Unlocking the age-old secrets of reward and substance use.

Although substance use is widespread across the lifespan from early adolescence to older adulthood, the prevalence of substance use disorder (SUD) differs between age groups. These age differences in SUD rates necessitate an investigation into how age moderates reward sensitivity, and consequently influences the risks and consequences related to substance use. This theoretical review integrates evidence from the literature to address the dynamic interplay between age and reward in the context of substance use. Overall, increasing evidence demonstrates that age moderates reward sensitivity and underlying reward system neurobiology. Reward sensitivity undergoes a non-linear trajectory across the lifespan. Low levels of reward sensitivity are associated with childhood and late adulthood. In contrast, high levels are associated with early to late adolescence, followed by a decline in the twenties. These fluctuations in reward sensitivity across the lifespan contribute to complex associations with substance use. This lends support to adolescence and young adulthood as vulnerable periods for the risk of subsequent SUD. More empirical research is needed to investigate reward sensitivity during SUD maintenance and recovery. Future research should also involve larger sample sizes and encompass a broader range of age groups, including older adults.

Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity: A Systematic Review.

Importance: In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers. Objective: To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts. Evidence Review: The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders. Findings: There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes. Conclusions and Relevance: Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments.

Associations between hair-derived cannabinoid levels, self-reported use, and cannabis-related problems.

RATIONALE: As cannabis potency and cannabis use are increasing in newly legalized markets, it is increasingly important to measure and examine the effects of cannabinoid exposure. OBJECTIVES: The current study aims to assess how hair-derived cannabinoid concentrations - offering insight into three-month cumulative exposure - are associated with common self-report measures of cannabis use and cannabis use-related problems. METHODS: 74 near-daily dependent cannabis users self-reported their quantity of cannabis use, cannabis use-related problems, and estimated cannabis potency. Hair samples were provided to quantify Δ9-THC, CBD, and CBN using LC-MS/MS and THC-consumption was verified by analyzing THC-COOH in hair using GC-MS/MS. RESULTS: Cannabinoids were detectable in 95.95% of the hair samples from individuals who tested positive on a urine screen for cannabis. Δ9-THC concentrations were positively associated with measures of self-reported potency (relative potency, potency category, and perceived 'high'), but Δ9-THC, CBD, CBN concentrations and THC/CBD ratio were not associated with self-reported quantity of use. Self-reported potency, but not hair-derived concentrations, were associated with withdrawal and craving. Self-reported quantity of cannabis use, but not cannabinoid concentrations, were associated with cannabis use-related problems. CONCLUSIONS: The use of hair-derived cannabinoid quantification is supported for detecting cannabis use in near-daily users, but the lack of associations between hair-derived cannabinoid concentrations and self-report measures of use does not support the use of hair analyses alone for quantification of cannabinoid exposure. Further research comparing hair-derived cannabinoid concentrations with other biological matrices (e.g. plasma) and self-report is necessary to further evaluate the validity of hair analyses for this purpose.

Sex Difference in Cigarette-Smoking Status and Its Association with Brain Volumes Using Large-Scale Community-Representative Data.

BACKGROUND: Cigarette smoking is believed to accelerate age-related neurodegeneration. Despite significant sex differences in both smoking behaviors and brain structures, the active literature is equivocal in parsing out a sex difference in smoking-associated brain structural changes. OBJECTIVE: The current study examined subcortical and lateral ventricle gray matter (GM) volume differences among smokers, active, past, and never-smokers, stratified by sex. METHODS: The current study data included 1959 Dallas Heart Study (DHS) participants with valid brain imaging data. Stratified by gender, multiple-group comparisons of three cigarette-smoking groups were conducted to test whether there is any cigarette-smoking group differences in GM volumes of the selected regions of interest (ROIs). RESULTS: The largest subcortical GM volumetric loss and enlargement of the lateral ventricle were observed among past smokers for both females and males. However, these observed group differences in GM volumetric changes were statistically significant only among males after adjusting for age and intracranial volumes. CONCLUSIONS: The study findings suggest a sex difference in lifetime-smoking-associated GM volumetric changes, even after controlling for aging and intracranial volumes.

A cross-cultural fMRI investigation of cannabis approach bias in individuals with cannabis use disorder.

