extPGS predicted both variance specific to conduct problems and variance shared by all measured problems. The extPGS also was associated with TGMV, which robustly predicted conduct problems. Statistical mediation analyses suggested the hypothesis that polygenic variation influences individual differences in brain development that are related to the likelihood of conduct problems during the adolescent transition, justifying new research to test this causal hypothesis.

Back to table of contents Previous article Next article EditorialsFull AccessWhy Psychological Problems Presage Cardiometabolic Health ProblemsBenjamin B. Lahey, Ph.D.Benjamin B. Lahey, Ph.D.Published Online:1 Jul 2024https://doi.org/10.1176/appi.ajp.20240370AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail The idea that the well-being of mind and body are intimately linked is an ancient one, dating back at least to the observations of Aristotle and Hippocrates. Carefully examining this notion in the age of science is a fundamentally important task for promoting the ability of medicine, psychiatry, and psychology to improve human lives (1). The remarkable article by Chen et al. in this issue (2) adds important new empirical information on the association between psychological problems and a cluster of often co-occurring cardiometabolic conditions—cardiac diseases, type 2 diabetes, hypertension, hyperlipidemia, and obesity. Using Swedish health registries, Chen and colleagues identified a cohort of over 670,000 persons for whom data were available on clinical diagnoses of mental disorders during early adulthood and on cardiometabolic conditions during middle adulthood. After excluding persons in whom cardiometabolic problems were already present during early adulthood, the authors replicated earlier findings of bivariate associations between a range of individual mental disorder diagnoses (depression, anxiety disorders, alcohol abuse, and others) and adjudicated criminal behavior (as an indicator of externalizing problems) at 18–25 years of age, on the one hand, and clinical diagnoses of cardiometabolic conditions during middle adulthood, on the other. These prospective replications are very useful because they are based on the largest sample and the longest follow-up period to date, leaving little doubt that psychological problems temporally precede cardiometabolic conditions that carry high risk of morbidity and mortality (3).Chen et al. go well beyond the robust replication of bivariate associations, however, and provide new information that should guide future theory and research on mind-health relationships. Using latent factor modeling, they provide novel evidence that the prospective association between mental disorder diagnoses and future cardiometabolic health is highly nonspecific. That is, they found that the variance common to all of the measured mental disorders—the shared variance that defines the general factor of psychological problems (4)—was the best predictor of future cardiometabolic health. Thus, no single kind of psychological problem predicted these health problems; it was (mostly) what all of the measured psychological problems share in common that predicted poor cardiometabolic outcomes. Thus, although focused studies of the health sequelae of single diagnoses may still be useful, our focus should shift to predictive relationships between the nonspecific causes and psychobiological mechanisms shared by all forms of psychological problems and future cardiometabolic health outcomes (5, 6).These findings from Chen et al. provide a platform for asking the next fundamentally important question. What psychobiological processes do all common forms of psychological problems share that might explain why psychological and cardiometabolic problems are linked? One obvious candidate is neuroticism. This refers to relatively stable individual differences in negative emotional response to threat, frustration, or loss (7, 8). Neuroticism is robustly correlated with essentially all diagnostic categories of mental disorder (9), with their co-occurrence (10), and, indeed, with the general factor of psychological problems (9, 11, 12). Furthermore, neuroticism is a robust prospective predictor of a broad range of physical health conditions, particularly cardiovascular illness, and predicts premature mortality (9, 13, 14). A second (and not mutually exclusive) process linking the general factor of psychological problems to physical health is intelligence. There is clear evidence from several robust studies of an inverse relationship between intelligence and both the general factor of psychological problems (15, 16) and physical health (17).Thus, growing evidence suggests that the variance shared by essentially all forms of psychological problems that is shared with cardiometabolic health is also shared with neuroticism and intelligence. Put plainly, people who are less intelligent and who experience greater negative emotional reactivity to stress are more likely to develop a broad range of psychological and cardiometabolic problems as they transact with their social and physical environments (1). It is worth noting that the more specific construct of cognitive control may prove to be more informative than general intelligence in understanding psychological and health problems. This highly heritable construct, also known as executive functions, refers to non-automatic processes of behavioral regulation that optimize goal-related behavior. Cognitive control is correlated with, but is separable from, general intelligence (18). Like intelligence, there are replicated findings of associations of measures of cognitive control with the general factor of psychological problems (18–22) and with physical health (23).What specific mechanisms link cognitive control and neuroticism with cardiometabolic health? Persons with high levels of neuroticism have been found to experience reduced immunity, increased inflammation, and disrupted sleep (24–26). In addition, neuroticism is associated with the generation of interpersonal stress (27) and with reduced social support, which are associated with both psychological and physical health problems (13). Smoking and other substance use also are more common in persons with greater neuroticism (28) and persons with lower intelligence (29). These and other factors, including neuroticism itself, may constitute mechanistic links that are feasible targets for public health interventions (9).Chen et al. also report findings from sibling analyses (30, 31) that need to be considered as we plan future studies of the casual risk factors underlying the prospective association between the general factor of psychological problems and cardiometabolic health. They estimated the strength of associations between psychological problems and cardiometabolic problems within pairs of full siblings in the same families and compared them to the magnitudes of the same associations across different families. Because all factors shared by siblings are held constant within sibling pairs, they do not contribute to the cross-trait (i.e., psychological and cardiometabolic problems) associations within sibling pairs. Therefore, the finding of attenuated associations within sibling pairs compared with the full sample argues that environmental factors typically shared by siblings, such as family and neighborhood-level poverty during childhood, are not likely to be causes, in any simple way, of the association between psychological and cardiometabolic problems (31, 32).Thus, the authors accurately conclude that the association between the general factor and the cardiometabolic conditions could either be causal in nature or be attributable to nonshared confounders. That is, it is possible that something about experiencing high general levels of psychopathology could cause an increased risk for cardiometabolic health problems later in adulthood. Nonetheless, the results do not exclude the possibility that the association between the general factor of psychological problems and health is attributable to influences not shared by the siblings (33). Because analyses of full siblings only hold half of their polymorphic genetic variation constant, this means that both genetic influences and environmental factors typically not shared by siblings, such as traumatic events, could explain the association between psychological and cardiometabolic problems. Consistent with this possibility, a considerable amount of evidence from a strong set of studies indicates that essentially all forms of psychological problems substantially share genetic influences with one another (34–36). Furthermore, the genetic influences on cardiometabolic problems (37, 38) appear to be shared with the general factor of psychological problems (39–41). In addition, neuroticism, cognitive control, and intelligence each substantially share genetic influences with both specific diagnosed mental disorders (42) and the general factor of psychological problems—that is, with the variance that psychological problems share in common (11, 12, 16, 19, 43).Much remains to be learned, of course, but a number of ongoing large population-based studies are likely to move the field forward on these topics. For example, a striking report based on data from the UK Biobank study of >40,000 adults showed that intelligence was genetically correlated with the structure of the right cardiac atrium and neuroticism was genetically correlated with the structure of the right cardiac ventricle, with evidence of direct or indirect pleiotropic genetic causation (44). The most informative future large-scale studies will be ones that not only include strong measures of a broad range of psychological problems and health, but also include measures of the key constructs, like neuroticism and cognitive control, that dispose individuals to develop a diverse range of psychological and health problems. This is because it is feasible to discover the causes and psychobiological mechanisms underlying a few uncorrelated dispositional dimensions, such as cognitive control and neuroticism, but very difficult or impossible to discover those underlying highly correlated forms of psychological and health problems (45).Department of Public Health Sciences, University of Chicago, Chicago.Send correspondence to Dr. Lahey ([email protected]).The author reports no financial relationships with commercial interests.References1. Lahey BB: Dimensions of Psychological Problems: Replacing Diagnostic Categories With a More Science-Based and Less Stigmatizing Alternative. 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Kalin, M.D.1 July 2024 | American Journal of Psychiatry, Vol. 181, No. 7 Volume 181Issue 7 July 01, 2024Pages 581-583 Metrics KeywordsMental DisordersComorbidityGeneral Psychopathology ModelMetabolic SyndromeCardiovascular DiseaseFamilial CoaggregationPDF download History Accepted 30 April 2024 Published online 1 July 2024 Published in print 1 July 2024

