Behavior Genetics

Neuropsychiatric polygenic indices (NPGIs) are used as genetic predictors of poor mental health. However, NPGIs are also associated with environmental factors which could affect mental health in adulthood, including the rearing environment. Hence, their "genetic" effects are both direct and environmentally mediated. There is a need to identify alternative genetic predictors without environmental signal. Endophenotype-based polygenic indices (EPGIs) trained on brain structure and function are under-studied alternatives which, due to their relative biological proximity, may exhibit associations with mental health outcomes which are less environmentally mediated than those of NPGIs. Using four representative UK samples (Understanding Society; UKHLS, NCDS, BCS70 and MCS) we employ sex-stratified path models to estimate the direct and environmentally mediated effects of eleven NPGIs and 30 EPGIs on adult mental health, focussing on the rearing environment. The depression NPGI is consistently associated with mental health symptoms across most sex-stratified sub-samples (best meta-analysis beta = 0.091, p 0.001) but demonstrates 1.6 - 24.5% environmental mediation. Seven other NPGIs and three EPGIs show sample- and sex-specific associations with mental health symptoms. NPGIs for attention deficit hyperactivity disorder, depression and substance use disorder are robustly associated with measures of the rearing environment, which in turn are frequently associated with mental health symptoms. Sensitivity analyses find that NPGI associations vary substantially depending on who is included in the sample. In conclusion, the rearing environment likely mediates a substantial portion of NPGIs' so-called "genetic" effects on mental health symptoms, but EPGIs are not currently powerful enough to replace them.

Animals fight for resources to obtain fitness benefits; most contests are intrasexual, and males tend to fight more than females. Although the genetic basis of male aggression is well studied, we know little about the genetic variation of female aggression. Female aggression varies with reproductive status and is potentially influenced not only by her genotype, but also by the genotype of her mate. Here we measured both male and female aggression in a set of Drosophila melanogaster inbred lines by competing each line against a standard competitor. Aggression varied among lines for both sexes, but male and female aggression were not correlated. Female aggression for many lines increased with mating, as expected, but not all lines changed aggression. However, when females were mated to males of different lines, male genotype did not affect the post-mating change in aggression, suggesting that ejaculate-mediated effects do not vary across these lines. The aggression level of the standard opponent was positively correlated with that of focal individuals indicating that individuals modulate their behavior according to the genotype of their opponent.

Objective: ADHD has been associated with obesity indicators, including BMI, across the lifespan. A possible mechanism linking ADHD and BMI is binge eating. Previous research has found associations between ADHD, binge eating and BMI. However, the role of genetic and environmental influences on these associations remains unclear. Method: We utilized data from the Twins Early Development Study (TEDS), comprising 3,675 monozygotic and 7,063 dizygotic twin pairs. ADHD symptoms in childhood and adolescence were assessed using parent-reported questionnaires. Adult ADHD symptoms were measured using both self-report and parent-report questionnaires. Phenotypic mediation models examined whether binge eating mediated the association between ADHD and BMI, without controlling for genetic confounding. Subsequently, the etiological architecture underlying the associations among the three traits across childhood, adolescence, and adulthood were investigated by incorporating genetic and environmental influences into the models. Results: Binge eating significantly mediated the association between ADHD symptoms and BMI in both adolescence and adulthood. However, these mediation effects were no longer present once genetic and environmental influences were incorporated into the models. The best-fitting model in childhood, adolescence and adulthood was Cholesky decomposition models, where covariance between traits was explained by shared aetiology. Conclusions: This twin study reveals shared liability across ADHD, binge eating, and BMI. The mediating role of binge eating in the relationship between ADHD symptoms and BMI was largely confounded by shared genetic influences. Intervention strategies could focus more on common underlying behavioural and self-regulatory mechanisms across these traits, as well as placing more emphasis on symptom patterns within families.

