酒精使用障碍患者从额叶皮层到纹状体的非典型有效连接

IF 5.8 1区 医学 Q1 PSYCHIATRY Translational Psychiatry Pub Date : 2024-09-18 DOI:10.1038/s41398-024-03083-8
Hongwen Song, Ping Yang, Xinyue Zhang, Rui Tao, Lin Zuo, Weili Liu, Jiaxin Fu, Zhuo Kong, Rui Tang, Siyu Wu, Liangjun Pang, Xiaochu Zhang
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引用次数: 0

摘要

酒精使用障碍(AUD)是一种严重的精神疾病,其特征是分布在大脑各区域之间的连接中断,表明功能整合受损。以往利用功能磁共振成像(fMRI)进行的连接组研究主要关注非定向功能连接,而与 AUD 相关的定向有效连接(EC)的具体改变仍不清楚。为了解决这一问题,本研究采用了多变量模式分析(MVPA)和频谱动态因果建模(DCM)。我们招募了 32 名患有 AUD 的禁欲男性和 30 名健康对照(HCs)男性,并收集了他们的静息态 fMRI 数据。我们采用基于区域同质性(ReHo)的 MVPA 方法对 AUD 和 HC 组进行了分类,并预测了 AUD 患者的成瘾严重程度。我们使用频谱 DCM 进一步研究了 MVPA 确定的信息量最大的脑区。结果表明,基于 ReHo 的支持向量分类法(SVC)在区分 AUD 患者和 HC 患者方面表现出最高的准确率(分类准确率: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,R2 = 0.38;密歇根酒精中毒筛查测试 MAST,R2 = 0.29)。对预测最有参考价值的脑区包括左侧前SMA、右侧dACC、右侧LOFC、右侧putamen和右侧NACC。这些结果在一个独立的数据集中得到了验证(35 名 AUD 患者和 36 名 HC,分类准确率:91.67%;AUDIT,R2 = 0.17;MAST,R2 = 0.20)。频谱 DCM 分析结果表明,与 HCs 相比,AUD 患者从左侧前 SMA 到右侧 Putamen、从右侧 dACC 到右侧 Putamen 以及从右侧 LOFC 到右侧 NACC 的 EC 均有所下降。此外,从右侧NACC到左侧前SMA以及从右侧dACC到右侧丘脑的EC强度介导了成瘾严重程度(MAST评分)和行为测量(冲动和强迫评分)之间的关系。这些发现为AUD患者自我控制能力受损、风险评估以及冲动性和强迫性饮酒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关系见解。
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Atypical effective connectivity from the frontal cortex to striatum in alcohol use disorder

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, R2 = 0.38; Michigan alcoholism screening test, MAST, R2 = 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, R2 = 0.17; MAST, R2 = 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.

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来源期刊
CiteScore
11.50
自引率
2.90%
发文量
484
审稿时长
23 weeks
期刊介绍: Psychiatry has suffered tremendously by the limited translational pipeline. Nobel laureate Julius Axelrod''s discovery in 1961 of monoamine reuptake by pre-synaptic neurons still forms the basis of contemporary antidepressant treatment. There is a grievous gap between the explosion of knowledge in neuroscience and conceptually novel treatments for our patients. Translational Psychiatry bridges this gap by fostering and highlighting the pathway from discovery to clinical applications, healthcare and global health. We view translation broadly as the full spectrum of work that marks the pathway from discovery to global health, inclusive. The steps of translation that are within the scope of Translational Psychiatry include (i) fundamental discovery, (ii) bench to bedside, (iii) bedside to clinical applications (clinical trials), (iv) translation to policy and health care guidelines, (v) assessment of health policy and usage, and (vi) global health. All areas of medical research, including — but not restricted to — molecular biology, genetics, pharmacology, imaging and epidemiology are welcome as they contribute to enhance the field of translational psychiatry.
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