Sex Differences in Resting State Brain Function of Cigarette Smokers and Links to Nicotine Dependence

Sex – a marker of biological and social individual differences – matters for drug use, particularly for cigarette smoking, which is the leading cause of preventable death in the United States. More men than women smoke, but women are less likely than men to quit. Resting state brain function, or intrinsic brain activity that occurs in the absence of a goal-directed task, is important for understanding cigarette smoking, as it has been shown to differentiate between smokers and non-smokers. But, it is unclear whether and how sex influences the link between resting state brain function and smoking behavior. In this study, we demonstrate that sex is indeed associated with resting state connectivity in cigarette smokers, and that sex moderates the link between resting state connectivity and self-reported nicotine dependence. Using functional magnetic resonance imaging and behavioral data from 50 adult daily smokers (23 women), we found that women had greater connectivity than men within the default mode network, and that increased connectivity within the reward network was related to increased nicotine tolerance in women but to decreased nicotine tolerance in men. Findings highlight the importance of sex-related individual differences reflected in resting state connectivity for understanding the etiology and treatment of substance use problems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/123046/1/Beltz, Berenbaum, Wilson. Sex differences in Resting State brain function of cigarette smokers and links to nicotine dependence..pd

Ovarian hormones: a long overlooked but critical contributor to cognitive brain structures and function

Cognitive neuroscience research has traditionally overlooked half of the population. Arguing that variability in ovarian hormones confounds empirical findings, girls and women have been excluded from research for decades. But times are changing. This review summarizes historical trends that have led to a knowledge gap in the role of ovarian hormones in neuroscience, synthesizes recent findings on ovarian hormone contributions to cognitive brain structures and function, and highlights areas ripe for future work. This is accomplished by reviewing research that has leveraged natural experiments in humans across the life span that focus on puberty, the menstrual cycle, hormonal contraceptive use, menopause, and menopausal hormone therapy. Although findings must be considered in light of study designs (e.g., sample characteristics and group comparisons versus randomized crossover trials), across natural experiments there is consistent evidence for associations of estradiol with cortical thickness, especially in frontal regions, and hippocampal volumes, as well as with frontal regions during cognitive processing. There are also emerging investigations of resting state connectivity and progesterone along with exciting opportunities for future work, particularly concerning biopsychosocial moderators of and individual differences in effects in novel natural experiments. Thus, delineating complex ovarian hormone contributions to cognitive brain structures and function will advance neuroscience.This review summarizes historical trends that have led to a knowledge gap in the role of ovarian hormones in neuroscience, synthesizes recent findings on ovarian hormone contributions to cognitive brain structures and function, and highlights areas ripe for future work.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154624/1/nyas14255_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154624/2/nyas14255.pd

Early Androgen Effects on Spatial and Mechanical Abilities: Evidence from Congenital Adrenal Hyperplasia

There is considerable controversy about the origins of sex differences in cognitive abilities, particularly the male superiority in spatial abilities. We studied effects of early androgens on spatial and mechanical abilities in adolescents and young adults with congenital adrenal hyperplasia (CAH). On tests of three-dimensional mental rotations, geography, and mechanical knowledge, females with CAH scored higher than their unaffected sisters, and males with CAH scored lower than their unaffected brothers. Exploratory regression analyses suggest that androgens affect spatial ability in females directly and through male-typed activity interests. Findings indicate that early androgens influence spatial and mechanical abilities, and that androgen effects on abilities may occur in part through effects on sex-typed activity interests.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/123051/1/Early Androgen Effects on Spatial and Mechanical abilities_evidence from Congenital Adrenal Hyperplasia.pd

How early hormones shape gender development

https://deepblue.lib.umich.edu/bitstream/2027.42/137657/1/Brenebaum(2016)Howearlyhormones.pd

Mapping temporal dynamics in social interactions with unified structural equation modeling: A description and demonstration revealing time-dependent sex differences in play behavior