BackgroundEarly adolescence is a common period of onset for depressive symptoms. In part, this may reflect a developmental manifestation of individuals genetic propensities as they undergo physiological and hormonal changes and interact with new environments. Many commonly proposed mechanisms assume direct effects of an individuals own genes on emerging variation in their depressive symptomatology. However, estimates of genetic influence based on analyses in unrelated individuals capture not only direct genetic effects but also genetic effects from parents and other biologically related family members. AimIn data from the Norwegian Mother, Father and Child Cohort (MoBa), we used linear mixed models to distinguish developmentally-stable and adolescence-specific direct and parental indirect genetic effects. We examined effects of polygenic scores for major depressive disorder (MDD), ADHD, anxiety disorders, and educational attainment (EA) on depressive symptoms, which were assessed by maternal reports at ages 8 and 14. ResultsChildrens own MDD polygenic scores showed adolescence-specific effects on depressive symptoms ({beta}PGS*wave=0.041, [95% CI: 0.017, 0.065]). Developmentally-stable direct effects from childrens polygenic scores for MDD ({beta}=0.016, [0.006, 0.039]), ADHD ({beta}=0.024, [0.008, 0.041]) and EA ({beta}=-0.02, [-0.038, -0.002]) were also evident. The only evidence of indirect genetic effects was a stable effect of maternal EA polygenic scores ({beta}=0.04, [0.024, 0.054]). ConclusionDirect genetic effects linked to genetic liability to MDD accounted for emerging variation in depressive symptoms in adolescence. These results imply that specific etiological mechanisms related to MDD may become particularly relevant for depressive symptoms during early adolescence compared to at earlier ages.

SNP-heritability is defined as the fraction of variance of a trait that is explained by the SNPs in a genome-wide association study. Several methodologies have been proposed to estimate this quantity. More recent methods aim to do so with ancestrally diverse datasets and yet obtain a single heritability for an entire dataset, which we refer to as marginal heritability. However, the different underlying subpopulations that compose a genetically diverse dataset might have different environmental and genetic exposures, and thus may have different heritabilities. In order to address this, we propose a conditional SNP-heritability approach that allows to estimate multiple SNP-heritabilities on a dataset corresponding to different ancestral compositions and environmental exposures. We take a careful statistical approach, including estimation of conditional genetic and environmental variances, and calculation of standard errors via a combination of the delta method with bootstrapping. We validate our method via extensive simulations. We then apply it to an ancestrally and socio-economically diverse dataset of 6603 subjects aged around 9 to 11 from the Adolescent Brain Cognitive Development study, and illustrate how the SNP-heritability of intelligence scores can change due to differing extrinsic variances in different socio-economic groups, which coincides with previous work in the literature. This conditional estimation approach can be a valuable tool for understanding differences in risks across subpopulations. Our work here improves on existing methodology and allows us to leverage the heterogeneity of the data to obtain new insights.

ObjectiveObsessive-compulsive disorder (OCD) frequently co-occurs with bipolar disorder (BD) or schizophrenia (SCZ), and, importantly, can often precede their onset. However, the genetic architecture and directionality underlying these relationships remain unclear. We leveraged large-scale genome-wide association study (GWAS) data to examine shared genetic architecture and directional relationships among OCD, BD and SCZ, and used major depressive disorder (MDD) as a comparator. MethodsUsing linkage disequilibrium score regression (LDSC), MiXeR, and Generalized Summary-data-based Mendelian Randomization (GSMR) as well as complementary Mendelian randomization approaches, we characterized genetic correlations, polygenic overlap (Dice coefficient), and effect direction concordance ({rho}{beta}) across disorders. ResultsWe observed substantial genetic correlations between OCD and BD (rg=0.37), BD type 2 (BD2) (rg=0.54), and SCZ (rg=0.39), with a large proportion of shared causal variants between OCD and both BD (Dice=0.85) and SCZ (Dice=0.84). MiXeR analyses indicated that OCD and BD2 share a smaller proportion of causal variants (Dice=0.57) but there is a high concordance of effect directions amongst these causal variants ({rho}{beta}=0.96), whereas OCD and MDD showed minimal overlap but strong concordance among shared variants (Dice=0.09, {rho}{beta}=1). Directional GSMR and complementary TwoSampleMR analyses supported a causal effect of genetic risk to OCD on liability to BD (b=0.20, p=1.5x10{square}{square}), SCZ (b=0.52, p=9.5x10{square}{superscript 2}{superscript 1}), and MDD (b=0.24, p=1.06x10{square}{square}), with little evidence for reverse causal effects. ConclusionsTogether, these findings indicate that genetic liability to OCD can represent an early component of transdiagnostic psychiatric risk, with implications for understanding and potentially predicting the emergence of broader psychopathology across the life course.