Introduction: As cannabis policies and attitudes become more permissive, it is crucial to examine how the legal and social environment influence neurocognitive mechanisms underlying cannabis use disorder (CUD). The current study aimed to assess whether cannabis approach bias, one of the mechanisms proposed to underlie CUD, differed between environments with distinct recreational cannabis policies (Amsterdam, The Netherlands (NL) and Dallas, Texas, United States of America (TX)) and whether individual differences in cannabis attitudes affect those differences. Methods: Individuals with CUD (NL-CUD: 64; TX-CUD: 48) and closely matched non-using controls (NL-CON: 50; TX-CON: 36) completed a cannabis approach avoidance task (CAAT) in a 3T MRI. The cannabis culture questionnaire was used to measure cannabis attitudes from three perspectives: personal, family/friends, and state/country attitudes. Results: Individuals with CUD demonstrated a significant behavioral cannabis-specific approach bias. Individuals with CUD exhibited higher cannabis approach bias-related activity in clusters including the paracingulate gyrus, anterior cingulate cortex, and frontal medial cortex compared to controls, which was no longer significant after controlling for gender. Site-related differences emerged in the association between cannabis use quantity and cannabis approach bias activity in the putamen, amygdala, hippocampus, and insula, with a positive association in the TX-CUD group and a negative association in the NL-CUD group. This was not explained by site differences in cannabis attitudes. Conclusions: Pinpointing the underlying mechanisms of site-related differences-including, but not limited to, differences in method of administration, cannabis potency, or patterns of substance co-use-is a key challenge for future research.

Working memory-related brain activity in cannabis use disorder: The role of cross-cultural differences in cannabis attitudes.

Cannabis legislation and attitudes towards use are changing. Given that evidence from cultural neuroscience research suggests that culture influences the neurobiological mechanisms underlying behaviour, it is of great importance to understand how cannabis legislation and attitudes might affect the brain processes underlying cannabis use disorder. Brain activity of 100 dependent cannabis users and 84 controls was recorded during an N-back working memory (WM) task in participants from the Netherlands (NL; users = 60, controls = 52) and Texas, USA (TX; users = 40, controls = 32). Participants completed a cannabis culture questionnaire as a measure of perceived benefits (positive) and perceived harms (negative) of cannabis from their personal, friends-family's and country-state's perspectives. Amount of cannabis use (grams/week), DSM-5 CUD symptoms and cannabis use-related problems were assessed. Cannabis users self-reported more positive and less negative (personal and friends-family) cannabis attitudes than controls, with this effect being significantly larger in the TX cannabis users. No site difference in country-state attitudes was observed. TX cannabis users, compared with NL cannabis users, and those cannabis users perceiving more positive country-state attitudes showed a more positive association between grams/week and WM-related activity in the superior parietal lobe. NL cannabis users, compared with TX cannabis users, and those cannabis users with less positive personal attitudes showed a more positive association between grams/week and WM-load-related activity in the temporal pole. Both site and cultural attitudes moderated the association of quantity of cannabis use with WM- and WM-load-related activity. Importantly, differences in legislation did not align with perceived cannabis attitudes and appear to be differentially associated with cannabis use-related brain activity.

Latent profiles of substance use, early life stress, and attention/externalizing problems and their association with neural correlates of reinforcement learning in adolescents.

BACKGROUND: Adolescent substance use, externalizing and attention problems, and early life stress (ELS) commonly co-occur. These psychopathologies show overlapping neural dysfunction in the form of reduced recruitment of reward processing neuro-circuitries. However, it is unclear to what extent these psychopathologies show common v. different neural dysfunctions as a function of symptom profiles, as no studies have directly compared neural dysfunctions associated with each of these psychopathologies to each other. METHODS: In study 1, a latent profile analysis (LPA) was conducted in a sample of 266 adolescents (aged 13-18, 41.7% female, 58.3% male) from a residential youth care facility and the surrounding community to investigate substance use, externalizing and attention problems, and ELS psychopathologies and their co-presentation. In study 2, we examined a subsample of 174 participants who completed the Passive Avoidance learning task during functional magnetic resonance imaging to examine differential and/or common reward processing neuro-circuitry dysfunctions associated with symptom profiles based on these co-presentations. RESULTS: In study 1, LPA identified profiles of substance use plus rule-breaking behaviors, attention-deficit hyperactivity disorder, and ELS. In study 2, the substance use/rule-breaking profile was associated with reduced recruitment of reward processing and attentional neuro-circuitries during the Passive Avoidance task (p < 0.05, corrected for multiple comparisons). CONCLUSIONS: Findings indicate that there is reduced responsivity of striato-cortical regions when receiving outcomes on an instrumental learning task within a profile of adolescents with substance use and rule-breaking behaviors. Mitigating reward processing dysfunction specifically may represent a potential intervention target for substance-use psychopathologies accompanied by rule-breaking behaviors.

