Disparities in Depression and Anxiety That Impact Self-Identified Sexual Minority People Affect a Broader Group of Same-Gender Attracted Young Adults.

PURPOSE: Prior work suggests sexual minority (e.g., gay, bisexual) young adults are at greater risk for depression and anxiety. However, the majority of said work focuses exclusively on self-reported sexual minority identity and neglects same-gender attraction. The current study aimed to characterize links between identity- and attraction-based indicators of sexual minority status and depression and anxiety in young adults, and to examine the ongoing significance of caregiver support in mental health during this key developmental period. METHODS: 386 youth (mean age = 19.92 years; SD = 1.39) reported their sexual orientation identity and experiences of attraction toward men and/or women. Participants also reported on anxiety, depression, and caregiver social support. RESULTS: While less than 16% of participants identified as sexual minority individuals, nearly half reported same-gender attraction. Self-identified sexual minority participants reported significantly higher depression and anxiety than self-identified heterosexual participants. Similarly, same-gender attracted individuals exhibited heightened depression and anxiety compared to exclusively different-gender attracted individuals. Greater caregiver social support predicted lower depression and anxiety. DISCUSSION: The present findings suggest that not only are self-identified sexual minority individuals at heightened risk for depression and anxiety symptoms, but also that this risk extends to a larger group of young people who experience same-gender attraction. These results demonstrate that better mental health supports may be needed for youth who identify as sexual minority individuals or report same-gender attraction. That higher caregiver social support was associated with lower mental illness risk suggests caregivers may be key to mental health promotion during young adulthood.

Value-based neural representations predict social decision preferences.

Social decision-making is omnipresent in everyday life, carrying the potential for both positive and negative consequences for the decision-maker and those closest to them. While evidence suggests that decision-makers use value-based heuristics to guide choice behavior, very little is known about how decision-makers' representations of other agents influence social choice behavior. We used multivariate pattern expression analyses on fMRI data to understand how value-based processes shape neural representations of those affected by one's social decisions and whether value-based encoding is associated with social decision preferences. We found that stronger value-based encoding of a given close other (e.g. parent) relative to a second close other (e.g. friend) was associated with a greater propensity to favor the former during subsequent social decision-making. These results are the first to our knowledge to explicitly show that value-based processes affect decision behavior via representations of close others.

Sensory processing challenges as a novel link between early caregiving experiences and mental health.

Early caregiving adversity (ECA) is associated with elevated psychological symptomatology. While neurobehavioral ECA research has focused on socioemotional and cognitive development, ECA may also increase risk for "low-level" sensory processing challenges. However, no prior work has compared how diverse ECA exposures differentially relate to sensory processing, or, critically, how this might influence psychological outcomes. We examined sensory processing challenges in 183 8-17-year-old youth with and without histories of institutional (orphanage) or foster caregiving, with a particular focus on sensory over-responsivity (SOR), a pattern of intensified responses to sensory stimuli that may negatively impact mental health. We further tested whether sensory processing challenges are linked to elevated internalizing and externalizing symptoms common in ECA-exposed youth. Relative to nonadopted comparison youth, both groups of ECA-exposed youth had elevated sensory processing challenges, including SOR, and also had heightened internalizing and externalizing symptoms. Additionally, we found significant indirect effects of ECA on internalizing and externalizing symptoms through both general sensory processing challenges and SOR, covarying for age and sex assigned at birth. These findings suggest multiple forms of ECA confer risk for sensory processing challenges that may contribute to mental health outcomes, and motivate continuing examination of these symptoms, with possible long-term implications for screening and treatment following ECA.

Maternal early life stress is associated with pro-inflammatory processes during pregnancy.

Early life stress (ELS) is common in the United States and worldwide, and contributes to the development of psychopathology in individuals with these experiences and their offspring. A growing body of research suggests that early life stress may contribute to adverse health partly through modulation of immune (and particularly inflammatory) responses. Therefore, increased maternal prenatal inflammation has been proposed as a mechanistic pathway by which the observed cross-generational effects of parental early life stress on child neuropsychiatric outcomes may be exerted. We examined associations between early life stress and molecular markers of inflammation (specifically pro-inflammatory gene expression and receptor-mediated transcription factor activity) and a commonly studied circulating marker of inflammation (C-Reactive Protein) in a diverse group of women in or near their third trimester of pregnancy, covarying for age, race/ethnicity, BMI, concurrent infection, concurrent perceived stress, and per capita household income. Mothers who experienced higher levels of early life stress had significantly increased pro-inflammatory (NF-κB) and decreased anti-viral (IRF) transcription factor activity. Transcripts that were up or down regulated in mothers with high ELS were preferentially derived from both CD16+ and CD16- monocytes. Early life stress was not associated with elevated CRP. Taken together, these findings provide preliminary evidence for an association between ELS and a pro-inflammatory transcriptional phenotype during pregnancy that may serve as a mechanistic pathway for cross-generational transmission of the effects of early life stress on mental and physical health.

Early caregiving adversity differentially shapes behavioral sensitivity to reward and risk during decision-making.

