ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2018
Machine Learning of Functional Magnetic Resonance Imaging Network Connectivity Predicts Substance Abuse Treatment Completion.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Using physiological biomarkers in forensic psychiatry: a scoping review.Frontiers in psychiatry · 2025Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Transcranial electrical and magnetic stimulation (tES and TMS) for addiction medicine: A consensus paper on the present state of the science and the road ahead.Neuroscience and biobehavioral reviews · 2019Pooled it
- Predicting IGD treatment outcomes across interventions using a whole-brain functional connectome biomarker.Dialogues in clinical neuroscience · 2027Trial
- Anticipation of monetary reward in amygdala, insula, caudate are predictors of pleasure sensitivity to d-Amphetamine administration.Drug and alcohol dependence · 2020Trial
- Craving for a Robust Methodology: A Systematic Review of Machine Learning Algorithms on Substance-Use Disorders Treatment Outcomes.International journal of mental health and addiction · 2026Article
- Repetitive transcranial magnetic stimulation for substance use disorders and chronic pain: A review of the evidence and call for increased mechanistic understanding.Current addiction reports · 2025Article
- Salience Network Connectivity Predicts Response to Repetitive Transcranial Magnetic Stimulation in Smoking Cessation: A Preliminary Machine Learning Study.Brain connectivity · 2025Article
- Speak and You Shall Predict: Evidence That Speech at Initial Cocaine Abstinence Is a Biomarker of Long-Term Drug Use Behavior.Biological psychiatry · 2025Article
- Development of New Diagnostic Techniques: Machine Learning.Advances in experimental medicine and biology · 2025Review
- Neuroimaging Biomarkers in Addiction.medRxiv : the preprint server for health sciences · 2024Article
- Masked features of task states found in individual brain networks.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Recovery of superior frontal gyrus cortical thickness and resting-state functional connectivity in abstinent heroin users after 8 months of follow-up.Human brain mapping · 2022Article
- Distinct neural activation patterns of age in subcomponents of inhibitory control: A fMRI meta-analysis.Frontiers in aging neuroscience · 2022Article
- Towards the Development of a Substance Abuse Index (SEI) through Informatics.Healthcare (Basel, Switzerland) · 2021Article
- Treating cocaine and opioid use disorder with transcranial magnetic stimulation: A path forward.Pharmacology, biochemistry, and behavior · 2021Review
- Connectome-Based Prediction of Optimal Weight Loss Six Months After Bariatric Surgery.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer.Personality neuroscience · 2021Article
- Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020Review
- Toward Addiction Prediction: An Overview of Cross-Validated Predictive Modeling Findings and Considerations for Future Neuroimaging Research.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020Review
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundSuccessfully treating illicit drug use has become paramount, yet elusive. Devising specialized treatment interventions could increase positive outcomes, but it is necessary to identify risk factors of poor long-term outcomes to develop specialized, efficacious treatments. We investigated whether functional network connectivity (FNC) measures were predictive of substance abuse treatment completion using machine learning pattern classification of functional magnetic resonance imaging data.
methodsTreatment-seeking stimulant- or heroin-dependent incarcerated participants (n = 139; 89 women) volunteered for a 12-week substance abuse treatment program. Participants performed a response inhibition Go/NoGo functional magnetic resonance imaging task prior to onset of the substance abuse treatment. We tested whether FNC related to the anterior cingulate cortex would be predictive of those who would or would not complete a 12-week substance abuse treatment program.
resultsMachine learning pattern classification models using FNC between networks incorporating the anterior cingulate cortex, striatum, and insula predicted which individuals would (sensitivity: 81.31%) or would not (specificity: 78.13%) complete substance abuse treatment. FNC analyses predicted treatment completion above and beyond other clinical assessment measures, including age, sex, IQ, years of substance use, psychopathy, anxiety and depressive symptomatology, and motivation for change.
conclusionsAberrant neural network connections predicted substance abuse treatment outcomes, which could illuminate new targets for developing interventions designed to reduce or eliminate substance use while facilitating long-term outcomes. This work represents the first application of machine-learning models of FNC analyses of functional magnetic resonance imaging data to predict which substance abusers would or would not complete treatment.
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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.