Ma Liangsuo, Steinberg Joel L, Hasan Khader M, Narayana Ponnada A, Kramer Larry A, Moeller F Gerard
Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center, Houston, Texas.
Hum Brain Mapp. 2014 Mar;35(3):760-78. doi: 10.1002/hbm.22212. Epub 2012 Nov 14.
Although reduced working memory brain activation has been reported in several brain regions of cocaine-dependent subjects compared with controls, very little is known about whether there is altered connectivity of working memory pathways in cocaine dependence. This study addresses this issue by using functional magnetic resonance imaging-based stochastic dynamic causal modeling (DCM) analysis to study the effective connectivity of 19 cocaine-dependent subjects and 14 healthy controls while performing a working memory task. Stochastic DCM is an advanced method that has recently been implemented in SPM8 that can obtain improved estimates, relative to deterministic DCM, of hidden neuronal causes before convolution with the hemodynamic response. Thus, stochastic DCM may be less influenced by the confounding effects of variations in blood oxygen level-dependent response caused by disease or drugs. Based on the significant regional activation common to both groups and consistent with previous working memory activation studies, seven regions of interest were chosen as nodes for DCM analyses. Bayesian family level inference, Bayesian model selection analyses, and Bayesian model averaging (BMA) were conducted. BMA showed that the cocaine-dependent subjects had large differences compared with the control subjects in the strengths of prefrontal-striatal modulatory (B matrix) DCM parameters. These findings are consistent with altered cortical-striatal networks that may be related to reduced dopamine function in cocaine dependence. As far as we are aware, this is the first between-group DCM study using stochastic methodology.
尽管与对照组相比，已报道可卡因依赖者的几个脑区存在工作记忆脑激活降低的情况，但对于可卡因依赖中工作记忆通路的连通性是否改变却知之甚少。本研究通过使用基于功能磁共振成像的随机动态因果模型（DCM）分析来研究19名可卡因依赖者和14名健康对照在执行工作记忆任务时的有效连通性，从而解决了这个问题。随机DCM是一种先进的方法，最近已在SPM8中实现，相对于确定性DCM，它在与血液动力学响应卷积之前可以获得对隐藏神经元原因的改进估计。因此，随机DCM可能较少受到疾病或药物引起的血氧水平依赖性反应变化的混杂效应的影响。基于两组共有的显著区域激活，并与先前的工作记忆激活研究一致，选择了七个感兴趣区域作为DCM分析的节点。进行了贝叶斯家族水平推断、贝叶斯模型选择分析和贝叶斯模型平均（BMA）。BMA表明，可卡因依赖者与对照者在前额叶-纹状体调节（B矩阵）DCM参数强度方面存在很大差异。这些发现与皮质-纹状体网络改变一致，这可能与可卡因依赖中多巴胺功能降低有关。据我们所知，这是第一项使用随机方法的组间DCM研究。
