Xu Jianxia, Chen Yubing, Wang Hui, Li Yuqian, Li Lanting, Ren Jingru, Sun Yu, Liu Weiguo
Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
Department of Neurology, Lianyungang Hospital of Traditional Chinese Medicine, Lianyungang, China.
Front Neurosci. 2022 Mar 4;16:828651. doi: 10.3389/fnins.2022.828651. eCollection 2022.
Depression, one of the most frequent non-motor symptoms in Parkinson's disease (PD), was proposed to be related to neural network dysfunction in advanced PD patients. However, the underlying mechanisms in the early stage remain unclear. The study was aimed to explore the alterations of large-scale neural networks in PD patients with depression.
We performed independent component analysis (ICA) on the data of resting-state functional magnetic resonance imaging from 21 PD patients with depression (dPD), 34 PD patients without depression (ndPD), and 43 healthy controls (HCs) to extract functional networks. Intranetwork and internetwork connectivity was calculated for comparison between groups, correlation analysis, and predicting the occurrence of depression in PD.
We observed an ordered decrease of connectivity among groups within the ventral attention network (VAN) (dPD < ndPD < HCs), mainly located in the left middle temporal cortex. Besides, dPD patients exhibited hypoconnectivity between the auditory network (AUD) and default mode network (DMN) or VAN compared to ndPD patients or healthy controls. Correlation analysis revealed that depression severity was negatively correlated with connectivity value within VAN and positively correlated with the connectivity value of AUD-VAN in dPD patients, respectively. Further analysis showed that the area under the curve (AUC) for dPD prediction was 0.863 when combining the intranetwork connectivity in VAN and internetwork connectivity in AUD-DMN and AUD-VAN.
Our results demonstrated that early dPD may be associated with abnormality of attention bias and especially auditory attention processing. Altered neural network connectivity is expected to be a potential neuroimaging biomarker to predict depression in PD.
抑郁症是帕金森病（PD）最常见的非运动症状之一，被认为与晚期PD患者的神经网络功能障碍有关。然而，早期阶段的潜在机制仍不清楚。本研究旨在探讨伴有抑郁症的PD患者大规模神经网络的改变。
我们对21例伴有抑郁症的PD患者（dPD）、34例无抑郁症的PD患者（ndPD）和43名健康对照者（HCs）的静息态功能磁共振成像数据进行独立成分分析（ICA），以提取功能网络。计算网络内和网络间的连通性，用于组间比较、相关性分析以及预测PD患者抑郁症的发生。
我们观察到腹侧注意网络（VAN）内各群组间的连通性呈有序下降（dPD < ndPD < HCs），主要位于左侧颞中皮质。此外，与ndPD患者或健康对照者相比，dPD患者在听觉网络（AUD）与默认模式网络（DMN）或VAN之间表现出低连通性。相关性分析显示，dPD患者的抑郁严重程度分别与VAN内的连通性值呈负相关，与AUD-VAN的连通性值呈正相关。进一步分析表明，结合VAN内的网络连通性以及AUD-DMN和AUD-VAN的网络间连通性时，dPD预测的曲线下面积（AUC）为0.863。
我们的结果表明，早期dPD可能与注意力偏差异常尤其是听觉注意力处理异常有关。神经网络连通性的改变有望成为预测PD患者抑郁症的潜在神经影像学生物标志物。
