Brain Imaging Center, McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA 02478, USA.
Drug Alcohol Depend. 2012 Oct 1;125(3):252-9. doi: 10.1016/j.drugalcdep.2012.02.020. Epub 2012 Mar 27.
Brain dysfunction in prefrontal cortex (PFC) and dorsal striatum (DS) contributes to habitual drug use. These regions are constituents of brain networks thought to be involved in drug addiction. To investigate whether networks containing these regions differ between nicotine dependent female smokers and age-matched female non-smokers, we employed functional MRI (fMRI) at rest.
Data were processed with independent component analysis (ICA) to identify resting state networks (RSNs). We identified a subcortical limbic network and three discrete PFC networks: a medial prefrontal cortex (mPFC) network and right and left lateralized fronto-parietal networks common to all subjects. We then compared these RSNs between smokers and non-smokers using a dual regression approach.
Smokers had greater coupling versus non-smokers between left fronto-parietal and mPFC networks. Smokers with the greatest mPFC-left fronto-parietal coupling had the most DS smoking cue reactivity as measured during an fMRI smoking cue reactivity paradigm. This may be important because the DS plays a critical role in maintaining drug-cue associations. Furthermore, subcortical limbic network amplitude was greater in smokers.
Our results suggest that prefrontal brain networks are more strongly coupled in smokers, which could facilitate drug-cue responding. Our data also are the first to document greater reward-related network fMRI amplitude in smokers. Our findings suggest that resting state PFC network interactions and limbic network amplitude can differentiate nicotine-dependent smokers from controls, and may serve as biomarkers for nicotine dependence severity and treatment efficacy.
前额叶皮层(PFC)和背侧纹状体(DS)的脑功能障碍导致习惯性药物使用。这些区域是被认为与药物成瘾有关的大脑网络的组成部分。为了研究这些区域包含的网络是否在尼古丁依赖的女性吸烟者和年龄匹配的非吸烟女性之间存在差异,我们在静息状态下进行了功能磁共振成像(fMRI)。
使用独立成分分析(ICA)对数据进行处理,以识别静息状态网络(RSN)。我们确定了一个皮质下边缘网络和三个离散的 PFC 网络:一个内侧前额叶皮质(mPFC)网络和右侧和左侧侧化的额顶网络,适用于所有受试者。然后,我们使用双回归方法比较了吸烟者和非吸烟者之间的这些 RSN。
与非吸烟者相比,吸烟者的左额顶和 mPFC 网络之间的耦合更强。在 fMRI 吸烟线索反应性范式中,mPFC-左额顶耦合最强的吸烟者的 DS 吸烟线索反应性最强。这可能很重要,因为 DS 在维持药物线索关联中起着关键作用。此外,吸烟者的皮质下边缘网络振幅更大。
我们的结果表明,吸烟者的前额叶大脑网络耦合更强,这可能促进药物线索反应。我们的数据也是首次记录到吸烟者奖赏相关网络 fMRI 振幅增加。我们的发现表明,静息状态 PFC 网络相互作用和边缘网络振幅可以区分尼古丁依赖的吸烟者和对照组,并可能作为尼古丁依赖严重程度和治疗效果的生物标志物。