Utility of Machine-Learning Approaches to Identify Behavioral Markers for Substance Use Disorders: Impulsivity Dimensions as Predictors of Current Cocaine Dependence

Alterations in the brain functional network of abstinent male individuals with methamphetamine use disorder

Methamphetamine is a highly addictive psychostimulant drug that is abused globally and is a serious threat to health worldwide. Unfortunately, the specific mechanism underlying addiction remains unclear. Thus, this study aimed to investigate the characteristics of functional connectivity in the brain network and the factors influencing methamphetamine use disorder in patients using magnetic resonance imaging. We included 96 abstinent male participants with methamphetamine use disorder and 46 age- and sex-matched healthy controls for magnetic resonance imaging. Compared with healthy controls, participants with methamphetamine use disorder had greater impulsivity, fewer small-world attributes of the resting-state network, more nodal topological attributes in the cerebellum, greater functional connectivity strength within the cerebellum and between the cerebellum and brain, and decreased frontoparietal functional connectivity strength. In addition, after controlling for covariates, the partial correlation analysis showed that small-world properties were significantly associated with methamphetamine use frequency, psychological craving, and impulsivity. Furthermore, we revealed that the small-word attribute significantly mediated the effect of methamphetamine use frequency on motor impulsivity in the methamphetamine use disorder group. These findings may further improve our understanding of the neural mechanism of impulse control dysfunction underlying methamphetamine addiction and assist in exploring the neuropathological mechanism underlying methamphetamine use disorder-related dysfunction and rehabilitation.

Behavioral and neurocognitive factors distinguishing post-traumatic stress comorbidity in substance use disorders

Significant trauma histories and post-traumatic stress disorder (PTSD) are common in persons with substance use disorders (SUD) and often associate with increased SUD severity and poorer response to SUD treatment. As such, this sub-population has been associated with unique risk factors and treatment needs. Understanding the distinct etiological profile of persons with co-occurring SUD and PTSD is therefore crucial for advancing our knowledge of underlying mechanisms and the development of precision treatments. To this end, we employed supervised machine learning algorithms to interrogate the responses of 160 participants with SUD on the multidimensional NIDA Phenotyping Assessment Battery. Significant PTSD symptomatology was correctly predicted in 75% of participants (sensitivity: 80%; specificity: 72.22%) using a classification-based model based on anxiety and depressive symptoms, perseverative thinking styles, and interoceptive awareness. A regression-based machine learning model also utilized similar predictors, but failed to accurately predict severity of PTSD symptoms. These data indicate that even in a population already characterized by elevated negative affect (individuals with SUD), especially severe negative affect was predictive of PTSD symptomatology. In a follow-up analysis of a subset of 102 participants who also completed neurocognitive tasks, comorbidity status was correctly predicted in 86.67% of participants (sensitivity: 91.67%; specificity: 66.67%) based on depressive symptoms and fear-related attentional bias. However, a regression-based analysis did not identify fear-related attentional bias as a splitting factor, but instead split and categorized the sample based on indices of aggression, metacognition, distress tolerance, and interoceptive awareness. These data indicate that within a population of individuals with SUD, aberrations in tolerating and regulating aversive internal experiences may also characterize those with significant trauma histories, akin to findings in persons with anxiety without SUD. The results also highlight the need for further research on PTSD-SUD comorbidity that includes additional comparison groups (i.e., persons with only PTSD), captures additional comorbid diagnoses that may influence the PTSD-SUD relationship, examines additional types of SUDs (e.g., alcohol use disorder), and differentiates between subtypes of PTSD.

Distinct neurocognitive fingerprints reflect differential associations with risky and impulsive behavior in a neurotypical sample

Engagement in risky and impulsive behavior has long been associated with deficits in neurocognition. However, we have a limited understanding of how multiple subfunctions of neurocognition co-occur within individuals and which combinations of neurocognitive subfunctions are most relevant for risky and impulsive behavior. Using the neurotypical Nathan Kline Institute Rockland Sample (N = 673), we applied a Bayesian latent feature learning model-the Indian Buffet Process-to identify nuanced, individual-specific profiles of multiple neurocognitive subfunctions and examine their relationship to risky and impulsive behavior. All features were within a relatively normative range of neurocognition; however, there was subtle variability related to risky and impulsive behaviors. The relatively overall poorer neurocognition feature correlated with greater affective impulsivity and substance use patterns/problems. The poorer episodic memory and emotion feature correlated with greater trait externalizing and sensation-seeking. The poorer attention feature correlated with increased trait externalizing and negative urgency but decreased positive urgency and substance use. Finally, the average or mixed features negatively correlated with various risky and impulsive behaviors. Estimating nuanced patterns of co-occurring neurocognitive functions can inform our understanding of a continuum of risky and impulsive behaviors.

Impulsivity and Treatment Outcomes in Individuals with Cocaine Use Disorder: Examining the Gap between Interest and Adherence

Background: Impulsivity is implicated in the development and maintenance of Cocaine Use Disorder (CUD). Less work has examined impulsivity's role on interest in initiating treatment, treatment adherence, or treatment response. No pharmacotherapies are approved for CUD, so efforts to understand and bolster the effects of psychotherapy are important in guiding and refining treatment. The present study examined the impact of impulsivity on interest in treatment, treatment initiation, treatment adherence, and treatment outcomes in individuals with CUD. Methods: Following the completion of a larger study on impulsivity and CUD participants were offered 14 sessions of (12 weeks) Cognitive Behavioral Relapse Prevention (CBT-RP). Before starting treatment, participants completed seven self-report and four behavioral measures of impulsivity. Sixty-eight healthy adults (36% female) with CUD (aged 49.4 ± 7.9) expressed an interest in treatment. Results: Greater scores on several self-report measures of impulsivity, and fewer difficulties with delayed gratification were associated with increased interest in treatment in both males and females. 55 participants attended at least 1 treatment session, while 13 participants did attend a single session. Individuals who attended at least one treatment session scored lower on measures of lack of perseverance and procrastination. Still, measures of impulsivity did not reliably predict session attendance nor the frequency of cocaine-positive urine samples throughout treatment. Males attended nearly twice as many treatment sessions as females despite nonsignificant associations between impulsivity in males and the number of sessions attended. Conclusions: Greater impulsivity in individuals with CUD was associated with expressing an interest in treatment, but not treatment adherence or response.