Standard genome-wide association studies (GWASs) are vulnerable to confounding factors, including stratification, assortative mating, and dynastic effects. Family studies such as sibling-based GWAS (sib-GWAS) mitigate such confounding and are becoming the tool of choice for teasing apart direct genetic effects--causal effects of ones genotype on ones own phenotype-- from other factors. However, due in part to their smaller sample sizes, sib-GWAS allelic effect estimates are substantially more variable than standard (i.e., population-based) GWAS estimates. The quantification of this uncertainty is essential for many uses of sib-GWAS, including polygenic scoring, causal inference (e.g., Mendelian randomization), disentangling direct from indirect familial effects, and measuring assortative mating. Here, we investigate sources of uncertainty in sib-GWAS allelic effect estimators. We study their impacts on the biases of three uncertainty measurement methods, including two that are commonly used and a new resampling-based approach we propose. We find that heterogeneity in allelic effects or heteroskedasticity across families (e.g., due to variation in genetic backgrounds or environments) can bias existing methods, and that this bias is more severe for small samples and rare variants. In contrast, the resampling-based approach we propose is approximately unbiased under all scenarios we considered. We validate our theoretical predictions, as well as the importance of effect heterogeneity and heteroskedasticity, using simulations and empirical analysis in the UK Biobank. In sum, this study helps understand the sources of uncertainty in family-based genotype-phenotype association studies and provides a robust method to estimate uncertainty.

BackgroundScreen use is pervasive in childhood and adolescence, yet its role in antisocial behaviour (ASB) remains uncertain. While cross-sectional studies consistently link higher screen use to elevated ASB, longitudinal evidence is mixed, and few studies have controlled adequately for prior behaviour and genetic liability. Thus, it remains unclear whether these associations reflect prospective influences of screen exposure, or underlying vulnerabilities shared with ASB. We investigated whether screen use is a modifiable risk factor or a marker of underlying vulnerability. MethodsWe analysed data from up to 41,562 children in the Norwegian Mother, Father, and Child Cohort Study (MoBa). ASB traits and ICD-10-based conduct disorder (CD) diagnoses were assessed at ages 5, 8 and 14 years, together with screen use (total exposure and modality). Cross-sectional logistic regression models examined associations between screen use and ASB traits/CD at each age, adjusting for sex and parental education. Polygenic risk scores for ASB (PRSASB) were used to assess genetic susceptibility and gene-environment interplay. Lagged logistic models tested whether screen use predicted later ASB, adjusting for prior ASB. Linear mixed-effects models examined developmental patterns across age. ResultsHigher screen use was positively associated with ASB traits and CD across all ages, with dose-response patterns across screen-use modalities. Social media showed the strongest modality-specific association at adolescence. In lagged models, screen use did not predict later ASB after adjustment for prior ASB. Longitudinal models showed significant but attenuating associations across development. PRSASB was independently and additively associated with ASB outcomes but did not interact with screen use. ConclusionsWe found that higher screen use was consistently associated with antisocial outcomes across childhood and adolescence. However, the absence of prospective associations after accounting for prior behaviour, together with independent genetic contributions, suggests that screen use may be better understood as a marker of underlying vulnerability rather than an independent driver of antisocial development.

Importance: Avoidant/restrictive food intake disorder (ARFID) is a feeding and eating disorder characterized by extremely restricted dietary variety and/or quantity resulting in significant physical health impairment and psychosocial dysfunction. ARFID frequently co-occurs with neurodevelopmental conditions, yet the extent to which this co-occurrence reflects shared genetic or environmental influences remains largely unknown, as few twin or genetic studies of ARFID have been conducted. Objective: To examine the extent to which genetic and environmental influences contribute to the association between a broad ARFID phenotype and neurodevelopmental traits. Design, Setting, and Participants: Population-based twin study using data from the Child and Adolescent Twin Study in Sweden, including 30,374 twins born 1992-2008. Main Outcomes and Measures: A broad ARFID phenotype was identified using a composite measure derived from parent reports and national health registers between ages 6 and 12 years. Parents completed measures of neurodevelopmental traits at age 9 or 12 years, including autism (subdomains: social communication problems and restricted/repetitive behaviors), attention-deficit/hyperactivity disorder (ADHD, subdomains: inattention and impulsivity/hyperactivity), tic disorders, learning disorders, oppositional defiant disorder, conduct disorder, obsessive-compulsive disorder (OCD), sensory perception problems, and sleep problems. Phenotypic associations were estimated using polyserial correlations. Bivariate twin models decomposed variance and covariance into genetic and environmental components. Results: Phenotypic correlations with the broad ARFID phenotype ranged from 0.18 (95% CI: 0.15-0.21) for OCD to 0.36 (95% CI: 0.33-0.38) for autism. Broad genetic correlations (rH; additive plus dominant genetic influences) ranged from 0.27 (95% CI: 0.21-0.33) for conduct disorder to 0.52 (95% CI: 0.44-0.60) for autism-restricted/repetitive behaviors. Genetic factors explained 77% to 95% of all phenotypic correlations. Non-shared environmental correlations were minimal to small, with the largest observed for autism (0.17; 95% CI: 0.08-0.26). Conclusions and Relevance: The broad ARFID phenotype shares substantial genetic influences with a number of neurodevelopmental traits. These findings suggest that the frequent co-occurrence of ARFID with neurodevelopmental traits largely reflects shared genetic influences rather than overlapping environmental influences, supporting the conceptualization of ARFID within a broader neurodevelopmental framework.

