Hormonal contraceptives and behavior: Updating the potent state of the nascent science.

Hundreds of millions of people worldwide use hormonal contraceptives (HCs), which have been an essential part of women's reproductive health care for decades. Throughout that time, however, research on the neural and behavioral consequences of HCs was minimal and plagued by poor methodology. HC effects - and users - were assumed to be homogenous. Fortunately, there has been a recent upswell in the number and quality of investigations, affording tentative conclusions about the roles of HCs in spatial cognition and mental health, particularly depression. Thus, this paper leverages findings from the past few years to highlight the heterogeneous aspects of use that seem to matter for behavior - ranging from variation in hormonal contraceptive formulations and routes of administration to individual differences among users linked to age and reproductive health history. This paper closes with five tips for future research that will help capture and clarify heterogeneity in potential relations between HCs and behavior, namely data collection, regional access, lifespan factors, gender, and collaboration. HCs are sociopolitically provocative and research on their potential behavioral neuroendocrine impacts is becoming increasingly popular. It is, therefore, imperative for scientists to conduct replicable and robust empirical investigations, and to communicate findings with the nuance that the heterogeneity among users and effects requires.

Like No Other? A Family-Specific Network Approach to Parenting Adolescents.

Numerous theories suggest that parents and adolescents influence each other in diverse ways; however, whether these influences differ between subgroups or are unique to each family remains uncertain. Therefore, this study explored whether data-driven subgroups of families emerged that exhibited a similar daily interplay between parenting and adolescent affective well-being. To do so, Subgrouping Group Iterative Multiple Model Estimation (S-GIMME) was used to estimate family-specific dynamic network models, containing same- and next-day associations among five parenting practices (i.e., warmth, autonomy support, psychological control, strictness, monitoring) and adolescent positive and negative affect. These family-specific networks were estimated for 129 adolescents (Mage = 13.3, SDage = 1.2, 64% female, 87% Dutch), who reported each day on parenting and their affect for 100 consecutive days. The findings of S-GIMME did not identify data-driven subgroups sharing similar parenting-affect associations. Instead, each family displayed a unique pattern of temporal associations between the different practices and adolescent affect. Thus, the ways in which parenting practices were related to adolescents' affect in everyday life were family specific.

Hormonal contraceptive influences on cognition and psychopathology: Past methods, present inferences, and future directions.

In the last decade, there has been a remarkable surge in research on the neural and behavioral correlates of hormonal contraceptive use, particularly oral contraceptive use. Questions have evolved swiftly and notably, with studies no longer revealing if hormonal contraceptives matter for the brain and behavior, but rather how, when, and for whom they matter most. Paralleling this shift, the goal of this review is to move beyond an average synthesis of hormonal contraceptive influences on human cognition and psychopathology (and their neural substrates) in order to consider the nature and specificity of effects. Accompanied by an evaluation of study methods and informed by findings from animal models, this consideration uncovers promising areas of research in the next ten years, including potential activational and organizational effects of hormonal contraceptive use, individual differences in effects that matter for the wellbeing of unique individuals, and correlates of intrauterine device use.

Prenatal androgen influences on the brain: A review, critique, and illustration of research on congenital adrenal hyperplasia.

Sex hormones, especially androgens, contribute to sex and gender differences in the brain and behavior. Organizational effects are particularly important because they are thought to be permanent, reflecting hormone exposure during sensitive periods of development. In human beings, they are often studied with natural experiments in which sex hormones are dissociated from other biopsychosocial aspects of development, such as genes and experiences. Indeed, the greatest evidence for organizational effects on sex differences in human behavior comes from studies of females with congenital adrenal hyperplasia (CAH), who have heightened prenatal androgen exposure, female-typical rearing, and masculinized toy play, activity and career interests, spatial skills, and some personal characteristics. Interestingly, however, neuroimaging studies of females with CAH have revealed few neural mechanisms underlying these hormone-behavior links, with the exception of emotion processing; studies have instead shown reduced gray matter volumes and reduced white matter integrity most consistent with other disease-related processes. The goals of this narrative review are to: (a) describe methods for studying prenatal androgen influences, while offering a brief overview of behavioral outcomes; (b) provide a critical methodological review of neuroimaging research on females with CAH; (c) present an illustrative analysis that overcomes methodological limitations of previous work, focusing on person-specific neural reward networks (and their associations with sensation seeking) in women with CAH and their unaffected sisters in order to inform future research questions and approaches that are most likely to reveal organizational hormone effects on brain structure and function.

