Functional connectivity MRI for the aging brain.

尽管在描绘衰老大脑的结构MRI方面已经取得了相当大的进展，但在评估其脑区之间的解剖和功能关系方面进展相对较小。在早期的研究中，一些研究人员假设了老年大脑中某种类型的连接性减少模型。在这项研究中，我们计划开发分析系统，利用功能连接磁共振成像(FcMRI)和弥散张量磁共振成像(DTI)来研究这种联系在病理生理上的变化。因此，我们采用了一种新颖的综合方法，如独立分量分析(ICA)和互信息。在验证阶段的结果中，我们发现在预处理中滤除生理信号(与心动周期、呼吸周期和容量有关的磁共振信号的变化)对于fcMRI分析的去噪至关重要。在应用阶段，我们已将该方法应用于检测脑内存在一定损害区域的中风患者功能连通性的病理生理变化。我们发现卒中患者颈内动脉的功能连接性较正常志愿者相对降低。然而，由于中风患者的生理信号存在较大的波动性，因此本分析系统的前处理阶段还在不断完善。

Although considerable progress has been made in delineating structural MRI of the aging brain, relatively little progress has been made in evaluating its anatomical and functional relationship between brain areas. Some investigators in the earlier studies have hypothesized some type of connectivity reduction model in aged brain. In this study, we have planed to develop analysis system to investigate such connection changes in the pathophysiology by using functional connectivity MRI (fcMRI) and diffusion tensor MRI (DTI). Accordingly, we have employed a novel and integrative methods such as Independent Component Analysis (ICA) and mutual information. In the results of validation stage, we found that filtering out of physiological signals (MR signal change related to cardiac cycle, respiration cycle and volumes) in pre-processing is extremely important for de-noising of fcMRI analysis. In the application stage, we have applied this method to detect the pathophysiological changes of functional connectivity in stroke patients that have certain damaged region in the brain. We found that functional connectivity in ICA results in stroke patients is relatively decreased than normal volunteer. However there are substantial fluctuation of the physiological signals in stroke patients, so we keep working further improvement in pre-processing stage of this analysis system.