AbstractBackgroundStudies suggest that prosocial behavior, having high empathy and engaging in behaviors intended to benefit others, may predict mental health or vice versa; however, these findings have been mixed. The purpose of the current study was to examine the bidirectional relationships between prosocial behavior and dimensions of psychopathology in children.MethodsThe relationships between prosocial behavior and four dimensions of psychopathology (general psychopathology, internalizing symptoms, conduct problems, and attention‐deficit/hyperactivity disorder symptoms) were examined longitudinally in children 9–12 years of age from the Adolescent Brain Cognitive Development Study (N = 9122). We used a random intercept cross‐lagged panel model to distinguish between stable, trait‐like (between‐person) and time‐dependent (within‐person) fluctuations across a 24‐month period.ResultsBetween‐person results revealed that prosocial behavior was negatively associated with general psychopathology and conduct problems while being positively associated with internalizing symptoms. Within‐person results demonstrated that, out of four possible directional paths tested, one was significant. This path showed that greater general psychopathology and conduct problems at the first‐year follow‐up predicted fewer prosocial behaviors at the second‐year follow‐up, although the effect size was small. In contrast, prosocial behavior did not predict psychopathology dimensions for any year.ConclusionsThe results of this study suggest that prosocial behaviors have stable associations with psychopathology across preadolescence; however, evidence of a directional association in which psychopathology predicts fewer prosocial behaviors in the future was only modest.

