Functional connectivity MRI for the aging brain.

虽然在描绘衰老大脑的结构MRI方面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取得的进展相对较小。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减少模型。在本研究中，我们计划开发分析系统，以探讨这种连接的变化，在病理生理学，通过使用功能连接MRI（fcMRI）和扩散张量MRI（DTI）。因此，我们采用了一种新的和综合的方法，如独立分量分析（伊卡）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、呼吸周期和体积相关的MR信号变化）对于fcMRI分析的去噪非常重要。在应用阶段，我们已经将该方法应用于检测脑内特定区域受损的脑卒中患者的功能连接的病理生理变化。我们发现脑卒中患者伊卡的功能连接性比正常志愿者相对降低。但是脑卒中患者的生理信号存在较大的波动性，因此本分析系统的预处理阶段还需要进一步的改进。

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.