Multimodal differences in brain structure and function in heroin dependence vs healthy controls
Poster No:
472
Submission Type:
Abstract Submission
Authors:
Danielle Kurtin1, Brianna Austin2, Anne Lingford-Hughes2, Louise Paterson2
Institutions:
1Imperial College London, London, UK, 2Imperial College London, London, London
First Author:
Co-Author(s):
Introduction:
Current therapies for addiction are ineffective, as >66% of people relapse within 6 months of treatment (Hayes, 2020). Advancing the neurobiological understanding of addiction may improve therapeutic targeting. Using a multimodal approach, we aim to identify generalisable and process-dependent addiction biomarkers. Our multimodal approach addressed three gaps identified in our review of differences in connectivity between healthy controls (HC) vs people with heroin dependence (HD) (Kurtin, 2024b): limited focus on reward/anti-reward VentroMedial Network (VMN) function despite its central role in addiction (Koob 2010; Koob 2021); a dearth of task-fMRI studies; and no work relating brain function to structural and molecular properties. Therefore, we collected fMRI data while HD (n=25) and HC (n=22) performed the Monetary Incentive Delay (MID) and Cue Reactivity (CR) tasks, which reliably elicit hypo/hyper-reactivity of reward circuitry during monetary rewards and drug-related cues in HD, respectively (Hayes, 2020). We then assessed how differences in function related to brain volume or density of receptors associated with HD, such as Dopamine 2 Receptors (D2DR) and μ-Opioid Receptors (MOR). We are currently employing this approach to the international, multicentre ENIGMA addiction working group to assess substance specificity.
Methods:
Mutual information functional connectivity (miFC) and large-scale nonlinear Granger Causality (lsnGC) was computed from 200-region Schaefer atlas transformed to subject space; 14 subcortical regions were defined with FreeSurfer. Graph theoretic metrics (regional eigenvector centrality, degree) were computed from each subject's pairwise miFC matrix. A SHAP analysis assessed which lsnGC edges had the greatest power to distinguish HD vs HC. Permutation tests with maxT familywise correction for multiple comparisons assessed group differences in miFC, lsnGC, and regional brain volume (corrected for estimated total intracranial volume). Mediation analysis assessed whether brain volume significantly mediated group differences in miFC. Partial Least Squared (PLS) analysis identified latent variables capturing the relationship between structural and molecular predictors (regional MOR, D2DR, and VolumeHD-HC) and functional responses (regional DegreeHD-HC and Eigenvector CentralityHD-HC) for each task.
Results:
Across both tasks, most edges with different miFC or lsnGC were among Control, DMN, and Somatomotor networks. During the CR task, miFC and lsnGC was weaker in HD vs HC. During the MID task, ~50% of edges were significantly stronger in HD vs HC, which often included VMN regions. Thirty-one regions had significantly different volume in HD vs HC (15 HC>HD). Mediation analysis revealed HD's lower volume of the right anterior insula, a Salience network region, drove weaker miFC in HD vs HC in both tasks. SHAP analysis showed Salience network regions were among the 3 edges with the greatest discriminative weight in classification between HD or HC. Across all PLS-derived latent variables, regional MOR and MID Eigenvector CentralityHD-HC had the greatest loading to the structural predictors and functional responses, respectively. The VMN, visual, and somatomotor networks consistently had the greatest latent predictor and response scores.
·Figure 1
·Figure 2
Conclusions:
Across both tasks, differences in miFC and lsnGC were concentrated among DMN, Control, and Somatomotor networks. However, mediation and SHAP analysis revealed consistent differences in HD vs HC among Salience network regions. Surprisingly, stronger VMN miFC and lsnGC was observed in HD vs HC in the MID. PLS revealed functional differences between HD vs HC during the MID consistently related to structural and molecular properties. These results inform our hypothesis that analysis of the ENIMGA datasets will show broad, task-generalisable differences in brain function, with VMN regions driving the structure-function disruptions in addiction.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Other Methods
Keywords:
Addictions
FUNCTIONAL MRI
Limbic Systems
RECEPTORS
STRUCTURAL MRI
Sub-Cortical
Other - Connectivity
1|2Indicates the priority used for review
Abstract Information
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Koob. (2010). Neurocircuitry of Addiction. Neuropsychopharmacology 35, 217–238.
Koob. (2021). Drug Addiction: Hyperkatifeia/Negative Reinforcement as a Framework for Medications Development. Pharmacological Reviews 73, 163–201.
Kurtin, D. (2024a). Stronger connectivity among reward, cognitive, and attention networks in people with severe Opioid Use Disorder compared with healthy controls. Under Review, Translational Psychiatry.
Kurtin, D. (2024b). Differences in fMRI-based functional connectivity during abstinence or interventions between heroin-dependent individuals and healthy controls. Under Review, Neuroscience and BioBehavioural Reviews.