Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load.

Abstract

BackgroundPeriventricular leukoaraiosis may be an important pathological change in postural instability gait disorder (PIGD), a motor subtype of Parkinson's disease (PD). Clinical diagnosis of PIGD may be challenging for the general neurologist.PurposeTo evaluate 1) the utility of a fully automated volume‐based morphometry (Vol‐BM) in characterizing imaging diagnostic markers in PD and PIGD, including, 2) novel deep gray nuclear lesion load (GMab), and 3) discriminatory performance of a Vol‐BM model construct in classifying the PIGD subtype.Study TypeProspective.SubjectsIn all, 23 PIGD, 21 PD, and 20 age‐matched healthy controls (HC) underwent MRI brain scans and clinical assessments.Field Strength/Sequence3.0T, sagittal 3D‐magnetization‐prepared rapid gradient echo (MPRAGE), and fluid‐attenuated inversion recovery imaging (FLAIR) sequences.AssessmentClinical assessment was conducted by a movement disorder neurologist. The MR brain images were then segmented using an automated multimodal Vol‐BM algorithm (MorphoBox) and reviewed by two authors independently.Statistical TestingBrain segmentation and clinical parameter differences and dependence were assessed using analysis of variance (ANOVA) and regression analysis, respectively. Logistic regression was performed to differentiate PIGD from PD, and discriminative reliability was evaluated using receiver operating characteristic (ROC) analysis.ResultsSignificantly higher white matter lesion load (WMab) (P < 0.01), caudate GMab (P < 0.05), and lateral and third ventricular (P < 0.05) volumetry were found in PIGD, compared with PD and HC. WMab, caudate and putamen GMab, and caudate, lateral, and third ventricular volumetry showed significant coefficients (P < 0.005) in linear regressions with balance and gait assessments in both patient groups. A model incorporating WMab, caudate GMab, and caudate GM discriminated PIGD from PD and HC with a sensitivity = 0.83 and specificity = 0.76 (AUC = 0.84).Data ConclusionFast, unbiased quantification of microstructural brain changes in PD and PIGD is feasible using automated Vol‐BM. Composite lesion load in the white matter and caudate, and caudate volumetry discriminated PIGD from PD and HC, and showed potential in classification of these disorders using supervised machine learning. Level of Evidence: 1 Technical Efficacy: Stage 1J. Magn. Reson. Imaging 2020;51:748–756.