» Articles » PMID: 38169807

Connectome-based Predictive Modelling Can Predict Follow-up Craving After Abstinence in Individuals with Opioid Use Disorders

Overview
Journal Gen Psychiatr
Specialty Psychiatry
Date 2024 Jan 3
PMID 38169807
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Individual differences have been detected in individuals with opioid use disorders (OUD) in rehabilitation following protracted abstinence. Recent studies suggested that prediction models were effective for individual-level prognosis based on neuroimage data in substance use disorders (SUD).

Aims: This prospective cohort study aimed to assess neuroimaging biomarkers for individual response to protracted abstinence in opioid users using connectome-based predictive modelling (CPM).

Methods: One hundred and eight inpatients with OUD underwent structural and functional magnetic resonance imaging (fMRI) scans at baseline. The Heroin Craving Questionnaire (HCQ) was used to assess craving levels at baseline and at the 8-month follow-up of abstinence. CPM with leave-one-out cross-validation was used to identify baseline networks that could predict follow-up HCQ scores and changes in HCQ (HCQ-HCQ. Then, the predictive ability of identified networks was tested in a separate, heterogeneous sample of methamphetamine individuals who underwent MRI scanning before abstinence for SUD.

Results: CPM could predict craving changes induced by long-term abstinence, as shown by a significant correlation between predicted and actual HCQ (r=0.417, p<0.001) and changes in HCQ (negative: r=0.334, p=0.002;positive: r=0.233, p=0.038). Identified craving-related prediction networks included the somato-motor network (SMN), salience network (SALN), default mode network (DMN), medial frontal network, visual network and auditory network. In addition, decreased connectivity of frontal-parietal network (FPN)-SMN, FPN-DMN and FPN-SALN and increased connectivity of subcortical network (SCN)-DMN, SCN-SALN and SCN-SMN were positively correlated with craving levels.

Conclusions: These findings highlight the potential applications of CPM to predict the craving level of individuals after protracted abstinence, as well as the generalisation ability; the identified brain networks might be the focus of innovative therapies in the future.

Citing Articles

An electroencephalography connectome predictive model of craving for methamphetamine.

Zhang H, Yu Q, Zhang X, Zhang Y, Huang T, Ding J Int J Clin Health Psychol. 2025; 25(1):100551.

PMID: 40007948 PMC: 11850752. DOI: 10.1016/j.ijchp.2025.100551.


Prediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods.

Antons S, Yip S, Lacadie C, Dadashkarimi J, Scheinost D, Brand M J Behav Addict. 2024; 13(3):695-701.

PMID: 39356557 PMC: 11457034. DOI: 10.1556/2006.2024.00050.


Stress and substance use disorders: risk, relapse, and treatment outcomes.

Sinha R J Clin Invest. 2024; 134(16).

PMID: 39145454 PMC: 11324296. DOI: 10.1172/JCI172883.

References
1.
Nutt D, Hayes A, Fonville L, Zafar R, Palmer E, Paterson L . Alcohol and the Brain. Nutrients. 2021; 13(11). PMC: 8625009. DOI: 10.3390/nu13113938. View

2.
Tiffany S, Carter B, Singleton E . Challenges in the manipulation, assessment and interpretation of craving relevant variables. Addiction. 2000; 95 Suppl 2:S177-87. DOI: 10.1080/09652140050111753. View

3.
Nathan Spreng R, Stevens W, Chamberlain J, Gilmore A, Schacter D . Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition. Neuroimage. 2010; 53(1):303-17. PMC: 2914129. DOI: 10.1016/j.neuroimage.2010.06.016. View

4.
Tolomeo S, Yu R . Brain network dysfunctions in addiction: a meta-analysis of resting-state functional connectivity. Transl Psychiatry. 2022; 12(1):41. PMC: 8799706. DOI: 10.1038/s41398-022-01792-6. View

5.
Conio B, Martino M, Magioncalda P, Escelsior A, Inglese M, Amore M . Opposite effects of dopamine and serotonin on resting-state networks: review and implications for psychiatric disorders. Mol Psychiatry. 2019; 25(1):82-93. DOI: 10.1038/s41380-019-0406-4. View