The youth self-report version of the Child and Adolescent Dispositions Scale (CADS) measures three orthogonal temperament dimensions that are hypothesized to dispose children and adolescents to develop psychological problems through transactions with the environment. The CADS was developed using exploratory and confirmatory factor analyses (CFA) of ratings of a pool of items from which synonyms and antonyms of DSM symptoms were excluded in two population-based samples. The criterion validity of the resulting three CADS dimensions of negative emotionality, prosociality, and daring has been well established in multiple samples. We examined the factor structure and criterion validity correlations of the CADS using data on 14.0–18.0 year olds in the English community based Neuroscience in Psychiatry 2400 Study. Using CFA, the three hypothesized CADS factors were identified, which were validated by their predicted cross-cutting correlations with measures of anxiety, depression, and conduct problems. Nonetheless, the fit of the CFA was not acceptable, likely because the expectation of simple structure in CFA was not met. The same data were re-analyzed using exploratory structural equation modeling (ESEM) to identify sources of poor fit. Two of the three fit indices suggested acceptable fit for the three-factor ESEM solution (Standardized Root Mean-Square Residual = 0.047; Root Mean-Square Error of Approximation = 0.067; Comparative Fit Index = 0.878). If replicated in other samples, these results suggest that some prosociality items with cross-loadings on more than one factor should be considered for elimination or replacement to improve the measurement of these dispositional dimensions.

Studies of the genetic and environmental factors that make children more or less likely to develop distressing and impairing psychological problems, and studies of the psychobiological pathways through which these causal factors operate, have the goal of improving our understanding of the basic nature of psychological problems to develop better methods of prevention and treatment. For this reason, we have long had our eye on the prize of discovering the causes and psychobiological mechanisms underlying each dimension of psychological problems. There are compelling reasons, however, to seek a different and more achievable prize to understand psychological problems. Dimensions of psychological problems are both far too heterogeneous and too highly correlated to line up with distinct causal pathways. In contrast, a small number of orthogonal cognitive and socioemotional dispositional dimensions are correlated with psychological problems in revealing cross-cutting patterns. Each of these dispositions shares its independent causal pathways with psychological problems and help us understand the complex shared and heterogeneous nature of their causal processes. I outline a strategy for understanding the causes and mechanisms of psychological problems using studies of independently measured dispositions.

The Hurst exponent (H) isolated in fractal analyses of neuroimaging time-series is implicated broadly in cognition. The connection between H and the mathematics of criticality makes it a candidate measure of individual differences in cognitive resource allocation. Relationships between H and multiple mental disorders have been detected, suggesting that H is transdiagnostically associated with psychopathology. Here, we demonstrate a gradient of decreased H with increased general psychopathology and attention-deficit/hyperactivity extracted factor scores during a working memory task which predicts concurrent and future working memory performance in 1,839 children. This gradient defines psychological and functional axes which indicate that psychopathology is associated with an imbalance in resource allocation between fronto-parietal and sensory-motor regions, driven by reduced resource allocation to fonto-parietal regions. This suggests the hypothesis that impaired cognitive function associated with psychopathology follows from a reduced cognitive resource pool and a reduction in resources allocated to the task at hand.