Cannabis Use Disorder Symptoms in Weekly Cannabis Users: A Network Comparison Between Daily Cigarette Users and Nondaily Cigarette Users.

Background: Concurrent use (co-use) of cannabis and tobacco is common and associated with worse clinical outcomes compared with cannabis use only. The mechanisms and interactions of cannabis use disorder (CUD) symptoms underlying co-use remain poorly understood. Methods: We examined differences in the symptom presence and symptom network configurations between weekly cannabis users who use tobacco daily (co-users, n=789) or non- or nondaily (nondaily co-users, n=428). Results: First, we identified a range of symptoms (craving, failed reduce or quit attempts, neglected responsibilities, and negative social effects) that are most central to the highly interconnected CUD symptom network. Risky cannabis use was mostly associated with negative social and health effects, and independent of other CUD symptoms. Craving symptoms act as a bridge between different CUD and withdrawal symptoms. Among co-users, (1) craving is more strongly associated with negative psychosocial effects, (2) feelings of depression and negative health effects are more central to the network, and (3) the negative health effects are more strongly associated with failed attempts to reduce or quit attempts compared with nondaily co-users. Discussion: Our results go beyond existing findings focused on the mere increase in CUD symptom presence, and speak to the potential synergistic effects of co-use on dependence and withdrawal symptoms. We outline clinical implications with respect to targeting specific CUD symptoms in co-users, and point to future research to disentangle tobacco and cannabis craving symptoms.

Functional hyperconnectivity between corticocerebellar networks and altered decision making in young adult cannabis users: Evidence from 7T and multivariate pattern analysis.

Decision-making (DM) impairments are important predictors of cannabis initiation and continued use. In cannabis users, how decision-making abnormalities related to structural and functional connectivity in the brain are not fully understood. We employed a three-method multimodal image analysis and multivariate pattern analysis (MVPA) on high dimensional 7 tesla MRI images examining functional connectivity, white matter microstructure and gray matter volume in a group of cannabis users and non-users. Neuroimaging and cognitive analyses were performed on 92 CU and 92 age- matched NU from a total of 187 7T scans. CU were selected on the basis of their scores on the Semi-Structured Assessment for the Genetics of Alcoholism. The groups were first compared on a decision-making test and then on ICA based functional connectivity between corticocerebellar networks. An MVPA was done as a confirmatory analysis. The anatomy of these networks was then assessed using Diffusion Tensor imaging (DTI) and cortical volume analyses. Cannabis Users had significantly higher scores on the Iowa Gambling task (IGT) [Gambling task Percentage larger] and significantly lower scores on the [Gambling task reward Percentage smaller]. Left accumbens (L NAc) volume was significantly larger in cannabis users. DTI analysis between the groups yielded no significant (FWE corrected) differences. Resting state FC analysis of the left Cerebellum region 9 showed enhanced functional connectivity with the right nucleus accumbens and left pallidum and left putamen in CU. In addition, posterior cerebellum showed enhanced functional connectivity (FWE corrected) with 2 nodes of the DMN and left and right paracingulate (sub genual ACC) and the sub callosal cortex in CU. IGT percentage larger scores correlated with posterior cerebellar functional connectivity in non-user women. A multivariate pattern analysis confirmed this cerebellar hyperconnectivity in both groups. Our results demonstrate for the first time that deficits in DM observed in cannabis users are mirrored in hyper connectivity in corticocerebellar networks. Cortical volumes of some of the nodes of these networks showed increases in users. However, the underlying white matter was largely intact in CU. The observed DM deficits and hyper connectivity in resting networks may contribute to difficulties in quitting and/or facilitating relapse.

