Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, Baltimore, Maryland 21224, and.
Research Center of Basic Space Science, Harbin Institute of Technology, Nangang Qu, Haerbin Shi 150001, Heilongjiang Sheng, People's Republic of China.
J Neurosci. 2019 Jun 19;39(25):5028-5037. doi: 10.1523/JNEUROSCI.0140-19.2019. Epub 2019 Apr 16.
Although 60% of the US population have tried smoking cigarettes, only 16% smoke regularly. Identifying this susceptible subset of the population before the onset of nicotine dependence may encourage targeted early interventions to prevent regular smoking and/or minimize severity. While prospective neuroimaging in human populations can be challenging, preclinical neuroimaging models before chronic nicotine administration can help to develop translational biomarkers of disease risk. Chronic, intermittent nicotine (0, 1.2, or 4.8 mg/kg/d; = 10-11/group) was administered to male Sprague Dawley rats for 14 d; dependence severity was quantified using precipitated withdrawal behaviors collected before, during, and following forced nicotine abstinence. Resting-state fMRI functional connectivity (FC) before drug administration was subjected to a graph theory analytical framework to form a predictive model of subsequent individual differences in nicotine dependence. Whole-brain modularity analysis identified five modules in the rat brain. A metric of intermodule connectivity, participation coefficient, of an identified insular-frontal cortical module predicted subsequent dependence severity, independent of nicotine dose. To better spatially isolate this effect, this module was subjected to a secondary exploratory modularity analysis, which segregated it into three submodules (frontal-motor, insular, and sensory). Higher FC among these three submodules and three of the five originally identified modules (striatal, frontal-executive, and sensory association) also predicted dependence severity. These data suggest that predispositional, intrinsic differences in circuit strength between insular-frontal-based brain networks before drug exposure may identify those at highest risk for the development of nicotine dependence. Developing biomarkers of individuals at high risk for addiction before the onset of this brain-based disease is essential for prevention, early intervention, and/or subsequent treatment decisions. Using a rodent model of nicotine dependence and a novel data-driven, network-based analysis of resting-state fMRI data collected before drug exposure, functional connections centered on an intrinsic insular-frontal module predicted the severity of nicotine dependence after drug exposure. The predictive capacity of baseline network measures was specific to inter-regional but not within-region connectivity. While insular and frontal regions have consistently been implicated in nicotine dependence, this is the first study to reveal that innate, individual differences in their circuit strength have the predictive capacity to identify those at greatest risk for and resilience to drug dependence.
尽管 60%的美国人口尝试过吸烟，但只有 16%的人经常吸烟。在尼古丁依赖之前确定人群中易受影响的亚组可能会鼓励有针对性的早期干预，以预防常规吸烟和/或最大限度地减少严重程度。虽然前瞻性神经影像学在人类群体中可能具有挑战性，但在慢性尼古丁给药之前的临床前神经影像学模型可以帮助开发疾病风险的转化生物标志物。慢性、间歇性尼古丁（0、1.2 或 4.8mg/kg/d；=10-11/组）给予雄性 Sprague Dawley 大鼠 14 天；使用在强制尼古丁戒断前、期间和之后收集的戒断行为来量化依赖严重程度。在药物给药之前，静息状态 fMRI 功能连接（FC）进行了图论分析框架，以形成对随后个体尼古丁依赖差异的预测模型。大鼠大脑中有五个模块。在鉴定的脑岛-额皮质模块中，模块间连接的度量，即参与系数，预测了随后的依赖严重程度，与尼古丁剂量无关。为了更好地在空间上隔离这种影响，对该模块进行了二次探索性模块化分析，将其分为三个子模块（额-运动、脑岛和感觉）。这些子模块之间以及五个原始鉴定模块（纹状体、额执行和感觉联想）中的三个之间更高的 FC 也预测了依赖严重程度。这些数据表明，在暴露于药物之前，基于脑岛-额的网络之间的电路强度的预先存在的、内在的差异可能会确定那些发展尼古丁依赖的风险最高的人。在这种基于大脑的疾病发生之前，开发对成瘾高风险个体的生物标志物对于预防、早期干预和/或后续治疗决策至关重要。使用尼古丁依赖的啮齿动物模型和在药物暴露前收集的静息状态 fMRI 数据的新型数据驱动、基于网络的分析，以固有脑岛-额模块为中心的功能连接预测了药物暴露后的尼古丁依赖严重程度。基线网络测量的预测能力是特定于区域间而不是区域内连接的。虽然脑岛和额叶区域一直与尼古丁依赖有关，但这是第一项揭示其电路强度的固有个体差异具有预测能力以识别对药物依赖风险最高和恢复力最强的人的研究。
