Cocaine self-administration behavior is associated with subcortical and cortical morphometry measures in individuals with cocaine use disorder.

Background: Individual differences in gray-matter morphometry in the limbic system and frontal cortex have been linked to clinical features of cocaine use disorder (CUD). Self-administration paradigms can provide more direct measurements of the relationship between the regulation of cocaine use and gray-matter morphometry when compared to self-report assessments.Objectives: Our goal was to investigate associations with self-administration behavior in subcortical and cortical brain regions. We hypothesized the number of cocaine infusions self-administered would be correlated with gray-matter volumes (GMVs) in the striatum, amygdala, and hippocampus. Due to scarcity in human studies, we did not hypothesize subcortical directionality. In the frontal cortex, we hypothesized thickness would be negatively correlated with self-administered cocaine.Methods: We conducted an analysis of cocaine self-administration and structural MRI data from 33 (nFemales = 10) individuals with moderate-to-severe CUD. Self-administration lasted 60-minutes and cocaine (8, 16, or 32 mg/70 kg) was delivered on an FR1 schedule (5-minute lockout). Subcortical and cortical regression analyses were performed that included combined bilateral regions and age, experimental variables and use history as confounders.Results: Self-administered cocaine infusions were positively associated with caudal GMV (b = 0.18, p = 0.030) and negatively with putamenal GMV (b = -0.10, p = 0.041). In the cortical model, infusions were positively associated with insular thickness (b = 0.39, p = 0.008) and women appeared to self-administer cocaine more frequently (b = 0.23, p = 0.019).Conclusions: Brain morphometry features in the striatum and insula may contribute to cocaine consumption in CUD. These differences in morphometry may reflect consequences of prolonged use, predisposed vulnerability, or other possibilities.Clinical Trial Numbers: NCT01978431; NCT03471182.

Investigating brain dynamics and their association with cognitive control in opioid use disorder using naturalistic and drug cue paradigms.

Objectives: Opioid use disorder (OUD) impacts millions of people worldwide. The prevalence and debilitating effects of OUD present a pressing need to understand its neural mechanisms to provide more targeted interventions. Prior studies have linked altered functioning in large-scale brain networks with clinical symptoms and outcomes in OUD. However, these investigations often do not consider how brain responses change over time. Time-varying brain network engagement can convey clinically relevant information not captured by static brain measures. Methods: We investigated brain dynamic alterations in individuals with OUD by applying a new multivariate computational framework to movie-watching (i.e., naturalistic; N=76) and task-based (N=70) fMRI. We further probed the associations between cognitive control and brain dynamics during a separate drug cue paradigm in individuals with OUD. Results: Compared to healthy controls (N=97), individuals with OUD showed decreased variability in the engagement of recurring brain states during movie-watching. We also found that worse cognitive control was linked to decreased variability during the rest period when no opioid-related stimuli were present. Conclusions: These findings suggest that individuals with OUD may experience greater difficulty in effectively engaging brain networks in response to evolving internal or external demands. Such inflexibility may contribute to aberrant response inhibition and biased attention toward opioid-related stimuli, two hallmark characteristics of OUD. By incorporating temporal information, the current study introduces novel information about how brain dynamics are altered in individuals with OUD and their behavioral implications.

Neural correlates of altered emotional responsivity to infant stimuli in mothers who use substances.

Mothers who use substances during pregnancy and postpartum may have altered maternal behavior towards their infants, which can have negative consequences on infant social-emotional development. Since maternal substance use has been associated with difficulties in recognizing and responding to infant emotional expressions, investigating mothers' subjective responses to emotional infant stimuli may provide insight into the neural and psychological processes underlying these differences in maternal behavior. In this study, 39 mothers who used substances during the perinatal period and 42 mothers who did not underwent functional magnetic resonance imaging while viewing infant faces and hearing infant cries. Afterwards, they rated the emotional intensity they thought each infant felt ('think'-rating), and how intensely they felt in response to each infant stimulus ('feel'-rating). Mothers who used substances had lower 'feel'-ratings of infant stimuli compared to mothers who did not. Brain regions implicated in affective processing (e.g., insula, inferior frontal gyrus) were less active in response to infant stimuli, and activity in these brain regions statistically predicted maternal substance-use status. Interestingly, 'think'-ratings and activation in brain regions related to cognitive processing (e.g., medial prefrontal cortex) were comparable between the two groups of mothers. Taken together, these results suggest specific neural and psychological processes related to emotional responsivity to infant stimuli may reflect differences in maternal affective processing and may contribute to differences in maternal behavior in mothers who use substances compared to mothers who do not. The findings suggest potential neural targets for increasing maternal emotional responsivity and improving child outcomes.