Determining menstrual cycle phase: An empirical examination of methodologies and recommendations for improvement in behavioral and brain sciences.

The recent decade has brought an exciting proliferation of behavioral, psychological and neuroscientific research involving the menstrual cycle. However, the reliability and validity of many popular methodologies for determining menstrual cycle phase lack empirical examination. These under-investigated methods include: (1) predicting menstrual cycle phase using self-report information only (e.g., "count" methods), (2) utilizing ovarian hormone ranges to determine menstrual cycle phase, and (3) using ovarian hormone changes from limited measurements (e.g., two time points) to determine menstrual cycle phase. In the current study, we examine the accuracy of these methods for menstrual cycle phase determination using 35-day within-person assessments of circulating ovarian hormones from 96 females across the menstrual cycle. Findings indicate that all three common methods are error-prone, resulting in phases being incorrectly determined for many participants, with Cohen's kappa estimates ranging from -0.13 to 0.53 indicating disagreement to only moderate agreement depending on the comparison. Such methodological challenges are surmountable through careful study design, more frequent hormone assays (when possible), and utilization of sophisticated statistical methods. With increased methodological rigor in behavioral, psychological and neuroscientific research, the field will be poised to detect biobehavioral correlates of ovarian hormone fluctuations for the betterment of the mental health and wellbeing of millions of females.

Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity.

BACKGROUND: Stressful events, such as the COVID-19 pandemic, are major contributors to anxiety and depression, but only a subset of individuals develop psychopathology. In a population-based sample (N = 174) with a high representation of marginalized individuals, this study examined adolescent functional network connectivity as a marker of susceptibility to anxiety and depression in the context of adverse experiences. METHODS: Data-driven network-based subgroups were identified using an unsupervised community detection algorithm within functional neural connectivity. Neuroimaging data collected during emotion processing (age 15) were extracted from a priori regions of interest linked to anxiety and depression. Symptoms were self-reported at ages 15, 17, and 21 (during COVID-19). During COVID-19, participants reported on pandemic-related economic adversity. Differences across subgroup networks were first examined, then subgroup membership and subgroup-adversity interaction were tested to predict change in symptoms over time. RESULTS: Two subgroups were identified: Subgroup A, characterized by relatively greater neural network variation (i.e., heterogeneity) and density with more connections involving the amygdala, subgenual cingulate, and ventral striatum; and the more homogenous Subgroup B, with more connections involving the insula and dorsal anterior cingulate. Accounting for initial symptoms, subgroup A individuals had greater increases in symptoms across time (ß = .138, p = .042), and this result remained after adjusting for additional covariates (ß = .194, p = .023). Furthermore, there was a subgroup-adversity interaction: compared with Subgroup B, Subgroup A reported greater anxiety during the pandemic in response to reported economic adversity (ß = .307, p = .006), and this remained after accounting for initial symptoms and many covariates (ß = .237, p = .021). CONCLUSIONS: A subgrouping algorithm identified young adults who were susceptible to adversity using their personalized functional network profiles derived from a priori brain regions. These results highlight potential prospective neural signatures involving heterogeneous emotion networks that predict individuals at the greatest risk for anxiety when experiencing adverse events.

Personalized Neural Networks Underlie Individual Differences in Ethnic Identity Exploration and Resolution.

This study examined how ethnic identity relates to large-scale brain networks implicated in social interactions, social cognition, self-definition, and cognitive control. Group Iterative Multiple Model Estimation (GIMME) was used to create sparse, person-specific networks among the default mode and frontoparietal resting-state networks in a diverse sample of 104 youths aged 17-21. Links between neural density (i.e., number of connections within and between these networks) and ethnic identity exploration and resolution were evaluated in the full sample. Ethnic identity resolution was positively related to frontoparietal network density, suggesting that having clarity about one's ethnic group membership is associated with brain network organization reflecting cognitive control. These findings help fill a critical knowledge gap about the neural underpinnings of ethnic identity.

Daily Gender and Cognition: A Person-Specific Behavioral Network Analysis.

