No abstract

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.

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.

Early life adversity (ELA) exposure (including 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 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. Additionally, we describe how the onset of puberty interacts with each of these factors, and explore recent findings that point to possible "pubertal recalibration" of ELA exposure as an opportunity for intervention. Lastly, we conclude by describing considerations and future directions for resilience research in adolescents, with a focus on understanding developmental trajectories using dimensional, holistic models of resilience.

Exposure to early adversity, defined as negative experiences during childhood that deviate from the expected environment (e.g., absence of a primary caregiver) and require significant psychological, social, or neurological adaptation for a typical child (McLaughlin, 2016), significantly increases the risk of psychopathology, including mood and anxiety disorders (McLaughlin et al., 2010). This is true even for individuals for whom adversity exposure ceased in early childhood: for example, previously institutionalized (PI) youth who experienced orphanage care as infants and were adopted as young children remain at elevated risk for internalizing symptoms (Hawk & McCall, 2010;Humphreys et al., 2015;Tottenham, 2012). Despite this fact, many PI youth do not develop psychopathology, underscoring how a combination of risk and resilience factors after exposure to early adversity impact mental health (Bimmel et al., 2003;. Emotion regulation ability is thought to be a potent source of resilience to internalizing disorders (and psychopathology in general) following early adversity (Jenness et al., 2020;Weissman et al., 2019), and variability in emotion regulation strategy use may, therefore, contribute to individual differences in internalizing symptoms present in PI youth. The current study sought to examine the differential use of emotion regulation strategies as factors that confer risk or resilience for internalizing symptomatology (anxiety, depression, somatic complaints, and withdrawal) among PI youth.

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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 related to 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 20001 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 PFC and a multivariate pattern of cognitive control both predicted a reduced likelihood of risk-taking, whereas increased activity in the NAcc 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.

System-based theories are a popular approach to explaining the psychology of human decision making. Such theories posit that decision-making is governed by interactions between different psychological processes that arbitrate amongst each other for control over behavior. To date, system-based theories have received inconsistent support at the neural level, leading some to question their veracity. Here we examine the possibility that prior attempts to evaluate system-based theories have been limited by their reliance on predicting brain activity from behavior, and seek to advance evaluations of system-based models through modeling approaches that predict behavior from brain activity. Using within-subject decision-level modeling of fMRI data from a risk-taking task in a sample of over 2000 decisions across 51 adolescents—a population in which decision-making processes are particularly dynamic and consequential—we find support for system-based theories of decision-making. In particular, neural activity in lateral prefrontal cortex and a multivariate pattern of cognitive control both predicted a reduced likelihood of making a risky decision, whereas increased activity in the ventral striatum—a region typically associated with valuation processes—predicted a greater likelihood of engaging in risk-taking. These results comprise the first formalized within-subjects neuroimaging test of system-based theories, garnering support for the notion that competing systems drive decision behaviors.Significance StatementDecision making is central to adaptive behavior. While dominant psychological theories of decision-making behavior have found empirical support, their neuroscientific implementations have received inconsistent support. This may in part be due to statistical approaches employed by prior neuroimaging studies of system-based theories. Here we use 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. We found broad support for system-based theories such that that neural systems involved in cognitive control predicted a reduced likelihood to make risky decisions, whereas value-based systems predicted greater risk-taking propensity.