An Interpretable and Predictive Connectivity-Based Neural Signature for Chronic Cannabis Use.

BACKGROUND: Cannabis is one of the most widely used substances in the world, with usage trending upward in recent years. However, although the psychiatric burden associated with maladaptive cannabis use has been well established, reliable and interpretable biomarkers associated with chronic use remain elusive. In this study, we combine large-scale functional magnetic resonance imaging with machine learning and network analysis and develop an interpretable decoding model that offers both accurate prediction and novel insights into chronic cannabis use. METHODS: Chronic cannabis users (n = 166) and nonusing healthy control subjects (n = 124) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic users and nonusers based on whole-brain functional connectivity. Network analysis was used to identify the most predictive regions and communities. RESULTS: We obtained high (∼80% out-of-sample) accuracy across 4 different classification models, demonstrating that task-evoked connectivity can successfully differentiate chronic cannabis users from nonusers. We also identified key predictive regions implicating motor, sensory, attention, and craving-related areas, as well as a core set of brain networks that contributed to successful classification. The most predictive networks also strongly correlated with cannabis craving within the chronic user group. CONCLUSIONS: This novel approach produced a neural signature of chronic cannabis use that is both accurate in terms of out-of-sample prediction and interpretable in terms of predictive networks and their relation to cannabis craving.

Gender differences in cannabis use disorder symptoms: A network analysis.

BACKGROUND: While cannabis use in women is increasing worldwide, research into gender differences in cannabis use disorder (CUD) symptomology is lacking. In response to limited effectiveness of addiction treatment, research focus has been shifting from clinical diagnoses towards interactions between symptoms, as patterns of symptoms and their interactions could be crucial in understanding etiological mechanisms in addiction. The aim of this study was to evaluate the CUD symptom network and assess whether there are gender differences therein. METHODS: A total of 1257 Dutch individuals reporting weekly cannabis use, including 745 men and 512 women, completed online questionnaires assessing DSM-5 CUD symptoms and additional items on plans to quit or reduce use, cigarette use, and the presence of psychological diagnoses. Gender differences were assessed for all variables and an Ising model estimation method was used to estimate CUD symptom networks in men and women using network comparison tests to assess differences. RESULTS: There were gender differences in the prevalence of 6 of the 11 symptoms, but symptom networks did not differ between men and women. Cigarette use appeared to only be connected to the network through withdrawal, indicating a potential role of cigarette smoking in enhancing cannabis withdrawal symptoms. Furthermore, there were gender differences in the network associations of mood and anxiety disorders with CUD symptoms. CONCLUSION: The association between smoking and withdrawal as well as gender differences in the role of comorbidities in the CUD network highlight the value of using network models to understand CUD and how symptom interactions might affect treatment.

Intersection between social inequality and emotion regulation on emerging adult cannabis use.

Emerging adulthood (EA; ages 18-25) is characterized by socioemotional and neurodevelopmental challenges. Cannabis is a widely used substance among EAs, and hazardous use may increase risk for sustained use patterns and related health consequences. Research shows differential increases in hazardous use by objective as well as subjective measures of social inequality, with more concerning trajectories for youth with greater experiences of social inequality. Learning how to flexibly monitor and modify emotions in proactive ways (i.e., emotion regulation) is a central developmental task navigated during the EA window. Challenges to and with emotion regulation processes can contribute to the emergence of mental health symptoms during EA, including hazardous cannabis use. In this perspective, we highlight emotion dysregulation and social inequality as two critical factors that interact to either buffer against or exacerbate cannabis use during the EA period, noting critical gaps in the literature that merit additional research. We recommend novel methods and longitudinal designs to help clarify how dynamic cognition-emotion interplay predicts trajectories of negative emotional experiences and cannabis use in EA.

A Bayesian learning model to predict the risk for cannabis use disorder.

