S. Alexandra Burt

Background: Prior work suggests girls with earlier subjective pubertal timing (i.e. girls who perceive themselves as maturing earlier than peers) engage in more antisocial behavior (ASB). Theories regarding mechanisms predominantly focus on social/environmental processes, but genetic influences may contribute, particularly given that ASB is heritable, and the stress associated with earlier subjective timing may activate or enhance these genetic vulnerabilities to increase risk. To date, no study has examined these gene × environment interactions (G×E) for ASB and subjective pubertal timing relative to peers. Methods: Participants included 995 female twins (ages 8–16 years) from the MSU Twin Registry. Mothers reported children’s aggressive and rule-breaking behaviors using the Child Behavior Checklist. Subjective timing was assessed with a modified Pubertal Development Scale where twins reported their maturation on pubertal markers (e.g. breast development) as earlier, the same, or later compared to two reference groups: their co-twin and other girls their age. Results: Twin models revealed that genetic influences on aggression and rule breaking were stronger in earlier versus on-time and later-maturing girls. These effects were present when examining timing relative to co-twin and other girls, although effects were somewhat stronger for co-twin. Environmental influences on aggression and rule breaking were greater in later-maturing girls across both reference groups. Conclusions: Results significantly inform etiologic models by highlighting G×E for associations between subjective timing and ASB in girls. Findings underscore the importance of considering genetic influences and the need to further explore the specific genetic/environmental factors that shift across perceptions of pubertal timing.

Background: Multiple dimensions of parenting–including hostility, conflict, and warmth–are associated with an array of mental disorders in youth. We tested two possible explanations for these associations: Parenting may shape a shared liability to experience many forms of psychopathology (“shared‐liability” hypothesis), or parenting may shape unique liabilities to externalizing and internalizing psychopathology (“unique‐liability” hypothesis). Methods: Participants were from the Early Growth and Development Study (EGDS; N = 561 adoptive families) and the Michigan Twin Neurogenetics Study (MTwiNS; N = 354 twin families). In EGDS, we measured parental hostility and warmth at child age 27 months. In MTwiNS, we measured parent‐child conflict and parental involvement at child mean age 8.1 years. We extracted shared and unique liabilities from child‐psychopathology data using structural equation models. We tested whether parenting longitudinally predicted psychopathology, and to what extent associations between parenting and children's externalizing and internalizing psychopathology liabilities were explained by associations with a general psychopathology liability (“p”). We addressed genetic confounding by testing associations of adoptive parents' parenting with psychopathology in their genetically unrelated children, and by using a twin‐difference design. Results: Across ages and cohorts, parenting characterized by hostility and conflict was associated with greater p, consistent with the shared‐liability hypothesis. Also consistent, parental warmth was associated with lower p in middle and late childhood in EGDS participants, though parental involvement was not associated with p in adolescent MTwiNS participants. There was limited support for the unique‐liability hypothesis; only one association (with externalizing psychopathology) remained after accounting for p. In childhood, but not adolescence, findings remained after accounting for genetic confounding. Conclusion: Parenting behaviors, particularly hostility and conflict, are associated with children's broad vulnerability to psychopathology, and these associations are evident in childhood even after controlling for genetic influences. These findings support parenting interventions as a means to reduce children's liability to diverse mental‐health problems.