Identification of a Composite Latent Dimension of Reward and Impulsivity Across Clinical, Behavioral, and Neurobiological Domains Among Youth.

BACKGROUND: Individual differences in reward processing are central to heightened risk-taking behaviors during adolescence, but there is inconsistent evidence for the relationship between risk-taking phenotypes and the neural substrates of these behaviors. METHODS: Here, we identify latent features of reward in an attempt to provide a unifying framework linking together aspects of the brain and behavior during early adolescence using a multivariate pattern learning approach. Data (N = 8295; n male = 4190; n female = 4105) were acquired as part of the Adolescent Brain Cognitive Development (ABCD) Study and included neuroimaging (regional neural activity responses during reward anticipation) and behavioral (e.g., impulsivity measures, delay discounting) variables. RESULTS: We revealed a single latent dimension of reward driven by shared covariation between striatal, thalamic, and anterior cingulate responses during reward anticipation, negative urgency, and delay discounting behaviors. Expression of these latent features differed among adolescents with attention-deficit/hyperactivity disorder and disruptive behavior disorder, compared with those without, and higher expression of these latent features was negatively associated with multiple dimensions of executive function and cognition. CONCLUSIONS: These results suggest that cross-domain patterns of anticipatory reward processing linked to negative features of impulsivity exist in both the brain and in behavior during early adolescence and that these are representative of 2 commonly diagnosed reward-related psychiatric disorders, attention-deficit/hyperactivity disorder and disruptive behavior disorder. Furthermore, they provide an explicit baseline from which multivariate developmental trajectories of reward processes may be tracked in later waves of the ABCD Study and other developmental cohorts.

Brain Networks and Adolescent Alcohol Use.

Importance: Alcohol misuse in adolescence is a leading cause of disability and mortality in youth and is associated with higher risk for alcohol use disorder. Brain mechanisms underlying risk of alcohol misuse may inform prevention and intervention efforts. Objective: To identify neuromarkers of alcohol misuse using a data-driven approach, with specific consideration of neurodevelopmental sex differences. Design, Setting, and Participants: Longitudinal multisite functional magnetic resonance imaging (fMRI) data collected at ages 14 and 19 years were used to assess whole-brain patterns of functional organization associated with current and future alcohol use risk as measured by the Alcohol Use Disorder Identification Test (AUDIT). Primary data were collected by the IMAGEN consortium, a European multisite study of adolescent neurodevelopment. Model generalizability was further tested using data acquired in a single-site study of college alcohol consumption conducted in the US. The primary sample was a developmental cohort of 1359 adolescents with neuroimaging, phenotyping, and alcohol use data. Model generalizability was further assessed in a separate cohort of 114 individuals. Main Outcomes and Measures: Brain-behavior model accuracy, as defined by the correspondence between model-predicted and actual AUDIT scores in held-out testing data, Bonferroni corrected across the number of models run at each time point, 2-tailed α < .008, as determined via permutation testing. Results: Among 1359 individuals in the study, the mean (SD) age was 14.42 (0.40) years, and 729 individuals (54%) were female. The data-driven, whole-brain connectivity approach identified networks associated with vulnerability for future and current AUDIT-defined alcohol use risk (primary outcome, as specified above, future: ρ, 0.22; P < .001 and present: ρ, 0.27; P < .001). Results further indicated sex divergence in the accuracies of brain-behavior models, such that female-only models consistently outperformed male-only models. Specifically, female-only models identified networks conferring vulnerability for future and current severity using data acquired during both reward and inhibitory fMRI tasks. In contrast, male-only models were successful in accurately identifying networks using data acquired during the inhibitory control-but not reward-task, indicating domain specificity of alcohol use risk networks in male adolescents only. Conclusions and Relevance: These data suggest that interventions focusing on inhibitory control processes may be effective in combating alcohol use risk in male adolescents but that both inhibitory and reward-related processes are likely of relevance to alcohol use behaviors in female adolescents. They further identify novel networks of alcohol use risk in youth, which may be used to identify adolescents who are at risk and inform intervention efforts.

