ArticleDrug and alcohol dependence2016
Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence.
Article in Drug and alcohol dependence, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.
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Who cites it
54 citing papers in PubMed, 2 syntheses or guidelines pooled it, 131 citations in OpenAlex.
- Common and distinct patterns of brain activity alterations during inhibitory control in depression and psychostimulant users: a comparative meta-analysis of task-based fMRI studies.Psychological medicine · 2025Pooled it
- Risky decision-making in individuals with substance use disorder: A meta-analysis and meta-regression review.Psychopharmacology · 2020Pooled it
- Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.Journal of medical Internet research · 2021Trial
- Neurodevelopmental burden in young adults undergoing opioid substitution therapy: The role of inattentive and hyperactive/impulsive symptoms.The American journal on addictions · 2026Article
- A Virtual Reality Intervention to Promote Uptake of Medications for Opioid Use Disorder in the Emergency Department Following Opioid-Involved Overdose Based on Input From People With Lived Experience: Development Study.JMIR XR and spatial computing · 2026Article
- Measurement of Impulsivity and Its Role in Drug Use Behaviours and Related Health, Harm and Treatment Outcomes Among People Who Use Illicit Drugs-A Scoping Review.International journal of mental health and addiction · 2026Article
- The impact of chronic heroin addiction on creative cognition: an EEG study based on divergent thinking.Translational psychiatry · 2025Article
- Comparing likelihood-based and likelihood-free approaches to fitting and comparing models of intertemporal choice.Behavior research methods · 2025Article
- Negative affectivity drivers of impulsivity in opioid use disorder.Nature reviews. Psychology · 2025Article
- Development of New Diagnostic Techniques: Machine Learning.Advances in experimental medicine and biology · 2025Review
- Commonalities and differences in trait-like, risky, and utilitarian decision-making styles between abstinent heroin-dependent individuals and their siblings.Frontiers in psychiatry · 2025Article
- Functional connectivity in resting-state fMRI (rs-fMRI) in opioid use disorder.The European physical journal. Special topics · 2025Review
- Common and distinct drug cue reactivity patterns associated with cocaine and heroin: An fMRI meta-analysis.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Application of machine learning in predicting adolescent Internet behavioral addiction.Frontiers in psychiatry · 2024Article
- The Effects of Cognitive Impulsivity on the Duration of Remission in Alcohol-Dependent Patients.Consortium psychiatricum · 2023Article
- Comparing psychopathy across measurement modalities.Personality disorders · 2023Article
- Virtual Digital Psychotherapist App-Based Treatment in Patients With Methamphetamine Use Disorder (Echo-APP): Single-Arm Pilot Feasibility and Efficacy Study.JMIR mHealth and uHealth · 2023Article
- Neurocognitive Impulsivity in Opiate Users at Different Lengths of Abstinence.International journal of environmental research and public health · 2023Article
- The Neural Signature of Impaired Inhibitory Control in Individuals with Heroin Use Disorder.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
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Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundRecent 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.
methodsThe 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.
resultsThe 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.
conclusionsThese 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.