Impaired learning that both novel and previously dangerous stimuli are safe (safety and extinction learning, respectively) are long standing, robust, and heritable features of anxiety disorders, representing potential endophenotypes. The computational mechanisms underpinning them have demonstrated associations with anxiety severity in recent studies. We undertook a pre-registered replication in a tenfold larger independent sample of twins (n = 925). Extinction learning rates were associated with anxiety severity ({rho}replication = -0.14, BFr0 = 1189. 67) but safety learning rates were not. Conversely, although safety learning rates showed modest heritability (h2safety = 0.16), extinction learning rates were not heritable. Accordingly, we were unable to identify genetic overlap between anxiety and either learning rate. Although this suggests neither learning rate is an anxiety endophenotype, we confirmed a cognitive-behavioral mechanism underpinning a robust marker of anxiety severity. Furthermore, we demonstrated heritability of a computationally modelled learning parameter, a key step towards establishing its biological basis.

Background: In prospective cohort studies, where an exposure is collected repeatedly, interest often lies in determining whether the timing of that exposure has a differential effect on a later outcome. The Structured Life Course Modeling Approach (SLCMA), where users select between temporal hypotheses of exposure specified a priori, provides one way to analyse such longitudinal data. However, few studies using SLCMA consider the effect of time-varying covariates (TVC) which may impact associations. Methods: We present a modified version of the SLCMA - called direct and mediated effects (DME)-SLCMA - which corrects for TVC. We first develop the DME-SLCMA method, test it through simulation, and apply it to psychosocial data from the Drakenstein Child Health Study (DCHS, n=336) to investigate relationships between maternal psychopathology, TVC of socioeconomic status, and offspring depressive symptoms. Results: We found that, on average, offspring depressive symptoms score increased by 3.9% (95% CI: 1.0%-6.9%, p = 0.039) for each unit of maternal psychopathology (SRQ) at 48 months whilst adjusting for time-varying socioeconomic status (at 18, 30, 42 and 54 months). Our simulations identified several realistic scenarios where selections ignoring TVC - with TVC mediated exposure effects present - were prone to be incorrect, including our DCHS example. Conclusion: DME-SLCMA is a robust new approach for life course modelling in the presence of time-varying covariates. We recommend adjusting for TVC whenever possible, and, when not possible, our simulation study identified that scenarios where mediated effects are comparable, or greater, in magnitude to direct effects are most prone to confounding.

Abstract Background Gene dosage disorders impact cognition and psychopathology, but outcomes vary widely amongst carriers of the same variant. Recent work has sought to better predict proband outcomes using measures of corresponding traits in family members. However, family-based models have not yet been prospectively quantified across several traits in different genetic disorders, nor evaluated for the precision they afford: both crucial issues for clinical implementation. Methods In a first test case for these questions, we apply regression analyses to quantify and compare family-based prediction of 12 traits (including IQ, autism- and ADHD-related traits) in 433 individuals from families including a proband with XXY or XYY syndrome (N=93 and 58, respectively). Results The 12 traits vary substantially in their proband-family associations (0.001<|r|<0.55) - with differences emerging between XXY and XYY syndrome. Only two traits also show significant and similar proband-family associations in both aneuploidies, with the greatest concordance found for IQ. A family-based model for IQ prediction in male sex chromosome trisomies significantly reduces error vs. a group mean IQ model (F = 7.4, p = 0.006), but only in 65% of probands, and with mean error reduction of ~2 IQ points. Conclusions Family-based prediction of neuropsychiatric traits in genetic syndromes likely requires trait- and syndrome- specific models. Family models can significantly improve outcome prediction for IQ, but to variable degrees across individuals and with a small mean improvement. By mapping and quantifying these limits, our work helps draft a roadmap for refinement of family-based prediction of proband outcomes in gene dosage disorders.