From Computation to Clinic.

Theory-driven and data-driven computational approaches to psychiatry have enormous potential for elucidating mechanism of disease and providing translational linkages between basic science findings and the clinic. These approaches have already demonstrated utility in providing clinically relevant understanding, primarily via back translation from clinic to computation, revealing how specific disorders or symptoms map onto specific computational processes. Nonetheless, forward translation, from computation to clinic, remains rare. In addition, consensus regarding specific barriers to forward translation-and on the best strategies to overcome these barriers-is limited. This perspective review brings together expert basic and computationally trained researchers and clinicians to 1) identify challenges specific to preclinical model systems and clinical translation of computational models of cognition and affect, and 2) discuss practical approaches to overcoming these challenges. In doing so, we highlight recent evidence for the ability of computational approaches to predict treatment responses in psychiatric disorders and discuss considerations for maximizing the clinical relevance of such models (e.g., via longitudinal testing) and the likelihood of stakeholder adoption (e.g., via cost-effectiveness analyses).

Distinct neural networks predict cocaine versus cannabis treatment outcomes.

Treatment outcomes for individuals with substance use disorders (SUDs) are variable and more individualized approaches may be needed. Cross-validated, machine-learning methods are well-suited for probing neural mechanisms of treatment outcomes. Our prior work applied one such approach, connectome-based predictive modeling (CPM), to identify dissociable and substance-specific neural networks of cocaine and opioid abstinence. In Study 1, we aimed to replicate and extend prior work by testing the predictive ability of the cocaine network in an independent sample of 43 participants from a trial of cognitive-behavioral therapy for SUD, and evaluating its ability to predict cannabis abstinence. In Study 2, CPM was applied to identify an independent cannabis abstinence network. Additional participants were identified for a combined sample of 33 with cannabis-use disorder. Participants underwent fMRI scanning before and after treatment. Additional samples of 53 individuals with co-occurring cocaine and opioid-use disorders and 38 comparison subjects were used to assess substance specificity and network strength relative to participants without SUDs. Results demonstrated a second external replication of the cocaine network predicting future cocaine abstinence, however it did not generalize to cannabis abstinence. An independent CPM identified a novel cannabis abstinence network, which was (i) anatomically distinct from the cocaine network, (ii) specific for predicting cannabis abstinence, and for which (iii) network strength was significantly stronger in treatment responders relative to control particpants. Results provide further evidence for substance specificity of neural predictors of abstinence and provide insight into neural mechanisms of successful cannabis treatment, thereby identifying novel treatment targets. Clinical trials registation: "Computer-based training in cognitive-behavioral therapy web-based (Man VS Machine)", registration number: NCT01442597 . "Maximizing the Efficacy of Cognitive Behavior Therapy and Contingency Management", registration number: NCT00350649 . "Computer-Based Training in Cognitive Behavior Therapy (CBT4CBT)", registration number: NCT01406899 .

Connectome-based prediction of craving in gambling disorder and cocaine use disorder.

INTRODUCTION: Craving, involving intense and urgent desires to engage in specific behaviours, is a feature of addictions. Multiple studies implicate regions of salience/limbic networks and basal ganglia, fronto-parietal, medial frontal regions in craving in addictions. However, prior studies have not identified common neural networks that reliably predict craving across substance and behavioural addictions. METHODS: Functional magnetic resonance imaging during an audiovisual cue-reactivity task and connectome-based predictive modelling (CPM), a data-driven method for generating brain-behavioural models, were used to study individuals with cocaine-use disorder and gambling disorder. Functions of nodes and networks relevant to craving were identified and interpreted based on meta-analytic data. RESULTS: Craving was predicted by neural connectivity across disorders. The highest degree nodes were mostly located in the prefrontal cortex. Overall, the prediction model included complex networks including motor/sensory, fronto-parietal, and default-mode networks. The decoding revealed high functional associations with components of memory, valence ratings, physiological responses, and finger movement/motor imagery. CONCLUSIONS: Craving could be predicted across substance and behavioural addictions. The model may reflect general neural mechanisms of craving despite specificities of individual disorders. Prefrontal regions associated with working memory and autobiographical memory seem important in predicting craving. For further validation, the model should be tested in diverse samples and contexts.

