Differential associations of adolescent versus young adult cannabis initiation with longitudinal brain change and behavior.

Leveraging ~10 years of prospective longitudinal data on 704 participants, we examined the effects of adolescent versus young adult cannabis initiation on MRI-assessed cortical thickness development and behavior. Data were obtained from the IMAGEN study conducted across eight European sites. We identified IMAGEN participants who reported being cannabis-naïve at baseline and had data available at baseline, 5-year, and 9-year follow-up visits. Cannabis use was assessed with the European School Survey Project on Alcohol and Drugs. T1-weighted MR images were processed through the CIVET pipeline. Cannabis initiation occurring during adolescence (14-19 years) and young adulthood (19-22 years) was associated with differing patterns of longitudinal cortical thickness change. Associations between adolescent cannabis initiation and cortical thickness change were observed primarily in dorso- and ventrolateral portions of the prefrontal cortex. In contrast, cannabis initiation occurring between 19 and 22 years of age was associated with thickness change in temporal and cortical midline areas. Follow-up analysis revealed that longitudinal brain change related to adolescent initiation persisted into young adulthood and partially mediated the association between adolescent cannabis use and past-month cocaine, ecstasy, and cannabis use at age 22. Extent of cannabis initiation during young adulthood (from 19 to 22 years) had an indirect effect on psychotic symptoms at age 22 through thickness change in temporal areas. Results suggest that developmental timing of cannabis exposure may have a marked effect on neuroanatomical correlates of cannabis use as well as associated behavioral sequelae. Critically, this work provides a foundation for neurodevelopmentally informed models of cannabis exposure in humans.

Machine learning approaches linking brain function to behavior in the ABCD STOP task.

The stop-signal task (SST) is one of the most common fMRI tasks of response inhibition, and its performance measure, the stop-signal reaction-time (SSRT), is broadly used as a measure of cognitive control processes. The neurobiology underlying individual or clinical differences in response inhibition remain unclear, consistent with the general pattern of quite modest brain-behavior associations that have been recently reported in well-powered large-sample studies. Here, we investigated the potential of multivariate, machine learning (ML) methods to improve the estimation of individual differences in SSRT with multimodal structural and functional region of interest-level neuroimaging data from 9- to 11-year-olds children in the ABCD Study. Six ML algorithms were assessed across modalities and fMRI tasks. We verified that SST activation performed best in predicting SSRT among multiple modalities including morphological MRI (cortical surface area/thickness), diffusion tensor imaging, and fMRI task activations, and then showed that SST activation explained 12% of the variance in SSRT using cross-validation and out-of-sample lockbox data sets (n = 7298). Brain regions that were more active during the task and that showed more interindividual variation in activation were better at capturing individual differences in performance on the task, but this was only true for activations when successfully inhibiting. Cortical regions outperformed subcortical areas in explaining individual differences but the two hemispheres performed equally well. These results demonstrate that the detection of reproducible links between brain function and performance can be improved with multivariate approaches and give insight into a number of brain systems contributing to individual differences in this fundamental cognitive control process.

A machine learning-derived neuroanatomical pattern predicts delayed reward discounting in the Human Connectome Project Young Adult sample.

Delayed reward discounting (DRD) is defined as the extent to which person favors smaller rewards that are immediately available over larger rewards available in the future. Higher levels of DRD have been identified in individuals with a wide range of clinical disorders. Although there have been studies adopting larger samples and using only gray matter volume to characterize the neuroanatomical correlates of DRD, it is still unclear whether previously identified relationships are generalizable (out-of-sample) and how cortical thickness and cortical surface area contribute to DRD. In this study, using the Human Connectome Project Young Adult dataset (N = 1038), a machine learning cross-validated elastic net regression approach was used to characterize the neuroanatomical pattern of structural magnetic resonance imaging variables associated with DRD. The results revealed a multi-region neuroanatomical pattern predicted DRD and this was robust in a held-out test set (morphometry-only R2 = 3.34%, morphometry + demographics R2  = 6.96%). The neuroanatomical pattern included regions implicated in the default mode network, executive control network, and salience network. The relationship of these regions with DRD was further supported by univariate linear mixed effects modeling results, in which many of the regions identified as part of this pattern showed significant univariate associations with DRD. Taken together, these findings provide evidence that a machine learning-derived neuroanatomical pattern encompassing various theoretically relevant brain networks produces robustly predicts DRD in a large sample of healthy young adults.

