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Investigating hierarchal control among functional networks disrupted by Opioid Use Disorder

Poster No:

1271

Submission Type:

Late-Breaking Abstract Submission

Authors:

Brianna Austin¹, Danielle Kurtin¹, Katherine Herlinger¹, Alexandra Hayes¹, Alexandra Hand¹, Leon Fonville¹^,2, Raymond Hill³, David Nutt¹, Anne Lingford-Hughes¹, Louise Paterson¹

Institutions:

¹Division of Psychiatry, Faculty of Medicine, Imperial College London, London, United Kingdom, ²Invicro London LLC, London, United Kingdom, ³Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom

First Author:

Brianna Austin
Division of Psychiatry, Faculty of Medicine, Imperial College London
London, United Kingdom

Co-Author(s):

Danielle Kurtin, PhD
Division of Psychiatry, Faculty of Medicine, Imperial College London
London, United Kingdom

Katherine Herlinger
Division of Psychiatry, Faculty of Medicine, Imperial College London
London, United Kingdom

Alexandra Hayes
Division of Psychiatry, Faculty of Medicine, Imperial College London
London, United Kingdom

Alexandra Hand
Division of Psychiatry, Faculty of Medicine, Imperial College London
London, United Kingdom

Leon Fonville
Division of Psychiatry, Faculty of Medicine, Imperial College London|Invicro London LLC
London, United Kingdom|London, United Kingdom

Raymond Hill
Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London
London, United Kingdom

David Nutt
Division of Psychiatry, Faculty of Medicine, Imperial College London
London, United Kingdom

Anne Lingford-Hughes, Prof
Division of Psychiatry, Faculty of Medicine, Imperial College London
London, United Kingdom

Louise Paterson, PhD
Division of Psychiatry, Faculty of Medicine, Imperial College London
London, United Kingdom

Late Breaking Reviewer(s):

Fernando Barrios, Ph.D.
Universidad Nacional Autónoma de México
Querétaro, Querétaro

Stephanie Forkel, PhD
Donders Institute for Brain, Cognition, and Behaviour
Nijmegen, Gelderland

Casey Paquola
Institute for Neuroscience and Medicine, INM-7, Forschungszentrum Jülich
Jülich, NA

Anna Roe, Phd
Zhejiang University
Hangzhou, Zhejiang

Introduction:

Opioid use disorder (OUD) poses a significant public health challenge, with >60% of individuals relapsing within six months post-treatment (Hayes et al., 2020). While harm reduction strategies and opioid substitution therapies have shown some success, improved relapse prevention strategies are needed. One way to develop more effective therapies is to improve the understanding of the neural underpinnings of OUD, and this can be done using fMRI. Here, we used Effective Connectivity (EC) to determine whether there is hierarchical control among functional networks disrupted by OUD, and whether whole-brain EC is an effective feature to between distinguish Healthy controls (HC) from people with OUD.

Methods:

Task-based fMRI data was collected from HC (n=22) and OUD participants receiving methadone (MD; n=25). Participants performed the Cue Reactivity (CR) and Monetary Incentive Delay (MID) tasks, which were chosen as they reliably engage reward/anti-reward pathways disrupted in OUD (Fonville et al., 2021). MRI data were acquired on a 3T Siemens Magnetom Verio scanner using a T2*-weighted echo-planar imaging sequence with GRAPPA and multiband acceleration factors:2 (Fonville et al., 2021). Data were preprocessed using fMRI Expert Analysis Tool (v6.0)(FEAT). Timeseries were extracted using the Schaefer atlas and Freesurfer, with select regions reassigned to the ventromedial network (VMN) to form a reward/anti-reward network. EC was quantified using large-scale nonlinear granger causality (Wismüller et al., 2021). To assess whether whole-brain EC is an effective distinguishing feature between HC vs MD participants, we used unsupervised hierarchal density-based spatial clustering with applications of noise (HDBSCAN). Dimensionality reduction of the EC matrices for clustering was performed with Uniform Manifold Approximation and Projection (UMAP) for subsequent clustering analysis. Trustworthiness and silhouette scores assessed UMAP's ability to represent the high-dimensional EC patterns in the reduced space. Permutation tests were used for statistical significance testing, with results considered significant at p<0.05 after max-T familywise correction.

Results:

We found significant EC differences between HC and MD participants across both tasks. In the MID task, 7.5% of all unique edges had significantly different EC between HC and MD participants, and about half of significant differences in EC showed HC had stronger connectivity than MD participants. In the CR task, 17.2% of all unique edges had significantly different EC between HC and MD participants, and most (>80%) of the significant differences in EC showed HC had stronger connectivity than MD participants. Edges with significantly stronger EC in HC vs MD participants were concentrated within and between the Control, Somatomotor, and Default Mode (DMN) networks. In the MID task, MD participants showed a greater proportion of edges with significantly higher EC in the VMN network as compared to HC and the CR task. UMAP quality assessment metrics (Trustworthiness score=0.72, silhouette score=64.2%) showed UMAP provided a moderately reliable representation of the high-dimensional EC patterns in the reduced space. However, EC patterns were unable to form distinct clusters that corresponded to clinical groups.

Conclusions:

EC was consistently weaker in MD participants compared with HC. Differences were concentrated in Control, Somatomotor, and Default Mode network. While these networks were dominant across both tasks, no one network drove dysfunction in others. Our findings suggest a key feature of OUD is impaired ability to dynamically regulate and shift between brain states. This network-level dysfunction implies OUD is not merely driven by an overactive reward system but rather compromised flexibility in engaging and transitioning between functional networks-particularly those involved in cognitive control and sensorimotor integration.

Disorders of the Nervous System:

Psychiatric (eg. Depression, Anxiety, Schizophrenia)

Modeling and Analysis Methods:

Classification and Predictive Modeling

Connectivity (eg. functional, effective, structural) ¹

fMRI Connectivity and Network Modeling ²

Keywords:

Addictions

Computational Neuroscience

FUNCTIONAL MRI

Machine Learning

Psychiatric

^1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

Task-activation

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was this research conducted in the United States?

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.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Yes

Please indicate which methods were used in your research:

Functional MRI

Computational modeling

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

FSL

Free Surfer

Provide references using APA citation style.

Hayes, A., Herlinger, K., Paterson, L., & Lingford-Hughes, A. (2020). The neurobiology of substance use and addiction: Evidence from neuroimaging and relevance to treatment. BJPsych Advances, 26(6), 367–378.

Fonville, L., Paterson, L., Herlinger, K., Hayes, A., Hill, R., Nutt, D., & Lingford-Hughes, A. (2021). Functional evaluation of NK1 antagonism on cue reactivity in opiate dependence: An fMRI study. Drug and Alcohol Dependence, 221, 108564.

Wismüller, A., Dsouza, A. M., Vosoughi, M. A., & Abidin, A. (2021). Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data. Scientific Reports, 11, 7817.

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