A multidimensional approach to understanding the emergence of sex differences in internalizing symptoms in adolescence.

Women are more vulnerable to internalizing disorders (e.g., depression and anxiety). This study took an integrative developmental approach to investigate multidimensional factors associated with the emergence of sex differences in internalizing symptoms, using data from the Adolescent Brain Cognitive Development (ABCD) study. Indices of sex hormone levels (dehydroepiandrosterone, testosterone, and estradiol), physical pubertal development, task-based functional brain activity, family conflict, and internalizing symptoms were drawn from the ABCD study's baseline sample (9- to 10-year-old; N = 11,844). Principal component analysis served as a data-driven dimensionality reduction technique on the internalizing subscales to yield a single robust measure of internalizing symptoms. Moderated mediation analyses assessed whether associations between known risk factors and internalizing symptoms vary by sex. Results revealed direct and indirect effects of physical pubertal development on internalizing symptoms through family conflict across sexes. No effects were found of sex hormone levels or amygdala response to fearful faces on internalizing symptoms. Females did not report overall greater internalizing symptoms relative to males, suggesting that internalizing symptoms have not yet begun to increase in females at this age. Findings provide an essential baseline for future longitudinal research on the endocrine, neurocognitive, and psychosocial factors associated with sex differences in internalizing symptoms.

Task-based co-activation patterns reliably predict resting state canonical network engagement during development.

Neurodevelopmental research has traditionally focused on development of individual structures, yet multiple lines of evidence indicate parallel development of large-scale systems, including canonical neural networks (i.e., default mode, frontoparietal). However, the relationship between region- vs. network-level development remains poorly understood. The current study tests the ability of a recently developed multi-task coactivation matrix approach to predict canonical resting state network engagement at baseline and at two-year follow-up in a large and cohort of young adolescents. Pre-processed tabulated neuroimaging data were obtained from the Adolescent Brain and Cognitive Development (ABCD) study, assessing youth at baseline (N = 6073, age = 10.0 ± 0.6 years, 3056 female) and at two-year follow-up (N = 3539, age = 11.9 ± 0.6 years, 1726 female). Individual multi-task co-activation matrices were constructed from the beta weights of task contrasts from the stop signal task, the monetary incentive delay task, and emotional N-back task. Activation-based predictive modeling, a cross-validated machine learning approach, was adopted to predict resting-state canonical network engagement from multi-task co-activation matrices at baseline. Note that the tabulated data used different parcellations of the task fMRI data ("ASEG" and Desikan) and the resting-state fMRI data (Gordon). Despite this, the model successfully predicted connectivity within the default mode network (DMN, rho = 0.179 ± 0.002, p < 0.001) across participants and identified a subset of co-activations within parietal and occipital macroscale brain regions as key contributors to model performance, suggesting an underlying common brain functional architecture across cognitive domains. Notably, predictive features for resting-state connectivity within the DMN identified at baseline also predicted DMN connectivity at two-year follow-up (rho = 0.258). These results indicate that multi-task co-activation matrices are functionally meaningful and can be used to predict resting-state connectivity. Interestingly, given that predictive features within the co-activation matrices identified at baseline can be extended to predictions at a future time point, our results suggest that task-based neural features and models are valid predictors of resting state network level connectivity across the course of development. Future work is encouraged to verify these findings with more consistent parcellations between task-based and resting-state fMRI, and with longer developmental trajectories.

Bayesian network mediation analysis with application to the brain functional connectome.

The brain functional connectome, the collection of interconnected neural circuits along functional networks, facilitates a cutting-edge understanding of brain functioning, and has a potential to play a mediating role within the effect pathway between an exposure and an outcome. While existing mediation analytic approaches are capable of providing insight into complex processes, they mainly focus on a univariate mediator or mediator vector, without considering network-variate mediators. To fill the methodological gap and accomplish this exciting and urgent application, in the article, we propose an integrative mediation analysis under a Bayesian paradigm with networks entailing the mediation effect. To parameterize the network measurements, we introduce individually specified stochastic block models with unknown block allocation, and naturally bridge effect elements through the latent network mediators induced by the connectivity weights across network modules. To enable the identification of truly active mediating components, we simultaneously impose a feature selection across network mediators. We show the superiority of our model in estimating different effect components and selecting active mediating network structures. As a practical illustration of this approach's application to network neuroscience, we characterize the relationship between a therapeutic intervention and opioid abstinence as mediated by brain functional sub-networks.

