Social connectedness strongly influences health and longevity, and adult pair bonds provide psychological benefits distinct from other social relationships. Oxytocin (OT), corticotropin-releasing hormone (CRH), and opioids, play an important role in the formation and maintenance of pair bonds. Evidence suggests that OT modulates the stress response via the hypothalamic-pituitary-adrenal (HPA) axis, while the kappa (κ) opioid system interacts with and may modulate OT signaling in contexts of stress and separation. In this study 20 coppery titi monkeys were exposed to a physical stressor under three social conditions: baseline (no stressor, partner present), stress (stressor, no partner present) and buffering (stressor, partner present). We predicted stress-induced dynorphin release would reduce κ-opioid receptor availability measured via [¹¹C]GR103545 Positron Emission Tomography (PET) and lower cerebrospinal fluid (CSF) OT, whereas partner presence would mitigate dynorphin release and increase CSF OT, with reduced dynorphin inferred from higher κ-opioid receptor radioligand binding. Our results show condition-dependent differences in [¹¹C]GR103545 binding in several brain regions, including the amygdala and hippocampus, with altered binding in both the stress and social buffering conditions. Cortisol levels were elevated in the stress condition compared to baseline. Females exhibited lower CSF OT levels during stress than at baseline, whereas plasma OT levels did not differ across conditions or between sexes. Spearman correlations revealed no significant associations between plasma and CSF OT. Together, these findings highlight the complex interaction between κ-opioid signaling, OT, and HPA axis activity in the context of social relationships and highlight neuroendocrine mechanisms underlying stress regulation in pair-bonded species.

The last two decades have seen a dramatic increase in using intensive longitudinal data to capture psychological processes. Intensive longitudinal data allow researchers to study intraindividual change and variability. Multiple modeling approaches have been developed to examine these dynamics in a process as it unfolds over time. What is often not considered in these models are factors that can influence the dynamics of the given process. In this article, we describe a state space model to examine the dynamics of daily affect and combine it with covariates that moderate the parameters describing such dynamics. In our approach, the moderators represent sentiment values from open responses to a daily questionnaire. Unlike standard Likert-type measures, open text allows individuals to express their thoughts and feelings without numerical or wording restrictions. We apply natural language processing to quantify positive and negative sentiment associated with such written responses reported daily. The implemented model utilizes a functional relationship between the variability in dynamic parameters and a time-varying covariate. The target of the moderator covariates are the autoregressive and cross-lag parameters in a vector autoregressive model. We find that such moderation effects from the covariates are small, yet robust. However, when the covariates are specified as predictors of the process instead of the dynamic parameters, their effects are strong. Our analyses show that, overall, qualitative measures are valuable to help understand dynamic processes.

Children who start school behind in mathematics are at-risk of staying behind throughout schooling and into adulthood. Unfortunately, there is no consensus on the best criteria for the early identification of these at-risk students. Using a unique 1st to 10th grade study of academic development (n = 445), we assessed the sensitivity and specificity of different ability-achievement and achievement-only criteria for identifying risk of mathematics learning difficulties (MLD) and adolescent innumeracy. The outcome criterion was based on the risk of innumeracy in 10th grade (< 25th mathematics percentile). The achievement-only cutoffs were mathematics scores below the 10th to 35th percentile (at 5 percentile increments, inclusive) in 1st grade and across 1st and 2nd grade, and teacher-reported in-class attentive behavior. The best combination of sensitivity (.589) and specificity (.878) was found for an achievement cutoff below the 25th percentile in 1st grade. We then examined growth in mathematics from 1st to 10th grade for different at-risk and typically achievement groups using latent score change (LCS) models. The models revealed that at-risk students were behind their peers in 1st grade, with a widening gap across the school years. However, not all at-risk students were identified indicating that additional factors need to be considered in follow-up studies. Until that time, low-achieving 1st graders, independent of cognitive ability, attentive behavior, and parental socioeconomic status, are at risk for long-term difficulties in mathematics and innumeracy in adolescence.

