Identifying Depression in Parkinson's Disease by Using Combined Diffusion Tensor Imaging and Support Vector Machine

Abstract

ObjectiveTo investigate white matter microstructural alterations in Parkinson's disease (PD) patients with depression using the whole-brain diffusion tensor imaging (DTI) method and to explore the DTI–based machine learning model in identifying depressed PD (dPD).MethodsThe DTI data were collected from 37 patients with dPD and 35 patients with non-depressed PD (ndPD), and 25 healthy control (HC) subjects were collected as the reference. An atlas-based analysis method was used to compare fractional anisotropy (FA) and mean diffusivity (MD) among the three groups. A support vector machine (SVM) was trained to examine the probability of discriminating between dPD and ndPD.ResultsAs compared with ndPD, dPD group exhibited significantly decreased FA in the bilateral corticospinal tract, right cingulum (cingulate gyrus), left cingulum hippocampus, bilateral inferior longitudinal fasciculus, and bilateral superior longitudinal fasciculus, and increased MD in the right cingulum (cingulate gyrus) and left superior longitudinal fasciculus-temporal part. For discriminating between dPD and ndPD, the SVM model with DTI features exhibited an accuracy of 0.70 in the training set [area under the receiver operating characteristic curve (ROC) was 0.78] and an accuracy of 0.73 in the test set (area under the ROC was 0.71).ConclusionDepression in PD is associated with white matter microstructural alterations. The SVM machine learning model based on DTI parameters could be valuable for the individualized diagnosis of dPD.

Cingulum and Uncinate Fasciculus Microstructural Abnormalities in Parkinson’s Disease: A Systematic Review of Diffusion Tensor Imaging Studies

“…The pattern of decreased FA and increased mean diffusivity (MD) has also been shown in several studies [ 33 , 47 , 62 ].…”

Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome

“…The dataset was different; however, our results are also inline. Yang et al [22] investigates white matter changes in depressed PD patients, utilizing a DTI-based SVM model for individualized diagnosis with 73% acc. Zhao et al [23] assesses a CNN’s diagnostic efficacy on PD using DTI, demonstrating its potential with a satisfactory performance.…”

“…Several recent approaches have been proposed to assess AD and PD. For example, support vector machine recursive feature elimination (SVM-RFE) [18] , CT-GAN [19] , SVM-RBF [20] , SVM [21] , [22] CNN [23] , 3D-CNN [23] . These approaches achieve state-of-the-art classification accuracy on similar datasets.…”

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

“…In AD, ML methods were applied for defining DTI metrics ( Konukoglu et al, 2016 ; Lombardi et al, 2020 ; Xu et al, 2021 ; Agostinho et al, 2022 ) to characterize MCI ( Velazquez and Lee, 2022 ; Zhou et al, 2022a , b ; Cheng et al, 2023 ) and to predict AD early ( Savarraj et al, 2022 ). The characterization of MCI and cognitive impairment in PD ( Xu et al, 2021 ; Yang Y. et al, 2022 ; Chen B. et al, 2023 ; Huang et al, 2023 ) or the investigation of progression in PD ( Prasuhn et al, 2020 ; Yang et al, 2021a , b ) has also been addressed by the application of ML methods. Furthermore, ML was applied to the differentiation of parkinsonian syndromes ( Haller et al, 2012 ; Du et al, 2017 ; Chougar et al, 2021 ; Talai et al, 2021 ).…”