The effects of prenatal stress on neural responses to infant cries in expectant mothers and fathers.

The perinatal period is characterized by distinct neurobiological and psychological changes initiated prenatally, which may both facilitate postpartum caregiving and increase vulnerability to stress. Parents need to adapt to the high demands of caregiving, which include responding to salient infant cues, such as infant cries. Therefore, assessing the impact of prenatal stress exposure on parents' neural processing of infant cries may elucidate mechanisms conferring early risk for detrimental perinatal outcomes. Using event-related potentials, we examined whether prenatal perceived stress affected neural markers of perceptual (N1, P2) and attentional (LPP) processes elicited by high- and low-distress infant cries in expectant mothers (n = 38) and fathers (n = 30). Results evidenced that prenatal perceived stress impacted parents' sustained attentional processing (LPP) of infant cries, but not early perceptual responses (N1, P2). Specifically, higher levels of prenatal perceived stress were associated with a greater LPP response to low-distress, but not high-distress, infant cries. There were no parental sex differences in prenatal perceived stress or neural responses to infant cries. Increased attentional processing of low-distress cries in highly stressed parents may reflect uncertainty regarding infant distress level, thereby requiring more attentional resources. Overall, our results suggest that prenatal stress impacts processing of infant cues, even before birth.

Hyperbolic discounting rates and risk for problematic alcohol use in youth enrolled in the Adolescent Brain and Cognitive Development study.

Adolescence is the peak period for the emergence of substance use, which can lead to long-term psychosocial, occupational and interpersonal complications. Ongoing large-scale, longitudinal, consortium initiatives, such as the Adolescent Brain and Cognitive Development (ABCD) study, offer unprecedented opportunities to elucidate key risk factors for problematic substance use in a well-powered sample and to examine how changes in risk factors relate to symptoms across time. Delay discounting has been proposed as a putative risk marker for early substance-use initiation and other forms of psychopathology. However, the extent to which other factors (e.g., socio-economic status and cognitive ability) influence discounting behaviour in young adolescents is not well established. The present study leverages data from the ABCD study (n = 11 045) to assess associations between core demographic and familial variables and delay discounting in youth-operationalized using hyperbolic discounting rates (k)-before the onset of significant psychopathology. Model estimates revealed significant effects of individual difference factors (e.g., sex and socio-economic status) and alcohol risk status (based on family history) on delay discounting. No significant differences were observed in the primary sample when comparing the presence of parent drug problems or prenatal drug exposures. These effects will require replication in later waves of ABCD. Nonetheless, these results provide support for delay discounting as a potential risk marker for problematic alcohol use and demonstrate a relationship between key demographic variables and adolescent discounting behaviour. Further, these results provide an empirical baseline from which developmental trajectories of delay discounting and substance use may be tracked throughout future waves of ABCD.

Multivariate, Transgenerational Associations of the COVID-19 Pandemic Across Minoritized and Marginalized Communities.