BACKGROUND: The prevalence of cannabis use disorder (CUD) has been increasing recently and is expected to increase further due to the rising trend of cannabis legalization. To help stem this public health concern, a model is needed that predicts for an adolescent or young adult cannabis user their personalized risk of developing CUD in adulthood. However, there exists no such model that is built using nationally representative longitudinal data. METHODS: We use a novel Bayesian learning approach and data from Add Health (n = 8712), a nationally representative longitudinal study, to build logistic regression models using four different regularization priors: lasso, ridge, horseshoe, and t. The models are compared by their prediction performance on unseen data via 5-fold-cross-validation (CV). We assess model discrimination using the area under the curve (AUC) and calibration by comparing the expected (E) and observed (O) number of CUD cases. We also externally validate the final model on independent test data from Add Health (n = 570). RESULTS: Our final model is based on lasso prior and has seven predictors: biological sex; scores on personality traits of neuroticism, openness, and conscientiousness; and measures of adverse childhood experiences, delinquency, and peer cannabis use. It has good discrimination and calibration performance as reflected by its respective AUC and E/O of 0.69 and 0.95 based on 5-fold CV and 0.71 and 1.10 on validation data. CONCLUSION: This externally validated model may help in identifying adolescent or young adult cannabis users at high risk of developing CUD in adulthood.

The role of sex in the association between cannabis use and working memory-related brain activity.

Although cannabis use patterns differ between men and women, studies on sex differences on the effects of cannabis on the brain and cognitive control are largely lacking. Working memory (WM) is a component of cognitive control believed to be involved in the development and maintenance of addiction. In this study, we evaluated the association between cannabis use and WM (load) related brain activity in a large sample, enabling us to assess sex effects in this association. The brain activity of 104 frequent cannabis users (63% men) and 85 controls (53% men) was recorded during an N-back WM task. Behavioral results showed a significant interaction between WM load and group for both accuracy and reaction time, with cannabis users showing a relatively larger decrease in performance with increasing WM load. Cannabis users compared to controls showed a relatively smaller reduction in WM (load) related activity in the precuneus and posterior cingulate cortex at higher WM load. This WM (load) related activity was not associated with performance nor cannabis use and related problems. An exploratory analysis showed higher WM-related activity in the superior frontal gyrus in men compared to women. While cannabis users showed higher WM (load) related activity in central nodes of the default mode network, this was not directly attributable to group specific worsening of performance under higher cognitive load. Further research is necessary to assess whether observed group differences increase with higher cognitive load, how group differences relate to measures of cannabis use, and how sex affects these group differences.

Joint risk prediction for hazardous use of alcohol, cannabis, and tobacco among adolescents: A preliminary study using statistical and machine learning.

For some, substance use during adolescence may be a stepping stone on the way to substance use disorders in adulthood. Risk prediction models may help identify adolescent users at elevated risk for hazardous substance use. This preliminary analysis used cross-sectional data (n = 270, ages 13-18) from the baseline dataset of a randomized controlled trial intervening with adolescent alcohol and/or cannabis use. Models were developed for jointly predicting quantitative scores on three measures of hazardous substance use (Rutgers Alcohol Problems Index, Adolescent Cannabis Problem Questionnaire, and Hooked on Nicotine Checklist) based on personal risk factors using two statistical and machine learning methods: multivariate covariance generalized linear models (MCGLM) and penalized multivariate regression with a lasso penalty. The predictive accuracy of a model was evaluated using root mean squared error computed via leave-one-out cross-validation. The final proposed model was an MCGLM model. It has eleven risk factors: age, early life stress, age of first tobacco use, age of first cannabis use, lifetime use of other substances, age of first use of other substances, maternal education, parental attachment, family cigarette use, family history of hazardous alcohol use, and family history of hazardous cannabis use. Different subsets of these risk factors feature in the three outcome-specific components of this joint model. The quantitative risk estimate provided by the proposed model may help identify adolescent substance users of cannabis, alcohol, and tobacco who may be at an elevated risk of developing hazardous substance use.

Use of Marijuana: Effect on Brain Health: A Scientific Statement From the American Heart Association.

