Structural architecture and brain network efficiency link polygenic scores to intelligence.

Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

The association between history of prenatal loss and maternal psychological state in a subsequent pregnancy: an ecological momentary assessment (EMA) study.

BACKGROUND: Prenatal loss which occurs in approximately 20% of pregnancies represents a well-established risk factor for anxiety and affective disorders. In the current study, we examined whether a history of prenatal loss is associated with a subsequent pregnancy with maternal psychological state using ecological momentary assessment (EMA)-based measures of pregnancy-specific distress and mood in everyday life. METHOD: This study was conducted in a cohort of N = 155 healthy pregnant women, of which N = 40 had a history of prenatal loss. An EMA protocol was used in early and late pregnancy to collect repeated measures of maternal stress and mood, on average eight times per day over a consecutive 4-day period. The association between a history of prenatal loss and psychological state was estimated using linear mixed models. RESULTS: Compared to women who had not experienced a prior prenatal loss, women with a history of prenatal loss reported higher levels of pregnancy-specific distress in early as well as late pregnancy and also were more nervous and tired. Furthermore, in the comparison group pregnancy-specific distress decreased and mood improved from early to late pregnancy, whereas these changes across pregnancy were not evident in women in the prenatal loss group. CONCLUSION: Our findings suggest that prenatal loss in a prior pregnancy is associated with a subsequent pregnancy with significantly higher stress and impaired mood levels in everyday life across gestation. These findings have important implications for designing EMA-based ambulatory, personalized interventions to reduce stress during pregnancy in this high-risk group.

Modeling dynamic personality theories in a continuous-time framework: An illustration.

OBJECTIVE: Personality psychology has traditionally focused on stable between-person differences. Yet, recent theoretical developments and empirical insights have led to a new conceptualization of personality as a dynamic system (e.g., Cybernetic Big Five Theory). Such dynamic systems comprise several components that need to be conceptually distinguished and mapped to a statistical model for estimation. METHOD: In the current work, we illustrate how common components from these new dynamic personality theories may be implemented in a continuous time-modeling framework. RESULTS: As an empirical example, we reanalyze experience sampling data with N = 180 persons (with on average T = 40 [SD = 8] measurement occasions) to investigate four different effects between momentary happiness, momentary extraverted behavior, and the perception of a situation as social: (1) between-person effects, (2) contemporaneous effects, (3) autoregressive effects, and (4) cross-lagged effects. CONCLUSION: We highlight that these four effects must not necessarily point in the same direction, which is in line with assumptions from dynamic personality theories.

Resource-Building Processes Across Life Domains: Father-Child Interactions as Starting Points for Resource Caravans.

In two studies, we examined preconditions of resource-building processes between family and work. Focusing on positive father-child interactions, we investigated positive mood states as links between the two life domains. Fathers employed in information technology (N 1 = 59) and the retail sector (N 2 = 75) participated in micro-longitudinal studies, both for eight consecutive workdays. Study 1 revealed that fathers with more positive interactions with a child also reported more positive mood states and fathers with more positive mood states perceived more social resources from their supervisor during the week. The indirect effect was small but significant. In Study 2, multilevel structural-equation models did not find indirect effects at the within-person level but did show that positive father-child interactions after work were related to fathers' positive mood states before going to bed and positive mood in the morning predicted perceived social resources from supervisors (but not from coworkers) in the forenoon. There were also positive effects of perceived social resources from supervisors on positive mood states, after work. But these did not translate into an increase in positive father-child interactions, in the evening. Hence, only single elements were supported but not the overall resource caravan. Supplementary Information: The online version contains supplementary material available at 10.1007/s10902-022-00523-4.

Estimating Reliability of Within-Person Couplings in a Multilevel Framework.