Impaired risk avoidance in bipolar disorder and substance use disorders

Comorbid substance use disorders are highly prevalent in bipolar disorder, and research suggests that individuals with the comorbid presentation typically have worse outcomes than individuals with bipolar disorder without this comorbidity. However, psychosocial treatments for the comorbid presentation have not demonstrated effectiveness for both mood and substance use symptom domains, suggesting novel treatments are needed. An alternative path to treatment development is to identify mechanisms that underlie comorbid bipolar disorder and substance use disorders that can subsequently be targeted in treatment. We evaluated neurocognitive markers for impairments in risk avoidance (the tendency to engage in a persistent pattern of problematic behaviors despite negative outcomes resulting from such behaviors) as potential mechanistic variables underlying negative illness outcomes in the comorbid population. Participants with bipolar disorder (n = 45) or comorbid bipolar disorder and substance use disorders (n = 31) in a relatively euthymic mood state completed clinical risk behavior assessments, task-based risk avoidance assessments, and neurocognitive assessments. Results indicated a lack of notable between-group differences in the clinical risk composite score, task-based risk avoidance assessments, and neurocognitive assessments, with the exception of self-reported executive dysfunction which was elevated among the comorbid sample. Collapsing across group, we found that increased discounting of delayed rewards, older age, and an earlier age of (hypo)mania onset predicted an increased clinical risk composite score. These findings underscore the potential importance of delay discounting as a novel mechanistic target for reducing clinical risk behaviors among individuals with bipolar disorder both with and without comorbid substance use disorders.

Regional Homogeneity Abnormalities and Its Correlation With Impulsivity in Male Abstinent Methamphetamine Dependent Individuals

Methamphetamine (MA) use affects the brain structure and function. However, no studies have investigated the relationship between changes in regional homogeneity (ReHo) and impulsivity in MA dependent individuals (MADs). The aim of this study was to investigate the changes of brain activity under resting state in MADs and their relationship to impulsivity using ReHo method. Functional magnetic resonance imaging (fMRI) was performed to collect data from 46 MADs and 44 healthy controls (HCs) under resting state. ReHo method was used to investigate the differences in average ReHo values between the two groups. The ReHo values abnormalities of the brain regions found in inter-group comparisons were extracted and correlated with impulsivity. Compared to the HCs, MADs showed significant increased ReHo values in the bilateral striatum, while the ReHo values of the bilateral precentral gyrus and the bilateral postcentral gyrus decreased significantly. The ReHo values of the left precentral gyrus were negatively correlated with the BIS-attention, BIS-motor, and BIS-nonplanning subscale scores, while the ReHo values of the postcentral gyrus were only negatively correlated with the BIS-motor subscale scores in MADs. The abnormal spontaneous brain activity in the resting state of MADs revealed in this study may further improve our understanding of the neuro-matrix of MADs impulse control dysfunction and may help us to explore the neuropathological mechanism of MADs related dysfunction and rehabilitation.

A Peer-Led, Artificial Intelligence-Augmented Social Network Intervention to Prevent HIV Among Youth Experiencing Homelessness

BACKGROUND

Youth experiencing homelessness (YEH) are at elevated risk of HIV/AIDS and disproportionately identify as racial, ethnic, sexual, and gender minorities. We developed a new peer change agent (PCA) HIV prevention intervention with 3 arms: (1) an arm using an artificial intelligence (AI) planning algorithm to select PCAs; (2) a popularity arm, the standard PCA approach, operationalized as highest degree centrality (DC); and (3) an observation-only comparison group.

SETTING

A total of 713 YEH were recruited from 3 drop-in centers in Los Angeles, CA.

METHODS

Youth consented and completed a baseline survey that collected self-reported data on HIV knowledge, condom use, and social network information. A quasi-experimental pretest/posttest design was used; 472 youth (66.5% retention at 1 month postbaseline) and 415 youth (58.5% retention at 3 months postbaseline) completed follow-up. In each intervention arm (AI and DC), 20% of youth was selected as PCAs and attended a 4-hour initial training, followed by 7 weeks of half-hour follow-up sessions. Youth disseminated messages promoting HIV knowledge and condom use.

RESULTS

Using generalized estimating equation models, there was a significant reduction over time (P < 0.001) and a significant time by AI arm interaction (P < 0.001) for condomless anal sex act. There was a significant increase in HIV knowledge over time among PCAs in DC and AI arms.

CONCLUSIONS

PCA models that promote HIV knowledge and condom use are efficacious for YEH. Youth are able to serve as a bridge between interventionists and their community. Interventionists should consider working with computer scientists to solve implementation problems.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Des repères pour la conception des apps ?

Objectif Proposer quelques repères pour faciliter le processus de création d’applications pour téléphones intelligents (apps) en santé mentale.

Méthode Présentation brève de l’intérêt potentiel des apps et proposition argumentée d’étapes clés pour la création des apps. L’article se base sur une revue narrative, un retour d’expérience et des discussions de groupes d’experts.

Résultats Les apps ont des caractéristiques ubiquitaires particulièrement intéressantes pour le domaine de la santé mentale. Potentiellement connectées à de multiples technologies, mobiles et disponibles en tout temps, elles permettent une grande flexibilité de conception. Afin d’augmenter les chances d’efficacité et de bonne dissémination d’une app donnée, certains principes pourraient guider de manière utile le travail de conception des apps : 9 repères sont proposés, en particulier une bonne intégration des utilisateurs finaux autour d’objectifs cibles bien définis durant tout le processus de création de tels outils.

Conclusion Les repères proposés pourraient faciliter le processus de création d’apps pour la santé mentale.

Impulsivity in cocaine users compared to matched controls: Effects of sex and preferred route of cocaine use

BACKGROUND

Impulsivity has been identified as playing a role in cocaine use. The purpose of this study was to explore self-report measures of impulsivity in large groups of male and female cocaine users and matched controls and to determine if differences in impulsivity measures within a group of cocaine users related to self-reported money spent on cocaine and route of cocaine use.

