In this section
@article{Austin:2026:10.1162/imag.a.1373,
author = {Austin, B and Kurtin, DL and Herlinger, K and Hayes, A and Hand, LJ and Fonville, L and Hill, RG and Nutt, DJ and Lingford-Hughes, AR and Paterson, LM},
doi = {10.1162/imag.a.1373},
journal = {Imaging Neuroscience},
title = {Investigating hierarchical control among functional networks disrupted by Opioid Use Disorder using effective connectivity},
url = {http://dx.doi.org/10.1162/imag.a.1373},
year = {2026}
}
TY - JOUR
AB - <jats:title>Abstract</jats:title> <jats:p>Opioid use disorder (OUD) poses a significant public health challenge. Developing a better understanding of the brain mechanisms and potential markers of OUD would facilitate the development of therapeutic interventions. While we recently showed that between-network connectivity is disrupted in people with OUD compared with healthy controls, it remains unclear what mechanisms may drive these disruptions and how dysfunctional interactions propagate across large scale functional networks. To advance the mechanistic understanding of the disrupted processes in OUD, this study used Effective Connectivity (EC) to quantify disrupted hierarchical control among functional networks governing cognition, attention, and reward. We also explored whether whole-brain patterns of EC were effective markers to distinguish people with severe OUD from controls. We hypothesised that the ventromedial network (VMN) would drive dysfunction in cognitive and attentional networks in people with OUD. Task-fMRI data was collected from healthy controls (HC; n=22) and OUD participants on methadone maintenance treatment (OUD; n=25), during a heroin cue reactivity (CR) and monetary incentive delay (MID) task. Following brain parcellation (214 regions) and network assignment (7 functional networks), EC was quantified using large scale nonlinear Granger causality. Dimensionality reduction was performed using uniform manifold approximation and projection, followed by hierarchical density-based spatial clustering of applications with noise to assess whether EC patterns could form clusters corresponding to group labels.</jats:p> <jats:p>Contrary to our hypothesis, the VMN did not drive dysfunction in cognitive and attentional networks. Instead, edges with significantly stronger EC in HC vs OUD participants were within and between the control, somatomotor, and default mode networks. EC patterns were u
AU - Austin,B
AU - Kurtin,DL
AU - Herlinger,K
AU - Hayes,A
AU - Hand,LJ
AU - Fonville,L
AU - Hill,RG
AU - Nutt,DJ
AU - Lingford-Hughes,AR
AU - Paterson,LM
DO - 10.1162/imag.a.1373
PY - 2026///
TI - Investigating hierarchical control among functional networks disrupted by Opioid Use Disorder using effective connectivity
T2 - Imaging Neuroscience
UR - http://dx.doi.org/10.1162/imag.a.1373
UR - https://doi.org/10.1162/imag.a.1373
ER -
Dr Nir Grossman
Senior Lecturer in Dementia Research and Group Leader at the UK DRI
nirg@imperial.ac.uk
+44 (0)20 7594 6805
