发表机构
University of Oklahoma; University of Tennessee Medical Center; University of Oklahoma Health Campus（俄克拉荷马大学; 田纳西大学医学中心; 俄克拉荷马大学健康校区）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究引入基于结构的动态功能连接框架，发现可卡因使用障碍患者脑网络整合增强、灵活性降低，且该特征可预测每周可卡因使用量，为可卡因成瘾的网络改变研究提供了有价值的测量方法。
AI 中文摘要
可卡因使用障碍（CUD）与大规模功能性脑网络的广泛改变相关，但这些改变的机制及其与临床和认知结果的关系仍知之甚少。为解决这一空白，我们引入了一个框架，用于提取基于结构的动态功能连接模式。随后，我们利用这些连接模式来表征与CUD相关的功能性脑网络组织差异，并探究其与临床指标的关系。具体而言，我们使用从扩散成像得到的个体化结构先验，对每个参与者的功能连接矩阵应用拉普拉斯谱平滑。这些基于结构的连接特征随后被用于检验跨网络相互作用，并表征功能性脑状态下的动态社区组织。我们的发现表明，可卡因使用障碍患者的功能性脑网络表现出整合和募集增加，同时灵活性降低，其中视觉、注意力和控制系统的改变最为显著。此外，基于结构的功能连接特征可预测CUD队列内的每周可卡因使用量。总体而言，这些结果凸显了基于结构的动态连接测量在表征与可卡因成瘾相关的网络水平改变，以及将这些改变与具有临床意义的可卡因使用严重程度指标关联起来的价值。
英文摘要
Cocaine Use Disorder (CUD) is associated with widespread alterations in large-scale functional brain networks, yet the mechanisms contributing to these changes and their relationship to clinical and cognitive outcomes remain poorly understood. To address this gap, we introduce a framework to extract structurally informed dynamic functional connectivity patterns. We then leverage these connectivity patterns to characterize differences in functional brain network organization associated with CUD and to examine their relationship with clinical measures. Specifically, we applied Laplacian spectral smoothing to each participant's functional connectivity matrix using individualized structural priors derived from diffusion imaging. These structurally informed connectivity features were subsequently used to examine cross-network interactions and characterize dynamic community organization across functional brain states. Our findings indicate that individuals with cocaine use disorder exhibit increased integration and recruitment accompanied by reduced flexibility in the functional brain networks, with the most pronounced alterations in visual, attentional, and control systems. In addition, structurally informed functional connectivity features were predictive of weekly cocaine use within the CUD cohort. Overall, these results highlight the value of structurally informed dynamic connectivity measures for characterizing network-level alterations associated with cocaine addiction and for linking these alterations to clinically meaningful measures of cocaine use severity.