Although positive parenting in childhood consistently predicts less adolescent antisocial behavior (ASB) over time, the etiology and direction of their association remain unclear. To fill this gap, we sought to illuminate prospective associations and potential changes in the etiology of the relationship between maternal positive parenting and youth ASB from middle childhood to adolescence using a cross-lagged, twin differences design. Participants were drawn from a longitudinal study with planned missingness (1,422 twins ages 6–11 at Wave 1 and 852 twins ages 11–19 at Wave 2) within the Michigan State University Twin Registry. Phenotypic analyses indicated that more childhood ASB significantly predicted less positive parenting in adolescence, but not the reverse. Twin difference-score analyses similarly revealed that, within both monozygotic (MZ) and dizygotic (DZ) twin pairs, co-twin differences in the positive parenting received in middle childhood did not significantly predict differences in adolescent ASB. However, DZ (but not MZ) co-twin differences in childhood ASB significantly predicted differences in positive parenting during adolescence, whereby the DZ co-twin exhibiting more ASB in childhood received less positive parenting in adolescence. These results collectively suggest a child-driven, genetically mediated effect of early ASB on later positive parenting, such that genetic influences on ASB in middle childhood appear to evoke reductions in positive parenting during adolescence. Moreover, because effect sizes did not differ across zygosity, results also suggest that shared familial/environmental confounds at least partially underlie the association. These findings have important implications for interventions and etiologic models of youth ASB across development.

The brain matures rapidly during childhood and adolescence. The environment may calibrate the pace of this process to shape cognition and mental health. Extending its utility as a risk marker from older to younger populations, brain age has been proposed to capture relative brain maturity in youth. Multiple algorithms have been developed to estimate brain age in predominantly White advantaged adults. Whether these models are useful in youth, particularly in more representative cohorts, remains unclear. Here, we systematically compare five influential algorithms (Drobinin, Whitmore, Pyment, Kaufmann, Centile) in two population‐based youth cohorts as a benchmark for future applied research. We examined (a) prediction accuracy (correlation with chronological age, mean absolute error), (b) sensitivity to scanning parameters (acquisition sequence, image quality), demographics (sex, puberty), and genetic similarity (intraclass correlations in pairs of monozygotic twins), and (c) strength of convergence between algorithms. In our primary sample of twins recruited from birth records to represent families in disadvantaged neighborhoods ( N = 593; 9–19 years), three algorithms (Drobinin, Pyment, Centile) exhibited strong predictions from structural MRI data (correlations with chronological age = 0.51–0.68, mean absolute error = 1.60–3.02). These algorithms also generated correlated brain age values and gaps, and the expected pattern of strong but not identical intraclass correlations in monozygotic twins. Pyment exhibited the strongest correlation with age and was not sensitive to acquisition sequence, image quality, sex, and puberty. In a second sample of predominantly Black, low‐income youth with a narrow age range ( N = 198; 15–17 years), these five algorithms exhibited weak predictions. This study raises critical questions about what “brain age” means, how it can best be estimated depending on the research question and study population, and whether it can be universally applied across samples with heterogeneous backgrounds and age ranges that are narrow or misaligned with the training data.

Conduct disorder (CD) is the leading global cause of mental health burden in children and adolescents and has recently been hypothesized to be a neurodevelopmental disorder. Although prior research has identified neuroanatomical differences associated with CD, it remains unclear whether these differences reflect atypical brain development. Here, we investigated the difference between an individual’s brain age and chronological age as a proxy for variations in brain maturation. Using a pretrained model, we estimated brain age from structural neuroimaging data obtained from 1,119 youth with CD and 1,183 typically developing controls across 14 international cohorts participating in the ENIGMA-Antisocial Behavior Working Group. Youth with CD exhibited a statistically robust but small acceleration in brain age compared to typically developing youth (around 0.50 years), which was restricted to the adolescence-onset subtype of the disorder. Our large-scale, coordinated analysis provides the first evidence of accelerated neurodevelopment as a potential mechanism underlying CD.

