Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures.
Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures.
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DOI：
10.3390/bs12050128
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发表时间：
2022-04-28
影响因子：
2.6
通讯作者：
Porjesz, Bernice
中科院分区：
文献类型：
--
作者：
Kamarajan, Chella;Ardekani, Babak A.;Pandey, Ashwini K.;Kinreich, Sivan;Pandey, Gayathri;Chorlian, David B.;Meyers, Jacquelyn L.;Zhang, Jian;Bermudez, Elaine;Kuang, Weipeng;Stimus, Arthur T.;Porjesz, Bernice
关键词：
患有酒精使用障碍（AUD）的人可能会表现出一系列神经和行为异常，包括大脑网络改变、神经认知功能受损和冲动性增强。当前的研究旨在利用多域测量，利用随机森林 (RF) 分类模型，识别可以将 AUD 患者与健康对照 (CTL) 患者区分开来的特定特征。特征包括奖励网络中基于功能磁共振成像的静息态功能连接 (rsFC)、神经心理学任务表现和行为冲动评分，这些评分是从 30 名有 AUD 病史的禁欲成年男性和 30 名没有 AUD 病史的 CTL 个体中收集的。结果发现，RF 模型的分类准确率达到 86.67%（AUC = 93%），并识别出 FC 和冲动性的关键特征，这些特征对 CTL 个体的 AUD 分类有显着贡献。冲动评分是最重要的预测因子，其次是 12 个 rsFC 特征，涉及大脑中 17 个关键奖励区域，例如腹侧被盖区、伏隔核、前岛叶、前扣带皮层以及其他皮质和皮质下结构。与对照组相比，患有 AUD 的个体在冲动性和功能连接性改变方面表现出显着差异。具体而言，AUD 在 13 个区域的 9 个连接中显示出较高的冲动性和低连接性，在涉及 6 个区域的 3 个连接中显示出高连接性。相对于对照组，AUD 的视觉空间短期工作记忆也受到损害。总之，大脑连接性、冲动性和神经心理学表现的特定多域特征可以在机器学习框架中使用，以有效地将 AUD 个体与健康对照进行分类。
Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls.
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DOI：
10.1111/acer.12266
发表时间：
2014-02
期刊：
Alcoholism, clinical and experimental research
影响因子：
--
作者：
Cyders MA;Dzemidzic M;Eiler WJ;Coskunpinar A;Karyadi K;Kareken DA
通讯作者：
Kareken DA
DOI：
10.1111/acer.14650
发表时间：
2021-08
期刊：
Alcoholism, clinical and experimental research
影响因子：
--
作者：
Arias AJ;Ma L;Bjork JM;Hammond CJ;Zhou Y;Snyder A;Moeller FG
通讯作者：
Moeller FG
影响因子：
3.7
作者：
Carlson, SR;Katsanis, J;Mertz, AK
通讯作者：
Mertz, AK
DOI：
10.1002/ajmg.b.30080
发表时间：
2005-01-05
影响因子：
2.8
作者：
Bowirrat, A;Oscar-Berman, M
通讯作者：
Oscar-Berman, M
DOI：
10.1006/cbmr.1996.0014
发表时间：
1996-06-01
期刊：
COMPUTERS AND BIOMEDICAL RESEARCH
影响因子：
--
作者：
Cox, RW
通讯作者：
Cox, RW