IMPORTANCE: The experienced consequences of the COVID-19 pandemic have diverged across individuals, families, and communities, resulting in inequity within a host of factors. There is a gap of quantitative evidence about the transgenerational impacts of these experiences and factors. OBJECTIVE: To identify baseline predictors of COVID-19 experiences, as defined by child and parent report, using a multivariate pattern-learning framework from the Adolescent Brain and Cognitive Development (ABCD) cohort. DESIGN, SETTING, AND PARTICIPANTS: ABCD is an ongoing prospective longitudinal study of child and adolescent development in the United States including 11 875 youths, enrolled at age 9 to 10 years. Using nationally collected longitudinal profiling data from 9267 families, a multivariate pattern-learning strategy was developed to identify factor combinations associated with transgenerational costs of the ongoing COVID-19 pandemic. ABCD data (release 3.0) collected from 2016 to 2020 and released between 2019 and 2021 were analyzed in combination with ABCD COVID-19 rapid response data from the first 3 collection points (May-August 2020). EXPOSURES: Social distancing and other response measures imposed by COVID-19, including school closures and shutdown of many childhood recreational activities. MAIN OUTCOMES AND MEASURES: Mid-COVID-19 experiences as defined by the ABCD's parent and child COVID-19 assessments. RESULTS: Deep profiles from 9267 youth (5681 female [47.8%]; mean [SD] age, 119.0 [7.5] months) and their caregivers were quantitatively examined. Enabled by a pattern-learning analysis, social determinants of inequity, including family structure, socioeconomic status, and the experience of racism, were found to be primarily associated with transgenerational impacts of COVID-19, above and beyond other candidate predictors such as preexisting medical or psychiatric conditions. Pooling information across more than 17 000 baseline pre-COVID-19 family indicators and more than 280 measures of day-to-day COVID-19 experiences, non-White (ie, families who reported being Asian, Black, Hispanic, other, or a combination of those choices) and/or Spanish-speaking families were found to have decreased resources (mode 1, canonical vector weight [CVW] = 0.19; rank 5 of 281), escalated likelihoods of financial worry (mode 1, CVW = -0.20; rank 4), and food insecurity (mode 1, CVW = 0.21; rank 2), yet were more likely to have parent-child discussions regarding COVID-19-associated health and prevention issues, such as handwashing (mode 1, CVW = 0.14; rank 9), conserving food or other items (mode 1, CVW = 0.21; rank 1), protecting elderly individuals (mode 1, CVW = 0.11; rank 21), and isolating from others (mode 1, CVW = 0.11; rank 23). In contrast, White families (mode 1, CVW = -0.07; rank 3), those with higher pre-COVID-19 income (mode 1, CVW = -0.07; rank 5), and presence of a parent with a postgraduate degree (mode 1, CVW = -0.06; rank 14) experienced reduced COVID-19-associated impact. In turn, children from families experiencing reduced COVID-19 impacts reported longer nighttime sleep durations (mode 1, CVW = 0.13; rank 14), less difficulties with remote learning (mode 2, CVW = 0.14; rank 7), and decreased worry about the impact of COVID-19 on their family's financial stability (mode 1, CVW = 0.134; rank 13). CONCLUSIONS AND RELEVANCE: The findings of this study indicate that community-level, transgenerational intervention strategies may be needed to combat the disproportionate burden of pandemics on minoritized and marginalized racial and ethnic populations.

Systematic review of structural and functional neuroimaging studies of cannabis use in adolescence and emerging adulthood: evidence from 90 studies and 9441 participants.

Cannabis use peaks in adolescence, and adolescents may be more vulnerable to the neural effects of cannabis and cannabis-related harms due to ongoing brain development during this period. In light of ongoing cannabis policy changes, increased availability, reduced perceptions of harm, heightened interest in medicinal applications of cannabis, and drastic increases in cannabis potency, it is essential to establish an understanding of cannabis effects on the developing adolescent brain. This systematic review aims to: (1) synthesize extant literature on functional and structural neural alterations associated with cannabis use during adolescence and emerging adulthood; (2) identify gaps in the literature that critically impede our ability to accurately assess the effect of cannabis on adolescent brain function and development; and (3) provide recommendations for future research to bridge these gaps and elucidate the mechanisms underlying cannabis-related harms in adolescence and emerging adulthood, with the long-term goal of facilitating the development of improved prevention, early intervention, and treatment approaches targeting adolescent cannabis users (CU). Based on a systematic search of Medline and PsycInfo and other non-systematic sources, we identified 90 studies including 9441 adolescents and emerging adults (n = 3924 CU, n = 5517 non-CU), which provide preliminary evidence for functional and structural alterations in frontoparietal, frontolimbic, frontostriatal, and cerebellar regions among adolescent cannabis users. Larger, more rigorous studies are essential to reconcile divergent results, assess potential moderators of cannabis effects on the developing brain, disentangle risk factors for use from consequences of exposure, and elucidate the extent to which cannabis effects are reversible with abstinence. Guidelines for conducting this work are provided.

Identification and Validation of Distinct Latent Neurodevelopmental Profiles in the Adolescent Brain and Cognitive Development Study.

