Random forest based classification of alcohol dependence patients and healthy controls using resting state MRI.
Random forest based classification of alcohol dependence patients and healthy controls using resting state MRI.
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DOI：
10.1016/j.neulet.2018.04.007
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发表时间：
2018-05-29
影响因子：
2.5
通讯作者：
Momenan R
中科院分区：
文献类型：
--
作者：
Zhu X;Du X;Kerich M;Lohoff FW;Momenan R
目前，酒精使用障碍（AUD）的分类是根据临床情况进行的；然而，强有力的证据表明，长期饮酒会导致神经化学和神经回路的适应。识别受酒精影响的神经元网络将提供更系统的诊断方法，并为 AUD 的病理生理学提供新的见解。在这项研究中，我们确定了 AUD 的网络级大脑特征，并以多元方式进一步量化了网络内的静息状态和网络间的连接特征，这些特征对 AUD 进行了分类，从而提供了有关每个网络如何导致酗酒的额外信息。静息态 fMRI 采集自 92 名个体（46 名对照者和 46 名 AUD）。使用概率独立成分分析 (PICA) 来提取 AUD 和对照的大脑功能网络及其相应的时间进程。每个网络的网络内连接性和每对网络的网络间连接性都被用作特征。应用随机森林进行模式分类。结果表明，网络内特征能够分别以 87.0% 的准确率和 90.5% 的精度识别 AUD 和控制。信息最丰富的网络包括两个：执行控制网络（ECN）和奖励网络（RN）。网络间特征达到了 67.4% 的准确率和 70.0% 的精度。 RN-默认模式网络 (DMN) 和 RN-ECN 之间的网络间连接对预测贡献最大。总之，与网络间连接相比，网络内功能连接为 AUD 分类提供了最大的信息。此外，我们的结果表明 ECN 和 RN 内的连通性对于 AUD 的分类提供了信息。我们的研究结果表明，机器学习算法提供了一种替代技术来量化大规模网络差异，并为识别 AUD 临床诊断的潜在生物标志物提供新见解。
Currently, classification of alcohol use disorder (AUD) is made on clinical grounds; however, robust evidence shows that chronic alcohol use leads to neurochemical and neurocircuitry adaptations. Identifications of the neuronal networks that are affected by alcohol would provide a more systematic way of diagnosis and provide novel insights into the pathophysiology of AUD. In this study, we identified network-level brain features of AUD, and further quantified resting-state within-network, and between-network connectivity features in a multivariate fashion that are classifying AUD, thus providing additional information about how each network contributes to alcoholism. Resting-state fMRI were collected from 92 individuals (46 controls and 46 AUDs). Probabilistic Independent Component Analysis (PICA) was used to extract brain functional networks and their corresponding time-course for AUD and controls. Both within-network connectivity for each network and between-network connectivity for each pair of networks were used as features. Random forest was applied for pattern classification. The results showed that within-networks features were able to identify AUD and control with 87.0% accuracy and 90.5% precision, respectively. Networks that were most informative included two; Executive Control Networks (ECN), and Reward Network (RN). The between-network features achieved 67.4% accuracy and 70.0% precision. The between-network connectivity between RN-Default Mode Network (DMN) and RN-ECN contribute the most to the prediction. In conclusion, within-network functional connectivity offered maximal information for AUD classification, when compared with between-network connectivity. Further, our results suggest that connectivity within the ECN and RN are informative in classifying AUD. Our findings suggest that machine-learning algorithms provide an alternative technique to quantify large-scale network differences and offer new insights into the identification of potential biomarkers for the clinical diagnosis of AUD.
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大规模功能性大脑网络的模式分类：癫痫信息性神经影像标记物的识别
影响因子：
3.7
作者：
Zhang J;Cheng W;Wang Z;Zhang Z;Lu W;Lu G;Feng J
通讯作者：
Feng J
影响因子：
3.5
作者：
Abraham A;Pedregosa F;Eickenberg M;Gervais P;Mueller A;Kossaifi J;Gramfort A;Thirion B;Varoquaux G
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Varoquaux G
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
影响因子：
64.8
作者：
Whelan, Robert;Watts, Richard;Orr, Catherine A.;Althoff, Robert R.;Artiges, Eric;Banaschewski, Tobias;Barker, Gareth J.;Bokde, Arun L. W.;Buechel, Christian;Carvalho, Fabiana M.;Conrod, Patricia J.;Flor, Herta;Fauth-Buehler, Mira;Frouin, Vincent;Gallinat, Juergen;Gan, Gabriela;Gowland, Penny;Heinz, Andreas;Ittermann, Bernd;Lawrence, Claire;Mann, Karl;Martinot, Jean-Luc;Nees, Frauke;Ortiz, Nick;Paillere-Martinot, Marie-Laure;Paus, Tomas;Pausova, Zdenka;Rietschel, Marcella;Robbins, Trevor W.;Smolka, Michael N.;Stroehle, Andreas;Schumann, Gunter;Garavan, Hugh
通讯作者：
Garavan, Hugh
静止状态功能磁共振成像揭示了重大麻使用者的不同大脑活动 - 多素模式分析。
DOI：
10.1177/0269881114550354
发表时间：
2014-11
期刊：
Journal of psychopharmacology (Oxford, England)
影响因子：
--
作者：
Cheng H;Skosnik PD;Pruce BJ;Brumbaugh MS;Vollmer JM;Fridberg DJ;O'Donnell BF;Hetrick WP;Newman SD
通讯作者：
Newman SD