Investigating hierarchical control among functional networks disrupted by opioid use disorder using effective connectivity.

Imaging neuroscience (Cambridge, Mass.) Pub Date : 2026-09-17 eCollection Date: 2026-01-01 DOI:10.1162/IMAG.a.1373
Brianna Austin, Danielle L Kurtin, Katherine Herlinger, Alexandra Hayes, Lexi J Hand, Leon Fonville, Raymond G Hill, David J Nutt, Anne R Lingford-Hughes, Louise M Paterson
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Abstract

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 hypothesized that the ventromedial network (VMN) would drive dysfunction in cognitive and attentional networks in people with OUD. Task-fMRI data were 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. 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 unable to form distinct clusters that corresponded to clinical groups; instead, clusters separated by task. Little evidence was found that disrupted VMN-mediated reward drove dysregulated whole-brain function in OUD.

利用有效连通性研究阿片类药物使用障碍破坏的功能网络中的层次控制。
阿片类药物使用障碍(OUD)是一个重大的公共卫生挑战。更好地了解OUD的大脑机制和潜在标志物将促进治疗干预措施的发展。虽然我们最近发现,与健康对照组相比,OUD患者的网络间连接受到破坏,但目前尚不清楚是什么机制导致了这些破坏,以及功能失调的相互作用如何在大规模功能网络中传播。为了促进对OUD中中断过程的机制理解,本研究使用有效连通性(Effective Connectivity, EC)来量化控制认知、注意和奖励的功能网络中中断的层次控制。我们还探讨了EC的全脑模式是否是区分严重OUD患者和对照组的有效标记。我们假设腹内侧网络(VMN)会导致OUD患者的认知和注意网络功能障碍。任务-功能磁共振成像数据来自健康对照(HC, n = 22)和接受美沙酮维持治疗的OUD参与者(OUD, n = 25),他们在海洛因提示反应性(CR)和货币激励延迟(MID)任务中。在对214个脑区进行脑分割和对7个功能网络进行网络分配后,采用大规模非线性格兰杰因果关系对EC进行量化。使用均匀流形近似和投影进行降维,然后对带有噪声的应用进行基于层次密度的空间聚类,以评估EC模式是否可以形成与组标签对应的聚类。与我们的假设相反,VMN并没有导致认知和注意力网络的功能障碍。相反,HC与OUD参与者的EC明显更强的边缘位于控制网络、躯体运动网络和默认模式网络内部和之间。EC模式无法形成与临床组相对应的明显簇;相反,集群按任务分开。几乎没有证据表明vmn介导的奖励中断导致OUD患者全脑功能失调。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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