Although adverse childhood experiences (ACEs) increase risk for mental illness at the population level, existing ACEs screens are less helpful in forecasting individual outcomes, suggesting they may not capture significant elements of childhood adversity. We have previously identified unpredictable parental and household experiences as an ACE that portends poorer cognitive and mental health. However, the contribution of unpredictability to established ACEs in real-world settings is unknown. Here, leveraging existing ACEs screening in California, we added the five-item Questionnaire on Unpredictability in Childhood (QUIC-5) in 19 pediatric clinics spanning broad sociodemographic constituencies and compared in ~30,000 children the link of each screen with mental health diagnoses. Scores on either the ACEs or QUIC-5 associated with probabilities of depression, externalizing symptoms, sleep disorders, anxiety and somatic symptoms. Each screen provided unique contributions and combining them often doubled the strength of associations. For depression and sleep disorders, the QUIC-5 identified vulnerable individuals missed by ACEs screen, improving risk detection and facilitating future interventions.

Reward seeking is an essential component of human behavior. Anhedonia (reduced motivation for and pleasure in reward) is a core feature of several mental illnesses. Early life adversity associates with later impairments in reward behaviors and related psychopathology; however, specific aspects of early adversity that lead to disrupted reward behaviors are unknown. Capitalizing on evidence that unpredictable signals to the developing brain constitute adversity and influence neurodevelopment, we examined whether exposure to perinatal unpredictable maternal signals associated with diminished reward responsivity, and probed causality using experiments in mice. We analyzed a longitudinal human study (N=158 mother/child dyads) and a controlled prospective mouse study (N=111 pups; 17 litters). In humans, exposure to unpredictable signals during late gestation was assessed by maternal mood entropy (a measure of how disordered or random signals are). In mice, unpredictable maternal care behaviors were experimentally induced during the neonatal period, and maternal care entropy was similarly computed. Aspects of anhedonia were determined by adolescent self-report and diminished behavioral responses to palatable food in young adult mice. Across species, unpredictable maternal signals were associated with reduced reward responsivity in males, but not females. Associations persisted after accounting for income and maternal psychopathology in humans, and for cohort and litter effects in mice. These findings indicate a robust, sex-dependent link between early exposure to maternal unpredictability and disrupted reward processing. The convergence of cross-species results indicates that unpredictability of maternal signals plays a role in shaping reward behaviors in males, with implications for the developmental origins of mental illness.

Anhedonia - the diminished capacity to experience or anticipate pleasure - is among the most common consequences of early-life unpredictability, yet how these co-occurring conditions jointly shape real-world decision-making remains unknown. Here, we use a sequential foraging-under-threat task to probe motivational conflict decisions in 357 individuals varying in early-life unpredictability and anhedonia symptoms. We find that unpredictability and anhedonia exert opposing influences on choice: unpredictability shifts behavior away from the survival-optimal policy in a sex-dependent manner, while anhedonia promotes adherence to it, partly through heightened sensitivity to unexpected threatening outcomes. A mediation analysis reveals that anhedonia partially buffers the deleterious effects of unpredictability on decision quality. These results demonstrate that co-occurring conditions can mask one another's behavioral signatures and suggest that the heterogeneous expression of transdiagnostic constructs like anhedonia may reflect context-dependent adaptations to distinct underlying etiologies.

Puberty is a period of profound behavioral reorganization that recalibrates social motivation, risk-taking, and sexual behavior in ways that shape lifelong human health. Yet its characterization in population-based studies relies largely on self-report, which reflects perceived physical changes rather than the neuroendocrine substrates driving the transition. The pituitary gland sits at the center of this reorganization, coordinating hypothalamic-pituitary axes that orchestrate puberty. Leveraging 11,818 adolescents (ABCD Study; 30,276 MRI observations), we show that pituitary volume is a precise, scalable marker of pubertal progression carrying non-redundant information beyond chronological age, salivary hormones, and self-reported stage. Sex-specific non-linear trajectories, accelerated expansion anchored to menarche, and distinct patterns across menarche timing subgroups capture both the timing and tempo of puberty at population scale. Pituitary volume was further associated with ACEs and decreased within-person growth following hormonal contraception initiation, positioning it as a sensitive index of the biological embedding of exogenous exposures known to influence pubertal maturation.

OBJECTIVE:Unpredictability in early life is an understudied aspect of early life adversity that provides important signals to the child about their environment. Recent work shows the predictability of patterns of parental signals on a moment-to-moment timescale sculpts the developing brain during sensitive windows, offering a potential pathway through which early life experiences shape child development. Both experimental non-human animal models and observational human research show sex-specific links between early life exposure to unpredictable patterns of parental sensory signals and aberrant offspring development. The impact of predictability of sensory signals on physical health outcomes, such as BMI, remains unknown. Here, we examined the sex-specific association between unpredictable maternal sensory signals during infancy and child BMI trajectories from infancy through adolescence. METHODS:In a prospective cohort ( N =190 mother/child dyads), we quantified the unpredictability of maternal sensory signals (unpredictability of transitions between visual, auditory, and tactile signals mothers provide their infant) from free-play interactions at 6 and 12 month of age. Child BMI was measured longitudinally 7 times from infancy through adolescence. General linear mixed models assessed relations of unpredictability with BMI trajectories. RESULTS:Greater unpredictability of maternal sensory signals in infancy was associated with higher BMI over time for females (b=0.07, SE=0.03, P =.047 for overall trajectory difference), but not males (b=-0.03, SE=0.04, P =.450 for overall trajectory difference). CONCLUSIONS:Findings suggest early life unpredictability may be an important signal shaping biobehavioral pathways to later child weight status among females.

Background:Unpredictable childhood experiences are an understudied form of early-life adversity that impact neurodevelopment. The neurobiological processes by which exposure to early-life unpredictability impact development and vulnerability to psychopathology remain poorly understood. In the current study, we investigated the sex-specific consequences of early-life unpredictability on the limbic network, focusing on the hippocampus and the amygdala. Methods:Participants included 150 youths (54% female). Early-life unpredictability was assessed using the Questionnaire of Unpredictability in Childhood (QUIC). Participants engaged in 1 or more task-functional magnetic resonance imaging scans between the ages of 8 and 17 (223 total observations) measuring blood oxygen level-dependent (BOLD) responses to novel and familiar scenes. Results:Exposure to early-life unpredictability was associated with BOLD contrast (novel vs. familiar) in a sex-specific manner. For boys, but not girls, higher QUIC scores were associated with lower BOLD activation in response to novel versus familiar stimuli in the hippocampal head and amygdala. Secondary psychophysiological interaction analyses revealed complementary sex-specific associations between QUIC scores and condition-specific functional connectivity between the right and left amygdala, as well as between the right amygdala and hippocampus bilaterally. Conclusions:Exposure to unpredictability in early life has persistent implications for the functional operations of limbic circuits. Importantly, consistent with emerging experimental animal and human studies, the consequences of early-life unpredictability differ for boys and girls. Furthermore, impacts of early-life unpredictability were independent of other risk factors including lower household income and negative life events, indicating distinct consequences of early-life unpredictability beyond more commonly studied types of early-life adversity.

Background and Objectives:Whereas adverse early life experiences (ACEs) correlate with cognitive, emotional and physical health at the population level, existing ACEs screens are only weakly predictive of outcomes for an individual child. This raises the possibility that important elements of the early-life experiences that drive vulnerability and resilience are not being captured. We previously demonstrated that unpredictable parental and household signals constitute an ACE with cross-cultural relevance. We created the 5-item Questionnaire of Unpredictability in Childhood (QUIC-5) that can be readily administered in pediatric clinics. Here, we tested if combined screening with the QUIC-5 and an ACEs measure in this real-world setting significantly improved prediction of child health outcomes. Methods:Leveraging existing screening with the Pediatric ACEs and Related Life Events Screener (PEARLS) at annual well-child visits, we implemented QUIC-5 screening in 19 pediatric clinics spanning the diverse sociodemographic constituency of Orange County, CA. Children (12yr+) and caregivers (for children 0-17years) completed both screens. Health diagnoses were abstracted from electronic health records (N=29,305 children). Results:For both screeners, increasing exposures were associated with a higher probability of a mental (ADHD, anxiety, depression, externalizing problems, sleep disorder) or physical (obesity abdominal pain, asthma, headache) health diagnosis. Across most diagnoses, PEARLS and QUIC provided unique predictive contributions. Importantly, for three outcomes (depression, obesity, sleep disorders) QUIC-5 identified vulnerable individuals that were missed by PEARLS alone. Conclusions:Screening for unpredictability as an additional ACE in primary care is feasible, acceptable and provides unique, actionable information about child psychopathology and physical health.