Attentional biases for sad faces in offspring of mothers with a history of major depression: trajectories of change from childhood to adolescence.

BACKGROUND: Theorists have proposed that the way children process social-emotional information may serve as a mechanism of risk for the intergenerational transmission of depression. There is growing evidence that infants and children of mothers with a history of major depressive disorder (MDD) during the child's life exhibit attentional avoidance of sad faces, which has been proposed as an early emerging emotion regulation strategy. In contrast, there is clear evidence that at-risk and depressed adolescents and adults exhibit difficulty disengaging attention from sad faces. METHODS: Seeking to link these two literatures, the current U.S.-based study used eye tracking within the context of an accelerated longitudinal design to assess attentional biases in 8-14-year-old offspring of mothers with a history MDD during the child's life (n = 123) or no history of MDD (n = 119) every six months for two years, allowing us to map trajectories of attention from age 8 to 16. RESULTS: Mother MDD history moderated age-based changes in children's gaze duration to sad (t[240] = 2.44, p = .02), but not happy (t[240] = 0.11, p = .91) or angry (t[240] = 0.67, p = .50), faces. Consistent our hypotheses, offspring of mothers with MDD exhibited significantly less attention to sad faces than offspring of never depressed mothers before age 8.5 but significantly more attention to sad faces after age 14.5, which was due to an increase in gaze duration to sad faces from childhood to adolescence among offspring of mothers with MDD (t[122] = 5.44, p < .001) but not among offspring of never depressed mothers (t[118] = 1.49, p = .14). CONCLUSIONS: It appears that the form, and perhaps function, of attentional bias may shift across development in at-risk youth. To the extent that this is true, it has significant implications not only for theories of the intergenerational transmission of depression risk but also for prevention and early intervention efforts designed to reduce this risk.

Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study.

The use of predefined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed preprocessed structural magnetic resonance imaging (sMRI) data from the Adolescent Brain Cognitive Development Study® to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9- to 10-year-old children (N = 9,432). Choice of machine learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger number of parcels (up to ~4,000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector-based pipeline, and ensembling across multiple parcellations, respectively, as the highest performing. These findings highlight the choice of parcellation as an important influence on downstream predictive performance, showing in some cases that switching to a higher resolution parcellation can yield a relatively large boost to performance.

Neuroanatomical foundations of delayed reward discounting decision making II: Evaluation of sulcal morphology and fractal dimensionality.

Delayed reward discounting (DRD) is a form of decision-making reflecting valuation of smaller immediate rewards versus larger delayed rewards, and high DRD has been linked to several health behaviors, including substance use disorders, attention-deficit/hyperactivity disorder, and obesity. Elucidating the underlying neuroanatomical factors may offer important insights into the etiology of these conditions. We used structural MRI scans of 1038 Human Connectome Project participants (Mage = 28.86, 54.7% female) to explore two novel measures of neuroanatomy related to DRD: 1) sulcal morphology (SM; depth and width) and 2) fractal dimensionality (FD), or cortical morphometric complexity, of parcellated cortical and subcortical regions. To ascertain unique contributions to DRD preferences, indicators that displayed significant partial correlations with DRD after family-wise error correction were entered into iterative mixed-effect models guided by the association magnitude. When considering only SM indicators, the depth of the right inferior and width of the left central sulci were uniquely associated with DRD preferences. When considering only FD indicators, the FD of the left middle temporal gyrus, right lateral orbitofrontal cortex, and left lateral occipital and entorhinal cortices uniquely contributed DRD. When considering SM and FD indicators simultaneously, the right inferior frontal sulcus depth and left central sulcus width; and the FD of the left middle temporal gyrus, lateral occipital cortex and entorhinal cortex were uniquely associated with DRD. These results implicate SM and FD as features of the brain that underlie variation in the DRD decision-making phenotype and as promising candidates for understanding DRD as a biobehavioral disease process.

Brain Predictability toolbox: a Python library for neuroimaging-based machine learning.