Autism spectrum disorder (ASD) is estimated to be up to four times as common in males as in females, yet the causes of this prevalence difference are not well established. One possible driver is genetic variation on the X chromosome, as it contains genes capable of contributing to ASD (e.g., PTCHD1, MECP2) and is known to play a role in genetic disorders with differential sex prevalence (e.g., color blindness). However, a lack of power compared to the autosomes combined with the complexities of modeling its biology have led to the X being largely overlooked in sequencing studies. Here, we develop quantitative X-linked TADA, a new model designed specifically for application to this chromosome, and use it to analyze rare variation from 50,663 individuals with ASD (and 136,670 individuals total). We find 9 genes on the X associated with ASD at a false discovery rate (FDR) < 0.05 and an additional 9 genes at FDR < 0.2, with many of these previously identified as involved in specific neurodevelopmental disorders. Point estimates of the liability conferred by de novo variants on the X are similar in females and males, with both sexes estimates elevated >20% above the corresponding autosomal values. We also develop a general theory of how X-linked variation of any additive or non-additive effect influences liability and describe its implications for prevalence. Using this theory and our empirical results, we show how genetic variation on the X could contribute to the sex-differential prevalence of ASD.

Population fertility patterns are closely linked to socioeconomic inequality, with educational attainment (EA) being a key predictor of completed fertility. While EA is partially heritable, the extent to which EA-associated genetic variation relates to fertility independently of education remains unclear, particularly outside Western European and North American populations. Using data from [~]40,000 women and [~]10,000 men in the Estonian Biobank, we examine sex-specific associations between EA polygenic scores (PGSEA) and completed fertility. We extend prior work by distinguishing cognitive and non-cognitive EA components, accounting for age at first pregnancy (AFP), and applying within-family analyses to assess the role of direct genetic effects. Among women, PGSEA is negatively associated with fertility, with a significantly stronger association for the non-cognitive than the cognitive EA polygenic score. The association between PGSEAand fertility is moderated by EA and changes sign across AFP strata, from negative among women with earlier AFP to positive among those with later AFP. Importantly, this association is not attenuated in within-family models, consistent with a predominant role of direct genetic effects. Among men, associations are weak or slightly positive and stable across education groups. Overall, EA-related genetic variation is associated with fertility through pathways that appear largely independent of educational attainment, suggesting that shared genetic influences operate through multiple mechanisms that differ by sex and reproductive timing. SignificanceEducational attainment is closely linked to completed fertility, yet the mechanisms behind this relationship remain not fully understood. Using a population-based cohort from Estonia, we show that genetic variants associated with education relate to fertility in markedly different ways for women and men and that these associations cannot be explained by education level alone. Differences between cognitive and non-cognitive education-related genetic components further point to multiple life-course pathways linking genetics and reproduction. Family-based analyses suggest that these associations are largely consistent with direct genetic effects and not driven by correlated family environments. Together, our findings suggest that education-related genetic variation shapes fertility through multiple sex-specific and life-course-dependent pathways, rather than acting solely through educational attainment.

Testing for Hardy-Weinberg equilibrium (HWE) is a fundamental component of genetic data analysis, widely used for quality control and model validation. Although HWE testing is well established for autosomal loci, inference on the X chromosome is more complex due to sex-specific genotype structures and potential sex differences in minor allele frequency (sdMAF). Existing tests differ in their assumptions about sdMAF and male sample inclusion, often leading to distinct but poorly characterized null hypotheses. We develop a general statistical framework for HWE inference using the robust allele-based regression model. By formulating HWE testing as an assessment of allele-level dependence, the framework directly parameterizes Hardy-Weinberg disequilibrium, unifies existing Pearson{chi} 2-based tests under explicit modeling assumptions, and clarifies their null hypotheses, degrees of freedom, and sensitivity to sdMAF. The framework also accommodates covariate and population-structure adjustment within a unified regression-based formulation. The proposed framework provides robust, interpretable, and flexible inference, establishing a unified statistical foundation for HWE testing across autosomal and X-chromosomal regions. Simulation studies and analysis of high-coverage 1000 Genomes Project data demonstrate that commonly used X-chromosome tests can exhibit inflated type I error or misleading inference when sdMAF is present.

