Abstract
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.
Highlights
Parkinson’s disease (PD) is a major neurodegenerative disease influenced by both genetic and environmental factors [1]
Patients in comparison to the healthy controls (HC), while significant differences were detected with respect to the Hamilton depression rating scale (HAMD) and H & Y scores among the three groups
To the best of our knowledge, this study was the first attempt to construct a tensorregression-based platform for structurally discriminating among non-depressed PD (NDPD), DPD, and HC
Summary
Parkinson’s disease (PD) is a major neurodegenerative disease influenced by both genetic and environmental factors [1]. As the second most common neurodegenerative disorder, PD is characterized by the degeneration of dopamine-producing cells in the brain, presenting a broad range of symptoms from motor dysfunctions to nonmotor psychobehavioral manifestations [2,3]. Nonmotor features can appear in the earliest phase of the disease even before clinical motor impairment [4,5,6]. Depression is a prominent nonmotor feature which is highly prevalent early in the disease process and has a significant impact on quality of life and disability [7,8,9]. It is generally accepted that clinically significant depressive disturbances occur in 40–50% of patients with PD [13]
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