SUMMARY: Brain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools designed to work with both tabulated data (e.g. brain derived, psychiatric, behavioral and physiological variables) and neuroimaging specific data (e.g. brain volumes and surfaces). This package is suitable for investigating a wide range of different neuroimaging-based ML questions, in particular, those queried from large human datasets. AVAILABILITY AND IMPLEMENTATION: BPt has been developed as an open-source Python 3.6+ package hosted at https://github.com/sahahn/BPt under MIT License, with documentation provided at https://bpt.readthedocs.io/en/latest/, and continues to be actively developed. The project can be downloaded through the github link provided. A web GUI interface based on the same code is currently under development and can be set up through docker with instructions at https://github.com/sahahn/BPt_app.

Structural brain differences do not mediate the relations between sex and personality or psychopathology.

INTRODUCTION: Males and females tend to exhibit small but reliable differences in personality traits and indices of psychopathology that are relatively stable over time and across cultures. Previous work suggests that sex differences in brain structure account for differences in domains of cognition. METHODS: We used data from the Human Connectome Project (N = 1098) to test whether sex differences in brain morphometry account for observed differences in the personality traits neuroticism and agreeableness, as well as symptoms of internalizing and externalizing psychopathology. We operationalized brain morphometry in three ways: omnibus measures (e.g., total gray matter volume), Glasser regions defined through a multi-modal parcellation approach, and Desikan regions defined by structural features of the brain. RESULTS: Most expected sex differences in personality, psychopathology, and brain morphometry were observed, but the statistical mediation analyses were null: sex differences in brain morphometry did not account for sex differences in personality or psychopathology. CONCLUSIONS: Men and women tend to exhibit meaningful differences in personality and psychopathology, as well as in omnibus morphometry and regional morphometric differences as defined by the Glasser and Desikan atlases, but these morphometric differences appear unrelated to the psychological differences.

Investigation of Psychiatric and Neuropsychological Correlates of Default Mode Network and Dorsal Attention Network Anticorrelation in Children.

The default mode network (DMN) and dorsal attention network (DAN) demonstrate an intrinsic "anticorrelation" in healthy adults, which is thought to represent the functional segregation between internally and externally directed thought. Reduced segregation of these networks has been proposed as a mechanism for cognitive deficits that occurs in many psychiatric disorders, but this association has rarely been tested in pre-adolescent children. The current analysis used data from the Adolescent Brain Cognitive Development study to examine the relationship between the strength of DMN/DAN anticorrelation and psychiatric symptoms in the largest sample to date of 9- to 10-year-old children (N = 6543). The relationship of DMN/DAN anticorrelation to a battery of neuropsychological tests was also assessed. DMN/DAN anticorrelation was robustly linked to attention problems, as well as age, sex, and socioeconomic factors. Other psychiatric correlates identified in prior reports were not robustly linked to DMN/DAN anticorrelation after controlling for demographic covariates. Among neuropsychological measures, the clearest correlates of DMN/DAN anticorrelation were the Card Sort task of executive function and cognitive flexibility and the NIH Toolbox Total Cognitive Score, although these did not survive correction for socioeconomic factors. These findings indicate a complicated relationship between DMN/DAN anticorrelation and demographics, neuropsychological function, and psychiatric problems.

Recent cannabis use is associated with smaller hippocampus volume: High-resolution segmentation of structural subfields in a large non-clinical sample.

There is mixed evidence that individuals who use cannabis have reduced hippocampal and amygdalar gray matter volume, potentially because of small sample sizes and imprecise morphological characterization. New automated segmentation procedures have improved the measurement of these structures and allow better examination of their subfields, which have been linked to distinct aspects of memory and emotion. The current study applies this new segmentation procedure to the Human Connectome Project Young Adult dataset (N = 1080) to investigate associations of cannabis use with gray matter volume in the hippocampus and amygdala. Results revealed significant bilateral inverse associations of hippocampal volume with recent cannabis use (THC+ urine drug screen; P < .005). Hippocampal subfield analyses indicated these associations were primarily driven by the head of the hippocampus, the first section of the cornu amonis (CA1), the subicular complex, and the molecular layer of the hippocampus. No associations were detected for age of cannabis initiation, the frequency of cannabis use across the lifespan, or the lifetime presence of cannabis use disorder. In one of the largest studies to date, these results support the hypothesis that recent cannabis use is linked to reduced hippocampal volume, but that this effect may dissipate following prolonged abstinence. Furthermore, these results clarify the specific subfields which may be most associated with recent cannabis use.

