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Article

Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis

1
Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45241, USA
2
Department of Mathematics and Statistics, Auburn University, Auburn, AL 36849, USA
3
Department of Radiology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210019, China
*
Author to whom correspondence should be addressed.
Academic Editor: Moon-Soo Lee
J. Pers. Med. 2022, 12(1), 89; https://doi.org/10.3390/jpm12010089
Received: 22 November 2021 / Revised: 6 January 2022 / Accepted: 7 January 2022 / Published: 11 January 2022
Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD. View Full-Text
Keywords: Parkinson’s disease; depression; mood disorders; MRI; structural MRI; diagnosis; prognosis; tensor regression; multinomial regression; gradient descent Parkinson’s disease; depression; mood disorders; MRI; structural MRI; diagnosis; prognosis; tensor regression; multinomial regression; gradient descent
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MDPI and ACS Style

Cao, X.; Yang, F.; Zheng, J.; Wang, X.; Huang, Q. Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis. J. Pers. Med. 2022, 12, 89. https://doi.org/10.3390/jpm12010089

AMA Style

Cao X, Yang F, Zheng J, Wang X, Huang Q. Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis. Journal of Personalized Medicine. 2022; 12(1):89. https://doi.org/10.3390/jpm12010089

Chicago/Turabian Style

Cao, Xuan, Fang Yang, Jingyi Zheng, Xiao Wang, and Qingling Huang. 2022. "Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis" Journal of Personalized Medicine 12, no. 1: 89. https://doi.org/10.3390/jpm12010089

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