Research Article: Effectiveness of imaging genetics analysis to explain degree of depression in Parkinson’s disease

Date Published: February 11, 2019

Publisher: Public Library of Science

Author(s): Ji Hye Won, Mansu Kim, Bo-yong Park, Jinyoung Youn, Hyunjin Park, Pengfei Xu.


Depression is one of the most common and important neuropsychiatric symptoms in Parkinson’s disease and often becomes worse as Parkinson’s disease progresses. However, the underlying mechanisms of depression in Parkinson’s disease are not clear. The aim of our study was to find genetic features related to depression in Parkinson’s disease using an imaging genetics approach and to construct an analytical model for predicting the degree of depression in Parkinson’s disease. The neuroimaging and genotyping data were obtained from an openly accessible database. We computed imaging features through connectivity analysis derived from tractography of diffusion tensor imaging. The imaging features were used as intermediate phenotypes to identify genetic variants according to the imaging genetics approach. We then constructed a linear regression model using the genetic features from imaging genetics approach to describe clinical scores indicating the degree of depression. As a comparison, we constructed other models using imaging features and genetic features based on references to demonstrate the effectiveness of our imaging genetics model. The models were trained and tested in a five-fold cross-validation. The imaging genetics approach identified several brain regions and genes known to be involved in depression, with the potential to be used as meaningful biomarkers. Our proposed model using imaging genetic features predicted and explained the degree of depression in Parkinson’s disease appropriately (adjusted R2 larger than 0.6 over five training folds) and with a lower error and higher correlation than with other models over five test folds.

Partial Text

Parkinson’s disease (PD) is the second most common neurodegenerative disorder [1]. PD is characterized primarily as a movement disorder, but recent research indicates that a variety of non-motor symptoms including constipation, sleep disturbances, diabetes, cognitive decline, and depression may play a role in PD development [2]. Among these symptoms, depression is the most common non-motor symptom of PD, occurring in around 40–50% of all patients diagnosed with PD [3,4]. Depression can predate symptoms of PD for several years before the worsening of motor symptoms and belongs to the group of non-motor features that might predict the development of PD [5,6]. Depression in PD (DPD) can aggravate all other symptoms, including the worsening of motor symptoms, rapid disease progression, and reduced cognitive function [7]. DPD is one of the major causes of poor quality of life and disability in PD patients [8]. However, DPD has not yet been fully explored [9,10].

Psychological phenomena are difficult to characterize using neuroimaging or genetic analysis alone as the given diagnosis spans a wide spectrum of symptoms. Some argue that diagnosis is becoming less relevant due to this problem [41,42]. The DPD diagnostic criteria used in this study were also affected by this problem. Depression occurs in approximately 40% of PD patients and DPD and nDPD patients share many symptoms such as cognitive decline, motor impairment, and helplessness, which makes separating DPD from nDPD difficult [6,7,43]. Depression is multifactorial and its manifestation varies significantly when it accompanies neurodegenerative diseases [5–8,44]. Therefore, in this study, we constructed models to predict the degree of depression in PD patients rather than the diagnosis of depression. We identified several features that indicate the degree of depression in PD using the imaging genetics approach. The features were used to predict the degree of depression and the performance was enhanced when features derived from imaging genetics were used. If each of the resulting SNPs from imaging genetics is studied in more detail, these SNPs could be used as biomarkers related to DPD in the future.




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