Background:Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection. Additionally, there are few studies illustrating how differences between methods manifest in examining brain-behavior relationships. The purpose of this study was to exemplify how the choice of multivariate approach can change brain-behavior interpretations. Method:We used data from 9,027 9- to 10-year-old children from the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) to examine brain-behavior relationships with three commonly used multivariate approaches: canonical correlation analysis (CCA), partial least squares correlation (PLSC), and partial least squares regression (PLSR). We examined the associations between psychopathology dimensions including general psychopathology, attention-deficit/hyperactivity symptoms, conduct problems, and internalizing symptoms with regional brain volumes. Results:The results of CCA, PLSC, and PLSR showed both consistencies and differences in the relationship between psychopathology symptoms and brain structure. The leading significant component yielded by each method demonstrated similar patterns of associations between regional brain volumes and psychopathology symptoms. However, the additional significant components yielded by each method demonstrated differential brain-behavior patterns that were not consistent across methods. Conclusion:Here we show that CCA, PLSC, and PLSR yield slightly different interpretations regarding the relationship between child psychopathology and brain volume. In demonstrating the divergence between these approaches, we exemplify the importance of carefully considering the method's underlying assumptions when choosing a multivariate approach to delineate brain-behavior relationships.

Background:When brain networks deviate from typical development, this is thought to contribute to varying forms of psychopathology. However, research has been limited by the reliance on discrete diagnostic categories that overlook the potential for psychological comorbidity and the dimensional nature of symptoms. Methods:This study examined the topology of functional networks in association with 4 bifactor-defined psychopathology dimensions-general psychopathology, internalizing symptoms, conduct problems, and attention-deficit/hyperactivity disorder symptoms-via the Child Behavior Checklist in a sample of 3568 children from the ABCD (Adolescent Brain Cognitive Development) Study. Local and global graph theory metrics were calculated at rest and during tasks of reward processing, inhibition, and working memory. Results:Greater attention-deficit/hyperactivity disorder symptoms were associated with reduced modularity across rest and tasks as well as reduced local efficiency in motor networks at rest. Results survived sensitivity analyses for medication and socioeconomic status. Greater conduct problem symptoms were associated with reduced modularity on working memory and reward processing tasks; however, these results did not persist after sensitivity analyses. General psychopathology and internalizing symptoms showed no significant network associations. Conclusions:Our findings suggest reduced efficiency in topology in those with greater attention-deficit/hyperactivity disorder symptoms across 4 critical cognitive states, with conduct problems also showing network deficits, although less consistently. This may suggest that modularity deficits are a neurobiological marker of externalizing behavior in children. Such specificity has not been demonstrated before using graph theory metrics and has the potential to redefine our understanding of network deficits in children with psychopathology symptoms.

BACKGROUND: Early-life stressors can adversely affect the developing brain. While hierarchical modeling has established the existence of a general factor of psychopathology, no studies have modeled a general factor of environmental stress and related this factor to brain development. Using a large sample of children from the ABCD (Adolescent Brain Cognitive Development) Study, the current study aimed to identify general and specific factors of environmental stress and test their associations with brain structure and psychopathology.METHODS: In a sample of 11,878 children, bifactor modeling and higher-order (second-order) modeling identified general and specific factors of environmental stress: family dynamics, interpersonal support, neighborhood socioeconomic status deprivation, and urbanicity. Structural equation modeling was performed to examine associations between these factors and regional gray matter volume (GMV) and cortical thickness as well as general and specific factors of psychopathology.RESULTS: The general environmental stress factor was associated with globally smaller cortical and subcortical GMV as well as thinner cortices across widespread regions. Family dynamics and neighborhood socioeconomic status deprivation were associated with smaller GMV in focal regions. Urbanicity was associated with larger cortical and subcortical GMV and thicker cortices in frontotemporal regions. The environmental factors were associated with psychopathology in the expected directions. The general factors of environmental stress and psychopathology were both predictors of smaller GMV in children, while remaining distinct from each other. CONCLUSIONS: This study reveals a unifying model of environmental influences that illustrates the inherent orga-nization of environmental stressors and their relationship to brain structure and psychopathology.

When neural networks deviate from typical development, the resulting dysfunction is thought to contribute to varying forms of psychopathology. Although childhood networks are crucial for understanding neural and psychological development, research in this domain has been limited by psychological comorbidities, small sample sizes, and a reliance on traditional diagnostic categories.

In a previous issue of Clinical Psychological Science, Clark and colleagues asserted that lower order factors in second-order models are comparable with specific factors in bifactor models when residualized on the general factor. Modeling simulated data demonstrated that residualized lower order factors are correlated with bifactor-specific factors only to the extent that factor loadings are proportional. Modeling actual data with violations of proportionality showed that specific and residualized lower order factors are not always highly correlated and have differential correlations with criterion variables even when both models fit acceptably. Because proportionality constraints limit only second-order models, bifactor models should be the first option for hierarchical modeling.