Background: Girls with predispositions for disordered eating (DE) may select into weight-conscious peer groups (i.e. peer groups that emphasize body weight/shape). However, factors driving selection into these peer groups remain unknown, as genetic and/or environmental predisposition to DE may lead girls to select weight-conscious peers. To explore what may drive selection, the present study investigated whether genetic or shared environmental influences underlie associations between DE and exposure to weight-conscious peers and whether effects differ by pubertal status. Methods: Participants included 833 female twins (ages 8–15) from the Michigan State University Twin Registry. Bivariate twin models were conducted to explore etiologic overlap between DE and exposure to weight-conscious peers. Separate models were run for pre-early pubertal girls and mid-late pubertal girls given past research demonstrates differences in genetic and environmental contributions underlying eating pathology by pubertal status. Results: During pre-early puberty, shared and non-shared environmental correlations accounted for the overlap between DE and weight-conscious peer group exposure. Furthermore, shared environmental and non-shared environmental influences underlying DE contributed to 33.3% and 20.0% of the individual differences in weight-conscious peer group membership, respectively. In mid-late puberty, the genetic and non-shared environmental correlations accounted for the overlap between DE and weight-conscious peer group exposure. Genetic and non-shared environmental influences underlying DE contributed to 37.5% and 19.4% of the variance in weight-conscious peer group membership, respectively. Conclusions: While selection effects may exist across development, these effects may be driven by variance in DE due to shared environment in pre-early puberty and genes in mid-late puberty.

Introduction: Ovarian hormones (i.e., estradiol, progesterone) show robust phenotypic associations with binge eating and related behaviors (e.g., emotional eating) in females. Recent research suggests these associations may be due to ovarian hormone effects on genetic and environmental influences on dysregulated eating. However, no research has yet examined how within‐person fluctuations in hormone levels impact genetic/environmental influences on day‐to‐day changes in dysregulated eating from an individual's own mean. This omission is critical because phenotypic associations between ovarian hormones and dysregulated eating are strongest at a within‐person level in adult women, suggesting potentially unique and important within‐person effects. Method: Participants from same‐sex female twin pairs ( N = 468) from the MSU Twin Registry provided measures of ovarian hormones and emotional eating daily for 49 consecutive days. We used continuous twin moderation models to examine how within‐person centered estradiol and progesterone impacted genetic/environmental influences on within‐person shifts in emotional eating. Results: Within‐person fluctuations in estradiol and progesterone significantly moderated genetic influences on within‐person changes in emotional eating. Stronger genetic influences were observed when the ratio of within‐person estradiol to progesterone was in the moderate range. Conclusion: Cyclic, within‐person changes in ovarian hormones may contribute to phenotypic changes in dysregulated eating across the menstrual cycle by dynamically regulating expression of underlying genetic risk. Genetic influences may be particularly pronounced under the hormonal conditions characteristic of the mid‐luteal phase (i.e., moderate estradiol and progesterone).

Epigenetic processes, such as DNA methylation, show potential as biological markers and mechanisms underlying gene-environment interplay in the prediction of mental health and other brain-based phenotypes. However, little is known about how peripheral epigenetic patterns relate to individual differences in the brain itself. An increasingly popular approach to address this is by combining epigenetic and neuroimaging data; yet, research in this area is almost entirely comprised of cross-sectional studies in adults. To bridge this gap, we established the Methylation, Imaging and NeuroDevelopment (MIND) Consortium, which aims to bring a developmental focus to the emerging field of Neuroimaging Epigenetics by (i) promoting collaborative, adequately powered developmental research via multi-cohort analyses; (ii) increasing scientific rigor through the establishment of shared pipelines and open science practices; and (iii) advancing our understanding of DNA methylation-brain dynamics at different developmental periods (from birth to emerging adulthood), by leveraging data from prospective, longitudinal pediatric studies. MIND currently integrates 16 cohorts worldwide, comprising (repeated) measures of DNA methylation in peripheral tissues (blood, buccal cells, and saliva) and neuroimaging by magnetic resonance imaging across up to five time points over a period of up to 21 years (N _{pooled DNAm} = 12,877; N _{pooled neuroimaging} = 10,899; N _{pooled combined} = 6074). By triangulating associations across multiple developmental time points and study types, we hope to generate new insights into the dynamic relationships between peripheral DNA methylation and the brain, and how these ultimately relate to neurodevelopmental and psychiatric phenotypes.