Polygenic risk scores (PRS) improve progressively as genome-wide association studies (GWAS) increase in sample size and ancestral diversity, yet the effect of successive GWAS releases on individual PRS rankings remains poorly characterised. Here, we quantify how individual PRS rankings change across GWAS releases, whether those changes favour cases over controls, how consistently individuals maintain their relative position, and whether those in high-risk strata retain that classification over time. Using PRS derived from four GWAS releases for bipolar disorder, major depressive disorder, and schizophrenia in three Australian cohorts, we observed widespread bidirectional reclassification that exceeded the theoretical minimum of expected reclassification, and was directionally consistent with case-control status when discriminative performance improved. Rank variability was substantial and uniformly distributed across all levels of risk, rank persistence was limited across releases, and retention of high-risk classifications was variable across disorders and largely accounted for by the inter-release correlation. These findings demonstrate that individual PRS rankings are dynamic and shaped by progressive improvements in effect-size estimates, carrying important implications for PRS-based risk stratification strategies that rely on stable classifications in psychiatric research and clinical practice.

Differences in physical features and disease prevalence between men and women are examples of sexual dimorphisms. However, sex differences can manifest not only in trait means but also in how strongly risk factors are linked to diseases (e. g. BMI to cardiovascular disease), a question heavily under-researched. To fill this gap, we set out to identify sex differences in phenotype correlations (rP) and decompose them into genetic (rG) and environmental (rE) contributions. Our analysis revealed 250 trait pairs with significant sex-different phenotypic correlations in the UK Biobank. Overall, we observed a predominance of environmental contributions to sex-different effects: 182 trait pairs (73%) exhibited exclusively sex-different rE, while 68 (27%) showed sex differences in both rE and rG, and no trait pair was affected solely by sex-specific rG. For example, we detected sex-different environmental correlation between C-reactive protein and BMI (rE(men) = 0.07 vs rE(women) = 0.25), but no sex-difference in genetic correlation. On the contrary, glycated haemoglobin and LDL cholesterol showed genetic correlation only in women (rG(women) = 0.17; 95% CI = [0.1, 0.23]), but environmental correlation only in men (rE(men) = -0.18; 95% CI = [-0.19, -0.16]). Some of the observed sex differences - including those involving testosterone, SHBG, urate, waist-hip ratio, and triglycerides - may reflect underlying sex-specific genetic architectures, as evidenced by low between-sex genetic correlations. In conclusion, environmental factors are the predominant contributors to sex differences in phenotypic correlations between complex traits, with modest detectable contributions from sex-specific genetic architectures. Recognising these patterns can inform the development of more effective, sex-informed interventions.

Although female rats are typically less aggressive than male rats, lactating females will vigorously defend their nests and pups against an intruder. Much attention has been directed at the consequences of this aggression on the intruder and less on the consequences for the mother and her subsequent interactions with her pups. Here, we exposed resident Sprague-Dawley dams to the resident-intruder paradigm twice daily for five consecutive days, beginning when the dam's (RES) pups were 7 days old, to assess social stress effects on maternal behavior and neurobiology. Controls were dams that had time-matched (TMC) separation from their pups but were not exposed to intruders, and naive moms which were never separated nor exposed to an intruder (CTL). We assessed the dam's subsequent behavior and interactions with her pups on Day 1 and Day 5, and Fos expression after Day 5 in select regions of the prefrontal cortex, amygdala, hypothalamus and periaqueductal gray of the midbrain. In separate cohorts, after pups were weaned, the dams underwent restraint stress and plasma corticosterone assayed. PCA analysis of the dam's behaviors identified three components: normal self-focused behaviors; nurturing behaviors and rough non-nurturing behaviors. Relative to CTL, RES dams exhibited more disrupted behaviors towards their pups, including, rough transport, stepping on pups, and flinging/tossing pups around the cage. In contrast, TMC Dams showed some, but fewer changes relative to CTL, suggesting that separation from pups alone does not account for all disrupted behavior in RES dams. The bulk of these behavioral effects occurred in the first 5-10 min after reunion with the pups and were seen on both the first and fifth day of testing. Of the brain regions examined, the prefrontal cortex was activated by both the defeat/intruder stress (RES) and separation stress (TMC), whereas the dorsal PAG was activated specifically by the defeat/intruder stress. The medial and basolateral amygdala exhibited differential neuronal activity between the RES defeat/intruder-exposed dams and the other two groups. The RES moms exhibited an insufficient adrenocortical response to acute restraint stress. The results suggest that amygdala-dPAG activity is important for dissociating disrupted maternal care in RES (due to defense of the nest against an intruder) from simple pup separation, both of which activate the mPFC. The experience of repeatedly defending the nest may induce subsequent disruptions in HPA responses. The amygdala-dPAG pathway may regulate aspects of stress and emotional regulation exhibited by mothers who defend their offspring against intruders.