Huth et al. (1) claim that our finding of lower depression rates in larger US cities (2) is “unwarranted.” Their argument is based on an oversight of our fundamental assumption articulating rates of depression to city size: that cities are socioeconomic networks mediated by built environments. Problematically, their analysis is based on a flawed definition of city boundaries. Here, we address each of these points. First, the issue of functional city definitions: The fundamental insight of urban science (3) is that cities are spatially aggregated socioeconomic networks with properties self-consistently shaped by their built infrastructure. Consequently, meaningful spatial city boundaries must capture—in a single unit of analysis—where people live, socialize, and work. In the United States, the well-tested definition that fits these criteria is the metropolitan statistical area (MSA) as delineated by the US … [↵][1]2To whom correspondence may be addressed. Email: andrewstier{at}uchicago.edu or bermanm{at}uchicago.edu. [1]: #xref-corresp-1-1

Abstract Background An ongoing positive revolution advocates a new approach to the individual differences in human emotions, cognitions, and behavior that cause distress and impair functioning. This revolution endorses the long‐proposed, but still unrealized rejection of the medical model, which attributes psychological problems to a sick brain or mind. In addition, it advocates replacing the binary diagnoses used in ICD and DSM, which assume a clear discontinuity between “normal” and “abnormal” functioning, with continuous dimensions of psychological problems. Method Selective literature review. Results and Discussion Seven strong reasons are provided for adopting a dimensional approach.

A polygenic risk score (PRS) for attention-deficit/hyperactivity disorder (ADHD) has been found to be associated with ADHD in multiple studies, but also with many other dimensions of problems. Little is known, however, about the processes underlying these transdiagnostic associations. Using data from the baseline and 1-year follow-up assessments of 9- to 10-year-old children in the Adolescent Brain Cognitive Development™ (ABCD©) Study, associations were assessed between an ADHD PRS and both general and specific factors of psychological problems defined in bifactor modeling. Additionally, prospective mediated paths were tested from the ADHD PRS to dimensions of problems in the follow-up assessment through baseline measures of executive functioning (EF) and two facets of impulsivity: lower perseverance and greater impulsiveness in the presence of surgent positive emotions. Previous findings of modest but significant direct associations of the ADHD PRS with the general factor of psychological problems were replicated in both assessments in 4,483 children of European ancestry. In addition, significant statistical mediation was found from the ADHD PRS to the general factor, specific ADHD, and conduct problems in the follow-up assessment through each of the two facets of impulsivity. In contrast, EF did not statistically mediate associations between the ADHD PRS and psychological problems. These results suggest that polygenic risk transdiagnostically influences both psychological problems and facets of impulsivity, perhaps partly through indirect pathways via facets of impulsivity.

Psychopathology can be conceptualized as a hierarchy of dimensional symptom domains, with common and dissociable neural substrates. While many studies of brain-behavior relationships rely on univariate analyses, multivariate approaches allow us to examine relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate approaches to consider. Each approach has important effects on the interpretation of the results; however, there are few studies illustrating their potential differences.

There is an ongoing revolution in psychology and psychiatry that will likely change how we conceptualize, study and treat psychological problems.­ Many theorists now support viewing psychopathology as consisting of continuous dimensions rather than discrete diagnostic categories. Indeed, recent papers have proposed comprehensive taxonomies of psychopathology dimensions to replace the DSM and ICD taxonomies of categories. The proposed dimensional taxonomies, which portray psychopathology as hierarchically organized correlated dimensions, are now well supported at phenotypic levels. Multiple studies show that both a general factor of psychopathology at the top of the hierarchy and specific factors at lower levels predict different functional outcomes. Our analyses of data on a large representative sample of child and adolescent twins suggested the causal hypothesis that phenotypic correlations among dimensions of psychopathology are the result of many familial influences being pleiotropic. That is, most genetic variants and shared environmental factors are hypothesized to non‐specifically influence risk for multiple rather than individual dimensions of psychopathology. In contrast, person‐specific experiences tend to be related to individual dimensions. This hierarchical causal hypothesis has been supported by both large‐scale family and molecular genetic studies. Current research focuses on three issues. First, the field has not settled on a preferred statistical model for studying the hierarchy of causes and phenotypes. Second, in spite of encouraging progress, the neurobiological correlates of the hierarchy of dimensions of psychopathology are only partially described. Third, although there are potentially important clinical implications of the hierarchical model, insufficient research has been conducted to date to rec­ommend evidence‐based clinical practices.