Maternal criticism and children's neural responses to reward and loss.

Parental criticism is linked to a number of detrimental child outcomes. One mechanism by which parental criticism may increase risk for negative outcomes in children is through children's neural responses to valenced information in the environment. The goal of the current study, therefore, was to examine the relation between maternal criticism and children's neural responses to monetary gains and losses. To represent daily environmental experiences of reward and punishment, we focused on reactivity to monetary gains versus losses in a guessing task. Participants were 202 children and their mothers recruited from the community. The average age of the children was 9.71 years (SD = 1.38, range = 7-11), with 52.0% of them male and 72.8% Caucasian. Mothers completed the Five Minute Speech Sample to assess expressed emotion-criticism, and of these dyads 51 mothers were rated as highly critical. In addition, children completed a simple guessing game during which electroencephalography was recorded. Children of critical mothers displayed less neural reactivity to both monetary gain and loss than children without critical mothers. Our results were at least partially independent of children's and mothers' current levels of internalizing psychopathology. These findings suggest that children exposed to maternal criticism may exhibit disruptions in adaptive responses to environmental experiences regardless of valence. Targeted interventions aimed at reducing expressed emotion-criticism may lead to changes in a child's reward responsiveness and risk for psychopathology.

Attentional biases to emotional faces among women with a history of single episode versus recurrent major depression.

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, and recurrent depression is associated with severe and chronic impairment. Identifying markers of risk is imperative to improve our ability to predict which individuals are likely to experience a recurrence. According to cognitive theories, biases in attention for affectively-salient information may serve as one mechanism of risk. Existing research has combined participants with a single episode (sMDD) and those with recurrent MDD (rMDD); therefore, little is known about whether these biases track the severity of disease course. The current study examined attentional biases to facial displays of emotion among 115 women with a history of rMDD, sMDD, or no history of psychopathology using a passive viewing eye-tracking task. Women with rMDD exhibited significantly lower sustained attention to happy faces compared to both healthy controls and sMDD women. These results extend previous research on the presence of attentional avoidance of positive stimuli in individuals with a history of MDD and provide preliminary evidence that this bias is strongest among individuals with a history of rMDD.

The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables.

Although covarying for potential confounds or nuisance variables is common in psychological research, relatively little is known about how the inclusion of covariates may influence the relations between psychological variables and indices of brain structure. In Part 1 of the current study, we conducted a descriptive review of relevant articles from the past two years of NeuroImage in order to identify the most commonly used covariates in work of this nature. Age, sex, and intracranial volume were found to be the most commonly used covariates, although the number of covariates used ranged from 0 to 14, with 37 different covariate sets across the 68 models tested. In Part 2, we used data from the Human Connectome Project to investigate the degree to which the addition of common covariates altered the relations between individual difference variables (i.e., personality traits, psychopathology, cognitive tasks) and regional gray matter volume (GMV), as well as the statistical significance of values associated with these effect sizes. Using traditional and random sampling approaches, our results varied widely, such that some covariate sets influenced the relations between the individual difference variables and GMV very little, while the addition of other covariate sets resulted in a substantially different pattern of results compared to models with no covariates. In sum, these results suggest that the use of covariates should be critically examined and discussed as part of the conversation on replicability in structural neuroimaging. We conclude by recommending that researchers pre-register their analytic strategy and present information on how relations differ based on the inclusion of covariates.

Cannabis involvement and neuropsychological performance: findings from the Human Connectome Project

Background: There is evidence that heavy cannabis use is associated with decrements in cognitive performance, but findings are mixed and studies are often limited by small sample sizes and narrow adjustment for potential confounding variables. In a comparatively large sample, the current study examined associations between multiple indicators of cannabis use in relation to performance on a variety of neuropsychological tasks. Methods: Participants were 1121 adults (54% female) enrolled in the Human Connectome Project. Cannabis involvement comprised recent cannabis use (positive tetrahydrocannabinol screen), total number of lifetime uses, cannabis use disorder and age at first use. The neuropsychological battery comprised performance in episodic memory, fluid intelligence, attention, working memory, executive function, impulsive decision-making, processing speed and psychomotor dexterity. Covariates were age, sex, income, family structure and alcohol and tobacco use. Results: Positive urinary tetrahydrocannabinol status was associated with worse performance in episodic memory and processing speed, and positive cannabis use disorder status was associated with lower fluid intelligence (all p < 0.005). No other significant associations were present. Limitations: The sample was limited to young adults aged 22­36 years. The measures of cannabis involvement were relatively coarse. Conclusion: Beyond an array of potential confounders, recent cannabis use was associated with deficits in memory and psychomotor performance, and cannabis use disorder was associated with lower overall cognitive functioning in a large normative sample of adults. The findings pertaining to recent use have particular relevance for occupational settings.