Gender is person-specific, and it influences and is influenced by a breadth of multidimensional psychological factors, including cognition. Directionality is important for research on gender and cognition, as debate surrounds, for instance, whether masculine self-concepts precede spatial skills, or whether the reverse is true. In order to provide novel insights into the individualized nature of these relations, a person-specific network approach devised by Peter Molenaar and the first author - group iterative multiple model estimation for multiple solutions (GIMME-MS) - was applied to 75-day intensive longitudinal data on gender self-concept (i.e., femininity-masculinity, instrumentality, and expressivity) and cognition (i.e., mental rotations and verbal recall) from 103 young adults. GIMME-MS estimates individualized networks that contain same-day and next-day directed relations, prioritizing relations common across participants. It is ideal for analyzing behavioral time series with unclear directionality, as it generates multiple solutions from which an optimal one is selected. GIMME-MS revealed notable heterogeneity in the presence, direction, and nature of relations from gender self-concept to cognition (∼26% of participants) and vice versa (∼21% of participants). Findings are wholly novel in revealing the person-specific nature of gender and its cognitive dynamics, yet somehow, unsurprising given the revolutionary corpus of Peter Molenaar.

Neural heterogeneity underlying late adolescent motivational processing is linked to individual differences in behavioral sensation seeking.

Adolescent risk-taking, including sensation seeking (SS), is often attributed to developmental changes in connectivity among brain regions implicated in cognitive control and reward processing. Despite considerable scientific and popular interest in this neurodevelopmental framework, there are few empirical investigations of adolescent functional connectivity, let alone examinations of its links to SS behavior. The studies that have been done focus on mean-based approaches and leave unanswered questions about individual differences in neurodevelopment and behavior. The goal of this paper is to take a person-specific approach to the study of adolescent functional connectivity during a continuous motivational state, and to examine links between connectivity and self-reported SS behavior in 104 adolescents (MAge  = 19.3; SDAge  = 1.3). Using Group Iterative Multiple Model Estimation (GIMME), person-specific connectivity during two neuroimaging runs of a monetary incentive delay task was estimated among 12 a priori brain regions of interest representing reward, cognitive, and salience networks. Two data-driven subgroups were detected, a finding that was consistent between both neuroimaging runs, but associations with SS were only found in the first run, potentially reflecting neural habituation in the second run. Specifically, the subgroup that had unique connections between reward-related regions had greater SS and showed a distinctive relation between connectivity strength in the reward regions and SS. These findings provide novel evidence for heterogeneity in adolescent brain-behavior relations by showing that subsets of adolescents have unique associations between neural motivational processing and SS. Findings have broader implications for future work on reward processing, as they demonstrate that brain-behavior relations may attenuate across runs.

Visual speech differentially modulates beta, theta, and high gamma bands in auditory cortex.

Speech perception is a central component of social communication. Although principally an auditory process, accurate speech perception in everyday settings is supported by meaningful information extracted from visual cues. Visual speech modulates activity in cortical areas subserving auditory speech perception including the superior temporal gyrus (STG). However, it is unknown whether visual modulation of auditory processing is a unitary phenomenon or, rather, consists of multiple functionally distinct processes. To explore this question, we examined neural responses to audiovisual speech measured from intracranially implanted electrodes in 21 patients with epilepsy. We found that visual speech modulated auditory processes in the STG in multiple ways, eliciting temporally and spatially distinct patterns of activity that differed across frequency bands. In the theta band, visual speech suppressed the auditory response from before auditory speech onset to after auditory speech onset (-93 to 500 ms) most strongly in the posterior STG. In the beta band, suppression was seen in the anterior STG from -311 to -195 ms before auditory speech onset and in the middle STG from -195 to 235 ms after speech onset. In high gamma, visual speech enhanced the auditory response from -45 to 24 ms only in the posterior STG. We interpret the visual-induced changes prior to speech onset as reflecting crossmodal prediction of speech signals. In contrast, modulations after sound onset may reflect a decrease in sustained feedforward auditory activity. These results are consistent with models that posit multiple distinct mechanisms supporting audiovisual speech perception.

Personalized models of personality disorders: using a temporal network method to understand symptomatology and daily functioning in a clinical sample.

BACKGROUND: An ongoing challenge in understanding and treating personality disorders (PDs) is a significant heterogeneity in disorder expression, stemming from variability in underlying dynamic processes. These processes are commonly discussed in clinical settings, but are rarely empirically studied due to their personalized, temporal nature. The goal of the current study was to combine intensive longitudinal data collection with person-specific temporal network models to produce individualized symptom-level structures of personality pathology. These structures were then linked to traditional PD diagnoses and stress (to index daily functioning). METHODS: Using about 100 daily assessments of internalizing and externalizing domains underlying PDs (i.e. negative affect, detachment, impulsivity, hostility), a temporal network mapping approach (i.e. group iterative multiple model estimation) was used to create person-specific networks of the temporal relations among domains for 91 individuals (62.6% female) with a PD. Network characteristics were then associated with traditional PD symptomatology (controlling for mean domain levels) and with daily variation in clinically-relevant phenomena (i.e. stress). RESULTS: Features of the person-specific networks predicted paranoid, borderline, narcissistic, and obsessive-PD symptom counts above average levels of the domains, in ways that align with clinical conceptualizations. They also predicted between-person variation in stress across days. CONCLUSIONS: Relations among behavioral domains thought to underlie heterogeneity in PDs were indeed associated with traditional diagnostic constructs and with daily functioning (i.e. stress) in person-specific networks. Findings highlight the importance of leveraging data and models that capture person-specific, dynamic processes, and suggest that person-specific networks may have implications for precision medicine.