METHODS

Eight self-report impulsivity measures yielding 34 subscales were obtained in 230 cocaine users (180 M, 50 F) and a matched group of 119 healthy controls (89 M, 30 F). Correlational analysis of the questionnaires revealed 2 factors: Impulsive Action (Factor 1) consisting of many traditional impulsivity measures and Thrill-seeking (Factor 2) consisting of delay discounting, sensation and thrill seeking.

RESULTS

Sex influenced within group comparisons. Impulsive Action scores did not vary as a function of sex within either group. But, male controls and male cocaine users had greater Thrill-seeking scores than females within the same group. Sex also influenced between group comparisons. Male cocaine users had greater Impulsive Action scores while female cocaine users had greater Thrill-seeking scores than their sex-matched controls. Among cocaine users, individuals who preferred insufflating ("snorting") cocaine had greater Thrill-seeking scores and lower Impulsive Action scores than individuals who preferred smoking cocaine. Individuals who insufflate cocaine also spent less money on cocaine.

CONCLUSIONS

Greater Impulsive Action scores in males and Thrill-seeking scores in females were associated with cocaine use relative to controls.

Relationships between Non-Suicidal Self-Injury and Other Maladaptive Behaviors: Beyond Difficulties in Emotion Regulation

Non-suicidal self-injury (NSSI) co-occurs with both other maladaptive behaviors (e.g., aggression) and emotion dysregulation. However, the extent to which these maladaptive behaviors are linked to NSSI independent of emotion dysregulation is unclear. The present study examined relationships between NSSI and six other maladaptive behaviors among university undergraduates. When controlling for demographic variables, emotion dysregulation, and other maladaptive behaviors, binge eating, purging, illicit drug use, and physical aggression were each related to lifetime NSSI history and/or severity. No maladaptive behaviors were significantly related to the presence of current diagnostic-level NSSI in these multivariate analyses. Results suggest that some maladaptive behaviors may relate uniquely to NSSI risk independent of emotion dysregulation, highlighting the importance of considering such behaviors in self-injury assessment and treatment.

Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial

BACKGROUND

The digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes.

OBJECTIVE

This study aims to use a machine learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy.

METHODS

We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics.

RESULTS

A higher engagement rate was associated with higher weight loss at 8 weeks (r=-0.59; P<.001) and 24 weeks (r=-0.52; P=.001). Applying the machine learning approach, lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, 16 types of digital phenotypes (ie, lower intake of high-calorie food and evening snacks and higher interaction frequency with mentors) predicted engagement rates (mean R² 0.416, SD 0.006). The prediction of short-term weight change (mean R² 0.382, SD 0.015) was associated with 13 different digital phenotypes (ie, lower intake of high-calorie food and carbohydrate and higher intake of low-calorie food). Finally, 8 measures of digital phenotypes (ie, lower intake of carbohydrate and evening snacks and higher motivation) were associated with a long-term weight change (mean R² 0.590, SD 0.011).

CONCLUSIONS

Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306.

Social Information Processing in Substance Use Disorders: Insights From an Emotional Go-Nogo Task

Positive social connections are crucial for recovery from Substance Use Disorder (SUD). Of interest is understanding potential social information processing (SIP) mediators of this effect. To explore whether persons with different SUD show idiosyncratic biases toward social signals, we administered an emotional go-nogo task (EGNG) to 31 individuals with Cocaine Use Disorder (CoUD), 31 with Cannabis Use Disorder (CaUD), 79 with Opioid Use Disorder (OUD), and 58 controls. Participants were instructed to respond to emotional faces (Fear/Happy) but withhold responses to expressionless faces in two task blocks, with the reverse instruction in the other two blocks. Emotional faces as non-targets elicited more "false alarm" (FA) commission errors as a main effect. Groups did not differ in overall rates of hits (correct responses to target faces), but participants with CaUD and CoUD showed reduced rates of hits (relative to controls) when expressionless faces were targets. OUD participants had worse hit rates [and slower reaction times (RT)] when fearful faces (but not happy faces) were targets. CaUD participants were most affected by instruction effects (respond/"go" vs withhold response/"no-go" to emotional face) on discriminability statistic A. Participants were faster to respond to happy face targets than to expressionless faces. However, this pattern was reversed in fearful face blocks in OUD and CoUD participants. This experiment replicated previous findings of the greater salience of expressive face images, and extends this finding to SUD, where persons with CaUD may show even greater bias toward emotional faces. Conversely, OUD participants showed idiosyncratic behavior in response to fearful faces suggestive of increased attentional disruption by fear. These data suggest a mechanism by which positive social signals may contribute to recovery.

Identifying factors associated with opioid cessation in a biracial sample using machine learning

Aim

Racial disparities in opioid use disorder (OUD) management exist, however, and there is limited research on factors that influence opioid cessation in different population groups.

Methods

We employed multiple machine learning prediction algorithms least absolute shrinkage and selection operator, random forest, deep neural network, and support vector machine to assess factors associated with ceasing opioid use in a sample of 1,192 African Americans (AAs) and 2,557 individuals of European ancestry (EAs) who met Diagnostic and Statistical Manual of Mental Disorders, 5th Edition criteria for OUD. Values for nearly 4,000 variables reflecting demographics, alcohol and other drug use, general health, non-drug use behaviors, and diagnoses for other psychiatric disorders, were obtained for each participant from the Semi-Structured Assessment for Drug Dependence and Alcoholism, a detailed semi-structured interview.