Developmental science is rich with observations of social interactions, but few available methodological and statistical approaches take full advantage of the information provided by these data. The authors propose implementation of the unified structural equation model (uSEM), a network analysis technique, for observational data coded repeatedly across time; uSEM captures the temporal dynamics underlying changes in behavior at the individual level by revealing the ways in which a single person influences – concurrently and in the future – other people. To demonstrate the utility of uSEM, the authors applied it to ratings of positive affect and vigor of activity during children’s unstructured laboratory play with unfamiliar, same-sex peers. Results revealed the time-dependent nature of sex differences in play behavior. For girls more than boys, positive affect was dependent upon peers’ prior positive affect. For boys more than girls, vigor of activity was dependent upon peers’ current vigor of activity.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/123050/1/Mapping temporal dynamics in social interactions with unified structural equation modeling_ A description and demonstration revealing time-dependent sex differences in play behavior.pd

Modeling Pubertal Timing and Tempo and Examining Links to Behavior Problems

Research on the role of puberty in adolescent psychological development requires attention to the meaning and measurement of pubertal development. Particular questions concern the utility of self-report, the need for complex models to describe pubertal development, the psychological significance of pubertal timing vs. tempo, and sex differences in the nature and psychological significance of pubertal development. We used longitudinal self-report data to model linear and logistic trajectories of pubertal development, and used timing and tempo estimates from these models, and from traditional approaches (age at menarche and time from onset of breast development to menarche), to predict psychological outcomes of internalizing and externalizing behavior problems, and early sexual activity. Participants (738 girls, 781 boys) reported annually from ages 9 through 15 on their pubertal development, and they and their parents reported on their behavior in mid-to-late adolescence and early adulthood. Self-reports of pubertal development provided meaningful data for both boys and girls, producing good trajectories, and estimates of individuals' pubertal timing and tempo. A logistic model best fit the group data. Pubertal timing was estimated to be earlier in the logistic compared to linear model, but linear, logistic, and traditional estimates of pubertal timing correlated highly with each other and similarly with psychological outcomes. Pubertal tempo was not consistently estimated, and associations of tempo with timing and with behavior were model dependent. Advances in modeling facilitate the study of some questions about pubertal development, but assumptions of the models affect their utility in psychological studies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/123048/1/Modeling Pubertal Timing and Tempo and Examining Links to Behavior Problems.pd

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

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148344/1/jora12371.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148344/2/jora12371_am.pd

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

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

ObjectiveEmotional 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.MethodGroup 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.ResultsGIMME 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.DiscussionThis 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.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146371/1/eat22902.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146371/2/eat22902_am.pd

Examining the Dynamic Structure of Daily Internalizing and Externalizing Behavior at Multiple Levels of Analysis

Psychiatric diagnostic covariation suggests that the underlying structure of psychopathology is not one of circumscribed disorders. Quantitative modeling of individual differences in diagnostic patterns has uncovered several broad domains of mental disorder liability, of which the Internalizing and Externalizing spectra have garnered the greatest support. These dimensions have generally been estimated from lifetime or past-year comorbidity patters, which are distal from the covariation of symptoms and maladaptive behavior that ebb and flow in daily life. In this study, structural models are applied to daily diary data (Median = 94 days) of maladaptive behaviors collected from a sample (N = 101) of individuals diagnosed with personality disorders. Using multilevel and unified structural equation modeling, between-person, within-person, and person-specific structures were estimated from 16 behaviors that are encompassed by the Internalizing and Externalizing spectra. At the between-person level (i.e., individual differences in average endorsement across days) we found support for a two-factor Internalizing-Externalizing model, which exhibits significant associations with corresponding diagnostic spectra. At the within-person level (i.e., dynamic covariation among daily behavior pooled across individuals) we found support for a more differentiated, four-factor, Negative Affect-Detachment-Hostility-Impulsivity structure. Finally, we demonstrate that the person-specific structures of associations between these four domains are highly idiosyncratic