Networks Are Associated With Depression in Patients With Parkinson’s Disease: A Resting-State Imaging Study

• Main Authors: Haiyan Liao, Sainan Cai, Qin Shen, Jie Fan, Tianyu Wang, Yuheng Zi, Zhenni Mao, Weijun Situ, Jun Liu, Ting Zou, Jinyao Yi, Xiongzhao Zhu, Changlian Tan
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-02-01
• Series: Frontiers in Neuroscience
• Subjects: Parkinson’s disease; depression; independent component analysis; triple-network model; resting state
• Online Access: https://www.frontiersin.org/articles/10.3389/fnins.2020.573538/full

BackgroundDisturbance of networks was recently proposed to be associated with the occurrence of depression in Parkinson’s disease (PD). However, the neurobiological mechanism of depression underlying PD remains unclear.ObjectiveThis study was conducted to investigate whether intra-network and inter-network brain connectivity is differently changed in PD patients with and without depression (PDD and PDND patients, respectively).MethodsForty-one PDD patients, 64 PDND patients, and 55 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (fMRI). The default mode network (DMN), executive control network (ECN), salience network (SN), precuneus network (PCUN), and sensorimotor network (SMN) were extracted using independent component analysis (ICA), and then the functional connectivity (FC) values within and between these networks were measured.ResultsPDD patients exhibited abnormal FC values within the DMN, ECN, SN, PCUN, and SMN. In addition, PDD patients demonstrated decreased connectivity between anterior SN (aSN) and bilateral ECN, between posterior SN (pSN) and dorsal DMN (dDMN), and between PCUN and dDMN/SMN/bilateral ECN. Connectivity within the left hippocampus of dDMN and the right medial superior frontal gyrus of aSN was a significant predictor of depression level in PD patients.ConclusionsAberrant intra- and inter-network FC is involved in several important hubs in the large-scale networks, which can be a biomarker for distinguishing PDD from PDND.