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酒精使用障碍患者前额皮质到纹状体的非典型有效连接。

Atypical effective connectivity from the frontal cortex to striatum in alcohol use disorder.

机构信息

Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China.

Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, Hefei, China.

出版信息

Transl Psychiatry. 2024 Sep 18;14(1):381. doi: 10.1038/s41398-024-03083-8.

Abstract

Alcohol use disorder (AUD) is a profound psychiatric condition marked by disrupted connectivity among distributed brain regions, indicating impaired functional integration. Previous connectome studies utilizing functional magnetic resonance imaging (fMRI) have predominantly focused on undirected functional connectivity, while the specific alterations in directed effective connectivity (EC) associated with AUD remain unclear. To address this issue, this study utilized multivariate pattern analysis (MVPA) and spectral dynamic causal modeling (DCM). We recruited 32 abstinent men with AUD and 30 healthy controls (HCs) men, and collected their resting-state fMRI data. A regional homogeneity (ReHo)-based MVPA method was employed to classify AUD and HC groups, as well as predict the severity of addiction in AUD individuals. The most informative brain regions identified by the MVPA were further investigated using spectral DCM. Our results indicated that the ReHo-based support vector classification (SVC) exhibits the highest accuracy in distinguishing individuals with AUD from HCs (classification accuracy: 98.57%). Additionally, our results demonstrated that ReHo-based support vector regression (SVR) could be utilized to predict the addiction severity (alcohol use disorders identification test, AUDIT, R = 0.38; Michigan alcoholism screening test, MAST, R = 0.29) of patients with AUD. The most informative brain regions for the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. These findings were validated in an independent data set (35 patients with AUD and 36 HCs, Classification accuracy: 91.67%; AUDIT, R = 0.17; MAST, R = 0.20). The results of spectral DCM analysis indicated that individuals with AUD exhibited decreased EC from the left pre-SMA to the right putamen, from the right dACC to the right putamen, and from the right LOFC to the right NACC compared to HCs. Moreover, the EC strength from the right NACC to left pre-SMA and from the right dACC to right putamen mediated the relationship between addiction severity (MAST scores) and behavioral measures (impulsive and compulsive scores). These findings provide crucial evidence for the underlying mechanism of impaired self-control, risk assessment, and impulsive and compulsive alcohol consumption in individuals with AUD, providing novel causal insights into both diagnosis and treatment.

摘要

酒精使用障碍(AUD)是一种严重的精神疾病,其特征是大脑不同区域之间的连接中断,表明功能整合受损。以前使用功能磁共振成像(fMRI)的连接组学研究主要集中在无向功能连接上,而与 AUD 相关的定向有效连接(EC)的具体变化尚不清楚。为了解决这个问题,本研究利用多元模式分析(MVPA)和频谱动态因果建模(DCM)。我们招募了 32 名戒酒的 AUD 男性和 30 名健康对照组(HC)男性,并采集了他们的静息态 fMRI 数据。采用基于局部一致性(ReHo)的 MVPA 方法对 AUD 和 HC 组进行分类,并预测 AUD 个体成瘾的严重程度。MVPA 确定的最具信息量的大脑区域进一步通过频谱 DCM 进行了研究。我们的结果表明,基于 ReHo 的支持向量分类(SVC)在区分 AUD 个体和 HC 方面表现出最高的准确性(分类准确性:98.57%)。此外,我们的结果表明,基于 ReHo 的支持向量回归(SVR)可用于预测 AUD 患者的成瘾严重程度(酒精使用障碍识别测试,AUDIT,R=0.38;密歇根酒精筛查测试,MAST,R=0.29)。用于预测的最具信息量的大脑区域包括左侧前 SMA、右侧 dACC、右侧 LOFC、右侧壳核和右侧 NACC。这些发现通过独立数据集(35 名 AUD 患者和 36 名 HC,分类准确性:91.67%;AUDIT,R=0.17;MAST,R=0.20)进行了验证。频谱 DCM 分析的结果表明,与 HC 相比,AUD 患者从左侧前 SMA 到右侧壳核、从右侧 dACC 到右侧壳核以及从右侧 LOFC 到右侧 NACC 的 EC 减少。此外,右侧 NACC 到左侧前 SMA 和右侧 dACC 到右侧壳核的 EC 强度介导了成瘾严重程度(MAST 评分)与行为测量(冲动和强迫评分)之间的关系。这些发现为 AUD 个体中自我控制、风险评估以及冲动和强迫性饮酒受损的潜在机制提供了重要证据,为诊断和治疗提供了新的因果见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b222/11411137/a1e9bf5c4d22/41398_2024_3083_Fig1_HTML.jpg

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