Within-person couplings play a prominent role in psychological research and previous studies have shown that interindividual differences in within-person couplings predict future behavior. For example, stress reactivity-operationalized as the within-person coupling of stress and positive or negative affect-is an important predictor of various (mental) health outcomes and has often been assumed to be a more or less stable personality trait. However, issues of reliability of these couplings have been largely neglected so far. In this work, we present an estimate for the reliability of within-person couplings that can be easily obtained using the user-modifiable R code accompanying this work. Results of a simulation study show that this index performs well even in the context of unbalanced data due to missing values. We demonstrate the application of this index in a measurement burst study targeting the reliability and test-retest correlation of stress reactivity estimates operationalized as within-person couplings in a daily diary design. Reliability and test-retest correlations of stress reactivity estimates were rather low, challenging the implicit assumption of stress reactivity as a stable person-level variable. We highlight key factors that researchers planning studies targeting interindividual differences in within-person couplings should consider to maximize reliability.

Maternal pro-inflammatory state during pregnancy and newborn leukocyte telomere length: A prospective investigation.

INTRODUCTION: Telomere biology plays a fundamental role in maintaining the integrity of the genome and cell, and shortened telomeres have been linked to several age-related diseases. The initial (newborn) telomere length (TL) represents a critically important feature of the telomere biology system. Exposure to a variety of adverse prenatal conditions such as maternal stress, suboptimal diet, obesity, and obstetric complications, is associated with shorter offspring TL at birth and in adult life. Many, if not all, of these exposures are believed to have an inflammatory component. In this context, stress-related immunological processes during pregnancy may constitute a potential additional biological pathway because they can affect telomere length and telomerase activity via transcriptions factors such as cyclic adenosine monophosphate-dependent transcription factor (ATF7) and nuclear factor-kappa B (NF-κB). Thus, in the present study we examined the hypothesis that maternal pro-inflammatory state across pregnancy, operationalized as the balance between tumor necrosis factor (TNF)-α, a major pro-inflammatory cytokine, and interleukin-10 (IL-10), the major anti-inflammatory cytokine, is associated with newborn leukocyte telomere length (LTL) at birth. METHODS AND MATERIALS: Participants were healthy women (N = 112) recruited in early pregnancy. Concentrations of TNF- α and IL-10 were quantified in early, mid and late pregnancy from maternal blood samples. Telomere length was assessed in newborn blood samples soon after birth. RESULTS: After adjusting for maternal age, maternal pre-pregnancy BMI, birth weight percentile, and infant sex, a higher mean TNF-α/IL-10 ratio across pregnancy was significantly associated with shorter newborn TL (ß = -.205, p = .030). Newborn TL was, on average, 10% shorter in offspring of women in the upper compared to lower quartile of the TNF-α/IL-10 ratio during pregnancy. DISCUSSION: These findings provide new evidence in humans for a potential "programming" mechanism linking maternal systemic pro-inflammatory processes during pregnancy with the initial (newborn) setting of her offspring's telomere system.

General self-efficacy as a driving factor of post-stroke depression: A longitudinal study.

Post-stroke depression (PSD) is the most common psychiatric condition after stroke, affecting one third of survivors. Despite identification of meaningful predictors, knowledge about the interplay between these factors remains fragmentary. General self-efficacy (GSE) is closely linked to PSD, yet direction and magnitude of this relationship remains unclear. The authors assessed the relationship between GSE and depression during the first two years post-stroke while controlling for stable inter-individual differences using continuous time (CT) structural equation modelling (SEM). Patients of two German rehabilitation centres (N = 294, mean age = 63.78 years, SD = 10.83) were assessed six weeks after ischemic stroke and at four follow-ups covering two years. GSE Scale and Geriatric Depression Scale (GDS) were used to assess GSE and depression. CT-analysis revealed significantly higher within-person cross-effects of GSE on GDS (a21 = -.29) than vice versa (a12 = -.17). Maximal cross-lagged effects emerged six months post-stroke. Our results show that decreasing GSE led to increasing depressiveness, and only to a smaller extent vice versa. This suggests that fostering GSE by strengthening perceived control after stroke can counter PSD emersion and exacerbation. Six months post-stroke, when patients face social re-integration, programmes focusing on GSE could potentially help to prevent later PSD.