Results

Support vector machine models performed marginally better on average than other machine learning methods with maximum prediction accuracies of 75.4% in AAs and 79.4% in EAs. Subsequent stepwise regression considered the 83 most highly ranked variables across all methods and models and identified less recent cocaine use (AAs: odds ratio (OR) = 1.82, P = 9.19 × 10^-5; EAs: OR = 1.91, P = 3.30 × 10^-15), shorter duration of opioid use (AAs: OR = 0.55, P = 5.78 × 10^-6; EAs: OR = 0.69, P = 3.01 × 10^-7), and older age (AAs: OR = 2.44, P = 1.41 × 10^-12; EAs: OR = 2.00, P = 5.74 × 10^-9) as the strongest independent predictors of opioid cessation in both AAs and EAs. Attending self-help groups for OUD was also an independent predictor (P < 0.05) in both population groups, while less gambling severity (OR = 0.80, P = 3.32 × 10^-2) was specific to AAs and post-traumatic stress disorder recovery (OR = 1.93, P = 7.88 × 10^-5), recent antisocial behaviors (OR = 0.64, P = 2.69 × 10^-3), and atheism (OR = 1.45, P = 1.34 × 10^-2) were specific to EAs. Factors related to drug use comprised about half of the significant independent predictors in both AAs and EAs, with other predictors related to non-drug use behaviors, psychiatric disorders, overall health, and demographics.

Conclusions

These proof-of-concept findings provide avenues for hypothesis-driven analysis, and will lead to further research on strategies to improve OUD management in EAs and AAs.

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks

Neurocognitive tasks are frequently used to assess disordered decision making, and cognitive models of these tasks can quantify performance in terms related to decision makers' underlying cognitive processes. In many cases, multiple cognitive models purport to describe similar processes, but it is difficult to evaluate whether they measure the same latent traits or processes. In this article, we develop methods for modeling behavior across multiple tasks by connecting cognitive model parameters to common latent constructs. This approach can be used to assess whether 2 tasks measure the same dimensions of cognition, or actually improve the estimates of cognitive models when there are overlapping cognitive processes between 2 related tasks. The approach is then applied to connecting decision data on 2 behavioral tasks that evaluate clinically relevant deficits, the delay discounting task and Cambridge gambling task, to determine whether they both measure the same dimension of impulsivity. We find that the discounting rate parameters in the models of each task are not closely related, although substance users exhibit more impulsive behavior on both tasks. Instead, temporal discounting on the delay discounting task as quantified by the model is more closely related to externalizing psychopathology like aggression, while temporal discounting on the Cambridge gambling task is related more to response inhibition failures. The methods we develop thus provide a new way to connect behavior across tasks and grant new insights onto the different dimensions of impulsivity and their relation to substance use. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Evaluation of Risk Behavior in Gambling Addicted and Opioid Addicted Individuals

Evidence suggests that both opioid addicted and gambling addicted individuals are characterized by higher levels of risky behavior in comparison to healthy people. It has been shown that the administration of substitution drugs can reduce cravings for opioids and the risky decisions made by individuals addicted to opioids. Although it is suggested that the neurobiological foundations of addiction are similar, it is possible that risk behaviors in opioid addicts may differ in detail from those addicted to gambling. The aim of this work was to compare the level of risk behavior in individuals addicted to opioid, with that of individuals addicted to gambling, using the Iowa Gambling Task (IGT). The score and response time during the task were measured. It was also observed, in the basis of the whole IGT test, that individuals addicted to gambling make riskier decisions in comparison to healthy individuals from the control group but less riskier decisions in comparison to individuals addicted to opioids, before administration of methadone and without any statistically significant difference after administration of methadone-as there has been growing evidence that methadone administration is strongly associated with a significant decrease in risky behavior.

A comparison of penalised regression methods for informing the selection of predictive markers

Background

Penalised regression methods are a useful atheoretical approach for both developing predictive models and selecting key indicators within an often substantially larger pool of available indicators. In comparison to traditional methods, penalised regression models improve prediction in new data by shrinking the size of coefficients and retaining those with coefficients greater than zero. However, the performance and selection of indicators depends on the specific algorithm implemented. The purpose of this study was to examine the predictive performance and feature (i.e., indicator) selection capability of common penalised logistic regression methods (LASSO, adaptive LASSO, and elastic-net), compared with traditional logistic regression and forward selection methods.

Design

Data were drawn from the Australian Temperament Project, a multigenerational longitudinal study established in 1983. The analytic sample consisted of 1,292 (707 women) participants. A total of 102 adolescent psychosocial and contextual indicators were available to predict young adult daily smoking.

Findings

Penalised logistic regression methods showed small improvements in predictive performance over logistic regression and forward selection. However, no single penalised logistic regression model outperformed the others. Elastic-net models selected more indicators than either LASSO or adaptive LASSO. Additionally, more regularised models included fewer indicators, yet had comparable predictive performance. Forward selection methods dismissed many indicators identified as important in the penalised logistic regression models.

Conclusions

Although overall predictive accuracy was only marginally better with penalised logistic regression methods, benefits were most clear in their capacity to select a manageable subset of indicators. Preference to competing penalised logistic regression methods may therefore be guided by feature selection capability, and thus interpretative considerations, rather than predictive performance alone.

Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls

Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response-based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

BACKGROUND

The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders.

METHODS

PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models.

FINDINGS

Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy.

INTERPRETATION

To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.

A Machine-Learning Approach to Predicting Smoking Cessation Treatment Outcomes

AIMS

Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thirds of individuals relapse when provided even the most robust treatments. Identifying for whom treatment is effective will improve the success of our treatments and perhaps identify strategies for improving current approaches.

METHODS

Two cohorts (training: N = 90, validation: N = 71) of cigarette smokers enrolled in group cognitive-behavioral therapy (CBT). Generalized estimating equations were used to identify baseline predictors of outcome, as defined by breath carbon monoxide and urine cotinine. Significant measures were entered as candidate variables to predict quit status. The resulting decision trees were used to predict cessation outcomes in a validation cohort.

RESULTS

In the training cohort, the decision trees significantly improved on chance classification of smoking status following treatment and at 6-month follow-up. The first split of all decision trees, which was delay discounting, significantly improved on chance classification rates in both the training and validation cohort. Delay discounting emerged as the single best predictor of group CBT treatment response with an average baseline discount rate of ln(k) = -7.1, correctly predicting smoking status of 80% of participants at posttreatment and 81% of participants at follow-up.

CONCLUSIONS

This study provides a first step toward personalized care for smoking cessation though future work is needed to identify individuals that are likely to be successful in treatments beyond group CBT.