Objective: Several standardized measures are routinely used to estimate autism severity in research and clinical settings with the assumption that they measure the same construct and can be used interchangeably. Here, we tested this assumption with a systematic comparison of multiple measures from two large cohorts. Methods: We extracted Autism Diagnostic Observation Schedule-2 (ADOS-2), Autism Diagnostic Interview–Revised (ADI-R), Social Responsiveness Scale (SRS), and Repetitive Behaviors Scale–Revised (RBS-R) scores from 3,038 autistic children, 2-18-years-old, in the Simons Simplex Collection and UC-Davis M.I.N.D Institute Autism Phenome Project cohorts. Pair-wise correlation coefficients were calculated across measures, and a factor analysis was performed to determine their shared variance structure. Results: Parent-reported ADI-R, SRS, and RBS-R scores were moderately correlated with each other (0.34 ≤ r ≤ 0.67) but were weakly correlated with clinician-reported ADOS-2 (0.02 ≤ r ≤ 0.40) or teacher-reported SRS (0 ≤ r ≤ 0.41) scores. A confirmatory factor analysis identified a common autism severity factor across measures, which explained a considerable proportion of shared variance in parent-reported measures, but little variance in clinician-reported ADOS-2 scores. Conclusions: Standardized measures of autism symptoms exhibit weak agreement, likely due to differences in how informants perceive and interpret autistic behaviors, as well as differences in how symptoms present across environments, time-periods, and contexts. This poses a considerable challenge for autism research, suggesting that measures cannot be used interchangeably. Achieving accurate and reliable estimates of autism severity may, therefore, require integrating information from multiple informants and incorporating digital-phenotyping techniques that measure autistic behaviors directly.

Accelerated longitudinal designs (ALDs) offer an efficient means of studying large developmental periods by combining shorter longitudinal samples across overlapping cohorts. ALDs have been used to study processes that span large developmental epochs, such as cognitive development. However, traditional parametric approaches may impose structural assumptions that limit the accurate reconstruction of individual developmental trajectories. We investigate a nonparametric functional data analytic (FDA) approach for modeling latent trajectories in accelerated longitudinal data. This method allows for the recovery of smooth, continuous time trajectories that account for both shared and idiosyncratic patterns of change. We compare the FDA approach to the continuous time latent change score model with simulations that emulate nonlinear change processes often seen in developmental research. We apply both methods to two empirical datasets with individuals aged 5- to 22-years, where each person is assessed two or three times. Our results demonstrate that the FDA method captures the shape and timing of individual trajectories more accurately beyond the observed data. These findings suggest that FDA offers a robust and flexible alternative for modeling developmental change in ALDs.

In this study we examine how a mixed-effects model with crossed random effects for individuals and variables estimates within- and between-variability in longitudinal multivariate trajectories from cohort-sequential designs. These designs are characterized by large proportions of planned missing data, and they usually require continuous-time metrics. Via simulations, we evaluated different model outcomes under various conditions regarding the size of clusters (individuals and variables) and the complexity of the trajectories. Results show that (a) this model can estimate the general trajectories (common to all individuals and variables) and their variability, plus the variable-specific trajectories through the predictions of the levels of the random factors; (b) the standard errors of the random effects are wide, yet they are important for making substantive decisions for specific variables; and (c) the model predictions can adequately forecast individual and variable-specific complete trajectories from just a few observations per individual. These results are supported in an empirical illustration using cognitive developmental data. These findings show that researchers can obtain complete individual trajectories for multiple variables throughout a target age range. The relative simplicity of this model in comparison with other alternatives makes it a promising and accessible tool for multivariate longitudinal data analysis.

