Key laboratory of Personality and Cognition, Faculty of Psychology, Southwest University, Chongqing, 400715, P.R. China.
Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, P.R. China.
Sci Rep. 2017 May 31;7(1):2568. doi: 10.1038/s41598-017-02127-y.
Much is known concerning the underlying mechanisms of Parkinson's disease (PD) with depression, but our understanding of this disease at the neural-system level remains incomplete. This study used resting-state functional MRI (rs-fMRI) and independent component analysis (ICA) to investigate intrinsic functional connectivity (FC) within and between large-scale neural networks in 20 depressed PD (dPD) patients, 35 non-depressed PD (ndPD) patients, and 34 healthy controls (HC). To alleviate the influence caused by ICA model order selection, this work reported results from analyses at 2 levels (low and high model order). Within these two analyses, similar results were obtained: 1) dPD and ndPD patients relative to HC had reduced FC in basal ganglia network (BGN); 2) dPD compared with ndPD patients exhibited increased FC in left frontoparietal network (LFPN) and salience network (SN), and decreased FC in default-mode network (DMN); 3) dPD patients compared to HC showed increased FC between DMN and LFPN. Additionally, connectivity anomalies in the DMN, LFPN and SN correlated with the depression severity in patients with PD. Our findings confirm the involvement of BGN, DMN, LFPN and SN in depression in PD, facilitating the development of more detailed and integrative neural models of PD with depression.
关于帕金森病（PD）伴发抑郁的潜在机制，人们已经了解了很多，但我们对这种疾病在神经系统层面的理解仍然不完整。本研究使用静息态功能磁共振成像（rs-fMRI）和独立成分分析（ICA），对 20 名抑郁性帕金森病（dPD）患者、35 名非抑郁性帕金森病（ndPD）患者和 34 名健康对照者（HC）的大尺度神经网络内和之间的固有功能连接（FC）进行了研究。为了缓解 ICA 模型阶数选择带来的影响，本工作报告了在 2 个水平（低阶和高阶）分析中的结果。在这两种分析中，均得出了相似的结果：1）dPD 和 ndPD 患者相对于 HC，其基底节网络（BGN）的 FC 降低；2）dPD 患者相较于 ndPD 患者，左侧额顶网络（LFPN）和突显网络（SN）的 FC 增加，默认模式网络（DMN）的 FC 降低；3）dPD 患者相对于 HC，DMN 和 LFPN 之间的 FC 增加。此外，DMN、LFPN 和 SN 的连接异常与 PD 患者的抑郁严重程度相关。本研究结果证实了 BGN、DMN、LFPN 和 SN 参与 PD 伴发抑郁，这有助于开发更详细、更具综合性的 PD 伴抑郁的神经模型。