Does puberty affect the development of behavior problems as a mediator, moderator, or unique predictor?

Pubertal timing matters for psychological development. Early maturation in girls is linked to risk for depression and externalizing problems in adolescence and possibly adulthood, and early and late maturation in boys are linked to depression. It is unclear whether pubertal timing uniquely predicts problems; it might instead mediate the continuity of behavior problems from childhood to adolescence or create psychological risk specifically in youth with existing problems, thus moderating the link. We investigated these issues in 534 girls and 550 boys, measuring pubertal timing by a logistic model fit to annual self-report measures of development and, in girls, age at menarche. Prepuberty internalizing and externalizing behavior problems were reported by parents. Adolescent behavior problems were reported by parents and youth. As expected, behavior problems were moderately stable. Pubertal timing was not predicted by childhood problems, so it did not mediate the continuity of behavior problems from childhood to adolescence. Pubertal timing did not moderate links between early and later problems for girls. For boys, early maturation accentuated the link between childhood problems and adolescent substance use. Overall, the replicated links between puberty and behavior problems appear to reflect the unique effects of puberty and child behavior problems on the development of adolescent behavior problems.

Understanding Puberty and Its Measurement: Ideas for Research in a New Generation.

The measurement of puberty is an intricate and precise task, requiring a match between participants' developmental age and appropriate techniques to identify and capture variations in maturation. Much of the foundational work on puberty and its psychosocial correlates was conducted several decades ago. In this article, we review the biological foundation of puberty; the operationalization of puberty in statistical analyses; and strategies for considering diversity and social context in research to help researchers align measurement with meaningful conceptual questions. These three areas are particularly important, given new statistical techniques, greater awareness of individual variations in development, and key differences between past cohorts and youth coming of age today.

Downstream consequences of pubertal timing for young women's body beliefs.

INTRODUCTION: Puberty is a critical biopsychosocial event that has long-term consequences for adolescents' behavior and well-being. Research has shown that developing earlier than one's peers may worsen adolescent girls' body image, in part due to weight redistribution and increases in adiposity resulting from pubertal development. However, research has yet to examine if pubertal timing is associated with girls' body beliefs related to self-objectification, self-sexualization, and positive body image beyond the adolescent years. We address that issue here. METHODS: Participants were 287 undergraduate White women (Mage = 18.62, SDage = 0.93) from the United States who completed surveys containing a retrospective report of pubertal timing and several scales assessing their current body beliefs. RESULTS: Regression analyses demonstrated that earlier pubertal timing was linked to greater body surveillance, greater sex appeal self-worth, and less body appreciation, but not to body shame or enjoyment of sexualization, which were negatively related to age. CONCLUSIONS: This study provides preliminary evidence that pubertal timing has downstream consequences for a variety of women's body beliefs. However, older women reported less body shame and less enjoyment of sexualization than younger women, suggesting possible age (and not puberty-) related developmental trends in these two body beliefs. These findings highlight a need for future work with larger, more diverse samples and longitudinal data.

Using person-specific neural networks to characterize heterogeneity in eating disorders: Illustrative links between emotional eating and ovarian hormones.

OBJECTIVE: Emotional eating has been linked to ovarian hormone functioning, but no studies to-date have considered the role of brain function. This knowledge gap may stem from methodological challenges: Data are heterogeneous, violating assumptions of homogeneity made by between-subjects analyses. The primary aim of this paper is to describe an innovative within-subjects analysis that models heterogeneity and has potential for filling knowledge gaps in eating disorder research. We illustrate its utility in an application to pilot neuroimaging, hormone, and emotional eating data across the menstrual cycle. METHOD: Group iterative multiple model estimation (GIMME) is a person-specific network approach for estimating sample-, subgroup-, and individual-level connections between brain regions. To illustrate its potential for eating disorder research, we apply it to pilot data from 10 female twins (N = 5 pairs) discordant for emotional eating and/or anxiety, who provided two resting state fMRI scans and hormone assays. We then demonstrate how the multimodal data can be linked in multilevel models. RESULTS: GIMME generated person-specific neural networks that contained connections common across the sample, shared between co-twins, and unique to individuals. Illustrative analyses revealed positive relations between hormones and default mode connectivity strength for control twins, but no relations for their co-twins who engage in emotional eating or who had anxiety. DISCUSSION: This paper showcases the value of person-specific neuroimaging network analysis and its multimodal associations in the study of heterogeneous biopsychosocial phenomena, such as eating behavior.