Social relationships play a critical role in modulating stress responses, adjusting behavior, and regulating physiology. Changes in the social environment, including cohabitation and partner status, can shape behavioral and physiological outcomes, yet the extent of influence to physiological changes remains unclear. In this study, we characterized the complexity of relationships across different social contexts in the prairie vole (Microtus ochrogaster) across four different social contexts including: isolated voles, females paired with vasectomized males (BV), females paired with intact males (BI), and same-sex sibling pairs (SS). We investigated the establishment and development of these varied social relationships through the use of four behavioral testing paradigms: partner preference testing (PPT), separation distress (SD), social buffering (SB), and homecage observations (HCO). In the PPT, we found that BI, BV, and SS subjects all preferred spending time with their partner rather than a stranger. In SD, subjects spent more time in the nest when their partners were present than when alone, whereas grooming increased during isolation. In SB, affiliative behaviors were elevated in the presence of a partner. In the homecage, SS subjects spent more time huddling and lunging was elevated in BV groups compared to BI, suggesting differences in baseline social dynamics with the presence of pups. We used these behavioral measures to identify two sets of factors that describe relationship quality. Together, the findings underscore that social relationships are not a unified construct, but reflect multiple complex responses. This study highlights the importance of characterizing complex social organization across different relationship types.

Adolescence is a sensitive period during which socioeconomic conditions can shape brain development and mental health. To further clarify such pathways, the present study examined how trajectories of different poverty indices across adolescence relate to brain structure and depressive symptomatology. In a sample of 206 Mexican-origin youth, latent growth curve modeling was used to assess parent-reported income-to-needs and economic hardship measured from ages 10 to 16 as predictors of self-reported depressive symptomatology at age 18. Youths also reported on perceived economic hardship at 14 and 16 years. Amygdala, hippocampus, anterior cingulate cortex, and ventromedial prefrontal cortex volumes assessed using structural magnetic resonance imaging at age 17 were examined as mediators within the models. Lower average income-to-needs across adolescence was associated with larger amygdala and hippocampal volumes, whereas youth-reported economic hardship predicted smaller anterior cingulate cortex volumes. Youth-reported economic hardship, but neither parent-reported economic hardship nor brain volumes, significantly predicted depression symptoms. Parent-reported economic hardship across adolescence was unrelated to both brain volume and depression symptoms. Results highlight that objective poverty and adolescents' own perceptions of hardship may each relate to neurodevelopment, and that youth-reported hardship may be more proximally linked to depressive symptomatology in late adolescence than parent-reported hardship or brain volume.

Physiological linkage, which refers to the degree that people's peripheral physiological responses change in coordinated ways, has been linked to a variety of psychiatric and developmental conditions. In contrast, physiological linkage in neurological conditions has been understudied. Behavioral variant frontotemporal dementia (bvFTD) is characterized by debilitating impairments in socioemotional functioning, including connections with others. We hypothesized that physiological linkage during interactions with loved ones would be reduced in bvFTD. During unrehearsed 10-min discussions of an area of disagreement in 86 dyads (n = 40 bvFTD; n = 35 Alzheimer's disease [AD]; n = 11 healthy controls), we computed dyadic physiological linkage using a composite of six peripheral physiological measures (i.e., heart rate, skin conductance, finger pulse amplitude, finger pulse transmission time, ear pulse transmission time, somatic activity). Specifically, we computed in-phase, anti-phase, and combined physiological linkage to examine each dyad's coordinated physiological changes that occur exclusively in the same direction (i.e., positively correlated), opposite direction (i.e., negatively correlated), or in either direction (i.e., correlated regardless of whether the correlation is positive or negative). Results indicate that bvFTD dyads had significantly lower combined (but not in-phase or anti-phase) physiological linkage compared to AD and healthy control dyads. To the extent that physiological linkage reflects social connection, these findings are consistent with the deficits in socio-emotional functioning that characterize bvFTD. We offer several possible explanations for this finding and consider implications for future research and clinical assessment of dyadic interpersonal processes in dementia and related disorders.

In the present study, we extend a stochastic differential equation (SDE) model, the Ornstein–Uhlenbeck (OU) process, to the simultaneous analysis of time series of multiple variables by means of random effects for individuals and variables using a Bayesian framework. This SDE model is a stationary Gauss‐Markov process that varies over time around its mean. Our extension allows us to estimate the variability of different parameters of the process, such as the mean ( μ ) or the drift parameter ( φ ), across individuals and variables of the system by means of marginalized posterior distributions. We illustrate the estimations and the interpretability of the parameters of this multilevel OU process in an empirical study of affect dynamics where multiple individuals were measured on different variables at multiple time points. We also conducted a simulation study to evaluate whether the model can recover the population parameters generating the OU process. Our results support the use of this model to obtain both the general parameters (common to all individuals and variables) and the variable‐specific point estimates (random effects). We conclude that this multilevel OU process with individual‐ and variable‐specific estimates as random effects can be a useful approach to analyse time series for multiple variables simultaneously.

Psychological theories are often expressed verbally using natural language, which may lead to varying interpretations of the phenomenon under study. This potential confusion can be mitigated by formalizing verbal theories using mathematical language, which can help in defining, analyzing, and interpreting one’s hypotheses in quantitative terms. Differential equations (DE) are a class of models in dynamical systems framework, particularly suited to many dynamic theories in psychology. However, there is a lack of tools for translating verbal theories into DE systems. To facilitate this translation, we introduce SimDE, an open access R Shiny application that allows users to specify a DE model for a multivariate system and then simulate the trajectories of each variable over time. SimDE provides an interface to simulate a range of DE models, with features such as: 1) first- or second-order DEs (e.g., exponential, oscillatory), 2) models with or without a dynamic error term (ordinary or stochastic DEs), 3) models with multivariate effects (coupling dynamics). Users have the flexibility of plotting these systems in order to see the pattern of changes over time and determine the appropriateness of the model for the phenomenon they are trying to study. The goal of our app is to serve as a tool for researchers who want to explore DE models for their psychological theories before they even collect data. It can also help researchers to study the implicit assumptions of their systems defined with such DEs and further refine them as needed.

Coppery titi monkeys (Plecturocebus cupreus) are socially monogamous monkeys that display strong pair bonds similar to human romantic attachments, preceded by infant attachment to their fathers. To understand how father-daughter bonds impact adult relationship dynamics, we established a novel method for quantifying expression of bond-related behaviors. We assessed behavioral and neural correlates of preference, stress buffering, and separation distress to identify how females’ current and former attachment figures impact female attachment. Whereas all females (n = 9) shifted to preferring their partner over father six-months post-pairing, females that exhibited higher expression of juvenile parent preference maintained a relationship with their father six-months post-pairing, as evidenced by higher-than-expected father proximity. Higher expression of juvenile measures of proximity following a brief separation predicted slightly increased partner proximity in adulthood. Neural activity patterns in brain regions assessed pre- and post-pairing showed high similarity in glucose metabolism, despite overall activity being lower post-pairing. While there was some inconsistency in results, higher expression of juvenile proximity following a separation was associated with enhanced reduction in activity within social bonding brain regions (social salience network, periaqueductal gray, cerebellum), suggesting a potential stress buffering benefit via reduced threat-related brain activation, like that seen in high-quality human relationships. These findings advance current knowledge of how early relationships may shape adult bond-related behavior and neural activity.

For decades, dyslexia interventions within the school setting are typically delivered at a low dosage, raising concerns that these interventions are not intensive enough to sustain long-term reading outcomes. We sought to investigate the impact of such interventions delivered in one state using data from the Connecticut Longitudinal Study. This unique study sample included both typically reading (n = 246) and dyslexic (n = 66) children beginning at age 5 who have been followed continuously and noninterruptedly through their current age of 45 years. Our findings indicate that the school-based interventions examined in our data did not lead to sustained outcomes for dyslexic readers. Findings suggest that participation in these interventions did not lead to improved outcomes for dyslexic readers in adulthood and may even have a detrimental effect on reading comprehension as adults. If students with dyslexia are to improve their reading, schools must adopt and use evidence-based interventions when reading difficulties are identified in the primary grades. Our findings provide strong empirical support for the need for more intensive and comprehensive interventions for students with dyslexia.