Directional data require specialized models because of the non-Euclidean nature of their domain. When a directional variable is observed jointly with linear variables, modeling their dependence adds an additional layer of complexity. A Bayesian nonparametric approach is introduced to analyze directional-linear data. Firstly, the projected normal distribution is extended to model the joint distribution of linear variables and a directional variable with arbitrary dimension projected from a higher-dimensional augmented multivariate normal distribution. The new distribution is called the semi-projected normal distribution (SPN) and can be used as the mixture distribution in a Dirichlet process model to obtain a more flexible class of models for directional-linear data. Then, a conditional inverse-Wishart distribution is proposed as part of the prior distribution to address an identifiability issue inherited from the projected normal and preserve conjugacy with the SPN. The SPN mixture model shows superior performance in clustering on synthetic data compared to the semi-wrapped Gaussian model. The experiments show the ability of the SPN mixture model to characterize bloodstain patterns. A hierarchical Dirichlet process model with the SPN distribution is built to estimate the likelihood of bloodstain patterns under a posited causal mechanism for use in a likelihood ratio approach to the analysis of forensic bloodstain pattern evidence.

Forensic science disciplines such as latent print examination, bullet and cartridge case comparisons, and shoeprint analysis, involve subjective decisions by forensic experts throughout the examination process. Most of the decisions involve ordinal categories. Examples include a three-category outcome for latent print comparisons (exclusion, inconclusive, identification) and a seven-category outcome for footwear comparisons (exclusion, indications of non-association, inconclusive, limited association of class characteristics, association of class characteristics, high degree of association, identification). As the results of the forensic examinations of evidence can heavily influence the outcomes of court proceedings, it is important to assess the reliability and accuracy of the underlying decisions. “Black box” studies are the most common approach for assessing the reliability and accuracy of subjective decisions. In these studies, researchers produce evidence samples consisting of a sample of questioned source and a sample of known source where the ground truth (same source or different source) is known. Examiners provide assessments for selected samples using the same approach they would use in actual casework. These studies often have two phases; the first phase comprises of decisions on samples of varying complexities by different examiners, and the second phase involves repeated decisions by the same examiner on a (usually) small subset of samples that were encountered by examiners in the first phase. We provide a statistical method to analyze ordinal decisions from black-box trials with the objective of obtaining inferences for the reliability of these decisions and quantifying the variation in decisions attributable to the examiners, the samples, and statistical interaction effects between examiners and samples. We present simulation studies to judge the performance of the model on data with known parameter values and apply the model to data from a handwritten signature complexity study, a latent fingerprint examination black-box study, and a handwriting comparisons black-box study.

BackgroundAnhedonia, an impairment in the motivation for or experience of pleasure, is a well-established transdiagnostic harbinger and core symptom of mental illness. Given increasing recognition of early life origins of mental illness, we posit that anhedonia should, and could, be recognized earlier if appropriate tools were available. However, reliable diagnostic instruments prior to childhood do not currently exist.MethodsWe developed an assessment instrument for anhedonia/reward processing in infancy, the Infant Hedonic/Anhedonic Processing Index (HAPI-Infant). Exploratory factor and psychometric analyses were conducted using data from 6- and 12-month-old infants from two cohorts (N = 188, N = 212). Then, associations were assessed between infant anhedonia and adolescent self-report of depressive symptoms.ResultsThe HAPI-Infant (47-items), exhibited excellent psychometric properties. Higher anhedonia scores at 6 (r = 0.23, p < .01) and 12 months (r = 0.19, p < .05) predicted elevated adolescent depressive symptoms, and these associations were stronger than for established infant risk indicators such as negative affectivity. Subsequent analyses supported the validity of short (27-item) and very short (12-item) versions of this measure.LimitationsThe primary limitations of this study are that the HAPI-Infant awaits additional tests of generalizability and of its ability to predict clinical diagnosis of depression.ConclusionsThe HAPI-Infant is a novel, psychometrically strong diagnostic tool suitable for recognizing anhedonia during the first year of life with strong predictive value for later depressive symptoms. In view of the emerging recognition of increasing prevalence of affective disorders in children and adolescents, the importance of the HAPI-Infant in diagnosing anhedonia is encouraging. Early recognition of anhedonia could target high-risk individuals for intervention and perhaps prevention of mental health disorders.