BACKGROUND: Regardless of the precise mechanism, all neurodevelopmental models of risk assume that, at the population level, there exist subgroups of individuals that share similar patterns of neural function and development-and that these subgroups somehow relate to psychiatric risk. However, the existence of multiple neurodevelopmental subgroups at the population level has not been assessed previously. METHODS: In the current study, cross-validated latent profile analysis was used to test for the presence of empirically derived, brain-based developmental subgroups using functional magnetic resonance imaging data from 6758 individuals (49.4% female; mean age = 9.94 years) in the Adolescent Brain and Cognitive Development (ABCD) study wave 1 release. Data were randomly split into training and testing samples. RESULTS: Analyses in the training sample (n = 3379) identified a seven-profile solution (entropy = 0.880) that was replicated in the held-out testing data (n = 3379, entropy = 0.890). Identified subgroups included a moderate group (66.8%), high reward (4.3%) and low reward (4.0%) groups, high inhibition (9.8%) and low inhibition (6.7%) groups, and high emotion regulation (4.0%) and low emotion regulation (4.3%) groups. Relative to the moderate group, other subgroups were characterized by more males (χ2 = 24.10, p = .0005), higher proportions of individuals from lower-income households (χ2 = 122.17, p < .0001), poorer cognitive performance (ps < .0001), more screen time (F = 6.80, p < .0001), heightened impulsivity (ps < .006), and higher rates of neurodevelopmental disorders (χ2 = 26.20, p = .0002). CONCLUSIONS: These data demonstrate the existence of multiple, distinct neurodevelopmental subgroups at the population level. They indicate that these empirically derived, brain-based developmental profiles relate to differences in clinical features, even at a young age, and prior to the peak period of risk for the development of psychopathology.

Effects of Smoking Status and State on Intrinsic Connectivity.

BACKGROUND: Smoking behavior during the first 24 hours of a quit attempt is a significant predictor of longer-term abstinence, yet little is known about the neurobiology of early tobacco abstinence. Specifically, the effects of acute tobacco deprivation and reinstatement on brain function-particularly at the level of large-scale network dynamics and assessed across the entire brain-remain incompletely understood. To address this gap, this study used a mixed within- and between-subjects design to assess the effects of smoking status (yes/no smoker) and state (deprived vs. satiated) on whole-brain patterns of intrinsic connectivity. METHODS: Participants included 42 tobacco smokers who underwent resting-state functional magnetic resonance imaging following overnight abstinence (deprived state) and following smoking reinstatement (satiated state, randomized order across participants). Sixty healthy control nonsmokers underwent a single resting-state scan using the same acquisition parameters. Functional connectivity data were analyzed using both a canonical network-of-interest approach and a whole-brain, data-driven approach, i.e., intrinsic connectivity distribution. RESULTS: Network-of-interest-based analyses indicated decreased functional connectivity within frontoparietal and salience networks among smokers relative to nonsmokers as well as effects of smoking state on default mode connectivity. In addition, intrinsic connectivity distribution analyses identified novel between-group differences in subcortical-cerebellar and corticocerebellar networks that were largely smoking state dependent. CONCLUSIONS: These data demonstrate the importance of considering smoking state and the utility of using both theory- and data-driven analysis approaches. These data provide much-needed insight into the functional neurobiology of early abstinence, which may be used in the development of novel treatments.

Discrete Roles for Impulsivity and Compulsivity in Gambling Disorder.

Background and Objective: Complex associations between gambling disorder (GD) and impulsivity have been identified. However, little is known regarding how compulsivity associates with different impulsivity domains in GD. In this study, we examined associations between self-reported and behavioral measures of impulsivity-assessed through the Barratt Impulsiveness Scale (BIS-11) and the Experiential Discounting Task (EDT), respectively- and compulsivity-measured using the Padua Inventory and the Wisconsin Card Sorting Test (WCST), respectively-, in an adult sample with GD (N = 132, 94 men and 38 women, ages ranging from 18 to 69 years). GD severity was assessed using the South Oaks Gambling Screen. Methods: Structural Equation Modeling was used to examine relationships between impulsivity and compulsivity measures, age, and GD severity. Results: BIS-11 non-planning and BIS-11 total scores positively correlated with GD severity. The standardized coefficients for the SEM showed direct positive contributions of BIS-11 non-planning, Padua and EDT scores to GD severity. Only participants' ages directly contributed to WCST perseverative errors, and no direct or indirect effects were found with respect to GD severity. Conclusion: The findings suggest that specific aspects of impulsivity and compulsivity contribute to GD severity. Interventions specifically targeting domains that are most relevant to GD severity may improve treatment outcomes.