Fluid intelligence and gross structural properties of the cerebral cortex in middle-aged and older adults: A multi-occasion longitudinal study.

According to Parieto-Frontal Integration Theory (P-FIT, Jung and Haier, 2007), individual differences in a circumscribed set of brain regions account for variations in general intelligence (g). The components of g, fluid (Gf) and crystallized (Gc) reasoning, exhibit distinct trajectories of age-related change. Because the brain also ages differentially, we hypothesized that age-related cognitive and neural changes would be coupled. In a sample of healthy middle-aged and older adults, we examined changes in Gf (operationalized by Cattell Culture Fair Test) and Gc (indexed by two vocabulary tests) as well as in structural properties of 19 brain regions. We fitted linear mixed models to the data collected on 73 healthy adults who participated in baseline assessment, with 43 returning for at least one follow-up, and 16 of them contributing four repeated assessments over seven years. We observed age differences as well as longitudinal decline in Gf, contrasted to a lack of age differences and stability in Gc. Cortical thickness and cortical volume exhibited significant age differences and longitudinal declines, which were accelerated in P-FIT regions. Gf (but not Gc) was associated with cortical thickness, but no such relationship was found for cortical volume. Uniformity of cognitive change (lack of reliable individual differences) precluded examination of the coupling between cognitive and brain changes. Cortical shrinkage was greater in high-Gc individuals, whereas in participants with higher Gf cortical volume slower volume shrinkage was observed.

SEM Based CARMA Time Series Modeling for Arbitrary N.

This article explains in detail the state space specification and estimation of first and higher-order autoregressive moving-average models in continuous time (CARMA) in an extended structural equation modeling (SEM) context for N = 1 as well as N > 1. To illustrate the approach, simulations will be presented in which a single panel model (T = 41 time points) is estimated for a sample of N = 1,000 individuals as well as for samples of N = 100 and N = 50 individuals, followed by estimating 100 separate models for each of the one-hundred N = 1 cases in the N = 100 sample. Furthermore, we will demonstrate how to test the difference between the full panel model and each N = 1 model by means of a subject-group-reproducibility test. Finally, the proposed analyses will be applied in an empirical example, in which the relationships between mood at work and mood at home are studied in a sample of N = 55 women. All analyses are carried out by ctsem, an R-package for continuous time modeling, interfacing to OpenMx.

The Role of Time in the Quest for Understanding Psychological Mechanisms.

The lead-lag structure of multivariate time-ordered observations and the possibility to disentangle between-person (BP) from within-person (WP) sources of variance are major assets of longitudinal (panel) data. Hence, psychologists are making increasing use of such data, often with the intent to delineate the dynamic properties of psychological mechanisms, understood as a sequence of causal effects that govern psychological functioning. However, even with longitudinal data, psychological mechanisms are not easily identified. In this article, we show how an adequate representation of time may enhance the tenability of causal interpretations in the context of multivariate longitudinal data analysis. We anchor our considerations with an example that illustrates some of the main problems and questions faced by applied researchers and practitioners. We distinguish between static versus dynamic and discrete versus continuous time modeling approaches and discuss their advantages and disadvantages. We place particular emphasis on different ways of addressing BP differences and stress their dual role as potential confounds versus valuable sources of information for improving estimation and aiding causal inference. We conclude by outlining an approach that offers the potential of better integration of information on BP differences and WP changes in the search for causal mechanisms along with a discussion of current problems and limitations.

Capturing Context-Related Change in Emotional Dynamics via Fixed Moderated Time Series Analysis.

