Rădulescu Anca R, Mujica-Parodi L R
Department of Applied Mathematics, UCB 526, University of Colorado at Boulder, Boulder, CO, USA.
Neuropsychobiology. 2008;57(4):206-16. doi: 10.1159/000151731. Epub 2008 Aug 29.
Using a prefrontal-limbic dysregulation model for schizophrenia, we tested whether a dynamic control systems approach in conjunction with neuroimaging might increase detection sensitivity in characterizing the illness. Our analyses were modeled upon diagnostic tests for other dysregulatory diseases, such as diabetes, in which trajectories for the excitatory and inhibitory components of the negative feedback loops that reestablish homeostasis are measured after system perturbation. We hypothesized that these components would show distinct coupling dynamics within the patient population, as compared to healthy controls, and that these coupling dynamics could be quantified statistically using cross-correlations between excitatory and inhibitory time series using fMRI.
As our perturbation, we activated neural regions associated with the emotional arousal response, using affect-valent facial stimuli presented to 11 schizophrenic patients (all under psychotropic medication) and 65 healthy controls (including 11 individuals age- and sex-matched to the patients) during fMRI scanning. We first performed a random-effects analysis of the fMRI data to identify activated regions. Those regions were then analyzed for group differences, using both standard analyses with respect to the time series peaks, as well as a dynamic analysis that looked at cross-correlations between excitatory and inhibitory time series and group differences over the entire time series.
Patients and controls showed significant differences in signal dynamics between excitatory and inhibitory components of the negative feedback loop that controls emotional arousal, specifically between the right amygdala and Brodmann area 9 (BA9), when viewing angry facial expressions (p = 0.002). Further analyses were performed with respect to activation amplitudes for these areas in response to angry faces, both over the entire time series as well as for each time point along the time series. While the amygdala responses were not significantly different between groups, patients showed significantly lower BA9 activation during the beginning of the response (0.000<or= p<or= 0.021) and significantly higher BA9 activation towards the end of the response (0.008<or= p<or= 0.025), suggesting longer time-lags between patients' excitatory responses and the inhibitory activation that modulates it.
Our results capture a significant dysregulation between the excitatory (amygdala) and inhibitory (prefrontal) limbic regions in medicated schizophrenic patients versus healthy controls. They suggest that, analogously to diagnostic tests used in other physiological diseases, quantifying dysregulation using a control systems approach may provide an appropriate model to investigate further in developing presymptomatic neurobiological assessments of risk, or illness severity in symptomatic patients.
我们采用精神分裂症的前额叶 - 边缘系统失调模型,测试了动态控制系统方法结合神经影像学是否能提高对该疾病特征的检测敏感性。我们的分析是基于对其他失调性疾病(如糖尿病)的诊断测试进行建模的,在这些疾病中,系统受到扰动后,会测量重新建立体内平衡的负反馈回路中兴奋性和抑制性成分的轨迹。我们假设,与健康对照组相比,这些成分在患者群体中会呈现出不同的耦合动力学,并且可以使用功能磁共振成像(fMRI)中兴奋性和抑制性时间序列之间的互相关进行统计量化。
作为我们的扰动,在fMRI扫描期间,我们向11名精神分裂症患者(均正在服用精神药物)和65名健康对照组(包括11名年龄和性别与患者匹配的个体)呈现具有情感效价的面部刺激,以激活与情绪唤醒反应相关的神经区域。我们首先对fMRI数据进行随机效应分析以识别激活区域。然后使用关于时间序列峰值的标准分析以及动态分析来研究这些区域的组间差异,动态分析着眼于兴奋性和抑制性时间序列之间的互相关以及整个时间序列上的组间差异。
在观看愤怒面部表情时,患者和对照组在控制情绪唤醒的负反馈回路的兴奋性和抑制性成分之间的信号动力学上存在显著差异,特别是在右侧杏仁核和布罗德曼区9(BA9)之间(p = 0.002)。针对这些区域对愤怒面孔的激活幅度,在整个时间序列以及沿时间序列的每个时间点都进行了进一步分析。虽然两组之间杏仁核反应没有显著差异,但患者在反应开始时BA9激活显著降低(0.000≤p≤0.021),而在反应结束时BA9激活显著升高(0.008≤p≤0.025),这表明患者的兴奋性反应与调节它的抑制性激活之间存在更长的时间滞后。
我们的结果揭示了正在服药的精神分裂症患者与健康对照组相比,兴奋性(杏仁核)和抑制性(前额叶)边缘区域之间存在显著失调。这表明,类似于用于其他生理疾病的诊断测试,使用控制系统方法量化失调可能为在开发症状前风险的神经生物学评估或症状性患者的疾病严重程度评估方面进行进一步研究提供一个合适的模型。