Impulsivity is a core dimension of ADHD and a transdiagnostic vulnerability factor for a wide range of adverse psychiatric and somatic outcomes, that could be mitigated through more effective screening of at-risk individuals. However, laboratory-based measures of impulsivity show weak convergence across paradigms and limited prediction of real-world behavior, constraining their utility. We tested whether combining repeated ecological assessment with computational modeling of response-time (RT) dynamics improves measurement of impulsivity and its cross-paradigm validity. Sixty participants, including adolescents with ADHD, individuals with 22q11.2 deletion syndrome, and healthy controls, completed a total of 1347 smartphone-based Balloon-Analogue-Risk-Task (D-BART) assessments repeatedly in daily life, alongside a single-session Conners CPT-3. RT was modeled using linear mixed-effects models as a function of objective risk and subjective uncertainty, with random effects capturing between- and within-person variability. Dynamic RT parameters were integrated with conventional performance metrics and related to CPT-3 variables using partial least squares analysis. External validity was evaluated against parent-rated behavioral symptoms. RT increased with both risk and uncertainty, consistent with adaptive modulation of speed-accuracy trade-offs. These effects varied substantially across individuals and repeated assessments. Dynamic RT parameters differentiated clinical from control participants, whereas traditional aggregate metrics did not. A PLS latent component linked D-BART and CPT-3 patterns and was associated with real-world hyperactivity/impulsivity, whereas CPT-3-derived scores alone were not. Experimental manipulation of ecological sampling density directly impacted D-BART predictive accuracy. These findings show that ecological repetition combined with parsimonious RT-dynamics modeling enhances construct validity, cross-paradigm convergence, and behavioral relevance of impulsivity measures, providing a scalable framework for capturing dynamic cognitive-control processes.

Objective: Attention-deficit/hyperactivity disorder (ADHD) is clinically and etiologically heterogeneous, and diagnostic decisions may benefit from integrating multiple sources of information. We developed an explainable deep learning approach to test whether genetic, environmental, cognitive, demographic, and temperament data could classify ADHD diagnosis and identify features contributing to model decisions. Method: We analyzed participants from the Oregon ADHD-1000 cohort split into training, validation, and test subsets. We trained modular neural network models classifying ADHD case-control status using SNP-level genotype data with biological annotations, polygenic scores, demographics, parenting and family conflict, stress and trauma, geocoded measures, cognitive task measures, temperament factor scores, and missingness indicators. Hyperparameter optimization selected model architecture and feature block inclusion. We evaluated model performance using AUC, precision-recall curves, calibration analyses, prediction certainty analyses, and decision curve analysis. We used integrated gradients to quantify block-level, feature-level, and individualized feature importance. Results: The best model using temperament features had an AUC of 0.97 in the held-out test subset, with high accuracy, sensitivity, and specificity and a Brier score of 0.06. The best model excluding temperament had an AUC of 0.75. Feature importance analyses highlighted temperament, demographic, and cognitive domains in the temperament-inclusive model. Individualized explanations showed that prediction drivers varied across participants and could help reveal conflicting or supporting evidence across domains. Conclusion: Explainable, multi-modal classification models can integrate heterogeneous ADHD-relevant information and identify features that contribute to individual predictions. These types of models may advance ADHD risk modeling research and clinician-led decision support, especially in complex or diagnostically uncertain cases.