Much of recent affect research relies on intensive longitudinal studies to assess daily emotional experiences. The resulting data are analyzed with dynamic models to capture regulatory processes involved in emotional functioning. Daily contexts, however, are commonly ignored. This may not only result in biased parameter estimates and wrong conclusions, but also ignores the opportunity to investigate contextual effects on emotional dynamics. With fixed moderated time series analysis, we present an approach that resolves this problem by estimating context-dependent change in dynamic parameters in single-subject time series models. The approach examines parameter changes of known shape and thus addresses the problem of observed intra-individual heterogeneity (e.g., changes in emotional dynamics due to observed changes in daily stress). In comparison to existing approaches to unobserved heterogeneity, model estimation is facilitated and different forms of change can readily be accommodated. We demonstrate the approach's viability given relatively short time series by means of a simulation study. In addition, we present an empirical application, targeting the joint dynamics of affect and stress and how these co-vary with daily events. We discuss potentials and limitations of the approach and close with an outlook on the broader implications for understanding emotional adaption and development.

The Subjective Health Horizon Questionnaire (SHH-Q): Assessing Future Time Perspectives for Facets of an Active Lifestyle.

BACKGROUND: A wider subjective time horizon is assumed to be positively associated with longevity and vitality. In particular, a lifestyle with exposure to novel and varied information is considered beneficial for healthy cognitive aging. At present, measures that specifically assess individuals' perceived temporal extension to engage in active lifestyles in the future are not available. OBJECTIVES: We introduce and validate a new self-report measure, the Subjective Health Horizon Questionnaire (SHH-Q). The SHH-Q assesses individuals' future time perspectives in relation to four interrelated but distinct lifestyle dimensions: (1) novelty-oriented exploration (Novelty), (2) bodily fitness (Body), (3) work goals (Work), and (4) goals in life (Life Goals). The present study aims at: (a) validating the hypothesized factor structure of the SHH-Q, according to which the SHH-Q consists of four interrelated but distinct subscales, and (b) testing the hypothesis that the Novelty and Body subscales of the SHH-Q show positive and selective associations with markers of cognition and somatic health, respectively. METHODS: Using structural equation modeling, we analyzed data from 1,371 healthy individuals (51% women) with a mean age of 70.1 years (SD = 3.6) who participated in the Berlin Aging Study II (BASE-II) and completed the SHH-Q. RESULTS: As predicted, the SHH-Q formed four correlated but distinct subscales: (1) Novelty, (2) Body, (3) Work, and (4) Life Goals. Greater self-reported future novelty orientation was associated with higher current memory performance, and greater future expectations regarding bodily fitness with better current metabolic status. CONCLUSION: The SHH-Q reliably assesses individual differences in four distinct dimensions of future time perspective. Two of these dimensions, Novelty and Body, show differential associations with cognitive status and somatic health. The SHH-Q may serve as a tool to assess how different facets of future time perspective relate to somatic health, cognition, motivation, and affect, and may help to identify the socioeconomic and individual antecedents, correlates, and consequences of an active lifestyle.

Toward a Unified Framework for the Study of Between-Person and Within-Person Structures: Building a Bridge Between Two Research Paradigms.

The vast majority of empirical research in the behavioral sciences is based on the analysis of between-person variation. In contrast, much of applied psychology is concerned with the analysis of variation within individuals. Furthermore, the mechanisms specified by psychological theories generally operate within, rather than across, individuals. This disconnect between research practice, applied demands, and psychological theories constitutes a major threat to the conceptual integrity of the field. Following groundbreaking earlier work, we propose a conceptual framework that distinguishes within-person (WP) and between-person (BP) sources of variation in psychological constructs. By simultaneously considering both sources of variation, it is shown how to identify possible reasons for nonequivalence of BP and WP structures as well as establishing areas of convergence. For this purpose, we first introduce the concept of conditional equivalence as a way to study partial structural equivalence of BP and WP structures in the presence of unconditional nonequivalence. Second, we demonstrate the construction of likelihood planes to explore the causes of structural nonequivalence. Third, we examine 4 common causes for unconditional nonequivalence-autoregression, subgroup differences, linear trends, and cyclic trends-and demonstrate how to account for them. Fourth, we provide an empirical example on BP and WP differences in attentiveness.