Marijuana is perceived as a harmless drug, and its recreational use has gained popularity among young individuals. The concentration of active ingredients in recreational formulations has gradually increased over time, and high-potency illicit cannabinomimetics have become available. Thus, the consumption of cannabis in the general population is rising. Data from preclinical models demonstrate that cannabinoid receptors are expressed in high density in areas involved in cognition and behavior, particularly during periods of active neurodevelopment and maturation. In addition, growing evidence highlights the role of endogenous cannabinoid pathways in the regulation of neurotransmitter release, synaptic plasticity, and neurodevelopment. In animal models, exogenous cannabinoids disrupt these important processes and lead to cognitive and behavioral abnormalities. These data correlate with the higher risk of cognitive impairment reported in some observational studies done in humans. It is unclear whether the effect of cannabis on cognition reverts after abstinence. However, this evidence, along with the increased risk of stroke reported in marijuana users, raises concerns about its potential long-term effects on cognitive function. This scientific statement reviews the safety of cannabis use from the perspective of brain health, describes mechanistically how cannabis may cause cognitive dysfunction, and advocates for a more informed health care worker and consumer about the potential for cannabis to adversely affect the brain.

A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus.

Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.

Time for a paradigm shift: The adolescent brain in addiction treatment.

OBJECTIVE: One route to improve adolescent addiction treatment outcomes is to use translational approaches to help identify developmental neuroscience mechanisms that undergird active treatment ingredients and advance adolescent behavior change. METHODS: This sample included 163 adolescents (ages 15-19) randomized to motivational interviewing (MI) vs. brief adolescent mindfulness (BAM). Youth completed an fMRI paradigm assessing adolescent brain response to therapist language (complex reflection vs. mindful; complex reflection vs. confront; mindful vs. confront) at pre- (prior to the completion of the full intervention) and post-treatment (at 3-month follow-up) and behavioral measures at 3, 6 and 12 months. RESULTS: Youth in both treatment groups showed significant problem drinking reductions at 3 and 6 months, but MI youth demonstrated significantly better treatment outcomes than BAM youth at 12 months. We observed several significant treatment group differences (MI > BAM) in neural response to therapist language, including at pre-treatment when examining complex reflection vs. mindful, and complex reflection vs. confront (e.g., superior temporal gyrus, lingual gyrus); and at post-treatment when examining mindful vs. confront (e.g., supplementary motor area; middle frontal gyrus). When collapsed across treatment groups (MI + BAM), we observed significant differences by time, with youth showing a pattern of brain change in response to complex reflection vs. mindful, and complex reflection vs. confront (e.g., precuneus; postcentral gyrus). There was no evidence of a significant group × time interaction. However, brain change in response to therapist language (complex reflection vs. confront) in regions such as middle frontal gyrus, was associated with reductions in problem drinking at 12 months. Yet, few treatment group differences were observed. CONCLUSIONS: These data underscore the need to better understand therapist language and it's impact on the developing brain, in order to inform and aggregate the most impactful elements of addiction treatment for future treatment development for adolescents.

Alcohol and Cannabis Use Disorder Symptom Severity, Conduct Disorder, and Callous-Unemotional Traits and Impairment in Expression Recognition.

Background: Alcohol and cannabis are commonly used by adolescents in the United States. Both alcohol use disorder (AUD) and cannabis use disorder (CUD) have been associated with reduced emotion expression recognition ability. However, this work has primarily occurred in adults and has not considered neuro-cognitive risk factors associated with conduct problems that commonly co-occur with, and precede, substance use. Yet, conduct problems are also associated with reduced emotion expression recognition ability. The current study investigated the extent of negative association between AUD and CUD symptom severity and expression recognition ability over and above any association of expression recognition ability with conduct problems [conduct disorder (CD) diagnostic status]. Methods: In this study, 152 youths aged 12.5-18 years (56 female; 60 diagnosed with CD) completed a rapid presentation morphed intensity facial expression task to investigate the association between relative severity of AUD/CUD and expression recognition ability. Results: Cannabis use disorder identification test (CUDIT) scores were negatively associated with recognition accuracy for higher intensity (particularly sad and fearful) expressions while CD diagnostic status was independently negatively associated with recognition of sad expressions. Alcohol use disorder identification test (AUDIT) scores were not significantly associated with expression recognition ability. Conclusions: These data indicate that relative severity of CUD and CD diagnostic status are statistically independently associated with reduced expression recognition ability. On the basis of these data, we speculate that increased cannabis use during adolescence may exacerbate a neuro-cognitive risk factor for the emergence of aggression and antisocial behavior.