BACKGROUND: Fetal exposure to maternal mood dysregulation influences child cognitive and emotional development, which may have long-lasting implications for mental health. However, the neurobiological alterations associated with this dimension of adversity have yet to be explored. Here, we tested the hypothesis that fetal exposure to entropy, a novel index of dysregulated maternal mood, would predict the integrity of the salience network, which is involved in emotional processing. METHODS: A sample of 138 child-mother pairs (70 females) participated in this prospective longitudinal study. Maternal negative mood level and entropy (an index of variable and unpredictable mood) were assessed 5 times during pregnancy. Adolescents engaged in a functional magnetic resonance imaging task that was acquired between 2 resting-state scans. Changes in network integrity were analyzed using mixed-effect and latent growth curve models. The amplitude of low frequency fluctuations was analyzed to corroborate findings. RESULTS: Prenatal maternal mood entropy, but not mood level, was associated with salience network integrity. Both prenatal negative mood level and entropy were associated with the amplitude of low frequency fluctuations of the salience network. Latent class analysis yielded 2 profiles based on changes in network integrity across all functional magnetic resonance imaging sequences. The profile that exhibited little variation in network connectivity (i.e., inflexibility) consisted of adolescents who were exposed to higher negative maternal mood levels and more entropy. CONCLUSIONS: These findings suggest that fetal exposure to maternal mood dysregulation is associated with a weakened and inflexible salience network. More broadly, they identify maternal mood entropy as a novel marker of early adversity that exhibits long-lasting associations with offspring brain development.

Background: Patterns of sensory inputs early in life play an integral role in shaping the maturation of neural circuits, including those implicated in emotion and cognition. In both experimental animal models and observational human research, unpredictable sensory signals have been linked to aberrant developmental outcomes, including poor memory and effortful control. These findings suggest that sensitivity to unpredictable sensory signals is conserved across species and sculpts the developing brain. The current study provides a novel inves-tigation of unpredictable maternal sensory signals in early life and child internalizing behaviors. We tested these associations in three independent cohorts to probe the generalizability of associations across continents and cultures. Method: The three prospective longitudinal cohorts were based in Orange, USA (n = 163, 47.2 % female, Mage = 1 year); Turku, Finland (n = 239, 44.8 % female, Mage = 5 years); and Irvine, USA (n = 129, 43.4 % female, Mage = 9.6 years). Unpredictability of maternal sensory signals was quantified during free-play interactions. Child internalizing behaviors were measured via parent report (Orange & Turku) and child self-report (Irvine). Results: Early life exposure to unpredictable maternal sensory signals was associated with greater child fearful-ness/anxiety in all three cohorts, above and beyond maternal sensitivity and sociodemographic factors. The association between unpredictable maternal sensory signals and child sadness/depression was relatively weaker and did not reach traditional thresholds for statistical significance. Limitations: The correlational design limits our ability to make causal inferences. Conclusions: Findings across the three diverse cohorts suggest that unpredictable maternal signals early in life shape the development of internalizing behaviors, particularly fearfulness and anxiety.

Adverse early-life experiences (ELA) affect a majority of the world’s children. Whereas the enduring impact of ELA on cognitive and emotional health is established, there are no tools to predict vulnerability to ELA consequences in an individual child. Epigenetic markers including peripheral-cell DNA-methylation profiles may encode ELA and provide predictive outcome markers, yet the interindividual variance of the human genome and rapid changes in DNA methylation in childhood pose significant challenges. Hoping to mitigate these challenges we examined the relation of several ELA dimensions to DNA methylation changes and outcome using a within-subject longitudinal design and a high methylation-change threshold.DNA methylation was analyzed in buccal swab / saliva samples collected twice (neonatally and at 12 months) in 110 infants. We identified CpGs differentially methylated across time for each child and determined whether they associated with ELA indicators and executive function at age 5. We assessed sex differences and derived a sex-dependent ‘impact score’ based on sites that most contributed to methylation changes.Changes in methylation between two samples of an individual child reflected age-related trends and correlated with executive function years later. Among tested ELA dimensions and life factors including income to needs ratios, maternal sensitivity, body mass index and infant sex, unpredictability of parental and household signals was the strongest predictor of executive function. In girls, high early-life unpredictability interacted with methylation changes to presage executive function. Thus, longitudinal, within-subject changes in methylation profiles may provide a signature of ELA and a potential predictive marker of individual outcome.

