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. 2019 Feb 11;14(2):e0211699.
doi: 10.1371/journal.pone.0211699. eCollection 2019.

Effectiveness of Imaging Genetics Analysis to Explain Degree of Depression in Parkinson's Disease

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Free PMC article

Effectiveness of Imaging Genetics Analysis to Explain Degree of Depression in Parkinson's Disease

Ji Hye Won et al. PLoS One. .
Free PMC article

Abstract

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.

Conflict of interest statement

I have read the journal's policy and the authors of this manuscript have the following competing interests: Imaging data were obtained from the PPMI (The Parkinson’s Progression Markers Initiative). The PPMI is a public-private partnership funded by: The Michael J. Fox Foundation for Parkinson's Research, Abbott, Avid Pharmaceuticals, Biogen Idec, Bristol-Myers Squibb, Covance, Elan, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Merck, Meso Scale Discovery, Pfizer, Roche, and UCB. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Overview of neuroimaging, reference-based genetics, and imaging genetics processing steps.
Fig 2
Fig 2. The prediction plots of the three models.
(a), (b), and (c) show the actual and predicted GDS from Models using neuroimaging features, conventional genetic features, and imaging genetics features, respectively. The dashed line indicates the identity line. (d) shows the actual GDS and predicted GDS for each subject using our proposed model using imaging genetics features (N = 81).

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Grant support

This study was supported by the Institute for Basic Science (http://www.ibs.re.kr/, grant number IBS-R015-D1, recipient: N/A). This work was also supported by the National Research Foundation of Korea (http://www.nrf.re.kr/, grant numbers NRF-2016R1A2B4008545 [recipient: HP] and NRF-2017H1A2A1043075 [recipient: MK]). This study was also supported by the Ministry of Science and ICT of Korea under the ITRC Program (grant number IITP-2018-2018-0-01798). Imaging data were obtained from the PPMI. The PPMI is a public-private partnership funded by The Michael J. Fox Foundation for Parkinson's Research, Abbott, Avid Pharmaceuticals, Biogen Idec, Bristol-Myers Squibb, Covance, Elan, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Merck, Meso Scale Discovery, Pfizer, Roche, and UCB (www.ppmi-info.org/fundingpartners). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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