Cognitive systems that track, update, and utilize information about reward (consequences) and risk (uncertainty) are critical for adaptive decision-making as well as everyday functioning and well-being. However, it remains unclear how individual differences in reward and risk sensitivity are independently shaped by environmental influences and give rise to decision-making. Here, we investigated the impact of early life experience-a potent sculptor of development-on behavioral sensitivity to reward and risk. We administered a widely used decision-making paradigm to 55 adolescents and young adults who were exposed to early deprivation in the form of early institutional (orphanage) care (a type of early life adversity) and 81 comparison individuals who were reared by their biological parents and did not experience institutional care. Leveraging random coefficient regression and computational models, we observed that previously institutionalized individuals displayed general reward hyposensitivity, contributing to a decreased propensity to make decisions that stood to earn relatively large rewards relative to comparison individuals. By contrast, group differences in risk sensitivity were selectively observed on loss, but not gain, trials. These results are the first to independently and explicitly link early experiences to reward and risk sensitivity during decision-making. As such, they lay the groundwork for therapeutic efforts to identify and treat adversity-exposed individuals at risk for psychiatric disorders characterized by aberrant decision-making processes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Revisiting the Neural Architecture of Adolescent Decision-Making: Univariate and Multivariate Evidence for System-Based Models.

Understanding adolescent decision-making is significant for informing basic models of neurodevelopment as well as for the domains of public health and criminal justice. System-based theories posit that adolescent decision-making is guided by activity amongst reward and control processes. While successful at explaining behavior, system-based theories have received inconsistent support at the neural level, perhaps because of methodological limitations. Here, we used two complementary approaches to overcome said limitations and rigorously evaluate system-based models. Using decision-level modeling of fMRI data from a risk-taking task in a sample of 2000+ decisions across 51 human adolescents (25 females, mean age = 15.00 years), we find support for system-based theories of decision-making. Neural activity in lateral prefrontal cortex and a multivariate pattern of cognitive control both predicted a reduced likelihood of risk-taking, whereas increased activity in the nucleus accumbens predicted a greater likelihood of risk-taking. Interactions between decision-level brain activity and age were not observed. These results garner support for system-based accounts of adolescent decision-making behavior.SIGNIFICANCE STATEMENT:Adolescent decision-making behavior is of great import for basic science, and carries equally consequential implications for public health and criminal justice. While dominant psychological theories seeking to explain adolescent decision-making have found empirical support, their neuroscientific implementations have received inconsistent support. This may be partly due to statistical approaches employed by prior neuroimaging studies of system-based theories. We used brain modeling-an approach that predicts behavior from brain activity-of univariate and multivariate neural activity metrics to better understand how neural components of psychological systems guide decision behavior in adolescents. We found broad support for system-based theories such that neural systems involved in cognitive control predicted a reduced likelihood to make risky decisions, whereas value-based systems predicted greater risk-taking propensity.

Emotion regulation strategy usage explains links between institutional caregiving and elevated internalizing symptoms.

Early adversity, including institutional orphanage care, is associated with the development of internalizing disorders. Previous research suggests that institutionalization can disrupt emotion regulation processes, which contribute to internalizing symptoms. However, no prior work has investigated how early orphanage care shapes emotion regulation strategy usage (e.g., cognitive reappraisal, expressive suppression) and whether the said strategy usage contributes to internalizing symptoms. This study probed emotion regulation strategy usage and internalizing symptoms in a sample of 36 previously institutionalized and 58 comparison youth. As hypothesized, previously institutionalized youth exhibited higher rates of internalizing symptoms than comparison youth, and more frequent use of suppression partially accounted for the relationship between early institutional care and elevated internalizing symptoms. Contrary to our initial hypotheses, reappraisal use did not buffer previously institutionalized or comparison youth against internalizing symptoms. Our findings highlight the potential utility of targeting emotion regulation strategy usage in adversity-exposed youth in future intervention work.

Neurobiological Markers of Resilience to Early-Life Adversity During Adolescence.

Early-life adversity (ELA) exposure (e.g., trauma, abuse, neglect, or institutional care) is a precursor to poor physical and mental health outcomes and is implicated in 30% of adult mental illness. In recent decades, ELA research has increasingly focused on characterizing factors that confer resilience to ELA and on identifying opportunities for intervention. In this review, we describe recent behavioral and neurobiological resilience work that suggests that adolescence (a period marked by heightened plasticity, development of key neurobiological circuitry, and sensitivity to the social environment) may be a particularly opportune moment for ELA intervention. We review intrapersonal factors associated with resilience that become increasingly important during adolescence (specifically, reward processing, affective learning, and self-regulation) and describe the contextual factors (family, peers, and broader social environment) that modulate them. In addition, we describe how the onset of puberty interacts with each of these factors, and we explore recent findings that point to possible "pubertal recalibration" of ELA exposure as an opportunity for intervention. We conclude by describing considerations and future directions for resilience research in adolescents, with a focus on understanding developmental trajectories using dimensional and holistic models of resilience.

BEAPP: The Batch Electroencephalography Automated Processing Platform.

Electroencephalography (EEG) offers information about brain function relevant to a variety of neurologic and neuropsychiatric disorders. EEG contains complex, high-temporal-resolution information, and computational assessment maximizes our potential to glean insight from this information. Here we present the Batch EEG Automated Processing Platform (BEAPP), an automated, flexible EEG processing platform incorporating freely available software tools for batch processing of multiple EEG files across multiple processing steps. BEAPP does not prescribe a specified EEG processing pipeline; instead, it allows users to choose from a menu of options for EEG processing, including steps to manage EEG files collected across multiple acquisition setups (e.g., for multisite studies), minimize artifact, segment continuous and/or event-related EEG, and perform basic analyses. Overall, BEAPP aims to streamline batch EEG processing, improve accessibility to computational EEG assessment, and increase reproducibility of results.

The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data.

Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.