This study examined how variations in signature complexity affected the ability of forensic document examiners (FDEs) and laypeople to determine whether signatures are authentic or simulated (forged), as well as whether they are disguised. Forty-five FDEs from nine countries evaluated nine different signature comparisons in this online study. Receiver Operating Characteristic (ROC) analyses revealed that FDEs performed in excess of chance levels, but performance varied as a function of signature complexity: Sensitivity (the true-positive rate) did not differ much between complexity levels (i.e., 65% vs. 79% vs. 79% for low vs medium vs high complexity), but specificity (the true-negative rate) was the highest (95%) for the medium complexity signatures and lowest (73%) for low complexity signatures. The specificity of high-complexity signatures (83%) was between these values. The sensitivity for disguised comparisons was only 11% and did not vary across complexity levels. One hundred-one novices also completed the study. A comparison of the area under the ROC curve (AUCs) revealed that FDEs outperformed novices in medium and high-complexity signatures but not low-complexity signatures. Novices also struggled to detect disguised signatures. While these findings elucidate the role of signature complexity in lay and expert evaluations, the error rates observed here may differ from those in forensic practice due to differences in the experimental stimuli and circumstances under which they were evaluated. This investigation of the role of signature complexity in the evaluation process was not intended to estimate error rates in forensic practice.

Spatial precision is often measured using the standard deviation (SD) of the eye position signal or the RMS of the sample-to-sample differences (StoS) signal during fixation. As both measures emerge from statistical theory applied to time-series, there are certain statistical assumptions that accompany their use. It is intuitively obvious that the SD is most useful when applied to unimodal distributions. Both measures assume stationarity, which means that the statistical properties of the signals are stable over time. Both metrics assume the samples of the signals are independent. The presence of autocorrelation indicates that the samples in the time series are not independent. We tested these assumptions with multiple fixations from two studies, a publicly available dataset that included both human and artificial eyes ("HA Dataset", N=224 fixations), and data from our laboratory of 4 subjects ("TXstate", N=37 fixations). Many position signal distributions were multimodal (HA: median=32%, TXstate: median=100%). No fixation position signals were stationary. All position signals were statistically significantly autocorrelated (p < 0:01). Thus, the statistical assumptions of the SD were not met for any fixation. All StoS signals were unimodal. Some StoS signals were stationary (HA: 34%, TXstate: 24%). Almost all StoS signals were statistically significantly autocorrelated (p < 0:01). For TXstate, 3 of 37 fixations met all assumptions. Thus, the statistical assumptions of the RMS were generally not met. The general failure of these assumptions calls into question the appropriateness of the SD or the RMS-StoS as metrics of precision for eye-trackers.

Studying the repeatability and reproducibility of decisions made during forensic examinations is important in order to better understand variation in decisions and establish confidence in procedures. For disciplines that rely on comparisons made by trained examiners such as for latent prints, handwriting, and cartridge cases, it has been recommended that ‘black-box’ studies be used to estimate the reliability and validity of decisions. In a typical black-box study, examiners are asked to judge samples of evidence as they would in practice, and their decisions are recorded; the ground truth about samples is known by the study designers. The design for such studies includes repeated assessments on forensic samples by different examiners and additionally, it is common for a subset of examiners to provide repeated assessments on the same evidence samples. We demonstrate a statistical approach to analyse the data collected across these repeated trials that offers the following advantages: i) we can make joint inference about repeatability and reproducibility while utilizing both the intra-examiner and inter-examiner data, ii) we can account for examiner–sample interactions that may impact the decision-making process. We demonstrate the approach first for continuous outcomes such as where decisions are made on an ordinal scale with many categories. The approach is next applied to binary decisions and results are presented on the data from two black-box studies.