Regularized continuous time structural equation models: A network perspective.

Regularized continuous time structural equation models are proposed to address two recent challenges in longitudinal research: Unequally spaced measurement occasions and high model complexity. Unequally spaced measurement occasions are part of most longitudinal studies, sometimes intentionally (e.g., in experience sampling methods) sometimes unintentionally (e.g., due to missing data). Yet, prominent dynamic models, such as the autoregressive cross-lagged model, assume equally spaced measurement occasions. If this assumption is violated parameter estimates can be biased, potentially leading to false conclusions. Continuous time structural equation models (CTSEM) resolve this problem by taking the exact time point of a measurement into account. This allows for any arbitrary measurement scheme. We combine CTSEM with LASSO and adaptive LASSO regularization. Such regularization techniques are especially promising for the increasingly complex models in psychological research, the most prominent example being network models with often dozens or hundreds of parameters. Here, LASSO regularization can reduce the risk of overfitting and simplify the model interpretation. In this article we highlight unique challenges in regularizing continuous time dynamic models, such as standardization or the optimization of the objective function, and offer different solutions. Our approach is implemented in the R (R Core Team, 2022) package regCtsem. We demonstrate the use of regCtsem in a simulation study, showing that the proposed regularization improves the parameter estimates, especially in small samples. The approach correctly eliminates true-zero parameters while retaining true-nonzero parameters. We present two empirical examples and end with a discussion on current limitations and future research directions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Intra-Subject Variability, Intelligence, and ADHD Traits in a Community-Based Sample.

OBJECTIVE: The present study investigates the predictive validity of intra-subject variability (ISV) for ADHD traits in a community-based sample and the stability of the relationship between ISV and fluid intelligence (gf) across the continuum of ADHD traits. METHOD: Age-residualized data from 426 participants (8-18 years, 6% ADHD) was used to investigate whether ex-Gaussian and DDM parameters derived from simple choice-reaction-time tasks can predict continuously assessed ADHD traits. Multiple-Group-Analyses and Latent-Moderated-Structural-Equations were used to test whether ADHD traits moderate the relationship between ISV and gf. RESULTS: σ and µ of the ex-Gaussian model as well as DDM parameters drift rate (v) and boundary separation (a) significantly predicted general ADHD traits, while τ predicted attention difficulties specifically. Across the ADHD continuum, σ and v were significant predictors of gf. CONCLUSION: The results confirm the link between ISV and ADHD. The relationship between ISV and gf appears stable across the ADHD continuum.

Heterogeneous Mental Health Responses to the COVID-19 Pandemic in Germany: An Examination of Long-Term Trajectories, Risk Factors, and Vulnerable Groups.

Abundant studies have examined mental health in the early periods of the COVID-19 pandemic. However, empirical work examining the mental health impact of the pandemic's subsequent phases remains limited. In the present study, we investigated how mental vulnerability and resilience evolved over the various phases of the pandemic in 2020 and 2021 in Germany. Data were collected (n = 3522) across seven measurement occasions using validated and self-generated measures of vulnerability and resilience. We found evidence for an immediate increase in vulnerability during the first lockdown in Germany, a trend towards recovery when lockdown measures were eased, and an increase in vulnerability with each passing month of the second lockdown. Four different latent trajectories of resilience-vulnerability emerged, with the majority of participants displaying a rather resilient trajectory, but nearly 30% of the sample fell into the more vulnerable groups. Females, younger individuals, those with a history of psychiatric disorders, lower income groups, and those with high trait vulnerability and low trait social belonging were more likely to exhibit trajectories associated with poorer mental well-being. Our findings indicate that resilience-vulnerability responses in Germany during the COVID-19 pandemic may have been more complex than previously thought, identifying risk groups that could benefit from greater support.

Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models.