IMPLICATIONS

This study provides a first step toward personalized care for smoking cessation. Using a novel machine-learning approach, baseline measures of clinical and executive functioning are used to predict smoking cessation outcomes following group CBT. A decision point is recommended for the single best predictor of treatment outcomes, delay discounting, to inform future research or clinical practice in an effort to better allocate patients to treatments that are likely to work.

Methylation Patterns of the HTR2A Associate With Relapse-Related Behaviors in Cocaine-Dependent Participants

Relapse during abstinence in cocaine use disorder (CUD) is often hastened by high impulsivity (predisposition toward rapid unplanned reactions to stimuli without regard to negative consequences) and high cue reactivity (e.g., attentional bias towards drug reward stimuli). A deeper understanding of the degree to which individual biological differences predict or promote problematic behaviors may afford opportunities for clinical refinement and optimization of CUD diagnostics and/or therapies. Preclinical evidence implicates serotonin (5-HT) neurotransmission through the 5-HT_2A receptor (5-HT_2AR) as a driver of individual differences in these relapse-related behaviors. Regulation of 5-HT_2AR function occurs through many mechanisms, including DNA methylation of the HTR2A gene, an epigenetic modification linked with the memory of gene-environment interactions. In the present study, we tested the hypothesis that methylation of the HTR2A may associate with relapse-related behavioral vulnerability in cocaine-dependent participants versus healthy controls. Impulsivity was assessed by self-report (Barratt Impulsiveness Scale; BIS-11) and the delay discounting task, while levels of cue reactivity were determined by performance in the cocaine-word Stroop task. Genomic DNA was extracted from lymphocytes and the bisulfite-treated DNA was subjected to pyrosequencing to determine degree of methylation at four cytosine residues of the HTR2A promoter (-1439, -1420, -1224, -253). We found that the percent methylation at site -1224 after correction for age trended towards a positive correlation with total BIS-11 scores in cocaine users, but not healthy controls. Percent methylation at site -1420 negatively correlated with rates of delay discounting in healthy controls, but not cocaine users. Lastly, the percent methylation at site -253 positively correlated with attentional bias toward cocaine-associated cues. DNA methylation at these cytosine residues of the HTR2A promoter may be differentially associated with impulsivity or cocaine-associated environmental cues. Taken together, these data suggest that methylation of the HTR2A may contribute to individual differences in relapse-related behaviors in CUD.

Using machine learning to predict opioid misuse among U.S. adolescents

This study evaluated prediction performance of three different machine learning (ML) techniques in predicting opioid misuse among U.S. adolescents. Data were drawn from the 2015-2017 National Survey on Drug Use and Health (N = 41,579 adolescents, ages 12-17 years) and analyzed in 2019. Prediction models were developed using three ML algorithms, including artificial neural networks, distributed random forest, and gradient boosting machine. The performance of the ML prediction models was compared with performance of the penalized logistic regression. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used as metrics of prediction performance. We used the AUPRC as the primary measure of prediction performance given that it is considered more informative for assessing binary classifiers on imbalanced outcome variable than AUROC. The overall rate of opioid misuse among U.S. adolescents was 3.7% (n = 1521). Prediction performance was similar across the four models (AUROC values range from 0.809 to 0.815). In terms of the AUPRC, the distributed random forest showed the best performance in prediction (0.172) followed by penalized logistic regression (0.162), gradient boosting machine (0.160), and artificial neural networks (0.157). Findings suggest that machine learning techniques can be a promising technique especially in the prediction of outcomes with rare cases (i.e., when the binary outcome variable is heavily lopsided) such as adolescent opioid misuse.

Identifying cognitive deficits in cocaine dependence using standard tests and machine learning

There is a growing need to address the variability in detecting cognitive deficits with standard tests in cocaine dependence (CD). The aim of the current study was to identify cognitive deficits by means of Machine Learning (ML) algorithms: Generalized Linear Model (Glm), Random forest (Rf) and Elastic Net (GlmNet), to allow more effective categorization of CD and Non-dependent controls (NDC and to address common methodological problems. For our validation, we used two independent datasets, the first consisted of 87 participants (53 CD and 34 NDC) and the second of 40 participants (20 CD and 20 NDC). All participants were evaluated with neuropsychological tests that included 40 variables assessing cognitive domains. Using results from the cognitive evaluation, the three ML algorithms were trained in the first dataset and tested on the second to classify participants into CD and NDC. While the three algorithms had a receiver operating curve (ROC) performance over 50%, the GlmNet was superior in both the training (ROC = 0.71) and testing datasets (ROC = 0.85) compared to Rf and Glm. Furthermore, GlmNet was capable of identifying the eight main predictors of group assignment (CD or NCD) from all the cognitive domains assessed. Specific variables from each cognitive test resulted in robust predictors for accurate classification of new cases, such as those from cognitive flexibility and inhibition domains. These findings provide evidence of the effectiveness of ML as an approach to highlight relevant sections of standard cognitive tests in CD, and for the identification of generalizable cognitive markers.

Applications of machine learning in addiction studies: A systematic review

This study aims to provide a systematic review of the applications of machine learning methods in addiction research. In this study, multiple searches on MEDLINE, Embase and the Cochrane Database of Systematic Reviews were performed. 23 full-text articles were assessed and 17 articles met the inclusion criteria for the final review. The selected studies covered mainly substance addiction (N = 14, 82.4%), including smoking (N = 4), alcohol drinking (N = 3), as well as uses of cocaine (N = 4), opioids (N = 1), and multiple substances (N = 2). Other studies were non-substance addiction (N = 3, 17.6%), including gambling (N = 2) and internet gaming (N = 1). There were eight cross-sectional, seven cohort, one non-randomized controlled, and one crossover trial studies. Majority of the studies employed supervised learning (N = 13), and others employed unsupervised learning (N = 2) and reinforcement learning (N = 2). Among the supervised learning studies, five studies used ensemble learning methods or multiple algorithm comparisons, six used regression, and two used classification. The two included reinforcement learning studies used the direct methods. These results suggest that machine learning methods, particularly supervised learning are increasingly used in addiction psychiatry for informing medical decisions.

Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation

BACKGROUND AND AIMS

The experience of alcohol use among adolescents is complex, with international differences in age of purchase and individual differences in consumption and consequences. This latter underlines the importance of prediction modeling of adolescent alcohol use. The current study (a) compared the performance of seven machine-learning algorithms to predict different levels of alcohol use in mid-adolescence and (b) used a cross-cultural cross-study scheme in the training-validation-test process to display the predictive power of the best performing machine-learning algorithm.

DESIGN

A comparison of seven machine-learning algorithms: logistic regression, support vector machines, random forest, neural network, lasso regression, ridge regression and elastic-net.

SETTING

Canada and Australia.

PARTICIPANTS

The Canadian sample is part of a 4-year follow-up (2012-16) of the Co-Venture cohort (n = 3826, baseline age 12.8 ± 0.4, 49.2% girls). The Australian sample is part of a 3-year follow-up (2012-15) of the Climate Schools and Preventure (CAP) cohort (n = 2190, baseline age 13.3 ± 0.3, 43.7% girls).

MEASUREMENTS

The algorithms used several prediction indices, such as F₁ prediction score, accuracy, precision, recall, negative predictive value and area under the curve (AUC).

FINDINGS

Based on prediction indices, the elastic-net machine-learning algorithm showed the best predictive performance in both Canadian (AUC = 0.869 ± 0.066) and Australian (AUC = 0.855 ± 0.072) samples. Domain contribution analysis showed that the highest prediction accuracy indices yielded from models with only psychopathology (AUC = 0.816 ± 0.044/0.790 ± 0.071 in Canada/Australia) and only personality clusters (AUC = 0.776 ± 0.063/0.796 ± 0.066 in Canada/Australia). Similarly, regardless of the level of alcohol use, in both samples, externalizing psychopathologies, alcohol use at baseline and the sensation-seeking personality profile contributed to the prediction.

CONCLUSIONS

Computerized screening software shows promise in predicting the risk of alcohol use among adolescents.

Current understanding of the neurobiology of opioid use disorder: An overview

PURPOSE OF REVIEW

This review provides an overview of the neurobiological mechanisms underlying opioid use disorder (OUD) drawing from genetic, functional and structural magnetic resonance imaging (MRI) research.

RECENT FINDINGS

Preliminary evidence suggests an association between OUD and specific variants of the DRD2, δ-opioid receptor 1 (OPRD1) and μ-opioid receptor 1 (OPRM1) genes. Additionally, MRI research indicates functional and structural alterations in striatal and corticolimbic brain regions and pathways underlying reward, emotion/stress and cognitive control processes among individuals with OUD.

SUMMARY

Individual differences in genetic and functional and structural brain-based features are correlated with differences in OUD severity and treatment outcomes, and therefore may potentially one day be used to inform OUD treatment selection. However, given the heterogeneous findings reported, further longitudinal research across different stages of opioid addiction is needed to yield a convergent characterization of OUD and improve treatment and prevention.

Gambling and its clinical correlates in university students

OBJECTIVE

This study sought to examine the prevalence of gambling disorder (GD) in a university sample and its associated physical and mental health correlates.

METHODS

A 156-item anonymous online survey was distributed via random email generation to a sample of 9449 university students. Current use of alcohol and drugs, psychological and physical status and academic performance were assessed, along with questionnaire-based measures of impulsivity and compulsivity. Positive screens for GD were based upon individuals meeting DSM-5 criteria.

RESULTS

A total of 3421 participants (59.7% female) were included in the analysis. The overall prevalence of GD was 0.4%, while an additional 8.4% reported subsyndromal symptoms of GD. GD was significantly associated with past-year use of cocaine, heroin/opiate pain medications, sedatives, alcohol and tobacco. Those with GD were more likely to have generalized anxiety, PTSD and compulsive sexual behavior. Questionnaire-based measures revealed higher levels of both compulsivity and impulsivity associated with disordered gambling.

CONCLUSIONS

Some level of gambling symptomatology is common in young adults and is associated with alcohol and drug use, as well as impulsive and compulsive behaviors. Clinicians should be aware of the presentation of problematic gambling and screen for it in primary care and mental health settings.

Impulsivities and addictions: a multidimensional integrative framework informing assessment and interventions for substance use disorders

Impulse control is becoming a critical survival skill for the twenty-first century. Impulsivity is implicated in virtually all externalizing behaviours and disorders, and figures prominently in the aetiology and long-term sequelae of substance use disorders (SUDs). Despite its robust clinical and predictive validity, the study of impulsivity is complicated by its multidimensional nature, characterized by a variety of trait-like personality dimensions, as well as by more state-dependent neurocognitive dimensions, with variable convergence across measures. This review provides a hierarchical framework for linking self-report and neurocognitive measures to latent constructs of impulsivity and, in turn, to different psychopathology vulnerabilities, including substance-specific addictions and comorbidities. Impulsivity dimensions are presented as novel behavioural targets for prevention and intervention. Novel treatment approaches addressing domains of impulsivity are reviewed and recommendations for future directions in research and clinical interventions for SUDs are offered. This article is part of the theme issue 'Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications'.

Time preferences are reliable across time-horizons and verbal versus experiential tasks

Individual differences in delay-discounting correlate with important real world outcomes, for example education, income, drug use, and criminality. As such, delay-discounting has been extensively studied by economists, psychologists and neuroscientists to reveal its behavioral and biological mechanisms in both human and non-human animal models. However, two major methodological differences hinder comparing results across species. Human studies present long time-horizon options verbally, whereas animal studies employ experiential cues and short delays. To bridge these divides, we developed a novel language-free experiential task inspired by animal decision-making studies. We found that the ranks of subjects' time-preferences were reliable across both verbal/experiential and second/day differences. Yet, discount factors scaled dramatically across the tasks, indicating a strong effect of temporal context. Taken together, this indicates that individuals have a stable, but context-dependent, time-preference that can be reliably assessed using different methods, providing a foundation to bridge studies of time-preferences across species.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity

Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression research has followed from discrete emotion theories, which posit a limited number of distinct affective states that are represented with specific patterns of facial action. Much less work has focused on dimensional features of emotion, particularly positive and negative affect intensity. This is likely, in part, because achieving inter-rater reliability for facial action and affect intensity ratings is painstaking and labor-intensive. We use computer-vision and machine learning (CVML) to identify patterns of facial actions in 4,648 video recordings of 125 human participants, which show strong correspondences to positive and negative affect intensity ratings obtained from highly trained coders. Our results show that CVML can both (1) determine the importance of different facial actions that human coders use to derive positive and negative affective ratings when combined with interpretable machine learning methods, and (2) efficiently automate positive and negative affect intensity coding on large facial expression databases. Further, we show that CVML can be applied to individual human judges to infer which facial actions they use to generate perceptual emotion ratings from facial expressions.

e-Addictology: An Overview of New Technologies for Assessing and Intervening in Addictive Behaviors

BACKGROUND

New technologies can profoundly change the way we understand psychiatric pathologies and addictive disorders. New concepts are emerging with the development of more accurate means of collecting live data, computerized questionnaires, and the use of passive data. Digital phenotyping, a paradigmatic example, refers to the use of computerized measurement tools to capture the characteristics of different psychiatric disorders. Similarly, machine learning-a form of artificial intelligence-can improve the classification of patients based on patterns that clinicians have not always considered in the past. Remote or automated interventions (web-based or smartphone-based apps), as well as virtual reality and neurofeedback, are already available or under development.

OBJECTIVE

These recent changes have the potential to disrupt practices, as well as practitioners' beliefs, ethics and representations, and may even call into question their professional culture. However, the impact of new technologies on health professionals' practice in addictive disorder care has yet to be determined. In the present paper, we therefore present an overview of new technology in the field of addiction medicine.

METHOD

Using the keywords [e-health], [m-health], [computer], [mobile], [smartphone], [wearable], [digital], [machine learning], [ecological momentary assessment], [biofeedback] and [virtual reality], we searched the PubMed database for the most representative articles in the field of assessment and interventions in substance use disorders.

RESULTS

We screened 595 abstracts and analyzed 92 articles, dividing them into seven categories: e-health program and web-based interventions, machine learning, computerized adaptive testing, wearable devices and digital phenotyping, ecological momentary assessment, biofeedback, and virtual reality.

CONCLUSION

This overview shows that new technologies can improve assessment and interventions in the field of addictive disorders. The precise role of connected devices, artificial intelligence and remote monitoring remains to be defined. If they are to be used effectively, these tools must be explained and adapted to the different profiles of physicians and patients. The involvement of patients, caregivers and other health professionals is essential to their design and assessment.

Discriminating Pathological and Non-pathological Internet Gamers Using Sparse Neuroanatomical Features

Internet gaming disorder (IGD) is often diagnosed on the basis of nine underlying criteria from the latest version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Here, we examined whether such symptom-based categorization could be translated into computation-based classification. Structural MRI (sMRI) and diffusion-weighted MRI (dMRI) data were acquired in 38 gamers diagnosed with IGD, 68 normal gamers diagnosed as not having IGD, and 37 healthy non-gamers. We generated 108 features of gray matter (GM) and white matter (WM) structure from the MRI data. When regularized logistic regression was applied to the 108 neuroanatomical features to select important ones for the distinction between the groups, the disordered and normal gamers were represented in terms of 43 and 21 features, respectively, in relation to the healthy non-gamers, whereas the disordered gamers were represented in terms of 11 features in relation to the normal gamers. In support vector machines (SVM) using the sparse neuroanatomical features as predictors, the disordered and normal gamers were discriminated successfully, with accuracy exceeding 98%, from the healthy non-gamers, but the classification between the disordered and normal gamers was relatively challenging. These findings suggest that pathological and non-pathological gamers as categorized with the criteria from the DSM-5 could be represented by sparse neuroanatomical features, especially in the context of discriminating those from non-gaming healthy individuals.

Ecological momentary assessment of daily discrimination experiences and nicotine, alcohol, and drug use among sexual and gender minority individuals

OBJECTIVE

Sexual and gender minority (SGM) individuals experience elevated rates of minority stress, which has been linked to higher rates of nicotine and substance use. Research on this disparity to date is largely predicated on methodology that is insensitive to within day SGM-based discrimination experiences, or their relation to momentary nicotine and substance use risk. We address this knowledge gap in the current study using ecological momentary assessment (EMA).

METHOD

Fifty SGM individuals, between 18 and 45 years of age, were recruited from an inland northwestern university, regardless of their nicotine or substance use history, and invited to participate in an EMA study. Each were prompted to provide data, six times daily (between 10:00 a.m. and 10:00 p.m.) for 14 days, regarding SGM-based discrimination, other forms of mistreatment, and nicotine, drug, and alcohol use since their last prompt.

RESULTS

Discrimination experiences that occurred since individuals' last measurement prompt were associated with greater odds of nicotine and substance use during the same measurement window. Substance use was also more likely to occur in relation to discrimination reported two measurements prior in lagged models. Relative to other forms of mistreatment, discrimination effects were consistently larger in magnitude and became stronger throughout the day/evening.

CONCLUSION

This study adds to existing minority stress research by highlighting the both immediate and delayed correlates of daily SGM-based discrimination experiences. These results also contribute to our understanding of daily stress processes and provide insight into ways we might mitigate these effects using real-time monitoring and intervention technology. (PsycINFO Database Record

Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations.

Validation of the short Arabic UPPS-P Impulsive Behavior Scale

BACKGROUND

Impulsivity is involved in numerous psychiatric and addictive disorders, as well as in risky behaviors. The UPPS-P scale highlights five complementary impulsivity constructs (i.e., positive urgency, negative urgency, lack of perseverance, lack of premeditation, and sensation seeking) that possibly work as different pathways linking impulsivity to other disorders. In this study, we aimed to evaluate the psychometric properties of the Arab language short 20-item UPPS-P scale and to eventually validate it.

METHODS

Participants were recruited online through e-mail invitations. After online informed consent was obtained, the questionnaires (the UPPS-P and the Compulsive Internet Use Scale [CIUS]) were completed anonymously. The five dimensions of the Arab UPPS-P model were assessed in a sample of 743 participants.

