Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
Brain Res. 2011 Jul 21;1402:46-53. doi: 10.1016/j.brainres.2011.05.054. Epub 2011 May 30.
Previous functional imaging studies on heroin addicts have focused on abnormal brain functions based on specific tasks, while few fMRI studies concentrated on the resting-state abnormalities of heroin-dependent individuals. In the current study, we applied the pattern classification technique, which employs the feature extraction method of non-negative matrix factorization (NMF) and a support vector machine (SVM) classifier. Its main purpose was to characterize the discrepancy in activation patterns between heroin-dependent individuals and healthy subjects during the resting state. The results displayed a high accuracy in the activation pattern differences of the two groups, which included the orbitofrontal cortex (OFC), cingulate gyrus, frontal and para-limbic regions such as the anterior cingulate cortex (ACC), hippocampal/parahippocampal region, amygdala, caudate, putamen, as well as the posterior insula and thalamus. These findings indicate that significant biomarkers exist among the network of circuits that are involved in drug abuse. The implications from our study may help explain the behavioral and neuropsychological deficits in heroin-dependent individuals and shed light on the mechanisms underlying heroin addiction.
先前关于海洛因成瘾者的功能影像学研究集中在基于特定任务的大脑功能异常上，而很少有 fMRI 研究关注海洛因依赖者的静息状态异常。在本研究中，我们应用了模式分类技术，该技术采用非负矩阵分解 (NMF) 和支持向量机 (SVM) 分类器的特征提取方法。其主要目的是描述海洛因依赖者和健康受试者在静息状态下激活模式的差异。结果显示，两组之间的激活模式差异具有很高的准确性，包括眶额皮质 (OFC)、扣带回、额极和边缘前区，如前扣带皮质 (ACC)、海马/海马旁回、杏仁核、尾状核、壳核以及后岛叶和丘脑。这些发现表明，在涉及药物滥用的电路网络中存在显著的生物标志物。我们研究的意义可能有助于解释海洛因依赖者的行为和神经心理学缺陷，并阐明海洛因成瘾的机制。
