ArticleThe American journal of psychiatry2019
Connectome-Based Prediction of Cocaine Abstinence.
Article in The American journal of psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers.
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Who cites it
125 citing papers in PubMed.
- The role of the dorsal attention network in attention bias modification for social anxiety disorder.Translational psychiatry · 2026Trial
- Acupuncture for Migraine Without Aura and Connection-Based Efficacy Prediction: A Randomized Clinical Trial.JAMA network open · 2026Trial
- Longitudinal changes in network engagement during cognitive control in cocaine use disorder.Drug and alcohol dependence · 2021Trial
- Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol.Scientific reports · 2020Trial
- Connectome-based substrates of emotional N-back performance in the adolescent brain cognitive development study.Developmental cognitive neuroscience · 2026Article
- Multivariate environmental exposures are reflected in whole-brain functional connectivity and cognition in youth.Developmental cognitive neuroscience · 2026Article
- M1 receptor modulator VU0364572 attenuates cortico-thalamo-striatal transmission and facilitates cocaine CPP extinction.Cellular and molecular life sciences : CMLS · 2026Article
- Functional Brain Network Predictors of Abstinence Treatment Outcomes in Methamphetamine Use Disorder.CNS neuroscience & therapeutics · 2026Article
- Dynamic Resting-State Network Markers of Disruptive Behavior Problems in Youth.Biological psychiatry global open science · 2026Article
- Flexible brain state engagement predicts cognitive control transdiagnostically.bioRxiv : the preprint server for biology · 2026Article
- Connectome-based predictive modelling of problematic gaming in youth from the ABCD study.Journal of behavioral addictions · 2026Article
- Brain Functional Connectivity as a Mediator Between Hematological Metrics and Cognitive Decline in Children With Beta-thalassemia Major.Brain and behavior · 2026Article
- Connectome-based prediction of problematic use of social media in adolescents: Findings from the ABCD study.NeuroImage · 2026Article
- Predicting individual incubation of opioid craving by whole-brain functional connectivity.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Optimizing functional connectivity scanning conditions for predicting autistic traits.medRxiv : the preprint server for health sciences · 2026Article
- 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
- 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
- Optimizing functional connectivity scanning conditions for predicting autistic traits.Nature. Mental health · 2026Article
- Subtypes of cocaine use disorder and their neurobehavioral profiles.Translational psychiatry · 2025Article
65 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
objectiveThe authors sought to identify a brain-based predictor of cocaine abstinence by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. CPM is a predictive tool and a method of identifying networks that underlie specific behaviors ("neural fingerprints").
methodsFifty-three individuals participated in neuroimaging protocols at the start of treatment for cocaine use disorder, and again at the end of 12 weeks of treatment. CPM with leave-one-out cross-validation was conducted to identify pretreatment networks that predicted abstinence (percent cocaine-negative urine samples during treatment). Networks were applied to posttreatment functional MRI data to assess changes over time and ability to predict abstinence during follow-up. The predictive ability of identified networks was then tested in a separate, heterogeneous sample of individuals who underwent scanning before treatment for cocaine use disorder (N=45).
resultsCPM predicted abstinence during treatment, as indicated by a significant correspondence between predicted and actual abstinence values (r=0.42, df=52). Identified networks included connections within and between canonical networks implicated in cognitive/executive control (frontoparietal, medial frontal) and in reward responsiveness (subcortical, salience, motor/sensory). Connectivity strength did not change with treatment, and strength at posttreatment assessment also significantly predicted abstinence during follow-up (r=0.34, df=39). Network strength in the independent sample predicted treatment response with 64% accuracy by itself and 71% accuracy when combined with baseline cocaine use.
conclusionsThese data demonstrate that individual differences in large-scale neural networks contribute to variability in treatment outcomes for cocaine use disorder, and they identify specific abstinence networks that may be targeted in novel interventions.
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