ReviewBiological psychiatry. Cognitive neuroscience and neuroimaging2020
Toward Addiction Prediction: An Overview of Cross-Validated Predictive Modeling Findings and Considerations for Future Neuroimaging Research.
Review in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Threats to external validity in the neuroprediction of substance use treatment outcomes.The American journal of drug and alcohol abuse · 2023Pooled it
- Predicting alcohol dependence from multi-site brain structural measures.Human brain mapping · 2022Pooled it
- Connectome-based encoding of subjective drug responses to acute oral methamphetamine.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Trial
- Connectome-based substrates of emotional N-back performance in the adolescent brain cognitive development study.Developmental cognitive neuroscience · 2026Article
- Personalized prediction of treatment response during the acute phase of schizophrenia based on the connectome-based predictive model.European archives of psychiatry and clinical neuroscience · 2026Article
- Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers.Nature human behaviour · 2026Article
- Functional Connectome Predicts Cognition and Links White Matter Hyperintensity Burden to Cognitive Impairment Across the Vascular Cognitive Impairment Continuum.Brain sciences · 2026Article
- Generative Artificial Intelligence Models for Developing Neuroimaging Markers of Psychiatric Disorders.Biological psychiatry · 2026Article
- Connectome-based predictive modelling of problematic gaming in youth from the ABCD study.Journal of behavioral addictions · 2026Article
- Connectome-based prediction of problematic use of social media in adolescents: Findings from the ABCD study.NeuroImage · 2026Article
- Brain Functional Connectivity as a Mediator Between Hematological Metrics and Cognitive Decline in Children With Beta-thalassemia Major.Brain and behavior · 2026Article
- Threading the Needle: Practical Considerations for Merging Theory-Driven Computational Psychiatry With Data-Driven Analytics to Enhance Precision Health at Scale.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Impulsivity and neuroticism share distinct functional connectivity signatures with alcohol-use risk in youth.Molecular psychiatry · 2026Article
- Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.bioRxiv : the preprint server for biology · 2026Article
- Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers.bioRxiv : the preprint server for biology · 2025Article
- Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine Learning Classification of Smoking Behaviours-From Social Environment to the Prefrontal Cortex.Addiction biology · 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
- Prediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods.Journal of behavioral addictions · 2024Review
- Risk and Promotive Factors Related to Cannabis Use Among American Indian Adolescents.Prevention science : the official journal of the Society for Prevention Research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Substance use is a leading cause of disability and death worldwide. Despite the existence of evidence-based treatments, clinical outcomes are highly variable across individuals, and relapse rates following treatment remain high. Within this context, methods to identify individuals at particular risk for unsuccessful treatment (i.e., limited within-treatment abstinence), or for relapse following treatment, are needed to improve outcomes. Cumulatively, the literature generally supports the hypothesis that individual differences in brain function and structure are linked to differences in treatment outcomes, although anatomical loci and directions of associations have differed across studies. However, this work has almost entirely used methods that may overfit the data, leading to inflated effect size estimates and reduced likelihood of reproducibility in novel clinical samples. In contrast, cross-validated predictive modeling (i.e., machine learning) approaches are designed to overcome limitations of traditional approaches by focusing on individual differences and generalization to novel subjects (i.e., cross-validation), thereby increasing the likelihood of replication and potential translation to novel clinical settings. Here, we review recent studies using these approaches to generate brain-behavior models of treatment outcomes in addictions and provide recommendations for further work using these methods.
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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.