The tip-of-the-tongue (ToT) phenomenon is a transient semantic memory retrieval failure. Here we examined to what extent different mnemonic factors (i.e., age of acquisition, frequency of retrieval, recency of last retrieval) impact ToTs during the retrieval of famous faces and places. Eighty adults completed a self-paced experiment for both stimuli. This required making judgements on whether they knew the name, were in a ToT state, the image was familiar or the name was unknown, as well as completing follow-up questions examining the mnemonic factors of interest. Results revealed that later acquired names, a lower frequency of retrieval, and less recently encountered names, all predicted an increase in ToT occurrences. These findings followed a similar pattern across faces and places, with places being stronger predictors for each mnemonic factor. By examining these factors simultaneously across these semantic categories, we provide further evidence regarding the variables determining transient retrieval failures.

Girls, more than boys, experience a decrease in the severity of autism symptoms during childhood. It is unclear, however, which specific autistic behaviors change more for girls than for boys. Trajectories of autism symptoms were evaluated using the Autism Diagnostic Observation Schedule-calibrated severity scores (ADOS-CSS). Change in the specific behavioral characteristics of autism was assessed by studying individual ADOS items for 183 children (55 girls) from age 3-to-11 years. Girls decreased in total autism symptom severity (ADOS-CSS) and restricted/repetitive behavior severity (RRB-CSS) across childhood, while boys remained stable in both. Girls showed decreasing-severity trajectories for seven ADOS items and an increasing-trajectory for one item. Boys showed decreasing-severity trajectories for six items and increasing-severity trajectories for three items. Girls with higher ADOS-CSS at age 3 were more likely to decrease in total symptom severity than other girls. Girls in our study mostly improved or remained stable in autism symptom severity and its specific behaviors during childhood, especially behaviors related to being socially engaged and responsive. Boys' symptom change was variable over time and included both improvement and worsening, especially in social behaviors that are key to interaction. Girls with high early severity levels can potentially decrease substantially in severity during childhood.Lay AbstractThe severity of overall autism symptoms tend to decrease more in autistic girls than in autistic boys during childhood, but we do not know which specific behaviors drive this difference. We studied how specific behaviors characteristic of autism change for girls and boys across childhood. We found that girls mostly improve or remain stable in the severity level of their autism symptoms and its specific behaviors during childhood. They improved mostly in behaviors related to being socially engaged and responsive to others. Furthermore, we found that it is possible for girls with high early autism symptoms to show major improvement during childhood. Boys improved in some specific behaviors but worsened in others. Boys worsened in some behaviors that are key to engaging in social interactions.

We implement an analytic approach for ordinal measures and we use it to investigate the structure and the changes over time of self-worth in a sample of adolescents students in high school. We represent the variations in self-worth and its various sub-domains using entropy-based measures that capture the observed uncertainty. We then study the evolution of the entropy across four time points throughout a semester of high school. Our analytic approach yields information about the configuration of the various dimensions of the self together with time-related changes and associations among these dimensions. We represent the results using a network that depicts self-worth changes over time. This approach also identifies groups of adolescent students who show different patterns of associations, thus emphasizing the need to consider heterogeneity in the data.

Many autistic children experience changes in core symptom severity across middle childhood, when co-occurring mental health conditions emerge. We evaluated this relationship in 75 autistic children from 6 to 11 years old. Autism symptom severity change was evaluated for total autism symptoms using the autism diagnostic observation schedule calibrated severity score, as well as social-communication symptoms calibrated severity score, and restricted/repetitive behaviors calibrated severity score. Children were grouped based on their symptom severity change patterns. Mental health symptoms (attention-deficit hyperactivity disorder, anxiety, disruptive behavior problems) were assessed via parental interview and questionnaire and compared across the groups. Co-occurring mental health symptoms were more strongly associated with change in social-communication symptom or restricted/repetitive behavior severity than with total autism symptom severity. Two relevant groups were identified. The social-communication symptomincreasing-severity-group (21.3%) had elevated and increasing levels of anxiety, attention-deficit hyperactivity disorder, and disruptive behavior problems compared with children with stable social-communication symptom severity. The restricted/repetitive behavior-decreasing-severity-group (22.7%) had elevated and increasing levels of anxiety; 94% of these children met criteria for an anxiety disorder. Autism symptom severity change during middle childhood is associated with co-occurring mental health symptoms. Children that increase in social-communication symptom severity are also likely to demonstrate greater psychopathology, while decreases in restricted/repetitive behavior severity are associated with higher levels of anxiety. Lay abstract For many autistic children, the severity of their autism symptoms changes during middle childhood. We studied whether these changes are associated with the emergence of other mental health challenges such as anxiety and attention-deficit hyperactivity disorder. Children who had increased social-communication challenges had more anxiety and attentiondeficit hyperactivity disorder symptoms and disruptive behavior problems than other children. Children who decreased their restricted and repetitive behaviors, on the contrary, had more anxiety. We discuss why these changes in autism symptoms may lead to increases in other mental health concerns.

