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. 2023 Dec 29;36(6):e101304.
doi: 10.1136/gpsych-2023-101304. eCollection 2023.

Connectome-based predictive modelling can predict follow-up craving after abstinence in individuals with opioid use disorders

Wenhan Yang  1 , Jungong Han  2 , Jing Luo  3 , Fei Tang  1 , Li Fan  3 , Yanyao Du  1 , Longtao Yang  1 , Jun Zhang  4 , Huiting Zhang  5 , Jun Liu  3   6   7
Affiliations

Connectome-based predictive modelling can predict follow-up craving after abstinence in individuals with opioid use disorders

Wenhan Yang et al. Gen Psychiatr. .

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 (HCQfollow-up-HCQbaseline). 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 HCQfollow-up (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.

Keywords: addictive; behavior mechanisms; behaviour; biological psychiatry; brain; psychiatry.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
The flowchart of the study. DSM-V, Diagnostic and Statistical Manual on Mental Disorders, Fifth Edition; fMRI, functional MRI; HCQ, Heroin Craving Questionnaire; MRI, magnetic resonance imaging; OUD, opioid use disorders; T1, T1-weighted.
Figure 2
Figure 2
Panel A shows negative craving (HCQfollow-up) networks; Panel B shows positive craving changes related (HCQfollow-up-HCQbaseline) networks; Panel C shows negative craving changes related networks. Larger spheres indicate nodes with more edges, and smaller spheres indicate fewer edges. HCQ: Heroin Craving Questionnaire. Positive network: positive correlation between edges and craving scores; Negative network: negative edges between edges and craving scores.
Figure 3
Figure 3
Positive and negative craving networks summarized by neural networks. Panel A shows negative craving (HCQfollow-up) networks; Panel B shows positive craving changes related (HCQfollow-up-HCQbaseline) networks; Panel C shows negative craving changes related networks. MF: Medial frontal; FP: Frontal parietal; DMN: Default mode; Mot: Motor/sensory; VI: Visual a; VII: Visual b; Vas: Visual assoc.; SAL: Salience; SC: Subcortical; CBL: Cerebellum/brainstem.Positive and negative craving networks summarized by neural networks.
Figure 4
Figure 4
Panel A illustrates the correspondence between actual (x-axis) and predicted (y-axis) follow-up craving scores (HCQfollow-up) from negative and positive edges generated using CPM. Panel B and C illustrates the correspondence between actual (x-axis) and predicted (y-axis) changes of craving scores (HCQfollow-up-HCQbaseline) from positive and negative edges generated using CPM. CPM successfully predicted within-abstinent follow-up craving scores (r=0.417, p<0.001) and changes of craving scores (negative: r=0.334, p=0.002, positive: r=0.233, p=0.038). HCQfollow-up: HCQ score of 8-month follow-up abstinent heroin users; HCQbaseline: HCQ score of baseline time abstinent heroin user.
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