Associations Between Drinking and Cortical Thickness in Younger Adult Drinkers: Findings From the Human Connectome Project.

BACKGROUND: Previous neuroimaging studies examining relations between alcohol misuse and cortical thickness have revealed that increased drinking quantity and alcohol-related problems are associated with thinner cortex. Although conflicting regional effects are often observed, associations are generally localized to frontal regions (e.g., dorsolateral prefrontal cortex [DLPFC], inferior frontal gyrus [IFG], and anterior cingulate cortex). Inconsistent findings may be attributed to methodological differences, modest sample sizes, and limited consideration of sex differences. METHODS: This study examined neuroanatomical correlates of drinking quantity and heavy episodic drinking in a large sample of younger adults (N = 706; Mage  = 28.8; 51% female) using magnetic resonance imaging data from the Human Connectome Project. Exploratory analyses examined neuroanatomical correlates of executive function (flanker task) and working memory (list sorting). RESULTS: Hierarchical linear regression models (controlling for age, sex, education, income, smoking, drug use, twin status, and intracranial volume) revealed significant inverse associations between drinks in past week and frequency of heavy drinking and cortical thickness in a majority of regions examined. The largest effect sizes were found for frontal regions (DLPFC, IFG, and the precentral gyrus). Follow-up regression models revealed that the left DLPFC was uniquely associated with both drinking variables. Sex differences were also observed, with significant effects largely specific to men. CONCLUSIONS: This study adds to the understanding of brain correlates of alcohol use in a large, gender-balanced sample of younger adults. Although the cross-sectional methodology precludes causal inferences, these findings provide a foundation for rigorous hypothesis testing in future longitudinal investigations.

Age differences in electrocortical reactivity to fearful faces following aversive conditioning in youth.

Although biases in the processing of affectively salient stimuli are thought to increase risk for psychopathology across the lifespan, questions remain regarding how these biases develop. The current study tested an aversive conditioning model for the development of children's sensitivity in detecting fearful faces at varying levels of emotional intensity and their facilitated attention to fearful faces assessed via the late positive potential (LPP) event-related potential component. Participants (N = 144, ages 7-11 years) were randomly assigned to one of three conditions: an active training condition in which an 85-dB white noise burst was paired with fearful faces, an active control condition in which the white noise was presented randomly throughout the task, and a no-sound condition. Children completed a separate task in which they viewed happy, sad, and fearful child faces at varying levels of emotional intensity while electroencephalography (EEG) was recorded. Although there were no conditioning group differences in children's sensitivity in detecting facial displays of emotion, there were group differences in LPP magnitude that were moderated by children's age. Among younger children, those in the active conditioning group exhibited smaller LPP amplitudes to high-intensity fearful faces than children in the control groups. However, among older youth, those in the active conditioning group exhibited larger LPP amplitudes to high-intensity fearful faces than children in the control groups. These findings provide insight into how attentional biases may develop in children and how period of development may influence these patterns.

Distinct functional and structural neural underpinnings of working memory.

Working memory (WM), the short-term abstraction and manipulation of information, is an essential neurocognitive process in daily functioning. Few studies have concurrently examined the functional and structural neural correlates of WM and the current study did so to characterize both overlapping and unique associations. Participants were a large sample of adults from the Human Connectome Project (N = 1064; 54% female) who completed an in-scanner visual N-back WM task. The results indicate a clear dissociation between BOLD activation during the WM task and brain structure in relation to performance. In particular, while activation in the middle frontal gyrus was positively associated with WM performance, cortical thickness in this region was inversely associated with performance. Additional unique associations with WM were BOLD activation in superior parietal lobule, cingulate, and fusiform gyrus and gray matter volume in the orbitofrontal cortex and cuneus. Across findings, substantially larger effects were observed for functional associations relative to structural associations. These results provide further evidence implicating frontoparietal subunits of the brain in WM. Moreover, these findings reveal the distinct, and in some cases opposing, roles of brain structure and neural activation in WM, highlighting the lack of homology between structure and function in relation to cognition.