RESULTS

As in other linguistic assessments of the UPPS-P, confirmatory factor analysis showed the validity of a model with five different, but nonetheless interrelated, facets of impulsivity. A three-factor model with two higher order factors-urgency (negative and positive) and lack of conscientiousness (lack of premeditation and lack of perseverance)-and a third sensation seeking factor fit the data well, but to a lesser extent. The results suggested good internal consistency, with external validity shown from correlations between some of the UPPS-P components and a measure of addictive Internet use (the CIUS).

CONCLUSION

The Arab short UPPS-P is a valid assessment tool with good psychometric properties and is suitable for online use.

Distinct Roles of Dopamine Receptors in the Lateral Thalamus in a Rat Model of Decisional Impulsivity

The thalamus and central dopamine signaling have been shown to play important roles in high-level cognitive processes including impulsivity. However, little is known about the role of dopamine receptors in the thalamus in decisional impulsivity. In the present study, rats were tested using a delay discounting task and divided into three groups: high impulsivity (HI), medium impulsivity (MI), and low impulsivity (LI). Subsequent in vivo voxel-based magnetic resonance imaging revealed that the HI rats displayed a markedly reduced density of gray matter in the lateral thalamus compared with the LI rats. In the MI rats, the dopamine D1 receptor antagonist SCH23390 or the D2 receptor antagonist eticlopride was microinjected into the lateral thalamus. SCH23390 significantly decreased their choice of a large, delayed reward and increased their omission of lever presses. In contrast, eticlopride increased the choice of a large, delayed reward but had no effect on the omissions. Together, our results indicate that the lateral thalamus is involved in decisional impulsivity, and dopamine D1 and D2 receptors in the lateral thalamus have distinct effects on decisional impulsive behaviors in rats. These results provide a new insight into the dopamine signaling in the lateral thalamus in decisional impulsivity.

Realising stratified psychiatry using multidimensional signatures and trajectories

Background

Stratified or personalised medicine targets treatments for groups of individuals with a disorder based on individual heterogeneity and shared factors that influence the likelihood of response. Psychiatry has traditionally defined diagnoses by constellations of co-occurring signs and symptoms that are assigned a categorical label (e.g. schizophrenia). Trial methodology in psychiatry has evaluated interventions targeted at these categorical entities, with diagnoses being equated to disorders. Recent insights into both the nosology and neurobiology of psychiatric disorder reveal that traditional categorical diagnoses cannot be equated with disorders. We argue that current quantitative methodology (1) inherits these categorical assumptions, (2) allows only for the discovery of average treatment response, (3) relies on composite outcome measures and (4) sacrifices valuable predictive information for stratified and personalised treatment in psychiatry.

Methods and findings

To achieve a truly ‘stratified psychiatry’ we propose and then operationalise two necessary steps: first, a formal multi-dimensional representation of disorder definition and clinical state, and second, the similar redefinition of outcomes as multidimensional constructs that can expose within- and between-patient differences in response. We use the categorical diagnosis of schizophrenia—conceptualised as a label for heterogeneous disorders—as a means of introducing operational definitions of stratified psychiatry using principles from multivariate analysis. We demonstrate this framework by application to the Clinical Antipsychotic Trials of Intervention Effectiveness dataset, showing heterogeneity in both patient clinical states and their trajectories after treatment that are lost in the traditional categorical approach with composite outcomes. We then systematically review a decade of registered clinical trials for cognitive deficits in schizophrenia highlighting existing assumptions of categorical diagnoses and aggregate outcomes while identifying a small number of trials that could be reanalysed using our proposal.

Conclusion

We describe quantitative methods for the development of a multi-dimensional model of clinical state, disorders and trajectories which practically realises stratified psychiatry. We highlight the potential for recovering existing trial data, the implications for stratified psychiatry in trial design and clinical treatment and finally, describe different kinds of probabilistic reasoning tools necessary to implement stratification.

Cognitive Mechanisms and Therapeutic Targets of Addiction

Fundamental to cognitive models of addiction is the gradual strengthening of automatic, urge-related responding that develops in tandem with the diminution of self-control-related processes aimed at inhibiting impulses. Recent conceptualizations of addiction also include a third set of cognitive processes related to self-awareness and superordinate regulation of self-control and other higher brain function. This review describes new human research evidence and theoretical developments related to the multicausal strengthening of urge-related responding and failure of self-control in addiction, and the etiology of disrupted self-awareness and rational decision-making associated with continued substance use. Recent progress in the development of therapeutic strategies targeting these mechanisms of addiction is reviewed, including cognitive bias modification, mindfulness training, and neurocognitive rehabilitation.

Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence

BACKGROUND

Recent animal and human studies reveal distinct cognitive and neurobiological differences between opiate and stimulant addictions; however, our understanding of the common and specific effects of these two classes of drugs remains limited due to the high rates of polysubstance-dependence among drug users.

METHODS

The goal of the current study was to identify multivariate substance-specific markers classifying heroin dependence (HD) and amphetamine dependence (AD), by using machine-learning approaches. Participants included 39 amphetamine mono-dependent, 44 heroin mono-dependent, 58 polysubstance dependent, and 81 non-substance dependent individuals. The majority of substance dependent participants were in protracted abstinence. We used demographic, personality (trait impulsivity, trait psychopathy, aggression, sensation seeking), psychiatric (attention deficit hyperactivity disorder, conduct disorder, antisocial personality disorder, psychopathy, anxiety, depression), and neurocognitive impulsivity measures (Delay Discounting, Go/No-Go, Stop Signal, Immediate Memory, Balloon Analogue Risk, Cambridge Gambling, and Iowa Gambling tasks) as predictors in a machine-learning algorithm.

RESULTS

The machine-learning approach revealed substance-specific multivariate profiles that classified HD and AD in new samples with high degree of accuracy. Out of 54 predictors, psychopathy was the only classifier common to both types of addiction. Important dissociations emerged between factors classifying HD and AD, which often showed opposite patterns among individuals with HD and AD.

CONCLUSIONS

These results suggest that different mechanisms may underlie HD and AD, challenging the unitary account of drug addiction. This line of work may shed light on the development of standardized and cost-efficient clinical diagnostic tests and facilitate the development of individualized prevention and intervention programs for HD and AD.