Altered Neural Network Connectivity Predicts Depression in de novo Parkinson's Disease

Jianxia Xu; Yubing Chen; Hui Wang; Yuqian Li; Lanting Li; Jingru Ren; Yu Sun; Weiguo Liu

doi:10.3389/fnins.2022.828651

Altered Neural Network Connectivity Predicts Depression in de novo Parkinson's Disease

Front Neurosci. 2022 Mar 4:16:828651. doi: 10.3389/fnins.2022.828651. eCollection 2022.

Authors

Jianxia Xu¹, Yubing Chen¹, Hui Wang^{1

2}, Yuqian Li³, Lanting Li¹, Jingru Ren¹, Yu Sun⁴, Weiguo Liu¹

Affiliations

¹ Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
² Department of Neurology, Lianyungang Hospital of Traditional Chinese Medicine, Lianyungang, China.
³ Department of Neurology, Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, China.
⁴ International Laboratory for Children's Medical Imaging Research, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.

Abstract

Background: 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 de novo PD patients with depression.

Methods: We performed independent component analysis (ICA) on the data of resting-state functional magnetic resonance imaging from 21 de novo PD patients with depression (dPD), 34 de novo 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.

Results: 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.

Conclusion: 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.

Keywords: Parkinson’s disease; depression; drug-naïve; independent component analysis; neural network.