Graph-based causal models are a flexible tool for causal inference from observational data. In this paper, we develop a comprehensive framework to define, identify, and estimate a broad class of causal quantities in linearly parametrized graph-based models. The proposed method extends the literature, which mainly focuses on causal effects on the mean level and the variance of an outcome variable. For example, we show how to compute the probability that an outcome variable realizes within a target range of values given an intervention, a causal quantity we refer to as the probability of treatment success. We link graph-based causal quantities defined via the do-operator to parameters of the model implied distribution of the observed variables using so-called causal effect functions. Based on these causal effect functions, we propose estimators for causal quantities and show that these estimators are consistent and converge at a rate of [Formula: see text] under standard assumptions. Thus, causal quantities can be estimated based on sample sizes that are typically available in the social and behavioral sciences. In case of maximum likelihood estimation, the estimators are asymptotically efficient. We illustrate the proposed method with an example based on empirical data, placing special emphasis on the difference between the interventional and conditional distribution.

Exploring the Structure and Interrelations of Time-Stable Psychological Resilience, Psychological Vulnerability, and Social Cohesion.

The current study explores the relationship between three constructs of high relevance in the context of adversities which have, however, not yet been systematically linked on the level of psychological dispositions: psychological vulnerability, psychological resilience, and social cohesion. Based on previous theoretical and empirical frameworks, a collection of trait questionnaires was assessed in a Berlin sample of 3,522 subjects between 18 and 65 years of age. Using a confirmatory factor analytical approach, we found no support for a simple three-factor structure. Results from exploratory structural analyses suggest that instead of psychological resilience and psychological vulnerability constituting two separate factors, respective indicators load on one bipolar latent factor. Interestingly, some psychological resilience indicators contributed to an additional specific latent factor, which may be interpreted as adaptive capacities, that is, abilities to adapt to changes or adjust to consequences of adversities. Furthermore, instead of evidence for one single social cohesion factor on the psychological level, indicators of perceived social support and loneliness formed another specific factor of social belonging, while indicators of prosocial competencies were found to form yet another distinct factor, which was positively associated to the other social factors, adaptive capacities and social belonging. Our results suggest that social cohesion is composed of different independent psychological components, such as trust, social belonging, and social skills. Furthermore, our findings highlight the importance of social capacities and belonging for psychological resilience and suggest that decreasing loneliness and increasing social skills should therefore represent a valuable intervention strategy to foster adaptive capacities.

Coping with the COVID-19 Pandemic: Perceived Changes in Psychological Vulnerability, Resilience and Social Cohesion before, during and after Lockdown.

The COVID-19 pandemic and associated lockdowns have posed unique and severe challenges to our global society. To gain an integrative understanding of pervasive social and mental health impacts in 3522 Berlin residents aged 18 to 65, we systematically investigated the structural and temporal relationship between a variety of psychological indicators of vulnerability, resilience and social cohesion before, during and after the first lockdown in Germany using a retrospective longitudinal study design. Factor analyses revealed that (a) vulnerability and resilience indicators converged on one general bipolar factor, (b) residual variance of resilience indicators formed a distinct factor of adaptive coping capacities and (c) social cohesion could be reliably measured with a hierarchical model including four first-order dimensions of trust, a sense of belonging, social interactions and social engagement, and one second-order social cohesion factor. In the second step, latent change score models revealed that overall psychological vulnerability increased during the first lockdown and decreased again during re-opening, although not to baseline levels. Levels of social cohesion, in contrast, first decreased and then increased again during re-opening. Furthermore, participants who increased in vulnerability simultaneously decreased in social cohesion and adaptive coping during lockdown. While higher pre-lockdown levels of social cohesion predicted a stronger lockdown effect on mental health, individuals with higher social cohesion during the lockdown and positive change in coping abilities and social cohesion during re-opening showed better mental health recovery, highlighting the important role of social capacities in both amplifying but also overcoming the multiple challenges of this collective crisis.

Psychopathological networks: Theory, methods and practice.

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.