进展型及稳定型轻度认知障碍的动态功能连接对比分析

Dynamic functional connectivity analysis of stable and progressive mild cognitive impairment

目的 本研究通过对轻度认知障碍(mild cognitive impairment, MCI)患者的静息态功能磁共振成像(resting-state functional magnetic resonance imaging, rs-fMRI)进行动态功能连接分析,以评估进展型MCI及稳定型MCI的功能连接变化特征及差异。材料和方法 本研究数据均来源于阿尔茨海默病神经影像学计划(Alzheimer’s Disease Neuroimaging Initiative,ADNI)数据库。检索MCI患者并根据其随访结果筛选进展型MCI患者入组,并选择性别年龄匹配的稳定型MCI患者作为对照组。基于独立成分分析(independent component analysis, ICA)对rs-f MRI进行处理提取并筛选感兴趣的独立成分(independent component, IC)。利用滑动时间窗法进行动态功能连接分析,应用k-means聚类及elbow方法将动态功能连接矩阵划分为具有代表性的动态功能连接状态,分析两组间动态功能连接状态的变化,并比较功能连接特征参数(各状态的时间比例、驻留时间及各状态间的转换次数)的差异。结果 最终23名进展型MCI及26名稳定型MCI患者纳入本研究。一系列动态功能连接矩阵被划分为4种动态功能连接状态:弱连接状态a,局部强连接状态,弱连接状态b及正相关强连接状态。与稳定型MCI组比较,进展型MCI组处于局部强连接状态的时间比例减少、驻留时间缩短(P=0.049,P=0.049),而处于弱连接状态b的时间比例增加、驻留时间延长(P=0.045,P=0.033)。结论 与稳定型MCI患者比较,进展型MCI患者具有局部强连接状态减少而弱连接状态增多的特点。动态功能连接能够客观反映进展型MCI及稳定型MCI的大脑功能变化,对于鉴别进展型及稳定型MCI具有一定价值。

Objective: In this study, resting-state functional magnetic resonance imaging(rs-fMRI) in patients with mild cognitive impairment(MCI) was analyzed using the dynamic functional connection(dFC) to evaluate the characteristics and differences of functional connectivity changes in patients with progressive and stable MCI. Materials and Methods: The data in this study were derived from the Alzheimer’s disease neuroimaging initiative(ADNI) database. Patients with MCI were retrieved and patients with progressive MCI were screened according to the follow-up results, and patients with stable MCI with matched gender and age were selected as the control group. Based on independent component analysis(ICA), rs-fMRI was processed and independent components(IC)of interest were extracted. The dFC analysis was performed by sliding time window method, and k-means clustering and elbow method were used to divide dFC matrix into several representative dFC states. The changes of dFC states were compared between progressive and stable MCI groups, and dFC feature parameters(fraction time and dwell time for each state, and the times of transition between states) were compared between the two groups. Results: Twenty-three patients with progressive MCI and twenty-six patients with stable MCI were included in this study. A series of real-time dFC matrix were divided into four kinds of dFC states: sparse connection state-a,strong local connection state, sparse connection state-b and strong positive-connection state. Compared with the stable MCI group,fraction time and dwell time in strong local connection state decreased significantly in patients with progressive MCI(P=0.049, P=0.049), fraction time and dwell time in sparse connection state-b increased significantly in patients with progressive MCI(P=0.045, P=0.033). Conclusions: Compared with the stable MCI, patients with progressive MCI showed a characteristic of increasing strong local connection state and decreasing sparse connection state. The rs-fMRI-based dFC analysis can objectively reflect the changes of brain function in patients with progressive and stable MCI, and may be helpful in differential diagnosis of progressive MCI from stable MCI.