Neural reward responsiveness in children who engage in nonsuicidal self-injury: an ERP study.

BACKGROUND: A better understanding of the correlates of nonsuicidal self-injury (NSSI) in children is important for the identification and prevention of future suicide risk. However, although abnormalities in reward responsiveness might constitute one potential transdiagnostic mechanism of risk for NSSI, no studies have examined initial response to reward in children with a history of NSSI. The goal of the present study was to address this important gap in the literature. To objectively assess initial response to reward, we utilized the feedback negativity (FN) event-related potential, a well-established psychophysiological marker of reward responsiveness. METHODS: Participants were 57 children (19 with a history of NSSI and 38 demographically matched controls) between the ages of 7 and 11. Diagnostic interviews were used to assess for current and past DSM-IV mood and anxiety diagnoses and NSSI history. Children also completed a guessing task, during which continuous electroencephalography was recorded. RESULTS: Children with a history of NSSI exhibited significantly more negative ΔFN (i.e., FN to losses minus FN to gains) than children without NSSI. These findings appeared to be at least partially independent of children's history of psychopathology and current symptoms, suggesting their specificity to NSSI. CONCLUSIONS: These results provide initial evidence for heightened neural initial reward responsiveness to losses versus rewards in children with a history of NSSI. Pending replications and longitudinal studies, the ΔFN might represent a psychophysiological marker of risk for self-harm.

Neural correlates of tobacco cue reactivity predict duration to lapse and continuous abstinence in smoking cessation treatment.

It has been hypothesized that neural reactivity to drug cues in certain limbic/paralimbic regions of the brain is an indicator of addiction severity and a marker for likelihood of success in treatment. To address this question, in the current study, 32 participants (44 percent female) completed a functional magnetic resonance imaging cigarette cue exposure paradigm 2 hours after smoking, and then enrolled in a 9-week smoking cessation treatment program. Neural activation to smoking cues was measured in five a priori defined limbic/paralimbic regions previously implicated with cue reactivity across substances. These included regions of the ventral striatum, anterior cingulate cortex and amygdala. Cox proportional hazard modeling was conducted to predict the number of days to first smoking lapse by using neural activation in these regions. Greater neural activation during pre-treatment exposure to smoking cues in the right ventral striatum, the left amygdala, and the anterior cingulate was associated with longer periods of abstinence following cessation. A similar pattern was present for continuous abstinence for the full duration of treatment. While baseline levels of nicotine dependence were strongly associated with treatment outcome, activation in the right ventral striatum predicted duration of abstinence beyond level of nicotine dependence. These results suggest that pre-treatment reactivity to smoking cues in areas associated with cue reactivity may be associated with successfully maintaining abstinence during treatment. This is consistent with models that propose that as addiction becomes more severe, motivational processing shifts from regions that subserve reward salience and learning to regions responsible motor behavior and habit learning.

Parental Expressed Emotion-Criticism and Neural Markers of Sustained Attention to Emotional Faces in Children.

There is growing evidence for the role of environmental influences on children's information-processing biases for affectively salient stimuli. The goal of this study was to extend this research by examining the relation between parental criticism (expressed emotion-criticism, or EE-Crit) and children's processing of facial displays of emotion. Specifically, we examined the relation between EE-Crit and children's sensitivity in detecting facial displays of emotion. We also examined a neural marker of sustained attention, the late positive potential (LPP) event-related potential component (ERP). Participants were 87 children (ages 7-11 years; 53.3% female, 77.8% Caucasian) and their parents (ages 24-71; 90% female, 88.9% Caucasian). Parents completed the Five-Minute Speech Sample to determine levels of EE-Crit toward their child. Children completed a morphed faces task during which behavioral and ERP responses were assessed. Although there were no group differences in sensitivity in detecting facial displays of emotion, we found that children of parents exhibiting high, compared to low, EE-Crit displayed less attention (smaller LPP magnitudes) to all facial displays of emotion (fearful, happy, sad). These results suggest that children of critical parents may exhibit an avoidant pattern of attention to affectively-salient interpersonal stimuli.