Gendered Peer Involvement in Girls with Congenital Adrenal Hyperplasia: Effects of Prenatal Androgens, Gendered Activities, and Gender Cognitions.

A key question in understanding gender development concerns the origins of sex segregation. Children's tendencies to interact with same-sex others have been hypothesized to result from gender identity and cognitions, behavioral compatibility, and personal characteristics. We examined whether prenatal androgen exposure was related to time spent with boys and girls, and how that gendered peer involvement was related to sex-typed activities and gender identity and cognitions. We studied 54 girls with congenital adrenal hyperplasia (CAH) aged 10-13 years varying in degree of prenatal androgen exposure: 40 girls with classical CAH (C-CAH) exposed to high prenatal androgens and 14 girls with non-classical CAH (NC-CAH) exposed to low, female-typical, prenatal androgens. Home interviews and questionnaires provided assessments of gendered activity interests and participation, gender identity, and gender cognitions. Daily phone calls over 7 days assessed time spent in gendered activities and with peers. Girls with both C-CAH and NC-CAH interacted more with girls than with boys, with no significant group differences. The groups did not differ significantly in gender identity or gender cognitions, but girls with C-CAH spent more time in male-typed activities and less time in female-typed activities than did girls with NC-CAH. Time spent with girls reflected direct effects of gender identity/cognitions and gender-typed activities, and an indirect effect of prenatal androgens (CAH type) through gender-typed activities. Our results extend findings that prenatal androgens differentially affect gendered characteristics and that gendered peer interactions reflect combined effects of behavioral compatibility and feelings and cognitions about gender. The study also shows the value of natural experiments for testing hypotheses about gender development.

From Genes to Behavior Through Sex Hormones and Socialization: The Example of Gender Development.

Irving Gottesman is known primarily for his work in psychopathology, but he also had a long-standing interest in understanding psychological development generally (typical and atypical). Through his mentorship, he also influenced work in gender development. Characteristics related to sex and gender are ideally suited to study the interplay of genes and environment across development. We discuss how gender development is influenced by gonadal hormones present during early life, but not in a simple way. We describe some of the challenges and opportunities to extend our understanding of the complexity of gender development. Throughout, we consider the kinds of questions Gottesman would likely have asked and emphasize his influence on our work.

Connecting Theory and Methods in Adolescent Brain Research.

Networks are often implicated in theories of adolescent brain development, but they are not regularly examined in empirical studies. The aim of this article is to address this disconnect between theory and quantitative methodology, using the dual systems model of adolescent decision making as a prototype. After reviewing the key task-related connectivity methods that have been applied in the adolescent neuroimaging literature (seed-based correlations, psychophysiological interactions, and dynamic causal modeling), a novel connectivity method is introduced (extended unified structural equation modeling). The potential of this method for understanding adolescent brain development is showcased with a simulation study: It creates person-specific networks that have direct and time-lagged connections that can be modulated by behavior.

Network Mapping with GIMME.

Network science is booming! While the insights and images afforded by network mapping techniques are compelling, implementing the techniques is often daunting to researchers. Thus, the aim of this tutorial is to facilitate implementation in the context of GIMME, or group iterative multiple model estimation. GIMME is an automated network analysis approach for intensive longitudinal data. It creates person-specific networks that explain how variables are related in a system. The relations can signify current or future prediction that is common across people or applicable only to an individual. The tutorial begins with conceptual and mathematical descriptions of GIMME. It proceeds with a practical discussion of analysis steps, including data acquisition, preprocessing, program operation, a posteriori testing of model assumptions, and interpretation of results; throughout, a small empirical data set is analyzed to showcase the GIMME analysis pipeline. The tutorial closes with a brief overview of extensions to GIMME that may interest researchers whose questions and data sets have certain features. By the end of the tutorial, researchers will be equipped to begin analyzing the temporal dynamics of their heterogeneous time series data with GIMME.

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps.

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample.