Cao Yuanyan, Si Qian, Tong Renjie, Zhang Xu, Li Chunlin, Mao Shanhong
School of Biomedical Engineering, Capital Medical University, Beijing, China.
Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China.
Front Neurosci. 2023 Mar 16;17:1116111. doi: 10.3389/fnins.2023.1116111. eCollection 2023.
Non-motor symptoms are common in Parkinson's disease (PD) patients, decreasing quality of life and having no specific treatments. This research investigates dynamic functional connectivity (FC) changes during PD duration and its correlations with non-motor symptoms.
Twenty PD patients and 19 healthy controls (HC) from PPMI dataset were collected and used in this study. Independent component analysis (ICA) was performed to select significant components from the entire brain. Components were grouped into seven resting-state intrinsic networks. Static and dynamic FC changes during resting-state functional magnetic resonance imaging (fMRI) were calculated based on selected components and resting state networks (RSN).
Static FC analysis results showed that there was no difference between PD-baseline (PD-BL) and HC group. Network averaged connection between frontoparietal network and sensorimotor network (SMN) of PD-follow up (PD-FU) was lower than PD-BL. Dynamic FC analysis results suggested four distinct states, and each state's temporal characteristics, such as fractional windows and mean dwell time, were calculated. The state 2 of our study showed positive coupling within and between SMN and visual network, while the state 3 showed hypo-coupling through all RSN. The fractional windows and mean dwell time of PD-FU state 2 (positive coupling state) were statistically lower than PD-BL. Fractional windows and mean dwell time of PD-FU state 3 (hypo-coupling state) were statistically higher than PD-BL. Outcome scales in Parkinson's disease-autonomic dysfunction scores of PD-FU positively correlated with mean dwell time of state 3 of PD-FU.
Overall, our finding indicated that PD-FU patients spent more time in hypo-coupling state than PD-BL. The increase of hypo-coupling state and decrease of positive coupling state might correlate with the worsening of non-motor symptoms in PD patients. Dynamic FC analysis of resting-state fMRI can be used as monitoring tool for PD progression.
非运动症状在帕金森病（PD）患者中很常见，会降低生活质量且没有特效治疗方法。本研究调查帕金森病病程中动态功能连接（FC）的变化及其与非运动症状的相关性。
收集PPMI数据集中的20例帕金森病患者和19例健康对照（HC）用于本研究。进行独立成分分析（ICA）以从全脑选择显著成分。将成分分为七个静息态固有网络。基于选定成分和静息态网络（RSN）计算静息态功能磁共振成像（fMRI）期间的静态和动态FC变化。
静态FC分析结果显示，帕金森病基线（PD-BL）组与健康对照组之间无差异。帕金森病随访（PD-FU）组额顶叶网络与感觉运动网络（SMN）之间的网络平均连接低于PD-BL。动态FC分析结果显示有四种不同状态，并计算了每种状态的时间特征，如分数窗口和平均停留时间。本研究的状态2显示感觉运动网络内部以及感觉运动网络与视觉网络之间存在正耦合，而状态3显示通过所有静息态网络的耦合减弱。PD-FU状态2（正耦合状态）的分数窗口和平均停留时间在统计学上低于PD-BL。PD-FU状态3（耦合减弱状态）的分数窗口和平均停留时间在统计学上高于PD-BL。帕金森病自主神经功能障碍评分中帕金森病随访的结果量表与帕金森病随访状态3的平均停留时间呈正相关。
总体而言，我们的研究结果表明，与PD-BL相比，PD-FU患者在耦合减弱状态下花费的时间更多。耦合减弱状态的增加和正耦合状态的减少可能与帕金森病患者非运动症状的恶化相关。静息态fMRI的动态FC分析可用作帕金森病进展的监测工具。