Social bonds influence physiology and behavior, which can shape how individuals respond to physical and affective challenges. Coppery titi monkey (Plecturocebus cupreus) offspring form selective bonds with their fathers, making them ideal for investigating how father-daughter bonds influence juveniles' responses to oxytocin (OT) and arginine-vasopressin (AVP) manipulations. We quantified the expression of father-daughter bond-related behaviors in females (n = 10) and gave acute intranasal treatments of saline, low/medium/high OT, low/high AVP, or an OT receptor antagonist (OTA) to subjects prior to a parent preference test. While females spent more time in proximity to their parents than strangers, we found a large degree of individual variation. Females with greater expression of bonding behaviors responded to OT treatments in a dose-dependent manner. Subjects also spent less time in proximity to strangers when treated with High OT (p = 0.003) and Low OT (p = 0.007), but more time when treated with High AVP (p = 0.007), Low AVP (p = 0.009), and OTA (p = 0.001). Findings from the present study suggest that variation in the expression of bond-related behaviors may alter responsiveness to OT and AVP, increasing engagement with unfamiliar social others. This enhanced sociality with strangers may promote the formation of pair bonds with partners.

Accelerated longitudinal designs allow researchers to efficiently collect longitudinal data covering a time span much longer than the study duration. One important assumption of these designs is that each cohort (a group defined by their age of entry into the study) shares the same longitudinal trajectory. Although previous research has examined the impact of violating this assumption when each cohort is defined by a single age of entry, it is possible that each cohort is instead defined by a range of ages, such as groups that experience a particular historical event. In this paper we examined how including cohort membership in linear and quadratic multilevel models performed in detecting and controlling for cohort effects in this scenario. Using a Monte Carlo simulation study, we assessed the performance of this approach under conditions related to the number of cohorts, the overlap between cohorts, the strength of the cohort effect, the number of affected parameters, and the sample size. Our results indicate that models including a proxy variable for cohort membership based on age at study entry performed comparably to using true cohort membership in detecting cohort effects accurately and returning unbiased parameter estimates. This indicates that researchers can control for cohort effects even when true cohort membership is unknown.

Romantic relationships are defined by emotion dynamics, or how the emotions of one partner at a single timepoint can affect their own emotions and the emotions of their partner at the next timepoint. Previous research has shown that the level of these emotion dynamics plays a role in determining the state and quality of the relationship. However, this research has not examined whether the estimated emotion dynamics change over time, and how the change in these dynamics might relate to relationship outcomes, despite changes in dynamics being likely to occur. We examined whether the magnitude of variation in emotion dynamics over time was associated with relationship outcomes in a sample of 148 couples. Time-varying vector autoregressive models were used to estimate the emotion dynamics for each couple, and the average and standard deviation of the dynamics over time was related to relationship quality and relationship dissolution 1-2 years later. Our results demonstrate that certain autoregressive and cross-lagged parameters do show significant variation over time, and that this variation is associated with relationship outcomes. Overall, this study demonstrates the importance of accounting for change in emotion dynamics over time, and the relevance of this change to the prediction of future outcomes.